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An Exploratory investigation of a stress model of medication adherence among elderly outpatients

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Title:
An Exploratory investigation of a stress model of medication adherence among elderly outpatients
Creator:
Gettman, David Allen
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Language:
English
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xviii, 260 leaves : ill. ; 29 cm.

Subjects

Subjects / Keywords:
Drug interactions ( jstor )
Medication adherence ( jstor )
Medications ( jstor )
Multiple regression ( jstor )
Older adults ( jstor )
Pharmacies ( jstor )
Pharmacists ( jstor )
Psychological stress ( jstor )
Questionnaires ( jstor )
Stress tests ( jstor )
Department of Pharmacy Health Care Administration thesis Ph.D ( mesh )
Dissertations, Academic -- College of Pharmacy -- Department of Pharmacy Health Care Administration -- UF ( mesh )
Drug Therapy -- Aged ( mesh )
Models, Psychological ( mesh )
Outpatients ( mesh )
Patient Compliance -- Aged ( mesh )
City of Gainesville ( local )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis (Ph. D.)--University of Florida, 1997.
Bibliography:
Includes bibliographical references (leaves 238-259).
Additional Physical Form:
Also available online.
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by David Allen Gettman.

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University of Florida
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University of Florida
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Copyright David Allen Gettman. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Resource Identifier:
028442610 ( ALEPH )
50414468 ( OCLC )

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AN EXPLORATORY INVESTIGATION OF A STRESS MODEL
OF MEDICATION ADHERENCE AMONG ELDERLY OUTPATIENTS













By

DAVID ALLEN GETTMAN













A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY


UNIVERSITY OF FLORIDA 1997

























Copyright 1997

by

DAVID ALLEN GETTMAN


























I wish to dedicate this dissertation to the veterans who have not only supported me during this academic endeavor but who have also defended the beliefs and values of my homeland.














ACKNOWLEDGMENTS


Most of us have found ourselves driving an automobile in city traffic during rush hour. If you were in a hurry to get to work on time and were trying to do so in the most efficient manner, you may have been required to deal with a number of irritations, minor annoyances, or hassles. If this situation occurred more than once, you may have at one time made the trip alone, another time with someone who was just there for the ride and did not do anything other than sit, and yet another time with someone in the car who was helpful, acting as "another set of eyes" to look for traffic hazards and making suggestions on how best to proceed. There may also have been a similar situation in which the traffic hassles had become so overwhelming that you felt the person wasn't helping at all and you reacted poorly by slowing down or stopping. This situation with its different scenarios could be understood as a metaphor for life. Ironically, it is also the focus of this dissertation.

During my graduate work at the University of Florida, I have not been alone on my

metaphorical trip. Neither have I been driving with persons who just sat there. The people along this part of my journey were genuinely interested in my well-being and proactively helped to keep my stress level down. In a real way, they all provided me with "another set of eyes."

First and foremost, I wish to thank Doug Ried, my chairperson, who is an extremely intelligent and articulate mentor. This rare combination of abilities served as a constant source of information and insight through a hassle-filled and stressful graduate experience. Like the proverbial patient, quiet farmer, he was there to cultivate my endeavors as they slowly grew from a master's focus on practice to a doctoral focus on research. He has been and probably will always be my role model



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of a thoughtful professor.

Second, I wish to thank Carole Kimberlin. She is also very bright and intuitive, but her real gifts are her frustration-alleviatmg skills. Several times she listened to my frustrations and then communicated enough emotional support to quickly and miraculously lower my levels of perceived stress, helping me refocus on the issues germane to this academic endeavor.

I wish to thank two other committee members. First, I wish to thank Rich Segal for finding me a home after Desert Storm/Desert Shield and for helping me appraise and deal constructively with a number of issues on both personal and academic levels. Second, I wish to thank Donna Berardo who brought to my attention the need for a better social support scale specific to medication-taking during a research project on renal transplantation.

I wish to thank four other professors whose membership on my dissertation committee changed over the years. First, I wish to thank Steve Dorman who opened my eyes to many health education frontiers and indirectly showed me some common intellectual ground I have with my wife. Second, I wish to thank Ron Stewart who lead me during a clinical clerkship with elderly patients to an examination of a stress model which can explain the antecedents of a number of outcomes besides depression. Third, I wish to thank John Henretta who showed me that the elderly are a vulnerable population who deserve our understanding and care. And, finally, I wish to thank John Lynch who showed me it is not enough to measure the variables in a study, but the real challenge is to elucidate the interactions.

I must also thank five other professors and one very special secretary who were never on my dissertation committee but were instrumental in my professional development. First, I wish to thank Paul Ranelli who helped me publish my first paper based on the data from his dissertation. Second, I wish to thank Doug Hepler who taught me to look for theoretical frameworks which may link to pharmaceutical health care to help explain health outcomes. Third, I wish to thank Carl

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Barfield who taught me that, by applying for grants, I might actually get paid to do the kind of research I have a passion to conduct. Finally, I wish to thank David Brushwood, Earlene Lipowski, and Delayne Redding, who showed me how difficult it really is to run a professional pharmacy conference.

This list would not be complete without mentioning at least a few of the gifted graduate students and a post doctoral fellow with whom I shared growth experiences. I wish to thank Mana Miralles, who showed me that statistical programs can be user friendly. I wish to thank Folake Odedina and Rami Ben-Joseph, who repeatedly demonstrated the need to ask carefully formulated questions. And, finally, I wish to thank "TJ" Grainger-Rousseau, who taught me not only how to be a good "TOM" pharmacist but also how to be a not so good "TOM" patient.

I wish to thank those I got to know at the Veterans Administration Medical Center in Gainesville, Florida, where I worked as a part-time pharmacist during my graduate school experience. I wish to thank the pharmacy staff and administrators who helped me in the data gathering process and with whom I can honestly say I learned the usefulness of a "good sense of humor." Also, I must thank the veterans who filled out the questionnaires. I hope this research can be used wisely to improve the well-deserved medical attention they receive at the medical center in Gainesville and others like it throughout the country. My limited active duty experiences in the Navy and Air Force have taught me firsthand that they deserve the best health care our nation can provide.

I wish to acknowledge the considerable skill of authors like Richard S. Lazarus and Sheldon Cohen whose talents are reflected in the pages of this dissertation and whose names appear in the references. I tend to think of this research project as an adaptation of their hard work. I can only hope that some day a graduate student will read a paper of mine and do the same.



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I wish to acknowledge my large and wonderful family. I want to thank my mother, Janet, who taught me many of her keen business skills at an early age. This was a difficult undertaking as our large family traveled over much of the world with the U.S. Air Force during the cold war. Also, I want to thank my many brothers and sisters who were always there and still, to this very day, provide me with an amusing and diverse set of stories, not only about their gifted children, but also on topics concerning their own interesting professions ranging from law enforcement to the aeronautics and space industries. And, I wish to thank my father, Frank Clifford Gettman. My earliest memories of him include his getting into his U.S. Air Force uniform to fly off to foreign countries to do studies on microorganisms. As a scientist/military reserve officer he fought an invisible war-within-a-war against organisms that affected many of our veterans before they could get to the battlefield. An old family joke is that my oldest sister's first spoken word was 'Shistosomias.' As a scientist/military reserve officer I now fight an invisible war-within-a-war against drug problems that are affecting many of our veterans after their return from the battlefield. During a recent conversation, my father told me how discouraged he was with health departments after his retirement, as for example, to see the resurgence in tuberculosis with the prevalence of HIV-infected individuals. I hope I am not similarly discouraged by a resurgence of drug problems if pharmaceutical health care providers should fail to fulfill their covenant with patients.

Finally, I want to thank my lovely, talented, and supportive wife, Mary Ann Harhi-Gettman. In addition to being the mother of my son, Paul James Gettman, she has aptly demonstrated to me that it takes someone close, concerned, and very special to keep all the things in your life in the perspective they truly belong.








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TABLE OF CONTENTS
page

ACKNOWLEDGMENTS ............. ............................... iv

LIST OF TABLES ................ ........................ xi

LIST OF FIGURES .................................................. xv

ABSTRACT ........ .... .................................... xvii

CHAPTERS

1 OVERVIEW OF THE ISSUES ADDRESSED BY THIS RESEARCH ............ 1

M edication Adherence ................................................... 1
Medication-related Stressors .................................. .. ........ 2
Perceived Medication Stress ................... .................... .. ..... 3
Medication-specific Social Support ................ ................... ... 3
Problem Statement ......................... ...................... 5

2 THEORETICAL FRAMEWORK ................ ....................... 6

Introduction ............ ................... ...................... 6
A Stress Model ..................................... .................. 6
The Stress Model of Medication Adherence ................. .................. 9
The "Nucleus" of the Stress Model of the Medication Adherence .................... 11
Research Q uestions .......................................... ........... 14
Summary .......................................................... 15

3 REVIEW OF THE LITERATURE ........................................ 16

Introduction ................ ...................................... 16
Medication Adherence ................... ................. .............. 18
Medication-related Stressors .................. .. ...................... 19
Historical Evolution of General Stressor Constructs .......... ......... .... 19
Development of the Medication-related Stressors Construct ................... 22
Perceived Medication Stress ................ ........................... 28
Medication-specific Social Support ........................................ 31
Other Conditioning Variables ...... ...... .......................... 35
Age ................... ...................................35


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In c o m e . . ... . . 3 7
H ealth Statu s ... .. .. .. ...... ... ........ .... ... ........ 38
Research Hypotheses ...................... ..... 39
First Set of Hypotheses ..... ................... ....... 41
Second Set of Hypotheses ............................... ....... 41
Third Set of Hypotheses ............... ....................... 42
Fourth Set of Hypotheses ..................................... .......... 43
Fifth Set of Hypotheses ................ ........................ ....... 43
Summary ....................... ..........................44

4 METHODOLOGY ....... ....... ........ ................ 46

Introduction ............ ........................................ 46
Study Instruments ........... ..................................46
Medication Adherence ..................... ............... ............. 46
Medication-related Stressors ........................................... 50
Perceived Medication Stress ................ .................. ......... 52
Medication-specific Social Support ................ ...................... 54
Other Conditioning Variables ................. ....................... 56
Study Phase I ................................................. 57
Study Phase I, Part One ............................................. 57
Study Phase I, Part Two ...................................... ........... 58
Study Phase 1, Part Three ................ ............................. 59
Study Phase II ...................... ............. ............ ..... .. 6 1
Study Phase I, Part One .............................................. 61
Study Phase II, Part Two ............................................ 63
Human Rights ....... ...... .......................................... 69
Summary ..................................................69

5 RESULTS ..................... ................ ................70

Introduction ..................................... ........... ........ 70
Study Phase I ...................................... ................... 70
Comparison between Phase I Lobby and Mail Samples ......................... 71
Phase I Sample Description and Measures .............. ................. 71
Study Phase II ....................................... ....... .. 78
Phase II Sample Description ................................. .... ........ 84
Factor Analyses ............. ............. ................ 87
Reliability Analyses ...................................... .............. 96
Phase II Measure Descriptions ................ ........................ 107
Content of the Four Scales .................. ........................... 109
Construct-related Validation of the Scales ................................ 110
Testing of the Five Sets of Hypotheses ................ ................. 111
Discussion of Results .................. ..... ....................... 175
Relationships Among Stressors, Perceived Stress, and Medication Adherence ........ 175 Mediating Effects of Perceived Stress ...................................... 181
Moderating Effects of Medication-specific Social Support ...................... 183

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Sum m ary ......................... ...................... ..... 192

6 LIMITATIONS, IMPLICATIONS, AND CONCLUSIONS ............... ... 194

Limitations on the Entire Study .... ............................. 194
Limitations on Mediating Effects ...... ............. ..... ............ 197
Limitations on Moderating Effects ..................................... 198
Implications for Pharmacy Practice .................. .................... 200
Implications for Future Research ....................................... 202
Conclusions ................... ............... ................... 206

APPENDICES

A INITIAL OUTPATIENT QUESTIONNAIRE ............................. 211

B REVISED OUTPATIENT QUESTIONNAIRE ............................. 223

REFERENCES ...................................................... 238

BIOGRAPHICAL SKETCH .............................. ............... 260
































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LIST OF TABLES

Table 2age

5.1 Phase I Comparison between Lobby and Mail Samples on Four Groups of
Questionnaire Items ................ ................ ............. 72

5.2 Phase I Sample Descnption ......................... ................. 73

5.3 Phase I Medication Adherence Items: Reliability Coefficients and Item-to-total
Correlations .................. ...................... ......... ... 75

5.4 Phase I Emotional Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations .............................. 76

5.5 Phase I Informational Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations .. ......................... 77

5.6 Phase I Instrumental Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations ................ ............... 78

5.7 Phase I Perceived Medication Stress Items: Reliability Coefficients and Item-to-total
Correlations .................. ................... .... ....... 80

5.8 Phase I Study Doctor Emotional Medication-specific Social Support Items: Reliability
Coefficients and Item-to-total Correlations ............................... 81

5.9 Phase I Study Pharmacist Emotional Medication-specific Social Support Items:
Reliability Coefficients and Item-to-total Correlations ........................ 82

5.10 Phase II of Study Sample Description ..................................... 86

5.11 Phase II Medication Adherence Items: Rotated Component Matrix using
Principal Components Analysis with Varimax Rotation and Kaiser Normalization .... 89

5.12 Phase II Advising Subjective Medication-related Stressors: Rotated Component
Matrix using Principal Components Analysis with Varimax Rotation and Kaiser
N orm alization ................ ................... ................. 91



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5 13 Phase II Isolating Subjective Medication-related Stressors: Rotated Component
Matnx using Principal Components Analysis with Varimax Rotation and Kaiser
Normalization ........ ............................ .......... ..... 92

5.14 Phase II Reminding Subjective Medication-related Stressors: Rotated Component
Matrix using Principal Components Analysis with Varimax Rotation and Kaiser
N orm alization .. ... ... ................ .......... ........... 93

5.15 Phase II Obtaining Subjective Medication-related Stressors: Rotated Component
Matrix using Principal Components Analysis with Varimax Rotation and Kaiser
Normalization ................... .................. .............. .. 94

5.16 Phase II Perceived Medication-related Stress Items: Rotated Component
Matrix using Principal Components Analysis with Varimax Rotation and Kaiser
Normalization ................................... ................. 96

5.17 Phase II Doctor Medication-specific Social Support Items: Rotated Component
Matrix using Principal Components Analysis with Varimax Rotation and Kaiser
Normalization ................... ..................... 97

5.18 Phase II Pharmacist Medication-specific Social Support Items: Rotated Component
Matrix using Principal Components Analysis with Varimax Rotation and Kaiser
Normalization .......... ............ ............ 98

5.19 Phase II Other Person Medication-specific Social Support Items: Rotated
Component Matrix using Principal Components Analysis with Varimax Rotation
and Kaiser Normalization ................... ............ ... 99

5.20 Phase II Medication Adherence Items: Reliability Coefficients and Item-to-total
Correlations ...................................... ................. 101

5.21 Phase II Advising Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations .............................. 102

5.22 Phase II Isolating Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations ............... .............. 103

5.23 Phase II Reminding Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations .............. .............. 104

5.24 Phase II Obtaining Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations .............................. 105

5.25 Phase II Perceived Medication Stress Items: Reliability Coefficients and Item-to-total
Correlations ...................................... .............. .. 106

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5.26 Descriptions of Phase II Measures .. ....... ...... ......... .... 108

5.27 Correlations between Subject Characteristics, Health Status, Medication Adherence,
Number of Medications, Type of Stressors, and Perceived Stress ................ 113

5.28 Raw Score Multiple Regression Analysis of Elderly Outpatient's Age, Income,
Self-rated Health Status and Chronic Disease Score on Medication Adherence,
Advising Stressors, Isolating Stressors, Reminding Stressors, Obtaining Stressors,
and Perceived Stress .................... ....... ............. 121

5.29 Correlations between Type of Support and Subject's Age, Income, Health Status,
Medication Adherence, Type of Stressors, and Perceived Stress ................. 130

5.30 Raw Score Multiple Regression Analysis of Elderly Outpatient's Age, Income,
Self-rated Health Status and Chronic Disease Score on Doctor Consultation Support,
Doctor Affirmation Support, Doctor Actuation Support, and Doctor Acquisition
Support ...................................... ............. ..... 132

5.31 Raw Score and Deviation Score Multiple Regression Analysis Relating Advising
stressors, Doctor Consultation Support, and Advising Stressors-Doctor Consultation
Support Interaction to Perceived Medication Stress ........ ........ ....... 135

5.32 Raw Score and Deviation Score Multiple Regression Analysis Relating Isolating
Stressors, Doctor Affirmation Support, and Isolating Stressors-Doctor Affirmation
Support Interaction to Perceived Medication Stress ......................... 137

5.33 Raw Score and Deviation Score Multiple Regression Analysis Relating Reminding
Stressors, Doctor Actuation Support, and Reminding Stressors-Doctor Actuation
Support Interaction to Perceived Medication Stress .......................... 140

5.34 Raw Score and Deviation Score Multiple Regression Analysis Relating Obtaining
Stressors, Doctor Acquisition Support, and Obtaining Stressors-Doctor Acquisition
Support Interaction to Perceived Medication Stress ................. ....... 142

5.35 Raw Score and Deviation Score Multiple Regression Analysis Relating Perceived
Medication Stress, Doctor Affirmation Support, and Perceived Medication
Stress-Doctor Affirmation Support Interaction to Medication Adherence ....... 144

5.36 Raw Score Multiple Regression Analysis of Elderly Outpatient's Age, Income,
Self-rated Health Status and Chronic Disease Score on Pharmacist Consultation
Support, Pharmacist Affirmation Support, Pharmacist Actuation Support, and
Pharmacist Acquisition Support .......................... .......... 149









5.37 Raw Score and Deviation Score Multiple Regression Analysis Relating Advising
Stressors, Pharmacist Consultation Support, and Advising Stressors-Pharmacist
Consultation Support Interaction to Perceived Medication Stress .............. 150

5.38 Raw Score and Deviation Score Multiple Regression Analysis Relating Isolating
Stressors, Pharmacist Affirmation Support, and Isolating Stressors-Pharmacist
Affirmation Support Interaction to Perceived Medication Stress ................. 152

5.39 Raw Score and Deviation Score Multiple Regression Analysis Relating Reminding
Stressors, Pharmacist Actuation Support, and Reminding Stressors-Pharmacist
Actuation Support Interaction to Perceived Medication Stress ................ 154

5.40 Raw Score and Deviation Score Multiple Regression Analysis Relating Obtaining
Stressors, Pharmacist Acquisition Support, and Obtaining Stressors-Pharmacist
Acquisition Support Interaction to Perceived Medication Stress ................. 156

5.41 Raw Score and Deviation Score Multiple Regression Analysis Relating Perceived
Medication Stress, Pharmacist Affirmation Support, and Perceived Medication
Stress-Pharmacist Affirmation Support Interaction to Medication Adherence ....... 158

5.42 Raw Score Multiple Regression Analysis of Elderly Outpatient's Age, Income,
Self-rated Health Status and Chronic Disease Score on Other Person Consultation Support, Other Person Affirmation Support, Other Person Actuation Support, and
Other Person Acquisition Support ................ ................. .. 163

5.43 Raw Score and Deviation Score Multiple Regression Analysis Relating Advising
Stressors, Other Person Consultation Support, and Advising Stressors-Other Person
Consultation Support Interaction to Perceived Medication Stress ............... 164

5.44 Raw Score and Deviation Score Multiple Regression Analysis Relating Isolating
Stressors, Other Person Affirmation Support, and Isolating Stressors-Other Person
Affirmation Support Interaction to Perceived Medication Stress ................. 166

5.45 Raw Score and Deviation Score Multiple Regression Analysis Relating Reminding
Stressors, Other Person Actuation Support, and Reminding Stressors-Other Person
Actuation Support Interaction to Perceived Medication Stress ......... ........ 169

5.46 Raw Score and Deviation Score Multiple Regression Analysis Relating Obtaining
Stressors, Other Person Acquisition Support, and Obtaining Stressors-Other Person
Acquisition Support Interaction to Perceived Medication Stress ................. 171

5.47 Raw Score and Deviation Score Multiple Regression with Analysis Relating Perceived
Stress, Other Person Affirmation Support, and Perceived Medication Stress-Other Person
Affirmation Support Interaction to Medication Adherence ........... ..... .. 173




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LIST OF FIGURES

Figure page 2.1 A stress model (Israel and Schurman, 1990) ............................. 8

2.2 The Stress Model of Medication Adherence (SMMA) ......................... 10

2.3 The "Nucleus" of the Stress Model of Medication Adherence (SMMA-n) ........... 12

3.1 Magnified view of the "Nucleus" of the Stress Model of Medication Adherence ...... 40

5.1 Path Diagram Using Multiple Regression with Raw Scores (r) and Deviation
Scores (d) for Testing Mediating effects of Perceived stress on the Relationship
between Advising Stressors and Medication Adherence ....................... 122

5.2 Path Diagram Using Multiple Regression with Raw Scores (r) and Deviation
Scores (d) for Testing Mediating effects of Perceived stress on the Relationship
between Isolating Stressors and Medication Adherence ....................... 124

5.3 Path Diagram Using Multiple Regression with Raw Scores (r) and Deviation
Scores (d) for Testing Mediating effects of Perceived stress on the Relationship
between Reminding Stressors and Medication Adherence .................. ... 126

5.4 Path Diagram Using Multiple Regression with Raw Scores (r) and Deviation
Scores (d) for Testing Mediating effects of Perceived stress on the Relationship
between Obtaining Stressors and Medication Adherence .............. ....... 128

5.5 The Effect of Advising Stressors on Perceived Medication Stress for Different
Levels of Doctor Consultation Support ............ ............ .... 136

5.6 The Effect of Isolating Stressors on Perceived Medication Stress for Different
Levels of Doctor Affirmation Support ................................. 138

5.7 The Effect of Reminding Stressors on Perceived Medication Stress for Different
Levels of Doctor Actuation Support .................. ................ 141

5.8 The Effect of Obtaining Stressors on Perceived Medication Stress for Different
Levels of Doctor Acquisition Support .............................. 143




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5.9 The Effect of Perceived Medication Stress on Medication Adherence for
Different Levels of Doctor Affirmation Support ........................... 145

5.10 The Effect of Advising Stressors on Perceived Medication Stress for Different
Levels of Pharmacist Consultation Support ...... ......... ........... 151

5.11 The Effect of Isolating Stressors on Perceived Medication Stress for Different
Levels of Pharmacist Affirmation Support ...... ........................ 153

5.12 The Effect of Reminding Stressors on Perceived Medication Stress for Different
Levels of Pharmacist Actuation Support ............................ ... 155

5.13 The Effect of Obtaining Stressors on Perceived Medication Stress for Different
Levels of Pharmacist Acquisition Support ............... .............. 157

5.14 The Effect of Perceived Medication Stress on Medication Adherence for
Different Levels of Pharmacist Affirmation Support ......................... 159

5.15 The Effect of Advising Stressors on Perceived Medication Stress for Different
Levels of Other Person Consultation Support ........................... 165

5.16 The Effect of Isolating Stressors on Perceived Medication Stress for Different
Levels of Other Person Affirmation Support ............................... 7

5.17 The Effect of Reminding Stressors on Perceived Medication Stress for Different
Levels of Other Person Actuation Support .............. .............. 170

5.18 The Effect of Obtaining Stressors on Perceived Medication Stress for Different
Levels of Other Person Acquisition Support ............................. 172

5.19 The Effect of Perceived Medication Stress on Medication Adherence for
Different Levels of Other Person Affirmation Support ........... .... .. 174


















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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy AN EXPLORATORY INVESTIGATION OF A STRESS MODEL
OF MEDICATION ADHERENCE AMONG ELDERLY OUTPATIENTS By

David Allen Gettman

December, 1997

Chairman: L. Douglas Ried, Ph.D.
Major Department: Pharmacy Health Care Administration


The study had two objectives: 1) to develop and validate constructs of a proposed Stress Model of Medication Adherence, and 2) to examine some of the more important relationships among these constructs. The study employed an exploratory, retrospective, cross-sectional design with a sample of veterans over 65 years of age living in north central Florida and southern Georgia and receiving one or more medications on a regular basis through the mail. The study was conducted in two phases. In the pilot phase, an initial questionnaire was developed and tested using a convenience sample of 94 subjects. In the main phase, a revised questionnaire was mailed to a convenience sample of 1,600 subjects. 1,017 responses were used to establish the internal validity of the constructs and to test hypothesized relationships in the proposed model. The dependent variable in the model was medication adherence. The independent variables included medication related stressors and medication specific social support. Perceived medication stress was either a dependent or independent variable depending on the context in which it was used. Descriptive



xvii









statistics and correlational analyses were used to initially address the validation of the four scales. Next, factor analyses were used to evaluate the dimensionality of the items in the questionnaire. Four types of stressors were found: advising, isolating, reminding, and obtaining. And, four types of social support were found for doctor, pharmacist and most concerned other person: consultation, affirmation, actuation, and acquisition. Next, multiple regression was used to test hypothesized main, mediator, and moderator relationships among adherence, stressors, perceived stress and social support. Perceived stress was found to partially mediate the relationships between all four types of stressors and medication adherence. This suggests that these stressors generate patient evaluations of medication taking that effect medication adherence negatively. Finally, some types of social support from the doctor and pharmacist were found to moderate the relationship between some types of stressors and perceived stress. This suggests that more than a threshold level of some types of social support from the doctor or pharmacist are required to prevent patient generation of perceived medication stress.

























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CHAPTER 1
OVERVIEW OF THE ISSUES ADDRESSED BY THIS RESEARCH


Medication Adherence

Drugs provide society with enormous benefits. They reduce mortality and morbidity
relieve pain and suffering, are less expensive forms of treatment than surgery and
hospitalization, are more readily accessible to a larger portion of the population than
are more expensive technologies, and have enabled physicians to see more patients with
improved outcomes (Feldstein, 1988, pp. 437)

Despite Feldstein's glowing portrayal of drugs, the real world results of drug use are far less than optimal. For the elderly, this may be due in part to medication nonadherence. Estimates of medication nonadherence in this population range from 40 to 75% (Cooper, Love and Raffoul, 1982; Lipton and Lee, 1988), and this nonadherence has been associated with a higher risk of hospitalization, which has been estimated to cost approximately $2,150 per admission (Col et al., 1991). Furthermore, it is anticipated that the problem of medication nonadherence among the elderly will become critical in the near future as the world's population grows progressively older (Pucino et al., 1985).

It will become increasingly necessary for health practitioners to improve medication adherence to obviate the need for more expensive medical care (Lipton, 1982; Gryfe and Gryfe, 1984). To improve medication adherence among the elderly will require a better understanding of three variables: stressors, perceived stress, and social support. The significance of these three variables will be discussed in the following sections. The chapter will conclude with a problem statement.






I











Medication-related Stressors

Medication nonadherence is not a unique phenomenon associated with aging. Many
of the same problems concerning taking medications most appropriately apply to 40 year
olds as well as to 80 year olds. What is unique about the elderly is their greater sensitivity
to medications, their greater propensity for the development of adverse effects, and the
greater complexity of their regimens as they develop chronic illnesses through the course
of their lives. In addition, decreasing acuity of special senses, problems with memory,
and the interposition of other care givers are all unique phenomena that have to be dealt
with. (Weintraub, 1990, pp. 445).

Weintraub's depiction of the serious problems faced by the elderly attempting to adhere to their medication regimens is insightful. However, for a more encompassing depiction of these problems one needs to examine the alarming statistics that underpin this rendering.

Elderly patients use more medications than younger patients, and the trend of increasing drug use continues through 80 years of age. Studies conducted in a variety of settings have shown that patients over 65 years of age use an average of 2 to 6 prescribed medications and 1 to 3.4 nonprescribed medications (Stewart and Cooper, 1994). It is known that the use of multiple medications increases the risks of adverse drug reactions, drug-drug interactions, and makes medication adherence more difficult (Fedder, 1984).

Elderly patients also have to deal with problems associated with altered pharmacokinetic and

pharmacodynamic effects of drugs (Hoffler, 1981; Roberts and Tumer, 1988; Dawling and Crome, 1989; Fox and Auestad, 1990; Taylor, 1990). There may be changes in the absorption of orally administered drugs, body composition, serum albumin and globulin concentration, cardiac output and hepatic metabolism, renal blood flow, renal function and homeostatic mechanisms (Shaw, 1982; Chapron, 1995).

Elderly patients also must struggle with a number of disease changes that occur with aging (Salzman, 1982; Tuck, 1988; Furberg and Black, 1988). For example, the incidence and prevalence of congestive heart failure increases exponentially with advancing age (Hunziker and








3

Bertel, 1995), and gastrointestinal problems become more common in the elderly (Levitan, 1989). Indeed, Dall (1989) has stated that side effects seem to increase and medication adherence seems to decrease as elderly patients suffer from increased comorbidities.

There are also age-related changes in vision, hearing, memory, and learning (Kimberlin, 1995). These changes and the inability to read and interpret prescription labels, open and close vials, remove tablets, and identify tablet colors have all been demonstrated to have a negative effect on medication adherence (Meyer and Schuna,1989).

Although numerous studies have specified the experience of these problems among the elderly, few studies have attempted to quantify these problems as stressors. Furthermore, few studies have attempted to place these stressors into a theoretical framework for understanding how these medication-related stressors might effect an elderly patient's perception of stress.

Perceived Medication Stress

Elderly patients may experience and then evaluate that experience with a medication-related stressor. The patient's evaluation of all these experiences may leave the patient with feelings that are favorable for medication nonadherence. In a recent study involving older adults taking prescribed medications, it was found that 21% had been nonadherent during the month preceding the study (Coons et al., 1994). Furthermore, higher perceived stress was significantly associated with medication nonadherence. Although this study specified an important correlation between perceived stress and medication nonadherence, the effect of social support on the relationship between perceived stress and medication nonadherence was not examined.

Medication-specific Social Support

The negative effects of a stress process among elderly outpatients taking medications may be alleviated with social support. As chronic "age-related" changes in physiology become more prevalent, increasing numbers of the elderly experience declined activities of daily living. These








4

patients not only require assistance with such activities as basic hygiene, dressing, eating, and answering the telephone, but also with taking their medication. For example, a significant association was found between spousal support and an elderly patient's adherence to coronary medications (Doherty et al., 1983). In another study, a considerable number of patients were found to benefit from the assistance of a relative or home helper in administering treatments (Gilmore, Temple, and Taggart, 1989). This study also recommended that a suitable helper be identified and counseled to assist at-risk elderly patients with their medication.

Among health care professionals, support activities specific to the needs of the elderly taking medications have been found to result in better medication adherence among elderly patients. For example, weekly pharmacist counseling sessions resulted in improvements in medication adherence among one subgroup of elderly patients (Wolf et al., 1989). In addition, among elderly patients belonging to the other subgroup, their identification as nonadherers helped pharmacists reduce medical misjudgement when making changes to a nonadherer's prescribed medications. In another study involving nurse practitioners and elderly women, a patient's perception of high psychosocial care was the only component of a nurse's visit that had an impact on the patient's intent to adhere (Chang et al., 1985).

In two older reviews of the literature, not specific to the elderly, these findings are indirectly supported, and one draws attention to an important methodological concern. The first review of the literature (Baekelund and Lundwall, 1975) found 19 studies on social support and dropping out of treatment. In all 19 studies, dropping out was associated with low social support. In the second review of the literature (Haynes and Sackett, 1977), 25 studies reported predictors which were indicators of social support. Sixteen of these studies supported a positive relationship between social support and medication adherence; only one study showed a negative relationship, and eight studies showed no relationship to medication adherence. Haynes and Sackett (1977) gave poor







5

ratings to four of these latter eight studies on the quality of the social support measures. Even if the other half of the "non-replications" had reliable, valid measures, the percentage of studies that suggest that social support improves medication adherence remains very high. These studies suggest that the reliability and validity of the social support measures will be critical when rigorously testing the impact of social support on medication adherence.

Problem Statement

Medication nonadherence is a significant problem among elderly outpatients and will assume even greater importance in the near future. Stressors, perceived stress, and social support have been connected with medication nonadherence among the elderly. Social support from health professionals and family members may help the elderly patient more easily deal with stressors and perceived stress and must be carefully measured. The next chapter will propose a Stress Model of Medication Adherence (SMMA).















CHAPTER 2
THEORETICAL FRAMEWORK


Introduction


This chapter begins by briefly describing a stress model. After this short depiction, the model will be adapted in the next section to describe the medication-taking experiences of elderly outpatients. It will be known as the Stress Model of Medication Adherence (SMMA). In the following section, some of the more important constructs in the Stress Model of Medication Adherence (SMMA) and the relationships among them will be initially examined by focusing on the "nucleus" of the Stress Model of Medication Adherence (SMMA-n). After developing and describing hypothesized relationships among the constructs in the SMMA-n, two "overarching" research questions will be presented.


A Stress Model


Soon after Lazarus (1966) published Psychological Stress and the Coping Process, cognitive approaches to stress were more fully developed by additional investigators. Along with renewed interest in emotions and behavioral medicine, the issues of stress in adult life and aging, as well as stress management, gained attention. Since that time, there has also been a dramatic increase of interest in the concept of social support as it affects health and well-being. This interest is reflected in an explosion of research as well as an increase m the number of treatment and intervention programs that use social support for therapeutic assistance.



6








7

Figure 2.1 presents a theoretical model (Israel and Schurman, 1990) that shows the general

relationships among the theoretical constructs that have been developed over the last three decades. This conceptual framework is based on earlier models of social-environmental determinants of health (Lazarus, 1966; McGrath, 1970; Katz and Kahn, 1978), occupational stress, social support, health (House, 1974, 1981), and stress control (Carver and Scheier, 1982; Leventhal and Nerenz, 1983). The model postulates the following set of five elements in the stress process: 1) People may experience a set of psychosocial-environmental conditions conducive to stress (stressors) that place them at risk for physical, psychological, and behavioral disorders. These conditions or stressors do not invariably result in long-term negative outcomes; rather, their effects depend on 2) the perceptions and responses to the stressors by the people involved. An individual may perceive a stressor as threatening, exciting, challenging, or stressful. When an individual perceives a stressor in the environment, he or she may respond in one or more ways: psychologically, behaviorally, and physiologically. The sequences of response to perceived stress are not necessarily bad, and, depending on the specific pattern of relationships among stressor, perception, and response, the stress reaction may be helpful and even pleasurable. However, when the demands placed on people by their environment exceed their abilities, or when people are not able to meet strong needs, conditions are perceived as stressful (McGrath, 1970). 3) Negative short-term responses to stress may occur that may lead, over time, to 4) enduring poor health outcomes. The framework further posits that no objective stressor is likely to produce the same perceptions of stress or resultant short-term responses or enduring outcomes in all people exposed to the stressor. Rather, 5) certain individual and situational characteristics influence how an individual experiences the stress process. Social, psychological, biophysical, and genetic "conditioning" factors influence how an individual experiences the stress process.



















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The Stress Model of Medication Adherence (SMMA)


Figure 2.1 presents a theoretical model that shows the general relationships among a number of variables that could explain medication adherence. An adapted model is presented in Figure 2.2-the Stress Model of Medication Adherence (SMMA). It incorporates medication-related stressors, medication-specific social support, ethnicity, income, age, gender, health status, perceived medication stress, medication adherence, and enduring health outcomes into a theoretical framework.

An example from a health care setting illustrates the components of this adapted framework and depicts the model as it relates to the research problems described in the first chapter. An elderly outpatient may experience one or more stressors (medication-related stressors) which may invoke perceived stress (perceived medication stress). This elderly individual may receive social support (medication-specific social support) from his or her prescribing physician or confidant. Also, a patient's ethnicity, income, age, gender, and health status may be important conditioning variables in the proposed model. For example, if the patient lives among other American Indians he or she may be encouraged to use instead a tribal remedy. Also, the patient may not be able to afford his or her medication. And, if the elderly patient is among the "young-old" elderly he or she may find it easier getting to the pharmacy than a patient that is among the "old-old" elderly. If the patient's health status is poor he or she may have difficulty remembering to take all of his or her medications. Finally, the model suggests how enduring outcomes (measured by a patient's quality of life) can have a feedback effect on medication adherence, as for example, a deterioration in quality of life can lead to a deterioration in medication adherence (Williams, 1987; Julius, 1988).







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The "Nucleus" of the Stress Model of Medication Adherence


The nucleus of the proposed Stress Model of Medication Adherence can be envisioned where perceived medication stress acts upon one relationship in the model, while medication-specific social support acts upon two other relationships in the model (see Figure 2.3). Each of these will be discussed m turn.

Perceived medication stress acts upon the relationship between medication-related stressors and medication adherence. It is important to understand that perceived medication stress is thought to have either a main (direct) effect, or a mediating (indirect) effect on the relationship between medication-related stressors and medication adherence. When perceived medication stress exhibits a main effect on the relationship between medication-related stressors and medication adherence, perceived medication stress will have a detrimental effect on medication adherence regardless of the level of medication-related stressors. For example, an elderly man who generates feelings of guilt or anxiety when he gets off track with his medication taking, may not adhere to his medication taking despite the frequency of his experiences with medication problems. When perceived medication stress exhibits a mediating effect on the relationship between medication-related stressors and medication adherence, a high level of experience with medication-related stressors might generate higher feelings of guilt or anxiety by various processes internal to the person and cause him not to adhere to his medication taking.

Medication-specific social support acts on two other relationships. In the first relationship, medication-specific social support acts upon the association between medication-related stressors and perceived medication stress. In the second relationship, medication-specific social support acts upon the association between perceived medication stress and medication adherence. It is important to understand that medication-specific social support is thought to have either a main (or















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13

direct) effect, or a moderating (or buffering) effect on these relationships. How medication-specific social support exhibits a main effect will be discussed, and then a discussion of how medicationspecific social support exhibits a moderating effect will follow.

When medication-specific social support exhibits a main effect on the relationship between

medication-related stressors and perceived medication stress, a threshold level of effort will have a certain beneficial effect regardless of the patient's level of medication-related stressors. For example, if a diabetic man knows that his family will pick up his insulin whenever he needs it, his perceived medication stress will be lower than if he didn't have his family to count on to do this for him. Also, for example, if the same diabetic man knows that his pharmacist will have his kind of insulin in the drug store when he needs it, the patient's perceived medication stress may be lower, contributing to better medication adherence. Finally, for example, if the same diabetic man knows that his doctor will schedule an appointment will him when he is experiencing a problem with his insulin medication, the patient's perceived medication stress may be lower, contributing to better medication adherence.

When social support exhibits a moderating effect on the relationship between medication-related stressors and perceived medication stress, the medication-specific social support will have a beneficial effect only if the medication-specific social support offered to the individual is high enough to help that person deal with the medication-related stressor he or she is experiencing. In this situation, medication-specific social support will require a higher level of effort to have a beneficial effect. For example, the same man's pharmacist may need to instruct this diabetic man on the use of a new electronic blood glucose monitor. It may take more than one counseling session with the pharmacist to bring the man's perceived medication stress level down. By offering more medication-specific social support to the patient to help him deal with this medication-related stressor he is experiencing often, the extra time spent is not only prudent but necessary.









14

In the present study, medication-related stressors, perceived medication stress, and medicationspecific social support will take on specific meanings. Medication-related stressors will represent the patient's expenence with problems related to his or her medication regimen. For example, an elderly man may state that very often he has needed something to remind him to take his medications on time. Perceived medication stress will represent the patient's evaluation of his or her experiences with these medication-related stressors. For example, an older man may assert that he feels very discouraged with his complex medication regimen. Medication-specific social support will represent the patient's perception of services received from a doctor, pharmacist, or "most concerned" other person, which may help the patient deal effectively with medication-related stressors or perceived medication stress. For example, an elderly man may declare that his wife shows little or no concern about his feelings of discouragement with his complex medication regimen. All these factors may influence his decision to not adhere to his medications.

This exploratory, cross-sectional, retrospective study will investigate only variables in the

"nucleus" of the proposed model. The dependent (outcome) variable will be medication adherence. The independent (predictor) variables will be medication-related stressors and medication-specific social support. Perceived medication stress will be either a dependent or independent variable depending on the context in which it is used.

An important goal of the proposed study will be to better understand the relationships among the variables of the nucleus of the model and, thus more clearly define the basic premises of model. Other variables and relationships, although interesting, will be reserved for subsequent studies. This present focus raises two research questions which will follow.

Research Ouestions

Relationships between four key constructs in the "nucleus" of the proposed Stress Model of

Medication Adherence (SMMA-n) will be the prime focus of this research. These four constructs









15

are medication adherence, medication-related stressors, perceived medication stress, and medication-specific social support. When this task has been accomplished, two "overarching" research questions can be addressed, which are as follows:

I. Does perceived medication stress have a main effect,
mediating effect, or combination of these effects on the
relationship between medication-related stressors and
medication adherence?

2. Does medication-specific social support have a main
effect, moderating effect, or combination of these effects on the following two relationships: 1.) the
relationship between medication-related stressors and
perceived medication stress, and 2.) the relationship between perceived medication stress and medication
adherence?

Summary

This chapter described both a stress model and an adapted one: the Stress Model of Medication Adherence (SMMA). The proposed study will only examine variables that form the "nucleus" of the proposed Stress Model of Medication Adherence (SMMA-n). The nucleus of the model is important because it is there that the consequential impact of perceived medication stress and medication-specific social support can be measured. An investigation of the nucleus of the proposed model will require a thorough review of the psychological, sociological, and medical practice literature germane to the variables and their inter-relationships. In the next chapter, past studies on the conceptualization and measurement of medication adherence, stressors, perceived stress, and social support will be examined.















CHAPTER 3
REVIEW OF THE LITERATURE


Introduction

In this chapter, literature relevant to the nucleus of the proposed Stress Model of Medication Adherence (SMMA-n) will be reviewed. During this review, it will become apparent that some of the variables in the proposed model have already undergone some development, whereas other variables still need a great amount of research. This makes the original, or parent, stress model (Israel and Schurman, 1990) challenging to adapt to the medication-taking experiences of the elderly. The handful of studies that will be reviewed here have used portions of the proposed model and, on the surface, seem to have conflicting results. However, when linking each of these studies using the proposed model, it will become clear that each study offers a piece of the puzzle which, when put together, will help us understand the more important relationships among the variables in this study's proposed model.

During this study, four variables will be utilized, and the more important relationships among them will be elucidated. Medication adherence will be a dependent variable; whereas, the independent variables will be medication-related stressors and medication-specific social support. Perceived medication stress will be either a dependent variable or independent variable according to the context in which it is used.

This chapter will begin with a discussion on medication adherence. This discussion will focus on the only study linking medication adherence to stressors and perceived stress. After a discussion of the positive and negative aspects of this study, an argument for the development of a 16








17

more specific stressor scale will be made to improve the prediction of medication adherence among elderly outpatients.

Next, this chapter will continue with a large section devoted to medication-related stressors. This large deliberation will be divided into two distinct sections. First, a review of the psychosocial literature germane to the historical evolution of general stressor constructs will be addressed. Second, the development of an improved medication-related stressor scale will be discussed. The argument will be made that the construct of medication-related stressors needs to be expanded to include both subjective and objective categorizations. The subjective category of medication-related stressors will include patient-reported measures of these stressors. These subjective medication-related stressors will be initially subclassified into informational, emotional, or instrumental categories. The objective category of medication-related stressors will be an impartial measure of these stressors taken from automated records, that is, the patient's number of prescribed medications.

Next, this chapter will continue with a discussion on perceived medication stress. This section will begin with a review of the literature germane to the historical development of the general perceived stress construct. This discussion will conclude with a review of scales which have been developed. The use of a perceived medication stress scale in the proposed study will aid not only in the elucidation of the proposed model, but also in the validation of the improved medication-related stressors construct.

This chapter will then continue with a discussion on medication-specific social support. This section will conclude with a section on statistical relationships that have been elucidated among stressors, social support, perceived stress, and outcome variables in other similar (but more general) studies. By reviewing the literature on each of these variables and describing relationships that may exist among them, the two overarching research questions from Chapter 2 will be









18

translated into five sets of research hypotheses suggested by the "magnified" nucleus of the proposed Stress Model of Medication Adherence (SMMA-n). A summary will conclude this chapter.

Medication Adherence

"[Adherence is] the extent to which a person's behavior (in terms of taking medications, following diets, or executing lifestyle changes) coincides with medical or health advice (Haynes, 1979)." Unfortunately, elderly outpatients seem to expenence multiple socio-medical problems that make high medication adherence rates difficult to achieve (Richardson, 1986; Entwhistle, 1989).

Medication adherence has been studied as an outcome variable in only one quantitative "stress model-like" study found in the literature. In this study, a group of investigators (Frenzel et al., 1988) found no link between a general daily hassles (stressors) scale (Kanner et al., 1981) and medication adherence. But, the study does seem to suggest that the development of a more "refined" daily hassles (stressors) measure tailored to medication use might be useful for demonstrating an association between medication-related stressors and medication adherence. This same investigation successfully demonstrated a link between perceived stress and medication adherence using a perceived stress scale developed by Cohen, Kamarck, and Mermelstein (1983). However, they did not investigate the effects of social support on the relationship between stressors and perceived stress and the relationship between perceived stress and medication adherence.

The findings from the study by Frenzel et al. (1988) suggest that "general" stressors have an

effect in the hypothesized direction on perceived stress. This opens the door to the use of the model proposed in this study using more specifically defined stressors. However, to move forward will require the development of a more specific construct to supplant the more general daily hassles scale.









19

Medication-related Stressors

The discussion on medication-related stressors will be divided into two sections. The first

section will review the historical evolution of general stressor constructs. The second section will discuss the development of an improved medication-related stressor scale.


Historical Evolution of General Stressor Constructs


There have been various attempts to evaluate the role of stressors using life events scales and daily hassles scales. It will be important to explore the differences between these two types of scales in order to develop a medication-related stressor construct that can be helpful in predicting medication adherence.

In the past, studies on the role of stressors have been approached from a major life events

perspective (Holmes and Rahe, 1967; Dohrenwend and Dohrenwend, 1974). Although much of this research has been criticized on methodological grounds (Mechanic, 1974; Sarason, Johnson and Siegel, 1978), it does provide consistent evidence for a link between social events and health disorders (Brown and Harris, 1978). Increased scores for major life events have been found to be related to dysfunction both retrospectively and prospectively (Monroe, 1983). However, the magnitude of the association found between major life events and health disorders has frequently been low (Sarason, de Monchaux, and Hunt, 1975; Rabkin and Struening, 1976).

In an attempt to improve this prediction, recent research has incorporated measures of everyday events, such as hassles. Lazarus and Folkman (1984) focused on everyday events or hassles as an alternative measure of stressors. Hassles are envisioned by Lazarus and Folkman (1984) as experiences and conditions of daily living that have been appraised as salient and harmful or threatening to the endorser's well-being.








20

Although daily hassles have been found to be correlated with major life events (Kanner et al., 198 1; Monroe, 1983), when the effects of both types of stressors were examined jointly, hassles were found to be a better predictor of dysfunction than major life events. Kanner et al. (1981) found daily hassles to outperform major life event scores as predictors of both current and subsequent symptoms. DeLongis et al. (1982) obtained essentially the same result using health status as the outcome variable. Both studies found hassles and outcomes to be significantly correlated when the effects of major life events were examined using partial correlation. However, when the partial correlation did not include hassles, major life events and outcomes were found to be unrelated. This finding has been replicated using different measures of hassles (Monroe, 1983; Burks and Martin, 1985) and different health outcome measures (Weinberger et al., 1987).

When the "original" Hassles Scale (Kanner et al., 1981) was published, the scale aroused considerable debate for two reasons. First, some critics contended that the Hassle Scale was confounded by an inability to separate the external, objective sources of stress from the internal, subjective reactions to it and that it measured, rather than predicted, perceived stress (Dohrenwend et al., 1984). Second, critics also contended that respondents were asked to endorse Hassle Scale items as having been "somewhat," "moderately," or "extremely" severe; there was no provision for rating a particular hassle as being "less than somewhat severe." They contend that, because of this format, endorsement of Hassle Scale items indicates difficulty in coping with the stressors mentioned in the items (Dohrenwend and Shrout, 1985).

In a newer instrument, the Survey of Recent Life Experiences, Kohn and Macdonald (1992) addressed both criticisms of the Hassles Scale for the general adult population. First, their itemselection strategy was to retain only items which correlated positively and significantly with Cohen et al.'s (1983) "gold standard": The Perceived Stress Scale. In this way, they ensured that the final form of the Survey of Recent Life Events would retain an indirect relationship to the stress-









21

appraisal process, which Lazarus and his associates maintained is a critical determinant of the adverse consequences of stress (e.g., Kanner et al., 1981; DeLongis et al., 1982, Lazarus and Folkmnan, 1984; Lazarus, 1984; Lazarus et al., 1985). They adopted this "indirect approach" to avoid the potential contamination inherent in the Kanner et al. (1981) method of tapping into stress appraisal, namely the use of severity ratings. Instead of having subjects rate each item for its severity, they had them indicate the extent of their experience with it over the past month. Second, they included another point in the Survey of Recent Life Experiences to denote no experience with hassles, as such addressing the second shortcoming alluded to earlier in the original Hassle Scale by Kanner et al. (1981).

Although the "general" Survey of Recent Life Events is a significant advance in hassle research, the development of special hassles measures for specific subgroups of the general population is clearly justified by substantial differences in the frequency of endorsement of various hassles among them (Kanner et al., 1981; Blankstein and Flett, 1991). This is why special scales have been developed to assess hassles among children (Elwood, 1987; Kanner et al., 1987), adolescents (Bobo et al., 1986; Compas et al., 1987), medical students (Wolf, Elston, and Kissling, 1989), college students (Blankenstein and Flett, 1991; Kohn et al., 1990), computer users (Hudiburg, 1991) and, in the health care field, patients on hemodialysis treatment for end-stage renal disease (Murphy, Powers, and Jalowiec, 1985).

Implicit in this view is the expectation that hassles will differ even within subgroups, reflecting their particular interpersonal and social contexts. It seems plausible that men and women, older and younger people, and individuals with different roles would report quite different hassles as a consequence of their personal situations. Furthermore, if hassles are to be a viable alternative to the major life events approach, they need to be predictive of significant outcomes, such as medication adherence.








22

Development of the Medication-related Stressors Construct


The notion of a "hassle-like" scale specific to medication use is not new. Medication-related stressors have been the focus of at least one qualitative study and two quantitative studies. However, by reviewing these related studies, an argument will be made for the development of an improved quantitative medication-related stressor scale that includes not only patient-reported measures of experiences with these stressors, but also an objective measure of these stressors taken from automated records.

One qualitative study has looked at the contribution of medications to stress among family

caregivers and the need of caregivers for services provided by a pharmacist (Ranelli and Aversa, 1994). An interview was designed to elicit information about stressors stemming directly from the medications (such as adverse effects) and arising from problems in managing the drug regimen (such as medication adherence), services received from health care providers (such as services received from the pharmacist), and the outcomes of stress (such as the caregiver's satisfaction with services). Medications were found to contribute substantially to caregivers' stress, with 32% of the caregivers reporting problems directly related to medications (primary medication-related stressors) and 19% and 52% reporting problems in managing the drug regimen (secondary medication-related stressors) currently or within the past year, respectively.

Two quantitative studies have looked at the contribution of medications to stress among patients suffering from schizophrenia (Weiden et al., 1994; Harvey, 1991). Weiden et al. (1994) constructed and validated a hassle "like" instrument specific to takers of lithium as part of a larger scale called the Rating of Medication Influences (ROMI). The ROMI was developed as part of a longitudinal study of neuroleptic nonadherence and was administered to 115 discharged schizophrenia outpatients. A principal components analysis of the "Reasons for Noncompliance"








23

items yielded five subscales related to nonadherence (Denial/Dysphona, Logistical Problems, Rejection of Label, Family Influence, and Negative Therapeutic Alliance). The authors concluded that the multidimensional aspects of the instrument make it ideal for research studies addressing the vanous reasons for medication nonadherence among schizophrenia outpatients. These five subscales do have a distinct advantage over the earlier unidimensional quantitative scale, the Lithium Attitudes Questionnaire (LAQ: Harvey, 1991). However, both of these scales are specific to patients suffering from schizophreniza and cannot be used as a more general scale for elderly outpatients suffering from one or more chronic conditions.

After a thorough examination of studies involving one or more medication-related stressors, it appears in general that subjective experiences of medication-related stressors can be subclassified (at least initially) into informational, instrumental, or emotional categories. Each of these categories, and the studies that suggest the specific subjective experiences of medication-related stressors within each category, will be addressed in turn.

