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Measuring patient acceptance of biomedical devices in cardiac patients

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Measuring patient acceptance of biomedical devices in cardiac patients
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Burns, Jason L
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viii, 89 leaves : ill. ; 29 cm.

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Defibrillators ( jstor )
Demography ( jstor )
Diseases ( jstor )
Dysrhythmias ( jstor )
Heart ( jstor )
International Statistical Classification of Diseases ( jstor )
Psychometrics ( jstor )
Psychosociology ( jstor )
Quality of life ( jstor )
Statistical discrepancies ( jstor )
Arrhythmia -- therapy ( mesh )
Defibrillators, Implantable ( mesh )
Pacemaker, Artificial ( mesh )
Patient Satisfaction ( mesh )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

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Thesis:
Thesis (Ph. D.)--University of Florida, 2003.
Bibliography:
Includes bibliographical references (leaves 81-88).
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by Jason L. Burns.

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MEASURING PATIENT ACCEPTANCE OF BIOMEDICAL DEVICES IN CARDIAC PATIENTS
















By

JASON L. BURNS














A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL AT THE
UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2003













ACKNOWLEDGMENTS

There are so many people to thank for helping me. First, and foremost, I would like to thank my dissertation supervisory chair, Dr. Samuel Sears, without whose guidance in the educational, professional, and oftentimes, personal arenas, this dissertation would never have happened. I would also like to thank the members of my dissertation committee (Dr. Garret Evans, Dr. Robert Glueckauf, and Dr. James Jessup) for their input, patience, and constructive critiques. Special thanks are also in order for Dr. Mike Ujhelyi and Medtronic, Inc. for their contributions, as well as Dr. Stephen Keim and his nursing staff at the Watson Clinic Electrophysiology Clinic in Lakeland, Florida. My gratitude goes out to Robyn Wallace, Amy Baughcum, Rebecca Sotile, and Karen Campbell for their contributions to this document and their contributions to my sanity during this process. I also want to thank Dr. Elise Labb6 for telling me I could do it if I wanted to bad enough; and Bryan Sullivan for changing the trajectory of my life by just listening. I thank Christa Burns Pazzaglia for comforting me with unconditional acceptance. I thank Ken Beatty for reality checks, especially those that came right after I talked to Christa. I thank my parents for always believing in me and supporting me, even when "tough love" was the trend. Finally, and undeniably most importantly, I thank Scott McLavy, my life-partner, for doing what all those mentioned above did and so much more. Words fall far short of conveying the ways he has been there for me.






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TABLE OF CONTENTS




ACKNOWLEDGMENTS ........................................ ii

LIST OF TABLES ...................................................... v

ABSTRACT .......................................................... vii

CHAPTER

1 INTRODUCTION ................................................... 1

2 LITERATURE REVIEW .............................................. 4

Cardiac Arrhythmias ................................................. 4
Treatments ..........................................................5
Implantable Cardiac Biomedical Devices ................ ............... 6
Quality of Life ..................................................... 14
The Construct of Patient Acceptance .................................... 23
Study Purposes and Hypotheses ..................................... ...24

3 M ETHOD ........................................................ 28

Participants ........................................................ 28
Procedure .......................................................... 30
Instrum ents ................. ...................................... 30

4 RESULTS ........................................................ 36

Assessment M easures ............................................... 36
Health-Related Quality of Life ......................................... 36
Biopsychosocial M odel ......................................... .....39
Development of the F-PAS ........................................... 40
Hypothesis 1: Factor Analysis ........................................ 41
Hypothesis 2: Convergent and Discriminant Validity of the F-PAS ............ 44
Device Group Comparisons ........................................... 58
Hypothesis 3: Group Differences on SF-36 Scale Scores ................. . 58
Hypotheses 4 and 5: Differences in Patient Acceptance ...................... 65


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5 DISCUSSION .................................................... 67

Hypotheses ........................................................ 69
Strengths and Limitations ............................................ 74
Implications and Conclusions ......................................... 75

APPENDIX GENERAL INFORMATION SHEET ..........................79

REFEREN CES ........................................................ 81

BIOGRAPHICAL SKETCH ............................................. 89











































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LIST OF TABLES

Table page

2-1. Initial items and hypothesized factors of the patient acceptance measure ...... 26 3-1. General characteristics of study participants ............................ 29

4-1. Obtained scores for total sample .................................... 37

4-2. Obtained scores on the SF-36 and statistics for comparison between
normative samples and current sample ............................... 38

4-3. Obtained scores on the biopsychosocial model of illness component
measures for normative samples and comparison statistics for current
sample ........................................................ 40

4-4. Initially retained items, factor loadings, and reliability coefficients .......... 43

4-5. Final factors and reliability coefficients ................................ 46

4-6. Pearson product moment correlation coefficients for F-PAS and SF-36 ....... 46

4-7. Pearson product moment correlation coefficients for F-PAS and biological
component measure ................................... ... ........ 47

4-8. Pearson product moment correlation coefficients for F-PAS and
psychological component measures ...................................49

4-9. Pearson product moment correlation coefficients for F-PAS and social
component measures ............................................. 49

4-10. Summary of hierarchical regression model for variables predicting SF-36
physical functioning scale scores using F-PAS subscale scores as a
measure of patient acceptance ..................................... . 52

4-11. Summary of hierarchical regression model for variables predicting SF-36
general health scale scores using F-PAS subscale scores as a measure of
patient acceptance ............................................... 53




V









4-12. Summary of hierarchical regression models for variables predicting SF-36
general health scale scores using the F-PAS total score as a measure of
patient acceptance ............................................... 54

4-13. Summary of hierarchical regression model for variables predicting SF-36
vitality scale scores using F-PAS subscale scores as a measure of patient
acceptance .................................... .................56

4-14. Summary of hierarchical regression model for variables predicting SF-36
social scale scores using F-PAS subscale scores as a measure of patient
acceptance .........................................57

4-15. Device group frequencies and means on demographic variables ............ 59

4-16. Analysis of variance between groups on demographics ................... 59

4-17. Obtained scores for pacemaker group ................................ 60

4-18. Obtained scores for ICD group ...................................... 61

4-19. Obtained scores for lAD group ...................................... 62

4-20. Analysis of variance between groups on SF-36 scales .................... 64

4-21. Tukey HSD homogenous subsets of device groups on scales of the SF-36 .... 64 4-22. Analysis of variance between groups on FPAS ......................... 66

4-23. Homogenous subsets of device groups on the F-PAS ............... . . 66






















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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

MEASURING PATIENT ACCEPTANCE OF BIOMEDICAL DEVICES IN CARDIAC PATIENTS

By

Jason L. Burns

August 2003

Chair: Samuel F. Sears
Major Department: Clinical and Health Psychology

Medicine has succeeded in the research and development of extraordinary implantable cardiac biomedical devices that provide targeted therapies for cardiac arrhythmias. However, a theoretical framework and common metric for studying the quality of life and psychosocial adjustment of these patients has not been empirically investigated. The purposes of our study were (a) to develop an instrument to measure patient acceptance of implantable devices; (b) to begin validation of the instrument; and

(c) to compare, using the developed and validated measure, acceptance and quality of life in patients with pacemakers, implantable cardioverter defibrillators (ICD), or implantable atrioverter defibrillators (IAD).

We constructed 47 items of patient acceptance. Through statistical analyses we reduced these to 15 essential items. By using exploratory factor analysis, we extracted four factors: Return to Life (RTL), Device-Related Distress (DRD), Positive Appraisal vii








(PA), and Body Image Concerns (BIC). The four factors were internally consistent, and a reliable higher-order factor also was found. The validity of the measure was addressed using a battery of instruments that assessed each component of the biopsychosocial model of illness. In between-group comparisons, the patient acceptance measure revealed that, overall, device acceptance was significantly greater for pacemaker patients then for ICD patients. Device-related distress was lower for pacemaker patients than for either ICD or IAD patients. Concerns about returning to their former lives are fewer for pacemaker and IAD patients than for ICD patients. The patient acceptance measure is the first empirically investigated device-specific instrument to provide a common metric used to study this unique group of patients. The information obtained has important implications for the development and implementation of psychosocial interventions that address the distress associated with the implantation of a cardiac device.


























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CHAPTER 1
INTRODUCTION

Cardiac arrhythmias play a role in over 500,000 deaths each year in the United States and are responsible for approximately 724,000 hospitalizations. The estimated prevalence of cardiac arrhythmias is 3,900,000; roughly 11.9 billion dollars is spent on treating arrhythmias annually (Smith et al., 2001). In an effort to find effective and costefficient methods for treating these patients, medicine has succeeded in the research and development of extraordinary implantable cardiac biomedical devices that provide targeted therapies and efficiency. While the biological benefits of these devices are welldocumented (Antiarrhythmics Versus Implantable Defibrillators [AVID] Investigators, 1997; Gold, Sulke, Schwartzman, Mehra, & Euler, 2001; Stofineel, Post, Kelder, Grobbee, & van Hemel, 2000), their psychosocial impact and effect on medical outcomes is less understood and warrants more concentrated effort by health psychology researchers (Aydemir et al., 1997; Sears & Conti, 2002).

The effect of psychosocial variables on medical end points was first addressed by Engel (1977) in his biopsychosocial model of illness. He stated that a "medical model must take into account the patient, the social context in which he lives and the complementary system devised by society to deal with the disruptive effects of illness" (p. 132). Engel's model is supported by recent research on the role of psychosocial distress in the prognosis of individuals with cardiovascular diseases. Several studies show that psychosocial variables have a significant impact on mortality and morbidity of


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cardiac patients (Carney et al., 1995; Frasure-Smith, Lesperance, Talajic, 1993, 1995; Ornish et al., 1990).

In the past decade, the goal of medical care providers has moved from merely

decreasing morbidity and mortality to the preservation of functioning and better quality of life than was experienced before medical treatment (American College of Physicians [ACP], 1988). Levine (1987) asserted that quality of life should become a primary criterion for evaluating the efficacy of medical treatments because psychosocial variables shape healthcare outcomes. They argued that quality of life must be evaluated because chronic medical conditions are now being managed but not cured and medical technologies that prolong life result in ethical dilemmas regarding end-of-life care. These ethical questions resulted in the conceptualization of health-related quality of life. Health-related quality of life has been defined as how health conditions, treatment modalities, and prognoses impact patients' preferences for treatments and their desire to continue treatment. Researchers in this area have consistently reported that individuals would trade more years of life for fewer years of higher quality life (Lawton, Moss, Hoffman et al., 1999), thus making quality-of-life outcomes even more important than prolonging life. A major concern for recipients of implantable cardiac biomedical devices is that the life-saving benefits may be diminished because of reduced quality of life (Sears, Todaro, Saia, Sotile, & Conti, 1999).

Formidable evidence increasingly shows that psychological distress undermines

health outcomes and quality of life across cardiac populations. Therefore, the importance of further scientific inquiry into the psychosocial factors affecting recipients of implantable cardiac biomedical devices should not be underestimated. A theoretical framework for explaining the unique psychosocial issues experienced by people with






3

these devices has not been empirically studied. Such a framework may allow healthcare providers to understand, measure, and develop interventions for treating psychosocial distress in device recipients, thereby improving medical outcomes and increasing recipients' quality of life. Our study provides a theoretical construct of patient acceptance by developing (and preliminary psychometric validation of) the Florida Patient Acceptance Survey (F-PAS). Our study also compared psychosocial adjustment for recipients of different implantable cardiac biomedical devices.













CHAPTER 2
LITERATURE REVIEW

This chapter provides a brief review of the cardiac arrhythmia nomenclature and a description of three implantable biomedical devices: the cardiac pacemaker, the implantable cardioverter defibrillator (ICD), and the atrioverter defibrillator (IAD). It also reviews the literature regarding the quality of life of device recipients and provides a rationale for the construct of patient acceptance.

Cardiac Arrhythmias

A cardiac arrhythmia is an abnormality of electrical impulse conduction within the heart. For a clear picture of conduction abnormalities seen in the heart, one needs a rudimentary understanding of the terms used to describe cardiac rhythms. The simple formula has three parts: (a) the location of the heart's pacemaker, (b) a description of any abnormalities, and (c) a classification of the rate or rhythm. For example, a rate of 60 to 100 beats per minute (bpm) with the sinoatrial node serving as the pacemaker and without any conduction abnormalities is called a normal sinus rhythm. A tachycardia is a fast heart rate of more than 100 bpm, and a bradycardia is a heart rate of less than 60 bpm. An example of an arrhythmia would be a supraventricular tachycardia, where the location of the heart's pacemaker would be supra (above) ventricular (the ventricles); and the rate of the heart would be tachycardic or too fast. In other words, a supraventricular tachycardia is an abnormally fast heart rate that is caused by pacemaker cells located above the ventricles. Some other important terms in the arrhythmia



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nomenclature are paroxysmal, starting and ending abruptly; sustained, without ending; and fibrillation, a fast twitching motion (Phibbs, 1996).

It is also important to know that pacemaker foci (concentrations of pacemaker cells) are scattered throughout the heart as the heart's own back-up system in case the normal electrical pathway becomes compromised. The compromising of the electrical pathway may be the result of atrioventricular block (AV block), the result of an aberrant conduction of electricity through the AV node; or bundle-branch block, an interruption of the conduction of electricity through the ventricles. Compromises to electrical conduction also may be caused by disturbances of serum electrolytes that regulate the ability of cell membranes to conduct electricity, or may be caused by disturbances of myocardial blood flow, which is the flow of oxygenated blood to the heart muscle itself. Arrhythmias are most often caused by the ectopic (outside the normal site) firings of back-up pacemaker foci (Phibbs, 1996).

Treatments

The treatment of cardiac arrhythmias varies based on the rhythm's characteristics and etiology and on the patient's symptomotology. The most efficient way to describe treatment options is to consider them on a scale of severity. The least severe (a non-lifethreatening and asymptomatic paroxysmal arrhythmia) may be treated with simple behavioral interventions such as dietary modifications and smoking cessation. A more severe symptomatic paroxysmal arrhythmia that results from a manageable disease process may be treated with anti-arrhythmic medications. The most severe arrhythmias are sustained, symptomatic, not drug-responsive, and/or life-threatening. These may call for more invasive interventions such as catheter ablation of conduction pathways, surgical ablation or resection of conduction pathways, or the implantation of a






6

biomedical device that controls the conduction of electricity within the heart or provides electrical countershock (Miller, Altemos, & Jay, 2001). As mentioned, the focus of our study was patient acceptance of treating severe arrhythmias with implantable cardiac biomedical devices.

Implantable Cardiac Biomedical Devices

Permanent Implantable Cardiac Pacemakers

Incidence. The first pacemaker was implanted in 1958 in a patient with viral myocarditis that resulted in AV block. The pacemaker lasted for only 8 hours. Improvements in battery storage of electricity, electrode lead placement, and computer technology have resulted in an implantable cardiac pacemaker that can last for 5 to 15 years; can provide electrical stimulation in both the atrium and the ventricle; and can sense and evaluate the appropriateness of cardiac rhythms (Luderitz, 2002). Today, permanent cardiac pacemaker implantation is the most common surgical procedure performed involving the heart. Each year 115,000 to 175,000 pacemakers are implanted in the United States (Xie, Thakur, Shah, & Hoon, 1998), and more than 400,000 pacemakers are implanted worldwide (Gregoratos, 1999).

Indications. The exponential increase in pacemaker implantations is a result of technological developments, the low rate of complications from the implant procedure, acceptance of the device by physicians and the lay public, and the growing number of indications for treatment with the device (Xie et al., 1999). Current recommendations for implantation of a cardiac pacemaker are categorized into six areas by the American College of Cardiology (ACC) and the American Heart Association (AHA; Dreifus et al., 1991):

* Acquired AV block (not associated with myocardial infarction)
* AV block associated with acute myocardial infarction






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* Chronic bifascicular and trifascicular block (block of specific pacemaker cell foci)
* Sinus node dysfunction (tachycardia-bradycardia syndrome)Hypersensitive carotid
sinus syndrome
* Pacing in children

Detailed descriptions of each of these implications may be found elsewhere

(Dreifus et al., 1991). However, an important common characterisitic of each of these implications is the experience of symptoms such as dizziness, shortness of breath, and loss of consciousness most often associated with bradycardia (abnormally slow heart rate).

Description. Pacemaker systems consist of two basic parts: the pulse generator and leads. The pulse generator is located in a pacemaker can that is made of titanium. The can is placed in a pectoral pocket on the nondominant side of the patient's chest. The pulse generator contains lithium batteries that are light and that have very gradual voltage decay. The pacemaker's electronic circuitry, responsible for sensing spontaneous intracardiac electrical impulses and providing discharges of electricity to the heart, are also a part of the pulse generator. To test the functioning of the pacemaker, a reed switch is also contained within the pulse generator. This switch allows the pacemaker to be disabled or reprogrammed using a magnet. The pacemaker's leads sense intracardiac signals and deliver the pulse generator charge of electricity to the walls of the heart. The leads of the pacemaker are metal conductors that are covered with polyurethane or silicone to insulate them. Leads can be epicardial (attached to the outside of the heart) or endocardial (attached to the inside of the heart), most contemporary pacemaker leads are endocardial for ease implantation (does not require a thoracotomy or open-heart surgery). An endocardial lead can be placed transvenously (threaded through the subclavian; cephalic; or internal or external jugular veins) under local anesthesia. Placement of an






8

epicardial lead involves a thoracotomy (opening the chest cavity) conducted under general anesthesia; it is more risky for the patient (Xie et al., 1998).

Function. There are four common pacemaker programs or modes: VVI, VVIR, DDD, and DDDR. The first letter of the mode signifies the chamber that is paced (Ventrical vs. Dual). The second letter is the chamber sensed by the pacemaker (Ventrical vs. Dual). The third letter is the response provided by the pacemaker (Inhibits vs. Dual). The pacemaker will either inhibit or trigger the electrical system of the heart when the heart falls below a normal rhythm. The existence of a fourth letter, always R, indicates if the pacemaker is rate-responsive. All four modes sense the electrical activity of the heart and provide electrical stimulation to the muscle fibers of the heart when the patient's heart rate drops below a preprogrammed rate. The electrical stimulation is rarely detected by the patient. Rate-responsive pacemakers provide discharges at appropriate cardiac output levels. Rate-responsive models are an improvement over nonrate-responsive models that pace the heart at a fixed rate without regard to cardiac-output needs (Xie et al., 1998). In other words, VVI and DDD modes cause the heart to beat at a fixed rate whether the patient is sleeping or walking quickly up a flight of stairs. Rate-responsive models react to cues of exertion and increase one's heart rate accordingly. Advances in cardiac pacemaker functioning (such as rate responsiveness) are continuing at a rapid rate. Newer programming features include advanced diagnostic capabilities, mode switching, and patient alerts to possible malfunctions (Wood, 2000). Implantable Cardioverter Defibrillators

Incidence. The first ICD was implanted by Mirowski in 1980 at Johns Hopkins Hospital as a last-resort effort to treat a patient with life-threatening ventricular fibrillation. A thoracotomy was needed to implant the bulky device; and FDA approval






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for its use was not obtained until 1985. As with the pacemaker, technological and surgical advances led to a smaller, more efficient, and more easily implanted device (Shah, Thakur, Xie, & Hoon, 1998). These advances led to its widespread use as a treatment option, as indicated by the 227% increase in implantations between 1993 when 15,000 ICDs were implanted and 1999 when over 50,000 were implanted (Cowen, 1999). Although ventricular arrhythmias are not listed as the cause of death from sudden cardiac arrest, most of the 220,000 people who die each year of cardiac arrest die as a result of ventricular fibrillation (Smith et al., 2001). This statistic is particularly significant when survival for ICD recipients is reported to be as high as 98% at 1-year follow-up, whereas survival for untreated individuals experiencing sudden cardiac arrest outside of the hospital is less than 10% (Smith et al., 2001). Although the ICD was originally designed as a last-resort treatment option for patients with non-drug-refractory ventricular arrhythmias, its efficacy and technological advances have lead to the ICD being considered the treatment of choice for potentially life-threatening arrhythmias. Two clinical trials, one reported in 1996 (MADIT; Multicenter Automatic Defibrillator Implantation Trial) and the other in 1997 (AVID; Antiarrhythmics Versus Implantable Defibrillators) compared treatment with an ICD to treatment with antiarrhythmic drugs (Buxton, 1996). The MADIT investigators terminated the 5-year long investigation in 1996 because of a 54% decrease in all mortality in patients treated with an ICD as compared to those treated with traditional pharmacological therapy (Moss et al., 1996). The AVID investigation also ended early after investigators observed a 39% reduction in mortality at 1-year follow-up for patients implanted with an ICD (AVID Investigators, 1997). These results gave antiarrhythmic medications a backseat to ICD in the treatment of patients diagnosed with life-threatening ventricular arrhythmias.






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Indications. The proper selection of patients is paramount to the success of ICD therapy. Selecting patients involves exploring the hemodynamic stability of the arrhythmia, underlying heart disease, left ventricular function, and the results of various electrophysiological studies including the inducibility of sustained ventricular tachycardia (Shah et al., 1998). The AHA, ACC, and the North American Society of Pacing and Electrophysiology (NASPE) have each made recommendations for ICD implantation. Their recommendations are as follows:

* One or more episodes of spontaneous ventricular tachycardia or ventricular
fibrillation in a patient for whom electrophysiologic (EP) testing and/or
spontaneous ventricular arrhythmias cannot be used accurately to predict the
efficacy of other therapies

* Ventricular fibrillation or hypotensive ventricular tachycardia with
contraindications to drug or surgical therapy

* Persistently inducible clinically relevant ventricular tachychardia or ventricular
fibrillation during EP testing despite drug therapy, corrective surgery, or catheter
ablation

* Recurrent episodes of spontaneous ventricular tachycardia or ventricular fibrillation
despite EP testing-guided or Holter-guided antiarrhythmic drug therapy

* Unexplained suncope in a patient with hypotensive ventricular tachycardia
inducible during EP testing with characteristics 1, 2, or 3 above

* Highly symptomatic long Q-T interval syndromes despite medical therapy (with or
without permanent pacemaker implantation)

Dreifus et al. (1991) give a complete explanation for each of the recommendations. More recent studies have attempted to assess the prophylactic use of the ICD with specific cardiac populations (heart failure patients, patients with nonischemic cardiomyopathy, patients who are at risk for myocardial infarction, those who experience unsustained tachycardia, and patients who have undergone coronary artery bypass grafting) (Prytowsky & Nisam, 2000). The ICD reduced mortality by more than 70% for






11

patients with unsustained tachycardia (Buxton et al., 1999). However, of patients who have undergone coronary artery bypass grafting, patients with an ICD have no significant survival advantage over those with no ICD. The influence of revascularization on ischemia and left ventricular functioning were cited as possible reasons for the lack of ICD benefit (Block & Breithardt, 1999). Perhaps as the results of these studies are reported, indications for the use of ICD will expand much as indications for the cardiac pacemaker did after its efficacy in atrioventricular block was established.

Description. Like the implantable cardiac pacemaker, the ICD consists of leads and a defibrillator can. The can of the defibrillator contains the circuitry of the defibrillator, batteries, and a capacitor that can store and provide high-voltage shocks when necessary. The ICD is also placed in a pectoral pocket, and the leads can be implanted endocardially or epicardially with the same limits and advantages as seen for pacemaker implantation. There are several configurations for lead placement. Coiled electrodes attached to the leads are placed in the right ventricle and the superior vena cava. These electrodes deliver defibrillation discharges. Sensor-pacer leads sense ventricular fibrillation and provide pacing when defibrillation is unnecessary (Xie et al., 1998).

Function. A major difference between cardiac pacemakers and ICDs is the modes or discharges of electrical stimulation that the ICD is capable of delivering to return a patient's heart to sinus rhythm. Four main types of ICD discharges are provided based on the severity of the arrhythmia; this four-discharge system is referred to as tiered therapy.

* Bradycardia pacing. This first type of ICD discharge, very low-energy pulses, is
delivered to the heart when the heartbeat slows down too much. This is the type of
intervention provided by a cardiac pacemaker. It is usually not felt by the patient.






12

* Antitachycardia pacing (ATP). This is the second, and most common, type of
pacing. Many patients also do not feel ATP when it is delivered. Patients who do
feel ATP describe it as a fluttering in the chest.

* Cardioversion. This is the third type of discharge delivered by the ICD. It consists
of low-energy shocks that can feel mildly uncomfortable. The patient typically
describes these shocks as feeling like a thump on the chest.

* Defibrillation. The fourth type is the strongest discharge delivered by the ICD.
Many patients will become faint or lose consciousness when their arrhythmia
becomes severe enough to warrant defibrillation. Because of the physical
symptoms of a severe arrhythmia, patients are unlikely to feel the high-energy shocks of defibrillation. However, if a patient is conscious, the shock will feel
much like a "kick in the chest" and will occur suddenly. It is described as painful
by most patients, but it is very brief, usually lasting for only a second.
(Medtronic, Inc., 2002).

Implantable Atrioverter Defibrillator

Incidence. The first implantable atrioverter defibrillator (IAD) was implanted in

1996 in Germany in a patient with symptomatic, drug refractory atrial fibrillation. While the device underwent clinical trials that resulted in its acceptance by the Federal Drug and Food Administration as a treatment plan for atrial fibrillation, only slightly more than 350 IADs have been successfully implanted in patients with atrial fibrillation that is not responsive to drug therapy (Gold et al., 2001; Luderitz & Jung, 2000). Atrial fibrillation

(AF) is the most common cardiac arrhythmia and is responsible for most hospital admissions related to cardiac arrhythmias. It increases the risk of stroke and thromboembolism and has hemodynamic effects that can result in decreased exercise tolerance and heart failure. Atrial fibrillation has become a focus of attention due to its growing prevalence in the elderly population (Lip, 2001) with 2% to 4% of people over the age of 65 being diagnosed with the condition (Luderitz & Jung, 2000).