Informational medication-related stressors represent the patient's subjective experience of a

subgroup of problems, irritants, or annoyances stemming from a lack of facts about the particular medication(s) he or she is taking, or the medical condition(s) he or she is experiencing. Nine "informational" medication-related stressors were identified in the medical literature. First, patients appear to have problems when they don't feel right, attribute it to their medication, and wonder about changing the dose of their medication. This has been seen in patients taking medication(s) for convulsions (Reynolds, 1978), nausea (Laszlo, 1983), angina (Scardi, 1989), sedation (Rhodes et al., 1978), diabetes (Paterson et al., 1989), anxiety (Henderson, 1982; Hellerstein et al., 1994), hypertension (Tunca and Agzitemiz, 1993), tuberculosis (Castelo et al., 1989), blood clots (SilvaSmith, 1994), psychosis (Salzman, 1993) and depression (Gram, 1990). Second, patients seem to have trouble choosing the appropriate nonprescription medication. This has been shown, for









24

example, among patients taking laxatives (Bruppacher et al., 1988), and antacids (Gerbino and Gans, 1982). In fact, the problems among the elderly with nonprescnption use has become so problematic that it has been advocated that counseling by pharmacists (Bayne et al., 1983) be augmented with help from social work practitioners (Giannetti, 1983) and trained volunteers (Fabacher et al. 1994). Third, patients appear to need information about how their medication interacts with other medications (Stewart and Cooper, 1994; and Silva-Smith, 1994), food (McCabe, 1986) and/or beverages (Lamy, 1984). Fourth, patients need information regarding the administration of eye drops (Smith and Drance, 1984) and metered-dose inhalers (Reardon and Bragdon, 1993; Clark, 1994), as well as the more common liquids, suppositories, and ointments (Anderson, 1977). Fifth, patients can undergo pressure when they do not know whether something that happens to them is a side effect of their medication. For example, medication adherence has been found to dramatically fall when patients suddenly complain of a change in sexual behavior (Papadopoulos, 1980; Strauss and Gross, 1984), or a change in taste (Coulter, 1988). Sixth, patients can feel anxiety when they do not know what to do when a side effect of their medication occurs. For example, patients often do not know that simple irritation of the skin from transdermal patches (Rayment et al., 1985; Carmichael, 1994), often doesn't require the changing of the prescription, whereas side effects from antidepressants often do (Somberg, 1984; Agosti et al., 1988; Stokes, 1993). Seventh, patients can experience a great deal of strain when they constantly miss doses of their medication. Often this will necessitate a change to drugs with more convenient dosage schedules (Ram and Featherston, 1988; Slinning, 1990; and Bialer, 1992), rather than the usage of depot administration (Barnes and Curson, 1994). Eighth, patients can become bewildered wondering whether a medication given to them by one physician should be taken with a medication given to them by a different physician (Lamy, 1989; Colley and Lucas, 1993; Kirchner, 1994). And, finally, ninth, patients may think or believe that their medication is not









25

working. For example, patients taking anticonvulsants (Reynolds, 1978) and patients takings antiemetics (Lazlo, 1983) may complain that they are uncertain about their drug's efficacy and need more information.

Instrumental medication-related stressors represent the patient's subjective experience of a

subgroup of problems, irritants, or annoyances stemming from a felt incapacity to acquire his or her medication(s) or items necessary to take his or her medication(s) correctly. Nine "instrumental" medication-related stressors were identified in the medical literature. First, elderly patients can experience difficulties getting transportation to a doctor's office and/or pharmacy (Wolfgang et al., 1993). These difficulties are compounded with problems involving opening hours (Crespo et al., 1992), physical disability (Smith and Drance, 1984), and uncommonly stocked medications (Miller, 1981; Bums et al., 1992; Rabon et al., 1993; Carlsson et al., 1993). Second, patients may need something to remind them to take their medications on time (Carswell, 1985; Fingeret and Schuettenberg, 1991; Rivers, 1992; Mackowiak et al., 1994; Spiers and Kutzik, 1995). Third, patients can need something to remind them to have their medications refilled on time (Smith et al., 1986). Fourth, patients can experience trouble having their blood pressure taken (National High Blood Pressure Education Program, 1983; Lebrec, 1990; Mejia et al., 1990; Costa, 1994; Schultz and Sheps, 1994) or some other type of monitoring, for example, drug levels in the blood (Reynolds,1978; Risch et al., 1979; Troupin, 1984; Squire et al., 1984; Ritschel et al. 1989; Javaid, 1994; Silva-Smith, 1994), peak-expiratory flow rates (Stafford, 1988), and occult blood testing (Simon, 1987). Fifth, patients may need an up-to-date list of their medications to show to all their doctors (Smith, 1994). Sixth, patients need to have easy-to-read information about their medications because they often do not receive or remember enough from their conversations with health-providers (Lee and Tan, 1979; Smith et al., 1986; Morrow et al., 1988). Seventh, elderly patients often have difficulty opening their medication containers (Schlumpf and Sonderegger,








26

1981; King and Palmisano, 1989; Burns et al., 1992). Eighth, elderly patients can have difficulty swallowing their medication and may need a suspension compounded (Sarkar et al., 1989; Andersen et al., 1995). And, finally, ninth, patients can have problems paying for their medication (Leppik, 1990; Shea et al., 1992).

Emotional medication-related stressors represent the patient's subjective experience of a

subgroup of problems, irritants, or annoyances stemming from a felt inability to deal with feelings about his or her medication(s) or his or her medical condition(s). Nine "emotional" medicationrelated stressors were identified in the medical literature. First, patients can presume that no one is really interested in their health. The impact of discussion which demonstrates patient interest by the health provider has been shown to be positively associated with patient medication adherence (Martin and Bass, 1989). Second, patients can feel afraid that no one can help them deal with their health problems. Without discussion on patient attitudes and expectations about their therapy, patients cannot hope to have realistic goals and may feel forsaken (Massey et al., 1980; Leppik 1990; Ranz et al., 1991; Unger, 1995; Donovan, 1995). Third, patients can lack confidence about taking their medication as prescribed. Techniques to improve patient confidence have repeatedly demonstrated a positive impact on medication adherence (Jones, 1976; Miller, 1981; Forman, 1985; Webb et al., 1990; Nespor, 1993; Miller, 1993; Lilja and Larsson, 1994; Costa, 1994; Robinson et al., 1995). Fourth, patients can feel embarrassed about taking their medications. These embarrassments may include problems with literacy (Hussey, 1991), urinary incontinence (Vernon, 1989), sexual inadequacies (Aizenberg et al., 1995), epilepsy (Krumholz et al., 1989), and body disfigurements (Stewart, 1983). Fifth, patients can feel that no one seems to listen to them about dealing with their medication problems (King et al., 1986). Sixth, patients can feel afraid to ask someone to make something that was told to them about their medication easier to understand. For this reason, physicians are shifting away from a rigid authoritative manner when








27

speaking with their patients (Martindale, 1990) to using a more collaborative manner (Heaton, 198 1; Larsen et al., 1985; Laage, 1988; Rost et al., 1989) which has been shown to have a positive impact on medication adherence among schizophrenics (Ranz et al., 1991), diabetics (Schifferdecker et al., 1994), and hypertensives (Schultz and Sheps, 1994; Kjellgren et al., 1995). Seventh, patients can feel judged because of the medications they take. Emotional counseling that includes respect and acceptance of the patient's condition must accompany medication therapy (Lieberman and Evans, 1985; Vinson and Cooley, 1993). Eighth, patients can experience a side effect of their medication that upsets them (Papadopoulos, 1980; Strauss and Gross, 1984). Ninth, patients can feel no need to take their medication. This often occurs when a patient suffers from a condition in which there are no readily discernable symptoms, as for example, hypertension (Nies, 1975; Moser, 1985; Black, 1990; Fotherby, 1995), or they suffer from a condition that renders them unable to discern such symptoms, as for example, depression (Guscott and Grof, 1991.)

Also from this thorough review of the literature, one might argue that there is an objective

measure of medication-related stress which act in unison with these subjective medication-related stressors to affect perceived medication stress. Specifically, there are the patient's number of prescribed medications. From the literature review, studies of drug use consistently show a negative relationship between the number of drugs taken by the patient and medication adherence (Weintraub, Au, and Lasagna, 1973; Hulka, Kupper, Cassel, and Efird, 1975; Damell et al., 1986).

In this segment on medication-related stressors, two literature reviews were accomplished: 1) a review of the psychosocial literature germane to the historical evolution of general stressor constructs, and 2) a review of the medical literature to make the argument that the medicationrelated stressor construct needs to include both a subjective and an objective scale and that the subjective stressors should be subclassified into three categories: informational, emotional or








28

instrumental. Both of these reviews will help in the conceptualization and construction of a new medication-related stressors scale.

Perceived Medication Stress

In this section, there will be a review of the literature germane to the historical development of the perceived stress construct. It will be argued that, although historically difficult, a distinction must be made between the experience of stressors and a patient's evaluation of those experiences. It will also be argued that perceived stress may have either main (direct) effects or mediating (indirect) effects on the relationship between stressor experience and medication adherence.

Perceived stress may arise when an individual perceives that it is important to respond to one or more of the hassles, but an appropriate response is not immediately available (Lazarus and Launier, 1978). Although a stressor may not place great demands on the abilities of most individuals, it is when multiple stressors accumulate, persisting and straining the problem-solving capacity of the individual, that the potential for serious perceived stress may occur (Wills and Langer, 1980).

Patients actively interact with their environments, appraising potentially threatening or challenging experiences (Lazarus, 1966, 1977). From this perspective, stressor effects are assumed to occur only when both 1) the experience is evaluated as threatening or otherwise demanding and 2) insufficient resources are available to deal with the experience. The argument is that the causal "event" is the emotional response to the stressor experience (Lazarus, 1977). An important part of this view is that the response to the experience is influenced by conditioning factors as well, such as social support.

The centrality of this evaluation process suggests the desirability of measuring perceived stress in addition to the experience of stressors. If perceived stress is measured, it could also be used in conjunction with a medication-related stressors scale in an effort to determine whether such factors











as social support (Pearlim et al., 1981) protect people from the deleterious effects of stressors by altering the process or processes by which the evaluation of these experiences result in behavioral changes (Gore, 1981). Also, perceived stress can be viewed as an outcome vanable measuring the level of perceived stress as a function of medication-related stressors.

Measures of the evaluation of specific stressor experiences have been widely used, for example, measures of perceived occupational stress (Kahn et al., 1964). There are, however, some practical and theoretical limitations of measuring evaluations to specific stressors. Practically, it is difficult and time-consuming to adequately develop and psychometrically validate an individual perceived stress measure every timune a new stressor is studied. Theoretically, there is an issue of whether measures of perceived stress to a specific stressor really assess a person's evaluation of that stressor. There is, in fact, evidence that people often wrongly attribute their evaluations of stress to a particular source when the perceived stress is actually due to another source (Gochman, 1979). Another problem with measures of evaluation to specific stressors is that such measures imply the independence of that experience in the precipitation of an outcome. However, it is likely that the outcome process is affected by a combination of specific stressors that may be measured by a "global" measure of these stressors.

Cohen, Kamarck, and Mermelstein (1983) presented evidence from three samples, two of college students and one of participants in a community smoking-cessation program, for the reliability and validity of a 14-item instrument, the Perceived Stress Scale, designed to measure the degree to which situations in one's life are evaluated as stressful. The Perceived Stress Scale showed adequate reliability and, as predicted, was correlated with major life event scores, depressive and physical symptomatology, utilization of health services, social anxiety, and smoking-reduction maintenance. In all comparisons, the Perceived Stress Scale was a better predictor of the outcome in question than were major life events scores. In the years to follow this








30

study, the Perceived Stress Scale became the gold standard for measuring perceived stress in other studies (e.g., Linn, 1985; Levenstein et al., 1993). In two studies examining the multidimensionality and internal consistency of the Perceived Stress Scale (PSS: Hewitt et al., 1992; and Martin et al., 1995), it was established that the PSS consisted of two factors. The first factor appears to measure global feelings of stress arising from perceptions that one's life is stressful, unpredictable, uncontrollable, and overloading. As suggested by Hewitt et al. (1992), "perceiving oneself as stressed may involve a perception of one's ability to deal effectively with events or changes." The second factor appears to assess perceptions of an ability to cope with stressors in one's life. As suggested by Hewitt et al. (1992), "perceiving oneself as stressed may involve a negative affective experience reaction."

These general studies on perceived stress point us in the direction we need to go: a perceived stress scale specific to medication use will have to be utilized. This kind of scale must be utilized to test two effects: 1.) the effect of medication-specific social support on the relationship between medication-related stressors and perceived medication stress, and 2.) to test the effect of medication-specific social support on the relationship between perceived medication stress and medication adherence.

However, it may be as important or more important to understand the relationship than

underpins the entire model, that is, how perceived medication stress effects the relationship between medication-related stressor experience and medication adherence. A recent study has demonstrated that neuroticism has significant direct effects on all health outcomes, and substantial indirect effects, through perceived stress, on mental health outcomes (Hooker et al., 1992). This might suggest that the experience of medication-related stressors while taking multiple medications might have direct effects on medication adherence, and substantial indirect effects through perceived medication stress, on medication adherence.









31

The central idea in a mediation model is that the effects of stimunuli on behavior are mediated by vanous transformation processes internal to the organism (Baron and Kenny, 1986). Perceived medication stress can represent a property of the person that transforms a predictor by some "min the head" mechanism. And, it must be understood that mediating events "shift roles" from effects to causes, depending on the focus of the analysis. Therefore, during one part of the study, perceived medication stress will be treated as an independent variable having an effect on medication adherence, while during another part of the study it might be thought of as being acted upon (as a dependent variable) by medication-related stressors.

Medication-specific Social Support

Medication-specific social support was identified as a "social" conditioning variable (Israel and Schurman, 1990). Social support has been defined as "an input directly provided by another person (or group) which moves the receiving person towards goals which the receiver desires (Caplan et al., 1976)." There is evidence that supportive interactions among people are protective against the health consequences of life stress.

Several prospective epidemiological studies have shown that increases in "global" social

support are related to decreases in mortality. This was shown in 9- to 12-year prospective studies of community samples by Berkman and Syme (1979) and House, Robbins, and Metzner (1982) and in a 30-month follow-up of an aged sample by Blazer (1982). Similarly, several prospective studies using mental health outcome measures have shown a positive relation between social support and mental health (Henderson, Byme, and Duncan-Jones, 1981; Holahan and Moos, 1981; Turner, 1981; Williams, Ware, and Donaldson, 1981; Aneshensel & Frerichs, 1982; Billings and Moos, 1982).

In 1983, Levy presented a selective review and critique on social support as a factor in the

enhancement of a different outcome measure--medication adherence. Regarding the integrity of the








32

independent variable of social support, Levy suggested that future research should clearly specify the form of social support in the home, in training sessions, and in support groups. Levy stated that it may be necessary to devise instruments that will enable investigators to monitor "the kind and rate of social support" in several situations, such as support from family and friends. In other words, Levy has suggested that past studies have lacked an adequate detailing of the social support variable because they utilized primarily a "structural" perspective along the "structural-functional continuum." It appears that future investigators should make the shift from the "structural" perspective to the "functional" perspective.

Two comprehensive reviews in the sociological literature (Caldwell and Reinhart, 1988; Oxman and Berkman, 1990) suggest that to utilize the "functional" perspective, one should consider at least three functional domains of social support (i.e., appraisal, emotional, and tangible). These studies suggest that these three functional domains categorize the majority of support functions and that future studies using this typology could add significantly to this body of research.

It seems reasonable to assume that many studies in medical practice have been concerned with the domain of "appraisal" support, whereas studies in the social support literature involving caregivers or confidantes are concerned with the domains of "emotional" or "tangible" support. This is borne out in the only scale identified in the literature that has been used to study the effect of social support on medication adherence (Caplan et al., 1980).

Although some studies have provided evidence of a relationship between functional social support and outcomes, in theory this effect can be explained through very different processes. Social support seems to have two sorts of health effects: 1) a main or direct effect (e.g., Kessler and Essex, 1982), and 2) a moderating or buffering effect (e.g., Henderson, 1980; Wilcox, 1981). The main effect model proposes that social support has a beneficial effect regardless of whether persons are under stress. The moderating effect model proposes that social support will act only to










protect people from the deleterious effects of stress on health and well-being when the patient is experiencing stressors. Both of these types of effects will be discussed in turn.

A generalized beneficial effect of social support is hypothesized to occur because it provides people with regular positive experiences as well as a socially rewarded role (Kessler and Essex, 1982). This kind of support could be related to the overall well-being of the person because it provides positive effects, a sense of predictability and a stability in one's life situation, and a recognition of self-worth. Integration in a network that provides some minimum level of social support may also provide resources to cope with stressors and help one avoid negative experiences that otherwise would increase the probability of an enduring negative outcome.

This view of social support in general has been conceptualized from a sociological perspective as "embeddedness" in social roles (Thoits, 1985) and, from a psychological perspective, as social interaction, social integration, relational reward, or status support (Wills, 1985). This kind of network is hypothesized to be related to physical outcomes through emotionally induced effects on neuroendocnne or immune system functioning (Jemmott and Locke, 1984) or through an influence on health-related behavioral patterns that might include cigarette smoking, alcohol use, or medical help-seeking (Wills, 1983; Krantz, Grunberg, and Baum, 1985).

Shifting to an examination of the moderating (buffering) effects model, social support may moderate between the stressor, or expectation of the stressor, and perceived stress by preventing high levels of perceived stress. That is, the perception that another individual can and will provide necessary resources may redefine the potential for harm posed by the experience of a stressor and bolster one's perceived ability to deal with the imposed experience of a stressor, and hence prevent a particular stressor experience from being evaluated as highly stressful (Cohen and McKay, 1984).








34

Literature reviews reveal that the relevance of the type of social support to a particular stressor experience may also determine the likelihood of observing a moderating effect (e.g., House 1981, House and Kahn, 1985; House et al., 1988; Cohen and Willis, 1985). In 1984, Cohen and McKay published a study on their Interpersonal Support Evaluation List (ISEL). Basically, they did not find a moderating effect with a tangible social support subscale. The lack of evidence for a moderating effect for the tangible social support subscale suggests that tangible aid is not an important resource for buffering. However, they did find a moderating effect for an emotional social support subscale and for an appraisal social support subscale.

This research seems to suggest that informational medication-specific support from a doctor, pharmacist, or (most concemed) other person will demonstrate a moderating effect on the relationship between medication-related stressors and perceived medication stress. Also, this research seems to suggest that emotional medication-specific support from a doctor, pharmacist, or (most concerned) other person will demonstrate a moderating effect on the relationship between medication-related stressors and perceived medication stress. However, this research seems to suggest that tangible medication-specific support from a doctor, pharmacist, or (most concerned) other person will demonstrate a main effect on perceived medication stress rather than moderating effect on the relationship between medication-related stressors and perceived medication stress.

Given that perceived medication stress is an evaluation of his or her responses to medicationrelated stressors, it will be hypothesized that only emotional medication-specific support can effect the relationship between perceived medication stress and medication adherence. Furthermore, it will be hypothesized that emotional medication-specific support from a doctor, pharmacist, or (most concerned) other person will demonstrate a moderating effect on the relationship between perceived medication stress and medication adherence.








35

Other Conditioning Vanables

In the proposed model, there are six other conditioning variables in the nucleus of the Stress Model of Medication Adherence. Two variables are other social conditioning variables: income and ethnicity. The other four vanables are biophysical conditioning variables: age, gender, and health status. Since the study sample will be taken from an older veteran population, ethnicity and gender were expected to not vary much from white males and these variables will be excluded from discussion here. It is hypothesized that the other conditioning variables will have some direct effects on medication-related stressors, perceived medication stress, and medication-specific social support. These hypotheses will help validate the "nucleus" of the proposed Stress Model of Medication Adherence and are expressed below. Age

Advanced age should not be associated with medication nonadherence. Spiers and Kutzik (1995) found, among independently living persons at least 55 years of age participating in a "brown-bag" medication review, that age was the best predictor of medication-related problems. However, contrary to what is often believed, as a group these patients try harder to comply with medications m general and antihypertensive medications in particular than do younger patients (Klein, 1988), even in the presence of bothersome problems.

Advanced age should be positively associated with instrumental medication-related stressors. Subjects with advanced age suffer from declines in activities of daily living and might experience more stressors involving getting and taking their medications (Doherty et al., 1983). Older adults seem to experience motor difficulties that are problematic to obtaining or doing mechanical tasks associated with their medications. In one study, almost half of the noninstitutionalized elderly were found limited in mobility because of chronic conditions, especially heart disease and arthritis (Rice and Estes, 1984). Decreased activity and dexterity can limit a person ability and willingness to







36

have prescriptions filled, take difficult to swallow drugs regularly, and open and close the childproof containers that, to arthritic hands are unmanageable (Mallet, 1992). There are agerelated changes in vision, hearing, memory and learning (Kimberln, 1995). In a study by Meyer and Schuna (1989), Jacob's Cognitive Capacity Screening Examination (CCSE) was utilized successfully to assess skills including the abiity to read and interpret prescription labels, open and close vials, remove tablets, and identify tablet colors.

Advanced age should be positively associated with informational medication-related stressors. Older adults seem to experience difficulties with information when taking their medication. A study suggests that the failing eyesight of older adults reduces their ability to read small print on prescription labels and package inserts (Dirckx, 1979). Also, another study suggests that about 30% of the population aged 65 years and older suffer significant hearing loss (National Center for Health Statistics, 1985). Hearing loss limits patients' ability to hear directions for appropriate drug use and discourages them from asking questions (Ebersole and Hess, 1981). Also, the concept of "intelligent noncompliance" may be of special relevance to the elderly lacking proper information, for older people may omit medications in order to compensate for physiological changes accompanying aging or disease changes that make them more vulnerable to adverse drug reactions that may be unrecognized by their physicians (Lipton and Lee, 1988). Furthermore, memory of information has been shown to decline with age and information may have to be repeated by the health care provider during each visit (Light, 1991; Salthouse, 1991).

Pharmacokinetic and pharmacodynamic effects may be altered in the patient as he or she ages (Hoffler, 1981; Braverman, 1982; Roberts and Turner, 1988; Dawling and Crome, 1989; Fox and Auestad, 1990; Taylor, 1990). There may be changes in the absorption of orally administered drugs, body composition, serum albumin and globulin concentration, cardiac output and hepatic metabolism, renal blood flow and renal function and homoeostatic mechanisms (Shaw, 1982;








37

Chapron, 1995). For example, these changes are associated with adverse effects among the elderly from nonsteroidal anti-inflammatory drugs (Johnson and Day, 1991). Drugs which most often result in adverse reactions in the elderly have been developed (Goldberg and Roberts, 1983).

In addition to these physiologic changes there are a number of disease changes that occur with aging (Salzman, 1982; Tuck, 1988; Furberg and Black, 1988). For example, the incidence and prevalence of congestive heart failure increase exponentially with advancing age (Hunziker and Bertel, 1995). Also, gastrointestinal problems are very common in the elderly, which exacerbate oral medication adherence efforts (Levitan, 1989). Indeed, side effects occur and medication adherence seems to wain, as elderly patients suffer from concurrent existing illnesses, for example, diabetes and bronchitis (Dall, 1989).

Income

Higher income should be positively associated with medication adherence. Hattaway (1996) has reported the impact of the high cost of prescriptions on medication nonadherence among the elderly. In addition, the failure of Medicare to cover the cost of prescription drugs is widely perceived to be a primary factor in medication nonadherence (Arnold et al., 1995). However, it must be clear that in this study most subjects had service connected disabilities and did not have to pay for their medications (except for a $2.00 co-pay). The medication adherence rate might have be significantly different if the study was replicated where cost was a consideration.

Higher income should not be associated with perceived medication stress. One study has

suggested a socioeconomic effect on perceived stress in work and nonwork environments (Bednar, Marshall, and Bahouth, 1995). However, it is interesting to point out that in another closely related study, an inverse relationship was found between economic satisfaction and perceived stress. It would seem that perceived stress has more to do with a person satisfaction with their economic situation than their actual income (Krannich, Riley, and Leffler, 1988).








38

Health Status

An association should not be found between poor health status (using self-rated health and Chronic Disease Score) and medication adherence. This has been suggested in a recent study among elderly patients taking one or more chronic medications (Coons et al., 1994).

Poor health status should be positively related to the informational medication-related stressors. Subjects with more disease states should be more concerned about their medication taking and information that has a bearing on their medication taking. These particular subjects will likely experience more stressors related to specific information gathering efforts. For some older patients, diabetes mellitus or renal functional impairment can effect sodium fluid volume retention, which requires concommitant dosage adjustments to avoid the appearance of side effects (Weidmann, 1983).

Poor health status should be positively related to the instrumental medication-related stressors. Subjects with more disease states would seem to experience more stressors involving getting and remembering to take their medications. Older patients with hip fractures have indicated having transportation problems (Harrison and Kuric, 1989), which suggests they can have difficulty getting medications refilled. Older patients with cancer have reported decreased functional status, fatigue, pain and psychologic distress, that result in instrumental difficulties continuing to take medications (Craig and Powell, 1987; Fossa et al., 1990).

Poor health status should be positively related to the emotional medication-related stressors. As a subject's health deteriorates, them seem to experience problems related to communicating with other people about their medications. Those in the age group 65 or over who rate their health as poor visit the doctor most and take more medication than those who rate their health as good (Linn and Linn, 1980). Certain affective disorders among the elderly are associated with compromised cognitive and emotional capacities and thus, with one's ability to act as a fully autonomous







39

individual (Perry, 1985). These patients, presenting with psychiatric disorder coexisting with alcohol and/or drug abuse, present a major problem in treatment because of their emotional fragility, their propensity to impulsive acting-out behaviors and their adaptation to psychiatric symptomatology via self-medication with illicit drugs (Case, 1991). Recovering patients have complex attitudes and feelings toward medications that need to be explored, particularly as they affect adherence with prescribed regimens (Zweben and Smith, 1989). Major depression is associated with amplification of somatic symptoms and disability, poor self-care and adherence to medical regimens (Katon and Sullivan, 1990).

Poor health status should be negatively related to perceived medication stress (Chwalisz and Kisler, 1995). However, it should be noted that in a separate study, it was found that the strength of this association decreases over time (Schulz et al., 1995). This suggests that upon the first occurrence of one or more morbidities, a patient's perceived stress may be higher than it will be at a future point in time as he or she gets accustomed to living with their condition(s).

Research Hypotheses

The nature of the proposed research is exploratory. Nevertheless, based on this literature review, we now have a "magnified" view of the nucleus of the proposed Stress Model of Medication Adherence (see Figure 3.1). Five sets of hypotheses can now be stated. The first set of hypotheses deal with correlations between the exogenous and endogenous variables to help validate the "nucleus" of the Stress Model of Medication Adherence. The second set of hypotheses deal with hypothesized mediating effects of perceived medication stress on the relationships between type of stressors and medication adherence. The third, fourth, and fifth set of hypotheses deal with relationships between medication adherence, type of stressors, perceived medication stress and type of doctor, pharmacist, and (most concerned) other person support, respectively.


















40






































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41

First Set of Hypotheses

Hypothesis IAA,: Advanced age is not associated with medication adherence.

Hypothesis 1 A,: Advanced age is positively associated with instrumental
medication-related stressors

Hypothesis 1A3: Advanced age is positively associated with informational
medication-related stressors.

Hypothesis IB: Higher income is positively associated with medication adherence.

Hypothesis 1B,: Higher income is not associated with perceived medication stress.

Hypothesis 1 B3: Higher income is not associated with perceived medication stress.

Hypothesis IC,: Poor health status is not associated with medication adherence.

Hypothesis 1C,: Poor health status is associated with informational medication-related
stressors.

Hypothesis 1C3: Poor health status is associated with instrumental medication-related
stressors.

Hypothesis 1C4: Poor health status is associated with emotional medication-related
stressors.

Hypothesis IC5: Poor health status is associated with perceived medication stress. Second Set of Hypotheses

Hypothesis 2A,: There will be a positive main effect between informational stressors and
perceived stress, a negative main effect between informational stressors and medication
adherence, and a negative main effect between perceived stress and medication adherence.

Hypothesis 2A: There will be a positive main effect between emotional stressors and
perceived stress, a negative main effect between emotional stressors and medication
adherence, and a negative main effect between perceived stress and medication adherence.

Hypothesis 2A3: There will be a positive main effect between instrumental stressors and
perceived stress, a negative main effect between instrumental stressors and medication
adherence, and a negative main effect between perceived stress and medication adherence.

Hypothesis 2B: The associations between the exogenous and endogenous variables
found earlier using zero order correlations may no longer be significant when
using multiple regressions.








42

Hypothesis 2CI: There will be a mediatig effect of perceived stress on the relationship
between informational stressors and medication adherence.

Hypothesis 2C,: There will be a mediating effect of perceived stress on the relationship
between emotional stressors and medication adherence.

Hypothesis 2C3: There will be a mediating effect of perceived stress on the relationship
between instrumental stressors and medication adherence.

Third Set of Hypotheses

Hypothesis 3A,: There will be negative main effect between informational stressors and
doctor's appraisal support, a positive main effect between informational stressors and
perceived stress, and a negative main effect between doctor's appraisal support and
perceived stress.

Hypothesis 3A,: There will be negative main effect between emotional stressors and
doctor's emotional support, a positive main effect between emotional stressors and
perceived stress, and a negative main effect between doctor's emotional support and
perceived stress.

Hypothesis 3A3: There will be negative main effect between instrumental stressors and
doctor's tangible support, a positive main effect between instrumental stressors and
perceived stress, and a negative main effect between doctor's tangible support and
perceived stress.

Hypothesis 3A4: There will be negative main effect between perceived stress and doctor
emotional support, a positive main effect between perceived stress and medication
adherence, and a negative main effect between doctor's emotional support and medication
adherence.

Hypothesis 3B: The associations between the exogenous variables and doctor medicationspecific social support variables found earlier using zero order correlations may no longer
be significant when using multiple regressions.

Hypothesis 3C1: There will be a moderating effect of doctor appraisal support on the
relationship between informational stressors and perceived stress.

Hypothesis 3C2: There will be a moderating effect of doctor emotional support on the
relationship between emotional stressors and perceived stress.

Hypothesis 3C3: There will be a moderating effect of doctor tangible support on the
relationship between instrumental stressors and perceived stress.

Hypothesis 3C4: There will be a moderating effect of doctor emotional support on the
relationship between perceived stress and medication adherence.








43

Fourth Set of Hypotheses


Hypothesis 4A,: There will be negative main effect between informational stressors and pharmacist appraisal support, a positive main effect between informational stressors and
perceived stress, and a negative main effect between pharmacist appraisal support and
perceived stress.

Hypothesis 4A,: There will be negative main effect between emotional stressors and pharmacist emotional support, a positive main effect between emotional stressors and
perceived stress, and a negative main effect between pharmacist emotional support and
perceived stress.

Hypothesis 4A3: There will be negative main effect between instrumental stressors and pharmacist tangible support, a positive main effect between instrumental stressors and
perceived stress, and a negative main effect between pharmacist tangible support and
perceived stress.

Hypothesis 4A4: There will be negative main effect between perceived stress and
pharmacist emotional support, a negative main effect between perceived stress and
medication adherence, and a positive main effect between pharmacist emotional support
and medication adherence.

Hypothesis 4B: The associations between the exogenous variables and pharmacist
medication-specific social support vanables found earlier using zero order correlations
may no longer be significant when using multiple regressions.

Hypothesis 4CI: There will be a moderating effect of pharmacist appraisal support on the
relationship between informational stressors and perceived stress.

Hypothesis 4C2: There will be a moderating effect of pharmacist emotional support on the
relationship between emotional stressors and perceived stress.

Hypothesis 4C3: There will be a moderating effect of pharmacist tangible support on the
relationship between reminding stressors and perceived stress.

Hypothesis 4C4: There will be a moderating effect of pharmacist emotional support on the
relationship between perceived stress and medication adherence.


Fifth Set of Hypotheses


Hypothesis 5AI: There will be main effects between informational stressors and other
person appraisal support, nor will there be a main effect between other person appraisal support and perceived stress. There will be a positive main effect between informational
stressors and perceived stress.








44

Hypothesis 5A2: There will be negative main effect between emotional stressors and other person emotional support, a positive main effect between emotional stressors and perceived
stress, and a negative main effect between other person emotional support and perceived
stress.

Hypothesis 5A3: There will be negative main effect between instrumental stressors and other person tangible support, a positive main effect between instrumental stressors and
perceived stress, and a negative main effect between other person tangible support and
perceived stress.

Hypothesis 5A,: There will be negative main effect between perceived stress and other
person emotional support, a negative main effect between perceived stress and medication
adherence, and a positive main effect between other person emotional support and
medication adherence.

Hypothesis 5B: The associations between the exogenous variables and (most concerned)
other person medication-specific social support variables found earlier using zero order
correlations may no longer be significant when using multiple regressions.

Hypothesis 5C,: There will be a moderating effect of other person appraisal support on
the relationship between informational stressors and perceived stress.

Hypothesis 5C,: There will be a moderating effect of other person emotional support on
the relationship between emotional stressors and perceived stress.

Hypothesis 5C3: There will be a moderating effect of other person tangible support on the
relationship between instrumental stressors and perceived stress.

Hypothesis 5C3: There will be a moderating effect of other person emotional support on
the relationship between perceived stress and medication adherence.

Summary

The literature suggests certain hypothesized relationships between advanced age, higher income, and poor health status (exogenous variables) and medication-related stressors, perceived medication stress, and medication-specific social support (endogenous variables) which can help validate the "nucleus" of the Stress Model of Medication Adherence (SMMA-n). Also, the literature seems to suggest that perceived medication stress will demonstrate either a main (direct) effect, a mediating (or indirect) effect, or both on the relationship between medication-related stressors and medication adherence. And, the literature suggests that the (doctor, pharmacist, and








45

other person) appraisal medication-specific social support will demonstrate a moderating (or buffering) effect on the relationship between subjective informational medication-related stressors and perceived medication stress. Similarly, the literature suggests that the (doctor, pharmacist, and other person) emotional medication-specific social support will demonstrate a moderating (or buffenng) effect on the relationship between subjective emotional medication-related stressors and perceived medication stress. Finally, the literature suggests that the (doctor, pharmacist, and other person) tangible medication-specific social support will demonstrate a main (or direct) effect on the relationship between subjective instrumental medication-related stressors and perceived medication stress.

The relationships posited by this "magnified" view of the nucleus of the Stress Model of

Medication Adherence (SMMA) have taken into account the relevant literature. The limitations of previous theoretical work have lead to the development of theoretical hypotheses which must be tested to better explain why some elderly outpatients take one or more medications as prescribed while others do not. The next chapter will explain the methodology of the proposed research.














CHAPTER 4
METHODOLOGY


Introduction

This study utilized a cross-sectional retrospective design to explore relationships among four important variables in the proposed Stress Model of Medication Adherence (SMMA). A convenience sample of subjects for this study was taken from a sample frame of subjects over the age of 65 receiving one or more chronic medications from the Ambulatory Care Pharmacy of the Veterans Administration Medical Center in Gainesville, Florida.

This chapter will be divided into four sections. The first section will discuss study instruments. In the second section, the study will be outlined: Study Phase I, and Study Phase II will be discussed in turn. In the third section, human rights will be discussed. This chapter will conclude with a summary.

Study Instruments

As was discussed at the end of Chapter 3, four variables are examined in this study. These variables are: 1) medication adherence, 2) medication-related stressors, 3) perceived medication stress, and 4) medication-specific social support. Measurement of each of these variables will be discussed in turn.

Medication Adherence

Several methods exist for measuring medication adherence. However, all of the measures identified are troublesome (Norell, 1984). An examination of the strengths and weaknesses of these methods will follow, and, given the study's intent, the most appropriate method was chosen 46









47

and utilized.

There are several methods for measuring medication adherence which fall into two broad

categories: direct and indirect. Direct methods of measuring adherence include testing of blood levels and urinary excretion of the medication, a metabolite or marker (Ballmger et al., 1975; Bury and Mashford, 1981; Young et al., 1984; Kapur et al., 1991). While it appears at first glance that these would be fool-proof methods, one must remember that each individual's body will react to medication (even an identical dose) in differing ways. Also, different forms of the same medication will behave differently in each individual. This is especially true of elderly individuals whose pharmacokminetics increasingly change with advanced age (Chapron, 1995). Finally, it is important that the laboratory tests used to determine adherence be carried out accurately and in a timely manner (Sackett and Snow, 1979). Despite the relative disadvantages of this method, it continue to have proponents (e.g., McMurdo et al., 1991).

There are several methods for indirectly determining adherence. The first involves the measurement of outcomes. While it seems reasonable to attribute a successful outcome to adherence with a prescribed regimen, there may be other potentially confounding factors. Among them are support from the family, physician and lifestyle changes (Sackett and Snow, 1979) as suggested by the proposed Stress Model of Medication Adherence (SMMA).

A second indirect measure of adherence is a patient self-report or interview. Self-report may involve the answering of a few yes or no questions about their medication-taking behavior (Morisky et al., 1986; Gil et al., 1993; Brooks et al., 1994). Scientific investigations of patients often overestimate their adherence with treatment regimunens. For example, Park and his associates (1994) compared estimates of adherence using the interview and pill count methods among a group of psychiatric outpatients. Their study showed that 100 patients would be considered adherent by using the interview method. However, this dropped to 57 when using the pill count method.








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A third method for measuring medication adherence is the pill count method. In this method, the investigator (or patient) simply counts the number of pills remaining m the bottle at some point during treatment. This is compared to the fill date and directions to determine adherence. This method also results in an overestimation of adherence. Problems ensue when farmly members share medications or medications have more than one purpose (Sackett and Snow, 1979).

Physician assessment of adherence is a fourth method. Studies have shown that physicians are no better at assessing medication adherence than by chance (Caron and Roth, 1968; Sackett and Snow, 1979).

A fifth method is relatively new and involves the use of an electronic medication monitor. One method which has been utilized in numerous medication adherence studies is the Medication Event Monitoring System (Cramer et al., 1995; Wall et al., 1995; and Mason, Matsuyama, and Jue, 1995). Another such monitor has been developed by Seth A. Eisen and his associates (1987). Their device contains two 21-blister medication packets and an unobtrusive electronic chip that records the date and time each pill is removed. It has been shown to yield a highly reliable and valid measure of medication adherence (Eisen et al., 1990) and has been utilized in a clinical study among elderly patients (Carney et al., 1995). Devices have also been developed to record the use of inhalers (Gong et al., 1988; Bosley et al., 1995). There is yet another method involving the use of a portable bar code scanner which provides detailed information about the type of forgetting underlying nonadherence (Leirer, 1988). All of these electronic methodologies suffer from two deficiencies: 1) high cost, and 2) the inability to tell whether the patient really ingests the medication or throws it away once he or she has taken the medication from the dispenser which electronically recorded the medication's removal.

Most investigators have recommended a combination of the above methods (e.g., Gilmore, Temple, and Taggart, 1989). However, the constraints of this study suggest that the most









49

appropriate measure is the self-report. Pill counts would be difficult if not impossible given the methodology (i.e., using a mailed questionnaire). Furthermore, the high costs associated with the use of electronic monitoring devices or laboratory tests precludes the use of these methods. Furthermore, Craig (1985) and Westfall (1986) have suggested that despite its limitations, the selfreport may most accurately identify persons who adhere and many of those who do not adhere.

A combination of two self-report measures of medication adherence was used in the study. The first measure was the Medication-taking Behavior scale developed by Morisky, Green and Levine (1986). The second scale was a 24-hour recall scale (Johnson, 1993). Each of these scales will be discussed in turn.

Each subject was asked questions from the Medication-taking Behavior scale. Results of the Morisky, Green and Levine (1986) study have showed the four-item scale to demonstrate both concurrent and predictive validity with regard to blood pressure control at two years and five years, respectively. The specific questions on the Medication-taking Behavior scale are as follows: 1) Do you ever forget to take your medicine? 2) Are you careless at times about taking your medicine? 3) When you feel better do you sometimes stop taking your medicine? and 4) Sometimes if you feel worse when you take the medicine, do you stop taking it? Corrected item-to-total correlations were found to be 0.515, 0.479, 0.527, and 0.561, respectively, for each question. Cronbach's alpha was found in the original study to be low (0.61), but what is considered "low" for alpha depends on the purpose of the research (Churchill, 1979). The questions were scored so that yes=0 and no= 1, and the range was 0 to 4. The mean (weighted) for medication adherence in the Morisky, Green and Levine (1986) study (n=290) was found to be 2.31.

Each subject was also asked another simple yes or no question from another self-report scale

(Johnson, 1993). The question on this scale is as follows: In the last 24 hours, have you missed a dose of medication?








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In using both these instruments together it was important to do an item analysis to determine the final set of items for the revised questionnaire. The decision to retain or delete an item from combined medication adherence scale was loosely based upon the following two criteria: 1) an item-to-total correlation coefficient of 0.3, and 2) a Cronbach alpha of 0.7 (Feketich, 1991). However, as discussed earlier, self-report medication adherence scales are known to suffer from poor reliabilities and it is was likely to get a Cronbach alpha less than 0.7 (Morisky et al., 1986) which would make the results concerning medication adherence only tentative. Medication-related Stressors

As was discussed in Chapter 3, a new scale had to be constructed to measure medication-related stressors. One of the key issues in this study was to test the validity of this new scale. The procedures used in this study for determining scale validity are concerned with the relationships between performance on the scale and other independently collected facts. These procedures were employed sequentially at different stages of scale construction (Anastasi, 1986). The validation procedures began with a statement of the construct's formulated definition, derived from the review of psychosocial theory and the prior research in Chapter 3.

Medication-related stressors were both subjective and objective. The subjective medicationrelated stressors were the patient's report of extent of experiences with a broad group of problems, irritants, or annoyances stemming from the medication(s) he or she is currently taking and/or his or her current medical condition(s). These subjective medication-related stressors were initially subcategorized into three types of stressors: 1) informational, 2) emotional, and 3) instrumental. Informational medication-related stressors represented the patient's extent of experiences with a subgroup of problems, irritants, or annoyances stemming from a lack of facts about the particular medication(s) he or she is currently taking or his or her current medical condition(s). Emotional medication-related stressors represented the patient's extent of experiences with a subgroup of









51

problems, irritants, or annoyances stemming from a felt inability to deal with feelings about his or her current medication(s) or his or her current medical condition(s). Instrumental medicationrelated stressors represented the patient's extent of experiences with a subgroup of problems, irritants, or annoyances stemming from a felt incapacity to acquire his or her current medication(s) or items necessary to take his or her current medication(s) correctly. Finally, there was an objective measure of medication-related stressors which was thought to act in unison with these subjective medication-related stressors to affect perceived medication stress. Specifically, this item was answered by looking at each patient's automated records and is the patient's current number of prescnbed medications.

Content-related validation of the new medication-related stressor scale was examined. The

items comprising each subscale arose from the literature review in Chapter 3. Content validity for the Medication-related Stressors (MrS) Scale thus began by selection of items from the medical literature and placement into hypothesized informational, emotional, and instrumental subscales. An expert panel reviewed and critiqued the items for content validity, and about fifty subjects were interviewed about readability and understanding of these items. The fifty subjects were also asked if there were any other additional stressors they had experienced. Finally, the fifty subjects were asked if the items appeared to be irrelevant or inappropriate. The end product of these scale development procedures consisted of the items added to and deleted from the initial list generated from the literature review.

Criterion-related validation of the new medication-related stressor scale was also examined. Concurrent validity for subjective medication-related stressors was examined relative to objective medication-related stressors, that is, the total scale score on subjective medication-related stressors was tested for a significant correlation with the objective-medication related stressors. Predictive validity of subjective medication-related stressors was tested by a significant correlation between









52

the subjective medication-related stressors and perceived medication stress.

Construct-related validation of the new medication-related stressor scale was also examined. On a four-point Likert scale, patients were asked to rate the degree to which they experienced subjective medication-related stressors in the past year with I = "never," 2 = "once in a while," 3 = "fairly often," and 4 = "very often." A total score for the scale was computed by summing the responses to the 27 items which could range from 27 to 108. As was discussed earlier, these items might be classified as informational, emotional, and instrumental. Since there are nine items in each classification, the scores on these factors ranged from 9 to 36 each.

Construct-related validation of the subjective medication-related stressors scale consisted of factor analyses and internal consistency analyses. Each of these will be discussed in turn.

Construct validity was assessed for the new scale using the principal components model of factor analysis based on the correlation matrix and using varimax orthogonal rotation. It was hypothesized that using a factor analysis would confirm the existence of the three dimensions of the subjective medication-related stressors: 1) informational, 2) emotional, and 3) instrumental.

In using this scale it was important to do an item analysis to determine the final set of items for the revised questionnaire. The decision to retain or delete an item from its respective scale was loosely based upon the following two criteria: 1) an item-to-total correlation coefficient of 0.3, and 2) a Cronbach alpha of 0.7 (Feketich, 1991).


Perceived Medication Stress

Perceived medication stress was defined as representing the patient's evaluations of the medication-related stressors they experienced in the past year. These evaluations were hypothesized to depend on medication-specific social support which will be discussed in the following section.







53

As was discussed in the literature review in Chapter 3, the 14-item Perceived Stress Scale

(PSS) developed by Cohen, Karmack and Mermelstein (1983) is the "gold standard" in the field of perceived stress research. However, this scale is not as "adaptable" to medication-taking as another scale that was identified in the literature. This new scale was developed to measure psychosocial adjustment specific to diabetes and is called the Problem Areas in Diabetes Survey (PAID: Polonsky et al., 1995).

On a six-point Likert scale, patients rate the degree to which each item is currently problematic for them, from I ("no problem") to 6 ("serious problem"). A total score for the scale is computed by summing the responses to the 20 items which can range from 20 to 120.

However, there were two problems when adapting the PAID survey for use in this study. First, no attempt was made to address the multidimensional nature of this scale in the original study. A cursory examination of the 20 items comprising the scale suggested that some items deal with feelings about one's medication regimen, while other items deal with feelings about one's medical condition or social support. It was hypothesized that medication-related stressors would be more strongly associated with the feelings about one's medication regimen, as opposed to the other items. Second, there are the numerous references to diabetes in the PAID survey. For this study, these items were re-worded to reflect perceived stress from medication-taking in general, rather than from just medications taken for diabetes. This required, for example, deletion of the word "diabetes" from the item, or substitution of the words "your condition" for "diabetes."

Criterion-related validation of the revised scale consisted of concurrent and predictive

validation. Concurrent validity for the revised scale was studied by examining the distribution of the revised item scores to the established PAID item scores, indicating the percentage of subjects who reported each item as a "serious problem" (scoring 5) and as "no problem" (scoring 2). Also, the total scale score on the revised scale was tested for a significant correlation with the total score








54

of the established survey. Predictive validity of the revised instrument was also examined. Again, medication-related stressors should have been strongly correlated with this scale, because level of stressors should predict level of perceived stress. Also, predictive validity was tested with a correlation between the score on the revised perceived stress scale and the combined medication adherence scale.

Construct validity was assessed for the scale, using the principal components model of factor analysis based on the correlation matrix and using Varimax (Orthogonal) Rotation and Kaiser Normalization. It was hypothesized that using a factor analysis would confirm the existence of the three dimensions of the perceived medication stress: 1) medication-related, 2) disease-related, and 3) support related. For the purposes of testing the proposed model, only the medication-related dimension was used.

In using this scale it was important to do an item analysis to determine the final set of

medication-related items for the revised questionnaire. The decision to retain or delete an item from its respective scale was loosely based upon the following two criteria: 1) an item-to-total correlation coefficient of 0.3, and 2) a Cronbach alpha of 0.7 (Feketich, 1991). Medication-specific Social Support

Medication-specific social support was defined as representing the patient's perception of a broad group of services or activities he or she might receive from a doctor, a pharmacist, and a (most concerned) other person, which might help the patient deal effectively with medicationrelated stressors. Without this medication-specific social support, the patient may evaluate the medication-related stressors he or she is experiencing as overwhelming, threatening, or harmful, and he or she may stop taking his or her medication(s). Medication-specific social support services or activities were initially subcategorized into three types of support: 1) appraisal, 2) emotional, and 3) tangible. Appraisal medication-specific social support represented the patient's evaluation







55

of a subgroup of services or activities he or she rmght have received from a doctor, pharmacist, or (most concerned) other person which might have helped the patient understand his or her medication(s) and/or medical condition(s). Emotional medication-specific social support represented the patient's evaluation of a subgroup of services or activities he or she might have received from a doctor, pharmacist, or (most concerned) other person, which might have helped the patient deal effectively with emotional problems related to his or her medication(s) and/or medical condition(s). Tangible medication-specific social support represented the patient's evaluation of a subgroup of services or activities he or she might have received from a doctor, pharmacist, or (most concerned) other person, which might have helped the patient obtain or procure their medication(s) and/or items that assisted them in the administration of their medication(s).

The only instrument which was identified in the literature specific to medication-taking is a

social support scale developed by Caplan et al. (1980). There are specific items related to "doctor support" and specific items related to "(most concerned) other person support." For the purposes of this study, items representing doctor support were duplicated for use in another subscale for "pharmacist support."

Although these scales were originally developed to indicate structural support from a doctor or (most concerned) other person, three particular items in these scales seem to indicate functional support. One of the items in the doctor subscale seemed to indicate appraisal medication-specific social support, that is, "[this person] helped me fully understand when and how to follow my treatment." Also, one of the items in the (most concerned) other person subscale seemed to indicate emotional medication-specific social support, that is, "[this person] offers help and shows real concern about my health." Finally, one of the items in the (most concerned) other person subscale seemed to indicate tangible medication-specific social support, that is, "[this person] helps me remember things such as taking my medicine, refilling prescriptions, and keeping doctor's









56

appointments."


Other Conditioning Vanables


In addition to the four constructs above, descriptive statistics were gathered on ethnicity, income, age, gender, and health status. Each will be discussed in turn.

Each subject was asked about his or her ethnic origin. This question was answered by choosing one of the following: 1) White [not of Hispanic origin], 2) Black [not of Hispanic origin], 3) Hispanic, 4) Asian or Pacific Islander, and 5) American Indian or Native Alaskan.

Each subject was asked what was roughly his or her gross annual income in dollars. Subjects were asked to check one box which corresponded most closely to their annual income, marked as follows: 1) less than $5,000, 2) $5,000 to 9,999, 3) $10,000 to 14,999, 4) $15,000 to 19,999, 5) $20,000 to 24,999, 6) $25,000 to 29,999, 7) 30,000 to 34,999 8) 35,000 to 39,999, and 9) greater than $40,000.

The patient's age and gender was obtained from the pharmacy's automated records. The age was recorded in years (a whole number) up to the subject's last birthday. The scoring on the gender item was such that male=0 and female= 1.

Health status was recorded using two different methods. First, health status was measured using a self-rated health scale (Ware et al., 1980). The scale reads as follows: "In general, would you say your health is (circle one number): 5) Excellent, 4) Very Good, 3) Good, 2) Fair, or 1) Poor? Second, health status was determined using the Chronic Disease Score (CDS: Von Korffet al., 1992). The CDS algorithm utilizes data from computerized medication profiles. For example, a patient with an anticoagulant (=3), an oral hypoglycemic (=2), and a theophylline product (=2) would have a CDS score of 7.








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Study Phase I

Study Phase I consisted of three parts. In part one, a questionnaire was administered to a convenience sample of 50 subjects in the pharmacy lobby. After reading the questionnaire the subjects were queried about the readability and understanding of the items. Furthermore, they were asked whether any of the items should not be included in the scale and if any other items should be included that they have experienced. In part two, the responses from the questionnaire were subjected to an item-analysis. In part three, one hundred questionnaires were mailed to subjects to test for the response rate Given the data collected in Study Phase I, a more accurate estimation of response rate and correlations among the major variables was used in the computation of sample size.

Study Phase I, Part One

In this study phase, a scale was developed to measure a construct proposed in the "nucleus"of the Stress Model of Medication Adherence (SMMA-n): Subjective medication-related stressors. Items were generated from the literature review in Chapter 3 to include in each of the three proposed subscales and modeled after another more general instrument in the literature which has been discussed in Chapter 3. Expert judges provided additional review of these three subscales for their content validity, readability, and face validity.

A questionnaire containing the new medication-related stressors scale and all the other

instruments discussed in the above sections were administered to a convenience sample of 50 subjects who visited the lobby of the Ambulatory Care Pharmacy at the Veterans Administration Medical Center in Gainesville, FL, while having their prescriptions filled. The inclusion criteria required that subjects be over the age of 65 and currently receiving one or more chronic medications in the mail from the mail-out section of a Veterans' Administration Medical Center Ambulatory Care Pharmacy.









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To find out whether subjects adequately understood the questions, the sample of respondents who participated in the pretest were asked about the interpretation and clarity of the questions and directions after their completion of the questionnaire. For each question, patients were asked to restate the question in their own words and to give examples of what the question meant to them. It was hoped that the meaning associated to these questions by all the subjects would be congruent with the intended meaning of each question, indicating clarity of the items. Also, subjects were asked about the ease of recalling the experiences. Study Phase I, Part Two

After the responses for the medication-related stressor scale were entered into the computer, the items were grouped into subscales according to what they were intended to measure (i.e., informational, emotional, or instrumental.) The internal consistency of each subscale was calculated to obtain reliability estimates. Reliability of each subscale was assessed through the use of Cronbach's coefficient alpha statistic. The coefficient alpha provided an estimate of how consistently subjects performed across items measuring the same construct definition. A high value of coefficient alpha would indicate a consistent performance of respondents across items. It would also indicate that the performance is generalizable to other potential items pertaining to the same content domain (Crocker and Algina, 1986). Although what is considered "low" for alpha depends on the purpose of the research (Churchill, 1979), a reliability estimate of 0.70 is considered acceptable (Nunnally, 1978).

An item analysis was also examined to determine the final set of items for the revised

questionnaire. The decision to retam or delete an item from its respective subscale was loosely based upon the following two criteria: 1) an item-to-total correlation coefficient of 0.3, and 2) a Cronbach alpha of 0.7 (Feketich, 1991).









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Corrected item-to-total correlations involved the correlations of item score with the total score of the remaining items in the scale under examination. Corrected item-to-total correlations were calculated to adjust for spurious values that might have been obtained when the item scores contributed to the total scores (Crocker and Algina, 1986). Corrected item-to-total correlations were especially relevant when there is a small number of items in the scale (Ferketich, 1991). A more recent "rule of thumb" for corrected correlations is that they should be 0.50 or greater (Bearden et al., 1989). However, corrected correlations above 0.30 have been considered sufficient (Nunnally, 1978).

The revised coefficient alpha revealed the changes in alpha if the item was dropped from the scale. If there was a substantial improvement in alpha when the item was deleted, this was considered as some support for dropping the item. However, the revised alpha was most informative when it was used in combination with the aforementioned aspects of item analysis.

Individual scale items were evaluated in terms of their inter-item correlations, item-to-total

correlations, and revised coefficient alphas when an item was deleted from the scale. The decision to delete or retain an item was determined on both psychometric and conceptual grounds. Caution was taken not to eliminate items only on its psychometric characteristics because of the relatively small Study Phase I sample size.

Certain changes were made in the scales before it was mailed to the larger sample in Study

Phase II. These changes were outlined in the results section. Given that the subjects were elderly outpatients, the response burden was kept as low as possible to improve response rate. Study Phase I, Part Three

According to a statistical report generated at the Veterans Administration Medical CenterGainesville, there were 5,871 elderly patients (aged 65 and over) getting one or more medications from the Ambulatory Care Pharmacy. This represented the sample frame of subjects for this









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study

The most important considerations in the study for sample size considerations involved the needs for the multiple regressions and the factor analyses. Each of these considerations will be discussed in turn

The most accurate correlations found in the literature review were the following: a correlation of 0.48 between stressors and perceived stress (Frenzel, 1988) and a correlation of -0. 12 between perceived stress and medication nonadherence (Coons et al., 1994). Because the scales that were developed in this study were more specific (and hence higher correlations were anticipated), both of these correlations represented conservative estimations. However, it was anticipated that the medication-specific social support items would have much lower correlations (e.g., r < .10). By using the STAT-POWER program (Baroy, 1993), a sample size of 700 cases was required, for the latter (smaller) correlation. In a study which mailed a 63-item questionnaire to 800 senior citizens in a rural area of Idaho, there was a 65% response rate (Johnson, 1972). Although the present study's questionnaire is not about recreational pursuits, the fact that this survey was sent to senior citizens in a rural area and the questionnaire was about the same length appeared to suggest a 65% response rate for this study. With this response rate, I 100 cases would be required.

For factor analysis, the smallest subject-to-item ratio is often considered in the 5 to 10 subjectto-item range (Crocker and Algina, 1986). Since the new scale contained 27 items, a 5-to-i ratio required 135 subjects, and a 10-to-I ratio required 270 subjects. To be conservative an anticipated 65% response rate, required the mailing of approximately 446 questionnaires. Since the 446 questionnaires required is less than the 1100 questionnaires required from the previous calculation, it was anticipated that 1 100 questionnaires would have to be mailed.

In part three, one hundred more questionnaires were mailed to subjects at home to test the

response rate (see Appendix A). These questionnaires were mailed just as they were in Study Phase









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II. Given all the data collected in Study Phase I, a more accurate estimunation of response rate and correlations among the major variables was thought to aid in a better computation of sample size.


Study Phase II

The objectives of this study phase were to validate the new subjective medication-related

stressors scale and test the relationships among the four scales in the "nucleus" of the proposed Stress Model of Medication Adherence. In Study Phase II, the revised questionnaire was mailed to a convenience sample of approximately 1,100 veterans (depending on the results of the initial mailout) 65 years of age and older receiving one or more medications through the mail from the same Veterans Administration Medical Center Ambulatory Care Pharmacy.

This section will continue with a discussion on the data collection procedures. It will end with a discussion of the statistical analyses to be done with the data when the revised mail questionnaires are returned, which involved the following: 1) descriptive statistics, 2) zero-order correlations, 3) factor analyses, 4) reliability analyses, and 5) multiple regressions. Study Phase II, Part One

A convenience sample of approximately 1,100 patients were chosen from requests for prescriptions to be mailed to them from the Ambulatory Care Pharmacy at the Veterans Administration Medical Center in Gainesville. Each subject chosen had to be over 65 years of age and taking one or more prescribed medications on a regular basis.

Data collection required two important steps: 1) an initial questionnaire mailout, and 2) a postcard follow-up. It was hoped that by discontinuation of this data collection process, 700 completed, usable questionnaires would be analyzed for a response rate of 65%. If not, a return to the mail requests for more names and more mailings of the questionnaire would have been initiated until the necessary 700 usable questionnaire demand was met.









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Cover letter

The cover letter emphasized the usefulness of the study, explained the importance of the

respondent's response, and assured that confidentiality would be upheld. The signature of the chief of pharmacy was at the bottom. If subjects had questions regarding the questionnaire they were asked to call the VAMC-Gainesville ambulatory care pharmacy. Identification system

An identification system was used to facilitate the sending of follow-up mailings. Therefore, confidentiality but not anonymity was offered. Questionnaires were identified by a patient ID number written on the upper right-hand comer of the questionnaire, a position in which it was easily visible. The number corresponded to one written next to the respondent's name on the mailing list.

Precoding procedure

The questionnaires were preceded based upon pretest responses to the extent feasible.

Responses from each questionnaire were transferred to a SPSS 7.5 for Windows (SPSS Inc., 1997) computer file. Each response category was assigned an identifying number which was used to represent that response on the SPSS 7.5 for Windows (SPSS Inc., 1997) computer file. The columns to which a response was transferred were listed beside each question on the questionnaire. The result of this preceding effort was the ability to go quickly from the questionnaire to the SPSS

7.5 for Windows (SPSS Inc., 1997) computer file for analysis. Mail-out

This questionnaire mail-out occurred in April, 1997. As outlined above, a convenience sample of about 1100 subjects was surveyed. Surveys were mailed to the patients' homes. A copy of the revised questionnaire is contained in the Appendix B.









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Postcard follow-up

According to Dillman et al. (1974), most people who answer questionnaires do so almost immediately after they receive them. A questionnaire that lies unanswered for a week or more is not likely to be returned. In repeated studies, Dillman et al. (1974) observed that half the return envelopes were postmarked within two or three days after being received by respondents. After that time, the number of postmarked returns declined, sharply at first and then gradually, but nonetheless consistently.

One week after the initial mail-out date, a postcard follow-up was mailed to the entire sample of subjects. The postcard urged nonrespondents to complete and return the questionnaire. It also served to thank those who had already returned the questionnaire. This mailing seemed to produce more returned questionnaires (some usable and some not usable) within the span of four weeks, after which data collection was discontinued.


Study Phase II, Part Two


The two major goals of the study were to: 1) validate the new subjective medication-related stressors scale, and 2) to test the proposed Stress Model of Medication Adherence. In this phase, validation of the new scale involved both criterion-related validation and construct-related validation. Criterion-related validation required both concurrent and predictive validation. This required accumulation of information from two specific techniques: 1) descriptive statistics and 2) correlational analysis. Construct-related validation required the accumulation of information from two other techniques: 1) factor analysis and 2) Cronbach's Alpha internal reliability. Testing of the proposed Stress Model of Medication Adherence involved the use of multiple regressions. All analyses were done using SPSS 7.5 for Windows (SPSS, Inc., 1997).









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Descriptive statistics

In this initial phase of the analysis, means, standard deviations, ranges (with minimums and maximums), and 95% confidence intervals of all measures were calculated. For those measures that are not new, their descriptive statistics were compared to those obtained in previous studies.

The means and standard deviations of the objective (number of prescriptions) and subjective (informational, emotional, and instrumental) medication-related stressors, (appraisal, emotional, and tangible) medication-specific social support, perceived medication stress, medication adherence, age, gender, income, ethnicity, self-rated health, and Chronic Disease Score were calculated. Given these statistics and correlations between these variables, preliminary conclusions were made about both the concurrent and predictive validity of the new medication-related stressors measure

Another preliminary step in the analysis involved an examination of the descriptive statistics of the various measurements and a decision of the type (parametric versus nonparametnc) of the subsequent analyses. Most of the independent variables and the dependent variable were measured by summing items scores, that is, by adding items together to form a subscale using the mean of that subscale to represent the score on that variable. When the range of possible scores on an ordinal scale is increased, data begins to take on the appearance of being continuous. Therefore, the ordinal data was tested to see if it was normally distributed.

For each subscale mean and each independent item, the following hypothesis were tested: 1) the values are a random sample from a normal distribution, and 2) the data will test against a normal distribution with mean and variance equal to the sample mean and variance. The Kolomogoriv D statistic was computed. It was anticipated that the data would not fail this test of normality.

Although the data might not have passed the tests of normality, the distribution means may have approximated a normal distribution due to a large number of responses (n a 700). This is because









65

of the Central Limit Theorem. According to this theorem, even if the distribution of x was not normal, the distribution would become closer and closer to the normal distribution with mean and vanance' as x got larger (Armitage, 1973).

Parametric analyses were utilized for tests affected by the Central Limit Theorem. The researcher may have had to trade the advantages of nonparametric analysis, that is, 1) their freedom from assumptions about the distribution of the scores in the population and 2) their simplicity, for the advantages of parametric analyses. Parametric analyses are more powerful and provide more information from the data when the assumptions of the particular analysis are met (Keppel, 1991). For analyses not affected by the Central Limit Theorem, nonparametric tests would have be chosen.

Zero-order correlations

Zero-order correlations between exogenous variables and endogenous variables were used to help establish validity for the nucleus of the proposed Stress Model of Medication Adherence. Also zero-order correlations between independent variables and between the independent variables and the dependent variable(s) were examined. For the testing of hypotheses with the magnitude and directionality of the relationship between two variables, intercorrelational analyses and the correlation coefficient were used.

A correlation matrix showing the strength of association between all combinations of

independent and dependent variables addressed questions like these. However, as mentioned before, a major goal of the proposed study was to validate the new subjective medication-related stressors scale. Therefore, the subscale scores of the new subjective medication-related stressors scale was tested for significant correlations with the objective medication-related stressors measure (number of medications). Furthermore, predictive validity of the subjective medication-related stressors subscales was tested by significant correlations between the subscale scores on the








66

medication-related stressors scale and the score on the perceived medication stress scale. Factor analyses


A factor analysis was conducted for each construct. First, a factor analysis was used to

evaluate the unidimensional nature of the medication adherence scale. Second, a factor analysis was used to evaluate the multidimensional nature of the medication-related stressor scales (e.g., for informational, emotional, and instrumental factors). Third, a factor analysis was used to evaluate the unidimensional nature of the perceived medication stress scale. Finally, three factor analyses were used to evaluate the multidimensional nature the doctor, pharmacist and (most concerned) other person medication-specific social support scales (e.g., for appraisal, emotional, and tangible support factors).

Using a principal components analysis with Varimax Rotation and Kaiser Normalization, the number of items were reduced from the number of original items to a relatively small number of factors, or common traits. A factor analysis could discover pattems among the variations in values of several variables. This is done essentially through the generation of artificial dimensions (factors) that correlate highly with several of the real variables and that are independent of one another (Harman, 1976; Gorsuch, 1983).

Factor analysis does have its disadvantages, and the results were kept in perspective. Factors are generated without any regard to substantive meaning. Often researchers will find factors producing very high loadings for a group of substantively disparate variables. Factor analysis is like other complex modes of analysis. It should be encouraged whenever such activity may assist in understanding a body of data. As in all cases, the investigator must remain aware that such tools are only tools and never "magical" solutions (Crocker and Algina, 1986).








67

Cronbach's coefficient alpha reliability

As in Study Phase I, Cronbach's coefficient alpha statistic was calculated. However, unlike in Study Phase I, Cronbach's alpha was calculated for each factor identified for the four scales. Items with item-to-total correlations less than 0.50 may have been dropped. An index was constructed for each of the factors by summing the unweighted responses to each item included in the factor. Multiple regressions

The predictive validation of the scales used in the study partly would lie in four effects. These were as follows: 1) the mediating (indirect) effect of the perceived medication-stress on the relationship between each type of medication stressor and medication adherence, 2) the main (direct) effect of tangible medication-specific social support on the relationship between instrumental medication-related stressors and perceived medication stress, 3.) the moderating (buffering) effect of appraisal medication-specific social support on the relationship between informational medication-related stressors and perceived medication stress, and 4.) the moderating (buffering) effect of emotional medication-specific social support on the relationship between emotional medication-related stressors and perceived medication stress. The mediating and moderating effects were examined using procedures suggested by Baron and Kenny (1986).

To test the mediating effect of perceived medication stress, a series of multiple regression analyses were conducted. According to Baron and Kenny (1986), the mediating model can be substantiated by three regression equations. First, the medication adherence (outcome variable) was regressed on the medication stressors (predictor variable). In the second equation, perceived medication stress (mediator variable) was regressed on medication stressors (predictor variable). The third equation involved regressing medication adherence (outcome variable) simultaneously on medication stressors (predictor variable) and perceived medication stress (mediator variable).









68

In order to test medication-specific social support as a moderator (buffer) of medication-related stressors, another entry procedure suggested by Baron and Kenny (1986) was utilized. First, medication-related stressors (MrS) and medication-specific social support (MsSS) were entered in step one. The interaction term of medication-related stressors and medication-specific social support (MrS X MsSS) was entered on step two. Steps one and two were repeated for each subscale of medication-related stressors and medication-specific social support.

In these types of regression analyses, it has been suggested that deviation scores on the

variable(s) from their means be considered (Finney et al., 1984). The estimated effects of the constituent variables are still those at the zero point of the variables(s), but, after deviation, the zero point is the mean of the variable(s) (Southwood, 1978). Such average effects have been estimated in several studies of stress and coping or social support (e.g., Williams et al., 1981).

It has been suggested that deviation scores in these types of multiple regressions are a cure for the multicollinearity problem (high correlation between the product-term and one or more of its constituents) that often arises with the use of untransformed (raw) scores (Cronbach and Snow, 1977). Although deviation scores will reduce the product-term's correlation with the constituent variables, multicollinearity poses no threat to the analysis of interactions or main effects, unless the correlation is so high as to produce rounding errors in computer calculations (Southwood, 1978).

Since the standard regression coefficient denotes the linear effects of an independent variable, the actual magnitude of the coefficients will be tested by hierarchical multiple regression. For testing the goodness of fit of a linear model, R2 was used for coefficient determination. The most important set of hypotheses in the study treated the magnitude and model of the effects of medication-specific social support as competing models, that is, main versus moderating models. The interaction term, a linear X linear interaction, would enhance the prediction of the dependent variables beyond the first order term and would suggest a moderator effect of a type of doctor,









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pharmacist, or (most concerned) other person medication-specific social support.

Although such transformations are useful, they affect the interpret ability of the regression

coefficients; therefore, some investigators prefer to interpret standardized regression coefficients in the context of certain multiple regression applications (Jaccard, Turrisi and Wan, 1990). Therefore, both types of multiple regressions (with raw scores and deviation scores) were done.

Human Rights

The research protocol was reviewed by the Committee on Protection of Human Subjects of the University of Florida and the research service of the VAMC-Gainesville. Participants in Study Phase I were solicited voluntarily in person. Participants in Study Phase II were solicited with a recruitment letter and informed that their responses would be kept confidential.

Summary

To adapt a general stress model to explain elderly patients' medication-taking, elderly

outpatients were surveyed using an interview and mail survey methodology in two separate study phases. Study Phase I involved the development and initial validation of a medication-related stressors scale. Study Phase II involved further validation of medication-related stressors scale and an exploratory investigation of the proposed Stress Model of Medication Adherence using responses obtained from about 700 elderly outpatients in a mail survey. The dependent variable was medication adherence. The independent variables were medication-related stressors,, medication-specific social support income, ethnicity, age, gender, and health status. Perceived medication stress was either a dependent or independent variables depending on the context in which it was used. This exploratory investigation of the proposed Stress Model of Medication Adherence focused on the important main, mediator and moderator effects.

The next chapter will begin with a detailed presentation of the results of the analyses. And, then the chapter will end with a discussion and interpretation of these results.














CHAPTER 5
RESULTS


Introduction


The study was conducted in two phases. In Phase I, a questionnaire was administered to a

convenience sample of fifty subjects m the lobby of an outpatient pharmacy to test the readability and understanding of the items. Then, the same questionnaire was mailed to a convenience sample of one hundred elderly outpatients to test for the response rate. The data from both samples were then combined and analyzed and a revised questionnaire was generated. In Phase II, the revised questionnaire was mailed to a convenience sample of 1,600 elderly outpatients. The data from this sample was then analyzed, and reported here.


Study Phase I


Study Phase I consisted of two parts. In the first part of Phase I, fifty subjects filled out

questionnaires in the lobby of the outpatient pharmacy. In the second part of Phase I, one hundred questionnaires were mailed to a convenience sample of one hundred subjects. Forty-four of the one hundred subjects responded to this initial mailing. In the second part of Phase I, the data from the fifty subjects in the lobby of the outpatient pharmacy was compared to the data from the subjects who responded to the mailed questionnaire. After a comparison of the mean responses on several of the key items, the two data sets from Phase I were combined. The frequencies of responses to all categorical variables, the means and standard deviations of the summated scales, and the



70









71

demographic data on the sample of subjects in the pilot study are reported. A correlation matrix of the independent variables was examined to determine whether conceptually distinct scales had low correlations.

Item analyses for each of the four scales will be reported. Item-to-total correlations will be

examined, and Cronbach's coefficient alpha was recalculated each time it was decided to delete an item.


Comparison between Phase I Lobby and Mail Samples


Before the results from the two samples could be pooled, an examination was made of the two samples on four groups of items: 1.) medication adherence, 2.) subjective medication-related stressors, 3.) perceived medication stress, and 4.) medication-specific social support. There were no statistically significant differences (Table 5.1) Phase I Sample Description and Measures


About seventy percent of the respondents in Phase I were "young-old" elderly, whereas only twenty-seven percent of the respondents were "middle-old" elderly. Almost all the respondents were male and of white (Caucasian) ethnicity. About seventy percent of the respondents had an annual income of less than $20,000. About fifty-nine percent of the respondents rated their own health as fair or poor; whereas, about fifty percent had a Chronic Disease Score between 6 and 15. Medication adherence


The medication adherence items had a Cronbach's alpha of 0.5021 calculated using the four items from the Morisky, Green, and Levine scale (1986). However, it seems prudent to attempt to









72

Table 5.1 Phase I Comparison between Lobby and Mail Samples on Four Groups of Questionnaire Items

Group of Items Mean S.D t_(Si.)

Medication Adherence -.228(.912) (4 items)

Lobby 3.2500 .9785 Mail 3.2955 .9296

Subjective -.426(.452) Medication-related
Stressors
(27 items)

Lobby 39.4318 10.1119
Mail 40.4524 9.2161

Perceived Medication .198(.507) Stress
(20 items)

Lobby 45.6000 24.1345 Mail 44.6364 21.7385

Medication-specific .988(.561) Social Support
(15 items)

Lobby 63.3023 9.8381
Mail 61.1579 9.6493









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Table 5.2 Phase I Sample Description

Characteristic n* Category Frequency Percent

Age 94 65 to 74 ("young-old") 66 70.3 75 to 84 ("middle-old") 27 28.6 85 to 100 ("old-old") 1 1.1

Gender 94 Male 92 979 Female 2 2.1

Ethnicity 92 White (not of Hispanic origin) 85 92.4 Black (not of Hispanic ongin) 5 5.4 Hispanic 2 2.2 Amencan Indian or Native Alaskan 0 0.0 Asian or Pacific Islander 0 0.0

Income 84 Less than $5,000 5 6.0 $5,000 to $9,999 25 29.8 $10,000 to $14,999 16 19.0 $15,000 to $19,999 13 15.5 $20,000 to $24,999 11 13.1 $25,000 to $29,999 4 4.8 $30,000 to $34,999 5 6.0 $35,000 to $39,999 2 2.4 Greater than $40,000 3 3.6

Self-rated Health 93 Poor 23 24.7 Fair 32 34.4 Good 26 28.0 Very Good 10 10.8 Excellent 2 2.2

Chronic Disease 94 0 to 2 24 25.6 Score 3 to 5 23 24.5 6to 8 31 32.9 9 to 11 13 13.8 12 to 15 3 3.2

* The maximum number of responses that could have been collected on each of these characteristics was 94. However, some subjects did not wish to respond to some of these questions. For example, ten subjects (10.6% of the Phase I sample) did not wish to reveal their income, dropping n for the income characteristic from 94 to 84.








74

improve the psychometric properties of this group of items by including the 24-hour recall item used in similar research studies on assessing medication compliance (e.g., Johnson, 1993). When the 24-hour recall item was included, the Cronbach's alpha improved to 0 6089. Furthermore, when one of the original Monsky, Green, and Levine scale items concerning adherence when feeling worse was dropped because of a poor item-to-total correlation of 0.1280, the Cronbach's alpha imunproved to 0.6526 (Table 5.3).

Subjective medication-related stressors

Two of the emotional subjective medication-related stressor items had low item-to-total correlations, that is, "felt that no one would agree with you when you thought taking your medication was unnecessary" (.2504) and "felt that no one understood why you felt embarrassed about taking your medications in front of other people" (.2823). When these two items were dropped the Cronbach's alpha improved from 0.7757, to 0.7831 (Table 5.4). There was some apprehension about these items and their low correlation with number of prescriptions. Therefore, five more items were generated for the revised questionnaire: 1) "felt that taking your medications as directed by your doctor was easy," 2) "felt that you were taking too many medications,"3) "felt uncomfortable interrupting the pharmacist to ask a question,"4) "felt that if you had a medication problem you could deal with it the right way, and 5) "felt like you had a problem with your medication."

There were fewer concerns about the informational and instrumental medication-related stressor items. Only one item concerning nonprescription medications was dropped from the informational subscale, and only one item concerning the timeliness of refills was dropped from the instrumental subscale because of low item-to-total correlations. After these changes, the Cronbach's alpha for the informational subjective medication-related stressors improved to 0.7134 (Table 5.5). The









75

Table 5 3 Phase I Medication Adherence Items: Reliability Coefficients and Item-to-total Correlations (n=91)

Item-to-total
Correlations*

Medication-taking Behavior Scale I. Are you careless at tunes about taking .4981 your medication?

2. When you feel better do you sometimes .3786 stop taking your medicine?

3. Do you forget to take your medicine? .2562 4. Sometimes if you feel worse when you .1142 take the medicine, do you stop taking it? Medication-taking Behavior Scale and '24-Hour Recall' Item

1. Are you careless at tunes about taking your .5529 .5828
medication?

2. In the last 24 hours, have you missed a dose of medication? .4638 .4692

3. When you feel better do you sometimes stop taking your medicine? .4328 .3783 4. Do you forget to take your medicine? .3033 .3548

5. Sometimes if you feel worse when you take the medicine, do you stop taking it? .1280


*For the first group of items, all items are included in the first and only column and Cronbach's alpha was 0.5021. For the second groups of items, all items are included in the first column and the Cronbach's alpha was 0.6089. In the second column, item 5 was deleted and Cronbach's alpha improved to 0.6526.









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Table 5 4 Phase I Emotional Subjective Medication-related Stressor Items: Reliability Coefficients and Item-to-total Correlations (n=91)

Item-to-total
Correlations*

1. Felt that no one listened to you about .6602 .6498 .6447
your medication problems

2. Felt that no one seemed really interested .5694 .5856 .6152
in your medication taking

3. Felt that no one could help you deal with .6043 .6243 .6149
your medication problems

4. Felt that no one could help you feel more .5470 .5277 .4986
confident about taking your medications as
directed

5. Felt that no one could explain something to .4284 .4326 .4366
you about your medication

6. Felt that no one treated you normally because .4034 .4096 .4288
of the medication you take

7. Felt that no one understood why a side effect .3886 .3780 .3624
of your medication was upsetting you

8. Felt that no one understood why you felt .2823 .2702
embarrassed about taking your medications
in front of other people

9. Felt that no one would agree with you when .2504
you thought taking your medication was
unnecessary

*In the first column, all items are included and Cronbach's alpha was 0.7757. In the second column, item 9 was deleted and Cronbach's alpha improved to 0.7788. In the third column, item 8 was deleted and Cronbach's alpha improved to 0.7831.









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Table 5 5 Phase I Informational Subjective Medication-related Stressor Items: Reliability Coefficients and Item-to-total Correlations (n=89) Item-to-total
Correlations*

1. Not known what to do if you missed a dose of .5116 .5053
medication

2. Not known if you should change the dose of your .4346 .4684
medication when you did not feel right

3. Not known if something that happened to you was .4624 .4261
a side effect of your medication

4. Not known whether a medication given to you by .4602 .4142
one doctor should be taken with a medication
given to you by a different doctor

5. Not known if you should drink alcohol and/or .3418 .3849
smoke while taking your medication

6. Not known if your medication was working right .3921 .3819 7. Not known if you were taking your medication .3390 .3792
correctly

8. Not known what to do if a side effect of your .3422 .3473
medication occurs

9. Not known if you should use a nonprescription .2496
medication


*In the first column, all items are included and Cronbach's alpha was 0.7094. In the second column, item 9 was deleted and Cronbach's alpha improved to 0.7134.









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Table 5 6 Phase I Instrumental Subjective Medication-related Stressor Items: Reliability Coefficients and Item-to-total Correlations (n=93)


Item-to-total
Correlations*


1. Had trouble getting something to keep track of how .6133 .6049
well your medication is working

2. Had trouble getting easy to read information about .5861 .5926
the medications you take

3. Had difficulty getting an easy to open medication .5738 .5825
container
4. Had trouble finding something to remind you to take .5482 .5595
your medications on time

5. Had difficulty getting an up-to-date list of your .4728 .5153
medications

6. Had difficulty getting an easy to swallow medication .4501 .4552

7. Had trouble getting something to remind you to have .4053 .3997
your medications refilled on time

8. Had difficulty getting transportation to a doctor's .4138 .3820
office or pharmacy

9. Had problems getting your medication refilled before .2927
your current supply ran out


*In the first column, all items are included and Cronbach's alpha was 0.7871. In the second column, item 9 was deleted and Cronbach's alpha improved to 0.7972.








79

Cronbach's alpha for the instrumental subjective medication-related stressors imunproved to 0.7972 (Table 5.6).

The ninety-four Phase I subjects were taking a mean number of 8.31 (s.d.= 4.63) prescriptions. The number of prescriptions was the objective medication-related stressor measure that should be positively associated with subjective medication-related stressors to establish concurrent validity, that is, as the number of prescriptions increases, the amount of experienced subjective stressors should increase. This association was demonstrated when the informational (r=.284, p <.01) and instrumental (r=.298, p <.01) subscales.. However, there was no association between the emotional items and number of prescriptions.

Perceived medication stress

The four items that reflect the construct all had good item-to-total correlations. Cronbach's alpha for the four items was 0.8520 (Table 5.7). Medication-specific social support

There were only enough items from the adapted scale to perform reliability analyses for doctor emotional medication-specific social support (Table 5.8) and pharmacist emotional medicationspecific social support (Table 5.9). Both these analyses demonstrated good item-to-total correlations and a good Cronbach's alpha. However, more items needed to be generated and the existing items needed to be made more specific to medication taking.

It was decided that the doctor medication-specific social support items would read the same as the pharmacist items after all the modifications were made. One of the doctor emotional medication-specific social support items asking the subject if the doctor or pharmacist had "acted in a warm and friendly manner" did not seem to adequately reflect emotional medication-specific social support and was dropped. The appraisal medication-specific social support item asking the









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Table 5.7 Phase I Perceived Medication Stress Items: Reliability Coefficients and Item-to-total Correlations (n=91) Item-to-total
Correlations*



1. Feeling overwhelmed by your medication regimen .8018

2. Feeling discouraged with your
medication regimen .7271

3. Worrying about reactions
(between two or more medications) .6405

4. Feeling guilty or anxious when you
get off track with your medication .6088



*Cronbach's alpha = 0.8520









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Table 5.8 Phase I Doctor Emotional Medication-specific Social Support Items: Reliability Coefficients and Item-to-total Correlations (n=92)


Item-to-total
Correlations*


1. Helped me work through any worries or concerns .7460 related to my condition

2. Made me feel confident I can take medicines .6987 and can do what else was asked

3. Acted in a warm and friendly manner .6498


*Cronbach's alpha = .8344









82

Table 5.9 Phase I Pharmacist Emotional Medication-specific Social Support Items: Reliability Coefficients and Item-to-total Correlations (n=92)



Item-to-total
Correlations*


1. Helped me work through any worries or concerns .7751 related to my condition

2. Made me feel confident I can take medicines .6351 and can do what else was asked

3. Acted in a warm and friendly manner .5622


*Cronbach's alpha = .7981




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AN EXPLORATORY INVESTIGATION OF A STRESS MODEL
OF MEDICATION ADHERENCE AMONG ELDERLY OUTPATIENTS
By
DAVID ALLEN GETTMAN
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA

Copyright 1997
by
DAVID ALLEN GETTMAN

I wish to dedicate this dissertation to the veterans who have not only supported me during this
academic endeavor but who have also defended the beliefs and values of my homeland.

ACKNOWLEDGMENTS
Most of us have found ourselves driving an automobile in city traffic during rush hour. If you
were in a hurry to get to work on tune and were trying to do so in the most efficient manner, you
may have been required to deal with a number of irritations, minor annoyances, or hassles. If this
situation occurred more than once, you may have at one time made the tnp alone, another time with
someone who was just there for the nde and did not do anything other than sit, and yet another time
with someone in the car who was helpful, acting as “another set of eyes” to look for traffic hazards
and making suggestions on how best to proceed. There may also have been a similar situation in
which the traffic hassles had become so overwhelming that you felt the person wasn’t helping at all
and you reacted poorly by slowing down or stopping. This situation with its different scenarios
could be understood as a metaphor for life. Ironically, it is also the focus of this dissertation.
During my graduate work at the University of Florida, I have not been alone on my
metaphorical tnp Neither have I been driving with persons who just sat there. The people along
this part of my journey were genuinely interested in my well-being and proactively helped to keep
my stress level down. In a real way, they all provided me with “another set of eyes.”
First and foremost, I wish to thank Doug Ried, my chairperson, who is an extremely intelligent
and articulate mentor. This rare combination of abilities served as a constant source of information
and insight through a hassle-filled and stressful graduate experience. Like the proverbial patient,
quiet farmer, he was there to cultivate my endeavors as they slowly grew from a master's focus on
practice to a doctoral focus on research. He has been and probably will always be my role model
IV

of a thoughtful professor.
Second, 1 wish to thank Carole Kimberlin. She is also very bright and intuitive, but her real
gifts are her frustration-alleviating skills. Several times she listened to my frustrations and then
communicated enough emotional support to quickly and miraculously lower my levels of perceived
stress, helping me refocus on the issues germane to this academic endeavor.
I wish to thank two other committee members. First, I wish to thank Rich Segal for finding me
a home after Desert Storm/Desert Shield and for helping me appraise and deal constructively with
a number of issues on both personal and academic levels. Second, I wish to thank Donna Berardo
who brought to my attention the need for a better social support scale specific to medication-taking
during a research project on renal transplantation,
I wish to thank four other professors whose membership on my dissertation committee changed
over the years. First, I wish to thank Steve Dorman who opened my eyes to many health education
frontiers and indirectly showed me some common intellectual ground I have with my wife. Second,
I wish to thank Ron Stewart who lead me during a clinical clerkship with elderly patients to an
examination of a stress model which can explain the antecedents of a number of outcomes besides
depression. Third, I wish to thank John Henretta who showed me that the elderly are a vulnerable
population who deserve our understanding and care And, finally, I wish to thank John Lynch who
showed me it is not enough to measure the variables in a study, but the real challenge is to
elucidate the interactions.
I must also thank five other professors and one very special secretary who were never on my
dissertation committee but were instrumental in my professional development. First, I wish to
thank Paul Ranelli who helped me publish my first paper based on the data from his dissertation.
Second, I wish to thank Doug Hepler who taught me to look for theoretical frameworks which may
link to pharmaceutical health care to help explain health outcomes Third, I wish to thank Carl
v

Barfield who taught me that, by applying for grants, 1 might actually get paid to do the kind of
research I have a passion to conduct. Finally, I wish to thank David Brushwood, Earlene
Lipowski, and Delayne Redding, who showed me how difficult it really is to run a professional
pharmacy conference.
This list would not be complete without mentioning at least a few of the gifted graduate students
and a post doctoral fellow with whom I shared growth experiences I wish to thank Mana
Miralles, who showed me that statistical programs can be user friendly. I wish to thank Folake
Odedina and Rami Ben-Joseph, who repeatedly demonstrated the need to ask carefully formulated
questions And, finally, I wish to thank "TJ" Grainger-Rousseau, who taught me not only how to
be a good "TOM" pharmacist but also how to be a not so good "TOM" patient
I wish to thank those 1 got to know at the Veterans Administration Medical Center in
Gainesville, Flonda, where I worked as a part-time pharmacist during my graduate school
expenence. I wish to thank the pharmacy staff and administrators who helped me in the data
gathering process and with whom 1 can honestly say 1 learned the usefulness of a "good sense of
humor." Also, I must thank the veterans who filled out the questionnaires. I hope this research can
be used wisely to improve the well-deserved medical attention they receive at the medical center in
Gainesville and others like it throughout the country My limited active duty experiences in the
Navy and Air Force have taught me firsthand that they deserve the best health care our nation can
provide.
I wish to acknowledge the considerable skill of authors like Richard S. Lazarus and Sheldon
Cohen whose talents are reflected in the pages of this dissertation and whose names appear in the
references. I tend to think of this research project as an adaptation of their hard work. I can only
hope that some day a graduate student will read a paper of mine and do the same
vi

I wish to acknowledge my large and wonderful family. I want to thank my mother, Janet, who
taught me many of her keen business skills at an early age This was a difficult undertaking as our
large family traveled over much of the world with the U S Air Force during the cold war. Also, I
want to thank my many brothers and sisters who were always there and still, to this very day,
provide me with an amusing and diverse set of stones, not only about their gifted children, but also
on topics concerning their own interesting professions ranging from law enforcement to the
aeronautics and space industries. And, I wish to thank my father, Frank Clifford Gettman My
earliest memones of him mclude his getting into his US. Air Force uniform to fly off to foreign
countnes to do studies on microorganisms. As a scientist/military reserve officer he fought an
invisible war-within-a-war against organisms that affected many of our veterans before they could
get to the battlefield. An old family joke is that my oldest sister's first spoken word was
'Shistosomias.' As a scientist/military reserve officer I now fight an invisible war-within-a-war
against drug problems that are affecting many of our veterans after their return from the battlefield.
During a recent conversation, my father told me how discouraged he was with health departments
after his retirement, as for example, to see the resurgence in tuberculosis with the prevalence of
HTV-infected individuals. I hope I am not similarly discouraged by a resurgence of drug problems
if pharmaceutical health care providers should fail to fulfill their covenant with patients
Finally, I want to thank my lovely, talented, and supportive wife, Mary Ann Harhi-Gettman. In
addition to being the mother of my son, Paul James Gettman, she has aptly demonstrated to me that
it takes someone close, concerned, and very special to keep all the things in your life in the
vii
perspective they truly belong.

TABLE OF CONTENTS
page
ACKNOWLEDGMENTS iv
LIST OF TABLES xi
LIST OF FIGURES xv
ABSTRACT xvii
CHAPTERS
1 OVERVIEW OF THE ISSUES ADDRESSED BY THIS RESEARCH 1
Medication Adherence 1
Medication-related Stressors 2
Perceived Medication Stress 3
Medication-specific Social Support 3
Problem Statement 5
2 THEORETICAL FRAMEWORK 6
Introduction 6
A Stress Model 6
The Stress Model of Medication Adherence 9
The "Nucleus" of the Stress Model of the Medication Adherence 11
Research Questions 14
Summary 15
3 REVIEW OF THE LITERATURE 16
Introduction 16
Medication Adherence 18
Medication-related Stressors 19
Historical Evolution of General Stressor Constructs 19
Development of the Medication-related Stressors Construct 22
Perceived Medication Stress 28
Medication-specific Social Support 31
Other Conditioning Variables 35
Age 35
vui

Income 37
Health Status 38
Research Hypotheses 39
First Set of Hypotheses 41
Second Set of Hypotheses 41
Third Set of Hypotheses 42
Fourth Set of Hypotheses 43
Fifth Set of Hypotheses 43
Summary 44
4 METHODOLOGY 46
Introduction 46
Study Instruments 46
Medication Adherence 46
Medication-related Stressors 50
Perceived Medication Stress 52
Medication-specific Social Support 54
Other Conditioning Variables 56
Study Phase I 57
Study Phase I, Part One 57
Study Phase I, Part Two 58
Study Phase 1, Part Three 59
Study Phase II 61
Study Phase II, Part One 61
Study Phase II. Part Two 63
Human Rights 69
Summary 69
5 RESULTS 70
Introduction 70
Study Phase I 70
Comparison between Phase I Lobby and Mail Samples 71
Phase I Sample Description and Measures 71
Study Phase II 78
Phase II Sample Description 84
Factor Analyses 87
Reliability Analyses 96
Phase II Measure Descriptions 107
Content of the Four Scales 109
Construct-related Validation of the Scales 110
Testing of the Five Sets of Hypotheses 111
Discussion of Results 175
Relationships Among Stressors, Perceived Stress, and Medication Adherence 175
Mediating Effects of Perceived Stress 181
Moderating Effects of Medication-specific Social Support 183
IX

Summary
192
6 LIMITATIONS, IMPLICATIONS, AND CONCLUSIONS 194
Limitations on the Entire Study 194
Limitations on Mediating Effects 197
Limitations on Moderating Effects 198
Implications for Pharmacy Practice 200
Implications for Future Research 202
Conclusions 206
APPENDICES
A INITIAL OUTPATIENT QUESTIONNAIRE 211
B REVISED OUTPATIENT QUESTIONNAIRE 223
REFERENCES 238
BIOGRAPHICAL SKETCH 260
x

LIST OF TABLES
Table page
5.1 Phase I Comparison between Lobby and Mail Samples on Four Groups of
Questionnaire Items 72
5.2 Phase I Sample Description 73
5 3 Phase I Medication Adherence Items: Reliability Coefficients and Item-to-total
Correlations 75
5 4 Phase I Emotional Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations 76
5.5 Phase I Informational Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations 77
5 6 Phase I Instrumental Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations 78
5.7 Phase I Perceived Medication Stress Items Reliability Coefficients and Item-to-total
Correlations 80
5 8 Phase I Study Doctor Emotional Medication-specific Social Support Items: Reliability
Coefficients and Item-to-total Correlations 81
5 9 Phase I Study Pharmacist Emotional Medication-specific Social Support Items:
Reliability Coefficients and Item-to-total Correlations 82
5 10 Phase II of Study Sample Description 86
5.11 Phase II Medication Adherence Items: Rotated Component Matrix using
Principal Components Analysis with Vanmax Rotation and Kaiser Normalization .89
5 12 Phase II Advising Subjective Medication-related Stressors: Rotated Component
Matrix using Principal Components Analysis with Vanmax Rotation and Kaiser
Normalization 91
xi

5 13 Phase II Isolating Subjective Medication-related Stressors: Rotated Component
Matrix using Principal Components Analysis with Vanmax Rotation and Kaiser
Normalization 92
5.14 Phase II Reminding Subjective Medication-related Stressors Rotated Component
Matrix using Principal Components Analysis with Vanmax Rotation and Kaiser
Normalization 93
5.15 Phase II Obtaining Subjective Medication-related Stressors: Rotated Component
Matnx using Principal Components Analysis with Vanmax Rotation and Kaiser
Normalization 94
5 16 Phase II Perceived Medication-related Stress Items. Rotated Component
Matnx using Principal Components Analysis with Vanmax Rotation and Kaiser
Normalization 96
5.17 Phase II Doctor Medication-specific Social Support Items: Rotated Component
Matnx using Principal Components Analysis with Vanmax Rotation and Kaiser
Normalization 97
5.18 Phase II Pharmacist Medication-specific Social Support Items: Rotated Component
Matrix usmg Principal Components Analysis with Vanmax Rotation and Kaiser
Normalization 98
5 19 Phase II Other Person Medication-specific Social Support Items: Rotated
Component Matnx usmg Principal Components Analysis with Vanmax Rotation
and Kaiser Normalization 99
5.20 Phase II Medication Adherence Items: Reliability Coefficients and Item-to-total
Correlations 101
5.21 Phase II Advising Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations 102
5.22 Phase II Isolating Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Conelations 103
5 23 Phase II Reminding Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations 104
5 24 Phase II Obtaining Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations 105
5.25 Phase II Perceived Medication Stress Items: Reliability Coefficients and Item-to-total
Correlations 106
Xll

108
5.26 Descriptions of Phase II Measures
5 27 Correlations between Subject Characteristics, Health Status, Medication Adherence,
Number of Medications, Type of Stressors, and Perceived Stress 113
5.28 Raw Score Multiple Regression Analysis of Elderly Outpatient’s Age, Income,
Self-rated Health Status and Chronic Disease Score on Medication Adherence,
Advising Stressors, Isolating Stressors, Reminding Stressors, Obtaining Stressors,
and Perceived Stress 121
5.29 Correlations between Type of Support and Subject’s Age, Income, Health Status,
Medication Adherence, Type of Stressors, and Perceived Stress 130
5.30 Raw Score Multiple Regression Analysis of Elderly Outpatient’s Age, Income,
Self-rated Health Status and Chronic Disease Score on Doctor Consultation Support,
Doctor Affirmation Support, Doctor Actuation Support, and Doctor Acquisition
Support 132
5 31 Raw Score and Deviation Score Multiple Regression Analysis Relating Advising
stressors, Doctor Consultation Support, and Advising Stressors-Doctor Consultation
Support Interaction to Perceived Medication Stress 135
5.32 Raw Score and Deviation Score Multiple Regression Analysis Relating Isolating
Stressors, Doctor Affirmation Support, and Isolating Stressors-Doctor Affirmation
Support Interaction to Perceived Medication Stress 137
5.33 Raw Score and Deviation Score Multiple Regression Analysis Relating Reminding
Stressors, Doctor Actuation Support, and Reminding Stressors-Doctor Actuation
Support Interaction to Perceived Medication Stress 140
5.34 Raw Score and Deviation Score Multiple Regression Analysis Relating Obtaining
Stressors, Doctor Acquisition Support, and Obtaining Stressors-Doctor Acquisition
Support Interaction to Perceived Medication Stress 142
5.35 Raw Score and Deviation Score Multiple Regression Analysis Relating Perceived
Medication Stress, Doctor Affirmation Support, and Perceived Medication
Stress-Doctor Affirmation Support Interaction to Medication Adherence 144
5.36 Raw Score Multiple Regression Analysis of Elderly Outpatient’s Age, Income,
Self-rated Health Status and Chronic Disease Score on Pharmacist Consultation
Support, Pharmacist Affirmation Support, Pharmacist Actuation Support, and
Pharmacist Acquisition Support
Xlll
149

5.37 Raw Score and Deviation Score Multiple Regression Analysis Relating Advising
Stressors, Pharmacist Consultation Support, and Advising Stressors-Pharmacist
Consultation Support Interaction to Perceived Medication Stress 150
5.38 Raw Score and Deviation Score Multiple Regression Analysis Relating Isolating
Stressors, Pharmacist Affirmation Support, and Isolating Stressors-Pharmacist
Affirmation Support Interaction to Perceived Medication Stress 152
5.39 Raw Score and Deviation Score Multiple Regression Analysis Relating Reminding
Stressors, Pharmacist Actuation Support, and Reminding Stressors-Pharmacist
Actuation Support Interaction to Perceived Medication Stress 154
5.40 Raw Score and Deviation Score Multiple Regression Analysis Relatmg Obtaining
Stressors, Pharmacist Acquisition Support, and Obtaining Stressors-Pharmacist
Acquisition Support Interaction to Perceived Medication Stress 156
5 41 Raw Score and Deviation Score Multiple Regression Analysis Relatmg Perceived
Medication Stress, Pharmacist Affirmation Support, and Perceived Medication
Stress-Pharmacist Affirmation Support Interaction to Medication Adherence 158
5.42 Raw Score Multiple Regression Analysis of Elderly Outpatient’s Age, Income,
Self-rated Health Status and Chronic Disease Score on Other Person Consultation
Support, Other Person Affirmation Support, Other Person Actuation Support, and
Other Person Acquisition Support 163
5.43 Raw Score and Deviation Score Multiple Regression Analysis Relatmg Advising
Stressors, Other Person Consultation Support, and Advising Stressors-Other Person
Consultation Support Interaction to Perceived Medication Stress 164
5 44 Raw Score and Deviation Score Multiple Regression Analysis Relatmg Isolating
Stressors, Other Person Affirmation Support, and Isolating Stressors-Other Person
Affirmation Support Interaction to Perceived Medication Stress 166
5 45 Raw Score and Deviation Score Multiple Regression Analysis Relatmg Reminding
Stressors, Other Person Actuation Support, and Reminding Stressors-Other Person
Actuation Support Interaction to Perceived Medication Stress 169
5 46 Raw Score and Deviation Score Multiple Regression Analysis Relatmg Obtaining
Stressors, Other Person Acquisition Support, and Obtaining Stressors-Other Person
Acquisition Support Interaction to Perceived Medication Stress 171
5.47 Raw Score and Deviation Score Multiple Regression with Analysis Relatmg Perceived
Stress, Other Person Affirmation Support, and Perceived Medication Stress-Other Person
Affirmation Support Interaction to Medication Adherence 173
xiv

LIST OF FIGURES
Figure page
2.1 A stress model (Israel and Schurman, 1990) 8
2.2 The Stress Model of Medication Adherence (SMMA) 10
2.3 The “Nucleus” of the Stress Model of Medication Adherence (SMMA-n) 12
3.1 Magnified view of the “Nucleus” of the Stress Model of Medication Adherence 40
5.1 Path Diagram Using Multiple Regression with Raw Scores (r) and Deviation
Scores (d) for Testing Mediating effects of Perceived stress on the Relationship
between Advising Stressors and Medication Adherence 122
5.2 Path Diagram Using Multiple Regression with Raw Scores (r) and Deviation
Scores (d) for Testing Mediating effects of Perceived stress on the Relationship
between Isolating Stressors and Medication Adherence 124
5 3 Path Diagram Usmg Multiple Regression with Raw Scores (r) and Deviation
Scores (d) for Testing Mediating effects of Perceived stress on the Relationship
between Reminding Stressors and Medication Adherence 126
5 4 Path Diagram Using Multiple Regression with Raw Scores (r) and Deviation
Scores (d) for Testing Mediating effects of Perceived stress on the Relationship
between Obtaining Stressors and Medication Adherence 128
5.5 The Effect of Advising Stressors on Perceived Medication Stress for Different
Levels of Doctor Consultation Support 136
5 6 The Effect of Isolating Stressors on Perceived Medication Stress for Different
Levels of Doctor Affirmation Support 138
5.7 The Effect of Reminding Stressors on Perceived Medication Stress for Different
Levels of Doctor Actuation Support 141
5 8 The Effect of Obtaining Stressors on Perceived Medication Stress for Different
Levels of Doctor Acquisition Support 143
xv

5.9 The Effect of Perceived Medication Stress on Medication Adherence for
Different Levels of Doctor Affirmation Support 145
5 10 The Effect of Advising Stressors on Perceived Medication Stress for Different
Levels of Pharmacist Consultation Support 151
5 11 The Effect of Isolating Stressors on Perceived Medication Stress for Different
Levels of Pharmacist Affirmation Support 153
5.12 The Effect of Reminding Stressors on Perceived Medication Stress for Different
Levels of Pharmacist Actuation Support 155
5 13 The Effect of Obtaining Stressors on Perceived Medication Stress for Different
Levels of Pharmacist Acquisition Support 157
5.14 The Effect of Perceived Medication Stress on Medication Adherence for
Different Levels of Pharmacist Affirmation Support 159
5 15 The Effect of Advising Stressors on Perceived Medication Stress for Different
Levels of Other Person Consultation Support 165
5.16 The Effect of Isolating Stressors on Perceived Medication Stress for Different
Levels of Other Person Affirmation Support 1^7
5.17 The Effect of Reminding Stressors on Perceived Medication Stress for Different
Levels of Other Person Actuation Support 170
5 18 The Effect of Obtaining Stressors on Perceived Medication Stress for Different
Levels of Other Person Acquisition Support 172
5 19 The Effect of Perceived Medication Stress on Medication Adherence for
Different Levels of Other Person Affirmation Support 174
xvi

Abstract of Dissertation Presented to the Graduate School
of the University of Flonda in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
AN EXPLORATORY INVESTIGATION OF A STRESS MODEL
OF MEDICATION ADHERENCE AMONG ELDERLY OUTPATIENTS
By
David Allen Gettman
December, 1997
Chairman: L. Douglas Ried, Ph D
Major Department: Pharmacy Health Care Administration
The study had two objectives: 1) to develop and validate constructs of a proposed Stress Model
of Medication Adherence, and 2) to examine some of the more important relationships among these
constructs The study employed an exploratory, retrospective, cross-sectional design with a sample
of veterans over 65 years of age living in north central Flonda and southern Georgia and receiving
one or more medications on a regular basis through the mail. The study was conducted m two
phases. In the pilot phase, an initial questionnaire was developed and tested using a convenience
sample of 94 subjects. In the main phase, a revised questionnaire was mailed to a convenience
sample of 1,600 subjects. 1,017 responses were used to establish the internal validity of the
constructs and to test hypothesized relationships in the proposed model. The dependent variable in
the model was medication adherence. The independent variables mcluded medication related
stressors and medication specific social support. Perceived medication stress was either a
dependent or independent variable depending on the context in which it was used. Descriptive
XVII

statistics and correlational analyses were used to initially address the validation of the four scales
Next, factor analyses were used to evaluate the dimensionality of the items in the questionnaire.
Four types of stressors were found: advising, isolatmg, reminding, and obtaining. And, four types
of social support were found for doctor, pharmacist and most concerned other person: consultation,
affirmation, actuation, and acquisition Next, multiple regression was used to test hypothesized
main, mediator, and moderator relationships among adherence, stressors, perceived stress and
social support. Perceived stress was found to partially mediate the relationships between all four
types of stressors and medication adherence. This suggests that these stressors generate patient
evaluations of medication taking that effect medication adherence negatively. Finally, some types
of social support from the doctor and pharmacist were found to moderate the relationship between
some types of stressors and perceived stress. This suggests that more than a threshold level of some
types of social support from the doctor or pharmacist are required to prevent patient generation of
perceived medication stress.
XVlll

CHAPTER 1
OVERVIEW OF THE ISSUES ADDRESSED BY THIS RESEARCH
Medication Adherence
Drugs provide society with enormous benefits They reduce mortality and morbidity
relieve pain and suffering, are less expensive forms of treatment than surgery and
hospitalization, are more readily accessible to a larger portion of the population than
are more expensive technologies, and have enabled physicians to see more patients with
unproved outcomes (Feldstein, 1988, pp. 437)
Despite Feldstein’s glowing portrayal of drugs, the real world results of drug use are far less
than optimal. For the elderly, this may be due in part to medication nonadherence. Estimates of
medication nonadherence in this population range from 40 to 75% (Cooper, Love and Raffoul,
1982; Lipton and Lee, 1988), and this nonadherence has been associated with a higher risk of
hospitalization, which has been estimated to cost approximately $2,150 per admission (Col et al.,
1991). Furthermore, it is anticipated that the problem of medication nonadherence among the
elderly will become critical in the near future as the world’s population grows progressively older
(Pucino et al., 1985).
It will become increasingly necessary for health practitioners to unprove medication adherence
to obviate the need for more expensive medical care (Lipton, 1982; Gryfe and Gryfe, 1984). To
unprove medication adherence among the elderly will require a better understanding of three
variables: stressors, perceived stress, and social support. The significance of these three variables
will be discussed in the following sections. The chapter will conclude with a problem statement.
I

2
Medication-related Stressors
Medication nonadherence is not a unique phenomenon associated with aging. Many
of the same problems concerning taking medications most appropriately apply to 40 year
olds as well as to 80 year olds. What is unique about the elderly is their greater sensitivity
to medications, their greater propensity for the development of adverse effects, and the
greater complexity of their regimens as they develop chronic illnesses through the course
of their lives. In addition, decreasing acuity of special senses, problems with memory,
and the interposition of other care givers are all unique phenomena that have to be dealt
with. (Weintraub, 1990, pp 445).
Weintraub’s depiction of the senous problems faced by the elderly attempting to adhere to their
medication regimens is insightful However, for a more encompassing depiction of these problems
one needs to examine the alarming statistics that underpin this rendering.
Elderly patients use more medications than younger patients, and the trend of increasing drug
use continues through 80 years of age. Studies conducted in a variety of settings have shown that
patients over 65 years of age use an average of 2 to 6 prescribed medications and 1 to 3.4 non-
prescnbed medications (Stewart and Cooper, 1994). It is known that the use of multiple
medications increases the risks of adverse drug reactions, drug-drug interactions, and makes
medication adherence more difficult (Fedder, 1984).
Elderly patients also have to deal with problems associated with altered pharmacokinetic and
pharmacodynamic effects of drugs (Hoffler, 1981; Roberts and Turner, 1988; Dawling and Crome,
1989, Fox and Auestad, 1990; Taylor, 1990). There may be changes in the absorption of orally
administered drugs, body composition, serum albumin and globulin concentration, cardiac output
and hepatic metabolism, renal blood flow, renal function and homeostatic mechanisms (Shaw,
1982, Chapron, 1995).
Elderly patients also must struggle with a number of disease changes that occur with aging
(Salzman, 1982; Tuck, 1988, Furberg and Black, 1988). For example, the incidence and
prevalence of congestive heart failure increases exponentially with advancing age (Hunziker and

3
Bertel, 1995), and gastrointestinal problems become more common in the elderly (Levitan, 1989).
Indeed, Dali (1989) has stated that side effects seem to increase and medication adherence seems to
decrease as elderly patients suffer from increased comorbidities.
There are also age-related changes in vision, hearing, memory, and leammg (Kimberlin, 1995).
These changes and the inability to read and interpret prescription labels, open and close vials,
remove tablets, and identify tablet colors have all been demonstrated to have a negative effect on
medication adherence (Meyer and Schuna,1989).
Although numerous studies have specified the experience of these problems among the elderly,
few studies have attempted to quantify these problems as stressors. Furthermore, few studies have
attempted to place these stressors into a theoretical framework for understanding how these
medication-related stressors might effect an elderly patient’s perception of stress
Perceived Medication Stress
Elderly patients may experience and then evaluate that experience with a medication-related
stressor. The patient’s evaluation of all these experiences may leave the patient with feelings that
are favorable for medication nonadherence In a recent study involving older adults taking
prescnbed medications, it was found that 21% had been nonadherent during the month preceding
the study (Coons et al, 1994). Furthermore, higher perceived stress was significantly associated
with medication nonadherence Although this study specified an important correlation between
perceived stress and medication nonadherence, the effect of social support on the relationship
between perceived stress and medication nonadherence was not examined.
Medication-specific Social Support
The negative effects of a stress process among elderly outpatients taking medications may be
alleviated with social support. As chronic "age-related" changes in physiology become more
prevalent, increasing numbers of the elderly experience declined activities of daily living. These

4
patients not only require assistance with such activities as basic hygiene, dressing, eating, and
answering the telephone, but also with taking their medication. For example, a significant
association was found between spousal support and an elderly patient's adherence to coronary
medications (Doherty et al., 1983). In another study, a considerable number of patients were found
to benefit from the assistance of a relative or home helper in administering treatments (Gilmore,
Temple, and Taggart, 1989). This study also recommended that a suitable helper be identified and
counseled to assist at-nsk elderly patients with their medication.
Among health care professionals, support activities specific to the needs of the elderly taking
medications have been found to result in better medication adherence among elderly patients. For
example, weekly pharmacist counseling sessions resulted in improvements in medication adherence
among one subgroup of elderly patients (Wolf et al., 1989). In addition, among elderly patients
belongmg to the other subgroup, their identification as nonadherers helped pharmacists reduce
medical misjudgement when making changes to a nonadherer’s prescribed medications. In another
study involving nurse practitioners and elderly women, a patient's perception of high psychosocial
care was the only component of a nurse's visit that had an impact on the patient's intent to adhere
(Chang et al., 1985).
In two older reviews of the literature, not specific to the elderly, these findings are indirectly
supported, and one draws attention to an important methodological concern. The first review of
the literature (Baekelund and Lundwall, 1975) found 19 studies on social support and dropping out
of treatment In all 19 studies, dropping out was associated with low social support. In the second
review of the literature (Haynes and Sackett, 1977), 25 studies reported predictors which were
indicators of social support. Sixteen of these studies supported a positive relationship between
social support and medication adherence; only one study showed a negative relationship, and eight
studies showed no relationship to medication adherence. Haynes and Sackett (1977) gave poor

5
ratings to four of these latter eight studies on the quality of the social support measures Even if
the other half of the "non-replications" had reliable, valid measures, the percentage of studies that
suggest that social support unproves medication adherence remains very high. These studies
suggest that the reliability and validity of the social support measures will be critical when
rigorously testing the unpact of social support on medication adherence.
Problem Statement
Medication nonadherence is a significant problem among elderly outpatients and will assume
even greater importance in the near future. Stressors, perceived stress, and social support have
been connected with medication nonadherence among the elderly Social support from health
professionals and family members may help the elderly patient more easily deal with stressors and
perceived stress and must be carefully measured. The next chapter will propose a Stress Model of
Medication Adherence (SMMA).