Indications. It is important to note that atrial fibrillation is not always symptomatic and is not immediately life-threatening. Approximately one-third of people with atrial






13

with atrial fibrillation do not experience any symptoms. The other two-thirds experience fatigue, heart palpitations, dizziness, and presyncopal episodes that often interfere with their daily functioning. Most of these patients are successfully treated with antiarrhythmic drugs and/or dual chamber pacemaker therapy. A significant minority of patients have recurring symptoms and must be externally cardioverted. It is this minority of patients who may be helped by the IAD. The IAD alleviates symptoms but does not cure the underlying disorder. Disorders that may result in atrial fibrillation include ischemic heart disease, rheumatic heart disease, hypertension, sick sinus syndrome, cardiomyopathy, and other structural abnormalities. Atrial fibrillation can be paroxysmal or sustained; however, most patients have paroxysmal atrial fibrillation (Lip, 2001). Currently, there are no indications for the implantation of an IAD; however, the patients involved in clinical trials using the LAD have paroxysmal atrial fibrillation that is symptomatic drug-refractory. Patients have also been excluded if they have atrial fibrillation from a reversible cause; have Wolff-Parkinson-White Syndrome; have had a myocardial infarction in the last year, have cardiac ischemia and resultant angina; or have a history of thromboembolic events, congestive heart failure, and/or left ejection fraction of < 40% (Wellens et al., 1998).

Description. The IAD consists of the atrioverter can and three leads. As with the pacemaker and the ICD, the IAD can contains the circuitry and power supply. There are three leads connected to the can. The leads are placed transvenously: one in the coronary sinus, one in the right atrium, and one in the right ventricle. The can is pectorally implanted.

Function. The IAD is capable of delivering two types of discharges: one is a

defibrillation shock for the atrium (similar to the defibrillation discharge delivered by the






14

ICD to the ventricle), and the other is a pacing discharge delivered to the ventricle. The pacing discharge is delivered in the event that the defibrillation discharge results in a bradycardic response. A major, and significant, difference between the IAD and the other devices described, is that it can be programmed to discharge automatically when symptoms of atrial fibrillation occur; or it can be used in a patient- or physician-activation mode (Wellens et al., 1998). As with the ICD defibrillation discharge, the patient experiences a quick "kick in the chest" when the IAD delivers the intervention necessary to return the patient to sinus rhythm (Gold, Sulke, Schwartzman, Mehra, & Euler, 2001). A striking difference between patients with atrial fibrillation and those with ventricular fibrillation is the lack of immediate threat of death. For IAD patients, this important difference may increasethe psychological distress associated with receiving shock.

Quality of Life

Health-Related Quality of Life

Definition. Health-related quality of life has emerged as an end-point of interest because it reflects a patient's ability to function in a variety of life domains (including physical, social, emotional, and work-related). Some researchers believe that it is embodied in the approach of the biopsychosocial model of health (Engel, 1977) in which biological, psychological, and social functioning are interdependent. While an operational definition of quality of life has not been universally agreed upon, there is a general consensus that quality of life is a multidimensional construct. Ware (1993) contends that the primary source of information for understanding health-related quality of life is standardized patient surveys. During the past 10 years, a number of generic health-related quality-of-life patient surveys have been developed. The term generic is






15

used because such instruments are not "age, disease, or treatment specific," (Ware, 1993; p. 6). These instruments provide information on several domains of life and are useful for comparing quality of life across different patient populations (Stofmeel et al., 2000).

Measurement. Three of the most widely used and well-validated generic measures of health-related quality of life are the Sickness Impact Profile (SIP; Bergner, 1981), the Nottingham Health Profile (NHP; Erdman, Passchier, & Kooijman, 1993), and the Medical Outcomes Study Short-Form 36 Health Survey (SF-36; Ware, 1993). In a comparison study of the three instruments, Stofineel et al. (2000) reported that the SF-36 is the most efficient and sensitive of the instruments for evaluating the quality of life of pacemaker patients. The SF-36 has been consistently used in quality-of-life outcome studies of cardiac patients (Bosworth et al., 2001; Fritz, 2000; Lam et al., 2000). Its use to validate other measures in cardiac illness research has lead to its utilization as the gold standard measure of generic quality of life in cardiac populations (Hamilton & Haennel, 2000; Stofmeel, Post, Kelder, Grobbee, & van Hemel, 2001a).

The SF-36 resulted from the Health Insurance Experiment (HIE), an attempt to

develop and refine health status surveys that measured an array of health-status and wellbeing concepts for both adults and children. The HIE showed that self-administered survey scales were valid instruments for assessing health status in the general population. This validation of surveys lead to the Medical Outcomes Study (MOS). Ware (1993) used comprehensive surveys to assess people's perceptions of 40 physical and mental health concepts, using a sample of over 20,000 U.S. residents. The SF-36 was constructed to include the eight most salient concepts described by MOS participants. The eight concepts are represented by the eight scales of the SF-36 which are Physical Functioning, Physical Role, Bodily Pain, General Health, Vitality, Social Functioning,






16

Emotional Role, and Mental Health. A complete description of the psychometric properties of the SF-36 is given in Chapter 3. Disease Specific Ouality of Life

Definition. Generic measures of health-related quality of life such as the SF-36 are acceptable for comparisons between different disease samples, but they may not be sensitive enough to detect differences in unique patient characteristics or changes in health status for a specific category of diseased patients (Linde, 2000). For this reason, Linde recommends that a generic instrument and a disease-specific instrument be used in conjunction with one another to adequately measure the quality of life of patients with a specific disease or condition. Such instruments would detect the subtle changes in functioning that are associated with a specific condition. These changes are likely to be missed when looking at more global aspects of patient functioning.

Measurement. Instruments such as the Karolinska Questionnaire (Linde-Edelstam et al., 1992), the Minnesota Living with Heart Failure Questionnaire (Rector & Cohn, 1992), the Cardiac Arrythmia Suppression Trial Quality of Life Scale (CAST; Wiklund, Gorkin, Pawitan, & Schron, 1992) and other rationally developed instruments (Oto et al., 1991; Rose & Blackburn, 1968) have been designed to assess quality of life in cardiac patients. However, these instruments do not address many of the issues associated with the implantation of a biomedical device and the reliance on technology to sustain life or treat a life-threatening condition.

Linde (2000) asserts that the quality of life of pacemaker patients and their psychological adjustment to having a device rather than a disease has never been adequately measured due to the psychometric characteristics of instruments currently used with this population. As pacemakers are the most common implantable-cardiac






17

biomedical devices, it stands to reason that Linde's assertion about the adequacy of quality of life measurement for pacemaker recipients is reflected in the literature on newer devices such as the implantable cardioverter defibrillator (ICD; Sears et al., 1999), and the implantable atrioverter defibrillator (IAD; NASPE, 2000). Until the present study, there have been no attempts to validate a measure that specifically addresses the quality of life of ICD or IAD recipients.

Quality of Life and the Pacemaker

Incidence. As mentioned earlier, cardiac pacemakers are used to treat

bradyarrhythmias and other conduction defects that cause slow heart rhythms. These slow rhythms result in a deficit of oxygenated blood being pumped through the body and may cause dizziness, syncope, or, although rarely, cardiac arrest (Scheidt, 1996). The pacemaker has been accepted as a treatment option by the general public for more than three decades (Mcguire et al. 1966). Nonetheless, the combination of cardiac difficulties and the implantation of a pacemaker have resulted in a higher incidence of psychological distress for recipients than nonpatients (Aydemir et al., 1997). While extensive research on the psychosocial component of quality of life of pacemaker patients has not been reported in the literature, a few studies addressing the issue were found. Aydemir and colleagues (1997) reported that 19.1% of a sample of 84 pacemaker patients received a psychological diagnosis that involved depressive symptomatology. These diagnoses included major depression, mood disorder, and adjustment disorder with depressed mood. He also reported that pacemaker patients experienced device specific fears and anxiety--with a greater proportion of those experiencing these symptoms being of lower socioeconomic status and having less education than the total sample. It was also reported that women with pacemakers experienced more symptoms of depression and






18

greater severity of depressive symptomatology. The literature reveals that anxiety and fear are also seen in a large proportion of pacemaker patients. It is estimated that 11% to 66% of pacemaker patients experience pacemaker specific fears and symptoms of anxiety (Aydemir et al., 1997).

Research on pacemaker patients' adjustment is often secondary to research on the quality of life differences experienced between dual-chamber and single-chamber pacemaker patients. While comparisons are made between the groups, little information is provided on pacemaker recipients as a whole in comparison with other normative populations. In a dual- versus single-chamber pacemaker comparison study of 407 patients 65 years of age or older (Lamas et al., 1998), patients implanted with pacemakers (both dual and single chamber) showed improvements from before implantation on six scales of the SF-36 3 months after implantation. Pacemaker recipients showed improvements from baseline on the Social Functioning, Physical Role, Emotional Role, Mental Health, and Energy subscales. There were no significant differences between the single-chamber and the dual-chamber pacing groups on any of the SF-36 scales at 3- and 18-month follow-ups. However, there was a difference on the mental health scale score at 9-month follow-up, favoring the dual-chamber group. In a similar study using the Nottingham Health Profile, a generic quality-of-life measure, also reported a trend toward improvements in quality of life (Lau et al., 1989).

Measurement. In an attempt to remedy the measurement shortcomings posited by Linde (2000), Stofmeel and associates (2001a) developed the Aquarel Questionnaire. A disease-specific questionnaire designed to measure the quality of life of pacemaker patients. The 20-item measure is to be used in conjunction with the SF-36. The questionnaire was given to 74 pacemaker patients and test-retest reliability for one to two






19

weeks was adequate with correlation coefficients ranging from 0.79 to 0.92. A factor analysis resulted in four factors that resulted in subscales with loadings ranging from 0.303 to 0.838. The subscales were labeled (a) Cognition, (b) Chest Discomfort, (c) Dyspnea, and (d) Arrhythmias. Subscales were reported to be internally consistent with Cronbach's alphas > .70. Content validity was reported as adequate with moderate correlations with the SF-36. In a second validation study (Stofmeel et al., 2001b), 51 patients completed the SF-36 and the Aquarel Questionnaire preoperatively and at 4 to 6 weeks postimplant. The Aquarel Questionnaire was reported to be more sensitive to changes in health status after pacemaker implantation than the SF-36, with effect sizes ranging from 0.13 to 0.72. It is important to note that the Aquarel Questionnaire showed significant improvements on two of its subscales (Dyspnea and Arrhythmias) and the SF36 showed improvements on four of its eight subscales (Physical Functioning, Social Functioning, Role Functioning-Emotional, and Vitality) indicating that the SF-36 was also sensitive to changes in pacemaker patient functioning. Some limitations to the studies include the small sample sizes, small effect sizes, and the lack of a criterion validity measure. Also, an inspection of the questions reveal that they do not ask about pacemaker functioning or issues, rather they focus on arrhythmia symptoms and cognitive issues. Further validation of the Aquarel is needed before any definitive conclusions about its validity are posited.

Implications. In summary, studies comparing pacemaker recipients to the general population suggest greater levels of depression and anxiety in the pacemaker population. However, those studies comparing recipients to preimplantation functioning indicate that quality of life and psychosocial functioning are improved for the majority of pacemaker patients. This makes understanding the specific issues faced by pacemaker recipients






20

important if healthcare providers are to fine-tune and improve their already efficacious treatment.

Quality of Life and the ICD

Incidence. While the efficacy of the ICD in reducing mortality is well established (AVID Investigators, 1997; Buxton, 1999; Moss et al., 1996), the quality of life of ICD recipients has not been as well studied. Studies that have assessed quality of life outcomes indicate that ICD recipients report equal or better quality of life than patients receiving anti-arrhythmic drugs (Arteaga & Windle, 1995; Herbst, Goodman, Feldstein, & Reilly, 1999; Sears & Conti, 2002). One exception, the CABG-Patch study, reported significantly worse mental and physical quality of life outcomes for patients with ICDs when compared to those without ICDs following coronary artery bypass surgery (Namerow, Firth, Heywood, Windle, & Parides, 1999). Further analysis of the data revealed equivalent quality of life for nonshocked ICD and no-ICD patients, indicating that receipt of ICD shock may be the key factor in diminishing the quality of life of these patients.

Recent research assessing the views of attending physicians and nurses (N = 261) regarding specific ICD patient outcomes and common daily problems indicated that health care providers estimate that 10% to 20% of ICD recipients experience significantly worse quality of life and emotional and family functioning post ICDimplantation (Sears et al., 1999). Research specifically designed to address the concerns of younger ICD recipients suggests that recipients > 50 years of age have a more difficult time adjusting to their ICD. Their concerns focus on the appearance of the ICD, physical activity limitations, sexual relations, social interactions, and driving restrictions (Sears, Bums, Handburg, Sotile, & Conti, 2002; Vitale & Funk, 1995).






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Measurement. An empirical comparison of instruments for assessing quality of life in ICD patients is not available in the current research literature. However, an extensive literature review conducted by Sears et al. (1999) revealed the use of several protocolspecific rationally-derived measures. From their review, Sears and colleagues concluded that disease-specific instruments used to measure quality of life for ICD patients should include a focus on symptoms of cardiac illness and/or symptoms of cardiac arrhythmias.

Implications. Since the introduction of the ICD as a treatment recommendation for life-threatening ventricular arrhythmias, research has indicated that a significant minority of patients experience some psychological complications following ICD implantation. In their review of the literature on the psychosocial impact of the ICD, Sears et al. (1999) reported diagnosable levels of anxiety for ICD recipients ranging from 13% to 38%, and depressive symptoms prevalence rates ranging from 24% to 33%. The reported incidence of these patient complaints is significant but has varied greatly across studies, primarily due to variations in research design, device technology, and types of assessment instruments utilized. Despite these inconsistencies within the literature, a common theme has emerged, many ICD recipients experience psychological distress at some point following ICD implantation (Sears et al., 1999). This suggests that the psychosocial outcomes of the ICD recipient may be undermining the potential quality of life benefit of the ICD. Consistently and adequately measuring the quality of life of ICD recipients may allow healthcare providers to establish an index of suspicion for psychological distress and provide appropriate primary as well as secondary interventions.






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Quality of Life and the lAD

Incidence. As mentioned previously, the IAD is a new device that has undergone clinical trials sufficient for its FDA approval for the treatment of atrial fibrillation. The clinical trials indicated that the IAD is an efficacious device for treating atrial fibrillation (Lip, 2001). The quality of life of IAD patients has not been extensively studied due to the novelty of the device; however, a single study, presented as a conference abstract, on the quality of life of these patients reported that recipients of IADs had significantly lower quality of life before implantation of the device when compared to healthy controls (Fellows, Mcquillan, & Rodenhiser, 2000). The 105 IAD recipients who participated in the study had lower scores on all subscales of the SF-36 before implantation. At 1 and 3 months postimplant their quality of life showed significant improvements on the Physical Functioning, Physical Role, and Social Functioning subscales. Significant improvements were not seen on the Mental Health, Vitality, General Health, and Bodily Pain subscales.

Measurement. Currently there are no studies in the literature that have attempted to use an instrument designed specifically for the measurement of the quality of life of IAD patients.

Implications. In the case of IAD recipients, where the arrhythmia is not immediately life threatening, it may be especially important to understand the psychosocial issues associated with the device in order to improve recipient selection. A profile of the patient who will tolerate the discomfort of the discharges and report improved quality of life may increase the efficacy of the IAD. Comparing Quality of Life of Pacemaker, ICD, and IAD Recipients

A literature search resulted in a single study comparing pacemaker and ICD

patients (Duru et al., 2001). Patients undergoing an initial implantation of a pacemaker






23

(n = 124) or ICD (n = 86) were recruited to participate in the study. Participants ranged in age from 40 to 70 years. It was reported that there were no significant differences between pacemaker patients, ICD patients who had not experienced a device discharge (nonshocked), and ICD patients who had experienced a discharge (shocked) on the Hospital Anxiety and Depression Scale (HAD) or any of the subscales of the SF-36. The researchers reported that 18.5% of pacemaker patients, 16.2% of nonshocked ICD patients, and 19.3% of shocked ICD patients reported psychological sequealae. It was also reported that shocked ICD patients were more likely to report limitations to their leisure activities, to admit anxiety about technical problems with their device, and to want to be a part of a support group. Shocked patients were also more likely to consider their ICD to be a "life extender." A significant limitation to the study was the absence of a cardiac specific measure of quality of life. There were no studies found that compared psychosocial adjustment, quality of life, or patient acceptance between pacemaker and IAD recipients or IAD and ICD recipients.

The Construct of Patient Acceptance

Definition

The term patient acceptance has been used in the implantable device literature to describe patients' "perception of the device, the perception of possible discharge, changing body image, changes in lifestyle, patient and family perceptions, home going concerns, and fear of complications" (Luderitz et al., 1993; p. 1816). It has also been defined as "a process characterized by choosing to live with technology, integrating technology into life, and living life through technology" (Burke, 1996). For the purposes of the current research, patient acceptance is the psychological accommodation and understanding of the pros and cons of the device, the recommendation of the device to






24

others, and the derivation of benefit in terms of biomedical, psychological, and social functioning. It is hypothesized that patient acceptance is a latent construct comprised of the following variables or dimensions: return to life issues, device-related distress, positive appraisal, social issues and technology-education-understanding. It is theorized that patient acceptance is a device specific component of the higher order construct of quality of life. It is also believed that patient acceptance is moderately correlated with the components of quality of life as measured by the SF-36, but that it is sensitive to device-specific concerns that are not explained by other factors.

Measurement. Luderitz and associates (1993) designed an eight-item instrument to measure "patients' acceptance" of the ICD. The nonvalidated instrument was comprised of six yes or no questions, one open-ended question ("What is your greatest concern in regard to your ICD") and a single question ("How long did it take you to get used to the ICD?") with answer choices of < 2 months and > 2 months (Luderitz et al., 1993; p. 1817). The results of the study indicated that "patient acceptance" of the ICD was good. Major limitations of the study included the small sample size (N = 57), the lack of psychometric validation, and the fact that a generic health-related quality of life measure was not administered. This would have allowed for comparisons of the measures and might have provided evidence for the discriminant validity of their patient acceptance construct. Nevertheless, their study was among the first to consider a psychological component in ICD therapy.

Study Purposes and Hypotheses

Purposes

The purposes of the study were (a) to develop a measure of patient acceptance and examine the factor structure of the instrument in pacemaker, ICD, and IAD patients;






25

(b) to investigate the convergent and discriminant validity of the measure; and (c) to compare patient acceptance and quality of life in pacemaker, ICD and IAD patients via the biopsychosocial model. The development of a patient acceptance instrument and comparison of quality of life between device groups provides evidence for the theoretical construct of patient acceptance, begins the validation process of a patient acceptance measure, and provides information necessary for designing interventions that will improve the quality of life of patients that must rely on life sustaining technology. Hypotheses

Hypothesis 1. It was hypothesized that an exploratory factor analysis of 47 selfreport items designed to measure patient acceptance of implantable biomedical devices would result in five stable factors. These factors would serve as subscales of a patient acceptance measure, to be referred to as the Florida Patient Acceptance Survey (F-PAS). The factors were hypothesized to be Return to Life, Device-Related Distress, Positive Appraisal, Social Issues, and Technology-Education-Understanding. For a complete listing of the items and the factors they were hypothesized to measure refer to Table 2-1.

Hypothesis 2. It was hypothesized that the F-PAS would be moderately correlated with the subscales of the SF-36 and would account for a significant amount of the variance in scores on the SF-36 that was not accounted for by demographic variables or affective symptoms. It was hypothesized that all the measures in a biopsychosocial model of illness battery would be moderately correlated with the SF-36 due to its purported measuring of physical, mental and social functioning. However, it was asserted that the F-PAS would show divergence from the SF-36 because of its sensitivity to subtle changes in the functioning of biomedical device recipients.






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Table 2-1. Initial items and hypothesized factors of the patient acceptance measure
Factor Item

Return to life I am concerned about resuming a normal social schedule.
I am confident about my ability to return to work if I want to. I have continued my normal sex life. I have returned to a full life. I am concerned about resuming my daily physical activities. I am afraid to drive because of my device. When I think about the device I avoid doing things I enjoy.

Device-related I avoid my usual activities because I feel disfigured by my distress device.
Thinking about the device makes me depressed. When I think about the device I become anxious. It is hard to function without thinking about my device. I wear different clothes to cover my device because it makes me feel self-conscious.
I do not feel disfigured by the medical device. I feel that others see me as disfigured by my device. I feel less attractive because of my device. I keep calm because getting excited or upset may affect my device.

Positive appraisal I feel that I have no control over the device.
I would recommend this device to a friend. My device was my best treatment option. I am safer from harm because of my device. The positive benefits of this device out-weigh the negatives. My family feels more secure because of my device. I need my device to live normally. I have fewer symptoms because of my device. My device creates unnecessary pain for me. I would receive this device again. My device hurts me.
I am confident that my device will work when I need it to. The healthcare costs related to my device are unreasonable. I am not pleased with the technology of this device.

Social issues I am not able to do things for my family the way I used to.
I feel that my device frightens children. I am careful when hugging or kissing my loved ones. Other people are more concerned about me overdoing it than I am.
My family is knowledgeable about how my device works. People question me about the device.






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Table 2-1. Coninued

Factor Item Technology-education I avoid vacations because I want to be within reach of my understanding doctor.
I am concerned about needing parts of my device replaced. I am sure the device will function the way it is supposed to. I know enough about my device. I am knowledgeable about how the device works and what it does for me.
My device is easy to understand. I understand why I have my device. I must see a physician regularly to properly maintain the device.
I feel uncomfortable being alone because I might need someone in an emergency. I am afraid to travel because I fear that no one will understand my treatment device in an emergency. I am afraid of electric appliances/equipment affecting my device.


Hypothesis 3. It was hypothesized that the quality of life of different cardiac

biomedical device recipients would not be significantly different as measured by the SF36.

Hypotheses 4 and 5. It was hypothesized that pacemaker patients would have

significantly greater patient acceptance of their device than ICD or IAD patients, and

finally, it was hypothesized that ICD patients would have significantly greater patient

acceptance of their device than IAD patients.














CHAPTER 3
METHOD

Participants

The sample was 243 patients implanted with a cardiac biomedical device for at least 3 months. Ninety-six of the participants were IAD patients participating in the Patient Atrial Shock Survey of Acceptance and Tolerance Study (PASSAT Study). The PASSAT Study was a cross sectional study of patients that received the Jewel AF 7250, an IAD designed by Medtronic, Inc. Investigators participating in the 7250 Jewel AF-AF Only clinical trial were eligible for participation in the PASSAT Study if they had more than five patients enrolled in the study for 3 months or more. Seventeen of the 45 centers met this criteria and nine investigators agreed to participate (Bums et al., 2003). Fiftyeight participants were ICD patients and 84 were pacemaker patients recruited from the Watson Outpatient Electrophysiology Clinic in Lakeland, Florida; device information was not collected for five participants from the outpatient clinic. Participants in the PASSAT Study were given $50.00 for their participation. The ICD and pacemaker patients were volunteers and received no monetary compensation. Individuals were excluded from the study if they were (a) less than 18 years of age, (b) unable to read and write in English, or (c) were deemed cognitively impaired by recruiting healthcare providers. Informed consent was obtained via approved procedures as required by the Institutional Review Boards (IRB) at both the University of Florida Health Sciences Center and the Watson Outpatient Clinics. A total of 338 patients were approached to participate in the study, yielding a response rate of 72%.


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29

Most of the sample was male (62.2%) with a mean age of 69.3 years (range: 30 to

90). Most of the participants were married (74.6%) and retired (68.3%). Table 3-1

provides a summary of the general demographic characteristics of the study sample.

Ethnicity and education information was not gathered from IAD patients participating in

the PASSAT Study.

Table 3-1. General characteristics of study participants
Characteristic n Percent Device type
IAD 96 40.3% ICD 58 24.4% Pacemaker 84 35.3% Patient age
Under 50 17 7.3% 50-59 29 12.5% 60-69 51 22.0% 70-79 101 43.5% 80 and above 34 14.7% Sex
Male 140 62.2% Female 85 37.8%
Marital status
Married 176 74.6% Unmarried 60 25.4%

Employment status
Full-time 36 15.0% Part-time 19 7.9% Not employed 21 8.8% Retired 164 68.3%

Ethnicity*
Caucasian 121 95.3% African-American 4 3.1% Other 2 1.6%

Education*
Less than high school 28 20.6% High school 54 39.7% Greater than high school 54 39.7%
* Characteristic not reported for PASSAT Study (IAD) participants.







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Procedure

All participants were surveyed once. Data for assessing patient acceptance in IAD patients were gathered through the PASSAT Study. These patients were consented by the Jewel AF-AF Only investigators and the questionnaire battery was either mailed or directly handed to the patient. Surveys were specially coded so that neither the Jewel AF-AF Only investigator nor the current investigator could identify the patient. The patients were allotted a 3-week period to complete the battery. Data for assessing patient acceptance in pacemaker and ICD patients were gathered by having consecutive patients at the Watson Outpatient Electrophysiology Clinic approached to participate in the study during routine follow-up appointments. Informed consent was obtained by nurses who agreed to act as research assistants. Patients were handed the battery of self-report instruments in an addressed stamped envelope. Participants were asked to complete the battery at home and mail the completed battery back to the investigator.

Instruments

Patient Acceptance: Florida Patient Acceptance Survey

The F-PAS began as 47 self-report items composed with the help of experts on

psychosocial functioning in device patients, an expert in the field of psychometrics, and a cardiac electrophysiologist. Information obtained through surveys and interviews were used as guidelines for item construction. The results from this initial psychometric investigation are reported in the "Results" section of this manuscript.

The F-PAS was designed to measure all aspects of the biopsychosocial model of illness; therefore, validated instruments measuring each component of the biopsychosocial model were chosen for the purpose of providing convergent and divergent validity indices. Participants in the PASSAT Study completed all measures.






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In an effort to decrease participant burden, ICD and pacemaker patients completed a core set of measures and one of five noncore measures, including the Interpersonal Support Evaluation List-Short Form (ISEL), the State Trait Anxiety Inventory State Scale (STAI-State), the Life Orientation Test (LOT), the Medical Coping Modes Questionnaire (MCMQ), and the Positive and Negative Affect Scale (PANAS). The noncore measures were shuffled and randomly placed in the questionnaire batteries as they were being constructed.

Quality of Life: Short Form-36 Health Survey (SF-36)

The SF-36 (Ware, Snow, Kosinski, & Gandek, 1993) is a 36-item, self-report

questionnaire that measures health-related quality of life. The SF-36 was both rationally and empirically developed as part of the Medical Outcomes Study (Ware et al., 1993). It has eight health domain scales including: physical functioning, role limitations due to physical problems, role limitations due to emotional problems, social functioning, bodily pain, mental health, vitality and general health perceptions. Despite having only a few items on each scale, the internal reliabilities of the scales range from 0.77 (Social functioning) to 0.92 (Physical functioning) in a sample of 3,053 adults (Stewart, Hays, & Ware, 1988). The items and scales of the SF-36 are scored so that a higher score indicates a better health state.