CHAPTER 2
THEORETICAL FRAMEWORK
Introduction
This chapter begins by briefly describing a stress model. After this short depiction, the model
will be adapted in the next section to describe the medication-taking experiences of elderly
outpatients. It will be known as the Stress Model of Medication Adherence (SMMA). In the
following section, some of the more important constructs in the Stress Model of Medication
Adherence (SMMA) and the relationships among them will be initially examined by focusing on
the "nucleus" of the Stress Model of Medication Adherence (SMMA-n) After developing and
describing hypothesized relationships among the constructs in the SMMA-n, two “overarching”
research questions will be presented.
A Stress Model
Soon after Lazarus (1966) published Psychological Stress and the Coping Process, cognitive
approaches to stress were more fully developed by additional investigators. Along with renewed
interest in emotions and behavioral medicine, the issues of stress in adult life and aging, as well as
stress management, gamed attention Since that tune, there has also been a dramatic increase of
interest in the concept of social support as it affects health and well-being. This interest is reflected
in an explosion of research as well as an increase in the number of treatment and intervention
programs that use social support for therapeutic assistance.
6

7
Figure 2 1 presents a theoretical model (Israel and Schurman, 1990) that shows the general
relationships among the theoretical constructs that have been developed over the last three decades.
This conceptual framework is based on earlier models of social-environmental determinants of
health (Lazarus, 1966; McGrath, 1970; Katz and Kahn, 1978), occupational stress, social support,
health (House, 1974, 1981), and stress control (Carver and Scheier, 1982; Leventhal and Nerenz,
1983). The model postulates the following set of five elements in the stress process: 1) People may
experience a set of psychosocial-environmental conditions conducive to stress (stressors) that place
them at nsk for physical, psychological, and behavioral disorders. These conditions or stressors do
not invariably result in long-term negative outcomes, rather, their effects depend on 2) the
perceptions and responses to the stressors by the people involved. An individual may perceive a
stressor as threatening, exciting, challenging, or stressful. When an individual perceives a stressor
in the environment, he or she may respond in one or more ways: psychologically, behaviorally, and
physiologically. The sequences of response to perceived stress are not necessarily bad, and,
depending on the specific pattern of relationships among stressor, perception, and response, the
stress reaction may be helpful and even pleasurable. However, when the demands placed on people
by their environment exceed their abilities, or when people are not able to meet strong needs,
conditions are perceived as stressful (McGrath, 1970). 3) Negative short-term responses to stress
may occur that may lead, over time, to 4) enduring poor health outcomes. The framework further
posits that no objective stressor is likely to produce the same perceptions of stress or resultant
short-term responses or enduring outcomes in all people exposed to the stressor Rather, 5) certain
individual and situational characteristics influence how an individual experiences the stress
process. Social, psychological, biophysical, and genetic "conditioning" factors influence how an
individual experiences the stress process.

/â– 
Conditioning Vanables: individual or Situational Characteristics
Psychological / Social / Biophysical / Genetic
\
Psychosocial-Environmental
Conditions Conduciv e to
Stress (Stressors)
e g., major life events
daily hassles
chronic strains
cataclysmic events
1
1
1
1
sr
Perceived
1
1
1
1
Short-Term
Responses
to Stress
1
1
1
1
1
Enduring
Health
(Stressors)
1 Physiological
â–º
Outcomes
(defenses)
2 Psychological
3 Behavioral
(Physiological
2 Psychological
3 Behavioral
ambient environment
(coping)
T J
Figure 2.1 A stress model (Israel and Schurman, 1990)

9
The Stress Model of Medication Adherence (SMMA)
Figure 2.1 presents a theoretical model that shows the general relationships among a number of
variables that could explain medication adherence An adapted model is presented m Figure 2.2—
the Stress Model of Medication Adherence (SMMA). It incorporates medication-related stressors,
medication-specific social support, ethnicity, income, age, gender, health status, perceived
medication stress, medication adherence, and enduring health outcomes into a theoretical
framework.
An example from a health care setting illustrates the components of this adapted framework
and depicts the model as it relates to the research problems described in the first chapter. An
elderly outpatient may experience one or more stressors (medication-related stressors) which may
invoke perceived stress (perceived medication stress). This elderly individual may receive social
support (medication-specific social support) from his or her prescribing physician or confidant.
Also, a patient's ethnicity, income, age, gender, and health status may be important conditioning
variables in the proposed model For example, if the patient lives among other American Indians he
or she may be encouraged to use instead a tribal remedy. Also, the patient may not be able to
afford his or her medication And, if the elderly patient is among the “young-old” elderly he or she
may find it easier getting to the pharmacy than a patient that is among the “old-old” elderly. If the
patient’s health status is poor he or she may have difficulty remembering to take all of his or her
medications. Finally, the model suggests how enduring outcomes (measured by a patient's
quality of life) can have a feedback effect on medication adherence, as for example, a deterioration
in quality of life can lead to a deterioration in medication adherence (Williams, 1987; Julius, 1988).

Conditioning Variables: Individual or Situational Characteristics
Psychosocial-Environmental
Conditions Conducive to
Stress (Stressors)
Subjective Medication-related Stressors
Objective Medication-related Stressors
(daily hassles)
T
Payditilugiuii
loons of' control
expectancy
Stiual
social support
otHnicity
income
age
gender
health status
Perceived
Medication
Stress
(coping)
(defenses)
Short-Term
Responses
to Stress
(Stressors)
Medication
Adherence
(Behavioral)
Enduring
Health
Outcomes
1 Physiological
2.Psychological
3 Behavioral
Figure 2.2 The Stress Model of Medication Adherence (SMMA)

11
The "Nucleus" of the Stress Model of Medication Adherence
The nucleus of the proposed Stress Model of Medication Adherence can be envisioned where
perceived medication stress acts upon one relationship m the model, while medication-specific
social support acts upon two other relationships in the model (see Figure 2.3). Each of these will
be discussed in turn.
Perceived medication stress acts upon the relationship between medication-related stressors and
medication adherence. It is important to understand that perceived medication stress is thought to
have either a mam (direct) effect, or a mediating (indirect) effect on the relationship between
medication-related stressors and medication adherence. When perceived medication stress exhibits
a mam effect on the relationship between medication-related stressors and medication adherence,
perceived medication stress will have a detrimental effect on medication adherence regardless of
the level of medication-related stressors. For example, an elderly man who generates feelmgs of
guilt or anxiety when he gets off track with his medication taking, may not adhere to his medication
taking despite the frequency of his experiences with medication problems. When perceived
medication stress exhibits a mediating effect on the relationship between medication-related
stressors and medication adherence, a high level of experience with medication-related stressors
might generate higher feelmgs of guilt or anxiety by various processes mtemal to the person and
cause him not to adhere to his medication taking
Medication-specific social support acts on two other relationships. In the first relationship,
medication-specific social support acts upon the association between medication-related stressors
and perceived medication stress. In the second relationship, medication-specific social support acts
upon the association between perceived medication stress and medication adherence. It is
important to understand that medication-specific social support is thought to have either a mam (or

Conditioning Variables: Individual or Situational Characteristics
Smua I
social support
ethnicity
income
Minphy.sical
age
gender
health status
Psychosocial-Environmental
Conditions Conducive to
Stress (Stressors)
Subjective Medication-related Stressors
Objective Medication-related Stressors
(daily hassles)
1
1
1
1
1
1
1
Perceived
1
1
t
1
Medication
»
Stress
Short-Term
Responses
to Stress
(Stressors)
Medication
Adheroice
(Behavioral)
Figure 2.3 The "nucleus" of the Stress Model of Medication Adherence (SMMA-n)

13
direct) effect, or a moderating (or buffering) effect on these relationships. How medication-specific
social support exhibits a main effect will be discussed, and then a discussion of how medication-
specific social support exhibits a moderating effect will follow.
When medication-specific social support exhibits a mam effect on the relationship between
medication-related stressors and perceived medication stress, a threshold level of effort will have a
certain beneficial effect regardless of the patient's level of medication-related stressors. For
example, if a diabetic man knows that his family will pick up his insulin whenever he needs it, his
perceived medication stress will be lower than if he didn’t have his family to count on to do this for
him. Also, for example, if the same diabetic man knows that his pharmacist will have his kind
of insulin in the drug store when he needs it, the patient’s perceived medication stress may be
lower, contributing to better medication adherence. Finally, for example, if the same diabetic man
knows that his doctor will schedule an appointment will him when he is experiencing a problem
with his insulin medication, the patient’s perceived medication stress may be lower, contributing to
better medication adherence
When social support exhibits a moderating effect on the relationship between medication-related
stressors and perceived medication stress, the medication-specific social support will have a
beneficial effect only if the medication-specific social support offered to the individual is high
enough to help that person deal with the medication-related stressor he or she is experiencing. In
this situation, medication-specific social support will require a higher level of effort to have a
beneficial effect. For example, the same man’s pharmacist may need to instruct this diabetic man
on the use of a new electronic blood glucose monitor. It may take more than one counseling
session with the pharmacist to bring the man’s perceived medication stress level down. By offering
more medication-specific social support to the patient to help him deal with this medication-related
stressor he is experiencing often, the extra time spent is not only prudent but necessary

14
In the present study, medication-related stressors, perceived medication stress, and medication-
specific social support will take on specific meanings. Medication-related stressors will represent
the patient's experience with problems related to his or her medication regimen. For example, an
elderly man may state that very often he has needed something to remind him to take his
medications on time. Perceived medication stress will represent the patient's evaluation of his or
her experiences with these medication-related stressors. For example, an older man may assert that
he feels very discouraged with his complex medication regimen. Medication-specific social
support will represent the patient's perception of services received from a doctor, pharmacist, or
“most concerned” other person, which may help the patient deal effectively with medication-related
stressors or perceived medication stress. For example, an elderly man may declare that his wife
shows little or no concern about his feelmgs of discouragement with his complex medication
regimen. All these factors may influence his decision to not adhere to his medications.
This exploratory, cross-sectional, retrospective study will investigate only variables in the
"nucleus" of the proposed model. The dependent (outcome) variable will be medication adherence.
The independent (predictor) variables will be medication-related stressors and medication-specific
social support. Perceived medication stress will be either a dependent or independent variable
depending on the context in which it is used.
An important goal of the proposed study will be to better understand the relationships among
the variables of the nucleus of the model and, thus more clearly define the basic premises of model
Other variables and relationships, although interesting, will be reserved for subsequent studies.
This present focus raises two research questions which will follow.
Research Questions
Relationships between four key constructs in the "nucleus" of the proposed Stress Model of
Medication Adherence (SMMA-n) will be the prime focus of this research. These four constructs

15
are medication adherence, medication-related stressors, perceived medication stress, and
medication-specific social support. When this task has been accomplished, two "overarching"
research questions can be addressed, which are as follows:
1. Does perceived medication stress have a main effect,
mediating effect, or combination of these effects on the
relationship between medication-related stressors and
medication adherence?
2. Does medication-specific social support have a main
effect, moderating effect, or combination of these
effects on the following two relationships: 1.) the
relationship between medication-related stressors and
perceived medication stress, and 2.) the relationship
between perceived medication stress and medication
adherence?
Summary
This chapter described both a stress model and an adapted one: the Stress Model of Medication
Adherence (SMMA). The proposed study will only examine variables that form the "nucleus" of
the proposed Stress Model of Medication Adherence (SMMA-n). The nucleus of the model is
important because it is there that the consequential impact of perceived medication stress and
medication-specific social support can be measured An investigation of the nucleus of the
proposed model will require a thorough review of the psychological, sociological, and medical
practice literature germane to the variables and their inter-relationships In the next chapter, past
studies on the conceptualization and measurement of medication adherence, stressors, perceived
stress, and social support will be examined

CHAPTER 3
REVIEW OF THE LITERATURE
Introduction
In this chapter, literature relevant to the nucleus of the proposed Stress Model of Medication
Adherence (SMMA-n) will be reviewed. During this review, it will become apparent that some of
the variables in the proposed model have already undergone some development, whereas other
variables still need a great amount of research. This makes the original, or parent, stress model
(Israel and Schurman, 1990) challenging to adapt to the medication-taking experiences of the
elderly. The handful of studies that will be reviewed here have used portions of the proposed
model and, on the surface, seem to have conflicting results. However, when linking each of these
studies using the proposed model, it will become clear that each study offers a piece of the puzzle
which, when put together, will help us understand the more important relationships among the
variables in this study's proposed model.
During this study, four variables will be utilized, and the more important relationships among
them will be elucidated. Medication adherence will be a dependent variable; whereas, the
independent variables will be medication-related stressors and medication-specific social support.
Perceived medication stress will be either a dependent vanable or independent variable according to
the context m which it is used.
This chapter will begin with a discussion on medication adherence This discussion will focus
on the only study linking medication adherence to stressors and perceived stress. After a
discussion of the positive and negative aspects of this study, an argument for the development of a
16

17
more specific stressor scale will be made to improve the prediction of medication adherence among
elderly outpatients.
Next, this chapter will continue with a large section devoted to medication-related stressors.
This large deliberation will be divided into two distinct sections First, a review of the
psychosocial literature germane to the historical evolution of general stressor constructs will be
addressed. Second, the development of an improved medication-related stressor scale will be
discussed. The argument will be made that the construct of medication-related stressors needs to
be expanded to include both subjective and objective categorizations. The subjective category of
medication-related stressors will include patient-reported measures of these stressors. These
subjective medication-related stressors will be initially subclassified into informational, emotional,
or instrumental categories. The objective category of medication-related stressors will be an
impartial measure of these stressors taken from automated records, that is, the patient’s number of
prescribed medications.
Next, this chapter will continue with a discussion on perceived medication stress. This section
will begm with a review of the literature germane to the historical development of the general
perceived stress construct This discussion will conclude with a review of scales which have been
developed. The use of a perceived medication stress scale in the proposed study will aid not only in
the elucidation of the proposed model, but also in the validation of the improved medication-related
stressors construct.
This chapter will then continue with a discussion on medication-specific social support. This
section will conclude with a section on statistical relationships that have been elucidated among
stressors, social support, perceived stress, and outcome variables in other similar (but more
general) studies. By reviewing the literature on each of these variables and describing relationships
that may exist among them, the two overarching research questions from Chapter 2 will be

18
translated into five sets of research hypotheses suggested by the "magnified" nucleus of the
proposed Stress Model of Medication Adherence (SMMA-n). A summary will conclude this
chapter.
Medication Adherence
“[Adherence is] the extent to which a person's behavior (m terms of taking medications,
following diets, or executmg lifestyle changes) coincides with medical or health advice
(Haynes, 1979)." Unfortunately, elderly outpatients seem to experience multiple socio-medical
problems that make high medication adherence rates difficult to achieve (Richardson, 1986;
Entwhistle, 1989).
Medication adherence has been studied as an outcome variable in only one quantitative "stress
model-like" study found in the literature. In this study, a group of investigators (Frenzel et al.,
1988) found no link between a general daily hassles (stressors) scale (Kanner et al., 1981) and
medication adherence. But, the study does seem to suggest that the development of a more
"refined" daily hassles (stressors) measure tailored to medication use might be useful for
demonstrating an association between medication-related stressors and medication adherence This
same investigation successfully demonstrated a link between perceived stress and medication
adherence using a perceived stress scale developed by Cohen, Kamarck, and Mermelstein (1983).
However, they did not investigate the effects of social support on the relationship between stressors
and perceived stress and the relationship between perceived stress and medication adherence.
The findings from the study by Frenzel et al. (1988) suggest that “general” stressors have an
effect in the hypothesized direction on perceived stress. This opens the door to the use of the model
proposed in this study using more specifically defined stressors. However, to move forward will
require the development of a more specific construct to supplant the more general daily hassles
scale.

19
Medication-related Stressors
The discussion on medication-related stressors will be divided into two sections The first
section will review the historical evolution of general stressor constructs. The second section will
discuss the development of an improved medication-related stressor scale.
Historical Evolution of General Stressor Constructs
There have been various attempts to evaluate the role of stressors using life events scales and
daily hassles scales. It will be important to explore the differences between these two types of
scales in order to develop a medication-related stressor construct that can be helpful in predicting
medication adherence.
In the past, studies on the role of stressors have been approached from a major life events
perspective (Holmes and Rahe, 1967, Dohrenwend and Dohrenwend, 1974). Although much of
this research has been criticized on methodological grounds (Mechanic, 1974, Sarason, Johnson
and Siegel, 1978), it does provide consistent evidence for a link between social events and health
disorders (Brown and Hams, 1978). Increased scores for major life events have been found to be
related to dysfunction both retrospectively and prospectively (Monroe, 1983). However, the
magnitude of the association found between major life events and health disorders has frequently
been low (Sarason, de Monchaux, and Hunt, 1975, Rabkin and Struening, 1976).
In an attempt to unprove this prediction, recent research has incorporated measures of everyday
events, such as hassles. Lazarus and Folkman (1984) focused on everyday events or hassles as an
alternative measure of stressors. Hassles are envisioned by Lazarus and Folkman (1984) as
experiences and conditions of daily living that have been appraised as salient and harmful or
threatening to the endorser's well-being.

20
Although daily hassles have been found to be correlated with major life events (Kanner et al.,
1981; Monroe, 1983), when the effects of both types of stressors were examined jointly, hassles
were found to be a better predictor of dysfunction than major life events Kanner et al. (1981)
found daily hassles to outperform major life event scores as predictors of both current and
subsequent symptoms. DeLongis et al. (1982) obtained essentially the same result using health
status as the outcome variable. Both studies found hassles and outcomes to be significantly
correlated when the effects of major life events were examined using partial correlation. However,
when the partial correlation did not include hassles, major life events and outcomes were found to
be unrelated. This finding has been replicated using different measures of hassles (Monroe, 1983,
Burks and Martin, 1985) and different health outcome measures (Weinberger et al., 1987).
When the "original" Hassles Scale (Kanner et al., 1981) was published, the scale aroused
considerable debate for two reasons First, some critics contended that the Hassle Scale was
confounded by an inability to separate the external, objective sources of stress from the internal,
subjective reactions to it and that it measured, rather than predicted, perceived stress (Dohrenwend
et al., 1984). Second, critics also contended that respondents were asked to endorse Hassle Scale
items as having been "somewhat," "moderately," or "extremely" severe; there was no provision for
ratmg a particular hassle as being "less than somewhat severe." They contend that, because of this
format, endorsement of Hassle Scale items indicates difficulty in coping with the stressors
mentioned m the items (Dohrenwend and Shrout, 1985).
In a newer instrument, the Survey of Recent Life Experiences, Kohn and Macdonald (1992)
addressed both criticisms of the Hassles Scale for the general adult population First, their item-
selection strategy was to retain only items which correlated positively and significantly with Cohen
et al's (1983) "gold standard": The Perceived Stress Scale. In this way, they ensured that the final
form of the Survey of Recent Life Events would retain an indirect relationship to the stress-

21
appraisal process, which Lazarus and his associates maintained is a critical determinant of the
adverse consequences of stress (e g., Kanner et al., 1981; DeLongis et al., 1982, Lazarus and
Folkman, 1984; Lazarus, 1984, Lazarus et al., 1985). They adopted this "indirect approach" to
avoid the potential contamination inherent in the Kanner et al. (1981) method of tapping into stress
appraisal, namely the use of seventy ratings. Instead of having subjects rate each item for its
seventy, they had them indicate the extent of their experience with it over the past month. Second,
they included another point in the Survey of Recent Life Expenences to denote no expenence with
hassles, as such addressing the second shortcoming alluded to earlier in the onginal Hassle Scale
by Kanner et al. (1981).
Although the "general" Survey of Recent Life Events is a significant advance m hassle research,
the development of special hassles measures for specific subgroups of the general population is
clearly justified by substantial differences in the frequency of endorsement of various hassles
among them (Kanner et al., 1981, Blankstein and Flett, 1991). This is why special scales have
been developed to assess hassles among children (Elwood, 1987; Kanner et al., 1987), adolescents
(Bobo et al., 1986; Compás et al., 1987), medical students (Wolf, Elston, and Kissling, 1989),
college students (Blankenstein and Flett, 1991; Kohn et al., 1990), computer users (Hudiburg,
1991) and, in the health care field, patients on hemodialysis treatment for end-stage renal disease
(Murphy, Powers, and Jalowiec, 1985).
Implicit in this view is the expectation that hassles will differ even within subgroups, reflecting
their particular interpersonal and social contexts. It seems plausible that men and women, older
and younger people, and individuals with different roles would report quite different hassles as a
consequence of their personal situations. Furthermore, if hassles are to be a viable alternative to
the major life events approach, they need to be predictive of significant outcomes, such as
medication adherence.

22
Development of the Medication-related Stressors Construct
The notion of a “hassle-like" scale specific to medication use is not new. Medication-related
stressors have been the focus of at least one qualitative study and two quantitative studies
However, by reviewing these related studies, an argument will be made for the development of an
improved quantitative medication-related stressor scale that includes not only patient-reported
measures of experiences with these stressors, but also an objective measure of these stressors taken
from automated records.
One qualitative study has looked at the contribution of medications to stress among family
caregivers and the need of caregivers for services provided by a pharmacist (Ranelli and Aversa,
1994). An interview was designed to elicit information about stressors stemming directly from the
medications (such as adverse effects) and arising from problems in managing the drug regimen
(such as medication adherence), services received from health care providers (such as services
received from the pharmacist), and the outcomes of stress (such as the caregiver's satisfaction with
services). Medications were found to contribute substantially to caregivers' stress, with 32% of the
caregivers reporting problems directly related to medications (primary medication-related stressors)
and 19% and 52% reporting problems in managing the drug regimen (secondary medication-related
stressors) currently or within the past year, respectively.
Two quantitative studies have looked at the contribution of medications to stress among patients
suffering from schizophrenia (Weiden et al., 1994; Harvey, 1991). Weiden et al. (1994)
constructed and validated a hassle "like" instrument specific to takers of lithium as part of a larger
scale called the Rating of Medication Influences (ROMI) The ROMI was developed as part of a
longitudinal study of neuroleptic nonadherence and was administered to 115 discharged
schizophrenia outpatients. A principal components analysis of the “Reasons for Noncompliance”

23
items yielded five subscales related to nonadherence (Denial/Dysphona, Logistical Problems,
Rejection of Label, Family Influence, and Negative Therapeutic Alliance) The authors concluded
that the multidimensional aspects of the instrument make it ideal for research studies addressing the
various reasons for medication nonadherence among schizophrenia outpatients. These five
subscales do have a distinct advantage over the earlier unidimensional quantitative scale, the
Lithium Attitudes Questionnaire (LAQ Harvey, 1991). However, both of these scales are specific
to patients suffering from schizophremza and cannot be used as a more general scale for elderly
outpatients suffering from one or more chronic conditions.
After a thorough examination of studies involving one or more medication-related stressors, it
appears in general that subjective experiences of medication-related stressors can be subclassified
(at least initially) into informational, instrumental, or emotional categories. Each of these
categories, and the studies that suggest the specific subjective experiences of medication-related
stressors within each category, will be addressed in turn.
Informational medication-related stressors represent the patient's subjective experience of a
subgroup of problems, irritants, or annoyances stemming from a lack of facts about the particular
medication(s) he or she is taking, or the medical condition(s) he or she is experiencing. Nine
"informational" medication-related stressors were identified in the medical literature. First, patients
appear to have problems when they don't feel right, attribute it to their medication, and wonder
about changing the dose of their medication. This has been seen in patients taking medication(s)
for convulsions (Reynolds, 1978), nausea (Laszlo, 1983), angina (Scardi, 1989), sedation (Rhodes
et al. , 1978), diabetes (Paterson et al., 1989), anxiety (Henderson, 1982; Hellerstein et al., 1994),
hypertension (Tunca and Agzitemiz, 1993), tuberculosis (Castelo et al., 1989), blood clots (Silva-
Smith, 1994), psychosis (Salzman, 1993) and depression (Gram, 1990). Second, patients seem to
have trouble choosing the appropriate nonprescnption medication This has been shown, for

24
example, among patients taking laxatives (Bruppacher et al., 1988), and antacids (Gerbino and
Gans, 1982). In fact, the problems among the elderly with nonprescnption use has become so
problematic that it has been advocated that counseling by pharmacists (Bayne et al., 1983) be
augmented with help from social work practitioners (Giannetti, 1983) and trained volunteers
(Fabacher et al. 1994). Third, patients appear to need information about how their medication
interacts with other medications (Stewart and Cooper, 1994, and Silva-Smith, 1994), food
(McCabe, 1986) and/or beverages (Lamy, 1984). Fourth, patients need information regarding the
administration of eye drops (Smith and Drance, 1984) and metered-dose inhalers (Reardon and
Bragdon, 1993, Clark, 1994), as well as the more common liquids, suppositories, and ointments
(Anderson, 1977). Fifth, patients can undergo pressure when they do not know whether something
that happens to them is a side effect of their medication. For example, medication adherence has
been found to dramatically fall when patients suddenly complain of a change in sexual behavior
(Papadopoulos, 1980; Strauss and Gross, 1984), or a change in taste (Coulter, 1988). Sixth,
patients can feel anxiety when they do not know what to do when a side effect of their medication
occurs For example, patients often do not know that simple irritation of the skin from transdermal
patches (Rayment et al., 1985, Carmichael, 1994), often doesn’t require the changing of the
prescription, whereas side effects from antidepressants often do (Somberg, 1984; Agosti et al.,
1988, Stokes, 1993). Seventh, patients can experience a great deal of strain when they constantly
miss doses of their medication Often this will necessitate a change to drugs with more convenient
dosage schedules (Ram and Featherston, 1988; Slinning, 1990; and Bialer, 1992), rather than the
usage of depot administration (Bames and Curson, 1994). Eighth, patients can become
bewildered wondering whether a medication given to them by one physician should be taken with a
medication given to them by a different physician (Lamy, 1989; Colley and Lucas, 1993;
Kirchner, 1994). And, finally, ninth, patients may think or believe that their medication is not

25
working. For example, patients taking anticonvulsants (Reynolds, 1978) and patients takings
antiemetics (Lazio, 1983) may complam that they are uncertain about their drug’s efficacy and
need more information.
Instrumental medication-related stressors represent the patient's subjective experience of a
subgroup of problems, irritants, or annoyances stemming from a felt incapacity to acquire his or
her medication(s) or items necessary to take his or her medication(s) correctly. Nine "instrumental"
medication-related stressors were identified in the medical literature. First, elderly patients can
experience difficulties getting transportation to a doctor’s office and/or pharmacy (Wolfgang et al.,
1993). These difficulties are compounded with problems involving opening hours (Crespo et al.,
1992), physical disability (Smith and Dranee, 1984), and uncommonly stocked medications
(Miller, 1981, Bums et al., 1992; Rabón et al., 1993; Carlsson et al., 1993). Second, patients may
need something to remind them to take their medications on time (Carswell, 1985, Fingeret and
Schuettenberg, 1991; Rivers, 1992; Mackowiak et al., 1994, Spiers and Kutzik, 1995). Third,
patients can need something to remind them to have their medications refilled on time (Smith et al.,
1986). Fourth, patients can experience trouble having their blood pressure taken (National High
Blood Pressure Education Program, 1983; Lebrec, 1990; Mejia et al., 1990; Costa, 1994, Schultz
and Sheps, 1994) or some other type of monitoring, for example, drug levels in the blood
(Reynolds, 1978; Risch et al., 1979; Troupin, 1984; Squire et al., 1984; Ritschel et al. 1989;
Javaid, 1994; Silva-Smith, 1994), peak-expiratory flow rates (Stafford, 1988), and occult blood
testing (Simon, 1987). Fifth, patients may need an up-to-date list of their medications to show to
all their doctors (Smith, 1994). Sixth, patients need to have easy-to-read information about their
medications because they often do not receive or remember enough from their conversations with
health-providers (Lee and Tan, 1979; Smith et al., 1986; Morrow et al., 1988). Seventh, elderly
patients often have difficulty opening their medication containers (Schlumpf and Sonderegger,

26
1981; King and Palmisano, 1989; Bums et al., 1992). Eighth, elderly patients can have difficulty
swallowing their medication and may need a suspension compounded (Sarkar et al., 1989;
Andersen et al., 1995). And, finally, ninth, patients can have problems paying for their medication
(Leppik, 1990; Shea et al., 1992).
Emotional medication-related stressors represent the patient's subjective experience of a
subgroup of problems, irritants, or annoyances stemming from a felt inability to deal with feelings
about his or her medication(s) or his or her medical condition(s). Nine "emotional" medication-
related stressors were identified in the medical literature. First, patients can presume that no one is
really interested in their health. The impact of discussion which demonstrates patient interest by
the health provider has been shown to be positively associated with patient medication adherence
(Martin and Bass, 1989). Second, patients can feel afraid that no one can help them deal with
their health problems Without discussion on patient attitudes and expectations about their
therapy, patients cannot hope to have realistic goals and may feel forsaken (Massey et al., 1980;
Leppik , 1990; Ranz et al., 1991, Unger, 1995; Donovan, 1995). Third, patients can lack
confidence about taking their medication as prescribed. Techniques to improve patient confidence
have repeatedly demonstrated a positive impact on medication adherence (Jones, 1976; Miller,
1981, Forman, 1985; Webb et al., 1990; Nespor, 1993; Miller, 1993; Lilja and Larsson, 1994,
Costa, 1994; Robinson et al., 1995). Fourth, patients can feel embarrassed about taking their
medications. These embarrassments may include problems with literacy (Hussey, 1991), urinary
incontinence (Vernon, 1989), sexual inadequacies (Aizenberg et al., 1995), epilepsy (Krumholz et
al., 1989), and body disfigurements (Stewart, 1983). Fifth, patients can feel that no one seems to
listen to them about dealing with their medication problems (King et al., 1986). Sixth, patients can
feel afraid to ask someone to make something that was told to them about their medication easier to
understand. For this reason, physicians are shifting away from a rigid authoritative manner when

27
speaking with their patients (Martindale, 1990) to using a more collaborative manner (Heaton,
1981; Larsen et al , 1985, Laage, 1988; Rostetal., 1989) which has been shown to have a positive
impact on medication adherence among schizophrenics (Ranz et al., 1991), diabetics
(Schifferdecker et al., 1994), and hypertensives (Schultz and Sheps, 1994; Kjellgren et al., 1995).
Seventh, patients can feel judged because of the medications they take Emotional counseling that
mcludes respect and acceptance of the patient's condition must accompany medication therapy
(Lieberman and Evans, 1985; Vinson and Cooley, 1993). Eighth, patients can experience a side
effect of their medication that upsets them (Papadopoulos, 1980; Strauss and Gross, 1984). Ninth,
patients can feel no need to take their medication. This often occurs when a patient suffers from a
condition in which there are no readily discemable symptoms, as for example, hypertension (Nies,
1975; Moser, 1985; Black, 1990; Fotherby, 1995), or they suffer from a condition that renders
them unable to discern such symptoms, as for example, depression (Guscott and Grof, 1991.)
Also from this thorough review of the literature, one might argue that there is an objective
measure of medication-related stress which act m unison with these subjective medication-related
stressors to affect perceived medication stress. Specifically, there are the patient’s number of
prescribed medications. From the literature review, studies of drug use consistently show a
negative relationship between the number of drugs taken by the patient and medication adherence
(Weintraub, Au, and Lasagna, 1973; Hulka, Kupper, Cassel, and Efird, 1975; Darnell et al.,
1986).
In this segment on medication-related stressors, two literature reviews were accomplished: 1) a
review of the psychosocial literature germane to the historical evolution of general stressor
constructs, and 2) a review of the medical literature to make the argument that the medication-
related stressor construct needs to include both a subjective and an objective scale and that the
subjective stressors should be subclassified into three categories: informational, emotional or

28
instrumental Both of these reviews will help m the conceptualization and construction of a new
medication-related stressors scale.
Perceived Medication Stress
In this section, there will be a review of the literature germane to the historical development of
the perceived stress construct. It will be argued that, although historically difficult, a distinction
must be made between the experience of stressors and a patient's evaluation of those experiences.
It will also be argued that perceived stress may have either mam (direct) effects or mediating
(indirect) effects on the relationship between stressor experience and medication adherence.
Perceived stress may arise when an individual perceives that it is important to respond to one or
more of the hassles, but an appropriate response is not immediately available (Lazarus and
Launier, 1978). Although a stressor may not place great demands on the abilities of most
individuals, it is when multiple stressors accumulate, persisting and straining the problem-solving
capacity of the individual, that the potential for serious perceived stress may occur (Wills and
Langer, 1980).
Patients actively interact with their environments, appraising potentially threatening or
challenging experiences (Lazarus, 1966, 1977). From this perspective, stressor effects are
assumed to occur only when both 1) the experience is evaluated as threatening or otherwise
demanding and 2) insufficient resources are available to deal with the experience. The argument is
that the causal "event" is the emotional response to the stressor experience (Lazarus, 1977). An
important part of this view is that the response to the experience is influenced by conditioning
factors as well, such as social support
The centrality of this evaluation process suggests the desirability of measuring perceived stress
in addition to the experience of stressors. If perceived stress is measured, it could also be used in
conjunction with a medication-related stressors scale in an effort to determine whether such factors

29
as social support (Pearlin et al., 1981) protect people from the deleterious effects of stressors by
altering the process or processes by which the evaluation of these experiences result in behavioral
changes (Gore, 1981). Also, perceived stress can be viewed as an outcome vanable measuring the
level of perceived stress as a function of medication-related stressors.
Measures of the evaluation of specific stressor experiences have been widely used, for example,
measures of perceived occupational stress (Kahn et al ., 1964). There are, however, some practical
and theoretical limitations of measuring evaluations to specific stressors. Practically, it is difficult
and time-consuming to adequately develop and psychometncally validate an individual perceived
stress measure every time a new stressor is studied. Theoretically, there is an issue of whether
measures of perceived stress to a specific stressor really assess a person's evaluation of that
stressor. There is, in fact, evidence that people often wrongly attribute their evaluations of stress
to a particular source when the perceived stress is actually due to another source (Gochman, 1979).
Another problem with measures of evaluation to specific stressors is that such measures imply the
independence of that experience in the precipitation of an outcome. However, it is likely that the
outcome process is affected by a combination of specific stressors that may be measured by a
“global” measure of these stressors.
Cohen, Kamarck, and Mermelstein (1983) presented evidence from three samples, two of
college students and one of participants in a community smoking-cessation program, for the
reliability and validity of a 14-item instrument, the Perceived Stress Scale, designed to measure the
degree to which situations in one's life are evaluated as stressful. The Perceived Stress Scale
showed adequate reliability and, as predicted, was correlated with major life event scores,
depressive and physical symptomatology, utilization of health services, social anxiety, and
smoking-reduction maintenance. In all comparisons, the Perceived Stress Scale was a better
predictor of the outcome in question than were major life events scores. In the years to follow this

30
study, the Perceived Stress Scale became the gold standard for measuring perceived stress in other
studies (e g., Linn, 1985; Levenstein et al., 1993). In two studies examining the multi-
dimensionahtv and internal consistency of the Perceived Stress Scale (PSS: Hewitt et al., 1992;
and Martin et al., 1995), it was established that the PSS consisted of two factors. The first factor
appears to measure global feelings of stress arising from perceptions that one’s life is stressful,
unpredictable, uncontrollable, and overloading. As suggested by Hewitt et al. (1992), “perceiving
oneself as stressed may involve a perception of one’s ability to deal effectively with events or
changes.” The second factor appears to assess perceptions of an ability to cope with stressors in
one’s life. As suggested by Hewitt et al. (1992), “perceiving oneself as stressed may involve a
negative affective experience reaction.”
These general studies on perceived stress point us in the direction we need to go: a perceived
stress scale specific to medication use will have to be utilized. This kind of scale must be utilized
to test two effects: 1.) the effect of medication-specific social support on the relationship between
medication-related stressors and perceived medication stress, and 2.) to test the effect of
medication-specific social support on the relationship between perceived medication stress and
medication adherence
However, it may be as important or more important to understand the relationship than
underpins the entire model, that is, how perceived medication stress effects the relationship between
medication-related stressor experience and medication adherence. A recent study has demonstrated
that neuroticism has significant direct effects on all health outcomes, and substantial indirect
effects, through perceived stress, on mental health outcomes (Hooker et al., 1992). This might
suggest that the experience of medication-related stressors while taking multiple medications might
have direct effects on medication adherence, and substantial indirect effects through perceived
medication stress, on medication adherence

31
The central idea in a mediation model is that the effects of stimuli on behavior are mediated by
various transformation processes internal to the organism (Baron and Kenny, 1986). Perceived
medication stress can represent a property of the person that transforms a predictor by some “in the
head” mechanism. And, it must be understood that mediatmg events “shift roles” from effects to
causes, depending on the focus of the analysis. Therefore, during one part of the study, perceived
medication stress will be treated as an independent variable having an effect on medication
adherence, while during another part of the study it might be thought of as being acted upon (as a
dependent variable) by medication-related stressors.
Medication-specific Social Support
Medication-specific social support was identified as a "social" conditioning variable (Israel and
Schurman, 1990). Social support has been defined as "an input directly provided by another
person (or group) which moves the receiving person towards goals which the receiver desires
(Caplan et al., 1976)." There is evidence that supportive interactions among people are protective
against the health consequences of life stress.
Several prospective epidemiological studies have shown that increases in "global" social
support are related to decreases in mortality. This was shown in 9- to 12-year prospective studies
of community samples by Berkman and Syme (1979) and House, Robbins, and Metzner (1982)
and in a 30-month follow-up of an aged sample by Blazer (1982). Similarly, several prospective
studies using mental health outcome measures have shown a positive relation between social
support and mental health (Henderson, Byrne, and Duncan-Jones, 1981, Holahan and Moos,
1981, Turner, 1981; Williams, Ware, and Donaldson, 1981; Aneshensel & Frenchs, 1982; Billings
and Moos, 1982).
In 1983, Levy presented a selective review and critique on social support as a factor in the
enhancement of a different outcome measure—medication adherence. Regarding the integrity of the

32
independent variable of social support, Levy suggested that future research should clearly specify
the form of social support in the home, in training sessions, and in support groups. Levy stated
that it may be necessary to devise instruments that will enable investigators to monitor “the kind
and rate of social support” in several situations, such as support from family and friends In other
words, Levy has suggested that past studies have lacked an adequate detailing of the social support
variable because they utilized primarily a "structural" perspective along the “structural-functional
continuum.” It appears that future investigators should make the shift from the “structural”
perspective to the “functional” perspective.
Two comprehensive reviews in the sociological literature (Caldwell and Reinhart, 1988; Oxman
and Berkman, 1990) suggest that to utilize the “functional” perspective, one should consider at
least three functional domains of social support (i.e., appraisal, emotional, and tangible). These
studies suggest that these three functional domains categorize the majority of support functions and
that future studies using this typology could add significantly to this body of research.
It seems reasonable to assume that many studies m medical practice have been concerned with
the domam of "appraisal" support, whereas studies in the social support literature involving
caregivers or confidantes are concerned with the domains of "emotional" or "tangible" support.
This is borne out in the only scale identified m the literature that has been used to study the effect
of social support on medication adherence (Caplan et al., 1980).
Although some studies have provided evidence of a relationship between functional social
support and outcomes, in theory this effect can be exp lamed through very different processes.
Social support seems to have two sorts of health effects: 1) a mam or direct effect (eg., Kessler
and Essex, 1982), and 2) a moderating or buffering effect (e g., Henderson, 1980; Wilcox, 1981).
The mam effect model proposes that social support has a beneficial effect regardless of whether
persons are under stress. The moderating effect model proposes that social support will act only to

33
protect people from the deleterious effects of stress on health and well-being when the patient is
experiencing stressors Both of these types of effects will be discussed in turn.
A generalized beneficial effect of social support is hypothesized to occur because it provides
people with regular positive experiences as well as a socially rewarded role (Kessler and Essex,
1982). This kind of support could be related to the overall well-being of the person because it
provides positive effects, a sense of predictability and a stability in one's life situation, and a
recognition of self-worth Integration in a network that provides some minimum level of social
support may also provide resources to cope with stressors and help one avoid negative experiences
that otherwise would increase the probability of an enduring negative outcome.
This view of social support in general has been conceptualized from a sociological perspective
as "embeddedness" in social roles (Thoits, 1985) and, from a psychological perspective, as social
interaction, social integration, relational reward, or status support (Wills, 1985). This kind of
network is hypothesized to be related to physical outcomes through emotionally induced effects on
neuroendocrine or immune system functioning (Jemmott and Locke, 1984) or through an influence
on health-related behavioral patterns that might include cigarette smoking, alcohol use, or medical
help-seeking (Wills, 1983, Krantz, Grunberg, and Baum, 1985).
Shifting to an examination of the moderating (buffering) effects model, social support may
moderate between the stressor, or expectation of the stressor, and perceived stress by preventing
high levels of perceived stress. That is, the perception that another individual can and will provide
necessary resources may redefine the potential for harm posed by the experience of a stressor and
bolster one's perceived ability to deal with the unposed experience of a stressor, and hence prevent
a particular stressor experience from being evaluated as highly stressful (Cohen and McKay,
1984).

34
Literature reviews reveal that the relevance of the type of social support to a particular stressor
experience may also determine the likelihood of observing a moderating effect (e g., House 1981;
House and Kahn, 1985, House et al., 1988, Cohen and Willis, 1985). In 1984, Cohen and McKay
published a study on their Interpersonal Support Evaluation List (ISEL). Basically, they did not
find a moderatmg effect with a tangible social support subscale. The lack of evidence for a
moderating effect for the tangible social support subscale suggests that tangible aid is not an
important resource for buffering. However, they did find a moderatmg effect for an emotional
social support subscale and for an appraisal social support subscale
This research seems to suggest that informational medication-specific support from a doctor,
pharmacist, or (most concerned) other person will demonstrate a moderatmg effect on the
relationship between medication-related stressors and perceived medication stress. Also, this
research seems to suggest that emotional medication-specific support from a doctor, pharmacist, or
(most concerned) other person will demonstrate a moderatmg effect on the relationship between
medication-related stressors and perceived medication stress. However, this research seems to
suggest that tangible medication-specific support from a doctor, pharmacist, or (most concerned)
other person will demonstrate a mam effect on perceived medication stress rather than moderatmg
effect on the relationship between medication-related stressors and perceived medication stress.
Given that perceived medication stress is an evaluation of his or her responses to medication-
related stressors, it will be hypothesized that only emotional medication-specific support can effect
the relationship between perceived medication stress and medication adherence. Furthermore, it
will be hypothesized that emotional medication-specific support from a doctor, pharmacist, or
(most concerned) other person will demonstrate a moderatmg effect on the relationship between
perceived medication stress and medication adherence.

35
Other Conditioning Variables
In the proposed model, there are six other conditioning vanables in the nucleus of the Stress
Model of Medication Adherence. Two vanables are other social conditioning vanables: income
and ethnicity. The other four vanables are biophysical conditioning vanables: age, gender, and
health status. Since the study sample will be taken from an older veteran population, ethnicity and
gender were expected to not vary much from white males and these variables will be excluded from
discussion here. It is hypothesized that the other conditioning vanables will have some direct
effects on medication-related stressors, perceived medication stress, and medication-specific social
support. These hypotheses will help validate the “nucleus” of the proposed Stress Model of
Medication Adherence and are expressed below
Age
Advanced age should not be associated with medication nonadherence. Spiers and Kutzik
(1995) found, among independently living persons at least 55 years of age participating in a
"brown-bag" medication review, that age was the best predictor of medication-related problems.
However, contrary to what is often believed, as a group these patients try harder to comply with
medications in general and antihypertensive medications in particular than do younger patients
(Klein, 1988), even in the presence of bothersome problems.
Advanced age should be positively associated with instrumental medication-related stressors.
Subjects with advanced age suffer from declines in activities of daily living and might experience
more stressors involving getting and taking their medications (Doherty et al., 1983). Older adults
seem to experience motor difficulties that are problematic to obtaining or doing mechanical tasks
associated with their medications. In one study, almost half of the nonmstitutionalized elderly were
found limited in mobility because of chronic conditions, especially heart disease and arthritis (Rice
and Estes, 1984). Decreased activity and dexterity can limit a person ability and willingness to

36
have prescriptions filled, take difficult to swallow drugs regularly, and open and close the
childproof containers that, to arthritic hands are unmanageable (Mallet, 1992). There are age-
related changes in vision, hearing, memory and leammg (Kimberlin, 1995). In a study by Meyer
and Schuna (1989), Jacob's Cognitive Capacity Screening Examination (CCSE) was utilized
successfully to assess skills including the ability to read and interpret prescription labels, open and
close vials, remove tablets, and identify tablet colors.
Advanced age should be positively associated with informational medication-related stressors.
Older adults seem to experience difficulties with information when taking their medication. A study
suggests that the failing eyesight of older adults reduces their ability to read small print on
prescription labels and package inserts (Dirckx, 1979). Also, another study suggests that about
30% of the population aged 65 years and older suffer significant hearing loss (National Center for
Health Statistics, 1985). Hearing loss limits patients' ability to hear directions for appropriate
drug use and discourages them from asking questions (Ebersole and Hess, 1981). Also, the
concept of “intelligent noncompliance” may be of special relevance to the elderly lacking proper
information, for older people may omit medications in order to compensate for physiological
changes accompanying agmg or disease - changes that make them more vulnerable to adverse drug
reactions that may be unrecognized by their physicians (Lipton and Lee, 1988). Furthermore,
memory of information has been shown to decline with age and information may have to be
repeated by the health care provider during each visit (Light, 1991, Sahhouse, 1991).
Pharmacokinetic and pharmacodynamic effects may be altered in the patient as he or she ages
(Hoffler, 1981; Braverman, 1982; Roberts and Turner, 1988, Dawling and Crome, 1989; Fox and
Auestad, 1990; Taylor, 1990). There may be changes in the absorption of orally administered
drugs, body composition, serum albumin and globulin concentration, cardiac output and hepatic
metabolism, renal blood flow and renal function and homoeostatic mechanisms (Shaw, 1982;

37
Chapron, 1995). For example, these changes are associated with adverse effects among the elderly
from nonsteroidal anti-inflammatory drugs (Johnson and Day, 1991). Drugs which most often
result m adverse reactions in the elderly have been developed (Goldberg and Roberts, 1983).
In addition to these physiologic changes there are a number of disease changes that occur with
aging (Salzman, 1982, Tuck, 1988; Furberg and Black, 1988). For example, the incidence and
prevalence of congestive heart failure increase exponentially with advancing age (Hunziker and
Bertel, 1995). Also, gastrointestinal problems are very common in the elderly, which exacerbate
oral medication adherence efforts (Levitan, 1989). Indeed, side effects occur and medication
adherence seems to warn, as elderly patients suffer from concurrent existing illnesses, for example,
diabetes and bronchitis (Dali, 1989).
Income
Higher income should be positively associated with medication adherence. Hattaway (1996)
has reported the impact of the high cost of prescriptions on medication nonadherence among the
elderly. In addition, the failure of Medicare to cover the cost of prescription drugs is widely
perceived to be a primary factor in medication nonadherence (Arnold et al., 1995). However, it
must be clear that in this study most subjects had service connected disabilities and did not have to
pay for their medications (except for a $2.00 co-pay). The medication adherence rate might have
be significantly different if the study was replicated where cost was a consideration
Higher income should not be associated with perceived medication stress One study has
suggested a socioeconomic effect on perceived stress in work and nonwork environments (Bednar,
Marshall, and Bahouth, 1995). However, it is interesting to point out that in another closely
related study, an inverse relationship was found between economic satisfaction and perceived
stress. It would seem that perceived stress has more to do with a person satisfaction with their
economic situation than their actual income (Krannich, Riley, and Leffler, 1988).

38
Health Status
An association should not be found between poor health status (using self-rated health and
Chronic Disease Score) and medication adherence This has been suggested in a recent study
among elderly patients taking one or more chronic medications (Coons et al., 1994).
Poor health status should be positively related to the informational medication-related stressors.
Subjects with more disease states should be more concerned about their medication taking and
information that has a bearing on their medication taking. These particular subjects will likely
experience more stressors related to specific information gathering efforts. For some older patients,
diabetes mellitus or renal functional impairment can effect sodium fluid volume retention, which
requires concommitant dosage adjustments to avoid the appearance of side effects (Weidmann,
1983).
Poor health status should be positively related to the instrumental medication-related stressors
Subjects with more disease states would seem to experience more stressors involving getting and
remembering to take their medications. Older patients with hip fractures have indicated having
transportation problems (Harrison and Kune, 1989), which suggests they can have difficulty
getting medications refilled. Older patients with cancer have reported decreased functional status,
fatigue, pain and psychologic distress, that result in instrumental difficulties continuing to take
medications (Craig and Powell, 1987; Fossa et al., 1990).
Poor health status should be positively related to the emotional medication-related stressors. As
a subject’s health detenorates, them seem to expenence problems related to communicating with
other people about their medications. Those in the age group 65 or over who rate their health as
poor visit the doctor most and take more medication than those who rate their health as good (Linn
and Linn, 1980). Certain affective disorders among the elderly are associated with compromised
cognitive and emotional capacities and thus, with one's ability to act as a fully autonomous

39
individual (Perry, 1985). These patients, presenting with psychiatric disorder coexisting with
alcohol and/or drug abuse, present a major problem in treatment because of their emotional
fragility, their propensity to impulsive acting-out behaviors and their adaptation to psychiatric
symptomatology via self-medication with illicit drugs (Case, 1991). Recovering patients have
complex attitudes and feelings toward medications that need to be explored, particularly as they
affect adherence with prescribed regimens (Zweben and Smith, 1989). Major depression is
associated with amplification of somatic symptoms and disability, poor self-care and adherence to
medical regimens (Katon and Sullivan, 1990).
Poor health status should be negatively related to perceived medication stress (Chwalisz and
Kisler, 1995). However, it should be noted that in a separate study, it was found that the strength
of this association decreases over tune (Schulz et al., 1995). This suggests that upon the first
occurrence of one or more morbidities, a patient’s perceived stress may be higher than it will be at
a future point m tune as he or she gets accustomed to living with their condition(s).
Research Hypotheses
The nature of the proposed research is exploratory. Nevertheless, based on this literature
review, we now have a "magnified" view of the nucleus of the proposed Stress Model of
Medication Adherence (see Figure 3.1). Five sets of hypotheses can now be stated The first set
of hypotheses deal with correlations between the exogenous and endogenous variables to help
validate the “nucleus” of the Stress Model of Medication Adherence, The second set of hypotheses
deal with hypothesized mediating effects of perceived medication stress on the relationships
between type of stressors and medication adherence. The third, fourth, and fifth set of hypotheses
deal with relationships between medication adherence, type of stressors, perceived medication
stress and type of doctor, pharmacist, and (most concerned) other person support, respectively.

/
Conditioning Variables: Individual or Situational Characteristics
Social
social support
a appraisal
b emotional
c tangible
ethnicity
income
Biophysical
age
gender
health status
a sell-report
b patient iccords
Psychosocial-Environmental
Conditions Conducive to
Stress (Stressors)
Subjective Medication-related Stressors
a informational
b emotional
c instrumental
Objective Medieat ion-related Stressors
number of prescriptions
1
1
1
1
Short-Term
1
1
1
Perceived
Medication
1
1
•if
Responses
to Stress
Stress
(Stressors)
Medication
Adherence
(Behavioral)
Figure 3.1 Magnified view of the "nucleus" of the Stress Model of Medication Adherence (SMM A-n)

41
First Set of Hypotheses
Hypothesis 1 A,: Advanced age is not associated with medication adherence
Hypothesis 1 A;: Advanced age is positively associated with instrumental
medication-related stressors
Hypothesis 1A3: Advanced age is positively associated with informational
medication-related stressors.
Hypothesis IB,: Higher income is positively associated with medication adherence.
Hypothesis 1B;: Higher income is not associated with perceived medication stress.
Hypothesis IB,: Higher mcome is not associated with perceived medication stress.
Hypothesis 1C, Poor health status is not associated with medication adherence.
Hypothesis 1C2: Poor health status is associated with informational medication-related
stressors.
Hypothesis 1C3: Poor health status is associated with instrumental medication-related
stressors
Hypothesis 1C4: Poor health status is associated with emotional medication-related
stressors
Hypothesis 1C5: Poor health status is associated with perceived medication stress.
Second Set of Hypotheses
Hypothesis 2A,: There will be a positive main effect between informational stressors and
perceived stress, a negative mam effect between informational stressors and medication
adherence, and a negative mam effect between perceived stress and medication adherence.
Hypothesis 2A;: There will be a positive mam effect between emotional stressors and
perceived stress, a negative mam effect between emotional stressors and medication
adherence, and a negative mam effect between perceived stress and medication adherence
Hypothesis 2A3: There will be a positive mam effect between instrumental stressors and
perceived stress, a negative mam effect between instrumental stressors and medication
adherence, and a negative mam effect between perceived stress and medication adherence
Hypothesis 2B The associations between the exogenous and endogenous variables
found earlier usmg zero order correlations may no longer be significant when
usmg multiple regressions.

42
Hypothesis 2C,: There will be a mediating effect of perceived stress on the relationship
between informational stressors and medication adherence
Hypothesis 2C;: There will be a mediating effect of perceived stress on the relationship
between emotional stressors and medication adherence.
Hypothesis 2C3: There will be a mediating effect of perceived stress on the relationship
between instrumental stressors and medication adherence
Third Set of Hypotheses
Hypothesis 3A,: There will be negative mam effect between informational stressors and
doctor’s appraisal support, a positive mam effect between informational stressors and
perceived stress, and a negative mam effect between doctor’s appraisal support and
perceived stress.
Hypothesis 3A;: There will be negative mam effect between emotional stressors and
doctor’s emotional support, a positive mam effect between emotional stressors and
perceived stress, and a negative mam effect between doctor’s emotional support and
perceived stress.
Hypothesis 3A3: There will be negative mam effect between instrumental stressors and
doctor’s tangible support, a positive mam effect between instrumental stressors and
perceived stress, and a negative mam effect between doctor’s tangible support and
perceived stress.
Hypothesis 3A4: There will be negative mam effect between perceived stress and doctor
emotional support, a positive mam effect between perceived stress and medication
adherence, and a negative mam effect between doctor’s emotional support and medication
adherence
Hypothesis 3B The associations between the exogenous variables and doctor medication-
specific social support variables found earlier usmg zero order correlations may no longer
be significant when usmg multiple regressions.
Hypothesis 3C,: There will be a moderating effect of doctor appraisal support on the
relationship between informational stressors and perceived stress
Hypothesis 3C; There will be a moderating effect of doctor emotional support on the
relationship between emotional stressors and perceived stress
Hypothesis 3C3: There will be a moderatmg effect of doctor tangible support on the
relationship between instrumental stressors and perceived stress
Hypothesis 3C4: There will be a moderating effect of doctor emotional support on the
relationship between perceived stress and medication adherence.

43
Fourth Set of Hypotheses
Hypothesis 4A,: There will be negative main effect between informational stressors and
pharmacist appraisal support, a positive main effect between informational stressors and
perceived stress, and a negative main effect between pharmacist appraisal support and
perceived stress.
Hypothesis 4A:: There will be negative main effect between emotional stressors and
pharmacist emotional support, a positive mam effect between emotional stressors and
perceived stress, and a negative mam effect between pharmacist emotional support and
perceived stress.
Hypothesis 4A3: There will be negative mam effect between instrumental stressors and
pharmacist tangible support, a positive mam effect between instrumental stressors and
perceived stress, and a negative mam effect between pharmacist tangible support and
perceived stress
Hypothesis 4A4: There will be negative mam effect between perceived stress and
pharmacist emotional support, a negative mam effect between perceived stress and
medication adherence, and a positive mam effect between pharmacist emotional support
and medication adherence
Hypothesis 4B The associations between the exogenous variables and pharmacist
medication-specific social support variables found earlier usmg zero order correlations
may no longer be significant when usmg multiple regressions
Hypothesis 4C,: There will be a moderating effect of pharmacist appraisal support on the
relationship between informational stressors and perceived stress.
Hypothesis 4C2: There will be a moderating effect of pharmacist emotional support on the
relationship between emotional stressors and perceived stress.
Hypothesis 4C3: There will be a moderating effect of pharmacist tangible support on the
relationship between reminding stressors and perceived stress
Hypothesis 4C4: There will be a moderating effect of pharmacist emotional support on the
relationship between perceived stress and medication adherence.
Fifth Set of Hypotheses
Hypothesis 5A,: There will be mam effects between informational stressors and other
person appraisal support, nor will there be a mam effect between other person appraisal
support and perceived stress. There will be a positive mam effect between informational
stressors and perceived stress.

44
Hypothesis 5A: There will be negative main effect between emotional stressors and other
person emotional support, a positive mam effect between emotional stressors and perceived
stress, and a negative mam effect between other person emotional support and perceived
stress
Hypothesis 5A3: There will be negative mam effect between instrumental stressors and
other person tangible support, a positive mam effect between instrumental stressors and
perceived stress, and a negative mam effect between other person tangible support and
perceived stress.
Hypothesis 5A4: There will be negative mam effect between perceived stress and other
person emotional support, a negative mam effect between perceived stress and medication
adherence, and a positive mam effect between other person emotional support and
medication adherence
Hypothesis 5B The associations between the exogenous variables and (most concerned)
other person medication-specific social support variables found earlier usmg zero order
correlations may no longer be significant when usmg multiple regressions
Hypothesis 5C,: There will be a moderating effect of other person appraisal support on
the relationship between informational stressors and perceived stress
Hypothesis 5C;: There will be a moderatmg effect of other person emotional support on
the relationship between emotional stressors and perceived stress.
Hypothesis 5C3: There will be a moderatmg effect of other person tangible support on the
relationship between instrumental stressors and perceived stress
Hypothesis 5C3: There will be a moderatmg effect of other person emotional support on
the relationship between perceived stress and medication adherence
Summary
The literature suggests certain hypothesized relationships between advanced age, higher mcome,
and poor health status (exogenous variables) and medication-related stressors, perceived
medication stress, and medication-specific social support (endogenous variables) which can help
validate the “nucleus” of the Stress Model of Medication Adherence (SMMA-n) Also, the
literature seems to suggest that perceived medication stress will demonstrate either a mam (direct)
effect, a mediatmg (or indirect) effect, or both on the relationship between medication-related
stressors and medication adherence. And, the literature suggests that the (doctor, pharmacist, and

45
other person) appraisal medication-specific social support will demonstrate a moderating (or
buffering) effect on the relationship between subjective informational medication-related stressors
and perceived medication stress Similarly, the literature suggests that the (doctor, pharmacist, and
other person) emotional medication-specific social support will demonstrate a moderating (or
buffering) effect on the relationship between subjective emotional medication-related stressors and
perceived medication stress. Finally, the literature suggests that the (doctor, pharmacist, and other
person) tangible medication-specific social support will demonstrate a mam (or direct) effect on the
relationship between subjective instrumental medication-related stressors and perceived medication
stress.
The relationships posited by this “magnified” view of the nucleus of the Stress Model of
Medication Adherence (SMMA) have taken into account the relevant literature The limitations of
previous theoretical work have lead to the development of theoretical hypotheses which must be
tested to better explain why some elderly outpatients take one or more medications as prescribed
while others do not. The next chapter will exp lam the methodology of the proposed research.

CHAPTER 4
METHODOLOGY
Introduction
This study utilized a cross-sectional retrospective design to explore relationships among four
important variables in the proposed Stress Model of Medication Adherence (SMMA). A
convenience sample of subjects for this study was taken from a sample frame of subjects over the
age of 65 receiving one or more chronic medications from the Ambulatory Care Pharmacy of the
Veterans Administration Medical Center in Gainesville, Florida.
This chapter will be divided into four sections. The first section will discuss study instruments.
In the second section, the study will be outlined: Study Phase I, and Study Phase II will be
discussed in turn. In the third section, human rights will be discussed. This chapter will conclude
with a summary
Study Instruments
As was discussed at the end of Chapter 3, four variables are examined in this study. These
variables are: 1) medication adherence, 2) medication-related stressors, 3) perceived medication
stress, and 4) medication-specific social support. Measurement of each of these variables will be
discussed m turn
Medication Adherence
Several methods exist for measuring medication adherence. However, all of the measures
identified are troublesome (Norell, 1984). An examination of the strengths and weaknesses of
these methods will follow, and, given the study’s intent, the most appropriate method was chosen
46

47
and utilized
There are several methods for measuring medication adherence which fall into two broad
categories direct and indirect. Direct methods of measuring adherence include testing of blood
levels and urinary excretion of the medication, a metabolite or marker (Ballmger et al., 1975; Bury
and Mashford, 1981, Young et al., 1984; Kapur et al., 1991). While it appears at first glance that
these would be fool-proof methods, one must remember that each individual's body will react to
medication (even an identical dose) in differing ways. Also, different forms of the same medication
will behave differently in each individual. This is especially true of elderly individuals whose
pharmacokinetics increasingly change with advanced age (Chapron, 1995). Finally, it is important
that the laboratory tests used to determine adherence be earned out accurately and in a timely
manner (Sackett and Snow, 1979). Despite the relative disadvantages of this method, it continues
to have proponents (e g., McMurdo et al., 1991).
There are several methods for indirectly determining adherence. The first involves the
measurement of outcomes While it seems reasonable to attnbute a successful outcome to
adherence with a presenbed regimen, there may be other potentially confounding factors. Among
them are support from the family, physician and lifestyle changes (Sackett and Snow, 1979) as
suggested by the proposed Stress Model of Medication Adherence (SMMA).
A second indirect measure of adherence is a patient self-report or interview. Self-report may
involve the answering of a few yes or no questions about their medication-taking behavior
(Monsky et al., 1986; Gil et al., 1993; Brooks et al., 1994). Scientific investigations of patients
often overestimate their adherence with treatment regimens. For example, Park and his associates
(1994) compared estimates of adherence usmg the interview and pill count methods among a group
of psychiatnc outpatients. Their study showed that 100 patients would be considered adherent by
usmg the interview method. However, this dropped to 57 when usmg the pill count method.

48
A third method for measuring medication adherence is the pill count method. In this method,
the investigator (or patient) simply counts the number of pills remaining in the bottle at some point
during treatment This is compared to the fill date and directions to determine adherence This
method also results in an overestimation of adherence. Problems ensue when family members
share medications or medications have more than one purpose (Sackett and Snow, 1979).
Physician assessment of adherence is a fourth method. Studies have shown that physicians are
no better at assessing medication adherence than by chance (Caron and Roth, 1968, Sackett and
Snow, 1979).
A fifth method is relatively new and involves the use of an electronic medication monitor. One
method which has been utilized in numerous medication adherence studies is the Medication Event
Monitoring System (Cramer et al., 1995, Wall et al., 1995; and Mason, Matsuyama, and Jue,
1995). Another such monitor has been developed by Seth A. Eisen and his associates (1987).
Their device contains two 21-blister medication packets and an unobtrusive electronic chip that
records the date and time each pill is removed. It has been shown to yield a highly reliable and
valid measure of medication adherence (Eisen et al., 1990) and has been utilized in a clinical study
among elderly patients (Camey et al., 1995). Devices have also been developed to record the use
of inhalers (Gong et al., 1988, Bosley et al., 1995). There is yet another method involving the use
of a portable bar code scanner which provides detailed information about the type of forgetting
underlying nonadherence (Leirer, 1988). All of these electronic methodologies suffer from two
deficiencies: 1) high cost, and 2) the inability to tell whether the patient really ingests the
medication or throws it away once he or she has taken the medication from the dispenser which
electronically recorded the medication's removal.
Most investigators have recommended a combination of the above methods (e g., Gilmore,
Temple, and Taggart, 1989). However, the constraints of this study suggest that the most

49
appropriate measure is the self-report. Pill counts would be difficult if not unpossible given the
methodology (i.e , using a mailed questionnaire). Furthermore, the high costs associated with the
use of electronic monitoring devices or laboratory tests precludes the use of these methods.
Furthermore, Craig (1985) and Westfall (1986) have suggested that despite its limitations, the self-
report may most accurately identify persons who adhere and many of those who do not adhere.
A combination of two self-report measures of medication adherence was used in the study. The
first measure was the Medication-taking Behavior scale developed by Monsky, Green and Levine
(1986). The second scale was a 24-hour recall scale (Johnson, 1993). Each of these scales will be
discussed in turn.
Each subject was asked questions from the Medication-taking Behavior scale Results of the
Monsky, Green and Levine (1986) study have showed the four-item scale to demonstrate both
concurrent and predictive validity with regard to blood pressure control at two years and five years,
respectively. The specific questions on the Medication-taking Behavior scale are as follows: 1)
Do you ever forget to take your medicine9 2) Are you careless at tunes about taking your
medicine9 3) When you feel better do you sometimes stop taking your medicine? and 4) Sometimes
if you feel worse when you take the medicine, do you stop taking it? Corrected item-to-total
correlations were found to be 0.515, 0.479, 0.527, and 0.561, respectively, for each question
Cronbach's alpha was found in the original study to be low (0.61), but what is considered "low" for
alpha depends on the purpose of the research (Churchill, 1979). The questions were scored so that
yes=0 and no=l, and the range was 0 to 4. The mean (weighted) for medication adherence in the
Morisky, Green and Levine (1986) study (n=290) was found to be 2.31.
Each subject was also asked another simple yes or no question from another self-report scale
(Johnson, 1993). The question on this scale is as follows: In the last 24 hours, have you missed a
dose of medication9

50
Ln using both these instruments together it was important to do an item analysis to determine the
final set of items for the revised questionnaire. The decision to retain or delete an item from
combined medication adherence scale was loosely based upon the following two entena: 1) an
item-to-total correlation coefficient of 0.3, and 2) a Cronbach alpha of 0.7 (Feketich, 1991).
However, as discussed earlier, self-report medication adherence scales are known to suffer from
poor reliabilities and it is was likely to get a Cronbach alpha less than 0.7 (Morisky et al., 1986)
which would make the results concerning medication adherence only tentative.
Medication-related Stressors
As was discussed in Chapter 3, a new scale had to be constructed to measure medication-related
stressors. One of the key issues in this study was to test the validity of this new scale. The
procedures used in this study for determining scale validity are concerned with the relationships
between performance on the scale and other independently collected facts. These procedures were
employed sequentially at different stages of scale construction (Anastasi, 1986). The validation
procedures began with a statement of the construct’s formulated definition, denved from the review
of psychosocial theory and the prior research in Chapter 3.
Medication-related stressors were both subjective and objective The subjective medication-
related stressors were the patient’s report of extent of experiences with a broad group of problems,
irritants, or annoyances stemming from the medication(s) he or she is currently taking and/or his or
her current medical condition(s). These subjective medication-related stressors were initially
subcategonzed into three types of stressors: 1) informational, 2) emotional, and 3) instrumental.
Informational medication-related stressors represented the patient's extent of experiences with a
subgroup of problems, irritants, or annoyances stemming from a lack of facts about the particular
medication(s) he or she is currently taking or his or her current medical condition(s). Emotional
medication-related stressors represented the patient's extent of experiences with a subgroup of

51
problems, irritants, or annoyances stemming from a felt inability to deal with feelings about his or
her current medication(s) or his or her current medical condition(s). Instrumental medication-
related stressors represented the patient's extent of experiences with a subgroup of problems,
irritants, or annoyances stemming from a felt incapacity to acquire his or her current medication(s)
or items necessary to take his or her current medication(s) correctly. Finally, there was an
objective measure of medication-related stressors which was thought to act in unison with these
subjective medication-related stressors to affect perceived medication stress. Specifically, this item
was answered by looking at each patient’s automated records and is the patient’s current number of
prescribed medications
Content-related validation of the new medication-related stressor scale was examined. The
items comprising each subscale arose from the literature review in Chapter 3. Content validity for
the Medication-related Stressors (MrS) Scale thus began by selection of items from the medical
literature and placement into hypothesized informational, emotional, and instrumental subscales
An expert panel reviewed and critiqued the items for content validity, and about fifty subjects were
interviewed about readability and understanding of these items. The fifty subjects were also asked
if there were any other additional stressors they had experienced. Finally, the fifty subjects were
asked if the items appeared to be irrelevant or inappropriate. The end product of these scale
development procedures consisted of the items added to and deleted from the initial list generated
from the literature review
Criterion-related validation of the new medication-related stressor scale was also examined
Concurrent validity for subjective medication-related stressors was examined relative to objective
medication-related stressors, that is, the total scale score on subjective medication-related stressors
was tested for a significant correlation with the objective-medication related stressors Predictive
validity of subjective medication-related stressors was tested by a significant correlation between

52
the subjective medication-related stressors and perceived medication stress.
Construct-related validation of the new medication-related stressor scale was also examined.
On a four-point Likert scale, patients were asked to rate the degree to which they experienced
subjective medication-related stressors in the past year with 1 = “never,” 2 = “once in a while,” 3
= “fairly often,” and 4 = “very often.” A total score for the scale was computed by summing the
responses to the 27 items which could range from 27 to 108. As was discussed earlier, these items
might be classified as informational, emotional, and instrumental. Since there are nme items m each
classification, the scores on these factors ranged from 9 to 36 each.
Construct-related validation of the subjective medication-related stressors scale consisted of
factor analyses and internal consistency analyses. Each of these will be discussed in turn.
Construct validity was assessed for the new scale using the principal components model of
factor analysis based on the correlation matrix and using vanmax orthogonal rotation It was
hypothesized that using a factor analysis would confirm the existence of the three dimensions of the
subjective medication-related stressors: 1) informational, 2) emotional, and 3) instrumental.
In using this scale it was important to do an item analysis to determine the final set of items for
the revised questionnaire. The decision to retain or delete an item from its respective scale was
loosely based upon the following two criteria: 1) an item-to-total correlation coefficient of 0.3, and
2) a Cronbach alpha of 0.7 (Feketich, 1991).
Perceived Medication Stress
Perceived medication stress was defined as representing the patient's evaluations of the
medication-related stressors they experienced in the past year. These evaluations were
hypothesized to depend on medication-specific social support which will be discussed m the
following section

53
As was discussed in the literature review in Chapter 3, the 14-item Perceived Stress Scale
(PSS) developed by Cohen, Karmack and Mermelstein (1983) is the “gold standard” in the field of
perceived stress research However, this scale is not as “adaptable” to medication-taking as
another scale that was identified in the literature. This new scale was developed to measure
psychosocial adjustment specific to diabetes and is called the Problem Areas in Diabetes Survey
(PAID: Polonsky et al., 1995).
On a six-point Likert scale, patients rate the degree to which each item is currently problematic
for them, from 1 (“no problem”) to 6 (“serious problem”). A total score for the scale is computed
by summing the responses to the 20 items which can range from 20 to 120.
However, there were two problems when adaptmg the PAID survey for use in this study. First,
no attempt was made to address the multidimensional nature of this scale in the original study. A
cursory examination of the 20 items comprising the scale suggested that some items deal with
feelings about one's medication regimen, while other items deal with feelings about one's medical
condition or social support. It was hypothesized that medication-related stressors would be more
strongly associated with the feelings about one's medication regimen, as opposed to the other items
Second, there are the numerous references to diabetes in the PAID survey. For this study, these
items were re-worded to reflect perceived stress from medication-taking m general, rather than
from just medications taken for diabetes. This required, for example, deletion of the word
“diabetes” from the item, or substitution of the words “your condition” for “diabetes.”
Criterion-related validation of the revised scale consisted of concurrent and predictive
validation. Concurrent validity for the revised scale was studied by examining the distribution of
the revised item scores to the established PAID item scores, indicating the percentage of subjects
who reported each item as a “serious problem” (scoring 5) and as “no problem” (scoring 2). Also,
the total scale score on the revised scale was tested for a significant correlation with the total score

54
of the established survey. Predictive validity of the revised instrument was also examined. Again,
medication-related stressors should have been strongly correlated with this scale, because level of
stressors should predict level of perceived stress. Also, predictive validity was tested with a
correlation between the score on the revised perceived stress scale and the combined medication
adherence scale
Construct validity was assessed for the scale, using the principal components model of factor
analysis based on the correlation matrix and using Vanmax (Orthogonal) Rotation and Kaiser
Normalization. It was hypothesized that using a factor analysis would confirm the existence of the
three dimensions of the perceived medication stress: 1) medication-related, 2) disease-related, and
3) support related. For the purposes of testing the proposed model, only the medication-related
dimension was used.
In using this scale it was important to do an item analysis to determine the final set of
medication-related items for the revised questionnaire. The decision to retain or delete an item
from its respective scale was loosely based upon the following two entena: 1) an item-to-total
correlation coefficient of 0.3, and 2) a Cronbach alpha of 0.7 (Feketich, 1991).
Medication-specific Social Support
Medication-specific social support was defined as representing the patient's perception of a
broad group of services or activities he or she might receive from a doctor, a pharmacist, and a
(most concerned) other person, which might help the patient deal effectively with medication-
related stressors. Without this medication-specific social support, the patient may evaluate the
medication-related stressors he or she is expenencing as overwhelming, threatening, or harmful,
and he or she may stop taking his or her medication(s). Medication-specific social support services
or activities were initially subcategonzed into three types of support: 1) appraisal, 2) emotional,
and 3) tangible. Appraisal medication-specific social support represented the patient's evaluation

55
of a subgroup of services or activities he or she might have received from a doctor, pharmacist, or
(most concerned) other person which might have helped the patient understand his or her
medication(s) and/or medical condition(s). Emotional medication-specific social support
represented the patient's evaluation of a subgroup of services or activities he or she might have
received from a doctor, pharmacist, or (most concerned) other person, which might have helped the
patient deal effectively with emotional problems related to his or her medication(s) and/or medical
condition(s). Tangible medication-specific social support represented the patient's evaluation of a
subgroup of services or activities he or she might have received from a doctor, pharmacist, or
(most concerned) other person, which might have helped the patient obtain or procure their
medication(s) and/or items that assisted them in the administration of their medication(s).
The only instrument which was identified in the literature specific to medication-taking is a
social support scale developed by Caplan et al. (1980). There are specific items related to “doctor
support” and specific items related to “(most concerned) other person support.” For the purposes
of this study, items representing doctor support were duplicated for use in another subscale for
"pharmacist support."
Although these scales were originally developed to indicate structural support from a doctor or
(most concerned) other person, three particular items in these scales seem to indicate functional
support. One of the items in the doctor subscale seemed to indicate appraisal medication-specific
social support, that is, “[this person] helped me fully understand when and how to follow my
treatment.” Also, one of the items in the (most concerned) other person subscale seemed to indicate
emotional medication-specific social support, that is, “[this person] offers help and shows real
concern about my health.” Finally, one of the items in the (most concerned) other person subscale
seemed to indicate tangible medication-specific social support, that is, “[this person] helps me
remember things such as taking my medicine, refilling prescriptions, and keeping doctor’s

56
appointments.”
Other Conditioning Variables
In addition to the four constructs above, descriptive statistics were gathered on ethnicity,
income, age, gender, and health status. Each will be discussed in turn.
Each subject was asked about his or her ethnic origin. This question was answered by choosing
one of the following: 1) White [not of Hispanic origin], 2) Black [not of Híspame origin], 3)
Hispanic, 4) Asian or Pacific Islander, and 5) American Indian or Native Alaskan
Each subject was asked what was roughly his or her gross annual income in dollars. Subjects
were asked to check one box which corresponded most closely to their annual income, marked as
follows. 1) less than $5,000, 2) $5,000 to 9,999, 3) $10,000 to 14,999, 4) $15,000 to 19,999, 5)
$20,000 to 24,999, 6) $25,000 to 29,999, 7) 30,000 to 34,999 8) 35,000 to 39,999, and 9) greater
than $40,000.
The patient’s age and gender was obtained from the pharmacy’s automated records The age
was recorded in years (a whole number) up to the subject's last birthday. The scoring on the
gender item was such that male=0 and female=l.
Health status was recorded usmg two different methods First, health status was measured
usmg a self-rated health scale (Ware et al., 1980). The scale reads as follows: “In general, would
you say your health is (circle one number): 5) Excellent, 4) Very Good, 3) Good, 2) Fair, or 1)
Poor? Second, health status was determined usmg the Chronic Disease Score (CDS: Von Korff et
al., 1992). The CDS algorithm utilizes data from computerized medication profiles For example,
a patient with an anticoagulant (=3), an oral hypoglycemic (=2), and a theophylline product (=2)
would have a CDS score of 7

57
Study Phase I
Study Phase I consisted of three parts. In part one, a questionnaire was administered to a
convenience sample of 50 subjects in the pharmacy lobby. After reading the questionnaire the
subjects were queried about the readability and understanding of the items. Furthermore, they were
asked whether any of the items should not be included in the scale and if any other items should be
included that they have experienced. In part two, the responses from the questionnaire were
subjected to an item-analysis. In part three, one hundred questionnaires were mailed to subjects to
test for the response rate Given the data collected in Study Phase I, a more accurate estimation of
response rate and correlations among the major variables was used in the computation of sample
size.
Study Phase I. Part One
In this study phase, a scale was developed to measure a construct proposed in the “nucleus’of
the Stress Model of Medication Adherence (SMMA-n): Subjective medication-related stressors.
Items were generated from the literature review in Chapter 3 to include in each of the three
proposed subscales and modeled after another more general instrument in the literature which has
been discussed in Chapter 3. Expert judges provided additional review of these three subscales for
their content validity, readability, and face validity
A questionnaire containing the new medication-related stressors scale and all the other
instruments discussed in the above sections were administered to a convenience sample of 50
subjects who visited the lobby of the Ambulatory Care Pharmacy at the Veterans Administration
Medical Center in Gainesville, FL, while having their prescriptions filled The inclusion entena
required that subjects be over the age of 65 and cunently receiving one or more chronic
medications in the mail from the mail-out section of a Veterans’ Administration Medical Center
Ambulatory Care Pharmacy

58
To find out whether subjects adequately understood the questions, the sample of respondents
who participated m the pretest were asked about the interpretation and clarity of the questions and
directions after their completion of the questionnaire. For each question, patients were asked to
restate the question in their own words and to give examples of what the question meant to them. It
was hoped that the meaning associated to these questions by all the subjects would be congruent
with the intended meaning of each question, indicating clarity of the items. Also, subjects were
asked about the ease of recalling the experiences.
Study Phase I. Part Two
After the responses for the medication-related stressor scale were entered into the computer, the
items were grouped into subscales according to what they were intended to measure (i.e.,
informational, emotional, or instrumental.) The internal consistency of each subscale was
calculated to obtain reliability estimates. Reliability of each subscale was assessed through the use
of Cronbach's coefficient alpha statistic. The coefficient alpha provided an estimate of how
consistently subjects performed across items measuring the same construct definition. A high
value of coefficient alpha would indicate a consistent performance of respondents across items It
would also indicate that the performance is generalizable to other potential items pertaining to the
same content domain (Crocker and Algina, 1986). Although what is considered "low" for alpha
depends on the purpose of the research (Churchill, 1979), a reliability estimate of 0.70 is
considered acceptable (Nunnally, 1978).
An item analysis was also examined to determine the final set of items for the revised
questionnaire. The decision to retain or delete an item from its respective subscale was loosely
based upon the following two entena: 1) an item-to-total correlation coefficient of 0.3, and 2) a
Cronbach alpha of 0.7 (Feketich, 1991).

59
Corrected item-to-total correlations involved the correlations of item score with the total score
of the remaining items in the scale under examination. Corrected item-to-total correlations were
calculated to adjust for spurious values that might have been obtained when the item scores
contributed to the total scores (Crocker and Algina, 1986). Corrected item-to-total correlations
were especially relevant when there is a small number of items in the scale (Ferketich, 1991). A
more recent “rule of thumb” for corrected correlations is that they should be 0.50 or greater
(Bearden et al., 1989). However, corrected correlations above 0.30 have been considered sufficient
(Nunnally, 1978).
The revised coefficient alpha revealed the changes in alpha if the item was dropped from the
scale. If there was a substantial improvement in alpha when the item was deleted, this was
considered as some support for dropping the item. However, the revised alpha was most
informative when it was used in combination with the aforementioned aspects of item analysis.
Individual scale items were evaluated in terms of their inter-item correlations, item-to-total
correlations, and revised coefficient alphas when an item was deleted from the scale. The decision
to delete or retain an item was determined on both psychometric and conceptual grounds Caution
was taken not to eliminate items only on its psychometric characteristics because of the relatively
small Study Phase I sample size
Certain changes were made in the scales before it was mailed to the larger sample in Study
Phase II. These changes were outlined in the results section. Given that the subjects were elderly
outpatients, the response burden was kept as low as possible to unprove response rate.
Study Phase 1, Part Three
According to a statistical report generated at the Veterans Administration Medical Center-
Gamesville, there were 5,871 elderly patients (aged 65 and over) getting one or more medications
from the Ambulatory Care Pharmacy. This represented the sample frame of subjects for this

60
study
The most important considerations in the study for sample size considerations involved the
needs for the multiple regressions and the factor analyses. Each of these considerations will be
discussed in turn
The most accurate correlations found in the literature review were the following: a correlation
of 0.48 between stressors and perceived stress (Frenzel, 1988) and a correlation of -0.12 between
perceived stress and medication nonadherence (Coons et al., 1994). Because the scales that were
developed in this study were more specific (and hence higher correlations were anticipated), both of
these correlations represented conservative estimations. However, it was anticipated that the
medication-specific social support items would have much lower correlations (e g., r < .10). By
using the STAT-POWER program (Baroy, 1993), a sample size of 700 cases was required, for the
latter (smaller) correlation. In a study which mailed a 63-item questionnaire to 800 senior citizens
in a rural area of Idaho, there was a 65% response rate (Johnson, 1972). .Although the present
study's questionnaire is not about recreational pursuits, the fart that this survey was sent to senior
citizens in a rural area and the questionnaire was about the same length appeared to suggest a 65%
response rate for this study. With this response rate, 1100 cases would be required.
For factor analysis, the smallest subject-to-item ratio is often considered in the 5 to 10 subject-
to-item range (Crocker and Algina, 1986). Since the new scale contained 27 items, a 5-to-1 ratio
required 135 subjects, and a 10-to-l ratio required 270 subjects. To be conservative an anticipated
65% response rate, required the mailing of approximately 446 questionnaires. Since the 446
questionnaires required is less than the 1100 questionnaires required from the previous calculation,
it was anticipated that 1100 questionnaires would have to be mailed
In part three, one hundred more questionnaires were mailed to subjects at home to test the
response rate (see Appendix A). These questionnaires were mailed just as they were in Study Phase

61
II Given all the data collected in Study Phase I, a more accurate estimation of response rate and
correlations among the major variables was thought to aid in a better computation of sample size.
Study Phase II
The objectives of this study phase were to validate the new subjective medication-related
stressors scale and test the relationships among the four scales in the "nucleus" of the proposed
Stress Model of Medication Adherence In Study Phase II, the revised questionnaire was mailed to
a convenience sample of approximately 1,100 veterans (depending on the results of the initial
mailout) 65 years of age and older receiving one or more medications through the mail from the
same Veterans Administration Medical Center Ambulatory Care Pharmacy.
This section will continue with a discussion on the data collection procedures It will end with
a discussion of the statistical analyses to be done with the data when the revised mail
questionnaires are returned, which involved the following: 1) descriptive statistics, 2) zero-order
correlations, 3) factor analyses, 4) reliability analyses, and 5) multiple regressions.
Study Phase II. Part One
A convenience sample of approximately 1,100 patients were chosen from requests for
prescriptions to be mailed to them from the Ambulatory Care Pharmacy at the Veterans
Administration Medical Center in Gainesville. Each subject chosen had to be over 65 years of age
and taking one or more prescribed medications on a regular basis.
Data collection required two important steps: 1) an initial questionnaire mailout, and 2) a
postcard follow-up. It was hoped that by discontinuation of this data collection process, 700
completed, usable questionnaires would be analyzed for a response rate of 65%. If not, a return to
the mail requests for more names and more mailmgs of the questionnaire would have been initiated
until the necessary 700 usable questionnaire demand was met.

62
Cover letter
The cover letter emphasized the usefulness of the study, explained the importance of the
respondent’s response, and assured that confidentiality would be upheld. The signature of the chief
of pharmacy was at the bottom. If subjects had questions regarding the questionnaire they were
asked to call the VAMC-Gainesville ambulatory care pharmacy.
Identification system
An identification system was used to facilitate the sending of follow-up mailings. Therefore,
confidentiality but not anonymity was offered. Questionnaires were identified by a patient ID
number written on the upper right-hand comer of the questionnaire, a position in which it was
easily visible The number corresponded to one written next to the respondent's name on the
mailing list.
Precoding procedure
The questionnaires were precoded based upon pretest responses to the extent feasible.
Responses from each questionnaire were transferred to a SPSS 7.5 for Windows (SPSS Inc , 1997)
computer file Each response category was assigned an identifying number which was used to
represent that response on the SPSS 7.5 for Windows (SPSS Inc., 1997) computer file The
columns to which a response was transferred were listed beside each question on the questionnaire
The result of this precoding effort was the ability to go quickly from the questionnaire to the SPSS
7.5 for Windows (SPSS Inc., 1997) computer file for analysis
Mail-out
This questionnaire mail-out occurred in April, 1997 As outlined above, a convenience sample
of about 1100 subjects was surveyed. Surveys were mailed to the patients’ homes. A copy of the
revised questionnaire is contained in the Appendix B

63
Postcard follow-up
According to Dillman et al. (1974), most people who answer questionnaires do so almost
immediately after they receive them. A questionnaire that lies unanswered for a week or more is
not likely to be returned. In repeated studies, Dillman et al. (1974) observed that half the return
envelopes were postmarked within two or three days after being received by respondents. .After
that tune, the number of postmarked returns declined, sharply at first and then gradually, but
nonetheless consistently.
One week after the initial mail-out date, a postcard follow-up was mailed to the entire sample of
subjects. The postcard urged nonrespondents to complete and return the questionnaire. It also
served to thank those who had already returned the questionnaire. This mailing seemed to produce
more returned questionnaires (some usable and some not usable) within the span of four weeks,
after which data collection was discontinued.
Study Phase II. Part Two
The two major goals of the study were to: 1) validate the new subjective medication-related
stressors scale, and 2) to test the proposed Stress Model of Medication Adherence In this phase,
validation of the new scale involved both criterion-related validation and construct-related
validation. Criterion-related validation required both concurrent and predictive validation This
required accumulation of information from two specific techniques: 1) descriptive statistics and 2)
correlational analysis. Construct-related validation required the accumulation of information from
two other techniques: 1) factor analysis and 2) Cronbach's Alpha internal reliability. Testing of the
proposed Stress Model of Medication Adherence involved the use of multiple regressions. All
analyses were done using SPSS 7.5 for Wmdows (SPSS, Inc , 1997).

64
Descriptive statistics
In this initial phase of the analysis, means, standard deviations, ranges (with mínimums and
máximums), and 95% confidence intervals of all measures were calculated. For those measures
that are not new, their descriptive statistics were compared to those obtained in previous studies.
The means and standard deviations of the objective (number of prescriptions) and subjective
(informational, emotional, and instrumental) medication-related stressors, (appraisal, emotional,
and tangible) medication-specific social support, perceived medication stress, medication
adherence, age, gender, mcome, ethnicity, self-rated health, and Chronic Disease Score were
calculated. Given these statistics and correlations between these variables, preliminary conclusions
were made about both the concurrent and predictive validity of the new medication-related
stressors measure
Another preliminary step in the analysis involved an examination of the descriptive statistics of
the various measurements and a decision of the type (parametric versus nonparametnc) of the
subsequent analyses. Most of the independent variables and the dependent variable were measured
by summing items scores, that is, by adding items together to form a subscale using the mean of
that subscale to represent the score on that variable When the range of possible scores on an
ordinal scale is increased, data begins to take on the appearance of being continuous. Therefore,
the ordinal data was tested to see if it was normally distributed.
For each subscale mean and each independent item, the following hypothesis were tested: 1) the
values are a random sample from a normal distribution, and 2) the data will test against a normal
distribution with mean and variance equal to the sample mean and variance. The Kolomogonv D
statistic was computed. It was anticipated that the data would not fail this test of normality.
.Although the data might not have passed the tests of normality, the distribution means may have
approximated a normal distribution due to a large number of responses (n * 700). This is because

65
of the Central Limit Theorem. According to this theorem, even if the distribution of x was not
normal, the distribution would become closer and closer to the normal distribution with mean and
variance2 as x got larger (Armitage, 1973).
Parametric analyses were utilized for tests affected by the Central Limit Theorem. The
researcher may have had to trade the advantages of nonparametnc analysis, that is, 1) their
freedom from assumptions about the distribution of the scores m the population and 2) their
simplicity, for the advantages of parametric analyses. Parametric analyses are more powerful and
provide more information from the data when the assumptions of the particular analysis are met
(Keppel, 1991). For analyses not affected by the Central Limit Theorem, nonparametnc tests
would have be chosen.
Zero-order correlations
Zero-order correlations between exogenous vanables and endogenous vanables were used to
help establish validity for the nucleus of the proposed Stress Model of Medication Adherence.
Also zero-order correlations between independent vanables and between the independent vanables
and the dependent vanable(s) were examined For the testing of hypotheses with the magnitude
and directionality of the relationship between two vanables, intercorrelational analyses and the
correlation coefficient were used.
A correlation matrix showing the strength of association between all combinations of
independent and dependent variables addressed questions like these However, as mentioned
before, a major goal of the proposed study was to validate the new subjective medication-related
stressors scale. Therefore, the subscale scores of the new subjective medication-related stressors
scale was tested for significant correlations with the objective medication-related stressors measure
(number of medications). Furthermore, predictive validity of the subjective medication-related
stressors subscales was tested by significant correlations between the subscale scores on the

66
medication-related stressors scale and the score on the perceived medication stress scale
Factor analyses
A factor analysis was conducted for each construct. First, a factor analysis was used to
evaluate the unidimensional nature of the medication adherence scale Second, a factor analysis
was used to evaluate the multidimensional nature of the medication-related stressor scales (eg., for
informational, emotional, and instrumental factors). Third, a factor analysis was used to evaluate
the unidimensional nature of the perceived medication stress scale. Finally, three factor analyses
were used to evaluate the multidimensional nature the doctor, pharmacist and (most concerned)
other person medication-specific social support scales (e g., for appraisal, emotional, and tangible
support factors).
Using a principal components analysis with Vanmax Rotation and Kaiser Normalization, the
number of items were reduced from the number of original items to a relatively small number of
factors, or common traits. A factor analysis could discover patterns among the variations in values
of several variables. This is done essentially through the generation of artificial dimensions
(factors) that correlate highly with several of the real variables and that are independent of one
another (Harman, 1976; Gorsuch, 1983).
Factor analysis does have its disadvantages, and the results were kept in perspective. Factors
are generated without any regard to substantive meaning Often researchers will find factors
producing very high loadings for a group of substantively disparate variables. Factor analysis is
like other complex modes of analysis. It should be encouraged whenever such activity may assist
in understanding a body of data. As in all cases, the investigator must remam aware that such
tools are only tools and never "magical" solutions (Crocker and Algina, 1986)

67
Cronbach's coefficient alpha reliability
As in Study Phase I, Cronbach's coefficient alpha statistic was calculated. However, unlike in
Study Phase I, Cronbach's alpha was calculated for each factor identified for the four scales Items
with item-to-total correlations less than 0.50 may have been dropped. An index was constructed
for each of the factors by summing the unweighted responses to each item included m the factor.
Multiple regressions
The predictive validation of the scales used in the study partly would lie in four effects. These
were as follows: 1) the mediating (indirect) effect of the perceived medication-stress on the
relationship between each type of medication stressor and medication adherence, 2) the main
(direct) effect of tangible medication-specific social support on the relationship between
instrumental medication-related stressors and perceived medication stress, 3.) the moderating
(buffering) effect of appraisal medication-specific social support on the relationship between
informational medication-related stressors and perceived medication stress, and 4.) the moderating
(buffering) effect of emotional medication-specific social support on the relationship between
emotional medication-related stressors and perceived medication stress The mediating and
moderatmg effects were examined using procedures suggested by Baron and Kenny (1986).
To test the mediating effect of perceived medication stress, a senes of multiple regression
analyses were conducted According to Baron and Kenny (1986), the mediating model can be
substantiated by three regression equations. First, the medication adherence (outcome vanable)
was regressed on the medication stressors (predictor vanable). In the second equation, perceived
medication stress (mediator vanable) was regressed on medication stressors (predictor vanable).
The third equation involved regressing medication adherence (outcome vanable) simultaneously on
medication stressors (predictor vanable) and perceived medication stress (mediator vanable).

68
In order to test medication-specific social support as a moderator (buffer) of medication-related
stressors, another entry procedure suggested by Baron and Kenny (1986) was utilized. First,
medication-related stressors (MrS) and medication-specific social support (MsSS) were entered in
step one. The interaction term of medication-related stressors and medication-specific social
support (MrS X MsSS) was entered on step two Steps one and two were repeated for each
subscale of medication-related stressors and medication-specific social support.
In these types of regression analyses, it has been suggested that deviation scores on the
vanable(s) from their means be considered (Finney et al., 1984). The estimated effects of the
constituent variables are still those at the zero point of the vanables(s), but, after deviation, the
zero point is the mean of the vanable(s) (Southwood, 1978). Such average effects have been
estimated in several studies of stress and coping or social support (e g., Williams et al., 1981).
It has been suggested that deviation scores in these types of multiple regressions are a cure for
the multicollineanty problem (high correlation between the product-term and one or more of its
constituents) that often arises with the use of untransformed (raw) scores (Cronbach and Snow,
1977). Although deviation scores will reduce the product-term’s correlation with the constituent
variables, multicollineanty poses no threat to the analysis of interactions or main effects, unless the
correlation is so high as to produce rounding errors in computer calculations (Southwood, 1978).
Since the standard regression coefficient denotes the linear effects of an independent vanable,
the actual magnitude of the coefficients will be tested by hierarchical multiple regression. For
testing the goodness of fit of a lmear model, R: was used for coefficient determination The most
important set of hypotheses in the study treated the magnitude and model of the effects of
medication-specific social support as competing models, that is, mam versus moderatmg models
The interaction term, a lmear X linear interaction, would enhance the prediction of the dependent
variables beyond the first order term and would suggest a moderator effect of a type of doctor,

69
pharmacist, or (most concerned) other person medication-specific social support.
Although such transformations are useful, they affect the interpret ability of the regression
coefficients; therefore, some investigators prefer to interpret standardized regression coefficients in
the context of certain multiple regression applications (Jaccard, Tumsi and Wan, 1990).
Therefore, both types of multiple regressions (with raw scores and deviation scores) were done.
Human Rights
The research protocol was reviewed by the Committee on Protection of Human Subjects of the
University of Florida and the research service of the VAMC-Gainesville Participants in Study
Phase I were solicited voluntarily in person Participants in Study Phase II were solicited with a
recruitment letter and informed that their responses would be kept confidential
Summary
To adapt a general stress model to explain elderly patients’ medication-taking, elderly
outpatients were surveyed using an interview and mail survey methodology in two separate study
phases. Study Phase I involved the development and initial validation of a medication-related
stressors scale. Study Phase II involved further validation of medication-related stressors scale and
an exploratory investigation of the proposed Stress Model of Medication Adherence using
responses obtained from about 700 elderly outpatients in a mail survey. The dependent variable
was medication adherence. The independent variables were medication-related stressors,,
medication-specific social support income, ethnicity, age, gender, and health status Perceived
medication stress was either a dependent or independent variables depending on the context in
which it was used. This exploratory investigation of the proposed Stress Model of Medication
Adherence focused on the important mam, mediator and moderator effects.
The next chapter will begin with a detailed presentation of the results of the analyses. And,
then the chapter will end with a discussion and interpretation of these results.

CHAPTER 5
RESULTS
Introduction
The study was conducted in two phases. In Phase I, a questionnaire was administered to a
convenience sample of fifty subjects in the lobby of an outpatient pharmacy to test the readability
and understanding of the items. Then, the same questionnaire was mailed to a convenience sample
of one hundred elderly outpatients to test for the response rate. The data from both samples were
then combined and analyzed and a revised questionnaire was generated. In Phase II, the revised
questionnaire was mailed to a convenience sample of 1,600 elderly outpatients The data from this
sample was then analyzed, and reported here.
Study Phase I
Study Phase I consisted of two parts In the first part of Phase I, fifty subjects filled out
questionnaires in the lobby of the outpatient pharmacy. In the second part of Phase I, one hundred
questionnaires were mailed to a convenience sample of one hundred subjects. Forty-four of the one
hundred subjects responded to this initial mailing In the second part of Phase I, the data from the
fifty subjects in the lobby of the outpatient pharmacy was compared to the data from the subjects
who responded to the mailed questionnaire. After a comparison of the mean responses on several
of the key items, the two data sets from Phase I were combmed. The frequencies of responses to all
categorical variables, the means and standard deviations of the summated scales, and the
70

71
demographic data on the sample of subjects in the pilot study are reported A correlation matrix of
the independent variables was examined to determine whether conceptually distinct scales had low
correlations.
Item analyses for each of the four scales will be reported, Item-to-total correlations will be
examined, and Cronbach's coefficient alpha was recalculated each time it was decided to delete an
item.
Comparison between Phase 1 Lobby and Mail Samples
Before the results from the two samples could be pooled, an examination was made of the two
samples on four groups of items: 1.) medication adherence, 2.) subjective medication-related
stressors, 3 .) perceived medication stress, and 4.) medication-specific social support. There were
no statistically significant differences (Table 5.1)
Phase I Sample Description and Measures
About seventy percent of the respondents in Phase I were “young-old” elderly, whereas only
twenty-seven percent of the respondents were “middle-old” elderly. Almost all the respondents
were male and of white (Caucasian) ethnicity. About seventy percent of the respondents had an
annual income of less than $20,000. About fifty-nine percent of the respondents rated their own
health as fair or poor; whereas, about fifty percent had a Chronic Disease Score between 6 and 15
Medication adherence
The medication adherence items had a Cronbach’s alpha of 0.5021 calculated using the four
items from the Monsky, Green, and Levine scale (1986). However, it seems prudent to attempt to

72
Table 5.1 Phase I Comparison between Lobby and Mail Samples on Four Groups of Questionnaire
Items
Group of Items
Mean SD t(Si2)
Medication Adherence
(4 items)
-.228( 912)
Lobby
Mail
3.2500 .9785
3.2955 .9296
Subjective
Medication-related
Stressors
(27 items)
-426(452)
Lobby
Mail
39.4318 10.1119
40.4524 9.2161
Perceived Medication
Stress
(20 items)
.198( 507)
Lobby
Mail
45.6000 24.1345
44.6364 21 7385
Medication-specific
Social Support
(15 items)
988( 561)
Lobby
Mail
63.3023 9.8381
61.1579 9.6493

73
Table 5.2 Phase I Sample Description
Characteristic
n*
Category
F requency
Percent
Age
94
65 to 74 (“young-old”)
66
70.3
75 to 84 (“middle-old”)
27
28 6
85 to 100 (“old-old”)
1
1.1
Gender
94
Male
92
97.9
Female
2
2.1
Ethnicity
92
White (not of Hispanic origin)
85
92.4
Black (not of Hispanic origin)
5
5.4
Hispanic
2
2.2
American Indian or Native Alaskan
0
0.0
Asian or Pacific Islander
0
0.0
Income
84
Less than $5,000
5
6.0
$5,000 to $9,999
25
29.8
$10,000 to $14,999
16
19 0
$15,000 to $19,999
13
15.5
$20,000 to $24,999
11
13.1
$25,000 to $29,999
4
4.8
$30,000 to $34,999
5
6.0
$35,000 to $39,999
2
2.4
Greater than $40,000
3
3.6
Self-rated Health
93
Poor
23
24.7
Fair
32
34.4
Good
26
28.0
Very Good
10
10.8
Excellent
2
2.2
Chronic Disease
94
0 to 2
24
25.6
Score
3 to 5
23
24.5
6 to 8
31
32.9
9 to 11
13
13.8
12 to 15
3
3.2
* The maximum number of responses that could have been collected on each of these
characteristics was 94 However, some subjects did not wish to respond to some of these
questions. For example, ten subjects (10 6% of the Phase I sample) did not wish to reveal their
income, dropping n for the income characteristic from 94 to 84.

74
improve the psychometric properties of this group of items by including the 24-hour recall item
used in similar research studies on assessing medication compliance (e g , Johnson, 1993) When
the 24-hour recall item was included, the Cronbach's alpha unproved to 0 6089. Furthermore, when
one of the original Monsky, Green, and Levine scale items concerning adherence when feeling
worse was dropped because of a poor item-to-total correlation of 0.1280, the Cronbach’s alpha
unproved to 0.6526 (Table 5,3).
Subjective medication-related stressors
Two of the emotional subjective medication-related stressor items had low item-to-total
correlations, that is, “felt that no one would agree with you when you thought taking your
medication was unnecessary” (.2504) and “felt that no one understood why you felt embarrassed
about taking your medications in front of other people” (.2823). When these two items were
dropped the Cronbach’s alpha unproved from 0.7757, to 0.7831 (Table 5.4). There was some
apprehension about these items and their low correlation with number of prescriptions Therefore,
five more items were generated for the revised questionnaire: 1) “felt that taking your medications
as directed by your doctor was easy,” 2) “felt that you were taking too many medications,”3) “felt
uncomfortable interrupting the pharmacist to ask a question,”4) “felt that if you had a medication
problem you could deal with it the right way, and 5) “felt like you had a problem with your
medication.”
There were fewer concerns about the informational and instrumental medication-related stressor
items Only one item concerning nonprescnption medications was dropped from the informational
subscale, and only one item concerning the timeliness of refills was dropped from the instrumental
subscale because of low item-to-total correlations. After these changes, the Cronbach’s alpha for
the informational subjective medication-related stressors unproved to 0.7134 (Table 5.5) The

Table 5 3 Phase I Medication Adherence Items: Reliability Coefficients and Item-to-total
Correlations (n=91)
75
Item-to-total
Correlations*
Medication-taking Behavior Scale
1. Are you careless at times about taking
your medication?
4981
2. When you feel better do you sometimes
stop taking your medicine9
.3786
3 Do you forget to take your medicine?
.2562
4. Sometimes if you feel worse when you
take the medicine, do you stop taking it9
.1142
Medication-taking Behavior Scale and ‘24-Hour Recall’ Item
1 Are you careless at times about taking your
medication9
5529
.5828
2. In the last 24 hours, have you missed a dose
of medication9
.4638
.4692
3. When you feel better do you sometimes stop
taking your medicine9
.4328
.3783
4 Do you forget to take your medicine9
.3033
.3548
5 Sometimes if you feel worse when you take
the medicine, do you stop taking it?
.1280
*For the first group of items, all items are included in the first and only column and Cronbach's
alpha was 0 5021. For the second groups of items, all items are mcluded in the first column and the
Cronbach’s alpha was 0.6089. In the second column, item 5 was deleted and Cronbach's alpha
improved to 0.6526.

76
Table 5 4 Phase I Emotional Subjective Medication-related Stressor Items Reliability Coefficients
and Item-to-total Correlations (n=91)
Item-to-total
Correlations*
1. Felt that no one listened to you about
your medication problems
.6602
.6498
6447
2. Felt that no one seemed really interested
in your medication taking
5694
.5856
6152
3. Felt that no one could help you deal with
your medication problems
.6043
6243
6149
4. Felt that no one could help you feel more
confident about taking your medications as
directed
.5470
.5277
.4986
5 . Felt that no one could explain something to
you about your medication
.4284
.4326
.4366
6. Felt that no one treated you normally because
of the medication you take
.4034
4096
.4288
7. Felt that no one understood why a side effect
of your medication was upsetting you
3886
.3780
3624
8 Felt that no one understood why you felt
.2823
.2702
embarrassed about taking your medications
in front of other people
9. Felt that no one would agree with you when .2504
you thought taking your medication was
unnecessary
♦In the first column, all items are included and Cronbach's alpha was 0 7757. In the second
column, item 9 was deleted and Cronbach’s alpha improved to 0.7788 In the third column, item 8
was deleted and Cronbach’s alpha improved to 0.7831.

Table 5,5 Phase I Informational Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations (n=89)
77
Item-to-total
Correlations*
1. Not known what to do if you missed a dose of
.5116
.5053
medication
2. Not known if you should change the dose of your
.4346
.4684
medication when you did not feel right
3. Not known if something that happened to you was
.4624
.4261
a side effect of your medication
4. Not known whether a medication given to you by
.4602
.4142
one doctor should be taken with a medication
given to you by a different doctor
5. Not known if you should drink alcohol and/or
.3418
.3849
smoke while taking your medication
6. Not known if your medication was working right
.3921
.3819
7. Not known if you were taking your medication
.3390
.3792
correctly
8. Not known what to do if a side effect of your
.3422
.3473
medication occurs
9. Not known if you should use a nonprescnption
.2496
medication
*In the first column, all items are included and Cronbach's alpha was 0.7094 In the second
column, item 9 was deleted and Cronbach’s alpha improved to 0.7134.

Table 5 6 Phase I Instrumental Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations (n=93)
78
Item-to-total
Correlations*
Had trouble getting something to keep track of how
well your medication is working
.6133
.6049
Had trouble getting easy to read information about
the medications you take
5861
5926
Had difficulty getting an easy to open medication
container
.5738
.5825
Had trouble finding something to remind you to take
your medications on time
.5482
.5595
Had difficulty getting an up-to-date list of your
medications
.4728
.5153
Had difficulty getting an easy to swallow medication
.4501
.4552
Had trouble getting something to remind you to have
your medications refilled on time
.4053
.3997
Had difficulty getting transportation to a doctor’s
office or pharmacy
.4138
.3820
Had problems getting your medication refilled before
your current supply ran out
.2927
* In the first column, all items are included and Cronbach’s alpha was 0.7871. In the second
column, item 9 was deleted and Cronbach’s alpha improved to 0.7972.