Biological Component Measure: AF Symptoms Severity Scale (AFSS)

The AFSS (Maglio, Sra, & Paquette, 1998) is a 13-item instrument that was rationally developed to assess disease specific symptom severity in atrial fibrillation patients. It has been found to have good internal reliability and a factor analysis revealed three subscales: AF Severity, Health Care Utilization and Overall Life Satisfaction. The






32

developers of the instrument reported that it was valid, reliable and sensitive to changes in the symptoms of AF patients (Maglio et al., 1998). Psychological Component Measures

Center for Epidemiological Studies-Depression Scale (CES-D). Depression will be measured using the CES-D (Radloff, 1977), a 20-item self-report measure that assesses depressive symptomology. Participants indicate how frequently they have experienced each symptom in the past week. Responses range from 0 (less than one day) to 3 (5-7 days). The total score can range from 0 to 60 and reflects both the number of depressive symptoms and their duration. A standard cut-off score of 16 can be used to indicate clinically significant symptoms of depression. The CES-D has been widely utilized in community surveys. Previous research has demonstrated that the CES-D is highly sensitive and specific and exhibits a high internal reliability coefficient of 0.85. Researchers have suggested that the CES-D is the most generally useful self-report measure of depression when compared to the Beck Depression Inventory, the MMPI Depression Scale, and the Zung Self Rating Scale of Depression (Turk & Okifuji, 1994).

State-Trait Anxiety Inventory (STAI). The STAI (Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983) is a 40-item self-report questionnaire designed to measure both state and trait anxiety. Trait anxiety is defined as a relatively enduring personality characteristic, or more specifically, as anxiety proneness. The internal reliability of both the state and trait anxiety scales has been shown to be uniformly high across samples of adults ranging from 0.89 to 0.96. Test-retest stability coefficients for multiple samples of college students ranged from 0.73 to 0.86, with test-retest validity specifically for the trait scale being reported at 0.73 for males and 0.77 for females. Concurrent validity






33

between the STAI and IPAT Anxiety Scale and the Taylor Manifest Anxiety Scale ranged from 0.83 to 0.73 (Spielberger et al., 1983).

Life Orientation Test (LOT). The LOT (Scheier & Carver, 1985) is an 8-item, selfreport questionnaire (with 4 filter items) which assesses generalized expectancies for positive versus negative outcomes. Subjects rate the extent to which they agree or disagree with each item using a 5-point scale. Half of the items are phrased in the positive direction (e.g., "In uncertain times, I usually expect the best"). The scores for the negative items are reversed, and all items are summed to yield an overall dispositional optimism score. The LOT has a reported reliability coefficient of 0.76 and test-retest reliability of 0.79 (Scheier & Carver, 1985). Although researchers have suggested that the measurement of dispositional optimism (using the LOT) overlaps with neuroticism or negative affectivity (see Smith, Pope, Rhodewalt, & Poulton, 1989), recent analyses conducted with the data from 4,309 subjects have demonstrated that associations between optimism and outcome variables remain significant even when the effects of neuroticism, trait anxiety, self-mastery, and self-esteem are statistically controlled (Scheier, Carver, & Bridges, 1994). The authors conclude that overall, the LOT has good predictive validity, and dispositional optimism (as measured by the LOT) is distinguishable as an independent construct, as compared to constructs of neuroticism and negative affectivity.

Positive and Negative Affect Scales (PANAS). The PANAS (Watson, Clark, & Tellegen, 1988) is a 20-item self-report measure comprised of two separate scales. The scales are designed to measure an individual's level of positive and negative affect. The respondent is asked to rate adjectives that they feel describe them over the past week on a 5-point scale ranging from not at all to extremely. Positive affect terms include






34

"inspired," "excited," and "attentive" and negative affect terms include "upset," "distressed," and "irritable." The 1-week time frame was used in an effort to yield more accurate recall of levels of affect that are ongoing. Research has shown that the positive and negative affect scores produced are minimally correlated and reliable (Cronbach's alpha in the 0.80 range). It has also been reported that the PANAS has good convergent and divergent validity with other instruments assessing affect (Watson et al., 1988).

Medical Coping Modes Questionnaire (MCMQ). The MCMQ (Feifel, Strack, & Nagy, 1987) is a 19-item questionnaire that assesses three types of coping responses to a medical situation, including confrontation, avoidance, and acceptance-resignation. Reliability coefficients range from 0.67 to 0.70; a total score for coping is not obtained. Social Component Measures

Illness Intrusiveness Rating Scale (IIRS). The IIRS (Devins, Orme, & Costello, 1988) is a 13-item self-report measure that asks respondents to rate how much their illness and consequent treatment interferes with each of 13 life domains. The domains relate to health, recreation, diet, work, financial situation, self-expression, family relations, relations with spouse, sex life, social relations, religious expression and community involvement. A total intrusiveness score and 13 domain scores can be calculated. Total scores range from 13 (where the illness is not intrusiveness) to 91 (where the illness is extremely intrusive in all domains). It has been reported that the IIRS has adequate to high internal consistency, reliability, and validity. Patients' appraisals of intrusiveness were corroborated by ratings from significant others (family and health care professionals). Studies also indicate that intrusiveness varies across treatment modalities and illnesses (Devins et al., 1993). The construct has been shown to be associated with a number of QOL endpoints including life satisfaction, mood states,






35

self esteem, depression, adjustment, and psychopathology (Devins et al., 1988; Devins et al., 1993).

Interpersonal Support Evaluation List-Short Form (ISEL). The short form ISEL (Cohen, Mermelstein, Karmack, & Hoberman, 1985) is a 16-item,; self-report measure that was developed to measure perceived availability of supportive social resources that might facilitate coping with stressful situations. The items are rated on the likelihood that the support described would be available if it was needed (probably true or false). Half of the items are phrased in the positive direction while the other half are phrased in the negative direction. This measure has been shown to have strong internal consistency and to moderately correlate with other measures of social support, such as the Inventory of Socially Supportive Behaviors (L= .46) and the Moos University Residence Environment Scale (r = .62; Cohen et al., 1985). There are 4 scales on this measure that assess the following domains of social support: (a) perceived ability of someone to talk to about one's problems (Appraisal), (b) perceived availability of people with whom to do things (Belonging), (c) perceived availability of positive social comparison (SelfEsteem), and (d) perceived availability of instrumental assistance (Tangible). For the purpose of this study these four scales will be combined into a total perceived social support score, which has been indicated in the literature as a valid indicator of perceived availability of social support (Bennett, 1993; Brookings & Bolton, 1988; Cohen at al., 1985).














CHAPTER 4
RESULTS

Assessment Measures

A summary of the scores obtained by the total sample on measures of health-related quality of life and of scores obtained on measures of each component of the biopsychosocial model of illness is presented in Table 4-1. Differences from normative samples on core battery measures were calculated using the Welch's T statistic for conservative comparisons between groups with unequal sample size and unequal variance (Howell, 1997).

Health-Related Quality of Life

The sample obtained a mean score of 57.19 (S~2 = 28.56) on the Physical

Functioning Scale of the SF-36. This score is significantly lower than the mean score obtained by a sample of 442 healthy 65 to 74 year olds, t (279) = 5.268, p < .001 and significantly higher than a sample of 216 congestive heart failure patients t (302) = 3.361, p < .001. On the Physical Role Scale, the current sample's mean score of 46.67 (SD = 42.15) was also significantly lower than the healthy sample's, t (293) = 4.264, p < .001 and significantly higher than the congestive heart failure sample's, t (301) 3.873, p < .001. The current sample's obtained mean score on the Bodily Pain Scale was 63.62 (SD = 24.76). This was significantly lower than the healthy sample's, t (312) = 2.303, p < .05, but not significantly different from the congestive heart failure sample's. The mean score of 56.86 (SD = 22.14) obtained by the current sample on the General Health


36






37

Table 4-1. Obtained scores for total sample Measure N M SD Range Quality of life
SF-36
Phy function 215 57.19 28.56 0-100 Phy role 214 49.67 42.15 0-100 Bodily pain 212 63.62 24.76 12-100 Gen health 214 56.86 22.14 0-100 Vitality 216 48.70 22.09 0-100 Social 217 76.59 24.65 0-100 Emotion role 215 68.99 40.51 0-100 Mental health 216 76.46 17.18 16-100

Biological Component
AFSS 217 26.52 12.90 1-61
Psychological component
CES-D 218 10.44 8.74 0-47 STAI
State 131 32.77 10.89 20-65 Trait 233 33.57 10.33 20-70 LOT 123 16.37 4.86 5-27 PANAS
Positive 117 33.52 7.36 17-50 Negative 116 17.09 5.99 10-36 MCMQ
Confrontation 229 18.91 2.08 12-26 Avoidance 232 14.30 2.23 9-20 Accept-Resign 232 10.06 1.00 7-13

Social component
IIRS 177 27.18 15.06 13-82 ISEL 125 10.28 5.58 0-16

Scale was significantly lower than the mean score obtained by the healthy sample, t (301)

= 3.079, p < .01 and significantly higher than the mean of scores obtained by the

congestive heart failure sample, t (301) = 4.389, p < .001. The current sample obtained a

mean score of 48.70 (SD = 22.09) on the Vitality Scale. This mean score is significantly

lower than the mean score of the healthy sample, t (302) = 6.126, p < .001 and

significantly higher than congestive heart failure patients, t (301) = 1.969, p < .05. The






38

current sample's mean score on the Social Functioning Scale (M = 76.59, SD = 24.65)

was not significantly different from the healthy sample's or the congestive heart failure

sample's. On the Emotional Role Scale the current sample's obtained mean score of

68.99 (SD = 40.51) was significantly lower than the healthy sample's obtained mean

score, t (262) = 3.873, < .001, but not significantly different than the mean score

obtained by congestive heart failure patients. The current sample's mean score on the

Mental Health Functioning Scale (M = 76.46) was not significantly different from the

healthy sample's or the congestive heart failure sample's. For a summary of the mean

comparison statistics for normative samples on the SF-36 scales, see Table 4-2.

Table 4-2. Obtained scores on the SF-36 and statistics for comparison between
normative samples and current sample
Normative Sample MSD df Welch's t

Healthy 65 to 75 year olds (n = 442)
Phy function 69.38 26.26 279 5.268*** Phy role 64.54 41.30 293 4.264***
Bodily pain 68.49 26.42 312 2.303*
Gen health 62.56 22.42 301 3.079**
Vitality 59.94 22.12 302 6.126***
Social 80.61 25.63 314 1.942
Emotion role 81.44 34.56 262 3.873***
Mental health 76.87 18.08 316 0.283

Congestive heart failure patients (n = 216)
Phy function 47.54 31.00 279 3.361*** Phy role 34.37 39.72 293 3.873***
Bodily pain 62.67 30.97 312 0.352
Gen health 47.05 24.17 301 4.389***
Vitality 44.29 24.41 302 1.969*
Social 71.31 33.06 314 1.883 Emotion role 63.67 43.00 262 1.322 Mental Hh 74.68 21.29 316 0.956

*p <.05; **p <.01; ***p < .001






39

Biopsychosocial Model

Biological

Atrial Fibrillation Severity Scale. The mean obtained score on the AFSS of 26.52 (SD = 12.90) was significantly higher than the mean score of 20.4 (SD = 9.4) obtained by a sample of 264 atrial fibrillation patients, t (273) = 5.831, p < .001 (Dorian et al., 2002). The most commonly endorsed symptom was "feeling fatigued," with 73% (n=158) of the sample indicating that they feel fatigued at least some of the time. Psychological

Center for Epidemiological Studies-Depression Scale. The current sample obtained a mean score of 10.4 (SD = 8.17) on the CES-D. The mean score was not significantly different from the mean scores obtained by a sample of 120 heart bypass surgery patients (M = 11.0, SD = 9.1; Pirraglia, Peterson, Williams-Russo et al., 1999) and a normative healthy adult sample of 2,514 (M - 9.3, SD = 8.6; Radloff, 1977).

State Trait Anxiety Inventory Trait Scale. The current sample obtained a mean score of 33.6 (S = 10.3) on the Trait Scale of the STAI. The mean score was significantly lower than the mean score of 41.3 (SD = 12.6) obtained by a sample of 110 medical procedure patients, t (128) = 5.645, p < .001. The mean score of the current sample was not significantly different from the mean score obtained by 106 healthy females (M = 31.8, SD = 7.8) or the mean score obtained by a sample of 382 healthy males (M = 33.9, SD = 8.9; Spielberger et al., 1983). Social: Illness Intrusiveness Rating Scale

The current sample obtained a mean score of 27.2 (SD = 15.1) on the IIRS. The mean score was significantly lower than the mean score 35.0 (SD = 15.0) obtained by a sample of 152 atrial fibrillation patients, t (226) = 4.706, p < .001 (Dorian et al., 2002).






40

The mean score of the current sample was not significantly different from the mean score

of 28.0 (SD = 19.0) obtained by a normative sample of healthy individuals. For a

summary of the means, standard deviations, and mean comparison statistics for

normative samples on the core battery measures of the biopsychosocial model of illness,

please see Table 4-3.

Table 4-3. Obtained scores on the biopsychosocial model of illness component
measures for normative samples and comparison statistics for current sample
Comparison Device group M/SD group M/SD df Welch's t
Comparison groups
AFSS
Atrial fibrillation patients
(N = 264) 20.4 + 9.4 26.5 + 12.9 273 5.831*

CES-D
Cardiac bypass patients
>65 yrs. (N = 120) 11.0 + 9.1 10.4 + 8.7 168 0.549
Healthy norms
(N= 2,514) 9.3+ 8.6 10.4+ 8.7 180 1.874

STAI trait scale scores Medical procedure patients
(N = 110) 41.3 + 12.6 33.6 + 10.3 128 5.645*
Healthy males
(N = 382) 33.9+ 8.9 33.6 + 10.3 307 0.319
Healthy females
(N = 106) 31.8 + 7.8 33.6 + 10.3 186 1.787

IIRS Scores
Atrial fibrillation patients
(N = 152) 35.0 + 15.0 27.2 + 15.1 226 4.706*
Healthy norms
(N = 47) 28.0 + 19.0 27.2 + 15.1 44 0.267
*p <.001

Development of the F-PAS

In a previous survey of 448 ICD patients, respondents were asked 51 questions

about their concerns about having an implantable device. The 10 most frequent concerns

of ICD patients were concerns about the quality of their health, the device's effects on






41
returning to former activities, emotional distress, and communication and education about their ICD and its functioning. As part of the PASSAT Study, qualitative interviews were conducted with 11 IAD patients and 12 physicians responsible for the care of IAD patients (Deaton, Dunbar, Moloney, Sears, & Ujhelyi, 2003). The patient interview consisted of open ended questions regarding the following seven themes:

* symptoms of AF
* prior treatments for AF (before receiving an IAD)
* reasons for trying IAD therapy
* surgical procedure and adjustment to the device
* experience with LAD shock
* self-activation and automatic activation
* overall satisfaction with the device

Information obtained from the interviews revealed that patients were concerned about knowing how their device worked, anticipating shock (anticipation was regarded as more distressing than the actual shock), and social support issues. The benefits of the device were most often related to being able to return to work, social activities, and having fewer AF symptoms. Physicians were asked questions regarding their selection of patients and patients' reactions to being asked to participate in a device based study, as well as what they thought the benefits to the patient would be, patients' acceptance of the device, and physician burden. Their answers indicated that patients did return to former activities, however, they often became anxious about future shocks. The most salient finding from the interviews was that physicians believed that patients should be emotionally ready before being implanted with a device for a non-lif-threatening condition.

Hypothesis 1: Factor Analysis

A principal components factor analysis with a varimax rotation was used to reduce the 47-item F-PAS to its simple structure. Thirteen components with eigenvalues >1






42

were extracted in 26 iterations. The 13 factors accounted for 62.1% of the variance in scores on the F-PAS. An examination of the scree plot (Figure 4-1), and an evaluation of the rotated sums of squares loadings indicated that seven factors accounted for the majority of the variance (43.0%).


Scree Plot
12 10 8


6


4





iD
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46

Component Number


Figure 4-1. Scree plot of rotated factors

Items with loadings of 0.50 or higher on one of these seven factors that did not have a loading of 0.30 or higher on any other factor were considered to be "good" measures of the factor on which they loaded (Grimm & Yarnold, 1997). These 20 items were retained for further evaluation. Factors containing at least two of the 20 items were examined and six factors were retained. Cronbach's alphas for each of the six factors were computed and ranged from 0.50 to 0.82. See Table 4-4 for a list of the 20 retained items, their






43

Table 4-4. Initially retained items, factor loadings, and reliability coefficients
Absolute
Item loading a
Factor 1
I am not able to do things for my family the way I used to. .754 I am confident about my ability to return to work ifI want to. .740 I am concerned about resuming my daily physical activities. .705 I have returned to a full life. .686 I have continued my normal sex life. .663 .50
Factor 2
When I think about the device I avoid doing things I enjoy. .798 I avoid my usual activities because I feel disfigured by my .738 device.
It is hard for me to function without thinking about my device. .735 Thinking about the device makes me depressed. .572 I am careful when hugging or kissing my loved ones. .536 .79
Factor 3
The positive benefits of this device out-weigh the negatives .760 I would receive this device again. .703 I am safer from harm because of my device. .683 My device was my best treatment option .680 .82
Factor 4
I feel less attractive because of my device. .716 I feel that others see me as disfigured by my device. .688 .74
Factor 5
I am knowledgable about how the device works and what it does for me. .687 I know enough about my device. .657 .59
Factor 6
My family feels more secure because of my device. .700 I have fewer symptoms because of my device. .606 .53

factor loadings, and Cronbach's alphas. The relatively low internal consistency 0.59 and

0.53 for Factors 5 and 6 respectively, and the small number of items in each factor

resulted in their being excluded as possible subscales of the F-PAS and from further

analyses in the current research. In order to determine if the internal consistency of

Factor 1 could be increased through the removal of a single item, item response






44

frequencies were examined. Item number 17, "I have continued my normal sex life," was the most frequently unanswered item on the measure (18 participants did not respond to the question) and the modal item response was "neither agree nor disagree." A Cronbach's Alpha of 0.89 was obtained when the item was removed from the factor. The item was consequently dropped from the factor and from further analyses.

A total of 15 items were retained for further validation procedures and Cronbach's Alpha was computed for the items (a = .83). The four remaining factors were labeled:

(a) Return to Life (RTL), (b) Device-Related Distress (DRD), (c) Positive Appraisal

(PA), and (d) Body Image Concerns (BIC). Scale scores were calculated for each of the factors. In order to decrease confusion resulting from subscale labels, negatively worded items in the RTL factor were reverse scored before a scale score was calculated. A higher order factor analysis procedure, using the factor scores, resulted in a single factor. The higher order factor allows for the calculation of a total F-PAS scale score; however, negatively worded items must be reverse scored for the total score to be interpretable. After calculating the factor scale scores, the negatively worded items were reverse scored and a Total F-PAS score was calculated for all participants. See Table 4-5 for the final factor items, labels, reverse scored items, and reliability coefficients.

Hypothesis 2: Convergent and Discriminant Validity of the F-PAS F-PAS and SF-36 Correlations

In order to determine if the F-PAS possessed convergent validity with the SF-36, Pearson product-moment correlation coefficients were calculated. The coefficients revealed that the F-PAS total score was moderately correlated with all eight scales of the SF-36:






45

* Physical Functioning (r = .260, p < .0001)
* Physical Role (r = .356,p < .0001)
* Body Pain (r = .206, p = .004)
* General Health (r = .470, p < .0001)
* Vitality (r = .359, p <.0001)
* Social (r = .290, p < .0001)
* Emotional Role (r = ..321, p < .0001)
* Mental Health (r = .351,p < .0001)

The Return to Life subscale of the F-PAS was also moderately correlated with the eight

scales of the SF-36:

* Physical Functioning (r = .436, p < .0001)
* Physical Role (r = .479, p < .0001)
* Body Pain (r = .323,p < .0001)
* General Health (r = .601,p < .0001)
* Vitality (r = .500,p <.0001)
* Social (r = .479, p < .0001)
* Emotional Role (r = .334, p < .0001)
* Mental Health (r = .387, p < .0001).

The Device-Related Distress subscale was significantly correlated with three of the

subscales of the SF-36:

* General Health (r = -.203, p = .003)
* Emotional Role (r = -.204, p = .003)
* Mental Health (r = -.195,p = .005)

The Positive Appraisal subscale was significantly correlated with six of the SF-36 scales:

* Physical Functioning (r = .147, p = .033)
* Physical Role (r = .159, p = .021)
* General Health (r = .185, p < .007)
* Vitality (r = .178,p =.010)
* Social (r = .146, p = .033)
* Mental Health (r = .151, p = .028)

And, the Body Image subscale was significantly correlated with the Mental Health

subscale of the SF-36 (r = -.162, p = .018). See Table 4-6 for all correlations between the

total score and subscale scores of the F-PAS and the eight scale scores of the SF-36.






46

Table 4-5. Final factors and reliability coefficients
Factor ao

Factor 1: Return to life .89 I am not able to do things for my family the way I used to.** I am confident about my ability to return to work if I want to.
I am concerned about resuming my daily physical activities.**
I have returned to a full life.
Factor 2: Device-related distress .79 When I think about the device I avoid doing things I enjoy.*
I avoid my usual activities because I feel disfigured by my device.*
It is hard for me to function without thinking about my device.*
Thinking about the device makes me depressed.*
I am careful when hugging or kissing my loved ones.*
Factor 3: Positive appraisal .82 The positive benefits of this device out-weigh the negatives.
I would receive this device again.
I am safer from harm because of my device.
My device was my best treatment option.
Factor 4: Body image concerns .74 I feel less attractive because of my device.*
I feel that others see me as disfigured by my device.*
Total F-PAS .83

*Items must be reversed before calculating a F-PAS total score.
**Items must be reversed scored before calculating subscale score and F-PAS total score.

Table 4-6. Pearson product moment correlation coefficients for F-PAS and SF-36
F-PAS
RTL DRD PA BIC Total F-PAS

SF-36
Phy funct .436*** -.013 .147* .067 .260*** Phy role .479*** -.102 .159* .028 .356*** Bodily pain .323*** -.012 .079 -.071 .206**
Gen health .601*** -.203** .185** -.017 .470*** Vitality .500*** -.070 .178** -.079 .359*** Social .479*** -.054 .146* .029 .290***
Emotion role .334*** -.204** .099 -.099 .321*** Mental .387*** -.195** .151* -.162* .351***

*p <.05; **p <.01; ***p <.001






47

F-PAS Correlations with Biological Component Measure

Pearson correlation coefficients were calculated to determine if the F-PAS total score and subscale scores were correlated with the AFSS, a measure of the biological component of the biopsychosocial model of illness. The AFSS was negatively correlated with the RTL subscale score (r = -.424, p < .001) and the F-PAS total score, (r = -.241, p = .001). See Table 4-7 for all correlations between the F-PAS and the measures of the biological component of the biopsychosocial model of illness. Table 4-7. Pearson product moment correlation coefficients for F-PAS and biological
component measure
F-PAS
RTL DRD PA BIC Total F-PAS AFSS -.424*** .065 -.039 -.047 -.241**
**p <.01; ***p <.001

F-PAS Correlations with Psychological Component Measures

Pearson correlation coefficients were also calculated to determine if the F-PAS total score and subscale scores were correlated with measures of the psychological component of the biopsychosocial model of illness. These measures were the CES-D, STAI, LOT, PANAS and MCMQ. The CES-D was negatively correlated with the RTL and PA subscale scores (r = -.510, p <.001; r = -.317, p <.001, respectively) and positively correlated with the DRD and BIC subscale scores, (r = .289, p < .001; r = .212, p = .002, respectively). The CES-D was negatively correlated with the F-PAS total score, (r = -.513,p < .001). The STAI Trait Scale score was negatively correlated with the RTL and PA subscale scores (r = -.403, p < .001; r = -.239, p < .001, respectively) and positively correlated with the DRD and BIC subscale scores, (r = .278, p < .001; r = .169, p = .011, respectively). It was negatively correlated with the F-PAS total score






48

(r = -.425,p < .001). The STAI State Scale score was also negatively correlated with the RTL and PA subscale scores (r = -.345, p < .001; r = -.366, p < .001, respectively) and positively correlated with the DRD and BIC subscale scores, (r = .311, p < .001; r = .256, p = .004, respectively). It was negatively correlated with the F-PAS total score (r = .447,p < .001). The LOT was positively correlated with the RTL subscale score (r = .309, p = .001) and negatively correlated with the DRD subscale score, (r = -.199, p = .030). It was positively correlated with the F-PAS total score (r = .270, p = .004).

The PANAS Positive Affect Scale score was positively correlated with the RTL and PA subscale scores (r = .430, p < .001; r = .235, p = .011, respectively) and negatively correlated with the DRD subscale score, (r = -.227, p = .016). It was positively correlated with the F-PAS total score (r = .372, p < .001). The PANAS Negative Affect Scale score was negatively correlated with the RTL and PA subscale scores (r = -.303, p = .001; r = -.319, p = .001, respectively) and positively correlated with the DRD and BIC subscale scores, (r = .329, p < .001; r = .233, p = .013, respectively). It was negatively correlated with the F-PAS total score (r = -.384, p < .001). The MCMQ Avoidance Scale score was negatively correlated with the RTL subscale score (r = -.183, p = .006). See Table 4-8 for all correlations between the FPAS and the measures of the psychological component of the biopsychosocial model of illness.

F-PAS Correlations with Social Component Measures

Finally, Pearson correlation coefficients were also calculated to determine if the F-PAS total score and subscale scores were correlated with measures of the social component of the biopsychosocial model of illness. The IIRS was negatively correlated with the RTL and PA subscale scores (r = -.592, p < .001; r = -.335, p < .001,






49

respectively) and postively correlated with the DRD and BIC subscale scores, (r = .428,

p < .001; r = .196, p = .010, respectively). It was negatively correlated to the F-PAS

total score, (r = -.599,p < .001). The ISEL was not significantly correlated with the FPAS subscale or total scores. See Table 4-9 for all correlations between the F-PAS and

the social component measures.