79
Cronbach’s alpha for the instrumental subjective medication-related stressors improved to 0.7972
(Table 5.6).
The ninety-four Phase I subjects were taking a mean number of 8.31 (s.d.= 4 63) prescriptions
The number of prescriptions was the objective medication-related stressor measure that should be
positively associated with subjective medication-related stressors to establish concurrent validity,
that is, as the number of prescriptions increases, the amount of experienced subjective stressors
should increase This association was demonstrated when the informational (r= 284, p <01) and
instrumental (r= 298, p <01) subscales.. However, there was no association between the emotional
items and number of prescriptions
Perceived medication stress
The four items that reflect the construct all had good item-to-total correlations Cronbach’s
alpha for the four items was 0 8520 (Table 5.7).
Medication-specific social support
There were only enough items from the adapted scale to perform reliability analyses for doctor
emotional medication-specific social support (Table 5 8) and pharmacist emotional medication-
specific social support (Table 5 9) Both these analyses demonstrated good item-to-total
correlations and a good Cronbach’s alpha However, more items needed to be generated and the
existing items needed to be made more specific to medication taking
It was decided that the doctor medication-specific social support items would read the same as
the pharmacist items after all the modifications were made One of the doctor emotional
medication-specific social support items asking the subject if the doctor or pharmacist had “acted
m a warm and friendly manner” did not seem to adequately reflect emotional medication-specific
social support and was dropped The appraisal medication-specific social support item asking the

Table 5 7 Phase I Perceived Medication Stress Items: Reliability Coefficients and Item-to-total
Correlations (n=91)
80
Item-to-total
Correlations*
1. Feeling overwhelmed by your
medication regimen
8018
2. Feeling discouraged with your
medication regimen
.7271
3 Worrying about reactions
(between two or more medications)
.6405
4. Feeling guilty or anxious when you
get off track with your medication
.6088
*Cronbach’s alpha = 0.8520

Table 5.8 Phase I Doctor Emotional Medication-specific Social Support Items: Reliability
Coefficients and Item-to-total Correlations (n=92)
81
Item-to-total
Correlations*
1. Helped me work through any womes or concerns
related to my condition
.7460
2. Made me feel confident I can take medicines
and can do what else was asked
.6987
3. Acted in a warm and friendly manner
.6498
*Cronbach’s alpha = .8344

82
Table 5.9 Phase I Pharmacist Emotional Medication-specific Social Support Items: Reliability
Coefficients and Item-to-total Correlations (n=92)
Item-to-total
Correlations*
1. Helped me work through any womes or concerns
related to my condition
.7751
2. Made me feel confident I can take medicines
and can do what else was asked
.6351
3 Acted in a warm and friendly manner
.5622
*Cronbach’s alpha = .7981

83
subject if the doctor and pharmacist “helped me work through any worries or concerns related to
my condition” was modified to make it more specific to medications, that is, “helped me work
through any womes or concerns related to my medications ” Also, another doctor and pharmacist
appraisal medication-specific social support item needed to be generated, that is, “helped me
become aware of things I didn’t know about my medications.” Finally, tangible items for doctor
and pharmacist medication-specific social support needed to be generated. These items asked the
subject if the doctor or pharmacist “helped me get the right medications” and if the doctor or
pharmacist “helped me get the things I needed to take my medications the right way.”
It was decided that the (most concerned) other person emotional and appraisal medication-
specific social support items would read the same as the doctor and pharmacist emotional and
appraisal medication-specific social support items. However, it was decided that the (most
concerned) other person tangible medication-specific social support items would read differently
from the doctor and pharmacist tangible medication-specific social support items. It was decided to
modify the original (most concerned) other person tangible medication-specific social support item
“helps me remember things such as taking my medicine, refilling prescriptions, and keeping
doctor’s appointments” to make it more specific to pharmacy, that is, “helped me remember things
such as taking my medicine and refilling my prescriptions.” Also, it was decided to modify the
original (most concerned) other person tangible medication-specific social support item “helps me
get to the doctor and the pharmacy” to make it more specific to pharmacy, that is, “helped me get
to the pharmacy.”
Study Phase II
After analyses of the Phase I data were completed, and a revised questionnaire was constructed,
Phase II of the study began. This section of the chapter will begin with a description of the Phase

84
II sample Then, results of the factor analyses of the medication adherence, stressor, perceived
stress, and social support measures will be discussed. Then, results of the reliability analyses of the
medication adherence, stressor, perceived stress, and social support measures will be discussed
Then, after the Phase II measures are described, five sets of hypotheses will be addressed
Phase II Sample Description
For Phase II of the study, 700 usable questionnaires were needed for the analyses. In the pilot
study there were forty-four responses to the one hundred mailed out questionnaires. Therefore,
anticipating a forty-four percent response rate, a revised questionnaire was mailed out to 1,600
elderly outpatients having one or more prescriptions mailed to them from a large Veterans
Administration Medical Center in north-central Florida. A week after each questionnaire was
mailed out, a follow-up postcard was mailed to each subject who had been mailed a questionnaire
As part of Phase II, the investigator answered phone calls from either the subjects or the
subjects’ care givers. Eighty phone calls were logged by the investigator. Fifty-four of these calls
were placed because the follow-up card reached subjects before their questionnaire Ten of these
calls were placed because a subject had received the questionnaire, but when the subject got the
postcard they had realized they had misplaced or lost the questionnaire and needed another one.
Eight questions were about how to answer the items on the questionnaire and revolved around two
areas of inquiry. First, subjects seemed upset about responding to the medication-related stressor
item about ‘medication taking being easy’ and the medication-related stressor item about ‘dealing
with a problem the right way.’ Respondents stated that these items seemed to ‘trick’ the subject
somehow because they seemed ‘the reverse’ of the other items about medication problems. Second,
subjects did not know how to answer the stressor item about a possible alcohol and/or smoking
interaction when they did not use these substances. The investigator also received five telephone

85
calls from subjects who had just received the follow-up postcard and felt they needed to tell the
investigator they had already mailed in their questionnaire. One telephone call was from a subject
who stated that he had been out of town for some tune, and didn’t know if it was too late to send
the questionnaire. Another two telephone calls concerned an inability for the subject to respond
and are discussed m the next paragraph.
Seven weeks after the last questionnaire had been mailed, and six weeks after the last follow-up
postcard, 1,090 questionnaires had been returned. Ten questionnaires had been returned as
undeliverable. One subject’s wife called the investigator to state that the subject had just died
Also, another care giver called the investigator to report that the elderly outpatient was legally
incompetent and would be unable to answer the questions. Given that twelve subjects could not be
reached, the denominator was changed to calculate response rate from 1,600 to 1,588 The
response rate was 1,090 divided by 1,588, or about 69%.
Twenty non-responders were followed-up by the investigator to identify specific reasons for
non-response. Eight non-responders were back in the hospital as inpatients, while three stated that
they did not respond to the study because they did not feel they had enough “medication problems”
to merit participation. Nine non-responders refused to participate in the study. These non¬
responders who refused to participate, were also followed-up to establish their reasons for choosing
not to participate Two refused because they felt they did not have enough “medication problems”
to merit inclusion in the study, while seven felt that they were too ill to complete the questionnaire
and they stated they did not have someone to help them complete it.
After the data from the 1,090 questionnaires was entered into the database, the information
from each subject’s medication profile was entered into the computer. After this process was
undertaken, 73 subjects were eliminated because they did not meet the study inclusion entena for

86
Table 5.10 Phase II of Study Sample Description
Characteristic
n
Category
Frequency
Percent
Age
1017
65 to 74 ("young-old")
625
61.5
75 to 84 ("middle-old")
368
362
85 to 100 ("old-old")
368
2.3
Gender
1017
Male
989
97.2
Female
28
2.8
Ethnicity
974
White
883
90.7
Black
60
6.2
Hispanic
17
1.7
American Indian or Native
13
1.3
Alaskan
Asian or Pacific Islander
1
0.1
Income
887
Less than $5,000
44
4.0
$5,000 to $9,999
175
16.1
$10,000 to 14,999
227
20.8
$15,000 to $19,999
145
13.3
$20,000 to $24,999
111
10.2
$25,000 to $29,999
68
6.2
$30,000 to $34,999
48
4.4
$35,000 to $39,000
25
2.3
$Greater than $40,000
44
4.0
Self-rated
982
Poor
256
26.1
Health
Fair
462
470
Good
216
22.0
Very Good
40
4.1
Excellent
8
0.8
Chronic Disease
1017
Oto 2
264
26.0
Score
3 to 5
331
32.5
6 to 8
281
27 6
9 to 11
120
11.8
12 to 15
21
2.1
♦The maximum number of responses that could have been obtained on each of these characteristics
was 1017. However, some subjects did not wish to respond to some of these questions For
example, 130 subjects (12 8% of the Phase II sample) did not wish to reveal their income dropping
n for the income characteristic from 1017 to 887.

87
one of three reasons: 1.) fifty-six were eliminated because they were less than 65 years old, 2.)
twelve were eliminated because they were not taking any medications on a regular basis, and 3.)
five were part of an investigational drug protocol and it was felt that the subjects might differ on
their medication-taking behaviors. Therefore, a maximum of 1,017 valid responses could possibly
be obtained on any one variable in the study. A description of the main study sample is presented
in Table 5.10
There were differences between the larger Phase II sample (Table 5.10) and the smaller Phase I
sample (Table 5 .2). Generally, the Phase II sample was older (61.5% “young-old’) than the Phase
I sample (70.3% “young-old”). Also, the Phase II sample had a more evenly distributed income.
For example, the Phase II sample did not have as many subjects concentrated in the $5,000 to
$9,000 category (16.1%) as did the Phase I sample (29.8%). Finally, the Phase II sample had both
lower evaluations of their health and lower Chrome Disease Scores For example, the Phase II
sample had more subjects (47.0%) concentrated in the lower “fair” self-rated health category than
the Phase I sample (34.4%). And, the Phase II sample had more subjects (32.5%) concentrated in
lower “3 to 5” Chrome Disease Score category than the Phase I sample (24.5%).
There were no large differences between the larger Phase II sample and the smaller Phase I
sample on ethnicity and gender. Furthermore, because of this lack of variability from white males,
ethnicity and gender were eliminated from any further analyses.
Factor Analyses
Six factor analyses were undertaken to assess the dimensionality of each of the hypothesized
subscales: 1.) medication adherence, 2.) subjective medication-related stressors, 3.) perceived
medication stress, 4 ) doctor medication-specific social support, 5 .) pharmacist medication-specific
social support, and 6.) most concerned other person medication-specific social support. Each

88
factor analysis will be discussed in turn.
Medication adherence
A factor analysis was conducted to assess the dimensionality of the four medication adherence
items. As hypothesized, they were unidimensional (see Table 5.11).
Subjective medication-related stressors
A factor analysis was conducted on the twenty-seven subjective medication-related stressors. It
was hypothesized that items would load on one of three possible dimensions: 1.) informational, 2.)
emotional, and 3.) instrumental. However, the factor analysis suggests that there may be as many
as four dimensions among the subjective medication-related stressor items (Tables 5 12-5.15).
The first dimension appears to reflect problems associated with informational or instructional
needs (Table 5.12) and will be referred to as the advising stressors. Five of the nine items that load
on this dimension were a priori thought to belong to an informational dimension (i.e., items 2, 4, 6,
8, and 9). The other four items that also loaded on the first dimension were initially thought to
belong to an emotional dimension (i.e., items 1, 3, 5 and 7). However, when grouped with the first
five items, all these items as a group appear to reflect problems associated with being uninformed
Advising medication-related stressors will therefore represent the patient's extent of experiences
with a subgroup of problems, irritants, or annoyances stemming from a lack of advice or
instructions about the particular medication(s) he or she is currently taking.
The second dimension appears to reflect emotional problems associated with feeling detached or
isolated (Table 5.13) and will be referred to as isolating stressors. Five of the six items that load on
this dimension were a prion thought to belong to an emotional dimension (i.e., items 1, 2, 3, 4, and
5). Item 6 was thought to belong to another dimension. However, when grouped with the other five
items, all these items as a group appear to reflect problems associated with feeling isolated and

89
Table 5.11 Phase II Medication Adherence Items: Rotated Component Matrix using Principal
Components Analysis with Vanmax Rotation and Kaiser Normalization
Component
1
Are you careless at tunes about taking
your medicine0
.726
Do you forget to take your medicine?
.702
In the last 24 hours, have you missed
a dose of medication?
618
When you feel better do you sometimes
stop taking your medicine0
.546

90
unable to talk to someone about their medications. Isolating medication-related stressors will
therefore represent the patient's extent of experiences with a subgroup of problems, irritants, or
annoyances stemming from a felt inability to communicate his or her feelings about his or her
current medication(s).
The third dimension reflects more instrumental problems associated with struggling to remain
on a medication regimen (Table 5 14) and will be referred to as reminding stressors Five of the six
items that load on this dimension were a priori thought to belong to an instrumental dimension (i e .,
items 1, 2, 4, 5 and 6). The third item was thought to belong to an informational dimension
However, when grouped with the other five items, all these items as a group appear to reflect
problems associated with remaining on a medication taking schedule. Reminding medication-
related stressors will therefore represent the patient's extent of experiences with a subgroup of
problems, irritants, or annoyances stemming from a felt incapacity to remain on his or her current
medication schedule.
The remaining “dimension” was unexpected. The fourth dimension consisted of three items
that were initially thought to load on an instrumental factor and will be referred to as obtaining
stressors (Table 5.15). Alone these three items seem to reflect a dimension associated with the
physical acquisition and taking of medications. Obtaining medication-related stressors will
therefore represent the patient's extent of experiences with a subgroup of problems, irritants, or
annoyances stemming from a felt incapacity to acquire his or her current medication(s) or items
necessary to take his or her current medication(s) correctly
Three items did not appear to load well on any of these factors. It was thought that two
of these items would load on an emotional factor, that is, the item about ‘medication taking being
easy’ and the item about ‘dealing with a problem the right way.’ Also, it was thought that another

91
Table 5.12 Phase II Advising Subjective Medication-related Stressors: Rotated Component Matrix
using Principal Components .Analysis with Vanmax Rotation and Kaiser Normalization (Rotation
converged in 9 iterations)
Component
1
2
3
4
5
6
1. Had a side effect of your
medication that has upset you
765
.040
-041
-.132
.168
-.064
2. Wondered if something that
happened to you was a side
effect of your medication
.760
.077
.031
- 100
.215
.019
3. Felt like you had a problem
with your medication
.730
.183
.132
-.048
.171
-.089
4 Believed that your medication
does not help you feel better
631
168
.206
-.019
.094
.030
5. Not felt confident about
taking your medication as
directed
.620
.315
.203
.026
.047
025
6 Felt that you were taking
too many medications
605
.159
.317
.028
.014
.087
7 Thought of changing the
dose of your medication
because you did not feel right
.604
.164
159
.009
.040
.021
8 Wondered why your medication
was prescribed
.529
.357
.228
162
-.036
151
9. Wondered how long you will
.508
.292
.248
.062
-.122
.225
have to take your medication

92
Table 5.13 Phase II Isolating Subjective Medication-related Stressors: Rotated Component Matrix
usmg Principal Components Analysis with Vanmax Rotation and Kaiser Normalization (Rotation
converged m 9 iterations)
Component
1
2
3
4
5
6
1. Felt that no one seemed
really interested in your
medication taking
.294
.749
.108
.007
-.026
-.094
2. Felt that no one listened
to you about your medication
problems
.309
709
110
.047
.047
-.100
3. Felt uncomfortable interrupting
the pharmacist to ask a question
-.007
616
083
-.112
.274
.028
4 Been afraid to ask someone to
better explain something about
your medication
.212
604
146
.120
.035
.136
5 Felt that no one could help
you deal with your medication
problems
484
.525
009
.099
.055
.142
6. Wondered whether a medication
.329
.351
.276
-Oil
.133
l
O
o
On
given to you by one doctor
should be taken with a
medication given to you by a
different doctor

93
Table 5.14 Phase II Reminding Subjective Medication-related Stressors: Rotated Component
Matrix using Principal Components Analysis with Vanmax Rotation and Kaiser Normalization
(Rotation converged in 9 iterations)
Component
1
2
3
4
5
6
1. Needed something to remind
you to take your medications
on tune
149
-.018
.757
-.028
.017
-.048
2. Needed something to remind
you to have your medications
refilled cm time
.137
.074
698
018
.117
013
3. Not known what to do if you
missed a dose of medication
.048
.197
.579
018
.183
.252
4 Had trouble keep mg track of
how well your medication is
working
.365
.225
.479
.024
-.013
131
5. Needed an up-to-date list of
your medications to show all
your doctors
.269
.265
.457
-.026
.194
- 148
6. Needed easy to read
.318
.314
.337
-.098
.276
.063
information about your
medications

94
Table 5.15 Phase II Obtaining Subjective Medication-related Stressors: Rotated Component
Matrix using Principal Components Analysis with Vanmax Rotation and Kaiser Normalization
(Rotation converged in 9 iterations)
Component
1
2
3
4
5
6
1. Had difficulty swallowing your
240
-068
.007
.051
619
187
your medication
2. Had difficulty opening a
.036
.258
.205
-.025
608
043
medication container
3 Had difficulty getting
134
.120
.253
109
.475
-.266
transportation to a pharmacy

95
item would load on an informational factor, that is, about “if you should dnnk alcohol or smoke
while taking medication ” But, from the telephone conversations the principal investigator had
with subjects or their care givers it seems these items were problematic. First, subjects seemed
upset about responding to these first two stressor items because these items seemed designed to
‘trick’ the subject somehow because they seemed ‘the reverse’ of the other items about medication
problems. Second, subjects did not know how to answer the stressor item about a possible alcohol
and/or smoking interaction when they did not use these substances. Given the discussions with
subjects and their care givers about the last three items, these three items were dropped from
further analyses.
Perceived medication stress
A separate factor analysis was undertaken to assess the dimensionality of the perceived
medication stress items. The four items were unidimensional (Table 5.16).
Medication-specific social support
Three factor analyses were undertaken to assess the dimensionality of doctor, pharmacist, and
(most concerned) other person medication-specific social support. The six medication-specific
social support items for the doctor, pharmacist, and (most concerned) other person were
hypothesized to load similarly (two items per dimension) on three possible dimensions: 1.)
appraisal, 2.) emotional, and, 3.) tangible.
The results of the factor analyses for doctor, pharmacist, and (most concerned) other person
medication-specific social support are summarized in Tables 5.17-5.19. For the doctor, the
pharmacist, and the (most concerned) other person the results were similar (“Full Matrix”) One of
the items that was hypothesized to load on an appraisal dimension (e g., “helped me become aware
of things I didn’t know about my medications”) loaded on both component 1 and 2 for each of the

96
Table 5.16 Phase II Perceived Medication Stress Items: Rotated Component Matrix using
Principal Components Analysis with Vanmax Rotation and Kaiser Normalization
Component
Feelmgs of being overwhelmed by your
medication regimen
891
Feelmgs of discouragement with your medication
regimen
870
Womes or concerns about reactions between two
or more medications
.799
Feelmgs of guilt or anxiety when you got off
track with your medication taking
.776

97
Table 5.17 Phase II Doctor Medication-specific Social Support: Rotated Component Matrices
using Principal Components Analysis with Vanmax Rotation and Kaiser Normalization*
Full Matnx Component
1
2
3
4
1. Helped me fully understand when
and how to follow my medication
treatment
.277
.842
.328
.235
2. Helped me become aware of things
I didn’t know about my medications
.646
.576
.137
.278
3. Helped me feel confident I can take
my medicines
.502
.398
.470
.374
4 Helped me work through and womes or
concerns related to my medications
.833
.234
.315
.279
5 Helped me get the nght medications
.248
.256
.882
.239
6 Helped me get the things I needed to
take my medications the nght way
318
.259
.277
.865
Reduced Matnx
Component
1
2
3
4
1 Helped me fully understand when
and how to follow my medication
treatment
.272
884
.255
.283
2. Helped me work through and womes or
concerns related to my medications
.261
.295
.303
868
3 Helped me get the nght medications
.897
.262
.258
.243
4. Helped me get the things I needed to
take my medications the nght way
.269
.257
881
.293
*In the full matnx, all items were included. Before the reduced matnx was calculated, items 2 and
item 3 were eliminated because of high loadings on more than one component

98
Table 5.18 Phase II Pharmacist Medication-specific Social Support: Rotated Component Matrices
using Principal Components Analysis with Varimax Rotation and Kaiser Normalization*
Full Matrix Component
1
2
3
4
1 Helped me fully understand when
and how to follow my medication
treatment
.837
.345
.272
268
2. Helped me become aware of things
I didn’t know about my medications
686
.547
.253
.262
3 Helped me feel confident I can take
my medicines
.538
.667
.293
.301
4 Helped me work through and womes or
concerns related to my medications
.393
.789
289
.311
5 Helped me get the right medications
.267
266
879
.291
6 Helped me get the things I needed to
take my medications the right way
.321
.342
.372
801
Reduced Matrix
Component
1
2
3
4
1 Helped me fully understand when
and how to follow my medication
treatment
.271
.853
.280
.348
2 Helped me work through and womes or
concerns related to my medications
.290
.395
.324
.809
3 Helped me get the right medications
881
.255
.312
.248
4 Helped me get the things I needed to
.372
.303
.818
.318
take my medications the right way
* In the full matrix, all items were included Before the reduced matrix was calculated, items 2 and
item 3 were eliminated because of high loadings on more than one component

99
Table 5.19 Phase II Other Person Medication-specific Social Support: Rotated Component
Matrices using Principal Components Analysis with Vanmax Rotation and Kaiser Normalization*
Full Matrix
Component
1
2
3
4
1 Helped me fully understand when
and how to follow my medication
treatment
.718
.358
.464
.296
2. Helped me become aware of things
I didn’t know about my medications
.761
.479
.255
.275
3. Helped me feel confident I can take
my medicines
.465
.716
.303
.337
4 Helped me work through and womes or
concerns related to my medications
.440
.695
.420
.276
5. Helped me remember things such as
taking my medicine and refill mg
my prescriptions
.331
.334
.816
.318
6. Helped me get to the pharmacy
.252
.256
.262
895
Reduced Matrix
Component
1
2
3
4
1 Helped me become aware of things
I didn’t know about my medications
.280
.832
.326
.352
2 Helped me feel confident I can take
my medicines
.322
.460
.352
.749
3 Helped me remember things such as
taking my medicine and refilling
my prescriptions
.319
.313
.850
.278
4 Helped me get to the pharmacy
.899
.243
.281
.231
*In the full matrix, all items were included. Before the reduced matrix was calculated, items 2 and
item 3 were eliminated because of high loadings on more than one component.

100
three “helpers.” Also, one of the items that was hypothesized to load on an emotional dimension
(e g., “helped me feel confident I can take my medicines”) loaded on both component 1 and 2 for
each of the three “helpers.”
After these preliminary analyses, the items that loaded on both component 1 and 2 were
dropped, and the three follow-up analyses were run agam (“Reduced Matrix”). Again, the second
analyses were similar for all three “helpers.” All four items loaded separately on one of the four
components Even the two items that were both hypothesized to load on a tangible dimension
loaded on separate components.
Because of these unexpected results, that is, having one item to represent one of the four kinds
of support as opposed to two items to represent one of the three kinds of support, it was decided to
rename the kinds of support to distinguish them from what had been hypothesized earlier. The
“helped me fully understand when and how to follow my medication treatment” item was termed
consultation medication-specific social support The “helped me work through and womes or
concerns related to my medications” item was termed affirmation medication-specific social
support. The “helped me get the right medications” item was termed acquisition medication-
specific social support. And, the “helped me get the things I needed to take my medications the
right way” item was termed actuation medication-specific social support.
Reliability Analyses
As discussed in the last section, the factor analyses for doctor, pharmacist and (most concerned)
other person medication-specific social support yielded unexpected results since the kinds of
medication-specific social support (consultation, affirmation, actuation, and acquisition) will be
represented by one item instead of two items. However, three separate reliability analyses were
undertaken on the three remaining item sets. Each will be discussed in turn

101
Table 5.20 Phase II Medication Adherence Items: Reliability Coefficients and Item-to-total
Correlations (n=971)
Item-to-total
Correlations*
1. Are you careless at times about taking
.4139
your medication9
2 Do you forget to take your medicine9
.3797
3. In the last 24 hours, have you missed a
dose of medication?
.3064
4. When you feel better do you sometimes
.2453
stop taking your medicine?
*Cronbach's alpha = 0.5435

Table 5.21 Phase II Advising Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations (n = 928)
Item-to-totai
Correlations*
1. Felt like you had a problem with your medication ,6602
2. Wondered if something that happened to you was a side ,6442
effect of your medication
3. Not felt confident about taking your medication as .6289
di retted
4 Believed that your medication does not help you feel .6043
better
5 Had a side effect of your medication that has upset 5959
you
6. Felt that you were taking too many medications .5795
7 Wondered why your medication was prescribed .5718
8, Thought of changing the dose of your medication .5380
because you did not feel nght
9 Wondered how long you will have to take your medication .5146
*Cronbach’s alpha = 0.8573

Table 5 22 Phase II Isolating Subjective Medication-related Stressor Items: Reliability
Coefficients and Item-to-total Correlations (n=943)
103
Item-to-total
Correlations*
Felt that no one seemed really interested in
your medication taking
6190
Felt that no one listened to you about your
medication problems
.6194
Been afraid to ask someone to better explain
something about your medication
.4645
Felt that no one could help you deal with your
medication problems
.3466
*Cronbach’s Alpha = 0 7170

104
Table 5.23 Phase II Reminding Subjective Medication-related Stressor Items: Reliability
Coefficients, and Item-to-total Correlations (n=956)
Item-to-total
Correlations*
Needed something to remind you to have your
medications refilled on time
.4962
Needed something to remind you to take your
medications on time
.4853
Needed an up-to-date list of your medications
to show all your doctors
.4423
Had trouble keepmg track of how well your
medication is working
.4332
Not known what to do if you missed a dose
.4180
*Cronbach’s Alpha = 0,6967

105
Table 5.24 Phase II Obtaining Subjective Medication-related Stressor Items Reliability
Coefficients, and Item-to-total Correlations (n=961)
Item-to-total
Correlations*
1 Had difficulty opening a medication container
.2389
2. Had difficulty getting transportation to a pharmacy
.2272
3 Had difficulty swallowing your medication
.1988
*Cronbach's Alpha = 0.3778

106
Table 5.25 Phase II Perceived Medication Stress Items Reliability Coefficients and Item-to-total
Correlations (n=964)
Item-to-total
Correlations*
1 Feelings of being overwhelmed by your medication . 7752
regimen
2 Feelings of discouragement with your medication .7389
regimen
3 Womes or concerns about reactions (between two .6498
or more medications)
4 Feeling guilty or anxious when you get off track .6220
with your medication
*Cronbach's alpha = 0.8531

107
The first reliability analysis was conducted on the four medication adherence items. Cronbach’s
alpha was 0 5435 (Table 5 20). The item “when you feel better do you sometimes stop taking your
medicine” had a low item-to-total correlation of 0,2453. Despite the poor Cronbach's alpha and
low item-to-total correlations, it was decided to retain all the medication adherence items given the
exploratory nature of the study.
The results from the reliability analyses conducted on the subjective medication-related stressor
items are summarized in Table 5.21-5.24. The first three dimensions each have a good Cronbach’s
alpha and each have good item-to-total correlations. However, the fourth dimension appears to
suffer from a poor Cronbach's alpha and low item-to-total correlations Despite the poor
Cronbach’s alpha and low item-to-total correlations, it was decided to retain this fourth dimension
given the exploratory nature of the study.
The last reliability analysis was conducted on the four perceived medication stress items.
Cronbach’s alpha was 0.8531 (Table 5.25).
Phase II Measure Descriptions
Phase II study measure descriptions are summarized in Table 5.26. It appears that on a scale
from one to four, four being perfect medication adherence, that the subjects rated themselves as a
group very high (i.e., mean = 3.42, standard deviation = 0.90). Furthermore, it appears that on a
scale from four to twenty-four, twenty-four being the highest ratmg of perceived stress, these
subjects rated themselves as a group rather low (i.e., mean = 8.33, standard deviation = 5.04)
Also, the subjects did not seem to rate themselves as a group very high on the three types of
stressors Furthermore, the subjects seemed to rate all the different types of medication-specific
social support from the doctor, pharmacist, and (most concerned) other person a little higher than
half-way up on a scale from one to five, one being “none” and five being “a great deal of support.”

108
Table 5 26 Descriptions of Phase II Measures
n*
# of items
Mean
Median
Min
Max
S D
Medication Adherence
971
4
3.42
4
0
4
0.90
Objective Stressors
Number of Prescriptions
1017
1
8.23
8
1
33
4.58
Subjective Stressors
Advising
928
9
14.12
13
9
36
4 63
Isolating
943
4
5.51
5
4
16
2.10
Reminding
956
5
7.77
7
5
20
2.63
Obtaining
961
3
4 26
4
3
12
1.50
Perceived Stress
964
4
8.33
7
4
24
5.04
Doctor Support
Consultation
975
1
3.84
4
1
5
111
Affirmation
978
1
3.44
4
1
5
1.32
Actuation
958
1
3 69
4
1
5
1.31
Acquisition
976
1
4.08
4
1
5
1.05
Pharmacist Support
Consultation
973
1
2.84
3
1
5
1.55
Affirmation
958
1
2 48
2
1
5
1.52
Actuation
955
1
2.72
3
1
5
1.56
Acquisition
962
1
3.07
3
1
5
1.58
Other Person Support
Consultation
962
1
2.96
3
1
5
1.58
Affirmation
961
1
3.19
4
1
5
1.59
Actuation
969
1
3.38
4
1
5
1.59
Acquisition
952
1
3.06
3
1
5
1.68
*n are different for different measures because some subjects chose not answer the question.

109
Kolmogorov-Smimov tests were conducted on each of the scales to test the normality of their
distributions Each test of normality passed with the Lilliefors correction to test for significance.
Content of the Four Scales
With the possible exception of the perceived medication stress scale, more items for each of the
scales should have been included in this study. The content of each of the scales will be discussed
in turn.
Medication adherence
One of the items m the self-report measure concerning adherence when feeling worse was not
used for the revised questionnaire, because of a poor item-to-total correlation generated during
Phase I of the study. In retrospect, it might have been wise to have included the item despite this
poor item-to-total correlation, as the inclusion of this item wouldn’t have posed a substantial
increase in response burden, and may have generated a better item-to-total correlation in Phase II
of the study. Furthermore, it was decided to included a 24-hour recall item to improve the scale’s
Cronbach’s alpha. Similarly, it might have been wise to have included items from other self-report
medication adherence measures. For example, another self-report scale could have been included
which asks the subject about intentional and unintentional reasons for not taking their medication
(Brooks et al., 1994). Perhaps, instead of relying on only two measures in this study, a battery of
scales could have been employed to unprove the measurement of medication adherence.
Medication-related stressors
Four of the items were not used for the revised questionnaire, because of a poor item-to-total
correlation generated during Phase I of the study. In retrospect, these items might have been
included concerning: 1.) timeliness of refills, 2 .) nonprescnption medications, 3 .) agreement with
feeling that a drug was unnecessary, and 4.) understanding of one’s embarrassment when taking a

110
drug in front of other people The inclusion of these items wouldn’t have posed a substantial
increase in response burden, and may have generated a better item-to-total correlation in Phase II
of the study Furthermore, it would have been helpful to have included a few more items that
reflect the obtaining medication-related stressor dimension to improve the reliability of the measure
using Cronbach’s alpha
Perceived medication stress
The four items that were adapted for use in this study appear to reflect the domain adequately.
However, this doesn’t rule out the possibility that another dimension to perceived medication stress
couldn’t exist and that items for this other dimension might need to be generated in a future study.
Medication-specific social support
Items for this scale were adapted from a previous study and it was hypothesized that three
distinct subscales (appraisal, emotional, and tangible) would exist for each “helper” (doctor,
pharmacist, and other person). It was thought that a factor analysis would reveal two items for
each subscale. However, two items were dropped from the scale because of higher inter
correlations between subscales. Furthermore, two items that were thought to belong to a single
subscale, seemed to belong to different dimensions. It would have been helpful to have included
more items that reflect the possible dimensions that were found in Phase II of the study (i .e .,
consultation, affirmation, actuation and acquisition)
Construct-related Validation of the Scales
Generally, it appears that the low reliability of some of the measures used, set the upper limit on
the size of the correlation that could be obtained between the measures (Thorndike, Cunningham,
Thorndike, and Hagen, 1991). This attenuation due to unreliability should be addressed in a
follow-up study by the inclusion of more carefully chosen items to increase Cronbach’s alpha

The unexpected results from the Vanmax Rotation and Kaiser Normalization have been already
been discussed for the medication-specific social support scale. However, convergent and
discriminant validity needs to be addressed by examining the subjective medication-related
stressors scale and the medication-specific social support scale in turn.
Items on each stressor subscale were examined for convergent and discriminant validity It was
demonstrated that there were stronger correlations among items on each of these subscales than
with items on the other subscales. Furthermore, it was demonstrated that there were stronger
correlations between items on these subscales, than between items on these subscales and the items
on the perceived stress scale. Also, it was demonstrated that there were stronger associations
among items on these subscales, than between items on these subscales and the items on the
medication adherence scale
Unfortunately, each item making up the medication-specific support seemed to be associated
with every type of medication-related stressor. This may either reflect a poor choice of items, or a
global response to all the medication-specific social support items by the respondents
Testing of the Five Sets of Hypotheses
Five sets of hypotheses will be discussed in turn. Testing of these five sets of hypotheses will
help establish validity for each of the four scales and the proposed model.
The first set of hypotheses will involve some preliminary hypothesized relationships between the
exogenous and endogenous variables in nucleus of the Stress Model of Medication Adherence The
results will be given and discussed. This will set the stage for a more thorough examination of
hypothesized mediating and moderating effects
The second set of hypotheses will involve hypothesized mediating effects The last three sets
will involve hypothesized moderating effects

112
First set of hypotheses
The first set of hypotheses will examine zero-order correlations (from Table 5 27) to answer the
following set of hypotheses These hypotheses deal with hypothesized associations between
exogenous and endogenous variables in the “nucleus” Stress Model of Medication Adherence An
examination of these associations will help validate the model by comparing it with what has been
found in other more general studies
Hypothesis 1 A,: Advanced age is not associated with medication adherence.
As hypothesized, advanced age was not associated with medication adherence. Spiers and
Kutzik (1995) found, among independently living persons at least 55 years of age participating in a
"brown-bag" medication review, that age was the best predictor of medication-related problems
However, contrary to what is often believed, as a group these patients try harder to comply with
medications in general than do younger patients (Klein, 1988), even in the presence of bothersome
problems.
Hypothesis 1 A:: Advanced age is positively associated with obtaining and reminding (instrumental)
medication-related stressors.
As hypothesized, advanced age was positively associated with obtaining medication-related
stressors (r= 089) Older adults seem to experience motor difficulties that are problematic to
obtaining or doing mechanical tasks associated with their medications. In one study, almost half of
the noninstitutionalized elderly were found limited in mobility because of chronic conditions,
especially heart disease and arthritis (Rice and Estes, 1984). Decreased activity and dexterity can
limit a person ability and willingness to have prescriptions filled, take difficult to swallow drugs
regularly, and open and close the childproof containers that, to arthritic hands are unmanageable

Table 5.27 Correlations between Subject Characteristics, Health Status, Medication Adherence, Number of Medications, Type of
Stressors, and Perceived Stress
1 2. 3. 4
1 Age
2 Income
Health Status
3 Self-rated
-036
033
.140**
4 CDS
-038
.026
-.286**
5. Medication
Adherence
-015
-010
044
-029
6 Number of
Medications
Stressors
-059
077*
-.342**
.654**
7 Advising
-092**
054
-.216**
187**
8 Isolating
-038
-.030
- 154**
026
9 Reminding
024
064
- 192**
162**
10 Obtaining
089**
-.028
-.218**
152**
11 Perceived
Stress
-023
048
-.189**
163**
5 6 7 8 9.
-Oil
-.316**
211**
-.177**
075*
.527**
-.449**
203**
.532**
426**
-172**
199**
.357**
.287**
401**
-.295**
203**
632**
454**
489**
10
310**

114
(Mallet, 1992)
Contrary to what was hypothesized, advanced age was not associated with reminding stressors.
It may be that for this sample, they have been taking their medications for so long that they
established routines or cues to help them remember to take their medications.
Hypothesis 1A3: Advanced age should be positively associated with (advising) informational
medication-related stressors.
Contrary to what was hypothesized, advanced age was negatively associated with advising
stressors (r=- 092). This finding seems to disagree with the literature suggesting that older adults
experience difficulties with information when taking their medication. A study has suggested that
the failing eyesight of older adults reduces their ability to read small print on prescription labels
and package inserts (Dirckx, 1979). Also, another study suggests that about 30% of the
population aged 65 years and older suffer significant hearing loss (National Center for Health
Statistics, 1985). Hearing loss limits patients’ ability to hear directions for appropriate drug use
and discourages them from asking questions (Ebersole and Hess, 1981). Also, the concept of
“intelligent noncompliance” may be of special relevance to the elderly lacking proper information,
for older people may omit medications in order to compensate for physiological changes
accompanying aging or disease - changes that make them more vulnerable to adverse drug
reactions that may be unrecognized by their physicians (Lipton and Lee, 1988). Furthermore,
memory of information has been shown to decline with age and information may have to be
repeated by the health care provider during each visit (Light, 1991; Sahhouse, 1991). What this
finding seems to suggest is that as the subjects advance in age, they do not have as many of these
advising medication-related stressors. Perhaps, they have been on their chronic medication
regimens for so long that they feel more information is unnecessary. This confusion might be

115
resolved in a future study including a duration of medication regimen variable
Hypothesis IB,: Higher mcome is not associated with medication adherence.
As hypothesized, an association was not found between mcome and medication adherence.
However, it must be clear that in this study most subjects had service connected disabilities and did
not have to pay for their medications. The medication adherence rate might have been significantly
different if the study was replicated using subjects who had to pay for their medications.
Hattaway (1996) has reported the impart of the high cost of prescriptions on medication
nonadherence among the elderly. In addition, the failure of Medicare to cover the cost of
prescription drugs is widely perceived to be a primary factor in medication nonadherence (Arnold
et al., 1995).
Hypothesis 1B;: Higher mcome is not associated with perceived medication stress.
As hypothesized, an association was not found between mcome and perceived stress. This
finding seems to agree with a study that examined the effects of demographic and socioeconomic
variables on perceived stress m work and nonwork environments (Bednar, Marshall, and Bahouth,
1995). However, it is interesting to pomt out that m another closely related study, an mverse
relationship was found between economic satisfaction and perceived stress. It would seem that
perceived stress has more to do with a person’s satisfaction with their economic situation than their
actual mcome (Kranmch, Riley, and Leffler, 1988).
Hypothesis 1C,: Poor health status is not associated with medication adherence.
As hypothesized, an association was not found between poor health status [usmg lower self-
rated health and higher Chronic Disease Score to indicate poor health status] and medication

116
adherence. This has been confirmed in a recent study among 1,028 elderly patients (Coons et al.,
1994).
Hypothesis 1C:: Poor health status is associated with advising (informational) medication-related
stressors
As hypothesized, poor health status was associated with a higher score on the advising
medication-related stressor subscale [lower self-rated health (r=-216), higher Chronic Disease
Score (r= 187)] . Subjects with a lower self-rating of their own health or more diseases states seem
to be concerned about their medication taking and therefore seek out needed information.
Hypothesis 1C3: Poor health status is associated with obtaining and reminding (instrumental)
medication-related stressors.
As hypothesized, poor health status was associated with a higher score on the obtaining
medication-related stressor subscale [lower self-rated health (r=-218), higher Chronic Disease
Score (r=. 152)] and reminding medication-related stressor subscale [lower self-rated health (r=-
192), higher Chronic Disease Score (r=. 162)] . Subjects with poor health status seem to
experience more stressors involving getting their medications, taking them, and remaining on their
medication schedules.
Hypothesis 1C4: Poor health status is associated with isolating (emotional) medication-related
stressors.
As hypothesized, poor health status was associated with a higher score on the isolating subscale
[lower self-rated health (r=-. 154)]. However, poor health (as measured more objectively with the
Chronic Disease Score) did not demonstrate an association. This seems to suggest that unless a
subject subjectively senses their deteriorating health, the subject will not experience an increase in
problems related to communicating with other people concerns about their medications

117
Hypothesis 1C5: Poor health status is associated with perceived medication stress
As hypothesized, poor health status was associated with perceived medication stress [lower self-
rated health (r=-. 189), higher Chronic Disease Score (r=. 163)]. This is in agreement with a study
that found a negative association between self-rated health status and perceived stress (Chwalisz
and Kisler, 1995). However, it should be noted that in a separate study, it was found that the
strength of this association decreases over tune (Schulz et al., 1995). This suggests that upon the
first occurrence of one or more morbidities, a patient’s perceived stress may be higher than it will
be at a future point in tune as he or she gets accustomed to living with their condition(s).
Second set of hypotheses
This second set of hypotheses revolve around the prediction that perceived stress will mediate
the relationship between type of stressors and medication adherence. To begin this set of
hypotheses, the zero order correlations (Table 5.27) are examined between the primary variables
(i.e., medication adherence, type of stressors and perceived stress). Then, the associations between
the subject’s age, income, health status (self-rated health and Chronic Disease score) and the
primary variables are reexamined using beta coefficients (Table 5.28). After this examination of
mam effects, the hypothesized mediating effects of perceived stress on the relationship between
type of stressors and medication adherence will be analyzed.
To test for the mediating effects of perceived stress, a senes of multiple regression analyses
were conducted, and the strengths of the paths were estimated. According to Baron and Kenny
(1986), the mediating effects can be substantiated by three regression equations. First, type of
stressors (the independent vanable) must affect perceived stress (the mediator vanable). Second,
type of stressors (the mdependent vanable) must affect the dependent vanable (medication

118
adherence) in the second equation Finally, perceived stress (the mediator variable) must affect
medication adherence (the dependent variable) in the third regression equation Perfect mediation
would hold if type of stressors (the independent variable) has no effect on medication adherence
(dependent variable) when perceived stress (the mediator) is controlled m this manner. But, if the
residual path is not zero, this would indicate the operation of multiple mediating factors, and that
the type of stressors variables are mediator candidates, albeit not both a necessary and a sufficient
candidate for an effect to occur.
The transformation of raw scores to deviation scores for use in the regressions to follow have
been recommended as a cure for the multicollmeanty problem (high correlation between the
product-term and one or more of its constituents) that often arises with the use of untransformed
(raw) scores (e g., Finney, Mitchell, Cronkite & Moos, 1984). However, deviation scores have the
problem of having results which are difficult to interpret. Therefore, regressions usmg raw scores
are used here too. Although deviation scores will reduce the product-term's correlation with the
constituent variables, mukicollinearity seems to pose no threat to the analysis of interaction or
mam effects unless the correlation is so high as to produce rounding errors m computer
calculations (e g., Cronbach and Snow, 1977).
Although this path-analytic method cannot rule out alternative causal explanations or reciprocal
effects, if mediating effects are found here they might provide important information about links
between the variables in the stress model. But, because of the cross-sectional nature of the present
study, all proposed mediational effects are considered exploratory.
Hypothesis 2A,: There will be positive mam effect between advising stressors and perceived
stress, a negative mam effect between advising stressors and medication adherence, and a negative
mam effect between perceived stress and medication adherence.

119
Correlation analyses were used to examine the hypothesized mam effects between the advising
stressors, perceived stress, and medication adherence (Table 5.27) A positive mam effect was
found between advising stressors and perceived stress (r = .632, p < .001). And, a negative
association was found between advising stressors and medication adherence (r = -.316, p < .001).
Finally, perceived stress was negatively associated with medication adherence (r = -.295, p < .001).
Hypothesis 2A2: There will be positive mam effect between isolating stressors and perceived
stress, a negative mam effect between isolating stressors and medication adherence, and a negative
mam effect between perceived stress and medication adherence.
Correlation analyses were used to examine the hypothesized mam effects between the isolating
stressors, perceived stress, and medication adherence (Table 5.27). A positive mam effect was
found between isolatmg stressors and perceived stress (r=.454, p< 001). Also, a negative
association was found between isolatmg stressors and medication adherence (r = -.177, p < .001).
Finally, perceived stress was negatively associated with medication adherence (r=-.295, p < .001).
Hypothesis 2A3: There will be positive mam effect between reminding stressors and perceived
stress, a negative mam effect between reminding stressors and medication adherence, and a
negative mam effect between perceived stress and medication adherence.
Correlation analyses were used to examine the hypothesized main effects between the reminding
stressors, perceived stress, and medication adherence (Table 5.27). A positive mam effect was
found between reminding stressors and perceived stress (r = .489, p < 001). Also, a negative
association was found between reminding stressors and medication adherence (r = -.449, p < .001).
Finally, perceived stress was negatively associated with medication adherence (r = -.295, p < .001).
Hypothesis 2A4: There will be positive mam effect between obtaining stressors and perceived
stress, a negative mam effect between obtaining stressors and medication adherence, and a negative
mam effect between perceived stress and medication adherence.

120
Correlation analyses were used to examine the hypothesized main effects between the obtaining
stressors, perceived stress, and medication adherence (Table 5 27). A positive main effect was
found between obtaining stressors and perceived stress (r = .310, p < .001). Also, a negative
association was found between obtaining stressors and medication adherence (r = - 172, p < 001).
Finally, perceived stress was negatively associated with medication adherence (r = -.295, p < .001).
Hypothesis 2B. The associations between the exogenous and endogenous variables found earlier
using zero-order correlations may no longer be significant when using multiple regression.
According to Table 5.28 the association between advanced age and obtaining stressors remains
significant (beta=. 122, p<001) when using multiple regression. However, the negative association
between advanced age and advising stressors no longer remains significant. Also, it is interesting
to note that the significant associations found between poor health status (using self-rated health
and Chronic Disease Score) and medication-related stressors using zero-order correlations remain
significant when using multiple regression. Note that an association between Chronic Disease
Score and isolating stressors was not significant using either a univariate or multivariate analysis.
Finally, the association between income and perceived stress that was not significant using zero
order correlations, does become significant when using multiple regression. This suggests that
income (or socioeconomic status) might have a positive association with a subjects’ generation of
evaluations of problems taking medications
Hypothesis 2C,: There will be a mediating effect of perceived stress on the relationship between
advising stressors and medication adherence.
Three sets of regression equations were used to test for the mediating effect of perceived stress
on the relationship between advising stressors and medication adherence. First, advising stressors
(the independent variable) affected perceived stress (the mediator variable) in the first set of

Table 5.28 Multiple Regression Analysis of Elderly Outpatient’s Age, Income, Self-rated Health Status and Chronic Disease Score on
Advising Stressors, Isolating Stressors, Reminding Stressors, Obtaining Stressors, Perceived Stress and Medication Adherence using
Raw Scores
Sociodemographic
Variables
Beta(significance)
Advising
Stressors
Isolating
Reminding
Obtaining
Perceived
Stress
Medication
Adherence
Age
- 061( 068)
- 023( 507)
059(076)
122(<001)
003( 925)
- 039( 259)
Income
.073(030)
-.009( 785)
.088(009)
-001(973)
071( 036)
-.019( 585)
Self-rated Health
- 195(< 001)
- 162(< 001)
- 186(< 001)
- 192(<001)
-. 181(< 001)
057(114)
Chronic Disease Score
128(< 001)
-.023( 527)
.111(001)
100(004)
097(005)
- 010( 777)
R: of model
.076
.026
.064
072
.053
005
F of model
17.127
5.572
14.698
16.505
12.117
1 076
p-value of model
000
000
000
000
000
367

122
Figure 5.1 Path diagram using multiple regression with raw scores (r) and deviation scores (d)
for testing the effects of perceived stress on the relationship between advising stressors
and medication adherence
Perceived
Stress
b
4
r=,632 (< 001)
r=-.163(<001)
d= 395 (<001)
d= .097 (.007)
Advising
a
/ \
« c
Medication
Stressors
r=- 316(< 001)
d=.145 (< 001)
Adherence

123
regression equations (using raw scores beta=.632, p< 001, using deviation scores beta=.395,
p<001) Second, advising stressors (the independent variable) affected the dependent variable
(medication adherence) in the second set of equations (using raw scores beta=-.316, p< 001; using
deviation scores beta= 145, p<.001). And, in the third set of regression equations perceived stress
(the mediator variable) affected medication adherence (the dependent variable) (using raw scores
beta=-.163, p< 001; using deviation scores beta=097, p= 007). Since all these regressions held in
the predicted direction, the effect of advising stressors (the independent variable) on medication
adherence (the dependent variable) had to be found less in the third set of equations (using raw
scores beta=-.215, p< 001; using deviation scores beta=.105, p=.003) than in the second set of
equations (using raw scores beta=-.316, p< 001; using deviation scores beta= 145, p<001).
Perfect mediation would have held if advising stressors (the independent variable) had no effect
when perceived stress (the mediator) was controlled in this manner. Since the residual path is not
zero, this indicates the operation of multiple mediating factors, and that the advising stressors
variable is a mediator candidate, albeit not both a necessary and a sufficient candidate for an effect
to occur.
Hypothesis 2C:: There will be a mediating effect of perceived stress on the relationship between
isolating stressors and medication adherence
Three sets of regression equations were used to test for the mediating effect of perceived stress
on the relationship between isolating stressors and medication adherence First, isolating stressors
(the independent variable) had an effect on perceived stress (the mediator variable) in the first set
of regression equations (usmg raw scores beta=454, p< 001; using deviation scores beta=.234,
p<001). Second, isolating stressors (the independent variable) had an effect on the dependent
variable (medication adherence) in the second set of equations (usmg raw scores beta=- 177,

124
Figure 5.2 Path diagram using multiple regression with raw scores (r) and deviation scores (d)
for testing the effects of perceived stress on the relationship between isolating stressors
and medication adherence
r=,454 (<.001)
d=234 (<001)
Isolating a
Stressors
Perceived
Stress
r=-.271 (< 001)
d= .120 (< 001)
" c Medication
r=-. 177 (< 001) Adherence
d= 080 ( .015)

125
p<001;using deviation scores beta=.080, p=.015). And, in the third regression set of equations
perceived stress (the mediator variable) had an effect on medication adherence (the dependent
variable) (using raw scores beta=- 271, pc.001; using deviation scores beta=.120, p< 001). Smce
all these regressions held in the predicted direction, the effect of isolating stressors (the independent
variable) on medication adherence (the dependent variable) had to be found less in the third set of
equations (using raw scores beta—.055, p=. 120; using deviation scores beta=.056, p=. 101) than in
the second set of equations (using raw scores beta—. 177, p<.001; usmg deviation scores
beta=080, p= 015). Perfect mediation would have held if isolating stressors (the independent
variable) had no effect when perceived stress (the mediator) was controlled in this manner. Since
the residual path is not zero, this indicates the operation of multiple mediating factors, and that the
isolating stressors variable is a mediator candidate, albeit not both a necessary and a sufficient
candidate for an effect to occur
Hypothesis 2C3: There will be a mediating effect of perceived stress on the relationship between
reminding stressors and medication adherence.
Three sets of regression equations were used to test for the mediating effect of perceived stress
on the relationship between reminding stressors and medication adherence. First, reminding
stressors (the independent variable) had an effect on perceived stress (the mediator variable) in the
first regression equation (usmg raw scores beta=489, p< 001; usmg deviation scores beta=.290,
p<001). Second, reminding stressors (the independent variable) had an effect on the dependent
variable (medication adherence) m the second set of equations (usmg raw scores beta— 449,
p<001;usmg deviation scores beta- 235, p<001). And, m the third regression set of equations
perceived stress (the mediator variable) had an effect on medication adherence (the dependent
variable) (usmg raw scores beta—.083, p=.014; usmg deviation scores beta=059, p= 077). Smce

126
Figure 5.3 Path diagram using multiple regression with raw scores (r) and deviation scores (d)
for testing the effects of perceived stress on the relationship between reminding
stressors and medication adherence
r=489 (<.001)
Perceived
Stress
b
f ^
r=- 083 (.014)
d=.290 (< 001)
/ \
/
/
d=.059 (.077)
Reminding
a
c
Medication
Stressors
r=-.449 (< 001)
Adherence
d=.235 (< 001)

127
all these regressions held in the predicted direction, the effect of reminding stressors (the
independent variable) on medication adherence (the dependent vanable) had to be found less m the
third set of equations (using raw scores beta—.412, p< 001, using deviation scores beta=.221,
pc.001) than in the second set of equations (using raw scores beta— 449, pc.001; using deviation
scores beta= 235, p< 001). Perfect mediation would have held if reminding stressors (the
independent vanable) had no effect when perceived stress (the mediator) was controlled in this
manner. Since the residual path is not zero, this indicates the operation of multiple mediating
factors, and that the reminding stressors vanable is a mediator candidate, albeit not both a
necessary and a sufficient candidate for an effect to occur.
Hypothesis 2C4: There will be a mediating effect of perceived stress on the relationship between
obtaining stressors and medication adherence.
Three sets of regression equations were used to test for the mediating effect of perceived stress
on the relationship between obtaining stressors and medication adherence First, obtaining stressors
(the independent vanable) had an effect on perceived stress (the mediator vanable) in the first set
of regression equations (using raw scores beta=.310, p< 001; using deviation scores beta=.156,
p< 001). Second, obtaining stressors (the independent vanable) had an effect on the dependent
vanable (medication adherence) in the second set of equations (using raw scores beta—. 172,
p< 001; using deviation scores beta=098, p=.003). And, in the third set of regression equations
perceived stress (the mediator vanable) had an effect on medication adherence (the dependent
vanable) (using raw scores beta—.263, pc.001; using deviation scores beta=. 118, p<001) Since
all these regressions held in the predicted direction, the effect of obtaining stressors (the
independent vanable) on medication adherence (the dependent vanable) had to be found less in the
third set of equations (using raw scores beta^.095, p=.004, using deviation scores beta=.076,

128
Figure 5.4 Path diagram usmg multiple regression with raw scores (r) and deviation scores (d)
for testing the effects of perceived stress on the relationship between obtaining
stressors and medication adherence
Perceived
Stress
b
4
r= 310(< 001)
d=. 156(< 001)
/
/
a *• c Medication
r=-.172(<001) Adherence
d= .098 (.003)
Obtaining
Stressors
r=-.263(<001)
d= 118(< 001)

129
p=.021) than in the second equation (using raw scores beta— 172, p< 001; using deviation scores
beta=.098, p=.003). Perfect mediation would have held if obtaining stressors (the independent
variable) had no effect when perceived stress (the mediator) was controlled in this manner Since
the residual path is not zero, this indicates the operation of multiple mediating factors, and that the
obtaining stressors vanable is a mediator candidate, albeit not both a necessary and a sufficient
candidate for an effect to occur.
Third set of hypotheses
The third set of hypotheses revolve around the prediction that type of doctor support will
moderate two types of relationships: 1.) the relationships between type of stressors and perceived
stress, and 2.) the relationship between perceived stress and medication adherence. To begin this
set of hypotheses, the zero order correlations will be examined (Table 5.29) for associations
between the endogenous variables. Then, beta coefficients will be examined (Table 5.30) for
associations between the exogenous and endogenous variables of concern. Finally, beta
coefficients will be examined using untransformed (raw) scores and transformed (deviation) scores
for significant interactions between type of stressor and type of doctor support.
Hypothesis 3A,: There will be negative mam effect between advising stressors and doctor
consultation support, a positive mam effect between advising stressors and perceived stress, and
negative mam effect between doctor consultation support and perceived stress.
A negative correlation (r=-.223, p< 001) was found between advising stressors and doctor
consultation support (Table 5.29). The correlation between advising stressors and perceived stress
(r= 632, p<001) was positive (Table 5.27). Doctor consultation support was negatively
associated with perceived medication stress (r=-. 150, p<001).