Table 4-8. Pearson product moment correlation coefficients for F-PAS and
psychological component measures
F-PAS
RTL DRD PA BIC Total F-PAS CES-D -.510*** .289*** -.317*** .212** -.513"** STAI
State -.345*** .311*** -.366*** .256** -.447*** Trait -.403*** .278*** -.239*** -.169* -.425*** LOT .309** -.199* .116 -.111 .270**
PANAS
Pos .430*** -.227* .235* -.064 .372*** Neg -.303** .329*** -.319** .233* -.384*** MCMQ
Con -.028 .110 .093 .007 -.027 Avoid -.183** .095 -.037 .087 -.124
Accpt -.079 -.097 -.034 .126 -.048
*p <.05; **p <.01; ***p <.001

Table 4-9. Pearson product moment correlation coefficients for F-PAS and social
component measures
F-PAS
RTL DRD PA BIC Total F-PAS IIRS -.592*** .428*** -.335*** .196* -.599*** ISEL .069 -.062 .091 .003 .113
*p < .05, ***p < .001

Regression on Scales of the SF-36

Hierarchical regressions were used to determine if the F-PAS predicted healthrelated quality of life as measured by the SF-36, when the variance in scores accounted

for by demographics, core battery measures of the biological, psychological, and social

components of the biopsychosocial model had been considered. Separate blocked






50

hierarchical regressions were computed for each of the eight scales of the SF-36. Demographic variables were placed in the first block, AFSS scores in the second block, CES-D and STAI Trait Scale scores in the third block, and IIRS scores in the fourth block. In the fifth and final block, patient acceptance was added. Separate regressions were computed using the subscale scores of the F-PAS in the fifth block and with the FPAS total score in the fifth block. Residuals, partial regressions, and normal probabilities were examined and no violations to the regression procedure's assumptions of linearity, constant variance, independence, or normality were found. Only those regression models where patient acceptance accounted for unique variance are summarized. The Mental Health scale equation is summarized due to its relevance to mental health care professionals.

Physical Functioning Scale. In the set of regressions on the Physical Functioning Scale of the SF-36, the demographic variables of age, gender, and marital status significantly accounted for 11.3% of the variance in scores. Arrhythmia symptoms significantly accounted for an additional 19.2%, depression and anxiety another 3.3%, and IIRS scores significantly accounted for an additional 2.5%. Patient acceptance, as measured by the F-PAS subscale scores, significantly predicted an additional 4.4% of the variance in scores when the variance accounted for by demographics and all other measures had been considered. In sum, the significant model accounted for 45.5% (p < .001) of the variance in physical functioning. A summary of model statistics for each block and regression coefficients for the final model are presented in Table 4-10. When patient acceptance was measured by the F-PAS total score, no significant additional variance was accounted for beyond that accounted for by demographics and the components of the biopsychosocial model. In all, the model significantly accounted for






51

36.7% (p < .001) of the variance in scores on the Physical Functioning Scale of the SF36.

General Health Scale. In the fourth set of regressions, demographic variables were not significant predictors of general health. Arrhythmia symptoms and psychological variables significantly predicted 41.4% of the variance (34.4% and 7.0% respectively). Illness intrusiveness was not a significant predictor. Patient acceptance as measured by the F-PAS subscale scores significantly predicted an additional 7.9% of the variance in general health. In all, the significant model accounted for 50.3% (p < .001) of the variance in General Health Scale scores. A summary of model statistics for each block and regression coefficients for the final model are presented in Table 4-11. When patient acceptance was measured by the F-PAS total score, an additional 3.0% of variance was accounted for beyond that accounted for by demographics and measures of the components of the biopsychosocial model of illness. In all, the model significantly accounted for 45.4% (p < .001) of the variance in scores on the General Health Scale of the SF-36. A summary of model statistics for each block and regression coefficients for the final model are presented in Table 4-12.

Vitality Scale. In the fifth set of regressions, demographic variables were not significant predictors of vitality. Arrhythmia symptoms significantly accounted for 25.3% of the variance in scores when they were added to the model and depression and anxiety significantly accounted for another 9.9% of the variance. Illness intrusiveness did not predict additional variance when it was added to the model. When measured by the subscales of the F-PAS, patient acceptance significantly accounted for an additional

4.6% of the variance in scores. In all, the significant model accounted for 41.7% (p < .001) of the variance in vitality. For a summary of model statistics for each block








Table 4-10. Summary of hierarchical regression model for variables predicting SF-36 physical functioning scale scores using FPAS subscale scores as a measure of patient acceptance
Variable B SE B t p R2 R2A Step 1
Age* -.585 .188 -.238 -3.104 .002 Gender -5.551 4.287 -.094 -1.295 .197 Marital status -3.590 4.679 -.055 -.767 .444
.113 .113
Step 2
ARSS* -.584 .178 -.264 -3.280 .001
.306 .192
Step 3
CES-D 4.034E-02 .372 .012 .109 .914 STAI trait -.190 .281 -.069 .677 .500
.339 .033
Step 4
IIRS -.286 .190 -.151 -1.504 .135
.364 .025
Step 5
RTL* .273 .099 .251 2.759 .007 DRD .110 .116 .075 .950 .344 PA 6.067E-02 .112 .039 .540 .590 BIC .151 .120 .094 1.263 .209
.408 .044
*Predictor of unique variance








Table 4-11. Summary of hierarchical regression model for variables predicting SF-36 general health scale scores using F-PAS
subscale scores as a measure of patient acceptance
Variable B SE B t p R2 R2A Step 1
Age .231 .134 .121 1.718 .088 Gender 5.601 3.056 .123 1.833 .069
Marital status -.475 3.335 -.009 -.142 .887
.018 .018
Step 2
ARSS* -.664 .127 -.387 -5.233 .000
.344 .326
Step 3
CES-D -.352 .265 -.139 -1.329 .186 STAI trait 2.316E-02 .201 .011 .115 .908
.414 .070
Step 4
IIRS 2.893E-02 .136 .020 .213 .831
.424 .010
Step 5
RTL* .313 .071 .370 4.434 .000
DRD -5.366E-02 .083 -.047 -.649 .517 PA 3.064E-02 .080 .026 .382 .703 BIC .144 .085 .115 1.689 .093
.503 .079
*Predictor of unique variance








Table 4-12. Summary of hierarchical regression models for variables predicting SF-36 general health scale scores using the F-PAS
total score as a measure of patient acceptance
Variable B SE B t p R2 R2A Step 1
Age .159 .135 .083 1.175 .242
Gender 5.514 3.158 .121 1.746 .083 Marital status 1.562 3.406 .031 .458 .647
.018 .018
Step 2
ARSS* -.801 .125 -.467 -6.406 .000
.344 .326
Step 3
CES-D -.418 .271 -.165 -1.541 .125 STAI trait 3.889E-02 .207 .018 .188 .851
.414 .070
Step 4
IIRS -5.378E-02 .135 -.037 -.399 .691
.424 .010
Step 5
FPAS total* .350 .123 .229 2.839 .005
.454 .030
*Predictor of unique variance






55

and regression coefficients for the final model, see Table 4-13. When patient acceptance was measured by the F-PAS total score, no additional variance in scores was significanlty predicted. In sum, the significant model accounted for 37.3% (p < .001) of the variance in scores on the Vitality Scale.

Social Scale. In the sixth set of regressions, demographic variables were not

significant predictors of social functioning. When arrhythmia symptoms were added to the model 16.5% of the variance was significantly predicted. Depression and anxiety significantly accounted for an additional 16.5% of the variance. Illness intrusiveness did not significantly account for additional variance. Patient acceptance, as measured by the F-PAS subscales, significantly accounted for another 5.0% of the variance in social functioning. In all, the significant model accounted for 41.4% (p < .001) of the variance in scores. A summary of model statistics for each block and regression coefficients for the final model are provided in Table 4-14. Patient acceptance, as measured by the F-PAS total score, was not a significant predictor of additional variance in social functioning. In sum, the significant model accounted for 36.9% (p < .001) of the variance in scores on the Social Functioning Scale of the SF-36.

Mental Health Scale. In the eighth and final set of regressions, demographic

variables were not significant in predicting mental health. When reported symptoms of arrhythmias were added to the model 8.9% of the variance in scores was significantly predicted. Depression and anxiety significantly accounted for the majority of the variance, accounting for an additional 39.3%. Illness intrusiveness did not significantly account for additional variance. Patient acceptance, when measured by the F-PAS subscale scores was not a predictor if significant variance in scores. In sum, the significant model using F-PAS subscale scores as measures of patient acceptance








Table 4-13. Summary of hierarchical regression model for variables predicting SF-36 vitality scale scores using F-PAS subscale
scores as a measure of patient acceptance
Variable B SE B t p R2 R2A Step 1
Age .125 .144 .066 .870 .386 Gender 3.463 3.279 .076 1.056 .293 Marital status -4.334 3.579 -.086 -1.211 .228
.011 .011
Step 2
ARSS* -.549 .136 -.320 -4.031 .000
.263 .253
Step 3
CES-D -.303 .284 -.120 -1.066 .288 STAI trait -.256 .215 -.120 -1.189 .236
.362 .099
Step 4
IIRS -.150 .146 -.102 -1.032 .304
.371 .009
Step 5
RTL* .203 .076 .241 2.687 .008 DRD* .197 .089 .174 2.224 .028 PA -2.246E-04 .086 .000 -.003 .998 BIC -4.465E-02 .091 -.036 -.488 .626
.417 .046
*Predictor of unique variance (p < .05)








Table 4-14. Summary of hierarchical regression model for variables predicting SF-36 social scale scores using F-PAS subscale
scores as a measure of patient acceptance
Variable B SE B 1 t p R R2A Step 1
Age -5.094E-02 .161 -.024 -.316 .752 Gender 6.957 3.667 .137 1.897 .060 Marital status -5.772 4.003 -.102 -1.142 .151
.025 .025
Step 2
ARSS -.349 .152 -.183 -2.294 .023
.191 .165
Step 3
CES-D -.960 .318 -.340 -3.019 .003 STAI trait 4.006E-02 .241 -.017 -.166 .868
.356 .165
Step 4
IIRS -.188 .163 -.115 -1.156 .260
.364 .008
Step 5
RTL .204 .085 .217 2.413 .017
DRD .161 .099 .128 1.623 .107 PA -5.822E-02 .096 -.044 -.605 .546 BIC .153 .102 .110 .495 .137
.414 .050
*Predictor of unique variance (p < .05)






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accounted for a total of 49.5% (p < .001) of the variance in scores on the Mental Health Scale of the SF-36. When F-PAS total scores were used as a measure of patient acceptance no additional variance was significantly predicted. The significant model accounted for a total of 48.8% (p < .001) of the variance in Mental Health Scale scores.

Device Group Comparisons

An analysis of variance (ANOVA) procedure was used to determine if there were significant differences between groups on demographic variables. Significant differences between groups were found for age, F (2, 226) = 29.024, p < .001, gender F (2, 220) = 5.483, p = .005, and marital status, F (2, 232) = 4.265, p = .015. Post hoc Tukey HSD (Tukey a) procedures were used to determine which groups were significantly different from one another. The IAD group was significantly younger than both the pacemaker (p < .001) and ICD (p < .001) groups. The pacemaker group was comprised of significantly more women than the IAD group (p = .004) and there were significantly more unmarried pacemaker patients than IAD patients (p = .011). A summary of the demographics for groups is presented in Table 4-15. A summary of demographic differences between groups is presented in Table 4-16. For a summary of the means, standard deviations, and range of scores on all measures for the three device groups see Tables 4-17, 4-18, and 4-19.

Hypothesis 3: Group Differences on SF-36 Scale Scores

Analysis of variance procedures were used to determine if there were differences

between pacemaker, ICD, and IAD groups on the eight scales of the SF-36. The analyses revealed significant group differences on the Physical Functioning Scale, F (2, 208) = 9.087, p < .001. A post hoc Tukey a procedure revealed that IAD patients reported significantly greater physical functioning than both pacemaker (p = .007) and






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Table 4-15. Device group frequencies and means on demographic variables
Group N Demographic n M SD

Pacemaker 81 Age 75.09 8.13 Gender
Male 37 Female 40 Marital status
Married 53 Unmarried 29 ICD 56 Age 72.00 9.60 Gender
Male 36 Female 18 Marital status
Married 42 Unmarried 13 IAD 92 Age 62.15 11.75 Gender
Male 66 Female 26 Marital status
Married 80 Unmarried 16

Table 4-16. Analysis of variance between groups on demographics

Source df MS F p

Age
Between groups 2 3586.522 29.024*** .000
Within groups 226 123.572

Gender
Between groups 2 1.234 5.483** .005
Within groups 220 .227

Marital Status
Between groups 2 .779 4.265* .015
Within groups 230 .183

**p < .001, **p< .005, *p < .05






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Table 4-17. Obtained scores for pacemaker group Measure n M SD Range

Quality of life
SF-36
Phy function 75 53.67 28.12 0-100 Phy role 74 45.68 42.32 0-100 Bodily pain 74 60.00 25.76 12-100 Gen health 75 58.39 22.96 0-100 Vitality 75 48.73 22.16 0-100 Social 75 76.83 24.89 0-100 Emotion role 75 72.44 38.12 0-100 Mental health 75 78.67 17.71 32-100

Biological component
AFSS 73 32.14 10.80 16-61


Psychological component
CES-D 78 9.46 9.15 0-47 STAI
State 25 28.60 8.81 20-50 Trait 82 33.61 10.12 20-70 LOT 16 13.37 3.44 8-20 PANAS
Positive 17 33.53 7.63 19-45 Negative 16 20.00 6.24 12-33 MCMQ
Confrontation 81 18.30 2.19 12-25 Avoidance 80 14.03 2.30 9-20 Accept-resign 81 9.93 1.06 7-13

Social component
IIRS 57 24.12 13.06 13-67 ISEL 22 2.23 2.16 0-9






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Table 4-18. Obtained scores for ICD group Measure n M SD Range

Quality of life
SF-36
Phy function 54 47.69 28.34 0-100 Phy role 53 38.21 40.61 0-100 Bodily pain 54 59.67 24.67 12-100 Gen health 54 48.33 21.82 0-92 Vitality 53 44.25 23.56 0-95 Social 54 71.44 26.38 12.5-100 Emotion role 53 64.15 41.79 0-100 Mental health 53 78.49 15.05 36-100

Biological component
AFSS 55 35.09 9.45 18-55

Psychological component
CES-D 54 11.07 8.75 0-38 STAI
State 16 29.19 9.41 20-48 Trait 58 32.88 10.13 20-63 LOT 12 14.08 5.05 8-24 PANAS
Positive 14 29.36 5.75 17-37 Negative 14 18.71 4.60 12-28 MCMQ
Confrontation 54 18.94 1.75 14-23 Avoidance 56 13.95 2.37 10-20 Accept-Resign 55 10.11 1.05 7-13


Social Component
IIRS 25 35.24 19.50 13-82 ISEL 12 2.50 2.61 0-8






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Table 4-19. Obtained scores for IAD group Measure n M SD Range

Quality of life
SF-36
Phy function 82 67.01 25.54 15-100 Phy role 83 61.45 41.19 0-100 Bodily pain 80 70.65 22.82 12-100 Gen health 81 61.58 20.52 15-100 Vitality 84 51.31 21.31 0-100 Social 84 79.61 23.43 25-100 Emotion role 83 69.88 41.85 0-100 Mental health 84 72.76 17.70 16-100


Biological component
AFSS 84 15.73 8.52 1-41


Psychological component
CES-D 81 11.07 8.57 0-35 STAI
State 88 34.73 11.33 20-65 Trait 88 33.99 10.76 20-70 LOT 93 17.25 4.80 5-27 PANAS
Positive 85 34.15 7.43 17-50 Negative 85 16.31 6.00 10-36

MCMQ
Confrontation 89 19.47 2.01 16-26 Avoidance 91 14.76 2.06 10-20 Accept-resign 91 10.19 0.89 8-12

Social component
IIRS 93 27.00 14.36 13-73 ISEL 91 13.25 2.84 3-16






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ICD (p < .001) patients. Significant group differences were also found on the Physical Role Scale, F (2, 207) = 5.712, p = .004. Implantable atrioverter defibrillator patients obtained significantly higher scores on the scale than the pacemaker (p =.046) and ICD (p = .004) patients. There were also significant differences between groups on the Bodily Pain Scale, F (2, 205) = 4.825, p = .009. The Tukey a procedure revealed that the IAD patients obtained higher scores on the Bodily Pain Scale than the other groups (p = .026 and p = .004; pacemaker and ICD patients respectively). Finally, significant differences between groups were also found on the General Health Scale F (2, 207) = 6.235, p < .002. Implantable cardioverter defibrillator patients reported significantly poorer general health than both pacemaker (p = .026) and IAD (p = .002) patients. See Table 4-20 for a summary of group differences and Table 23 for homogenous subsets. To determine if the significant differences between groups might be better accounted for by significant differences on demographic variables, univariate analysis of covariance procedures using age, gender, and marital status as covariates (ANCOVAs) were computed. On the Physical Functioning Scale, age was a significant covariate, F (1, 190) = 10.734, p = .001). When demographics were covaried, there was no significant main effect for Physical Functioning Scale by device group, F (2, 189) = 2.155, p = .119. There were no significant covariates in the evaluation of Physical Role Scale by device group, however when demographic variables were covaried, there was no significant main effect, F (2, 188) = 1.977, p = .141. Age was also a significant covariate in the differences between groups on the Bodily Pain Scale, F (1, 187) = 5.439, p = .021. Again, when demographics were covaried, the main effect for Bodily Pain Scale by group was not significant, F (2, 186)= .599, p = .551. There were no significant demographic covariates in the evaluation of General Health by device group and the main effect when demographics were covaried remained significant, F (2, 188) = 5.294, p = .006.






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Table 4-20. Analysis of variance between groups on SF-36 scales
Source df MS F p Physical functioning
Between groups 2 6858.391 9.087*** .000
Within groups 208 754.785 Physical role scale
Between groups 2 9813.721 5.712** .004
Within groups 207 1718.104 Bodily pain scale
Between groups 2 2867.890 4.825* .009
Within groups 205 594.362 General health scale
Between groups 2 2949.626 6.235** .002
Within groups 207 473.099 Vitality scale
Between groups 2 811.436 1.648 .195
Within groups 209 492.490 Social scale
Between groups 2 1103.421 1.807 .167
Within groups 210 610.799 Emotional role scale
Between groups 2 1088.576 .662 .517
Within groups 208 754.785 Mental health scale
Between groups 2 862.934 2.957 .054
Within groups 209 291.833
***p <.001, **p <.005, *p <.010

Table 4-21. Tukey HSD homogenous subsets of device groups on scales of the SF-36 Subsets a < .05
Device Groups N M M
Physical functioning scale
Pacemaker 75 53.667 ICD 54 47.685
IAD 82 67.012 Physical role scale
Pacemaker 74 45.676 45.676
ICD 53 38.208
IAD 83 61.446 Bodily pain scale
Pacemaker 74 60.000 ICD 54 59.667
IAD 80 70.650 General health scale
Pacemaker 75 58.387
ICD 54 48.333
IAD 81 61.580






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Hypotheses 4 and 5: Differences in Patient Acceptance

Differences between device groups in regard to patient acceptance were also evaluated using ANOVAs. Differences between groups were found on the RTL subscale, F (2, 227) = 8.293, p < .001. A post hoc Tukey a procedure revealed that ICD patients obtained significantly lower RTL subscale scores than pacemaker and IAD patietnts. Significant between group differences were found on the DRD subscale, F (2, 225) = 6.497, p = .002. Pacemaker patients reported significantly less device related distress than both ICD and IAD patients. Device groups did not differ significantly on the PA subscale of the F-PAS. Group differences were significant on the BIC subscale, F (2, 228) = 5.399, p = .005. Tukey a results indicated that IAD patients reported significantly more body image concerns than pacemaker patients. There were also significant differences between groups on the F-PAS total score, F (2, 209) = 6.698, p = .002. Implantable cardioverter defibrillator patients had significantly lower patient acceptance than pacemaker patients. For a summary of between group differences see Table 4-22. For a summary of homogenous subsets see Table 4-23. Again, demographic variables were covaried to determine if significant group differences were better accounted for by age, gender, and marital status. When demographic variables were covaried the main effect for RTL by device group was significant, F (2, 205) = 7.275, p = .001. Age was a significant covariate for scores on the DRD Scale, F (2, 203) = 8.321, p = .004; however, even after differences between groups on demographic variables were controlled, there was a significant main effect for DRD by device group, F (2, 202) = 6.097, p = .003. There were no significant covariates for scores on the BIC subscale and the main effect for BIC by device group was not significant, F (2, 206) =






66

2.693, p = .070. When demographic variables were covaried, the main effect for F-PAS

total score by device group was significant, F (2, 190) = 6.239, p = .002.

Table 4-22. Analysis of variance between groups on FPAS
Source df MS F p Return to life (RTL)
Between groups 2 5470.820 8.293** .000
Within groups 227 659.713 Device related distress (DRD)
Between groups 2 2372.534 6.497* .002
Within groups 225 365.162 Positive appraisal (PA)
Between groups 2 53.941 .153 .858
Within groups 229 594.362 Body image concerns (BIC)
Between groups 2 1653.394 5.399* .005
Within groups 228 306.238 FPAS total
Between groups 2 1361.472 6.698* .002
Within groups 209 203.274
**p < .001,p < .005

Table 4-23. Homogenous subsets of device groups on the F-PAS Device Groups Subsets a < .05

Tukey HSD N M M Return to Life Subscale
Pacemaker 79 72.152
ICD 56 54.799
IAD 95 69.3421 Device Related Distress Subscale Pacemaker 81 9.383
ICD 57 20.526 IAD 90 17.278 Body Image Concerns Subscale Pacemaker 79 5.063
ICD 57 10.088 10.088 IAD 80 13.816 F-PAS Total Score
Pacemaker 72 85.440
ICD 54 76.006
IAD 81 81.092 81.092













CHAPTER 5
DISCUSSION

The current study achieved its three main purposes of (a) developing a measure of patient acceptance of implantable devices, (b) beginning the psychometric validation of the patient acceptance measure, and (c) comparing patient acceptance and quality of life in pacemaker, implantable cardioverter defibrillator (ICD), and implantable atrioverter defibrillator (IAD) patients. Forty-seven items of patient acceptance were constructed from empirical data and information obtained from experts in the fields of psychology, electrophysiology, and psychometrics. Through statistical analyses these items were reduced to 15 essential items that comprise four factors, labeled Return to Life (RTL), Device-Related Distress (DRD), Positive Appraisal (PA), and Body Image Concerns (BIC). The four factors were found to be internally consistent and were used as subscales while a reliable higher order factor was used as a single score of patient acceptance. The Florida Patient Acceptance Survey (F-PAS) revealed that, overall, pacemaker patients have significantly greater patient acceptance of their device than ICD patients and less device-related distress than both ICD and IAD patients. It also indicated that pacemaker and IAD patients have fewer concerns about returning to their former lives than ICD patients. The F-PAS is the first self-report measure to distinguish significant differences in patients' acceptance of different implantable cardiac devices and the first empirically investigated device/treatment specific instrument to provide a common metric used to study this uniquely homogenous (arrhythmia) and heterogeneous (device type) group of



67






68

patients. The information obtained has important implications for the development and implementation of psychosocial interventions that address the distress associated with the implantation of a cardiac device, as well as, possible implications for the research and design of future implantable cardiac devices.

In order to evaluate the importance of the development of the F-PAS, it is necessary to consider the value of previous measures in regard to their role in the development of psychosocial interventions for increasing health-related quality of life in cardiac populations. The Karolinska Questionnaire (Linde-Edelstam et al., 1992), the Minnesota Living with Heart Failure Questionnaire (Rector & Cohn, 1992), the Cardiac Arrythmia Suppression Trial Quality of Life Scale (CAST; Wiklund et al., 1992) and other rationally developed instruments (Rose & Blackburn, 1968; Oto et al., 1991) have been used to assess quality of life in cardiac patients from before to after treatment and between cardiac illness samples. The Aquarel survey (Stofmeel, 2001 a) measured changes in functioning from before to after the implantation of a pacemaker. The F-PAS assesses patients' acceptance only after treatment. This is an important distinction due to the fact that pacemakers, ICDs and future implantable cardiac devices are likely to be a patient's only treatment option. Measuring their psychosocial functioning before and after the implantation of the device does not provide information that can be used to improve patients' health related quality of life after receiving a device they had no choice but to receive. While the Luderitz et al. (2000) questions regarding patient acceptance of ICD's was a post-implantation measure of acceptance, its dichotomous response options do not allow for incremental changes in acceptance. The F-PAS provides a continuum of acceptance on individual items, subscale scores, and total score; this continuum makes it possible to measure incremental increases or decreases in patient acceptance. Also,






69

previous studies have shown that the scales of the SF-36 are sensitive to changes in quality of life from before to after cardiac illness treatment (Bosworth et al., 2001; Lam et al., 2000; Fritz, 2000), suggesting that measuring quality of life using the SF-36 before and after implantation is adequate for measuring the efficacy of therapy. However, the SF-36 is limited in its ability to distinguish between cardiac illness samples and measure changes in health status for a specific category of disease, if all members of a sample have been treated or all members remain untreated (Linde, 2000). Evidence provided by the current research indicates that using the F-PAS in conjunction with the SF-36 when measuring post-implantation quality of life would be efficacious in determining what psychosocial interventions would improve patients' acceptance of the device and consequently increase patients' health-related quality of life. The post-implantation continuum of patient acceptance measured by the F-PAS makes its development an important and relevant addition to the study of implantable devices.

Hypotheses

In order to evaluate the current study's ability to provide evidence regarding the hypotheses put forth by the researcher, each of the hypotheses should be addressed separately. The extraction of four factors from the initial 47-item questionnaire and the extraction of a higher order factor partially support the first hypothesis: a factor analysis of the F-PAS, using participants' responses, will result in five stable factors and these factors will serve as subscales of the F-PAS. The factors hypothesized by the researcher were (a) Return to Life, (b) Device-Related Distress, (c) Positive Appraisal, (d) Social Issues, and (e) Technology-Education-Understanding. Three of the hypothesized factors, Return to Life, Device-Related Distress, and Positive Appraisal were extracted. These factors were found to be internally consistent with Cronbach's alphas ranging from 0.79






70

to 0.89. The fourth factor, Body Image Concerns, was also found to be internally consistent with a Cronbach's alpha of 0.74. The extraction of a single higher order factor supports the first hypothesis by allowing for the computation of a total score. The final 15 items of the F-PAS were found to be internally consistent with a Cronbach's alpha of

0.83.

Three of the items retained in the three final factors that were originally

hypothesized did not load on the factor on which they were predicted to load. The item "I am not able to do things for my family the way I used to," was predicted to load under a factor labeled "Social Issues." The social issues factor was not extracted. A possible explanation for the item loading under the RTL factor may be the inclusion of the phrase, "the way I used to." This phrase is suggestive of doing things the way they were done before the implantation, making the item interpreted as a return to life issue. The other two items that did not load on the scales they were predicted to load on were, "When I think about the device I avoid doing things I enjoy," and "I am careful when hugging or kissing my loved ones." A possible explanation for their loading under the DRD subscale may be that having to alter once pleasurable behaviors is distressing. The items loading on the BIC factor were predicted to load on the DRD factor. This finding indicates that concerns about body image are different from device-related distress and should be considered separately in future investigations of patient acceptance.

The second hypothesis--the F-PAS would be moderately correlated with the subscales of the SF-36 and would account for a significant amount of the variance in scores on the scales of the SF-36 that was not accounted for by demographic variables, physiological indicators, or affective symptoms-was also partially supported. Moderate correlations with the SF-36 were posited to provide construct validity of patient






71

acceptance by revealing both convergent and divergent validity. At least one subscale of the F-PAS was moderately correlated with at least one scale of the SF-36 and the F-PAS total score was moderately correlated with all eight scales of the SF-36. Construct validity was also supported by the finding that patient acceptance accounted for a significant amount of the variance in quality of life beyond that which was accounted for by demographics, physiological measures, and affective symptoms on four of the eight scales of the SF-36. The moderate correlations suggest that the F-PAS is measuring a construct that is related to quality of life, but is not the same construct. The significant variance in quality of life accounted for by the F-PAS suggests that the effects patient acceptance has on quality of life is not completely accounted for by demographics, physiological symptoms, or affective symptoms.

The third hypothesis-the quality of life of different implantable cardiac device recipients would not be significantly different as measured by the SF-36-was mostly supported. Only one of eight scales of the SF-36, the General Health scale, showed significant differences between device groups when demographic variables were controlled. Scores on the General Health scale indicated that ICD patients viewed their overall health as poorer than pacemaker and IAD patients. This suggests that the SF-36 addresses global differences between device recipients, however, specific areas where psychosocial interventions and changes in device development might improve recipients quality of life are not identified.

Findings from the research partially support hypothesis four: pacemaker patients will have significantly greater patient acceptance of their device than ICD or IAD patients. Scores on the subscales of the F-PAS reveal that pacemaker patients have fewer concerns about returning to the life they had before they received their device than






72

ICD patients. However, unexpectedly, IAD patients also have fewer concerns about returning to their former lives than ICD patients. Pacemaker patients and IAD patients are not significantly different in this respect. As predicted, pacemaker patients have much less device related distress than lAD and ICD patients. This is likely due to the lack of painful and/or unexpected therapies. While preliminary ANOVAs indicated that pacemaker patients have fewer body image concerns than IAD patients and similar concerns to ICD patients, when demographic variables are controlled, these differences disappear. Because no single demographic variable was found to be a significant covariate, a combination of age, gender, and marital status are assumed to be more important than the type of device received in determining if body image is an issue. A review of the significant demographic differences between groups revealed that IAD patients were younger than both pacemaker and ICD patients, the pacemaker group was comprised of more women than the IAD group, and pacemaker patients were more likely to be unmarried than were IAD patients. The F-PAS total score indicates that pacemaker patients, as expected, have greater patient acceptance than ICD patients.

With regard to hypothesis five-ICD patients will have significantly greater patient acceptance of their device than IAD patients-the hypothesis was not supported. On the RTL subscale of the F-PAS, significant differences between ICD and IAD patients were found. Implantable cardioverter defibrillator patients had more return to life concerns than IAD patients. A finding that contradicts what was hypothesized. While controlling for differences in demographic variables did not result in the eradication of this difference, it is important to note that selection criteria for IAD patients included the absence of a life-threatening condition. Implantable cardioverter defibrillator patients are, by definition, dealing with a life-threatening condition. Two explanations are






73

posited. One is that the IAD patients selected to participate in the study were generally healthier than the ICD patients in the study and therefore had less difficulty returning to their former life following the implantation of the device. This explanation is supported by the fact that the ICD patients had lower scores on the General Health scale of the SF36 and greater illness intrusiveness scores on the IIRS. The other explanation is that IAD patients have a choice in whether or not to receive IAD therapy and those who are willing to receive painful therapies to decrease the symptoms of a non-life-threatening condition may be more accepting of an implantable device.

There were no significant differences between groups in regard to participants'

positive appraisal of their device. This indicates that all three groups believe their device to be a positive treatment option. What is most salient about this finding is that positive appraisal of a device does not infer patient acceptance of the device. The device may likely be their best or only treatment option and, therefore, viewed positively. However, the device may still interfere with returning to life, may create device related distress, and may result in body image concerns. This finding also has implications for the need for both subscale scores and F-PAS total score. Higher or lower F-PAS total scores may be a more global indicator as to the necessity of a psychosocial intervention. Differences between subscale scores may indicate what domain of functioning should be the focus of a psychosocial intervention. For example, if the F-PAS total score is low, a review of the subscales might reveal that the low score is the result of a low score on the positive appraisal scale, a specific cognitive intervention that addresses erroneous beliefs about the device would be implemented. If a review of the subscales indicated that concerns about returning to a full life was lowering acceptance, a behavioral intervention that included strategies for increasing daily activities would be implemented. A high DRD






74

subscale score may indicate that relaxation strategies and cognitive restructuring are most appropriate.

While not all of the researcher's hypotheses were fully supported, the majority of them were partially or mostly supported. This suggests that the ability of the current study to evaluate the questions being asked was adequate. The support for a majority of hypotheses and a lack of support for aspects of some hypotheses is indicative of both strengths and limitations of the current investigation.

Strengths and Limitations

A major strength of the study is its uniqueness with regard to the current literature in the area of psychosocial adjustment of cardiac device patients. It is the first study to make group comparison across three different device groups. It is the first study to provide validity estimates for a common metric that measures differences between implantable cardiac device groups. It is also the first investigation of a self-report instrument that provides information that is efficacious in the application of psychosocial science in regard to implantable cardiac device patients.

The use of a mixed device group sample to validate the F-PAS contributes to the literature by increasing the likelihood that the measure is valid across device populations and provides an accurate picture of patients' acceptance of implantable technology. Another strength is the use of an assessment battery constructed of standardized and validated measures of psychosocial functioning chosen to measure each component of the biopsychosocial model of illness. The battery allowed the construct validity of the FPAS to be assessed via a multi-trait design and helped ensure that both discriminant and convergent validity would be assessed. Finally, a strength of the study that should be addressed is the inclusion of participants from both private and research settings. The






75

enrollment of patients from a private practice clinic, as well as, the enrollment of patients from a major research institution provides further support for the generalization of findings from the current research.

While the use of participants from different settings may be seen as a strength, the difference in the overall health of patients in the private sector compared to that of patients chosen to be a part of a randomized clinical trial is also a weakness of the current study. The IAD patients in the study were chosen due to their high level of overall health and may not represent the average drug refractory atrial fibrillation patient. However, as mentioned above, atrial fibrillation patients who choose to have a device that delivers uncomfortable electrical shock therapies, may be likely to be in better health than the average atrial fibrillation patient and may, contrary to the researcher's initial hypothesis, be self-selected to have greater acceptance of the device.

Another relative weakness of the study is the lack of pre-implant information

regarding patients' psychosocial functioning. This makes it impossible to determine if people with pre-implant adjustment difficulties or psychiatric concerns may be more or less likely to report lower levels of patient acceptance of their device. Previous research indicates that pre-implant psychological functioning is the best predictor of post-implant functioning in ICD patients (Wallace et al., 2002). Also a weakness of the study is the reliance on self-report. While self-report has been shown to be a valid way to assess physical and psychosocial functioning, the inclusion of clinical data for reliability comparisons would add to the validity of the findings being reported.

Implications and Conclusions

The strengths and weaknesses of the study are important when considering the implications for future research and the conclusions drawn from the current study. In






76

regard to future research and further validation of the F-PAS, it is important to note the extraction of two factors that did not have acceptable internal consistency. One of these factors contained two items hypothesized to measure technology-education understanding. The items were "I am knowledgable about how the device works and what it does for me," and "I know enough about my device." The items loaded on the factor at 0.687 and 0.657 respectively; however, the resulting factor had a Cronbach's alpha of 0.59. Also, the item, "I have continued my normal sex life," was removed from the first factor due to its effects on internal consistency. A review of the frequency statistics revealed that the item was not answered by eighteen participants and the modal answer was "neither agree nor disagree." It is important to note that several participants were older and widowed and many indicated through written notes on their questionnaires that this question was "too personal" or "not applicable." While these items were not found to be reliable in the current sample, education about an implantable device and sexual functioning have been identified as major concerns for both patients and physicians, and may be clinically relevant. Therefore, it is suggested that these items be retained for clinical use of the F-PAS and be investigated in future psychometric research.

Future research should also compare the factor structure found in the current sample of cardiac patients to the factor structure obtained in an implantable device sample that is not a cardiac device sample to determine if patient acceptance, as measured by the F-PAS, is applicable to other devices such as cochlear implants or spinal cord stimulators. While internal consistency is considered an adequate test of reliability, the test-retest reliability of the measure should also be assessed in further validation studies in an effort to provide further reliability and validity data for the measure.






77

While several conclusions can be drawn from the information obtained, one

important conclusion is that global measures of health-related quality of life such as the SF-36 may be adequate for assessing pre and post-implantation differences in quality of life, but they may not have the specificity needed to detect subtle differences and changes in health status in patients after they have been treated. Current disease specific measures for cardiac patients also provide adequate information regarding pre and posttreatment changes in cardiac patients. In many cases they are also able to distinguish between cardiac illness populations and may be able to detect changes in health status in similar cardiac illness patients. However, neither global measures of quality of life or current cardiac disease specific instruments are adequate in assessing the unique psychosocial issues associated with patients' acceptance of an implantable cardiac device. This preliminary investigation of the F-PAS indicates that it is a valid measure for assessing these issues.

The F-PAS also provides relevant information for the design and implementation of psychosocial interventions that would increase acceptance of a device and consequently improve health-related quality of life. For instance, the F-PAS revealed that ICD patients have a more difficult time returning to life and experience greater device related distress than other cardiac device patients. While the reasons for these finding are not assessed in the current study, a reasonable explanation is that ICD patients typically receive a device after they have experienced ventricular tachycardia or sudden death, and may, consequently, have mortality-related fears. These fears may be amenable to change via a psychosocial intervention. Another possible use of the F-PAS is in the area of research and design of implantable devices. Information obtained from the measure may allow






78

designers to improve patients' acceptance by changing less acceptable attributes of a device.

The most remarkable conclusion to be drawn from this study may be that

implantable cardiac device patients generally accept their devices and appraise them positively. This information is especially important when one considers the number of technological advances that have been made in recent years. As more and more devices are developed it will become paramount to measure patients' acceptance of these devices and to determine how mental healthcare professionals may be utilized to improve the quality of life of device recipients. In sum, the current research has provided evidence for the existence of the often referred to construct of patient acceptance; it has highlighted the important role of psychosocial issues in the treatment of cardiac arrhythmia patients; and it has laid the groundwork for future studies of this important facet of health-related quality of life.













APPENDIX GENERAL INFORMATION SHEET DATE:

Please indicate the following information about yourself: Social Security Number: Age:

Gender: MALE FEMALE Ethnic background: AA Wh Asn Hsp AInd OTHER:

Years of education: None Elementary (8) Some High School __ High School (12)

Some College College (16) Further College


M.D PHD

Marital Status: Single/Never Married Married Divorced Widowed Separated For each statement below, check the box which reflects the best answer.

1. Before receiving your device were you...
O Employed full-time O Employed part-time
U Not employed
U Retired

2. Currently are you....
O Employed full-time " Employed part-time
O Not employed
O Retired



79






80

3. Do you have a primary support person that you can count on for everyday support?
O Yes
D No (If "no," please skip to the instructions before question 6.)

4. If yes, what is that person's relationship to you?
O Spouse or partner
D Parent L Sibling U Friend 0 Child
Other (please write in)













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BIOGRAPHICAL SKETCH

Jason L. Bums graduated from high school in 1986 from the American School in Aberdeen, Scotland. He began his undergraduate work at Texas Christian University in Fort Worth, Texas, and obtained a Bachelor of Arts degree in public relations from Louisiana State University in Shreveport in 1993. He decided to return to school after an accident at the restaurant he managed resulted in his developing an infection on his heart. The experience led to his understanding of the saying, "life is not a dress rehearsal and we should do what we love and love what we do." He returned to school in 1996 and earned a Bachelor of Science degree in psychology from the University of South Alabama in Mobile, Alabama in 1997. He went on to obtain his Master of Science degree from the same institution in 1999. Jason began his doctoral work that year at the University of Florida. He completed the course work in 3 years and completed his internship at the New Orleans Veteran's Affairs Medical Center in July 2003. His dissertation, entitled "Measuring Patient Acceptance of Biomedical Devices in Cardiac Patients" was completed in May 2003. He began his postdoctoral fellowship in clinical psychology at the University of Florida in August 2003.












89








I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy.





amuel F. Sears, Jr., Chair
Associate Professor of Clinical and Health Psychology

I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy.





Garret D. Evans
Associate Professor of Clinical and Health Psychology

I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy.






Professorof 'kmi 1 and Health Psychology

I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy.





J pes V. Jessup .
associate e Profss4 of Nursini








This dissertation was submitted to the Graduate Faculty of the College of Health Professions and to the Graduate School and was accepted as partial fulfillment of the requirements for the degree of Doctor of Philosophy. August 2003


Dean, College of Health Professions





Dean, Graduate Sc ool













































233 FT4
R2/18/R43 947




Full Text
45
Physical Functioning (r = .260, p < .0001)
Physical Role (r = .356, p < .0001)
Body Pain (r = .206, p = .004)
General Health (r = .470, /? < .0001)
Vitality (r = .359,p <.0001)
Social (r = .290, p < .0001)
Emotional Role (r = ..321,/? < .0001)
Mental Health (r = 3 51,/? <.0001)
The Return to Life subscale of the F-PAS was also moderately correlated with the eight
scales of the SF-36:
Physical Functioning (r = .436, p < .0001)
Physical Role (r = .479, p < .0001)
Body Pain (r .323, p < .0001)
General Health (r = .601, p < .0001)
Vitality (r = .500,/? <.0001)
Social (r = .479,/? < .0001)
Emotional Role (r = .334, p < .0001)
Mental Health (r = .387, p < .0001).
The Device-Related Distress subscale was significantly correlated with three of the
subscales of the SF-36:
General Health (r = -.203,/? = .003)
Emotional Role (r = -.204, p = .003)
Mental Health (r = -.195,/? = .005)
The Positive Appraisal subscale was significantly correlated with six of the SF-36 scales:
Physical Functioning (r = A47,p = .033)
Physical Role (r 159, p = .021)
General Health (r = 185, p < .007)
Vitality (r = .178,/? =.010)
Social (r = .146,p = .033)
Mental Health (r = 151, p = .028)
And, the Body Image subscale was significantly correlated with the Mental Health
subscale of the SF-36 (r = -A62,p = .018). See Table 4-6 for all correlations between the
total score and subscale scores of the F-PAS and the eight scale scores of the SF-36.


55
and regression coefficients for the final model, see Table 4-13. When patient acceptance
was measured by the F-PAS total score, no additional variance in scores was significanlty
predicted. In sum, the significant model accounted for 37.3% (p < .001) of the variance
in scores on the Vitality Scale.
Social Scale. In the sixth set of regressions, demographic variables were not
significant predictors of social functioning. When arrhythmia symptoms were added to
the model 16.5% of the variance was significantly predicted. Depression and anxiety
significantly accounted for an additional 16.5% of the variance. Illness intrusiveness did
not significantly account for additional variance. Patient acceptance, as measured by the
F-PAS subscales, significantly accounted for another 5.0% of the variance in social
functioning. In all, the significant model accounted for 41.4% (p < .001) of the variance
in scores. A summary of model statistics for each block and regression coefficients for
the final model are provided in Table 4-14. Patient acceptance, as measured by the
F-PAS total score, was not a significant predictor of additional variance in social
functioning. In sum, the significant model accounted for 36.9% (p < .001) of the
variance in scores on the Social Functioning Scale of the SF-36.
Mental Health Scale. In the eighth and final set of regressions, demographic
variables were not significant in predicting mental health. When reported symptoms of
arrhythmias were added to the model 8.9% of the variance in scores was significantly
predicted. Depression and anxiety significantly accounted for the majority of the
variance, accounting for an additional 39.3%. Illness intrusiveness did not significantly
account for additional variance. Patient acceptance, when measured by the F-PAS
subscale scores was not a predictor if significant variance in scores. In sum, the
significant model using F-PAS subscale scores as measures of patient acceptance


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
MEASURING PATIENT ACCEPTANCE OF BIOMEDICAL DEVICES
IN CARDIAC PATIENTS
By
Jason L. Bums
August 2003
Chair: Samuel F. Sears
Major Department: Clinical and Health Psychology
Medicine has succeeded in the research and development of extraordinary
implantable cardiac biomedical devices that provide targeted therapies for cardiac
arrhythmias. However, a theoretical framework and common metric for studying the
quality of life and psychosocial adjustment of these patients has not been empirically
investigated. The purposes of our study were (a) to develop an instrument to measure
patient acceptance of implantable devices; (b) to begin validation of the instrument; and
(c) to compare, using the developed and validated measure, acceptance and quality of life
in patients with pacemakers, implantable cardioverter defibrillators (ICD), or implantable
atrioverter defibrillators (IAD).
We constructed 47 items of patient acceptance. Through statistical analyses we
reduced these to 15 essential items. By using exploratory factor analysis, we extracted
four factors: Return to Life (RTL), Device-Related Distress (DRD), Positive Appraisal
Vll




73
posited. One is that the IAD patients selected to participate in the study were generally
healthier than the ICD patients in the study and therefore had less difficulty returning to
their former life following the implantation of the device. This explanation is supported
by the fact that the ICD patients had lower scores on the General Health scale of the SF-
36 and greater illness intrusiveness scores on the IIRS. The other explanation is that IAD
patients have a choice in whether or not to receive IAD therapy and those who are willing
to receive painful therapies to decrease the symptoms of a non-life-threatening condition
may be more accepting of an implantable device.
There were no significant differences between groups in regard to participants
positive appraisal of their device. This indicates that all three groups believe their device
to be a positive treatment option. What is most salient about this finding is that positive
appraisal of a device does not infer patient acceptance of the device. The device may
likely be their best or only treatment option and, therefore, viewed positively. However,
the device may still interfere with returning to life, may create device related distress, and
may result in body image concerns. This finding also has implications for the need for
both subscale scores and F-PAS total score. Higher or lower F-PAS total scores may be
a more global indicator as to the necessity of a psychosocial intervention. Differences
between subscale scores may indicate what domain of functioning should be the focus of
a psychosocial intervention. For example, if the F-PAS total score is low, a review of the
subscales might reveal that the low score is the result of a low score on the positive
appraisal scale, a specific cognitive intervention that addresses erroneous beliefs about
the device would be implemented. If a review of the subscales indicated that concerns
about returning to a full life was lowering acceptance, a behavioral intervention that
included strategies for increasing daily activities would be implemented. A high DRD


27
Table 2-1. Coninued
Factor Item
Technology-education I avoid vacations because I want to be within reach of my
understanding doctor.
I am concerned about needing parts of my device replaced.
I am sure the device will function the way it is supposed to.
I know enough about my device.
I am knowledgeable about how the device works and what it
does for me.
My device is easy to understand.
I understand why I have my device.
I must see a physician regularly to properly maintain the
device.
I feel uncomfortable being alone because I might need
someone in an emergency.
I am afraid to travel because I fear that no one will
understand my treatment device in an emergency.
I am afraid of electric appliances/equipment affecting my
device.
Hypothesis 3. It was hypothesized that the quality of life of different cardiac
biomedical device recipients would not be significantly different as measured by the SF-
36.
Hypotheses 4 and 5. It was hypothesized that pacemaker patients would have
significantly greater patient acceptance of their device than ICD or IAD patients, and
finally, it was hypothesized that ICD patients would have significantly greater patient
acceptance of their device than IAD patients.


60
Table 4-17. Obtained scores for pacemaker group
Measure
n
M
SD
Range
Quality of life
SF-36
Phy function
75
53.67
28.12
0-100
Phy role
74
45.68
42.32
0-100
Bodily pain
74
60.00
25.76
12-100
Gen health
75
58.39
22.96
0-100
Vitality
75
48.73
22.16
0-100
Social
75
76.83
24.89
0-100
Emotion role
75
72.44
38.12
0-100
Mental health
75
78.67
17.71
32-100
Biological component
AFSS
73
32.14
10.80
16-61
Psychological component
CES-D
78
9.46
9.15
0-47
STAI
State
25
28.60
8.81
20-50
Trait
82
33.61
10.12
20-70
LOT
16
13.37
3.44
8-20
PANAS
Positive
17
33.53
7.63
19-45
Negative
16
20.00
6.24
12-33
MCMQ
Confrontation
81
18.30
2.19
12-25
Avoidance
80
14.03
2.30
9-20
Accept-resign
81
9.93
1.06
7-13
Social component
IIRS
57
24.12
13.06
13-67
ISEL
22
2.23
2.16
0-9


ACKNOWLEDGMENTS
There are so many people to thank for helping me. First, and foremost, I would
like to thank my dissertation supervisory chair, Dr. Samuel Sears, without whose
guidance in the educational, professional, and oftentimes, personal arenas, this
dissertation would never have happened. I would also like to thank the members of my
dissertation committee (Dr. Garret Evans, Dr. Robert Glueckauf, and Dr. James Jessup)
for their input, patience, and constructive critiques. Special thanks are also in order for
Dr. Mike Ujhelyi and Medtronic, Inc. for their contributions, as well as Dr. Stephen
Keim and his nursing staff at the Watson Clinic Electrophysiology Clinic in Lakeland,
Florida. My gratitude goes out to Robyn Wallace, Amy Baughcum, Rebecca Sotile, and
Karen Campbell for their contributions to this document and their contributions to my
sanity during this process. I also want to thank Dr. Elise Labb for telling me I could do
it if I wanted to bad enough; and Bryan Sullivan for changing the trajectory of my life by
just listening. I thank Christa Bums Pazzaglia for comforting me with unconditional
acceptance. I thank Ken Beatty for reality checks, especially those that came right after I
talked to Christa. I thank my parents for always believing in me and supporting me, even
when tough love was the trend. Finally, and undeniably most importantly, I thank
Scott McLavy, my life-partner, for doing what all those mentioned above did and so
much more. Words fall far short of conveying the ways he has been there for me.
11


BIOGRAPHICAL SKETCH
Jason L. Bums graduated from high school in 1986 from the American School in
Aberdeen, Scotland. He began his undergraduate work at Texas Christian University in
Fort Worth, Texas, and obtained a Bachelor of Arts degree in public relations from
Louisiana State University in Shreveport in 1993. He decided to return to school after an
accident at the restaurant he managed resulted in his developing an infection on his heart.
The experience led to his understanding of the saying, life is not a dress rehearsal and
we should do what we love and love what we do. He returned to school in 1996 and
earned a Bachelor of Science degree in psychology from the University of South
Alabama in Mobile, Alabama in 1997. He went on to obtain his Master of Science
degree from the same institution in 1999. Jason began his doctoral work that year at the
University of Florida. He completed the course work in 3 years and completed his
internship at the New Orleans Veterans Affairs Medical Center in July 2003. His
dissertation, entitled Measuring Patient Acceptance of Biomedical Devices in Cardiac
Patients was completed in May 2003. He began his postdoctoral fellowship in clinical
psychology at the University of Florida in August 2003.
89


8
epicardial lead involves a thoracotomy (opening the chest cavity) conducted under
general anesthesia; it is more risky for the patient (Xie et al., 1998).
Function. There are four common pacemaker programs or modes: VVI, VVIR,
DDD, and DDDR. The first letter of the mode signifies the chamber that is paced
(Ventrical vs. Dual). The second letter is the chamber sensed by the pacemaker
(Ventrical vs. Dual). The third letter is the response provided by the pacemaker (Inhibits
vs. Dual). The pacemaker will either inhibit or trigger the electrical system of the heart
when the heart falls below a normal rhythm. The existence of a fourth letter, always R,
indicates if the pacemaker is rate-responsive. All four modes sense the electrical activity
of the heart and provide electrical stimulation to the muscle fibers of the heart when the
patients heart rate drops below a preprogrammed rate. The electrical stimulation is
rarely detected by the patient. Rate-responsive pacemakers provide discharges at
appropriate cardiac output levels. Rate-responsive models are an improvement over non-
rate-responsive models that pace the heart at a fixed rate without regard to cardiac-output
needs (Xie et al., 1998). In other words, VVI and DDD modes cause the heart to beat at
a fixed rate whether the patient is sleeping or walking quickly up a flight of stairs.
Rate-responsive models react to cues of exertion and increase ones heart rate
accordingly. Advances in cardiac pacemaker functioning (such as rate responsiveness)
are continuing at a rapid rate. Newer programming features include advanced diagnostic
capabilities, mode switching, and patient alerts to possible malfunctions (Wood, 2000).
Implantable Cardioverter Defibrillators
Incidence. The first ICD was implanted by Mirowski in 1980 at Johns Hopkins
Hospital as a last-resort effort to treat a patient with life-threatening ventricular
fibrillation. A thoracotomy was needed to implant the bulky device; and FDA approval


58
accounted for a total of 49.5% (p < .001) of the variance in scores on the Mental Health
Scale of the SF-36. When F-PAS total scores were used as a measure of patient
acceptance no additional variance was significantly predicted. The significant model
accounted for a total of 48.8% (p < .001) of the variance in Mental Health Scale scores.
Device Group Comparisons
An analysis of variance (ANOVA) procedure was used to determine if there were
significant differences between groups on demographic variables. Significant differences
between groups were found for age, F (2, 226) = 29.024, p < .001, gender F (2, 220) =
5.483, p = .005, and marital status, F (2, 232) = 4.265, p = .015. Post hoc Tukey HSD
(Tukey a) procedures were used to determine which groups were significantly different
from one another. The IAD group was significantly younger than both the pacemaker
(p < .001) and ICD (p < .001) groups. The pacemaker group was comprised of
significantly more women than the IAD group (p = .004) and there were significantly
more unmarried pacemaker patients than IAD patients (p = .011). A summary of the
demographics for groups is presented in Table 4-15. A summary of demographic
differences between groups is presented in Table 4-16. For a summary of the means,
standard deviations, and range of scores on all measures for the three device groups see
Tables 4-17,4-18, and 4-19.
Hypothesis 3: Group Differences on SF-36 Scale Scores
Analysis of variance procedures were used to determine if there were differences
between pacemaker, ICD, and IAD groups on the eight scales of the SF-36. The analyses
revealed significant group differences on the Physical Functioning Scale, F
(2, 208) = 9.087, p < .001. A post hoc Tukey a procedure revealed that IAD patients
reported significantly greater physical functioning than both pacemaker (p = .007) and