Table 5 .29 Correlations between Type of Support and Subject’s Age, Income, Health Status, Medication Adherence, Type of Stressors, and
Perceived Stress
Health
Status Stressors
Age
Income
Self
rated
CDS Med
Adherence
#
Meds
Advising
Isolating
Reminding Obtaining Perceived
Stress
Doctor
Support
Consultation
054
000
009
016
.096**
.061
-.223**
-.318**
-.125**
-.079*
- 150**
Affirmation
076*
-.059
-.031
.028
.076*
.040
-.200**
-.296**
-.057
-.093**
-121**
Actuation
.108**
-.051
-Oil
053
.080*
.061
-.206**
-.259**
-.067*
-.046
-136**
Acquisition
048
.058
057
-.008
.112**
-008
-.278**
-.320**
-.124**
-.131**
-.216**
Pharmacist
Support
Consultation
-.025
-.080*
.029
.078*
048
073*
-.166**
-.251**
-055
-.090**
-.091**
Affirmation
-.008
- 072* -
•010
084**
.043
071*
-.140**
-.207**
-.024
-.071*
-.060
Actuation
.013
-121**
-003
089**
030
065*
-.145**
-.189**
-023
-.058
-051
Acquisition
-010
-.071*
008
087**
.051
061
-.185**
-.201**
-026
-.079*
-.089**
Other Person
Support
Consultation
.121**
-.177**
-.190** .098**
-002
137**
-.016
-.021
109**
086**
055
Affirmation
.107**
-.135**
-.161** 081*
.032
135**
-068*
-.022
.088**
080*
042
Actuation
065*
-.107**
-.150** 097**
-062
119**
020
-.038
.184**
075*
101**
Acquisition
082*
-.121**
-.196** .149**
-.004
.167**
-008
-.031
093**
094**
050

131
Hypothesis 3A:: There will be negative main effect between isolating stressors and doctor
affirmation support, a positive mam effect between isolating stressors and perceived stress, and a
negative mam effect between doctor affirmation support and perceived stress.
A negative correlation (r=-.296, p<001) was found between isolating stressors and doctor
affirmation support (Table 5.29). The correlation between isolating stressors and perceived stress
(r=.454, p< 001) was positive (Table 5.27) Doctor affirmation support was negatively associated
with perceived medication stress (r=-. 121, pc.001).
Hypothesis 3A3: There will be negative mam effect between reminding stressors and doctor
actuation support, a positive mam effect between reminding stressors and perceived stress, and a
negative mam effect between doctor actuation support and perceived stress
A negative correlation (r=-.067, p<.043) was found between reminding stressors and doctor
actuation support (Table 5 29). The correlation between reminding stressors and perceived stress
(r= 489, pc.001) was positive (Table 5.27). Doctor actuation support was negatively associated
with perceived medication stress (r=-.136, p<001).
Hypothesis 3A4: There will be negative mam effect between obtaining stressors and doctor
acquisition support, a positive mam effect between obtaining stressors and perceived stress, and a
negative mam effect between doctor acquisition support and perceived stress
A negative correlation (r=-.131, p<001) was found between obtaining stressors and doctor
acquisition support (Table 5 29). The correlation between obtaining stressors and perceived stress
(r= 310, p<.001) was positive (Table 5.27) Doctor acquisition support was negatively associated
with perceived medication stress (r=-.216, p<001).
Hypothesis 3A5: There will be negative mam effect between perceived stress and doctor
affirmation support, a negative mam effect between perceived stress and medication adherence, and
a positive mam effect between doctor affirmation support and medication adherence
A negative correlation (r=*. 121, p<001) exists between perceived stress and doctor affirmation
support (Table 5.29). The correlation between perceived stress and medication adherence (r=-

132
Table 5 30 Multiple Regression Analysis of Elderly Outpatient’s Age, Income, Self-rated Health
Status and Chronic Disease Score on Doctor Consultation Support, Doctor Affirmation Support,
Doctor Actuation Support, and Doctor Acquisition Support using Raw Scores
Sociodemographic
Variables
Beta(sigmficance)
Consultation
Doctor SuDDort
Affirmation Actuation
Acquisition
Age
.052( 127)
,072(.034)
.106(002)
.056( 098)
Income
-.003(939)
-.056( 102)
-052(. 132)
.050( 149)
Self-rated Health
.033( 363)
.0010978)
.031( 388)
.077( 031)
Chronic Disease Score
.029(408)
.031(375)
.069( 051)
.012( 735)
R: of model
004
.010
.018
.012
F of model
.897
2.071
3.939
2.694
p-value of model
.465
.083
.004
.030

133
.295, p< 001) was negative (Table 5.27). Doctor affirmation support was positively associated
with medication adherence (r= 076, p< 018)
Hypothesis 3B The associations between the exogenous variables and doctor medication-specific
social support variables found earlier usmg zero order correlations may no longer be significant
when usmg multiple regressions.
When comparing the results on Table 5.29 against the results on Table 5.30, it becomes clear
that age remains positively associated with doctor affirmation support (r=.076, p=.017; beta=.072,
p=.034), and doctor actuation support (r=. 108, p=001, beta=.106, p=002). The associations
between other exogenous variables and other types of doctor medication-specific social support
remain nonsignificant.
To test for the moderatmg effects of type of doctor support, a senes of hierarchical multiple
regression analyses were conducted usmg deviation (transformed) scores and raw (untransformed j
scores. A moderatmg effect requires that type of doctor support interacts with type of stressors to
modify perceived stress, or that doctor affirmation support interacts with perceived stress to
modify medication adherence. If the F ratio for the interaction term is significant, then the
moderatmg model is supported.
The moderatmg hypotheses of interaction effects specifies that type of stressors relates to
perceived stress differently depending on the level of type of doctor support, or that perceived
stress relates to medication adherence differently depending on the level of doctor affirmation
support. These effects will be charted with each set of multiple regressions. However, the
significance of the interaction (the slope) cannot be determined without looking at the results of the
product-term regression usmg deviation scores. The test of this moderating effect is achieved by the
following multiplicative regression models:
Y= b0 + b,X + b:Z + b3XZ + *

134
where Y = perceived stress, X = type of stressors, Z = type of Doctor support, and e = error term
and, where Y = medication adherence, X = perceived stress, Z = Doctor affirmation support, and e
- error term.
Using deviation scores in product-term regression has been recommended as a cure for the
multicollineanty problem (high correlation between the product-term and one or more of its
constituents) that often anses with the use of untransformed scores (e g., Finney, Mitchell,
Cronkite & Moos, 1984). Although deviation scores will reduce the product-term’s correlation
with the constituent variables, multicollineanty poses no threat to the analysis of interaction or
main effects unless the correlation is so high as to produce rounding errors in computer
calculations (e g., Cronbach and Snow, 1977).
Type of doctor support was expected to operate as a moderating (buffer) vanable to perceived
stress at high levels of type of stressors. And, doctor affirmation support was expected to operate
as a moderating (buffer) vanable to medication adherence at high levels of perceived stress. In
order to test these hypotheses, a multiple regression equation was generated for each hypothesis.
For example, for those hypotheses requiring a test of whether type of doctor support was a
moderator of type of stressors, the SPSS forced entry procedure suggested by Baron and Kenny
(1986) was employed in a stepwise fashion First, type of stressors and type of doctor support
were entered in two separate blocks. Then the interaction term was entered on the third step.
Hypothesis 3C,: There will be a moderating effect of doctor consultation support on the
relationship between advising stressors and perceived stress
As shown in Table 5.31, the interaction term (advising stressors X doctor consultation support)
did contribute to the prediction of perceived stress (F-ratio of R: Change = 6 202, p=.013). This
suggests that doctor consultation support moderates the relationship between advising stressors and

135
Table 5.31 Raw score and deviation score multiple regression analysis relating advising stressors,
doctor consultation support, and Advising stressors-Doctor consultation support interaction to
perceived medication stress using raw scores and deviation scores
Predictor
Beta
R2
Change
R2
F-ratio of R: Change(Sig )
Advising
Stressors
.621
.386
.386
556 930(< 001)
Doctor
Consultation
Support
-015
.386
.000
.326( 568)
Advising Stressors
X Doctor Consultation
Support
-.109
.387
.001
1.112(292)
Usina Deviation Scores:
Predictor
Beta
R2
Change
R2
F-ratio of R2 Change(Sig )
Advising
Stressors
.374
.140
.140
144.090(000)
Doctor
.038
.141
.001
1.439(231)
Consultation
Support
Advising Stressors
X Doctor Consultation
Support
.157 147
.006
6.202(013)

Effect of Advising Stressors on Perceived Medication Stress
Figure
Level of Doctor Consultation Support
5.5 The Effect of Advising Stressors on Perceived Medication Stress for Different Levels of Doctor Consultation Support

Table 5.32 Raw Score and deviation score multiple regression analysis relating isolating stressors,
Doctor affirmation support, and isolating stressors-Doctor affirmation support interaction to
perceived medication stress using raw scores and deviation scores
Using Raw Scores:
Predictor
Beta
R2
Change
R2
Isolating
Stressors
.463
.214
.214
Doctor
Affirmation
Support
.019
.215
.001
Isolating Stressors
X Doctor Affirmation
Support
.060
.215
.000
Using Deviation Scores:
Predictor
Beta
R2
Change
R2
Isolating
Stressors
.239
.057
.057
Doctor
Affirmation
Support
-.008
.057
.000
F-ratio of R: Change(Sig)
245.821(<.001)
361( 548)
492(483)
F-ratio of R: Change(Sig)
54.655(000)
.061( 805)
Isolating Stressors
X Doctor Affirmation
Support
.112 .060
.002
2.234( 135)

Effect of Isolating Stressors on Perceived Medication Stress
Level of Doctor Affirmation Support
Figure 5.6 The Effect of Isolating Stressors on Perceived Medication Stress for Different Levels of Doctor Affirmation Support

139
perceived stress
Hypothesis 3C;: There will be a moderating effect of doctor affirmation support on the relationship
between isolating stressors and perceived stress.
The interaction term (isolatmg stressors X doctor affirmation support) did not contribute to the
prediction of perceived stress (Table 5.32). This suggests that doctor affirmation support does not
moderate the relationship between isolating stressors and perceived stress. In contrast, a zero order
correlation was found earlier for Doctor affirmation support, accounting for a proportion of the
variance in perceived stress.
Hypothesis 3C3: There will be a moderating effect of doctor actuation support on the relationship
between reminding stressors and perceived stress.
As shown in Table 5.33, the interaction term (reminding stressors X doctor actuation support)
did contribute to the prediction of perceived stress (F-ratio of R: Change = 10.605, p=001). This
suggests that doctor actuation support moderates the relationship between reminding stressors and
perceived stress.
Hypothesis 3C4: There will be a moderating effect of doctor acquisition support on the relationship
between obtaining stressors and perceived stress.
As shown in Table 5.34, the interaction term (obtaining stressors X doctor acquisition support)
did contribute to the prediction of perceived stress (change in F = 4.158, p=042). This suggests
that doctor acquisition support moderates the relationship between obtaining stressors and perceived
stress.
Hypothesis 3C5: There will be a moderating effect of doctor affirmation support on the relationship
between perceived stress and medication adherence.
As shown in Table 5.35, the interaction term (perceived stress X doctor affirmation support) did
not contribute to the prediction of medication adherence. This suggests that doctor affirmation
support does not moderate the relationship between perceived stress and medication adherence. In

140
Table 5.33 Raw score and deviation score multiple regression analysis relating reminding stressors,
doctor actuation support, and reminding stressors-doctor actuation support interaction to perceived
medication stress using raw scores and deviation scores
Using Raw Scores:
Predictor
Beta
R2
Change
R2
F-ratio of R2 Change(Sig.)
Reminding
Stressors
.485
.235
.235
277.282(<001)
Doctor
Actuation
Support
-.099
.245
.010
11.525(.001)
Reminding Stressors
X Doctor Actuation
Support
-.255
.249
.004
5.033(.025)
Using Deviation Scores:
Predictor
Beta
R2
Change
R2
F-ratio of R2 Change(Sig.)
Reminding
Stressors
.295
.087
.087
86.135(.000)
Doctor
Actuation
Support
.038
.089
.001
1.462(.227)
Reminding Stressors
.219
.099
.011
10.605(.001)
X Doctor Actuation
Support

Effect of Reminding Stressors on Perceived Medication Stress
Figure 5.7 The Effect of Reminding Stressors on Perceived Medication Stress for Different Levels of Doctor Actuation Support

142
Table 5 34 Raw score and deviation score multiple regression analysis relating obtaining stressors,
doctor acquisition support, and obtaining stressors-doctor acquisition support interaction to
perceived medication stress
Using Raw Scores:
Predictor
Beta
R2
Change
R2
F-ratio of R: Change(Sig )
Obtaining
Stressors
.307
.094
.094
95.470(<001)
Doctor
Acquisition
Support
- 181
.126
.032
33.808(<001)
Obtaining Stressors
X Doctor Acquisition
Support
-.050
.126
.000
.163(687)
Using Deviation Scores:
Predictor
Beta
R2
Change
R2
F-ratio of R2 Change(Sig)
Obtaining
Stressors
.159
.025
.025
23.836( 000)
Doctor
Acquisition
Support
088
033
.008
7.259(007)
Obtaining Stressors
127
.037
004
4.158( 042)
X Doctor Acquisition
Support

Effect of Obtaining Stressors on Perceived Medication Stress
04
0 3
02
0 1
0 —
1 2 3 4 5
Level of Doctor Acquisition Support
Figure 5.8 The Effect of Obtaining Stressors on Perceived Medication Stress for Different Levels of Doctor Acquisition Support

Table 5.35 Raw score and deviation score multiple regression analysis relatmg perceived stress,
doctor affirmation support, and perceived stress-doctor affirmation support interaction to
medication adherence using raw scores and deviation scores
144
Using Raw Scores:
Predictor
Beta
R:
Change
R:
F-ratio of R: Change(Sig )
Perceived
Stress
-.290
.084
.084
85 585(< 001)
Doctor
Affirmation
Support
042
.086
.002
1.75 (.186)
Perceived Stress
X Doctor Affirmation
Support
.009
.086
.000
.010(919)
Using Deviation Scores:
Predictor
Perceived
Stress
Doctor
Affirmation
Support
Perceived Stress
X Doctor Affirmation
Support
Beta R: Change
R:
-.048 .020 .002
-.041 .020 .000
F-ratio of R: Change(Sig )
2.145( 143)
.293( 589)
.133 .018 .018
16.731(000)

Effect of Perceived Medication Stress on Medication Adherence
1 2 3 4 5
Level of Doctor Affirmation Support
Figure 5 9 The Effect of Perceived Medication Stress on Medication Adherence for Different Levels of Doctor Affirmation Support
4^
L/*

146
contrast, a zero order correlation was found earlier for doctor affirmation support, accounting for a
proportion of the variance in medication adherence
Fourth set of hypotheses
This set of hypotheses are similar to those for doctor support. To test for the moderating effects
of type of pharmacist support rather than doctor support, the same analytic strategy will be
employed, but this time the pharmacist support variables will be used instead of doctor support
variables.
Hypothesis 4A¡: There will be negative mam effect between advising stressors and pharmacist
consultation support, a positive mam effect between advising stressors and perceived stress, and
negative mam effect between pharmacist consultation support and perceived stress.
A negative correlation (r=-. 166, p<001) was found between advising stressors and pharmacist
consultation support (Table 5.29). The correlation between advising stressors and perceived stress
(r=.632, p<001) was positive (Table 5.27). Pharmacist consultation support was negatively
associated with perceived stress (r=-.091, p=005).
Hypothesis 4A:: There will be negative mam effect between isolating stressors and pharmacist
affirmation support, a positive mam effect between isolating stressors and perceived stress, and a
negative mam effect between pharmacist affirmation support and perceived stress
A negative correlation (r=-.207, p<001) was found between isolating stressors and pharmacist
affirmation support (Table 5.29). The correlation between isolating stressors and perceived stress
(r=.454, p<001) was positive (Table 5.27). Pharmacist affirmation support was not associated
with perceived stress.
Hypothesis 4A3: There will be negative mam effect between reminding stressors and pharmacist
actuation support, a positive mam effect between reminding stressors and perceived stress, and a
negative mam effect between pharmacist actuation support and perceived stress.

147
No association was found between reminding stressors and pharmacist actuation support (Table
5.29). The correlation between reminding stressors and perceived stress (r=.489, p<001) was
positive (Table 5.27). Pharmacist actuation support was not associated with perceived stress.
Hypothesis 4A4: There will be negative mam effect between obtaining stressors and pharmacist
acquisition support, a positive mam effect between obtaining stressors and perceived stress, and a
negative mam effect between pharmacist acquisition support and perceived stress.
A negative correlation (r=-079, p= 015) was found between obtaining stressors and pharmacist
acquisition support (Table 5.29). The correlation between obtaining stressors and perceived stress
(r=.310, p< 001) was positive (Table 5.27). Pharmacist acquisition support was negatively
associated with perceived stress (r=-089, p=.006).
Hypothesis 4A5: There will be negative mam effect between perceived stress and pharmacist
affirmation support, a negative mam effect between perceived stress and medication adherence, and
a positive mam effect between pharmacist affirmation support and medication adherence.
No association was found between perceived stress and pharmacist affirmation support (Table
5.29). The correlation between perceived stress and medication adherence (r=-.295, p<001) was
negative (Table 5 27). Pharmacist affirmation support was not associated with medication
adherence.
Hypothesis 4B: The associations between the exogenous variables and pharmacist medication-
specific social support variables found earlier usmg zero order correlations may no longer be
significant when usmg multiple regressions
It is interesting to note that income and Chronic Disease Score were associated with each type of
pharmacist medication-specific social support usmg both zero-order correlations (Table 5.29) and
the multiple regressions (Table 5.36). Furthermore, an association between self-rated health and
pharmacist consultation support that was not significant usmg zero-order correlations, was

148
significant when using multiple regression (Table 5.36).
Hypothesis 4C,: There will be a moderating effect of pharmacist consultation support on the
relationship between advising stressors and perceived stress.
As shown in Table 5.37, the interaction term (advising stressors X pharmacist consultation
support) did contribute to the prediction of perceived stress (F-ratio of R" Change = 9 400, p=.002).
This suggests that pharmacist consultation support moderates the relationship between advising
stressors and perceived stress.
Hypothesis 4C;: There will be a moderatmg effect of pharmacist affirmation support on the
relationship between isolating stressors and perceived stress.
The interaction term (isolating stressors X pharmacist affirmation support) did not contribute to
the prediction of perceived stress (Table 5 38). This suggests that pharmacist affirmation support
does not moderate the relationship between isolating stressors and perceived stress. In contrast, a
zero order correlation was found earlier. Furthermore, a mam effect was found earlier for
pharmacist affirmation support (r=-.207, p<.001), accounting for a proportion of the variance in
perceived stress.
Hypothesis 4C3: There will be a moderatmg effect of pharmacist actuation support on the
relationship between reminding stressors and perceived stress.
The interaction term (reminding stressors X pharmacist actuation support) did not contribute to
the prediction of perceived stress (Table 5.39). This suggests that pharmacist actuation support
does not moderate the relationship between reminding stressors and perceived stress Furthermore,
no association was found earlier (Table 5 29) between reminding stressors and pharmacist actuation
support, or between pharmacist actuation support and perceived stress.
Hypothesis 4C4: There will be a moderatmg effect of pharmacist acquisition support on the
relationship between obtaining stressors and perceived stress.

149
Table 5 36 Multiple Regression .Analysis of Elderly Outpatient's Age, Income, Self-rated Health
Status and Chronic Disease Score on Pharmacist Consultation Support, Pharmacist Affirmation
Support, Pharmacist Actuation Support, and Pharmacist Acquisition Support usmg Raw Scores
Variables
Beta(significance)
Consultation
Pharmacist
Affirmation
SuDDort
Actuation
Acquisition
Age
-.037(274)
-.028( 416)
.005( 877)
.000(992)
Income
-095(005)
-.082(018)
- 129(< 001)
-.082(017)
Self-
rated
Health
088( 013)
.047( 189)
.052( 145)
.061(086)
Chronic
Disease
Score
115(001)
.111(002)
117(001)
117(001)
R: of model
.023
.017
.027
.018
F of model
4.962
3.758
5 961
3 989
p-value of model
.001
.005
.000
003

150
Table 5.37 Raw score and deviation score multiple regression analysis relating advising stressors,
pharmacist consultation support, and advising stressors-pharmacist consultation support interaction
to perceived medication stress using raw scores and deviation scores
Using Raw Scores:
Predictor
Beta
R2
Change
R:
F-ratio of R2 Change(Sig )
Advising
Stressors
.628
.395
.395
579 168(< 001)
Pharmacist
Consultation
Support
-.002
.395
.000
.005( 945)
Advising Stressors
X Pharmacist
Consultation
Support
-.097
.396
.001
1.229(268)
Using Deviation Scores:
Predictor
Beta
R2
Change
R2
F-ratio of R2 Change(Sig )
Advising
Stressors
389
.152
.152
158 780( 000)
Pharmacist
Consultation
Support
-.019
.152
.000
365(546)
Advising Stressors
.264
.161
.009
9.400(002)
X Pharmacist
Consultation
Support

Effect of Advising Stressors on Perceived Medication Stress
Level of Pharmacist Consultation Support
Figure 5 10 The Effect of Advising Stressors on Perceived Medication Stress for Different Levels of Pharmacist Consultation Support

152
Table 5.38 Raw score and deviation score multiple regression analysis relatmg isolating stressors,
pharmacist affirmation support, and isolating stressors-pharmacist affirmation support interaction
to perceived medication stress
Usmg Raw Scores:
Predictor
Beta R: Change F-ratio of R: Change(Sig )
R:
Isolating .451 .203 .203 225 968(< 001)
Stressors
Pharmacist .041 .205 .002 1.834( 176)
Affirmation
Support
Isolating Stressors .095 .206 .001 1.055( 305)
X Pharmacist
.Affirmation
Support
Usmg Deviation Scores:
Predictor
Isolating
Stressors
Pharmacist
Affirmation
Support
Isolating Stressors
X Pharmacist
Affirmation
Support
Beta R: Change
R;
.233 .054 .054
-.033 .055 .001
.072 .056 .001
F-ratio of R: Change(Sig )
50.868(000)
1.039( 308)
572(450)

Effect of Isolating Stressors on Perceived Medication Stress
06
03
02
0.1
o ——-—— ■■■—
1 2 3 4 5
Level of Pharmacist Affirmation Support
Figure 5 11 The Effect of Isolating Stressors on Perceived Medication Stress for Different Levels of Pharmacist Affirmation Support

154
Table 5.39 Raw score and deviation score multiple regression analysis relating reminding stressors,
pharmacist actuation support, and reminding stressors-pharmacist actuation support interaction to
perceived medication stress
Usmg Raw Scores:
Predictor
Reminding
Stressors
Pharmacist
Actuation
Support
Reminding Stressors
X Pharmacist
Actuation
Support
Beta R2 Change
R2
483 .233 .233
-.040 .235 .002
-.100 .235 .001
F-ratio of R: Change(Sig)
273.842(<001)
1.872( 172)
.956( 329)
Using Deviation Scores:
Predictor
Reminding
Stressors
Pharmacist
Actuation
Support
Reminding Stressors
X Pharmacist
Actuation
Support
Beta R2 Change
R2
280 .078 .078
.013 .079 000
.027 079 .000
F-ratio of R2 Change(Sig )
76.681(000)
161( 688)
.098( 754)

Effect of Reminding Stressors on Perceived Medication Stress
06
0 3
02
0 1
Level of Pharmacist Actuation Support
Figure 5 12 The Effect of Reminding Stressors on Perceived Medication Stress for Different Levels of Pharmacist Actuation Support

156
Table 5.40 Raw score and deviation score multiple regression analysis relatmg obtaining stressors,
pharmacist acquisition support, and obtaining stressors-pharmacist acquisition support interaction
to perceived medication stress using raw scores and deviation scores
Usmg Raw Scores.
Predictor Beta
Obtaining .297
Stressors
Pharmacist -069
Acquisition
Support
Obtaining Stressors 099
X Pharmacist
Acquisition
Support
Using Deviation Scores:
Predictor Beta
Obtaining 144
Stressors
Pharmacist -012
Acquisition
Support
Obtaining Stressors 074
X Pharmacist
Acquisition
Support
R Change F-ratio of R: Change(Sig )
R:
.088 088 87.390(<001)
.093 .005 4.663(031)
.093 .001 .844( 359)
R: Change F-ratio of R: Change(Sig )
R-
.021 .021 19.211(000)
021 .000 .125(724)
.022 .001 .637(425)

Effect of Obtaining Stressors on Perceived Medication Stress
Level of Pharmacist Acquisition Support
Figure 5 13 The Effect of Obtaining Stressors on Perceived Medication Stress for Different Levels of Pharmacist Acquisition Support

158
Table 5.41 Raw score and deviation score multiple regression analysis relating perceived stress,
pharmacist affirmation support, and perceived stress-pharmacist affirmation support interaction to
medication adherence
Using Raw Scores:
Predictor
Beta
R:
Change
R:
F-ratio of R: Change! Sig)
Perceived
Stress
-.292
.085
.085
85 133(< 001)
Pharmacist
Affirmation
Support
.026
.086
.001
.672(413)
Perceived Stress
-.076
.087
.001
.983( 322)
X Pharmacist
Affirmation
Support
Using Raw Scores:
Predictor Beta R: Change F-ratio of R: Change(Sig )
R:
Perceived
Stress
.136
.018
.018
17.207(000)
Pharmacist
Affirmation
Support
-.045
.021
.002
1 884( 170)
Perceived Stress
-.069
.021
.001
.527( 468)
X Pharmacist
.Affirmation
Support

Effect of Perceived Medication Stress on Medication Adhereno
Level of Pharmacist Affirmation Support
Figure 5 14 The Effect of Perceived Medication Stress on Medication Adherence for Different Levels of Pharmacist Affirmation Support
L/t
O

160
The interaction term (obtaining stressors X pharmacist acquisition support) did not contribute to
the prediction of perceived stress (Table 5.40). This suggests that pharmacist acquisition support
does not moderate the relationship between obtaining stressors and perceived stress In contrast, an
association was found earlier (Table 5.29) between obtaining stressors and pharmacist acquisition
support (r=-.079, p= 015), and between pharmacist acquisition support and perceived stress (r=-
.089, p= 006).
Hypothesis 4CS: There will be a moderating effect of other pharmacist affirmation support on the
relationship between perceived stress and medication adherence
The interaction term (perceived stress X pharmacist affirmation support) did not contribute to
the prediction of medication adherence (Table 5.41). This suggests that pharmacist affirmation
support does not moderate the relationship between perceived stress and medication adherence.
Furthermore, no association was found earlier (Table 5.29) between perceived stress and pharmacist
affirmation support, nor between pharmacist affirmation support and medication adherence.
Fifth set of hypotheses
This set of hypotheses are similar to those for doctor support To test for the moderating efFects
of type of other person support rather than doctor support, the same analytic strategy will be
employed, but this time the other person support variables will be used instead of doctor support
variables.
Hypothesis 5 A,: There will be a mam effect between advising stressors and other person
consultation support, a mam effect between other person consultation support and perceived stress,
and a positive mam effect between advising stressors and perceived stress
No association was found between advising stressors and other person’s consultation support
(Table 5.29). The correlation between advising stressors and perceived stress (r=632, p<001) was

161
positive (Table 5.27). Other person consultation support was not associated with perceived stress.
Hypothesis 5 A;: There will be negative main effect between isolating stressors and other person’s
affirmation support, a positive mam effect between isolating stressors and perceived stress, and a
negative mam effect between other person's affirmation support and perceived stress.
No association was found between isolating stressors and other person’s affirmation support
(Table 5.29). The correlation between isolatmg stressors and perceived stress (r= 454, p<001) was
positive (Table 5.27). Other person’s affirmation support was not associated wrth perceived stress.
Hypothesis 5A3: There will be negative mam effect between reminding stressors and other person’s
actuation support, a positive mam effect between reminding stressors and perceived stress, and a
negative mam effect between other person’s actuation support and perceived stress.
A positive correlation (r=. 184, p<001) was found between reminding stressors and other
person’s actuation support (Table 5.29). The correlation between reminding stressors and perceived
stress (r=.489, p<001) was positive (Table 5.27). Other person’s actuation support was positively
associated with perceived stress (n=.101, p=002).
Hypothesis 5 A*: There will be negative mam effect between obtaining stressors and other person’s
acquisition support, a positive mam effect between obtaining stressors and perceived stress, and a
negative mam effect between other person’s acquisition support and perceived stress.
A positive correlation (r=.094, p=.004) was found between obtaining stressors and other person
acquisition support (Table 5.29). The correlation between obtaining stressors and perceived stress
was found earlier (Table 5.27) to be positive (r=.310, p<001). Other person acquisition support
was not associated with perceived stress.
Hypothesis 5A5: There will be negative mam effect between perceived stress and other person
affirmation support, a negative mam effect between perceived stress and medication adherence, and
a positive mam effect between other person affirmation support and medication adherence.

162
An association was not found between perceived stress and other person affirmation support
(Table 5.29). The correlation between perceived stress and medication adherence (r=-.295,
p<.001) was negative (see Table 5.27). Other person affirmation support was not associated with
medication adherence.
Hypothesis 5B: The associations between the exogenous variables and (most concerned) other
person medication-specific social support variables found earlier using zero-order correlations may
no longer be significant when using multiple regressions.
It is interesting to note that every type of (most concerned) other person medication-specific
social support was associated with every exogenous variable (age, income, self-rated health and
Chronic Disease Score) using zero order correlations (see Table 5 29). These same associations
were found using multiple regression with the exception of a nonsignificant association between
other person affirmation medication-specific social support and (the more objective measure of
health status) Chronic Disease Score (Table 5.42).
Hypothesis 5C,: There will be a moderating effect of other person consultation support on the
relationship between advising stressors and perceived medication stress
The interaction term (advising stressors X other person consultation support) did not contribute
to the prediction of perceived stress (Table 5.43). This suggests that other person consultation
support does not moderate the relationship between advising stressors and perceived stress
Furthermore, no mam effects were found earlier (Table 5.29) between advising stressors and other
person consultation support, nor were mam effects found between other person consultation support
and perceived stress
Hypothesis 5C2: There will be a moderatmg effect of other person affirmation support on the
relationship between isolatmg stressors and perceived medication stress.

163
Table 5.42 Multiple Regression Analysis of Elderly Outpatient’s Age, Income, Self-rated Health
Status and Chronic Disease Score on Other person Consultation Support, Other person Affirmation
Support, Other person Actuation Support, and Other person Acquisition Support
using Raw Scores
Sociodemographic
Variables Beta(significance)
Other person Support
Consultation
Affirmation
Actuation
Acquisition
Age
128(< 001)
.115(001)
.069(040)
.103(002)
Income
- 152(< 001)
-.114(001)
-.089( 008)
-097(004)
Self-
rated
Health
-.139(000)
.119(001)
-.119(001)
-.137(000)
Chronic
Disease
Score
.077( 026)
.053( 132)
.073(036)
.114(001)
R; of model
.077
.051
.040
064
F of model
17.868
11.421
8.926
14 538
p-value of model
.001
.000
000
.000

164
Table 5.43 Raw score and deviation score multiple regression analysis relating advising stressors,
other person consultation support, and advising stressors-other person consultation support
interaction to perceived medication stress using raw scores and deviation scores
Using Raw Scores:
Predictor
Beta
R2
Change
R2
F-ratio of R: Change(Sig )
Advising
Stressors
.629
.395
.395
575.207(< 001)
Other Person
Consultation
Support
.055
.398
.003
4418(036)
Advising Stressors
X Other Person
Consultation
Support
-.035
.398
.000
.145(704)
Using Deviation Scores:
Predictor
Beta
R2
Change
R:
F-ratio of R: Change(Sig.)
Advising
Stressors
.390
.152
.152
157.410(000)
Other Person
Consultation
Support
003
.152
.000
.011(918)
Advising Stressors
X Other Person
Consultation
Support
-.039
.152
.000
.201(654)

0 8
0.7
06
0 5
04
0 3
02
0 1
0
15 1
2 3 4 5
Level of Other Person Consultation Support
Effect of Advising Stressors on Perceived Medication Stress for Different Levels of Other Person Consultation Support

166
Table 5.44 Raw score and deviation score multiple regression analysis relatmg isolating stressors,
other person affirmation support, and isolating stressors-other person affirmation support
interaction to perceived medication stress usmg raw scores and deviation scores
Using Raw Scores:
Predictor
Beta
R;
Change
R:
F-ratio of R; Change(Sig )
Isolating
Stressors
.452
.204
.204
228.906(<001)
Other Person
Affirmation
Support
.058
.208
.003
3.799(052)
Isolating Stressors
X Other Person
Affirmation
Support
i
K>
K>
OO
.212
.005
5.355( 021)
Using Deviation Scores:
Predictor
Beta
R2
Change F-ratio of R: Change(Sig )
R:
Isolating
Stressors
.234
.055
.055 51.712(000)
Other Person
Affirmation
Support
.025
.055
.001 .606(436)
Isolating Stressors
-.037
.056
.000 .197(657)
X Other Person
Affirmation
Support

Effect of Isolating Stressors on Perceived Medication Stress
1 2 3 4 5
Level of Other Person Affirmation Support
Figure 5 16 The Effect of Isolating Stressors on Perceived Medication Stress for Different Levels of Other Person Affirmation Support

168
The interaction term (isolating stressors X other person affirmation support) did not contribute
to the prediction of perceived stress (Table 5.44), This suggests that other person affirmation
support does not moderate the relationship between isolating stressors and perceived stress.
Furthermore, a mam effect was not found earlier (Table 5.29) between isolating stressors and other
person affirmation support, nor was other person affirmation support associated with perceived
stress
Hypothesis 5C3: There will be a moderating effect of other person actuation support on the
relationship between reminding stressors and perceived stress.
The interaction term (reminding stressors X other person actuation support) did not contribute to
the prediction of perceived stress (Table 5.45). This suggests that other person actuation support
does not moderate the relationship between reminding stressors and perceived stress. In contrast,
mam effects were found earlier (Table 5 .29) between reminding stressors and other person actuation
support (r= 184, p<001), and between other person actuation support and perceived stress (r=. 101,
p=.002).
Hypothesis 5C4: There will be a moderating effect of other person acquisition support on the
relationship between obtaining stressors and perceived stress
The interaction term (obtaining stressors X other person acquisition support) did not contribute
to the prediction of perceived stress (Table 5.46). This suggests that other person acquisition
support does not moderate the relationship between obtaining stressors and perceived stress.
Furthermore, a mam effect was found earlier (Table 5.29) between obtaining stressors and other
person acquisition support (r=.094, p= 004), but not between other person acquisition support and
perceived stress

169
Table 5.45 Raw score and deviation score multiple regression analysis relating reminding stressors,
other person actuation support, and reminding stressors-other person actuation support interaction
to perceived medication stress using raw scores and deviation scores
Using Raw Scores:
Predictor
Beta
R:
Change F-ratio of R" Change( S lg.)
R:
Reminding
Stressors
.484
.234
.234 280.637(<001)
Other Person
Actuation
Support
.007
.234
.000 ,063(.802)
Reminding Stressors
-.356
.241
.007 8.001(005)
X Other Person
Actuation
Support
Using Deviation Scores:
Predictor Beta R: Change F-ratio of R: Change(Sig )
R:
Reminding
Stressors
.290
.084
.084
84.261(000)
Other Person
Actuation
Support
.056
087
.003
3.130(077)
Reminding Stressors
.053
.088
.000
373(.542)
X Other Person
Actuation
Support

Effect of Reminding Stressors on Perceived Medication Stress
Level of Other Person Actuation Support
Figure 5 17 The Effect of Reminding Stressors on Perceived Medication Stress for Different Levels of Other Person Actuation Support

171
Table 5.46 Raw score and deviation score multiple regression analysis relating obtaining stressors,
other person acquisition support, and obtaining stressors-other person acquisition support
interaction to perceived medication stress using raw scores and deviation scores
Using Raw Scores:
Predictor
Beta
R:
Change
R:
F-ratio of R; Change(Sig )
Obtaining
Stressors
.311
.097
.097
96.913(<001)
Other Person
Acquisition
Support
.025
.097
.001
603(.438)
Obtaining Stressors
X Other Person
Acquisition
Support
-.074
.098
.000
385( 535)
Using Deviation Scores
Predictor
Beta
R:
Change
R:
F-ratio of R: Change(Sig)
Obtaining
Stressors
.158
.025
.025
23.081( 000)
Other Person
Acquisition
Support
.039
.026
002
1.433( 232)
Obtaining Stressors
.057
027
.001
.471(493)
X Other Person
Acquisition
Support

Effect of Obtaining Stressors on Perceived Medication Stress
Level of Other Person Acquisition Support
Figure 5.18 The Effect of Obtaining Stressors on Perceived Medication Stress for Different Levels of Other Person Acquisition Support
f'O

173
Table 5.47 Raw score and deviation score multiple regression analysis relating perceived stress,
other person affirmation support, and perceived stress-other person affirmation support interaction
to medication adherence using raw scores and deviation scores
Using Raw Scores
Predictor
Beta
R2
Change
R2
F-ratio of R: Change(Sig )
Perceived
Stress
-.294
.086
.086
86.918(<001)
Other Person
Affirmation
Support
.038
.088
.001
1 455( 228)
Perceived Stress
X Other Person
Affirmation
Support
-Oil
.088
.000
.017( 897)
Using Deviation Scores:
Predictor
Beta
R:
Change
R:
F-ratio of R; Change! Sig)
Perceived
Stress
.134
018
.018
16.780(000)
Other Person
Affirmation
Support
-.026
.019
.001
.619(432)
Perceived Stress
.058
.019
.000
.430( 512)
X Other Person
Affirmation
Support

Effect of Perceived Medication Stress on Medication Adherenci
Level of Other Person Affirmation Support
Figure 5 19 The Effect of Perceived Medication Stress on Medication Adherence for Different Levels of Other Person Affirmation Support

175
Hypothesis 5C<: There will be a moderating effect of other person affirmation support on the
relationship between perceived stress and medication adherence.
The interaction term (perceived stress X other person affirmation support) did not contribute to
the prediction of medication adherence (Table 5.47). This suggests that other person affirmation
support does not moderate the relationship between perceived stress and medication adherence
Furthermore, no mam effects were found earlier (Table 5.29) between perceived stress and other
person affirmation support, nor between other person affirmation support and medication
adherence.
Discussion of Results
The discussion to follow will focus on the results of the study and how they help to validate the
proposed Stress Model of Medication Adherence among elderly outpatients. This section will be
divided into three subsections. The first subsection will examine the relationships among stressors,
perceived stress, and medication adherence. Then, the second subsection will examine the
mediating effects of perceived stress Finally, the third subsection will address the moderating
effects of medication-specific social support.
Relationships .Among Stressors. Perceived Stress, and Medication Adherence
A positive association was demonstrated between the objective medication-related stressor scale
(number of prescriptions) and each subscale of the subjective medication-related stressors scale
(see Table 5 .27). The strongest positive correlation was between number of prescriptions and
advising stressors (r=.211). As the number of prescriptions nse, the number of drug interactions
increase, the complexity of the medication regimen increases, and consequently more information is
required by a subject. Furthermore, the weakest positive correlation was between the objective
medication-related stressor scale (number of prescriptions) and isolating stressors (r= 075). How

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often a subject experiences an inability to communicate his or her concerns about medication
taking may not depend on how many prescriptions are being taken but more on the interpersonal
relationships between the patient and his or her doctor, the patient and his or her pharmacist, and
the patient and his or her (most concerned) other person.
There were positive associations found between all types of medication-related stressors and
perceived medication stress (see Table 5.27). This suggests that as subjects experience more
medication-related stressors, the subjects evaluate these experiences and generate higher levels of
perceived medication stress. In the main study, each medication-related stressor item belonged to
one of four domains: 1.) advising, 2.) reminding, 3 .) isolating, and 4 ) obtaining Perceived
medication stress was most strongly associated with advising stressors (r= 632), then reminding
stressors (r=.489), followed by isolating stressors (r=454), and finally obtaining stressors
(r=.310).
These findings seem to suggest that experiences with certain types of stressors generate a higher
level of perceived stress than the other medication-related stressors No past studies have
demonstrated clear links between these types of stressors and perceived stress. There are only
studies that demonstrate that elderly patients do experience these different types of stressors
Elderly patients have to deal with a myriad of advising stressors associated with altered
pharmacokinetic and pharmacodynamic effects of drugs (Hoffler, 1981; Braverman, 1982; Roberts
and Turner, 1988; Dawlmg and Crome, 1989; Fox and Auestad, 1990; Taylor, 1990). There may
also be changes in the absorption of orally administered drugs, body composition, serum albumin
and globulin concentration, cardiac output and hepatic metabolism, renal blood flow, renal function
and homeostatic mechanisms (Shaw, 1982, Chapron, 1995). Elderly patients also must struggle
with a number of disease changes that occur with aging (Salzman, 1982; Tuck, 1988; Furberg and

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Black, 1988). And, elderly patients have been found to need things to remind them to take their
medications on time (Carswell, 1985; Fingeret and Schuettenberg, 1991, Rivers, 1992, Mackowiak
et al., 1994; Spiers and Kutzik, 1995). Furthermore, elderly patients seem to need to communicate
with spouses (Doherty et al., 1983), home helpers (Gilmore, Temple, and Taggart, 1989), and
health care professionals (Wolf et al., 1989) about their medications or to get help in obtaining or
taking their medications (Chang et al., 1985).
These more specific findings seem to agree with more general studies of the association between
stressors and perceived stress described earlier in the literature review. Cohen, Karmack, and
Mermelstein (1983) designed a 14-item Perceived Stress Scale (PSS) to measure the degree to
which situation in one’s life are appraised as stressful The summated score on this scale was
positively associated with stressors (life-event scores). A similar but larger scale with 30-items,
called Perceived Stress Questionnaire (Levenstein et al., 1993), was also positively associated with
stressors (stressful life events).
A negative association was found between perceived medication stress and medication adherence
(r=-.295). This suggests that if subjects evaluate their stressor experiences and generate high
levels of perceived medication stress, the same subjects will not adhere to their medication taking
behavior Some past studies have suggested a link between perceived medication stress and
medication adherence. Cohen, Kamarck, and Mermelstein (1983) demonstrated a negative
association between then Perceived Stress Scale and smoking-reduction maintenance Frenzel et
al. (1988) did not find an association between perceived stress and adherence in a study using this
same Perceived Stress Scale and a measure of adherence to insulin injections. However, they did
find a positive association between the measure of perceived stress and adherence to caloric diet
intake. One could reasonably argue that this general perceived stress scale was more specific to

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medication administration (rather than to caloric intake), they would have found a significant
correlation between perceived stress and insulin injections
This negative relationship between perceived stress and medication adherence has been
demonstrated in two recent studies Polonsky et al. (1995) demonstrated a negative association
between a 20-item measure of diabetes-related distress and adherence to self-care behaviors (which
included the use of insulin). And, a negative association was found between a previously validated
12-item measure of psychological distress and medication adherence (Coons et al., 1994)
As hypothesized, there were negative associations found between all types of stressors and
medication adherence. This suggests that as subjects experience more stressors, they report poorer
medication adherence, i.e., that they are careless at times about taking their medications, they do
forget to take their medicine, they have missed a dose of medication in the last 24 hours, and that
when they feel better they sometimes stop taking their medicine
Advising stressors were the type of stressors most strongly associated with medication
nonadherence. This negative association suggests that as subjects experience more difficulties with
what they have been told or not been told about their medication the same subjects will report a
lower level of medication adherence behavior. This seems understandable concerning elderly
patients. For example, Dali (1989) has stated that as elderly patients suffer from increased
comorbidities as part of the aging process, unexpected side effects that they have not been informed
about seem to increase and medication adherence seems to decrease
Although this study can’t tell the difference between the informed and uninformed subjects,
there are studies that loosely agree with this association between advising stressors and medication
adherence When subjects are uninformed about the name and purpose of drugs, the dosage
schedules, and the duration of the regimen and its possible side effects, or adverse consequences,

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this ignorance seems to predispose patients to medication nonadherence (Schwartz et al., 1962,
Lundin et al., 1980, Klein et al., 1992). Studies indicate that physicians’ drug instructions are
given hastily at the end of patients’ visits and are usually fragmentary and general. Furthermore,
patients are not given basic information, such as the purpose of the drug, expected treatment
outcomes, duration of the drug regimen, and dosage, schedule, and frequency (Svarstad, 1986).
Sometimes patients receive no instructions at all, primarily because the physician assumes other
health professionals have done so
Reminding stressors were the next most strongly and negatively associated type of stressors
with medication adherence. This suggests that as a subject experiences more difficulties
maintaining their medication schedule, the same subjects will report a lower level of medication
adherence behavior.
At least one study appears to agree with this finding and suggests that people frequently miss
doses because of mistakes that occur when a well-established pattern of daily activities is changed
either unexpectantly or by planning (Lonsh, Richards, and Brown, 1989). In this study, 200
patients with rheumatoid arthritis typically explained that these changes directed their attention to
another activity and-or they would be removed from their normal environment that contained cues
to remind them to take the forgotten doses. The focus of attention upon other activities and-or the
absence of reminder cues in the patient’s immediate environment also accounted for missed doses
because of being in too much of a hurry to do something or go somewhere, as well as being too
absorbed or mentally focused on another activity Patients who relied cm their pain to remind them
were also subject to missing doses. Patients who did not miss for these reasons learned to plan
reminders and-or place extra supplies in appropriate places, so that a missed dose might be taken a
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Isolating stressors were the next most strongly associated type of stressors with medication
adherence This negative association suggests that as a subject experience more difficulties
communicating their emotions related to their medication taking the same subjects report a lower
level of medication adherence behavior.
Studies have demonstrated an association between isolation and medication adherence.
Patients can freely communicate with hospital staff about their emotions related to their medication
taking while hospitalized The diabetic can talk to a nurse about his insulin injections And, the
asthmatic can talk to his doctor about how he wakes up in the middle of the night out of breath and
looking for his inhaler Patients who are fully adherent during their hospitalization may become
medication nonadherent soon after discharge, in part because patients’ drug therapy is closely
supervised in the hospital and at initial diagnosis but is not monitored as carefully therafter when
they go home and are not cared for in the same manner (Hare and Willcox, 1967; Irwin, Weitzell.
and Morgan, 1971). Medication adherence tends to be a greater problem when patients are
socially isolated in their own homes and cannot communicate these feelings (Haynes, Sackett, and
Taylor, 1980).
Obtaining (or mechanical) stressors were the most weakly associated type of stressors with
perceived medication stress. An attenuation of the strength of the correlation between obtaining
stressors and perceived medication stress may be due to the low reliability of the subscale as
measured with Cronbach’s alpha This negative association suggests that as subjects experience
more problems physically getting and taking a medication, the same subjects report a lower level of
medication adherence behaviors At least one study has demonstrated an association between these
obtaining (or mechanical) stressors and medication adherence. For example, it has been
demonstrated that a major reason for one or more missed doses was running out of medicine and

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not being able to obtain a refill in time (Lonsh, Richards, and Brown, 1989)
Two more general studies have examined the direct effect of stressors on adherence and seem to
dovetail with the findings in this study on medication-related stressors. Each of these will be
discussed m turn. Hanson and Pichert (1986) found a significant negative correlation between
negative events and adherence in a study using a very general measure for negative events and
adherence to a diabetic diet. They did not find any association between a measure for positive
events and adherence to a diabetic diet. This would seem to loosely agree with the findings here
based on negative experiences with medication taking and adherence to medications. Whether or
not positive experiences with medication taking have an association with medication adherence will
have to be addressed in a future study.
Frenzel et al. (1988) did not find a significant negative correlation between general stressors
using the Daily Hassles Scale (Kanner, Coyne, Schaefer, and Lazarus, 1981) and a measure of
adherence to insulin injections However, they did find a significant negative correlation between
this measure of stressors and adherence to caloric diet intake One could reasonably argue that the
general stressors scale they used was more specific to caloric intake than insulin injections, and
that if the researchers used a stressor scale more specific to medication administration, they would
have found a significant correlation between stressors and insulin injections
Mediating Effects of Perceived Stress
From a cognitive phenomenological perspective (Lazaraus and Folkman, 1984), variation in
outcomes to objectively similar stressors suggests that people have different interpretations of, and
reactions to, stressful experiences (DeLongis, Folkman, and Lazarus, 1988). A person’s reaction
to a stressor is thought to be influenced by subjective appraisal of the stressor. The process by

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which subjective appraisal of the same stressful experiences results in different appraisals across
individuals is not well specified.
A study attempted to examine this phenomenon and found that there are individual differences
in the ability of some subjects to deal with what would seem to be objectively similar situations
(e g., Haley, Levine, Brown, and Bartolucci, 1987). It seems plausible from the present study that
perceived medication stress, which is designed to measure the degree to which one’s situation in
taking medications are appraised as stressful, mediates the relationships between some types of
stressors and medication adherence. In other words, perceived medication stress represents some
property of the individual that transforms some medication-related stressor experiences in some
way in a person’s head, which eventually has an effect on their medication adherence behavior
differently than another mdividual.
The mediating effect of perceived medication stress on the relationship between advising,
isolating, reminding and obtaining medication-related stressors and medication adherence was
tested If perfect mediation had been demonstrated, the experiences with these types of stressors
would have all wait into the generation of perceived medication stress, and that perceived
medication stress had the effect on medication adherence However, perfect mediation was not
demonstrated. Therefore, the experiences with these types of medication-related stressors went only
partially into the generation of perceived medication stress. In this instance, perceived medication
stress is not both a necessary and a sufficient candidate for medication adherence. From an
examination of the direct effects of advising, isolating, reminding and obtaining medication-related
stressors, it has been demonstrated that these medication-related stressors also have direct effects
on medication adherence

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Only one other study has been identified which has examined the mediating effects of perceived
stress. In an investigation conducted by Hooker et al. (1992), it was hypothesized that two types of
personality (neuroticism and optimism) would have direct effects, and indirect effects through
perceived stress, on health outcomes. Results showed that neuroticism had significant direct
effects on all of the health outcomes, and substantial indirect effects, through perceived stress, on
mental health outcomes. Certainly neuroticism and the experience of medication-related stressors
are not the same. However, they are both strong negative experiences which might share similar
indirect pathways through perceived stress to health outcomes, or similar direct pathways to health
outcomes.
Moderating Effects of Medication-specific Social Support
Four types of medication-specific social support from three helpmg individuals were
hypothesized to moderate relationships between four kinds of medication-related stressors and
perceived medication stress. Furthermore, one type of medication-specific social support (i.e.,
affirmation support) was hypothesized to also moderate the relationship between perceived
medication stress and medication adherence
As hypothesized, three types of medication-specific social support (i.e., consultation, actuation,
and acquisition support) from the doctor did seem to moderate some of the relationship between
certain kinds of medication-related stressors (i.e., advising, reminding, and obtaining stressors,
respectively) and perceived medication stress. And, as hypothesized, one type of medication-
specific social support (i.e., consultation support) from the pharmacist did seem to moderate some
of the relationship between one kind of medication-related stressors (i .e., advising stressors) and
perceived medication stress This suggests that the doctor and pharmacist seem to make an
assessment of each elderly patient’s perceived medication stress level, and then attempts to match

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their level of a certain kind of support to help that elderly patient deal effectively with that certain
type of stressor
Contrary to what was hypothesized, some kinds of medication-specific social support offered
from particular individuals did not seem to moderate the relationship between kinds of medication-
related stressors and perceived medication stress Affirmation support from the doctor,
pharmacist, and (most concerned) other person did not seem to moderate the relationship between
isolating stressors and perceived stress Also, affirmation support from the doctor, pharmacist,
and other person did not seem to moderate the relationship between perceived stress and medication
adherence In addition, neither actuation and acquisition support from either the pharmacist or
other person seemed to moderate the relationship between reminding and obtaining stressors,
respectively, and perceived medication stress. Finally, consultation support from the other person
did not seem to moderate the relationship between advising stressors and perceived medication
stress. This suggests that for these situations the helping individual does not need to make an
assessment of the elderly patient’s perceived medication stress level, and then attempt to match
their level of a kind of support to help the elderly patient deal effectively with a certain type of
stressor. Instead, it seems to suggest that the helping individual need only offer a threshold level of
a kind support to help the patient deal with a type of stressor. To offer the elderly patient a higher
level of a certain kind of support would not help bring the elderly patient ’s level of perceived stress
down any These unexpected results might be explained m three ways: 1.) the helping individual’s
kind of support really has no moderating effect on the hypothesized relationship for this sample, or
2.) there is a psychometric problem with the item for the helpmg individual's support, or 3.) a
combination of the first two reasons