41
returning to former activities, emotional distress, and communication and education
about their ICD and its functioning. As part of the PASSAT Study, qualitative interviews
were conducted with 11 I AD patients and 12 physicians responsible for the care of IAD
patients (Deaton, Dunbar, Moloney, Sears, & Ujhelyi, 2003). The patient interview
consisted of open ended questions regarding the following seven themes:
symptoms of AF
prior treatments for AF (before receiving an LAD)
reasons for trying IAD therapy
surgical procedure and adjustment to the device
experience with IAD shock
self-activation and automatic activation
overall satisfaction with the device
Information obtained from the interviews revealed that patients were concerned about
knowing how their device worked, anticipating shock (anticipation was regarded as more
distressing than the actual shock), and social support issues. The benefits of the device
were most often related to being able to return to work, social activities, and having
fewer AF symptoms. Physicians were asked questions regarding their selection of
patients and patients reactions to being asked to participate in a device based study, as
well as what they thought the benefits to the patient would be, patients acceptance of the
device, and physician burden. Their answers indicated that patients did return to former
activities, however, they often became anxious about future shocks. The most salient
finding from the interviews was that physicians believed that patients should be
emotionally ready before being implanted with a device for a non-lif-threatening
condition.
Hypothesis 1: Factor Analysis
A principal components factor analysis with a varimax rotation was used to reduce
the 47-item F-PAS to its simple structure. Thirteen components with eigenvalues >1


Table 4-13. Summary of hierarchical regression model for variables predicting SF-36 vitality scale scores using F-PAS subscale
scores as a measure of patient acceptance
Variable
B
SEB
P
t
P
R2
R2A
Step 1
Age
.125
.144
.066
.870
.386
Gender
3.463
3.279
.076
1.056
.293
Marital status
-4.334
3.579
-.086
-1.211
.228
.011
.011
Step 2
ARSS*
-.549
.136
-.320
-4.031
.000
.263
.253
Step 3
CES-D
-.303
.284
-.120
-1.066
.288
STAI trait
-.256
.215
-.120
-1.189
.236
.362
.099
Step 4
IIRS
o
yn
r
.146
-.102
-1.032
.304
.371
.009
Step 5
RTL*
.203
.076
.241
2.687
.008
DRD*
.197
.089
.174
2.224
.028
PA
-2.246E-04
.086
.000
-.003
.998
BIC
-4.465E-02
.091
-.036
-.488
.626
.417
.046
Predictor of unique variance (p < .05)


76
regard to future research and further validation of the F-PAS, it is important to note the
extraction of two factors that did not have acceptable internal consistency. One of these
factors contained two items hypothesized to measure technology-education
understanding. The items were I am knowledgable about how the device works and
what it does for me, and I know enough about my device. The items loaded on the
factor at 0.687 and 0.657 respectively; however, the resulting factor had a Cronbachs
alpha of 0.59. Also, the item, I have continued my normal sex life, was removed from
the first factor due to its effects on internal consistency. A review of the frequency
statistics revealed that the item was not answered by eighteen participants and the modal
answer was neither agree nor disagree. It is important to note that several participants
were older and widowed and many indicated through written notes on their
questionnaires that this question was too personal or not applicable. While these
items were not found to be reliable in the current sample, education about an implantable
device and sexual functioning have been identified as major concerns for both patients
and physicians, and may be clinically relevant. Therefore, it is suggested that these items
be retained for clinical use of the F-PAS and be investigated in future psychometric
research.
Future research should also compare the factor structure found in the current
sample of cardiac patients to the factor structure obtained in an implantable device
sample that is not a cardiac device sample to determine if patient acceptance, as
measured by the F-PAS, is applicable to other devices such as cochlear implants or spinal
cord stimulators. While internal consistency is considered an adequate test of reliability,
the test-retest reliability of the measure should also be assessed in further validation
studies in an effort to provide further reliability and validity data for the measure.


5 DISCUSSION
67
Hypotheses 69
Strengths and Limitations 74
Implications and Conclusions 75
APPENDIX GENERAL INFORMATION SHEET 79
REFERENCES 81
BIOGRAPHICAL SKETCH 89
IV


51
36.7% (p < .001) of the variance in scores on the Physical Functioning Scale of the SF-
36.
General Health Scale. In the fourth set of regressions, demographic variables were
not significant predictors of general health. Arrhythmia symptoms and psychological
variables significantly predicted 41.4% of the variance (34.4% and 7.0% respectively).
Illness intrusiveness was not a significant predictor. Patient acceptance as measured by
the F-PAS subscale scores significantly predicted an additional 7.9% of the variance in
general health. In all, the significant model accounted for 50.3% (p < .001) of the
variance in General Health Scale scores. A summary of model statistics for each block
and regression coefficients for the final model are presented in Table 4-11. When patient
acceptance was measured by the F-PAS total score, an additional 3.0% of variance was
accounted for beyond that accounted for by demographics and measures of the
components of the biopsychosocial model of illness. In all, the model significantly
accounted for 45.4% (p < .001) of the variance in scores on the General Health Scale of
the SF-36. A summary of model statistics for each block and regression coefficients for
the final model are presented in Table 4-12.
Vitality Scale. In the fifth set of regressions, demographic variables were not
significant predictors of vitality. Arrhythmia symptoms significantly accounted for
25.3% of the variance in scores when they were added to the model and depression and
anxiety significantly accounted for another 9.9% of the variance. Illness intrusiveness
did not predict additional variance when it was added to the model. When measured by
the subscales of the F-PAS, patient acceptance significantly accounted for an additional
4.6% of the variance in scores. In all, the significant model accounted for 41.7%
(p < .001) of the variance in vitality. For a summary of model statistics for each block


these devices has not been empirically studied. Such a framework may allow healthcare
providers to understand, measure, and develop interventions for treating psychosocial
distress in device recipients, thereby improving medical outcomes and increasing
recipients quality of life. Our study provides a theoretical construct of patient
acceptance by developing (and preliminary psychometric validation of) the Florida
Patient Acceptance Survey (F-PAS). Our study also compared psychosocial adjustment
for recipients of different implantable cardiac biomedical devices.


82
Bums, J. L., Sears, S. F., Sotile, R., Schwartzman, D. S., Hoyt, R. H., Alvarez, L., &
Ujhelyi, M. R. (2003). Do patients accept implantable atrial defibrillation therapy?
Results from the Patient Atrial Shock Survey of Acceptance and Tolerance Study.
Manuscript in preparation.
Buxton, A. E., Lee, K. L., & Fisher, J. D. (1999). A randomized study of the prevention
of sudden death in patients with coronary artery disease. New England Journal of
Medicine. 341. 1882-1890.
Carney, R. M., Saunders, R. D., Freedland, K. E., Stein, P., Rich, M. W., & Jaffe, A. S.
(1995). Association of depression with reduced heart rate variability in coronary
artery disease. American Journal of Cardiology, 76. 562-564.
Cohen, S., Mermelstein, R., Kamarck, T., & Hoberman, H. M. (1985). Measuring the
functional components of social support. In I. G. Sarason & B. R. Sarason (Eds.),
Social support: Theory, research and applications (pp. 73-94). The Hague, the
Netherlands: Martinus Njhoff.
Deaton, C., Dunbar, S. B., Moloney, M., Sears, S. F., & Ujhelyi, M. (in press). Patient
experiences with atrial fibrillation and treatment with the atrial device therapy.
Heart and Lung.
Devins, G. M., Edworthy, S. M., Seland, T. P., Klein, G. M., Paul, L. C., & Mandin, H.
(1993). Differences in illness intrusiveness across rheumatoid arthritis, end-stage
renal disease, and multiple sclerosis. Journal of Nervous and Mental Diseases. 181.
377-381.
Devins, G. M., Orme, C. M., & Costello, C. G.. (1988). Measuring depressive symptoms
in illness populations: Pscyhometric properties of the Center for Epidemiologic
Studies Depression (CES-D) scale. Psychology and Health. 2. 139-156.
Dreifus, L. S., Fisch, C., Griffin, J. C., Gillette, P. C., Mason, J. W., & Parsonnet, V.
(1991). Guidelines for implantation of cardiac pacemakers and antiarrhythmia
devices. A report of the American College of Cardiology/American Heart
Association Task Force on Assessment and Diagnostic and Therapeutic
Cardiovascular Procedures (Committee on Pacemaker Implantation). Journal of the
American College of Cardiology. 18. 1-13.
Dorian, P., Paquette, M., Newman, D., Green, M., Connolly, S. J., Talajic, M. & Roy, D.
(2002). Quality of life improves with treatment in the Canadian Trial of Atrial
Fibrillation. American Heart Journal. 143. 984-990.
Duru, F., Buchi, S., Klaghofer, R., Mattman, H., Sensky, T., Buddeberg, C., & Candnas,
R. (2001). How different from pacemaker patients are recipients of implantable
cardioverter defibrillator patients with respect to psychosocial adaptation, affective
disorders, and quality of life? Heart. 85. 375-379.


19
weeks was adequate with correlation coefficients ranging from 0.79 to 0.92. A factor
analysis resulted in four factors that resulted in subscales with loadings ranging from
0.303 to 0.838. The subscales were labeled (a) Cognition, (b) Chest Discomfort, (c)
Dyspnea, and (d) Arrhythmias. Subscales were reported to be internally consistent with
Cronbachs alphas > .70. Content validity was reported as adequate with moderate
correlations with the SF-36. In a second validation study (Stofmeel et al., 2001b), 51
patients completed the SF-36 and the Aquarel Questionnaire preoperatively and at 4 to 6
weeks postimplant. The Aquarel Questionnaire was reported to be more sensitive to
changes in health status after pacemaker implantation than the SF-36, with effect sizes
ranging from 0.13 to 0.72. It is important to note that the Aquarel Questionnaire showed
significant improvements on two of its subscales (Dyspnea and Arrhythmias) and the SF-
36 showed improvements on four of its eight subscales (Physical Functioning, Social
Functioning, Role Functioning-Emotional, and Vitality) indicating that the SF-36 was
also sensitive to changes in pacemaker patient functioning. Some limitations to the
studies include the small sample sizes, small effect sizes, and the lack of a criterion
validity measure. Also, an inspection of the questions reveal that they do not ask about
pacemaker functioning or issues, rather they focus on arrhythmia symptoms and
cognitive issues. Further validation of the Aquarel is needed before any definitive
conclusions about its validity are posited.
Implications. In summary, studies comparing pacemaker recipients to the general
population suggest greater levels of depression and anxiety in the pacemaker population.
However, those studies comparing recipients to preimplantation functioning indicate that
quality of life and psychosocial functioning are improved for the majority of pacemaker
patients. This makes understanding the specific issues faced by pacemaker recipients


69
previous studies have shown that the scales of the SF-36 are sensitive to changes in
quality of life from before to after cardiac illness treatment (Bosworth et al., 2001; Lam
et al., 2000; Fritz, 2000), suggesting that measuring quality of life using the SF-36 before
and after implantation is adequate for measuring the efficacy of therapy. However, the
SF-36 is limited in its ability to distinguish between cardiac illness samples and measure
changes in health status for a specific category of disease, if all members of a sample
have been treated or all members remain untreated (Linde, 2000). Evidence provided by
the current research indicates that using the F-PAS in conjunction with the SF-36 when
measuring post-implantation quality of life would be efficacious in determining what
psychosocial interventions would improve patients acceptance of the device and
consequently increase patients health-related quality of life. The post-implantation
continuum of patient acceptance measured by the F-PAS makes its development an
important and relevant addition to the study of implantable devices.
Hypotheses
In order to evaluate the current studys ability to provide evidence regarding the
hypotheses put forth by the researcher, each of the hypotheses should be addressed
separately. The extraction of four factors from the initial 47-item questionnaire and the
extraction of a higher order factor partially support the first hypothesis: a factor analysis
of the F-PAS, using participants responses, will result in five stable factors and these
factors will serve as subscales of the F-PAS. The factors hypothesized by the researcher
were (a) Return to Life, (b) Device-Related Distress, (c) Positive Appraisal, (d) Social
Issues, and (e) Technology-Education-Understanding. Three of the hypothesized factors,
Return to Life, Device-Related Distress, and Positive Appraisal were extracted. These
factors were found to be internally consistent with Cronbachs alphas ranging from 0.79


17
biomedical devices, it stands to reason that Lindes assertion about the adequacy of
quality of life measurement for pacemaker recipients is reflected in the literature on
newer devices such as the implantable cardioverter defibrillator (ICD; Sears et al., 1999),
and the implantable atrioverter defibrillator (IAD; NASPE, 2000). Until the present
study, there have been no attempts to validate a measure that specifically addresses the
quality of life of ICD or IAD recipients.
Quality of Life and the Pacemaker
Incidence. As mentioned earlier, cardiac pacemakers are used to treat
bradyarrhythmias and other conduction defects that cause slow heart rhythms. These
slow rhythms result in a deficit of oxygenated blood being pumped through the body and
may cause dizziness, syncope, or, although rarely, cardiac arrest (Scheidt, 1996). The
pacemaker has been accepted as a treatment option by the general public for more than
three decades (Mcguire et al. 1966). Nonetheless, the combination of cardiac difficulties
and the implantation of a pacemaker have resulted in a higher incidence of psychological
distress for recipients than nonpatients (Aydemir et al., 1997). While extensive research
on the psychosocial component of quality of life of pacemaker patients has not been
reported in the literature, a few studies addressing the issue were found. Aydemir and
colleagues (1997) reported that 19.1% of a sample of 84 pacemaker patients received a
psychological diagnosis that involved depressive symptomatology. These diagnoses
included major depression, mood disorder, and adjustment disorder with depressed
mood. He also reported that pacemaker patients experienced device specific fears and
anxietywith a greater proportion of those experiencing these symptoms being of lower
socioeconomic status and having less education than the total sample. It was also
reported that women with pacemakers experienced more symptoms of depression and


50
hierarchical regressions were computed for each of the eight scales of the SF-36.
Demographic variables were placed in the first block, AFSS scores in the second block,
CES-D and STAI Trait Scale scores in the third block, and IIRS scores in the fourth
block. In the fifth and final block, patient acceptance was added. Separate regressions
were computed using the subscale scores of the F-PAS in the fifth block and with the F-
PAS total score in the fifth block. Residuals, partial regressions, and normal probabilities
were examined and no violations to the regression procedures assumptions of linearity,
constant variance, independence, or normality were found. Only those regression models
where patient acceptance accounted for unique variance are summarized. The Mental
Health scale equation is summarized due to its relevance to mental health care
professionals.
Physical Functioning Scale. In the set of regressions on the Physical Functioning
Scale of the SF-36, the demographic variables of age, gender, and marital status
significantly accounted for 11.3% of the variance in scores. Arrhythmia symptoms
significantly accounted for an additional 19.2%, depression and anxiety another 3.3%,
and IIRS scores significantly accounted for an additional 2.5%. Patient acceptance, as
measured by the F-PAS subscale scores, significantly predicted an additional 4.4% of the
variance in scores when the variance accounted for by demographics and all other
measures had been considered. In sum, the significant model accounted for 45.5% (p <
.001) of the variance in physical functioning. A summary of model statistics for each
block and regression coefficients for the final model are presented in Table 4-10. When
patient acceptance was measured by the F-PAS total score, no significant additional
variance was accounted for beyond that accounted for by demographics and the
components of the biopsychosocial model. In all, the model significantly accounted for


40
The mean score of the current sample was not significantly different from the mean score
of 28.0 (SD = 19.0) obtained by a normative sample of healthy individuals. For a
summary of the means, standard deviations, and mean comparison statistics for
normative samples on the core battery measures of the biopsychosocial model of illness,
please see Table 4-3.
Table 4-3. Obtained scores on the biopsychosocial model of illness component
measures for normative samples and comparison statistics for current sample
Comparison
group M/SD
Device
group M/SD
df
Welchs t
Comparison groups
AFSS
Atrial fibrillation patients
(N = 264)
20.4 9.4
26.5 12.9
273
5.831*
CES-D
Cardiac bypass patients
> 65 yrs. (N = 120)
o
1+
vO
10.4 8.7
168
0.549
Healthy norms
(N = 2,514)
9.3 8.6
10.4 8.7
180
1.874
STAI trait scale scores
Medical procedure patients
(N= 110)
41.3 12.6
33.6 10.3
128
5.645*
Healthy males
(N = 382)
33.9 8.9
33.6 10.3
307
0.319
Healthy females
(N= 106)
31.8 7.8
33.6 10.3
186
1.787
IIRS Scores
Atrial fibrillation patients
(N= 152)
35.0 15.0
27.2 15.1
226
4.706*
Healthy norms
(N = 47)
28.0+19.0
27.2 + 15.1
44
0.267
< .001
Development of the F-PAS
In a previous survey of 448 ICD patients, respondents were asked 51 questions
about their concerns about having an implantable device. The 10 most frequent concerns
of ICD patients were concerns about the quality of their health, the device's effects on


11
patients with unsustained tachycardia (Buxton et al., 1999). However, of patients who
have undergone coronary artery bypass grafting, patients with an ICD have no significant
survival advantage over those with no ICD. The influence of revascularization on
ischemia and left ventricular functioning were cited as possible reasons for the lack of
ICD benefit (Block & Breithardt, 1999). Perhaps as the results of these studies are
reported, indications for the use of ICD will expand much as indications for the cardiac
pacemaker did after its efficacy in atrioventricular block was established.
Description. Like the implantable cardiac pacemaker, the ICD consists of leads and
a defibrillator can. The can of the defibrillator contains the circuitry of the defibrillator,
batteries, and a capacitor that can store and provide high-voltage shocks when necessary.
The ICD is also placed in a pectoral pocket, and the leads can be implanted endocardially
or epicardially with the same limits and advantages as seen for pacemaker implantation.
There are several configurations for lead placement. Coiled electrodes attached to the
leads are placed in the right ventricle and the superior vena cava. These electrodes
deliver defibrillation discharges. Sensor-pacer leads sense ventricular fibrillation and
provide pacing when defibrillation is unnecessary (Xie et al., 1998).
Function. A major difference between cardiac pacemakers and ICDs is the modes
or discharges of electrical stimulation that the ICD is capable of delivering to return a
patients heart to sinus rhythm. Four main types of ICD discharges are provided based
on the severity of the arrhythmia; this four-discharge system is referred to as tiered
therapy.
Bradycardia pacing. This first type of ICD discharge, very low-energy pulses, is
delivered to the heart when the heartbeat slows down too much. This is the type of
intervention provided by a cardiac pacemaker. It is usually not felt by the patient.


84
Lam, C. T., Lau, C. P., Leung, S. K., Tse, H. F., Lee, K. L., Tang, M. O., & Tsang, V.
(2000). Efficacy and tolerability of continuous overdrive atrial pacing in atrial
fibrillation. Europace. 2. 286-291.
Lamas, G. A., Orav, E. J., Stambler, B. S., Ellenbogen, K. A., Sgarbossa, E. B., Huang,
S. K., Marinchak, R. A., Estes, N. A. 3rd, Mitchell, G. F., Lieberman, E. H.,
Mangione, C.M ., & Goldman, L. (1998). Quality of life and clinical outcomes in
elderly patients treated with ventricular pacing as compared with dual-chamber
pacing. Pacemaker selection in the elderly investigators. New England Journal of
Medicine. 338. 1097-1104.
Lau, C. P., Rushby, J., Leigh-Jones, M., Tam, C. Y., Poloniecki, J., Ingram, A., Sutton,
R., & Camm, A. J. (1989). Symptomatology and quality of life in patients with rate-
responsive pacemakers: A double-blind, randomized, cross-over study. Clinical
Cardiology. 12. 505-512.
Lawton, M. P., Moss, M., Hoffman, C., Grant, R., Have, T. T., & Kleban, M. H. (1999).
Health, valuation of life, and the wish to live. Gerontologist. 39.406-415.
Levine, S. (1987). Contemporary issues in health, medicine and social policy. In S.
Levine & A. Lilienfeld (Eds.), Epidemiology and health policy. New York:
Tavistock.
Linde-Edelstam, C., Nordlander, R., Unden, A., Orth-Gomer, K., & Ryden, L. (1992).
Quality of life in patients treated with atrioventricular synchronous pacing compared
to rate modulated ventricular pacing: A long-term, double-blind, crossover study.
Pacing and Clinical Electrophvsiology. 15. 1467-1476.
Linde, C. (2000). Quality of life in pacemaker and implantable cardioverter defibrillator
recipients. Pacing and Clinical Electrophvsiology. 23. 931-932.
Lip, G. (2001). Atrial fibrillation in clinical practice. London: Martin Dunitz Ltd.
Luderitz, B. (2002). We have come a long way with device therapy: Historical
perspectives on antiarrhythmic therapy. Journal of Cardiovascular
Electrophvsiology. 13. S2-8.
Luderitz, B., & Jung, W. (2000). Quality of life in patients with atrial fibrillation.
Archives of Internal Medicine. 160. 1749-1757.
Luderitz, B., Wemer, J. Deister, A., Memaros, A., & Manz, M. (1993). Patient
acceptance of the implantable cardioverter defibrillator in ventricular
tachyarrhythmias. Pacing and Clinical Electrophvsiology. 16. 1815-1821.
Maglio, C., Sra, J., Paquette, M. S. (1998). Measuring quality of life and symptom
severity in patients with atrial fibrillation. North American Society of Pacing and
Electrophvsiology Abstracts in Pacing and Clinical Electrophvsiology. 21. 97.


12
Antitachycardia pacing (ATP). This is the second, and most common, type of
pacing. Many patients also do not feel ATP when it is delivered. Patients who do
feel ATP describe it as a fluttering in the chest.
Cardioversion. This is the third type of discharge delivered by the ICD. It consists
of low-energy shocks that can feel mildly uncomfortable. The patient typically
describes these shocks as feeling like a thump on the chest.
Defibrillation. The fourth type is the strongest discharge delivered by the ICD.
Many patients will become faint or lose consciousness when their arrhythmia
becomes severe enough to warrant defibrillation. Because of the physical
symptoms of a severe arrhythmia, patients are unlikely to feel the high-energy
shocks of defibrillation. However, if a patient is conscious, the shock will feel
much like a kick in the chest and will occur suddenly. It is described as painful
by most patients, but it is very brief, usually lasting for only a second.
(Medtronic, Inc., 2002).
Implantable Atrioverter Defibrillator
Incidence. The first implantable atrioverter defibrillator (LAD) was implanted in
1996 in Germany in a patient with symptomatic, drug refractory atrial fibrillation. While
the device underwent clinical trials that resulted in its acceptance by the Federal Drug
and Food Administration as a treatment plan for atrial fibrillation, only slightly more than
350 IADs have been successfully implanted in patients with atrial fibrillation that is not
responsive to drug therapy (Gold et al., 2001; Luderitz & Jung, 2000). Atrial fibrillation
(AF) is the most common cardiac arrhythmia and is responsible for most hospital
admissions related to cardiac arrhythmias. It increases the risk of stroke and
thromboembolism and has hemodynamic effects that can result in decreased exercise
tolerance and heart failure. Atrial fibrillation has become a focus of attention due to its
growing prevalence in the elderly population (Lip, 2001) with 2% to 4% of people over
the age of 65 being diagnosed with the condition (Luderitz & Jung, 2000).
Indications. It is important to note that atrial fibrillation is not always symptomatic
and is not immediately life-threatening. Approximately one-third of people with atrial




68
patients. The information obtained has important implications for the development and
implementation of psychosocial interventions that address the distress associated with the
implantation of a cardiac device, as well as, possible implications for the research and
design of future implantable cardiac devices.
In order to evaluate the importance of the development of the F-PAS, it is necessary
to consider the value of previous measures in regard to their role in the development of
psychosocial interventions for increasing health-related quality of life in cardiac
populations. The Karolinska Questionnaire (Linde-Edelstam et al., 1992), the Minnesota
Living with Heart Failure Questionnaire (Rector & Cohn, 1992), the Cardiac Arrythmia
Suppression Trial Quality of Life Scale (CAST; Wiklund et al., 1992) and other
rationally developed instruments (Rose & Blackburn, 1968; Oto et al., 1991) have been
used to assess quality of life in cardiac patients from before to after treatment and
between cardiac illness samples. The Aquarel survey (Stofmeel, 2001a) measured
changes in functioning from before to after the implantation of a pacemaker. The F-PAS
assesses patients acceptance only after treatment. This is an important distinction due to
the fact that pacemakers, ICDs and future implantable cardiac devices are likely to be a
patients only treatment option. Measuring their psychosocial functioning before and
after the implantation of the device does not provide information that can be used to
improve patients health related quality of life after receiving a device they had no choice
but to receive. While the Luderitz et al. (2000) questions regarding patient acceptance of
ICDs was a post-implantation measure of acceptance, its dichotomous response options
do not allow for incremental changes in acceptance. The F-PAS provides a continuum of
acceptance on individual items, subscale scores, and total score; this continuum makes it
possible to measure incremental increases or decreases in patient acceptance. Also,


83
Engel, G. L. (1977). The need for a new medial model: A challenge for biomedicine.
Science. 196. 129-136.
Erdman, R. A., Passchier, J., Koojiman, M., & Stronks, D. L. (1993). The Dutch version
of the Nottingham Health Profile: Investigations of psychometric aspects.
Psychological Reports. 72. 1027-1035.
Feifel, H., Strack, S. & Nagy, V. T. (1987). Coping strategies and associated features of
medically ill patients. Psychosomatic Medicine. 49. 616-625.
Fellows, C. L., Mcquillan, S., & Rodenhiser, K. L. (2000). Does the quality of life
improve over time in patients implanted with the Metrix atrial defibrillator? North
American Society of Pacing and Electrophvsiology Abstracts in Pacing and Clinical
Electrophvsiology. 23. 503.
Frasure-Smith, N., Lesperance, F., & Talajic, M. (1993). Depression following
myocardial infarction. Impact on six month survival. Journal of the American
Medical Association. 270. 1819-1825.
Frasure-Smith, N., Lesperance, F., & Talajic, M. (1995). Depression and eighteen month
prognosis after myocardial infarction. Circulation. 91. 999-1005.
Fritz, H. L. (2000). Gender-linked personality traits predict mental health and functional
status following a first coronary event. Health Psychology. 19. 420-428.
Gold, M. R., Sulke, N., Schwartzman, D. S., Mehra, R., & Euler, D. E. (2001). Clinical
experience with a dual-chamber implantable cardioverter defibrillator to treat atrial
tachyarrhythmias. Journal of Cardiovascular Electrophvsiology. 12.1247-1253.
Gregoratos, G. (1999). Permanent pacemakers in older persons. Journal of the American
Geriatrics Society. 47. 1125-1135.
Grimm, L. G., & Yamold, P. R. (1997). Reading and understanding more multivariate
statistics. Washington, DC: American Psychological Association.
Hamilton, D. M., & Haennel, R. G. (2000). Validity and reliability of the six minute walk
test in a cardiac rehabilitation population. Journal of Cardiopulmonary
Rehabilitation. 20. 156-164.
Herbst, J. H., Goodman, M., Feldstein, S., & Reilly, J. M. (1999). Health related quality
of life assessment of patients with life-threatening ventricular arrhythmias. Pacing
and Clinical Electrophvsiology. 22. 915-926.
Howell, D. C. (1997). Statistical methods for psychology (4th ed.T Belmont, CA:
Wadsworth.


70
to 0.89. The fourth factor, Body Image Concerns, was also found to be internally
consistent with a Cronbachs alpha of 0.74. The extraction of a single higher order factor
supports the first hypothesis by allowing for the computation of a total score. The final
15 items of the F-PAS were found to be internally consistent with a Cronbachs alpha of
0.83.
Three of the items retained in the three final factors that were originally
hypothesized did not load on the factor on which they were predicted to load. The item
I am not able to do things for my family the way I used to, was predicted to load under
a factor labeled Social Issues. The social issues factor was not extracted. A possible
explanation for the item loading under the RTL factor may be the inclusion of the phrase,
the way I used to. This phrase is suggestive of doing things the way they were done
before the implantation, making the item interpreted as a return to life issue. The other
two items that did not load on the scales they were predicted to load on were, When I
think about the device I avoid doing things I enjoy, and I am careful when hugging or
kissing my loved ones. A possible explanation for their loading under the DRD
subscale may be that having to alter once pleasurable behaviors is distressing. The items
loading on the BIC factor were predicted to load on the DRD factor. This finding
indicates that concerns about body image are different from device-related distress and
should be considered separately in future investigations of patient acceptance.
The second hypothesisthe F-PAS would be moderately correlated with the
subscales of the SF-36 and would account for a significant amount of the variance in
scores on the scales of the SF-36 that was not accounted for by demographic variables,
physiological indicators, or affective symptomswas also partially supported. Moderate
correlations with the SF-36 were posited to provide construct validity of patient


31
In an effort to decrease participant burden, ICD and pacemaker patients completed a core
set of measures and one of five noncore measures, including the Interpersonal Support
Evaluation ListShort Form (ISEL), the State Trait Anxiety Inventory State Scale
(STAI-State), the Life Orientation Test (LOT), the Medical Coping Modes Questionnaire
(MCMQ), and the Positive and Negative Affect Scale (PANAS). The noncore measures
were shuffled and randomly placed in the questionnaire batteries as they were being
constructed.
Quality of Life: Short Form-36 Health Survey (SF-361
The SF-36 (Ware, Snow, Kosinski, & Gandek, 1993) is a 36-item, self-report
questionnaire that measures health-related quality of life. The SF-36 was both rationally
and empirically developed as part of the Medical Outcomes Study (Ware et al., 1993). It
has eight health domain scales including: physical functioning, role limitations due to
physical problems, role limitations due to emotional problems, social functioning, bodily
pain, mental health, vitality and general health perceptions. Despite having only a few
items on each scale, the internal reliabilities of the scales range from 0.77 (Social
functioning) to 0.92 (Physical functioning) in a sample of 3,053 adults (Stewart, Hays, &
Ware, 1988). The items and scales of the SF-36 are scored so that a higher score
indicates a better health state.
Biological Component Measure: AF Symptoms Severity Scale (AFSS)
The AFSS (Maglio, Sra, & Paquette, 1998) is a 13-item instrument that was
rationally developed to assess disease specific symptom severity in atrial fibrillation
patients. It has been found to have good internal reliability and a factor analysis revealed
three subscales: AF Severity, Health Care Utilization and Overall Life Satisfaction. The


CHAPTER 2
LITERATURE REVIEW
This chapter provides a brief review of the cardiac arrhythmia nomenclature and a
description of three implantable biomedical devices: the cardiac pacemaker, the
implantable cardioverter defibrillator (ICD), and the atrioverter defibrillator (IAD). It
also reviews the literature regarding the quality of life of device recipients and provides a
rationale for the construct of patient acceptance.
Cardiac Arrhythmias
A cardiac arrhythmia is an abnormality of electrical impulse conduction within the
heart. For a clear picture of conduction abnormalities seen in the heart, one needs a
rudimentary understanding of the terms used to describe cardiac rhythms. The simple
formula has three parts: (a) the location of the hearts pacemaker, (b) a description of
any abnormalities, and (c) a classification of the rate or rhythm. For example, a rate of
60 to 100 beats per minute (bpm) with the sinoatrial node serving as the pacemaker and
without any conduction abnormalities is called a normal sinus rhythm. A tachycardia is
a fast heart rate of more than 100 bpm, and a bradycardia is a heart rate of less than 60
bpm. An example of an arrhythmia would be a supraventricular tachycardia, where the
location of the hearts pacemaker would be supra (above) ventricular (the ventricles);
and the rate of the heart would be tachycardic or too fast. In other words, a
supraventricular tachycardia is an abnormally fast heart rate that is caused by pacemaker
cells located above the ventricles. Some other important terms in the arrhythmia
4




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Table 4-14. Summary of hierarchical regression model for variables predicting SF-36 social scale scores using F-PAS subscale
scores as a measure of patient acceptance
Variable
B
SEB
P
t
P
R2
R2A
Step 1
Age
-5.094E-02
.161
-.024
-.316
.752
Gender
6.957
3.667
.137
1.897
.060
Marital status
-5.772
4.003
-.102
-1.142
.151
.025
.025
Step 2
ARSS
-.349
.152
-.183
-2.294
.023
.191
.165
Step 3
CES-D
-.960
.318
-.340
-3.019
.003
STAI trait
4.006E-02
.241
-.017
-.166
.868
.356
.165
Step 4
IIRS
-.188
.163
-.115
-1.156
.260
.364
.008
Step 5
RTL
.204
.085
.217
2.413
.017
DRD
.161
.099
.128
1.623
.107
PA
-5.822E-02
.096
-.044
-.605
.546
BIC
.153
.102
.110
.495
.137
.414
.050
* Predictor of unique variance (p < .05)


15
used because such instruments are not age, disease, or treatment specific, (Ware, 1993;
p. 6). These instruments provide information on several domains of life and are useful
for comparing quality of life across different patient populations (Stofmeel et ah, 2000).
Measurement. Three of the most widely used and well-validated generic measures
of health-related quality of life are the Sickness Impact Profile (SIP; Bergner, 1981), the
Nottingham Health Profile (NHP; Erdman, Passchier, & Kooijman, 1993), and the
Medical Outcomes Study Short-Form 36 Health Survey (SF-36; Ware, 1993). In a
comparison study of the three instruments, Stofmeel et al. (2000) reported that the SF-36
is the most efficient and sensitive of the instruments for evaluating the quality of life of
pacemaker patients. The SF-36 has been consistently used in quality-of-life outcome
studies of cardiac patients (Bosworth et ah, 2001; Fritz, 2000; Lam et ah, 2000). Its use
to validate other measures in cardiac illness research has lead to its utilization as the gold
standard measure of generic quality of life in cardiac populations (Hamilton & Haennel,
2000; Stofmeel, Post, Kelder, Grobbee, & van Hemel, 2001a).
The SF-36 resulted from the Health Insurance Experiment (HIE), an attempt to
develop and refine health status surveys that measured an array of health-status and well
being concepts for both adults and children. The HIE showed that self-administered
survey scales were valid instruments for assessing health status in the general population.
This validation of surveys lead to the Medical Outcomes Study (MOS). Ware (1993)
used comprehensive surveys to assess peoples perceptions of 40 physical and mental
health concepts, using a sample of over 20,000 U.S. residents. The SF-36 was
constructed to include the eight most salient concepts described by MOS participants.
The eight concepts are represented by the eight scales of the SF-36 which are Physical
Functioning, Physical Role, Bodily Pain, General Health, Vitality, Social Functioning,



PAGE 4

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Hypotheses 4 and 5: Differences in Patient Acceptance
Differences between device groups in regard to patient acceptance were also
65
evaluated using ANOVAs. Differences between groups were found on the RTL
subscale, F (2, 227) = 8.293, p < .001. A post hoc Tukey a procedure revealed that ICD
patients obtained significantly lower RTL subscale scores than pacemaker and IAD
patietnts. Significant between group differences were found on the DRD subscale, F (2,
225) = 6.497, p = .002. Pacemaker patients reported significantly less device related
distress than both ICD and IAD patients. Device groups did not differ significantly on
the PA subscale of the F-PAS. Group differences were significant on the BIC subscale, F
(2, 228) = 5.399, p = .005. Tukey a results indicated that IAD patients reported
significantly more body image concerns than pacemaker patients. There were also
significant differences between groups on the F-PAS total score, F (2, 209) = 6.698,
p = .002. Implantable cardioverter defibrillator patients had significantly lower patient
acceptance than pacemaker patients. For a summary of between group differences see
Table 4-22. For a summary of homogenous subsets see Table 4-23. Again, demographic
variables were covaried to determine if significant group differences were better
accounted for by age, gender, and marital status. When demographic variables were
covaried the main effect for RTL by device group was significant, F (2, 205) = 7.275,
p = .001. Age was a significant covariate for scores on the DRD Scale, F (2,
203) = 8.321, p = .004; however, even after differences between groups on demographic
variables were controlled, there was a significant main effect for DRD by device group, F
(2, 202) = 6.097, p = .003. There were no significant covariates for scores on the BIC
subscale and the main effect for BIC by device group was not significant, F (2,206) =


This dissertation was submitted to the Graduate Faculty of the College of Health
Professions and to the Graduate School and was accepted as partial fulfillment of the
requirements for the degree of Doctor of Philosophy.
August 2003
cr=j'^/'c^
Dean, College of Health Professions
Dean, Graduate Sc 100I


86
Rector, T. S., & Cohn, J. N. (1992). Assessment of patient outcome with the Minnesota
Living with Heart Failure questionnaire: Reliability and validity during a
randomized, double-blind, placebo-controlled trial of pimobendan. American Heart
Journal, 124. 1017-1025.
Rose, G. A., & Blackburn, H. (1968). Survey questionnaires. Cardiovascular methods.
Geneva: World Health Organization.
Scheidt, S. S. (1996). A whirlwind tour of cardiology for the mental health professional.
In R. Allan & S. S. Scheidt (Eds.), Heart and mind: The practice of cardiac
psychology (pp. 15-62). Washington, DC: American Psychological Association.
Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing optimism from
neuroticism (and trait anxiety, self-mastery, and self-esteem): A review of the Life
Orientation Test. Journal of Personality and Social Psychology, 67, 1063-1078.
Scheier, M. G., & Carver, C. S. (1985). Optimism, coping, and health: Assessment and
implications of generalized outcome expectancies. Health Psychology, 4, 219-247.
Sears, S. F., Bums, J. L., Handburg, E., Sotile, W. M., & Conti, J. B. (2002). Young at
heart: Understanding the unique psychosocial adjustment of young implantable
cardioverter defibrillator recipients. Pacing and Clinical Electrophvsiologv, 24,
1113-1117.
Sears, S. F., Bums, J. L., Schwartzman, D. S., Hoyt, R. H., Alvarez, L., & Ujhelyi, M.
(2002). Do patients accept implantable atrial therapy? Published abstract. North
American Society of Pacing and Electrophvsiologv: 23rd Annual Scientific Session.
San Diego, CA.
Sears, S. F., & Conti, J. B. (2002). Quality of life and the psychological functioning of
ICD patients. Heart. 87, 488-493.
Sears, S. F., Todaro, J. F., Saia, T. L., Sotile, W., & Conti, J. B. (1999). Examining the
psychosocial impact of implantable cardioverter defibrillators: A literature review.
Clinical Cardiology, 22, 481 -489.
Shah, C. P., Thakur, R. K., Xie, B., & Hoon, V. K. (1998). Implantable cardioverter
defibrillators. Emergency Medicine Clinics of North America, 16,463-489.
Smith, S. C., Jr., Blair, S. N., Bonow, R. O., Brass, L. M., Cerquira, M. D., Dracup, K.,
Fuster, V., Gotto, A., Grundy, S. M., Miller, N. H., Jacobs, A., Jones, D., Krauss, R.
M., Mosca, L, Ockene, L, Pasternak, R. C., Pearson, T., Pfeffer, M. A., Starke, R. D.,
& Taubert, K. A. (2001). AHA/ACC scientific statement: AHA/ACC guidelines for
preventing heart attack and death in patients with atherosclerotic cardiovascular
disease: 2001 update: A statement for healthcare professionals from the American
Heart Association and the American College of Cardiology. Circulation. 104,1577-
1579.


CHAPTER 5
DISCUSSION
The current study achieved its three main purposes of (a) developing a measure of
patient acceptance of implantable devices, (b) beginning the psychometric validation of
the patient acceptance measure, and (c) comparing patient acceptance and quality of life
in pacemaker, implantable cardioverter defibrillator (ICD), and implantable atrioverter
defibrillator (IAD) patients. Forty-seven items of patient acceptance were constructed
from empirical data and information obtained from experts in the fields of psychology,
electrophysiology, and psychometrics. Through statistical analyses these items were
reduced to 15 essential items that comprise four factors, labeled Return to Life (RTL),
Device-Related Distress (DRD), Positive Appraisal (PA), and Body Image Concerns
(BIC). The four factors were found to be internally consistent and were used as subscales
while a reliable higher order factor was used as a single score of patient acceptance. The
Florida Patient Acceptance Survey (F-PAS) revealed that, overall, pacemaker patients
have significantly greater patient acceptance of their device than ICD patients and less
device-related distress than both ICD and IAD patients. It also indicated that pacemaker
and IAD patients have fewer concerns about returning to their former lives than ICD
patients. The F-PAS is the first self-report measure to distinguish significant differences
in patients acceptance of different implantable cardiac devices and the first empirically
investigated device/treatment specific instrument to provide a common metric used to
study this uniquely homogenous (arrhythmia) and heterogeneous (device type) group of
67


35
self esteem, depression, adjustment, and psychopathology (Devins et al., 1988; Devins et
al 1993).
Interpersonal Support Evaluation ListShort Form (ISELT The short form ISEL
(Cohen, Mermelstein, Karmack, & Hoberman, 1985) is a 16-item, self-report measure
that was developed to measure perceived availability of supportive social resources that
might facilitate coping with stressful situations. The items are rated on the likelihood
that the support described would be available if it was needed (probably true or false).
Half of the items are phrased in the positive direction while the other half are phrased in
the negative direction. This measure has been shown to have strong internal consistency
and to moderately correlate with other measures of social support, such as the Inventory
of Socially Supportive Behaviors (r_= .46) and the Moos University Residence
Environment Scale (r = .62; Cohen et al., 1985). There are 4 scales on this measure that
assess the following domains of social support: (a) perceived ability of someone to talk to
about ones problems (Appraisal), (b) perceived availability of people with whom to do
things (Belonging), (c) perceived availability of positive social comparison (Self-
Esteem), and (d) perceived availability of instrumental assistance (Tangible). For the
purpose of this study these four scales will be combined into a total perceived social
support score, which has been indicated in the literature as a valid indicator of perceived
availability of social support (Bennett, 1993; Brookings & Bolton, 1988; Cohen at al.,
1985).


CHAPTER 3
METHOD
Participants
The sample was 243 patients implanted with a cardiac biomedical device for at
least 3 months. Ninety-six of the participants were IAD patients participating in the
Patient Atrial Shock Survey of Acceptance and Tolerance Study (PASSAT Study). The
PASSAT Study was a cross sectional study of patients that received the Jewel AF 7250,
an IAD designed by Medtronic, Inc. Investigators participating in the 7250 Jewel AF-AF
Only clinical trial were eligible for participation in the PASSAT Study if they had more
than five patients enrolled in the study for 3 months or more. Seventeen of the 45 centers
met this criteria and nine investigators agreed to participate (Bums et al., 2003). Fifty-
eight participants were ICD patients and 84 were pacemaker patients recruited from the
Watson Outpatient Electrophysiology Clinic in Lakeland, Florida; device information
was not collected for five participants from the outpatient clinic. Participants in the
PASSAT Study were given $50.00 for their participation. The ICD and pacemaker
patients were volunteers and received no monetary compensation. Individuals were
excluded from the study if they were (a) less than 18 years of age, (b) unable to read and
write in English, or (c) were deemed cognitively impaired by recruiting healthcare
providers. Informed consent was obtained via approved procedures as required by the
Institutional Review Boards (IRB) at both the University of Florida Health Sciences
Center and the Watson Outpatient Clinics. A total of 338 patients were approached to
participate in the study, yielding a response rate of 72%.
28


43
Table 4-4. Initially retained items, factor loadings, and reliability coefficients
Absolute
Item
loading
a
Factor 1
I am not able to do things for my family the way I used to.
.754
I am confident about my ability to return to work if I want to.
.740
I am concerned about resuming my daily physical activities.
.705
I have returned to a full life.
.686
I have continued my normal sex life.
.663
.50
Factor 2
When I think about the device I avoid doing things I enjoy.
.798
I avoid my usual activities because I feel disfigured by my
.738
device.
It is hard for me to function without thinking about my device.
.735
Thinking about the device makes me depressed.
.572
I am careful when hugging or kissing my loved ones.
.536
.79
Factor 3
The positive benefits of this device out-weigh the negatives
.760
I would receive this device again.
.703
I am safer from harm because of my device.
.683
My device was my best treatment option
.680
.82
Factor 4
I feel less attractive because of my device.
.716
I feel that others see me as disfigured by my device.
.688
.74
Factor 5
I am knowledgable about how the device works and what it does
for me.
.687
I know enough about my device.
.657
.59
Factor 6
My family feels more secure because of my device.
.700
I have fewer symptoms because of my device.
.606
.53
factor loadings, and Cronbachs alphas. The relatively low internal consistency 0.59 and
0.53 for Factors 5 and 6 respectively, and the small number of items in each factor
resulted in their being excluded as possible subscales of the F-PAS and from further
analyses in the current research. In order to determine if the internal consistency of
Factor 1 could be increased through the removal of a single item, item response


23
(n = 124) or ICD (n = 86) were recruited to participate in the study. Participants ranged
in age from 40 to 70 years. It was reported that there were no significant differences
between pacemaker patients, ICD patients who had not experienced a device discharge
(nonshocked), and ICD patients who had experienced a discharge (shocked) on the
Hospital Anxiety and Depression Scale (HAD) or any of the subscales of the SF-36. The
researchers reported that 18.5% of pacemaker patients, 16.2% of nonshocked ICD
patients, and 19.3% of shocked ICD patients reported psychological sequealae. It was
also reported that shocked ICD patients were more likely to report limitations to their
leisure activities, to admit anxiety about technical problems with their device, and to
want to be a part of a support group. Shocked patients were also more likely to consider
their ICD to be a life extender. A significant limitation to the study was the absence of
a cardiac specific measure of quality of life. There were no studies found that compared
psychosocial adjustment, quality of life, or patient acceptance between pacemaker and
IAD recipients or IAD and ICD recipients.
The Construct of Patient Acceptance
Definition
The term patient acceptance has been used in the implantable device literature to
describe patients perception of the device, the perception of possible discharge,
changing body image, changes in lifestyle, patient and family perceptions, home going
concerns, and fear of complications (Luderitz et al., 1993; p. 1816). It has also been
defined as a process characterized by choosing to live with technology, integrating
technology into life, and living life through technology (Burke, 1996). For the purposes
of the current research, patient acceptance is the psychological accommodation and
understanding of the pros and cons of the device, the recommendation of the device to


74
subscale score may indicate that relaxation strategies and cognitive restructuring are most
appropriate.
While not all of the researchers hypotheses were fully supported, the majority of
them were partially or mostly supported. This suggests that the ability of the current
study to evaluate the questions being asked was adequate. The support for a majority of
hypotheses and a lack of support for aspects of some hypotheses is indicative of both
strengths and limitations of the current investigation.
Strengths and Limitations
A major strength of the study is its uniqueness with regard to the current literature
in the area of psychosocial adjustment of cardiac device patients. It is the first study to
make group comparison across three different device groups. It is the first study to
provide validity estimates for a common metric that measures differences between
implantable cardiac device groups. It is also the first investigation of a self-report
instrument that provides information that is efficacious in the application of psychosocial
science in regard to implantable cardiac device patients.
The use of a mixed device group sample to validate the F-PAS contributes to the
literature by increasing the likelihood that the measure is valid across device populations
and provides an accurate picture of patients acceptance of implantable technology.
Another strength is the use of an assessment battery constructed of standardized and
validated measures of psychosocial functioning chosen to measure each component of
the biopsychosocial model of illness. The battery allowed the construct validity of the F-
PAS to be assessed via a multi-trait design and helped ensure that both discriminant and
convergent validity would be assessed. Finally, a strength of the study that should be
addressed is the inclusion of participants from both private and research settings. The


CHAPTER 4
RESULTS
Assessment Measures
A summary of the scores obtained by the total sample on measures of health-related
quality of life and of scores obtained on measures of each component of the
biopsychosocial model of illness is presented in Table 4-1. Differences from normative
samples on core battery measures were calculated using the Welchs T statistic for
conservative comparisons between groups with unequal sample size and unequal
variance (Howell, 1997).
Health-Related Quality of Life
The sample obtained a mean score of 57.19 (SD = 28.56) on the Physical
Functioning Scale of the SF-36. This score is significantly lower than the mean score
obtained by a sample of 442 healthy 65 to 74 year olds, t (279) = 5.268, £ < .001 and
significantly higher than a sample of 216 congestive heart failure patients t (302) = 3.361,
P < .001. On the Physical Role Scale, the current samples mean score of 46.67
(SD = 42.15) was also significantly lower than the healthy samples, t (293) = 4.264,
£ < .001 and significantly higher than the congestive heart failure samples, t (301) 3.873,
£ < .001. The current samples obtained mean score on the Bodily Pain Scale was 63.62
(SD = 24.76). This was significantly lower than the healthy samples, t (312) = 2.303,
£ < .05, but not significantly different from the congestive heart failure samples. The
mean score of 56.86 (SD = 22.14) obtained by the current sample on the General Health
36


I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
samuel F. Sears, Jr., Chair
Associate Professor of Clinical and Health
Psychology
I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
Garret D. Evans
Associate Professor of Clinical and Health
Psychology
I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
I certify that I have read this study and that in my opinion it conforms to
acceptable standards of scholarly presentation and is fully adequate, in scope and quality,
as a dissertation for the degree of Doctor of Philosophy.
Jqres V. Jessup
ssociate Pro


CHAPTER 1
INTRODUCTION
Cardiac arrhythmias play a role in over 500,000 deaths each year in the United
States and are responsible for approximately 724,000 hospitalizations. The estimated
prevalence of cardiac arrhythmias is 3,900,000; roughly 11.9 billion dollars is spent on
treating arrhythmias annually (Smith et al., 2001). In an effort to find effective and cost-
efficient methods for treating these patients, medicine has succeeded in the research and
development of extraordinary implantable cardiac biomedical devices that provide
targeted therapies and efficiency. While the biological benefits of these devices are well-
documented (Antiarrhythmics Versus Implantable Defibrillators [AVID] Investigators,
1997; Gold, Sulke, Schwartzman, Mehra, & Euler, 2001; Stofmeel, Post, Kelder,
Grobbee, & van Hemel, 2000), their psychosocial impact and effect on medical outcomes
is less understood and warrants more concentrated effort by health psychology
researchers (Aydemir et ah, 1997; Sears & Conti, 2002).
The effect of psychosocial variables on medical end points was first addressed by
Engel (1977) in his biopsychosocial model of illness. He stated that a medical model
must take into account the patient, the social context in which he lives and the
complementary system devised by society to deal with the disruptive effects of illness
(p. 132). Engels model is supported by recent research on the role of psychosocial
distress in the prognosis of individuals with cardiovascular diseases. Several studies
show that psychosocial variables have a significant impact on mortality and morbidity of
1


78
designers to improve patients acceptance by changing less acceptable attributes of a
device.
The most remarkable conclusion to be drawn from this study may be that
implantable cardiac device patients generally accept their devices and appraise them
positively. This information is especially important when one considers the number of
technological advances that have been made in recent years. As more and more devices
are developed it will become paramount to measure patients acceptance of these devices
and to determine how mental healthcare professionals may be utilized to improve the
quality of life of device recipients. In sum, the current research has provided evidence
for the existence of the often referred to construct of patient acceptance; it has
highlighted the important role of psychosocial issues in the treatment of cardiac
arrhythmia patients; and it has laid the groundwork for future studies of this important
facet of health-related quality of life.


63
ICD (p < .001) patients. Significant group differences were also found on the Physical
Role Scale, F (2, 207) = 5.712, p = .004. Implantable atrioverter defibrillator patients
obtained significantly higher scores on the scale than the pacemaker (p =.046) and ICD
(p = .004) patients. There were also significant differences between groups on the Bodily
Pain Scale, F (2, 205) = 4.825, p = .009. The Tukey a procedure revealed that the IAD
patients obtained higher scores on the Bodily Pain Scale than the other groups (p = .026
and p = .004; pacemaker and ICD patients respectively). Finally, significant differences
between groups were also found on the General Health Scale F (2, 207) = 6.235, p <
.002. Implantable cardioverter defibrillator patients reported significantly poorer general
health than both pacemaker (p = .026) and IAD (p = .002) patients. See Table 4-20 for a
summary of group differences and Table 23 for homogenous subsets. To determine if the
significant differences between groups might be better accounted for by significant
differences on demographic variables, univariate analysis of covariance procedures using
age, gender, and marital status as covariates (ANCOVAs) were computed. On the
Physical Functioning Scale, age was a significant covariate, F (1, 190) = 10.734,
p = .001). When demographics were covaried, there was no significant main effect for
Physical Functioning Scale by device group, F (2,189) = 2.155, p = .119. There were no
significant covariates in the evaluation of Physical Role Scale by device group, however
when demographic variables were covaried, there was no significant main effect, F (2,
188) = 1.977, p = 141. Age was also a significant covariate in the differences between
groups on the Bodily Pain Scale, F (1,187) = 5.439, p = .021. Again, when
demographics were covaried, the main effect for Bodily Pain Scale by group was not
significant, F (2, 186) = .599, p = .551. There were no significant demographic
covariates in the evaluation of General Health by device group and the main effect when
demographics were covaried remained significant, F (2,188) = 5.294, p = .006.