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The explanation for the results of each helping individual depend on the particular situation
Medication-specific social support given by the doctor, pharmacist, and (most concerned) other
person to the elderly outpatient will be discussed in turn.
Doctor medication-specific social support
As hypothesized, consultation, actuation, and acquisition support from the doctor did seem to
moderate some of the relationship between advising, reminding, and obtaining stressors,
respectively, and perceived medication stress. This suggests that doctors seem to make an
assessment of each elderly patient’s perceived medication stress level, and then attempt to match
their level of a particular type of support to help that patient deal effectively with a certain kind of
stressor
Contrary to what was hypothesized, doctor affirmation support did not seem to moderate the
relationship between isolating stressors and perceived medication stress, or the relationship
between perceived medication stress and medication adherence. An examination of the direct
effects of doctor affirmation support on perceived stress and medication adherence helps to
elucidate this relationship First, doctor affirmation support was negatively associated with
perceived stress, suggesting that when subjects rate their doctors high on helping them work
through any womes or concerns related to their medications, the same subjects evaluate the
experience of their medication-related stressors (perceived stress) as less stressful. Second, doctor
affirmation support was positively associated with medication adherence, suggesting that when
subjects rate their doctors high on helping them work through any womes or concerns related to
their medications, the same subjects rate higher on their medication adherence behavior items.
Since there were associations between the constructs in this situation as hypothesized, there
appears to be validity to the assertion the doctor affirmation support does not moderate these

186
relationships.
In another study, whether doctors made these patient assessments was examined. Rashid and his
associates (1989) studied 250 patients attending consecutive appointments with five doctors
(general practitioners). The investigators attempted to identify deficiencies within each
appointment as rated by patients and doctors. The patients and doctors significantly disagreed
about the doctors' ability to assess and put patients at ease, to offer explanations and advice on
treatment, and to allow expression of emotional feelmgs and about the overall benefit that the
patients gained from the appointment. In all cases of disagreement, the doctor had a more negative
view of the appointment than the patient. The investigators suggested that giving structured
questionnaires to both patients and doctors during appointments could be a useful teachmg tool for
established doctors or those in training to improve the quality and sensitivity of care they provide
Pharmacist medication-specific social support
As hypothesized, consultation support from the pharmacist did seem to moderate some of the
relationship between advising and perceived medication stress. Wolf and his associates (1989)
found that pharmacist consultation activities specific to the needs of the elderly taking medications
have been found to result in better medication adherence among elderly patients For example,
weekly pharmacist counseling sessions resulted in improvements in medication adherence among
one subgroup of elderly patients. And, among elderly patients belonging to the other subgroup,
their identification as nonadherers helped pharmacists reduce medical misjudgement when making
changes to a nonadherer’s prescribed medications
Contrary to what was hypothesized, pharmacist actuation support did not seem to moderate the
relationship between reminding stressors and perceived medication stress. An examination of the
direct effects of pharmacist actuation support on perceived stress and medication adherence helps

187
to elucidate this relationship. First, pharmacist actuation support was not associated with
perceived stress. This suggests that there is no association between pharmacists’ helping subjects
get the things they need to take their medications the right way and subjects’ evaluations of their
expenences with medication-related stressors (perceived stress). Perhaps, as suggested earlier,
pharmacists will need to take more tune to help subjects establish cues in their daily activities that
will prompt them to take their medications on time. Second, pharmacist actuation support was not
associated with medication adherence. This lack of an association between the pharmacist
actuation support item and medication adherence items was surprising given the results of studies
in the literature review (e g., Wolf et al, 1989). It seems reasonable to assume that this lack of
association is due to the latter combination of reasons. As pharmacists shift from their focus on
the product to a focus on patient care, the association between pharmacist actuation support and
medication adherence will probably become a positive association. And, as the pharmacist
actuation support items are generated and refined to reflect this focus, the association between
pharmacist actuation support and medication adherence will probably become an even stronger
positive association
Contrary to what was hypothesized, pharmacist acquisition support did not seem to moderate
the relationship between obtaining stressors and perceived medication stress. An examination of
the direct effects of pharmacist acquisition support on perceived stress and medication adherence
helps to elucidate this relationship First, pharmacist acquisition support was negatively associated
with perceived stress, although the association was a weak one. Second, pharmacist acquisition
support was not associated with medication adherence. The lack of an association between
pharmacist acquisition support and medication adherence seems surprising since it is pharmacists’
job to help the subjects’ obtain their medications It seems reasonable to assume that this lack of

188
association is due to the combination of reasons. Hopefully, the pharmacist will then demonstrate
to the subjects that they not only provide the patient with a medication, but with the right
medication as they do with the doctor Perhaps, subjects presently see the role of the pharmacist as
someone who fills their medication and then places it in the mail. They see pharmacist acquisition
support as irrelevant to them, i.e., something that can be done through automation Hopefully, the
pharmacist will take more time to communicate with the subject (or caregiver) what the pharmacist
does to monitor the subject’s medication regimen for drug-drug interactions, and to help the doctor
tailor the subjects medication regimen when warranted to assure that the subject gets the right
medication When this change in subjects' perception occurs and more pharmacist acquisition
support items are generated to reflect these activities, pharmacist acquisition support and
medication adherence will become a positive association.
Contrary to what was hypothesized, pharmacist affirmation support did not seem to moderate
the relationship between isolating stressors and perceived medication stress, or the relationship
between perceived medication stress and medication adherence. An examination of the direct
effects of pharmacist affirmation support on perceived stress and medication adherence helps to
elucidate this relationship. First, pharmacist affirmation support was not associated with perceived
stress. This suggests that there is no association between pharmacists’ helping work through any
womes or concerns related to their medications and subjects’ evaluations of their experiences with
medication-related stressors (perceived stress). Perhaps, as suggested earlier, pharmacists will need
to take more time to communicate with the elderly subjects about their emotional concerns related
to medication-taking. Second, pharmacist affirmation support was not associated with medication
adherence. It seems reasonable to assume that this lack of association is due more to the first
reason. As pharmacist affirmation support items are generated and refined to reflect this time

189
spent by pharmacists to communicate with the subject about their emotional concerns, the
association between pharmacist affirmation support and medication adherence will become a
positive association.
Other person medication-specific social support
Contrary to what was hypothesized, other person consultation support did not seem to moderate
the relationship between advising stressors and perceived medication stress. An examination of
the direct effects of other person consultation support on perceived stress and medication adherence
helps to elucidate this relationship. First, other person consultation support was not associated
with perceived stress. This suggests that there is no association between other persons' helping
subjects become aware of things they didn’t know about their medications and subjects’
evaluations of their experiences with medication-related stressors (perceived stress). Perhaps,
other person consultation support items should reflect an attempt by that person to contact a health
professional on the subject’s behalf for advice about medication-related stressors. Second, most
concerned other person consultation support was also not associated with medication adherence.
It also seems reasonable to assume that this lack of association is due to the latter combination of
reasons Maybe this other person (who is not a health professional) has nothing to say that can
change how a subject will evaluate his or her experiences with medication-related stressors. It
should also be noted that the other person consultation support item differed substantially from the
item used for doctor consultation support and pharmacist consultation support. It may have been
that the other person consultation support item “helped me become aware of things I didn’t know
about my medications” did not reflect the domain it was designed to measure.
Contrary to what was hypothesized, other person actuation support did not seem to moderate
the relationship between reminding stressors and perceived medication stress. An examination of

190
the direct effects of other person actuation support on perceived stress and medication adherence
helps to elucidate this relationship. First, other person actuation support was not associated with
perceived stress. This suggests that there is no association between other persons’ attempts to
help subjects' remember to take their medications and subjects’ evaluations of their experiences
with medication-related stressors (perceived stress). Perhaps, other persons will need to take more
time to help subjects establish cues in their daily activities that will prompt them to take their
medications on time. Second, other person actuation support was not associated with medication
adherence. This lack of an association between other person actuation support and medication
adherence items was surprising given the results of studies in the literature review on spousal
support (Doherty et al., 1983), relatives or home helpers (Gilmore, Temple, and Taggart, 1989).
It seems reasonable to assume that this lack of association is due to the first reason. The other
person actuation support item “helped me remember things such as taking my medicines and
refilling my prescriptions” seems suitable. Maybe the other person (who is not a health
professional) does not know what he or she can say to change the way the subject remembers
to take his or her medications. This seems to suggest that the pharmacist or doctor should spend
time with the (most concerned) other person to carefully formulated ways to make the patient
remember to take their medication
Contrary to what was hypothesized, other person acquisition support did not seem to moderate
the relationship between obtaining stressors and perceived medication stress. An examination of
the direct effects of other person acquisition support on perceived stress and medication adherence
helps to elucidate this relationship. First, other person acquisition support was not associated with
perceived stress. This suggests that there is no association between other persons’ helping get to
the pharmacy and subjects’ evaluations of their experiences with medication-related stressors

191
(perceived stress). Perhaps, this other person acquisition support item will need to be revised
Second, other person acquisition support was not associated with medication adherence. It seems
reasonable to assume that this lack of association is due to the second reason. The other person
acquisition support item, “helps me get to the pharmacy,” might be rephrased to reflect the real
help the other person offers. For example, if the other person acquisition support item had read
“helps me get my refill requests delivered in time,” an association might have been found. Other
items might be generated to more strongly reflect what the other person acquisition support really
entails. For example, other other person acquisition support items might read “helps me find out
why my medications were not delivered to me by calling the pharmacist,” or “helps me get another
prescription from my doctor when I need one.”
Contrary to what was hypothesized, other person affirmation support did not seem to moderate
the relationship between isolating stressors and perceived medication stress, or the relationship
between perceived medication stress and medication adherence. An examination of the direct
effects of other person affirmation support on perceived stress and medication adherence helps to
elucidate this relationship First, other person affirmation support was not associated with
perceived stress This suggests that there is no association between other persons’ helping work
through any womes or concerns related to their medications and subjects’ evaluations of their
experiences with medication-related stressors (perceived stress). Perhaps, other persons will need
to take more time to communicate with the elderly subjects about their emotional concerns related
to medication-taking. Second, other person affirmation support was not associated with
medication adherence This suggests that there is no association between other persons’ helpmg
subjects work through any womes related to their medications and subjects’ medication adherence.

192
Summary
The results of this exploratory study seem to suggest that for this sample of elderly outpatients
there may be as many as four types of medication-related stressors: 1.) advising, 2.) reminding, 3 .)
obtaining, and 4 ) isolating. The results also suggest that perceived medication stress partially
mediates the relationships between type of stressors and medication adherence
The results of this study seem to be theoretically consistent with the proposed model, in that the
associations between stressors and perceived stress, and the associations between perceived stress
and adherence are fairly strong. However, the weakness of the associations between support and
perceived stress and between support and adherence seem to suggest that the model needs
refinement to reflect the impact of specific types of support by incorporating specific types of
perceived stress and adherence behaviors. It may be that advising, reminding, obtaining, and
isolating types of perceived stress, all need to be considered for inclusion into the model. And, it
may be that an adherence scale will need to be considered with different dimensions to reflect
advising adherence behavior, reminding adherence behavior, obtaining adherence behavior, and
isolating adherence behavior Finally, more specific support items will need to be formulated for
the doctor, pharmacist, and other person to reflect each “persons” specific unpact on the specific
relationships between stressors and types of perceived medication stress and between types of
perceived medication stress and types of adherence.
This has implications for practice, in that the doctor, pharmacist, and (most concerned) other
person may need to learn a new set of skills to help the patient deal not only with the specific
relationships between specific stressors and different types of perceived medication stress, but
specific relationships between different types of perceived medication stress and different types of
medication adherence behaviors.

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The next chapter will discuss the limitations on the entire study, including a discussion on the
limitations on the mediating and moderatmg effects. The discussion will continue with a discussion
on the implications for pharmacy practice and future research and end with some preliminary
conclusions.

CHAPTER 6
DISCUSSION
The chapter will deliberate on the major limitations of the study and the major implications of
the study. The major limitations of the study are addressed in three sections: 1.) limitations on the
entire study, 2.) limitations on mediating effects, and 3.) limitations on moderating effects. The
major implications of the study are addressed in two sections: 1.) implications for future research,
and 2.) implications for pharmacy practice. The chapter will end with the conclusions.
Limitations on the Entire Study
There are a number of limitations that apply to the entire study. These limitations are: 1.) the
use of a cross-sectional design, 2 .) the failure to assess test-retest reliability, 3 .) response bias, 4.)
floor and ceiling effects, 5 .) generalizability, and 6.) attentuation of associations due to measures
with low reliability. Each of these will be discussed in turn.
This study used a cross-sectional, restrospective design It would have been better to have
employed prospective analyses. For example, the correlations found in this study between support
and medication adherence are amenable to three alternative causal interpretations. These
correlations may reflect support causing changes in medication adherence, medication adherence
causing changes in support level, or a third factor (e g., age) causmg changes in both support and
medication adherence When two-wave (Tune 1 and Time 2) longitudinal data are available, the
investigator should use Time 2 medication adherence as the dependent variable with Tune 1
stressors and support as the independent vanables and Time 1 medication adherence as a control
194

195
variable. By focusing on changes in medication adherence that occur as a function of Time 1
stress and support, this methodology would help rule out the possibility that results are attributable
to prexisting medication nonadherence causing subsequent stressors and decreases in support. The
use of multiple regression analysis would also make it possible to control for third variables (e g.,
age) that may be correlated with the independent variables and medication adherence
Test-retest reliability was not assessed for stability over a predetermined period of time. To
assess this, the revised questionnaire should be administered on two occasions to the subjects. The
correlations would be tested for stability on each of the scales comprising the questionnaire.
There were concerns about response bias. Subjects may have answered many of the items in
the direction of some ideal or socially desirable response. And, if they gave a very positive global
response to all the medication-specific social support items posed, this might explain a lack of
variability in the support measures. This could be tested by asking the same set of subjects the
same items on the questionnaire at two different times with two different sets of instructions. For
example, at Time 1 the subjects could be given the basic instructions about the purpose of the
questionnaire At Time 2, more directed instructions would be given, that is, to ask the subject to
fill out the questionnaire according to what the subject considers to be good or ideal circumstances
The results would be compared by calculating the average on each scale in the questionnaire for
each of the two administrations and then looking for significant differences using a paired t test.
Despite receiving a follow-up postcard, some subjects failed to return their questionnaire. The
investigator after having contacted five nonresponders in Phase I, believed that by strengthening the
message to the veterans about having responses from all veterans (even those veterans taking few
or many medications) the response rate would increase It did increase from 44% to 69%.
However, the investigator also contacted twenty nonresponders in Phase II, and ascertained that the

nonreponders seemed to think that they had to have several “problems” to respond, or they were
disabled and unable to fill out the questionnaire without help, or they were now living in the
hospital. Despite efforts to maximize the response rate, it may not be possible to get an accurate
picture of the outpatient elderly population by the use of mailed questionnaires This problem of
nonresponse bias has been demonstrated before for a similar elderly veteran population (Graveley
and Oseasohn, 1991). The methodology used in this study may have to be augmented with the use
of telephone calls and visits to the veterans’ homes. This nonresponse bias may also have
contributed to some lack of variability in the medication-specific social support scores and the
inability to demonstrate some moderator effect, as these subjects may be among those who have
little or no forms of medication-specific social support (who belong m the “lower tail” of the social
support curve).
Because of the nonresponse bias problem, it would have been difficult to get an accurate picture
of the floor and ceiling effects of the different types of stressors. Future studies will need to
ascertain these effects, and determine how these effects might establish bounds on variance in
perceived stress and medication adherence
The subjects were veterans (mostly male) over 65 years of age. Although methods are available
for over sampling females, it wasn’t possible to attain a proper over sampling Most of the
patients were from lower incomes, living in rural areas of north-central Florida and southern
Georgia. The results of the study will therefore be limited in generalizability to other elderly
populations Furthermore, vulnerable subpopulations of elderly patients taking medications need
to be investigated that may have higher levels of stressors and perceived stress, for example, cancer
(oncology) patients.

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Finally, one of the biggest limitations to this study were the measures themselves. In addition to
needing more social support items to adequately detail the dimensions, many of the measures suffer
from low reliabilities as measured by Cronbach’s alpha. The low reliability in many of the
measures, especially medication adherence, may be responsible for an attentuation in strength of
the associations between the measures. This may be addressed in a future study with the inclusion
of carefully selected items for each measure (especially medication adherence).
Limitations on Mediating Effects
An important feature of the study was to examine the effects of different types of stressors on
medication adherence behavior as mediated by a transformation process internal to the elderly
outpatient (perceived medication stress). There are limitations on this study that have effects on
the results. These are discussed in turn.
Again, there were problems concerning the low reliability with the obtaining stressors and
medication adherence measures The improvement of the subscale for obtaining stressors can occur
with the addition of items with good item-to-total correlations and which improve the Cronbach’s
alpha. And, improvement of the medication adherence scale might occur with the use of a battery
of self-report measures. However, the improvement of the medication adherence measurement
might only occur with more expensive methods, for example, the use of electronic medication
monitors such as the Medication Event Monitoring System (Cramer et al., 1995).
Because perceived stress has been described in the literature as an internal, psychological
variable, it is likely to have been measured with error The presence of measurement error in the
mediator can produce an underestimate of the effect of the mediator and an overestimate of the
effect of the independent variable on the dependent variable (Judd and Kenny, 1981).

198
An assumption was made by the investigator that perceived stress (the mediator) is not caused
by medication nonadherence (the dependent variable). It may be that medication nonadherence
causes perceived medication stress when statistically analyzing the relationships between types of
stressors and medication adherence Feedback effects like this one need to be estimated throughout
the model using structural modeling procedures (Jóreskog and Sorbom, 1984). Perceived stress
may be an important determinant of medication adherence behavior, but other factors are also
known in the literature to be important These other intervening factors could have significantly
decreased the association between medication-related stressors and medication adherence. These
factors include the subject’s personal history, experiences with the relief of complaints or
symptoms, familial and cultural influences, and other predisposing and enabling characteristics
(Kohn and White, 1975).
Limitations on Moderating Effects
Another important feature of the study was the moderating effects of different types of support
on the two relationships: 1.) between type of stressors and perceived stress, and 2.) between
perceived stress and medication adherence. There are limitations on this study that have affected
the results These are discussed in turn.
As was discussed in the results chapter, there is a serious measurement problem involving the
medication-specific social support items adapted from the work of Caplan et al (1980). There
were too few items available for an adequate detailing of the possible dimensions (i.e., consultation,
affirmation, actuation, and acquisition). The medication-specific social support measures from this
study will have to be augmented with the use of other items which reflect the particular dimension
of support. A subtle but important methodological issue derives from a possible overlap of the

199
isolating stressors and affirmation medication-specific social support measures (Gore, 1981;
Berkman. 1982, Thotts, 1982, Cohen and Willis, 1985). Most studies of this nature have
measured stress with a checklist of negative life events These measures typically include items
about interpersonal discord (e g., marital problems) and social exits (e g., moving, divorce, family
death). By definition, such events result in at least temporary loss of support resources. Hence,
there might be some confounding of stress and support measurement because the stress and
support measures might to some extent, be measuring the same thing, namely changes in social
relationships Therefore, the process of formulating or revising isolating stressor items will require
the careful construction and testing of more affirmation medication-specific social support items,
while at the same time attempting to diminish the overlapping to avoid any potential confounding
of these variables.
i
Only a small number of interactions specified in the hypotheses were tested in this study A
multitude of other two-way and possibly three-way interactions could and should be explored. For
example, it is possible that the doctor and pharmacist consultation support items could have an
interaction with the advising stressors to improve medication adherence. It seems plausible that if
a doctor provides advice about a medication, and a pharmacist voices the same advice to the
subject, this may help the subject evaluate experiences with advising stressors as less stressful.
The study only used affirmation medication-specific social support as a moderator between
perceived medication stress and medication adherence. It may be that the other types of support
moderate this relationship. It could be that another type of support from the doctor, pharmacist,
and (most concerned) other person can have an effect on this relationship. This possibility is
discussed more under implications for future study

200
The most critical findings seem to suggest that the model needs refinement to reflect dimensions
to perceived medication stress and medication adherence. Furthermore, medication specific social
support items will need to be formulated to reflect an unpact on the specific relationships between
lands of perceived medication stress and kinds of medication adherence.
Implications for Pharmacy Practice
It has been suggested from previous studies (Coyne and Delongis, 1986) that therapeutic and
preventative efforts will be most successful when they proceed from a specific understanding of the
target population, environmental setting, target behavior, and the nature of the optimal congruence
between them. Presently, the social support literature is lunrted in what it can offer to the
pharmacist. An important property of the study was that it makes the social support literature
relevant to the practice of pharmacy.
Perceived medication stress is thought to have both a mam (direct) effect, and a mediating
(indirect) effect on the relationship between medication-related stressors and medication adherence.
When perceived medication stress exhibits a mam effect on the relationship between medication-
related stressors and medication adherence, perceived medication stress will have a detrimental
effect on medication adherence regardless of the level of medication-related stressors. When
perceived medication stress exhibits a mediating effect on the relationship between medication-
related stressors and medication adherence, a high level of expenence with a certam type of
medication-related stressor might generate evaluations of these experiences as stressful by vanous
transformations processes internal to the person and that cause him to not perform medication
adherence behaviors. This partial mediating process seems to be true for each type of medication-
related stressor.

201
Medication-specific social support acts on two other relationships. In the first relationship,
medication-specific social support acts upon the association between medication-related stressors
and perceived medication stress. In the second relationship, medication-specific social support acts
upon the association between perceived medication stress and medication adherence. Medication-
specific social support has either a mam (or direct) effect, or a moderating (or buffering) effect on
these relationships. All the (most concerned) other person types of medication-specific social
support seem to exhibit mam effects, while m contrast the consultation type of medication-specific
social support from the doctor and pharmacist exhibits a moderating effect
When medication-specific social support exhibits a mam effect on the relationship between
medication-related stressors and perceived medication stress, a threshold level of effort will have a
certain beneficial effect regardless of the patient's level of medication-related stressors. For
example, if a diabetic man knows that his (most concerned) other person will pickup his msulm
whenever he needs it, his perceived medication stress will be lower than if he didn’t have his family
to count on to do this for him
When social support exhibits a moderating effect on the relationship between medication-related
stressors and perceived medication stress, the medication-specific social support will have a
beneficial effect only if the medication-specific social support offered to the individual is high
enough to help that person deal with the medication-related stressor he or she is experiencing In
this situation, medication-specific social support will require a higher level of effort to have a
beneficial effect For example, the same man’s doctor or pharmacist may need to advise this
diabetic man on the use of a new electronic blood glucose monitor It may take more than one
counseling session with the doctor or pharmacist to bring the man’s perceived medication stress
level down. By offering more medicgOon-specific social support to the patient to help him deal

202
with this medication-related stressor he is experiencing often, the extra time spent is not only
prudent but necessary
As with most practical applications, the understandable push for implementation often predates
an adequate knowledge base. The findings of this study are only a beginning and its use in the
specification of veterans administration policy may be premature
The most critical findings seem to suggest that the model needs refinement to reflect dimensions
to perceived medication stress and medication adherence. Furthermore, medication specific social
support items will need to be formulated to reflect an impact on the specific relationships between
kinds of perceived medication stress and kinds of medication adherence. If future studies reveal
this to be true, new skills may have to be developed to help the subject deal with advising,
isolating, reminding, and obtaining kinds of perceived medication stress Hopefully, these kinds of
perceived medication stress support will have a moderating effect on the relationships between the
specific kinds of perceived medication stress and specific lands of medication adherence.
Implications for Future Research
There are a number of implications for future research suggested by this study Each of these
will be discussed in turn.
In this study, the subjects were responding to questionnaires mailed to them from the same
place they were mailed their medications. It is therefore likely, that in this sample frame, the
subjects did not perceive themselves to be getting much support from the pharmacist who mailed
them their medications Future studies should focus on elderly populations who receive more
support from their pharmacist For example, the same methodology should be applied to elderly
patients that see pharmacists on a monthly basis, for example, in an anticoagulation clinic run by a

203
pharmacist
Perceived medication stress is probably a more complex phenomenon than what has been
demonstrated here. In this study, only four items served as a measure for perceived medication
stress and were adapted from a scale developed to measure psychosocial adjustment specific to
diabetes (Polonsky et al., 1995). The “gold standard” in the field of perceived stress is the 14-item
Perceived Stress Scale developed by Cohen, Karmack, and Mermelstein (1983). In a follow up
study conducted by Hewitt et al. (1992), it was found that the Cohen, Karmack, and Mermelstein
(1983) scale had two factors reflecting the following: 1.) adaptation, and 2.) coping ability. Since
many items in the “gold standard” could be adapted for medication taking, it might also be
reasonable to suggest that a larger perceived medication stress scale (larger than the four items
used in this study) may have two or more factors.
It has also been suggested that medication adherence is multidimensional m nature (Lee et al.,
1991). Medication adherence may have intentional and nomntentional components
If stressors, perceived stress, and medication adherence are all multidimensional concepts, the
chance for the elucidation of stronger mediating and moderating effects than the ones found in this
study are possible. It could be for example, that the relationship between advising stressors and
advising medication adherence are mediated by an advising perceived medication stress. It could
also be for example, that the relationship between advising perceived medication stress and
advising medication adherence is moderated by an advising perceived medication stress type of
support
The stress-amplifying effects of medication-specific social support were not addressed in this
study Okun, Meiichar, and Hill (1980) examined whether positive and negative social ties
moderate the effects of stressors on perceived stress. In accord with the stress-buffenng

204
hypothesis, the effect of stressors on perceived stress significantly decreases as positive social ties
increased. This was a particularly solid foundation for this more specific study. However, Okun,
Melichar, and Hill (1980) also found that, contrary to the stress-amplifying hypothesis, negative
social ties did not interact with stressors to influence perceived stress, but, instead, had a
significant additive efFect on it. This latter relationship should be addressed in a future study.
The conditioning variables that were examined in this study may only represent the “the tip of
the (conditioning variable) iceberg ” For example, the Multidimensional Health Locus of Control
(Wallston, Wallston, and DeVillis, 1978) might be significant in predicting adherence when used
analytically with or without social support (Lewis, Morisky, and Flynn, 1986). Previous research
has had disappointments usmg this variable (see e g., Gravely and Oseasohn, 1991). However,
examining it in conjunction with medication-specific social support may shed “light” on why it had
no significant effect on medication adherence. Mental status may also be another important
conditioning variable (Pfeiffer, 1975), especially with elderly outpatients when examined with or
without social support
Several variables suggested by the Stress Model of Medication Adherence were not developed
in this study One of the most important variables not addressed in this study was coping which
has been suggested m previous research (Kruse and Brandenburg, 1994). Lazarus (1977) has
proposed that the particular kinds of coping behaviors which people use are determined by both the
subject’s personal characteristics and those of their social environment, and, most importantly, by
the nature of the stress with which they are contending Copmg questionnaires have already been
developed for particular illnesses (e g., for depression, Kleinke, 1988) and it would seem prudent to
develop a copmg questionnaire for elderly outpatients copmg with medication-taking. When
responses from this copmg scale are inserted into a multiple regression equation with responses

205
from the other stress model scales, the explained variance may increase.
The measurement of medication adherence would be greatly enhanced with the use of a better
(more objective) measure of adherence. The newer Medication Event Monitoring System (MEMS-
3) should be considered for a more comprehensive longitudinal study.
Enduring health outcomes are not part of this study. There are a number of stable and
responsive health-related quality of life (HRQOL) scales which could be utilized. However, this
should not mean that other even more sensitive scales might not be developed For example,
among elderly outpatients, Avlund et al (1993), have developed a new measure of instrumental
activities of daily living (IADLs) that subdivides lADLs into 'tiredness” and “speed” This new
measure, for example, may be particularly useful in assessing medication-related stressors,
medication-specific social support, perceived medication stress, and medication adherence among
more vulnerable elderly outpatients taking medications to improve their mobility.
Variables from other models of medication adherence could be brought to bear in a follow-up
study to improve the prediction of medication adherence These factors include the subject’s
personal history, experiences with the relief of complaints or symptoms, familial and cultural
influences, and other predisposing and enabling characteristics (Kohn and White, 1975).
In a future study, it may be prudent to investigate how different classes of drugs invoke
different medication-related stressors It might be the drugs with narrow therapeutic indexes, may
not only invoke more types of stressors, but stressor expenences with more intensity among the
elderly.
Finally, experimental and longitudinal features will have to be incorporated in the future studies
to fully explicate the entire theoretical stress model of medication adherence Furthermore, the
results should be analyzed using a structural equation with latent vanables approach

206
Conclusions
There is actually no literature to support the notion that medication nonadherence is greater in
the elderly, but because of the potential harmful effects of under- and over-dosing combined with
agmg, nonadherence is more hazardous for this population segment (Cargill, 1992). This was one
of the major reasons for this study
The research reported here had two aims. The first aim was to explore and describe
medication-related stressors, perceived medication stress and medication-specific social support.
The second aun was to test for mediating effects of perceived stress to test for the moderating
effects of medication-specific social support.
The results of this exploratory study seem to suggest that for this sample of elderly outpatients
there may be as many as four types of medication-related stressors: 1.) advising, 2.) reminding, 3 .)
obtaining, and 4 ) isolating The results also suggest that perceived medication stress partially
mediates the relationships between type of stressors and medication adherence.
Three types of medication-specific social support (i.e., consultation, actuation, and acquisition
support) from the doctor did seem to moderate some of the relationship between certain kinds of
medication-related stressors (i.e., advising, reminding, and obtaining stressors, respectively) and
perceived medication stress. And, as hypothesized, one type of medication-specific social support
(i.e., consultation support) from the pharmacist did seem to moderate some of the relationship
between one kind of medication-related stressors (i.e., advising stressors) and perceived medication
stress This suggests that the doctor and pharmacist seem to make an assessment of each elderly
patient’s perceived medication stress level, and then attempts to match their level of a certain kind
of support to help that elderly patient deal effectively with that certain type of stressor.

207
Some kinds of medication-specific social support offered from particular individuals did not
seem to moderate the relationship between kinds of medication-related stressors and perceived
medication stress. Affirmation support from the doctor, pharmacist, and (most concerned) other
person did not seem to moderate the relationship between isolating stressors and perceived stress.
Also, affirmation support from the doctor, pharmacist, and other person did not seem to moderate
the relationship between perceived stress and medication adherence. In addition, neither actuation
and acquisition support from either the pharmacist or other person seemed to moderate the
relationship between reminding and obtaining stressors, respectively, and perceived medication
stress Finally, consultation support from the other person did not seem to moderate the
relationship between advising stressors and perceived medication stress. This suggests that for
these situations the helping individual does not need to make an assessment of the elderly patient’s
perceived medication stress level, and then attempt to match their level of a kind of support to help
the elderly patient deal effectively with a certain type of stressor. Instead, it seems to suggest that
the helping individual need only offer a threshold level of a kind support to help the patient deal
with a type of stressor. To offer the elderly patient a higher level of a certain kind of support
would not help bring the elderly patient’s level of perceived stress down any. These unexpected
results might be explained in three ways: 1.) the helping individual's kind of support really has no
moderating effect on the hypothesized relationship for this sample, or 2.) there is a psychometric
problem with the item for the helping individual's support, or 3 .) a combination of the first two
reasons
The results of this study seem to be theoretically consistent with the proposed model, in that the
associations between stressors and perceived stress, and the associations between perceived stress
and adherence are fairly strong. However, the weakness of the associations between support and

208
perceived stress and between support and adherence seem to suggest that the model needs
refinement to reflect the unpact of specific types of support by incorporating specific types of
perceived stress and adherence behaviors. It may be that advising, reminding, obtaining, and
isolatmg perceived stress, all need to be considered for inclusion into the model. And, it may be
that an adherence scale will need to be considered with different dimensions to reflect advising
adherence behavior, reminding adherence behavior, obtaining adherence behavior, and isolating
adherence behavior. Finally, more specific support items will need to be formulated for the doctor,
pharmacist, and other person to reflect each “persons” specific impact on the specific relationships
between stressors and types of perceived medication stress and between types of perceived
medication stress and types of adherence
This has implications for practice, in that the doctor, pharmacist, and (most concerned) other
person may need to leam a new set of skills to help the patient deal not only with the specific
relationships between specific stressors and different types of perceived medication stress, but
specific relationships between different types of perceived medication stress and different types of
medication adherence behaviors.
Although the hypotheses were not all supported, this study does make two important additions
to the literature on medication adherence among elderly outpatients: 1.) the study describes the
mediating effects of perceived medication stress on the relationship between stressors and
medication adherence by specifying a causal model, 2.) the study provides an incremental
contribution to the explanation of the moderating effects of different types of support.
The development of theory depends upon the continued confirmation of hypotheses, rather than
on the results of a single study (Dubin, 1978). The large sample size in this study, allowed very
low correlations to be statistically significant. Furthermore, it has been suggested that more and

209
better medication-specific social support items will need to be generated in future studies to tease
out the clinical versus statistical significance distinction concerning medication-specific social
support from the doctor, pharmacist, and most concerned other person. Research concerning
medication adherence in the context of elderly outpatients dealing with stressors is in the initial
stage of theory development and will require further replication before implementation of any
programs.

INITIAL OUTPATIENT QUESTIONNAIRE

Veterans
Administration
Our pharmacy is working with the University of Florida to improve the
quality of health care we offer to patients who take medications. We are
asking many patients, including yourself, for help in this effort.
How can you help? You can help by participating in a survey about
dealing with medication problems. Please fill out this written questionnaire.
After you fill out the questionnaire, please return it in the preaddressed,
postage paid envelope provided.
The questionnaire should take about twenty minutes to complete. You may
have someone help you complete the questionnaire. Your responses to the
questionnaire will be kept confidential. Only the researchers at the
University of Florida will have access to them.
If you can help, you may be making a valuable contribution to the
improvement of our service to you and your fellow veterans. You have no
obligation to participate in the survey and this will not affect your medical
care from the VA.
Cordially,
Max Dame, M.S., R.Ph.
Chief, Pharmacy Service
211

212
PATIENT ID NO. ( )
PART A. Problems with your medications
Sometimes people have problems taking their medications that can range
from minor annoyances to fairly major pressures, problems, or difficulties
with the medications they take.
Listed on the following pages are a number of reasons that a person
might feel stressed because of the medications they take. Read each
statement and tell us if you have felt this way in the past year. Check the box
on the right that best describes how often you felt that way IN THE PAST
YEAR.
In the past year, how often have you:
â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡
1. Not known what to do if a side effect of your
medication occurs
2. Needed something to remind you to take your
medications on time
3. Had difficulty opening a medication container
4. Not known how to take your medication
correctly
5. Had difficulty getting transportation to a
doctor's office or pharmacy
6. Had trouble choosing the right
NONprescription medication
7. Felt that no one listened to you about your
medication problems
** 0
PHARMACY SIRVTY
P**2-

213
8. Had trouble keeping track of how well your
medication is working â–¡ â–¡ â–¡ â–¡
9. Needed an up-to-date list of your medications to
show to all your doctors â–¡ â–¡ â–¡ â–¡
10. Believed that your medication does not help
you feel better â–¡ â–¡ â–¡ â–¡
11. Not known what to do if you missed a dose of
medication â–¡ â–¡ â–¡ â–¡
12. Wondered whether a medication given to you
by one doctor should be taken with a medication
given to you by a different doctor â–¡ â–¡ â–¡ â–¡
13. Needed something to remind you to have your
medications refilled on time â–¡ â–¡ â–¡ â–¡
14. Wondered if you should drink alcohol and/or
smoke while taking your medication â–¡ â–¡ â–¡ â–¡
15. Felt you were treated differently because of the
medication you take â–¡ â–¡ â–¡ â–¡
16. Felt that no one seemed really interested in
your health â–¡ â–¡ â–¡ â–¡
17. Felt embarassed about taking your
medications â–¡ â–¡ â–¡ â–¡
18. Been afraid to ask someone to better explain
something about your medication â–¡ â–¡ Q â–¡
19. Wondered if something that happened to you
was a side effect of your medication â–¡ â–¡ â–¡ â–¡
20. Had a side effect of your medication that has
upset you â–¡ â–¡ â–¡ â–¡
fHAJLVUCY smvxY

214
21. Needed easy to read information about the
medication you take
22. Thought of changing the dose of your
medication because you did not feel right
23. Been afraid that no one could help you deal
with your health problems
24. Not felt confident about taking your
medications as directed
25. Had problems paying for your medication ..
26. Had difficulty swallowing your medication ..
27. Felt that you did not need to take your
medication
â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡
Patients have many different experiences when they visit their doctor.
Sometimes these experiences are good. Sometimes there is room for
improvement
During your most recent visit to a doctor at the clinic, how much of the
following was done by the doctor? Check one box for each item.
PHARMACY SIRVTY

215
The doctor:
1. Made me feel confident I can take my
medicines and can do what else was asked. .
2. Helped me work through any worries or
concerns related to my condition
3. Acted in a warm and friendly manner ...
4. Helped me fully understand when and
how to follow my treatment
5. Overall, how much help and real concern
about you and your health has been shown
by your physician?
i
&
â–¡ â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡ â–¡
Patients have many different experiences when they visit their
pharmacist Sometimes these experiences are good. Sometimes there is
room for improvement
During your most recent visit to a pharmacist how much of the following
was done by the pharmacist? Check one box for each item.
The pharmacist:
1. Made me feel confident I can take my
medicines and can do what else was asked. .
2. Helped me work throueh any worries or
concerns related to my condition
â–¡ â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡ â–¡
PHARMACY SI RVTY
- 5 -

216
3. Acted in a warm and friendly manner ...
4. Helped me fully understand when and
how to follow my treatment
5. Overall, how much help and real concern
about you and your health has been shown
by your pharmacist?
â–¡ â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡ â–¡
You probably have someone you consider the person most concerned
about you and your health. This person may be a spouse or a good friend.
How much of the following does this most concerned person do? Check one
box for each item.
This most concerned person:
1. Makes me feel worthwhile and good
about myself.
2. Offers help and shows real concern about
my health
Jw Helps me remember things such as taking
ay medicine, refilling prescriptions, and
keeping doctor's appointments
4. Helps me get to the doctor and the
pharmacy
5. Overall, how much help and real concern
about you and your health has been shown
by this most concerned person?
PHARMACY SURVEY
r
â–¡ â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡ â–¡
â–¡ â–¡ â–¡ â–¡ â–¡

PART C. Feelings about your health
and medications
In this section, we would like you to tell us about your feelings and
thoughts about your health and medications during the last month. For
each statement, circle a number underneath the statement that reflects how
much of a problem you have considered that feeling to have been.
1.Feeling depressed when you think about having/living with your illness
No Problem 0 1 2 3 4 5 6 Serious Problem
2. Not "accepting" your illness
No Problem 0 1 2 3 4 5 6 Serious Problem
3. Feeling unsatisfied with your physician
No Problem 0 1 2 3 4 5 6 Serious Problem
4. Worrying about the future and the possibility of serious health problems
No Problem 0 1 2 3 4 5 6 Serious Problem
5. Feeling limited in what you can do physically because of your illness
No Problem 0 1 2 3 4 5 6 Serious Problem
PHARMACY SIRVTY

218
6.Feeling guilty or anxious when you get ofT track with your medication
taking
No Problem 0 1 2 3 4 5 6 Serious Problem
7.Feeling that your illness is taking up too much mental and physical
energy
No Problem 0 1 2 3 4 5 6 Serious Problem
8.Uncomfortable interactions with family/friends because of your illness
No Problem 0 1 2 3 4 5 6 Serious Problem
9.Feeling that friends/family are not supportive of your efforts to manage
your illness
No Problem 0 1 2 3 4 5 6 Serious Problem
10.Feeling alone with your illness
No Problem 0 1 2 3 4 5 6 Serious Problem
11. Feeling frustrated about things you would like to do but can't because of
your illness
No Problem 0 1 2 3 4 5 6 Serious Problem
12. Feeling "burned out" by the constant effort to manage your illness
No Problem 0 1 2 3 4 5 6 Serious Problem
PHARMACY SURVEY
PH»*-

219
13.Not having clear and concrete goals for your health care.
No Problem 0 1 2 3 4 5 6 Serious Problem
14.Feeling scared when you think about having/living with an illness
No Problem 0 1 2 3 4 5 6 Serious Problem
15.Not knowing if the mood or feelings you are experiencing are related to
your illness
No Problem 0 1 2 3 4 5 6 Serious Problem
16.Feeling angry when you think about having/living with your illness
No Problem 0 1 2 3 4 5 6 Serious Problem
17.Coping with your illness
No Problem 0 1 2 3 4 5 6 Serious Problem
18.Worrying about reactions (between two or more medications)
No Problem 0 1 2 3 4 5 6 Serious Problem
19.Feeling discouraged with your medication regimen
No Problem 0 1 2 3 4 5 6 Serious Problem
20.Feeling overwhelmed by your medication regimen
No Problem 0 1 2 3 4 5 6 Serious Problem
m vamacy si rvty

220
Part D. Medication Taking
The questions in this next section ask you about whether you sometimes
forget to take the medication you have been prescribed by your doctor.
Please answer by checking either the YES or NO box.
1. Do you forget to take your medicine? â–¡ â–¡
2. Are you careless at times about taking your medicine? â–¡ â–¡
3. When you feel better do you sometimes stop taking your
medicine? â–¡ â–¡
4. Sometimes if you feel worse when you take the medicine, do
you stop taking it? â–¡ â–¡
5. In the last 24 hours, have you missed a dose of medication? . â–¡ â–¡
People sometimes find it hard to follow the doctor's instructions for a
variety of reasons. We are going to ask three questions about those
medications your doctor prescribed that you are supposed to take every day
or every other day. In other words, this is only about those medicines your
doctor told you to take regularly. Thinking just about the past week, about
how many times did you:
a. Foreet to take a pill?
â–¡ o â–¡ 1
b. Add an extra pill?
â–¡ 2
â–¡ 3
â–¡ 4
â–¡ 5 OR MORE
â–¡o ai â–¡2
c. Not take a pill on purpose?
â–¡ 3
â–¡ 4
â–¡ 5 OR MORE
â–¡ o â–¡ 1
â–¡ 2
â–¡ 3 Q 4
PHARMACY SLKVTY
â–¡ 5 OR MORE

221
Part D. QUESTIONS ABOUT YOU
The following questions are questions about you. Please check ONE box
underneath each item which corresponds most closely to you.
1. What is your ethnic origin?
â–¡ White (not of Hispanic origin) â–¡ Asian or Pacific Islander
â–¡ Black (not of Hispanic origin) Q American Indian or Native Alaskan
Q Hispanic
2. W hat is your approximate gross annual income in dollars?
â–¡ less than S5.000 â–¡ $15,000 to $19,000 â–¡ $30,000 to $34,000
â–¡ S5,000 to $9,000 â–¡ $20,000 to $24,000 â–¡ $35,000 to $39,000
â–¡ $10,000 to $14,000 â–¡ $25,000 to $29,000 â–¡ Greater than $40,000
3. In general, would you say your health is?
Q Excellent Q Very Good Q Good Q Fair Q Poor
THANK YOU for your time in answering these questions and
mailing this questionnaire back to us at your earliest
convenience!
rHAJLMACY SI HVTY
Pag* II -

REVISED OUTPATIENT QUESTIONNAIRE

A SURVEY OF NORTH-CENTRAL FLORIDA AND SOUTHERN
GEORGIA VETERANS ABOUT MEDICATION-RELATED PROBLEMS
Medications are the most important way of treating many of the chronic medical
conditions that veterans have. Some veterans find it easy to take their medications.
Some veterans do not find it so easy for many reasons. The University of Florida
College of Pharmacy and the Gainesville VAMC are working together so that we can
find out those reasons. This will help us to better meet the needs of North-Central
Florida and Southern Georgia veterans who need to take medications for their
health. You have been selected to represent the veterans at the Gainesville VAMC
who take medications. Since you take medications from the Gainesville VAMC, your
opinions are very important to us because they represent the opinions of all the other
veterans who are not contacted or are not able to answer our questions. We would
like to know the needs of our veterans so that we can improve our services for you.
To meet those needs, we need to ask you some questions. We need to know how
many veterans have problems with taking their medications and what kinds of
problems that they have. Even if you are taking very few medications and do not
have any problems with them right now, your answers to these questions are still
very important to us. If you are taking many medications for your health, your
answers are very important to us too.
Please answer all the questions as best as you can. It should take you about 20
minutes to answer all of the questions. If you wish to comment on any question or
qualify your answers, please feel free to use the space in the margins. Your
comments will be read and taken into account. If you are unable to answer the
questionnaire because you cannot read it because the print is too small or for some
other reason, please contact David Gettman at (352) 392-9035. If he is not there at
that moment, he will call you back and ask you the questions over the telephone.
While your answers are very important to us, if you choose not to answer the
questions, it will not affect your health benefits.
Return this questionnaire to:
College of Pharmacy
Pharmacy Health Care
PO Box Í00496
Gainesville FL 32610-0496
FLORIDA
UNIVERSITY OF
223

224
1
PATIENT ID NO. ( )
Policymakers concerned with designing programs need to better understand how
people help our veterans to take their medications. Therefore, we would like to ask
you to describe your feelings about the medications that you take and the help that
you get from others in taking them. In this first section, we will first ask you about
your doctor. Next, we will ask you similar questions about your pharmacist. Finally,
we will ask you some questions about someone else in your life who helps you take
your medications.
Veterans have different experiences when they visit their doctor. Sometimes thes<
experiences are good. Sometimes there is room for improvement. We would like to
ask you about the last time you visited the doctor. FOR EXAMPLE: If during your
last visit your doctor acted in a warm and friendly manner “some” during your last
visit, you might circle “3.”
1 NONE OR VERY LITTLE
2 A LITTLE
(T) SOME
4 A LOT
5 A GREAT DEAL
During your most recent visit to a doctor at the clinic, how much of the time did
the doctor do the following things for you? Circle one number for each question.
Q-l The doctor helped me fully understand when and how to follow my medication
treatment. (Circle number)
1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL
Q-2
The doctor helped me become
medications. (Circle number)
aware of things I didn't know about my
1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL

225
2
Q-3 The doctor helped me feel confident I can take my medicines. (Circle number)
1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL
Q-4 The doctor helped me work through any worries or concerns related to my
medications. (Circle number) 1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL
Q-5 The doctor helped me get the right medications. (Circle number)
1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL
Q-6 The doctor helped me get the
way. (Circle number)
things I needed to take my medications the right
1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL
Next, we would like to find out about your experiences with the pharmacist.
Veterans also have different experiences when they visit their pharmacist.
Sometimes these experiences are good, but there may sometimes be room for
improvement.
During your most recent visit to a pharmacy, how much of the time did the
pharmacist do the following things for you? Circle one number for each question.
TURN TO PAGE 3

226
3
Q-7 The pharmacist helped me fully understand when and how to follow my
medication treatment. (Circle number)
1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL
Q-8 The pharmacist helped me become aware of things I didn't know about my
medications. (Circle number)
1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL
Q-9 The pharmacist helped me feel
number)
confident I can take my medicines. (Circle
1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL
Q-10 The pharmacist helped me work
medications. (Circle number) 1
2
3
4
5
through any worries or concerns related to my
NONE OR VERY LITTLE
A LITTLE
SOME
A LOT
A GREAT DEAL
Q-ll The pharmacist helped me get the right medications. (Circle number)
" 1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL

227
4
Q-12 The pharmacist helped me get
right way. (Circle number)
the things needed to take my medications the
1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL
Veterans have very different experiences with people other than health
professionals regarding their medication taking. You may know someone that shows
concern about you and your medication taking. This person may be a spouse, a good
friend or another person. How much of the time has this “most concerned other
person” done the following for you in the last month? Circle one number for each
question.
Q-13 This person helped me fully understand when and how to follow my medication
treatment. (Circle number)
1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL
Q-14 This person helped me become aware of things I didn’t know about my
medications. (Circle number) 1
2
3
4
5
NONE OR VERY LITTLE
A LITTLE
SOME
A LOT
A GREAT DEAL
Q-15 This person helped me feel confident I can take my medications. (Circle
number)
1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL
TURN TO PAGE 5

228
5
Q-16 This person helped me work through any worries or concerns related to my
medications. (Circle number) 1
2
3
4
5
NONE OR VERY LITTLE
A LITTLE
SOME
A LOT
A GREAT DEAL
Q-17 This person helped me remember things such as taking my medicine and
refilling my prescriptions. (Circle number)
1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL
Q-18 This person helped me get to the pharmacy. (Circle number)
1 NONE OR VERY LITTLE
2 A LITTLE
3 SOME
4 A LOT
5 A GREAT DEAL
An important part of understanding the problems veterans face taking their
medications has to do with understanding the feelings they have if they have
experienced one or more problems with their medications. Some veterans might say
that these feelings are no problem for them. Other veterans may say that these
feelings are a serious problem. Still others might say there is a problem, but the
seriousness of the problem lies somewhere “in-between” no problem and a serious
problem. FOR EXAMPLE: If this past year the feeling of being “burned out” by
the constant effort to manage your medications has been a pretty serious problem,
you might circle “4.”
NO PROBLEM 0 1 2 3 (4^ 5 6
If it is only a little problem, you might circle “2.”
NO PROBLEM 0
4 5 6
SERIOUS PROBLEM
SERIOUS PROBLEM

229
6
For each statement in this section, circle a number underneath the statement that
reflects how serious a problem you have considered that feeling or thought to have
been this past year on a scale of increasing seriousness from 0 (no problem) to 6
(serious problem).
Q-19 This past year how serious have been your feelings of guilt or anxiety when you
got off track with your medication taking. (Circle number)
NO PROBLEM 0 1 2 3 4 5 6 SERIOUS PROBLEM
Q-20 This past year how serious have been your worries or concerns about reactions
between two or more medications. (Circle number)
NO PROBLEM 0 1 2 3 4 5 6 SERIOUS PROBLEM
Q-21 This past year how serious have been your feelings of discouragement with
your medication regimen. (Circle number)
NO PROBLEM 0 1 2 3 4 5 6 SERIOUS PROBLEM
Q-22 This past year how serious have been your feelings of being overwhelmed by
your medication regimen. (Circle number)
NO PROBLEM 0 1 2 3 4 5 6 SERIOUS PROBLEM
Medications are the most important way of treating many diseases or medical
conditions. Some veterans find it very easy to take their medications as directed. It
is more difficult for other veterans. We would like to ask you about your experiences
with taking medications that have been prescribed to you by your doctor. Circle one
number for each question.
TURN TO PAGE 7

230
7
Q-23 Do you forget to take your medicine? (Circle number)
1 NO
2 YES
Q-24 Are you careless at times about taking your medicine? (Circle number)
1 NO
2 YES
Q-25 When you feel better do you sometimes stop taking your medicine? (Circle
number) 1 NO
2 YES
Q-26 In the last 24 hours, have you missed a dose of medication? (Circle number)
1 NO
2 YES
Another important way that can help us to help you better is to learn more about
the problems veterans face when taking their medications. Sometimes veterans
never experience any of these problems. And, sometimes veterans may experience a
problem very often. Listed on the next few pages are problems a veteran might hav
felt in the last year. Read each statement and tell us how often you may have
experienced this problem in the past year.
FOR EXAMPLE: If you think that in the past year, you have never had a problem
paying for your medications, you might circle “1.”
(/JNEVER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN
Circle the number that best describes how often you experienced each problem in
the past year.
Q-27 In the past year, how often have you felt that you were taking too many
medications? (Circle number) l NEVER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN

231
8
Q-28 In the past year, how often have you needed something to remind you to take
your medications on time? (Circle number)
1 NEVER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN
Q-29 In the past year, how often have you had difficulty opening a medication
container? (Circle number) l NEV ER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN
Q-30 In the past year, how often have you felt that taking your medications as
directed by your doctor was easy? (Circle number)
1 NEVER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN
Q-31 In the past year, how often have you had difficulty getting transportation to a
pharmacy? (Circle number) 1 NEV ER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN
Q-32 In the past year, how often have you felt uncomfortable interrupting the
pharmacist to ask a question? (Circle number)
1 NEVER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN
TI RN TO PAGE 9

232
Q-33 In the past year, how often have you felt that if you had a medication problem
you could deal with it the right way? (Circle number)
1 NEVER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN
Q-34 In the past year, how often have you had trouble keeping track of how well
your medication is working? (Circle number)
1 NEVER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN
Q-35 In the past year, how often have you needed an up-to-date list of your
medications? (Circle number) 1 NEVER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN
Q-36 In the past year, how often have you believed that your medication does not
help you feel better? (Circle number)
1 NEVER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN
Q-37 In the past year, how often have you not known what to do if you missed a dose
of medication? (Circle number)l NEVER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN
Q-38 In the past year, how often have you wondered whether a medication given to
you by one doctor should be taken with a medication given to you by a different
doctor? (Circle number) 1 NEVER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN

233
10
Q-39 In the past year, how often have you needed something to remind you to have
your medications refilled on time? (Circle number)
1 NEVER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN
Q-40 In the past year, how often have you wondered if you should drink alcohol
and/or smoke while taking your medication? (Circle number)
1 NEVER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN
Q-41 In the past year, how often have you felt like you had a problem with your
medication? (Circle number) 1 NEVER
2 ONCE IN A W HILE
3 FAIRLY OFTEN
4 VERY OFTEN
Q-42 In the past year, how often have you felt that no one listened to you about your
medication problems? (Circle number)
1 NEVER
2 ONCE IN A WHILE
3 FAIRLY OFTEN
4 VERY OFTEN
Q-43 In the past year, how often have you felt that no one seemed really interested in
your medication