16
Emotional Role, and Mental Health. A complete description of the psychometric
properties of the SF-36 is given in Chapter 3.
Disease Specific Quality of Life
Definition. Generic measures of health-related quality of life such as the SF-36 are
acceptable for comparisons between different disease samples, but they may not be
sensitive enough to detect differences in unique patient characteristics or changes in
health status for a specific category of diseased patients (Linde, 2000). For this reason,
Linde recommends that a generic instrument and a disease-specific instrument be used in
conjunction with one another to adequately measure the quality of life of patients with a
specific disease or condition. Such instruments would detect the subtle changes in
functioning that are associated with a specific condition. These changes are likely to be
missed when looking at more global aspects of patient functioning.
Measurement. Instruments such as the Karolinska Questionnaire (Linde-Edelstam
et al., 1992), the Minnesota Living with Heart Failure Questionnaire (Rector & Cohn,
1992), the Cardiac Arrythmia Suppression Trial Quality of Life Scale (CAST; Wiklund,
Gorkin, Pawitan, & Schron, 1992) and other rationally developed instruments (Oto et al.,
1991; Rose & Blackburn, 1968) have been designed to assess quality of life in cardiac
patients. However, these instruments do not address many of the issues associated with
the implantation of a biomedical device and the reliance on technology to sustain life or
treat a life-threatening condition.
Linde (2000) asserts that the quality of life of pacemaker patients and their
psychological adjustment to having a device rather than a disease has never been
adequately measured due to the psychometric characteristics of instruments currently
used with this population. As pacemakers are the most common implantable-cardiac


62
Table 4-19. Obtained scores for IAD group
Measure
n
M
SD
Range
Quality of life
SF-36
Phy function
82
67.01
25.54
15-100
Phy role
83
61.45
41.19
0-100
Bodily pain
80
70.65
22.82
12-100
Gen health
81
61.58
20.52
15-100
Vitality
84
51.31
21.31
0-100
Social
84
79.61
23.43
25-100
Emotion role
83
69.88
41.85
0-100
Mental health
84
72.76
17.70
16-100
Biological component
AFSS
84
15.73
8.52
1-41
Psychological component
CES-D
81
11.07
8.57
0-35
STAI
State
88
34.73
11.33
20-65
Trait
88
33.99
10.76
20-70
LOT
93
17.25
4.80
5-27
PANAS
Positive
85
34.15
7.43
17-50
Negative
85
16.31
6.00
10-36
MCMQ
Confrontation
89
19.47
2.01
16-26
Avoidance
91
14.76
2.06
10-20
Accept-resign
91
10.19
0.89
8-12
Social component
IIRS
93
27.00
14.36
13-73
ISEL
91
13.25
2.84
3-16


42
were extracted in 26 iterations. The 13 factors accounted for 62.1% of the variance in
scores on the F-PAS. An examination of the scree plot (Figure 4-1), and an evaluation of
the rotated sums of squares loadings indicated that seven factors accounted for the
majority of the variance (43.0%).
Scree Plot
Component Number
Figure 4-1. Scree plot of rotated factors
Items with loadings of 0.50 or higher on one of these seven factors that did not have
a loading of 0.30 or higher on any other factor were considered to be good measures of
the factor on which they loaded (Grimm & Yamold, 1997). These 20 items were retained
for further evaluation. Factors containing at least two of the 20 items were examined and
six factors were retained. Cronbachs alphas for each of the six factors were computed
and ranged from 0.50 to 0.82. See Table 4-4 for a list of the 20 retained items, their


80
3. Do you have a primary support person that you can count on for everyday support?
Yes
No (If no, please skip to the instructions before question 6.)
4. If yes, what is that persons relationship to you?
Spouse or partner
Parent
Sibling
Friend
Child
Other {please write in)


APPENDIX
GENERAL INFORMATION SHEET
DATE:
Please indicate the following information about yourself:
Social Security Number: -
Age:
Gender: MALE FEMALE
Ethnic background: AA Wh Asn Hsp AInd
OTHER:
Years of education: None Elementary (8) Some High School
School(12)
Some College College (16) Further College
M.D PHD
Marital Status: Single/Never Married Married Divorced
Widowed Separated
For each statement below, check the box which reflects the best answer.
1. Before receiving your device were you...
Employed full-time
Employed part-time
Not employed
Retired
2. Currently are you....
Employed full-time
Employed part-time
Not employed
Retired
High
79


71
acceptance by revealing both convergent and divergent validity. At least one subscale of
the F-PAS was moderately correlated with at least one scale of the SF-36 and the F-PAS
total score was moderately correlated with all eight scales of the SF-36. Construct
validity was also supported by the finding that patient acceptance accounted for a
significant amount of the variance in quality of life beyond that which was accounted for
by demographics, physiological measures, and affective symptoms on four of the eight
scales of the SF-36. The moderate correlations suggest that the F-PAS is measuring a
construct that is related to quality of life, but is not the same construct. The significant
variance in quality of life accounted for by the F-PAS suggests that the effects patient
acceptance has on quality of life is not completely accounted for by demographics,
physiological symptoms, or affective symptoms.
The third hypothesisthe quality of life of different implantable cardiac device
recipients would not be significantly different as measured by the SF-36was mostly
supported. Only one of eight scales of the SF-36, the General Health scale, showed
significant differences between device groups when demographic variables were
controlled. Scores on the General Health scale indicated that ICD patients viewed their
overall health as poorer than pacemaker and IAD patients. This suggests that the SF-36
addresses global differences between device recipients, however, specific areas where
psychosocial interventions and changes in device development might improve recipients
quality of life are not identified.
Findings from the research partially support hypothesis four: pacemaker patients
will have significantly greater patient acceptance of their device than ICD or IAD
patients. Scores on the subscales of the F-PAS reveal that pacemaker patients have
fewer concerns about returning to the life they had before they received their device than


22
Quality of Life and the IAD
Incidence. As mentioned previously, the IAD is a new device that has undergone
clinical trials sufficient for its FDA approval for the treatment of atrial fibrillation. The
clinical trials indicated that the IAD is an efficacious device for treating atrial fibrillation
(Lip, 2001). The quality of life of IAD patients has not been extensively studied due to
the novelty of the device; however, a single study, presented as a conference abstract, on
the quality of life of these patients reported that recipients of IADs had significantly
lower quality of life before implantation of the device when compared to healthy controls
(Fellows, Mcquillan, & Rodenhiser, 2000). The 105 IAD recipients who participated in
the study had lower scores on all subscales of the SF-36 before implantation. At 1 and 3
months postimplant their quality of life showed significant improvements on the Physical
Functioning, Physical Role, and Social Functioning subscales. Significant improvements
were not seen on the Mental Health, Vitality, General Health, and Bodily Pain subscales.
Measurement. Currently there are no studies in the literature that have attempted to
use an instrument designed specifically for the measurement of the quality of life of LAD
patients.
Implications. In the case of IAD recipients, where the arrhythmia is not
immediately life threatening, it may be especially important to understand the
psychosocial issues associated with the device in order to improve recipient selection. A
profile of the patient who will tolerate the discomfort of the discharges and report
improved quality of life may increase the efficacy of the IAD.
Comparing Quality of Life of Pacemaker. ICD. and IAD Recipients
A literature search resulted in a single study comparing pacemaker and ICD
patients (Duru et al., 2001). Patients undergoing an initial implantation of a pacemaker


66
2.693, p = .070. When demographic variables were covaried, the main effect for F-PAS
total score by device group was significant, F (2, 190) = 6.239, p = .002.
Table 4-22. Analysis of variance between groups on FPAS
Source
df
MS F
P
Return to life (RTL)
Between groups
2
5470.820 8.293**
.000
Within groups
227
659.713
Device related distress (DRD)
Between groups
2
2372.534 6.497*
.002
Within groups
225
365.162
Positive appraisal (PA)
Between groups
2
53.941 .153
.858
Within groups
229
594.362
Body image concerns (BIC)
Between groups
2
1653.394 5.399*
.005
Within groups
228
306.238
FPAS total
Between groups
2
1361.472 6.698*
.002
Within groups
209
203.274
**/? < .001,/? < .005
Table 4-23. Homogenous subsets of device groups on the F-PAS
Device Groups
Subsets a < .05
Tukey HSD
N
M
M
Return to Life Subscale
Pacemaker
79
72.152
ICD
56
54.799
IAD
95
69.3421
Device Related Distress Subscale
Pacemaker
81
9.383
ICD
57
20.526
IAD
90
17.278
Body Image Concerns Subscale
Pacemaker
79
5.063
ICD
57
10.088
10.088
IAD
80
13.816
F-PAS Total Score
Pacemaker
72
85.440
ICD
54
76.006
IAD
81
81.092
81.092


24
others, and the derivation of benefit in terms of biomedical, psychological, and social
functioning. It is hypothesized that patient acceptance is a latent construct comprised of
the following variables or dimensions: return to life issues, device-related distress,
positive appraisal, social issues and technology-education-understanding. It is theorized
that patient acceptance is a device specific component of the higher order construct of
quality of life. It is also believed that patient acceptance is moderately correlated with
the components of quality of life as measured by the SF-36, but that it is sensitive to
device-specific concerns that are not explained by other factors.
Measurement. Luderitz and associates (1993) designed an eight-item instrument to
measure patients acceptance of the ICD. The nonvalidated instrument was comprised
of six yes or no questions, one open-ended question (What is your greatest concern in
regard to your ICD) and a single question (How long did it take you to get used to the
ICD?) with answer choices of < 2 months and > 2 months (Luderitz et al., 1993;
p. 1817). The results of the study indicated that patient acceptance of the ICD was
good. Major limitations of the study included the small sample size (N = 57), the lack of
psychometric validation, and the fact that a generic health-related quality of life measure
was not administered. This would have allowed for comparisons of the measures and
might have provided evidence for the discriminant validity of their patient acceptance
construct. Nevertheless, their study was among the first to consider a psychological
component in ICD therapy.
Study Purposes and Hypotheses
Purposes
The purposes of the study were (a) to develop a measure of patient acceptance and
examine the factor structure of the instrument in pacemaker, ICD, and IAD patients;


64
Table 4-20. Analysis of variance between groups on SF-36 scales
Source
df
MS
F
P
Physical functioning
Between groups
2
6858.391
9.087***
.000
Within groups
208
754.785
Physical role scale
Between groups
2
9813.721
5.712**
.004
Within groups
207
1718.104
Bodily pain scale
Between groups
2
2867.890
4.825*
.009
Within groups
205
594.362
General health scale
Between groups
2
2949.626
6.235**
.002
Within groups
207
473.099
Vitality scale
Between groups
2
811.436
1.648
.195
Within groups
209
492.490
Social scale
Between groups
2
1103.421
1.807
.167
Within groups
210
610.799
Emotional role scale
Between groups
2
1088.576
.662
.517
Within groups
208
754.785
Mental health scale
Between groups
2
862.934
2.957
.054
Within groups
209
291.833
***/? < .001, **p < .005, *p < .010
Table 4-21. Tukey HSD homogenous subsets of device groups on scales of the SF-36
Device Groups
N
Subsets a < .05
M
M
Physical functioning scale
Pacemaker
75
53.667
ICD
54
47.685
IAD
82
67.012
Physical role scale
Pacemaker
74
45.676
45.676
ICD
53
38.208
IAD
83
61.446
Bodily pain scale
Pacemaker
74
60.000
ICD
54
59.667
IAD
80
70.650
General health scale
Pacemaker
75
58.387
ICD
54
48.333
IAD
81
61.580


6
biomedical device that controls the conduction of electricity within the heart or provides
electrical countershock (Miller, Altemos, & Jay, 2001). As mentioned, the focus of our
study was patient acceptance of treating severe arrhythmias with implantable cardiac
biomedical devices.
Implantable Cardiac Biomedical Devices
Permanent Implantable Cardiac Pacemakers
Incidence. The first pacemaker was implanted in 1958 in a patient with viral
myocarditis that resulted in AV block. The pacemaker lasted for only 8 hours.
Improvements in battery storage of electricity, electrode lead placement, and computer
technology have resulted in an implantable cardiac pacemaker that can last for 5 to 15
years; can provide electrical stimulation in both the atrium and the ventricle; and can
sense and evaluate the appropriateness of cardiac rhythms (Luderitz, 2002). Today,
permanent cardiac pacemaker implantation is the most common surgical procedure
performed involving the heart. Each year 115,000 to 175,000 pacemakers are implanted
in the United States (Xie, Thakur, Shah, & Hoon, 1998), and more than 400,000
pacemakers are implanted worldwide (Gregoratos, 1999).
Indications. The exponential increase in pacemaker implantations is a result of
technological developments, the low rate of complications from the implant procedure,
acceptance of the device by physicians and the lay public, and the growing number of
indications for treatment with the device (Xie et ah, 1999). Current recommendations for
implantation of a cardiac pacemaker are categorized into six areas by the American
College of Cardiology (ACC) and the American Heart Association (AHA; Dreifus et ah,
1991):
Acquired AV block (not associated with myocardial infarction)
AV block associated with acute myocardial infarction


10
Indications. The proper selection of patients is paramount to the success of ICD
therapy. Selecting patients involves exploring the hemodynamic stability of the
arrhythmia, underlying heart disease, left ventricular function, and the results of various
electrophysiological studies including the inducibility of sustained ventricular
tachycardia (Shah et al., 1998). The AHA, ACC, and the North American Society of
Pacing and Electrophysiology (NASPE) have each made recommendations for ICD
implantation. Their recommendations are as follows:
One or more episodes of spontaneous ventricular tachycardia or ventricular
fibrillation in a patient for whom electrophysiologic (EP) testing and/or
spontaneous ventricular arrhythmias cannot be used accurately to predict the
efficacy of other therapies
Ventricular fibrillation or hypotensive ventricular tachycardia with
contraindications to drug or surgical therapy
Persistently inducible clinically relevant ventricular tachychardia or ventricular
fibrillation during EP testing despite drug therapy, corrective surgery, or catheter
ablation
Recurrent episodes of spontaneous ventricular tachycardia or ventricular fibrillation
despite EP testing-guided or Holter-guided antiarrhythmic drug therapy
Unexplained suncope in a patient with hypotensive ventricular tachycardia
inducible during EP testing with characteristics 1,2, or 3 above
Highly symptomatic long Q-T interval syndromes despite medical therapy (with or
without permanent pacemaker implantation)
Dreifus et al. (1991) give a complete explanation for each of the recommendations.
More recent studies have attempted to assess the prophylactic use of the ICD with
specific cardiac populations (heart failure patients, patients with nonischemic
cardiomyopathy, patients who are at risk for myocardial infarction, those who experience
unsustained tachycardia, and patients who have undergone coronary artery bypass
grafting) (Prytowsky & Nisam, 2000). The ICD reduced mortality by more than 70% for


18
greater severity of depressive symptomatology. The literature reveals that anxiety and
fear are also seen in a large proportion of pacemaker patients. It is estimated that 11% to
66% of pacemaker patients experience pacemaker specific fears and symptoms of anxiety
(Aydemir et al., 1997).
Research on pacemaker patients adjustment is often secondary to research on the
quality of life differences experienced between dual-chamber and single-chamber
pacemaker patients. While comparisons are made between the groups, little information
is provided on pacemaker recipients as a whole in comparison with other normative
populations. In a dual- versus single-chamber pacemaker comparison study of 407
patients 65 years of age or older (Lamas et al., 1998), patients implanted with
pacemakers (both dual and single chamber) showed improvements from before
implantation on six scales of the SF-36 3 months after implantation. Pacemaker
recipients showed improvements from baseline on the Social Functioning, Physical Role,
Emotional Role, Mental Flealth, and Energy subscales. There were no significant
differences between the single-chamber and the dual-chamber pacing groups on any of
the SF-36 scales at 3- and 18-month follow-ups. However, there was a difference on the
mental health scale score at 9-month follow-up, favoring the dual-chamber group. In a
similar study using the Nottingham Health Profile, a generic quality-of-life measure, also
reported a trend toward improvements in quality of life (Lau et al., 1989).
Measurement. In an attempt to remedy the measurement shortcomings posited by
Linde (2000), Stofmeel and associates (2001a) developed the Aquarel Questionnaire. A
disease-specific questionnaire designed to measure the quality of life of pacemaker
patients. The 20-item measure is to be used in conjunction with the SF-36. The
questionnaire was given to 74 pacemaker patients and test-retest reliability for one to two


Table 4-11. Summary of hierarchical regression model for variables predicting SF-36 general health scale scores using F-PAS
subscale scores as a measure of patient acceptance
Variable
B
SEB
P
t
P
R2
R2A
Step 1
Age
.231
.134
.121
1.718
.088
Gender
5.601
3.056
.123
1.833
.069
Marital status
-.475
3.335
-.009
-.142
.887
.018
.018
Step 2
ARSS*
-.664
.127
-.387
-5.233
.000
.344
.326
Step 3
CES-D
-.352
.265
-.139
-1.329
.186
STAI trait
2.316E-02
.201
.011
.115
.908
.414
.070
Step 4
IIRS
2.893E-02
.136
.020
.213
.831
.424
.010
Step 5
RTL*
.313
.071
.370
4.434
.000
DRD
-5.366E-02
.083
-.047
-.649
.517
PA
3.064E-02
.080
.026
.382
.703
BIC
.144
.085
.115
1.689
.093
.503
.079
* Predictor of unique variance
U>


McGuire, L. B., Beckwith, J. R., Littlefield, J. B., & Muller, W. H. (1966). Experience
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Medtronic, Inc. (2002). Understanding cardiac resvchronization therapy for heart failure
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Miller, J. M., Alternse, G. T., & Jayachandran, J. V. (2001). Catheter ablation of
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Moss, A. J., Hall, W. J., Cannom, D. S., Daubert, J. P., Higgins, S. L., Klien, H., Levine,
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Namerow, P. B., Firth, B. R., Heywood, G. M., Windle, J.R ., & Parides, M. K. (1999).
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*


9
for its use was not obtained until 1985. As with the pacemaker, technological and
surgical advances led to a smaller, more efficient, and more easily implanted device
(Shah, Thakur, Xie, & Hoon, 1998). These advances led to its widespread use as a
treatment option, as indicated by the 227% increase in implantations between 1993 when
15,000 ICDs were implanted and 1999 when over 50,000 were implanted (Cowen, 1999).
Although ventricular arrhythmias are not listed as the cause of death from sudden cardiac
arrest, most of the 220,000 people who die each year of cardiac arrest die as a result of
ventricular fibrillation (Smith et al., 2001). This statistic is particularly significant when
survival for ICD recipients is reported to be as high as 98% at 1-year follow-up, whereas
survival for untreated individuals experiencing sudden cardiac arrest outside of the
hospital is less than 10% (Smith et al., 2001). Although the ICD was originally designed
as a last-resort treatment option for patients with non-drug-refractory ventricular
arrhythmias, its efficacy and technological advances have lead to the ICD being
considered the treatment of choice for potentially life-threatening arrhythmias. Two
clinical trials, one reported in 1996 (MADIT; Multicenter Automatic Defibrillator
Implantation Trial) and the other in 1997 (AVID; Antiarrhythmics Versus Implantable
Defibrillators) compared treatment with an ICD to treatment with antiarrhythmic drugs
(Buxton, 1996). The MADIT investigators terminated the 5-year long investigation in
1996 because of a 54% decrease in all mortality in patients treated with an ICD as
compared to those treated with traditional pharmacological therapy (Moss et al., 1996).
The AVID investigation also ended early after investigators observed a 39% reduction in
mortality at 1-year follow-up for patients implanted with an ICD (AVID Investigators,
1997). These results gave antiarrhythmic medications a backseat to ICD in the treatment
of patients diagnosed with life-threatening ventricular arrhythmias.


5
nomenclature are paroxysmal, starting and ending abruptly; sustained, without ending;
and fibrillation, a fast twitching motion (Phibbs, 1996).
It is also important to know that pacemaker foci (concentrations of pacemaker
cells) are scattered throughout the heart as the hearts own back-up system in case the
normal electrical pathway becomes compromised. The compromising of the electrical
pathway may be the result of atrioventricular block (AV block), the result of an aberrant
conduction of electricity through the AV node; or bundle-branch block, an interruption of
the conduction of electricity through the ventricles. Compromises to electrical
conduction also may be caused by disturbances of serum electrolytes that regulate the
ability of cell membranes to conduct electricity, or may be caused by disturbances of
myocardial blood flow, which is the flow of oxygenated blood to the heart muscle itself.
Arrhythmias are most often caused by the ectopic (outside the normal site) firings of
back-up pacemaker foci (Phibbs, 1996).
Treatments
The treatment of cardiac arrhythmias varies based on the rhythms characteristics
and etiology and on the patients symptomotology. The most efficient way to describe
treatment options is to consider them on a scale of severity. The least severe (a non-life-
threatening and asymptomatic paroxysmal arrhythmia) may be treated with simple
behavioral interventions such as dietary modifications and smoking cessation. A more
severe symptomatic paroxysmal arrhythmia that results from a manageable disease
process may be treated with anti-arrhythmic medications. The most severe arrhythmias
are sustained, symptomatic, not drug-responsive, and/or life-threatening. These may call
for more invasive interventions such as catheter ablation of conduction pathways,
surgical ablation or resection of conduction pathways, or the implantation of a




LIST OF TABLES
Table page
2-1. Initial items and hypothesized factors of the patient acceptance measure 26
3-1. General characteristics of study participants 29
4-1. Obtained scores for total sample 37
4-2. Obtained scores on the SF-36 and statistics for comparison between
normative samples and current sample 38
4-3. Obtained scores on the biopsychosocial model of illness component
measures for normative samples and comparison statistics for current
sample 40
4-4. Initially retained items, factor loadings, and reliability coefficients 43
4-5. Final factors and reliability coefficients 46
4-6. Pearson product moment correlation coefficients for F-PAS and SF-36 46
4-7. Pearson product moment correlation coefficients for F-PAS and biological
component measure 47
4-8. Pearson product moment correlation coefficients for F-PAS and
psychological component measures 49
4-9. Pearson product moment correlation coefficients for F-PAS and social
component measures 49
4-10. Summary of hierarchical regression model for variables predicting SF-36
physical functioning scale scores using F-PAS subscale scores as a
measure of patient acceptance 52
4-11. Summary of hierarchical regression model for variables predicting SF-36
general health scale scores using F-PAS subscale scores as a measure of
patient acceptance 53
v


39
Biopsvchosocial Model
Biological
Atrial Fibrillation Severity Scale. The mean obtained score on the AFSS of 26.52
(SD = 12.90) was significantly higher than the mean score of 20.4 (SD = 9.4) obtained by
a sample of 264 atrial fibrillation patients, t (273) = 5.831, p < .001 (Dorian et al., 2002).
The most commonly endorsed symptom was feeling fatigued, with 73% (n=158) of the
sample indicating that they feel fatigued at least some of the time.
Psychological
Center for Epidemiological StudiesDepression Scale. The current sample
obtained a mean score of 10.4 (SD = 8.17) on the CES-D. The mean score was not
significantly different from the mean scores obtained by a sample of 120 heart bypass
surgery patients (M = 11.0, SD = 9.1; Pirraglia, Peterson, Williams-Russo et al., 1999)
and a normative healthy adult sample of 2,514 (M = 9.3, SD = 8.6; Radloff, 1977).
State Trait Anxiety Inventory Trait Scale. The current sample obtained a mean
score of 33.6 (SD = 10.3) on the Trait Scale of the STAI. The mean score was
significantly lower than the mean score of 41.3 (SD = 12.6) obtained by a sample of 110
medical procedure patients, t (128) = 5.645, p < .001. The mean score of the current
sample was not significantly different from the mean score obtained by 106 healthy
females (M = 31.8, SD = 7.8) or the mean score obtained by a sample of 382 healthy
males (M = 33.9, SD = 8.9; Spielberger et al., 1983).
Social: Illness Intrusiveness Rating Scale
The current sample obtained a mean score of 27.2 (SD = 15.1) on the IIRS. The
mean score was significantly lower than the mean score 35.0 (SD = 15.0) obtained by a
sample of 152 atrial fibrillation patients, t (226) = 4.706, p < .001 (Dorian et al., 2002).


32
developers of the instrument reported that it was valid, reliable and sensitive to changes
in the symptoms of AF patients (Maglio et al., 1998).
Psychological Component Measures
Center for Epidemiological StudiesDepression Scale (CES-D). Depression will
be measured using the CES-D (Radloff, 1977), a 20-item self-report measure that
assesses depressive symptomology. Participants indicate how frequently they have
experienced each symptom in the past week. Responses range from 0 (less than one day)
to 3 (5-7 days). The total score can range from 0 to 60 and reflects both the number of
depressive symptoms and their duration. A standard cut-off score of 16 can be used to
indicate clinically significant symptoms of depression. The CES-D has been widely
utilized in community surveys. Previous research has demonstrated that the CES-D is
highly sensitive and specific and exhibits a high internal reliability coefficient of 0.85.
Researchers have suggested that the CES-D is the most generally useful self-report
measure of depression when compared to the Beck Depression Inventory, the MMPI
Depression Scale, and the Zung Self Rating Scale of Depression (Turk & Okifuji, 1994).
State-Trait Anxiety Inventory (STAI). The STAI (Spielberger, Gorsuch, Lushene,
Vagg, & Jacobs, 1983) is a 40-item self-report questionnaire designed to measure both
state and trait anxiety. Trait anxiety is defined as a relatively enduring personality
characteristic, or more specifically, as anxiety proneness. The internal reliability of both
the state and trait anxiety scales has been shown to be uniformly high across samples of
adults ranging from 0.89 to 0.96. Test-retest stability coefficients for multiple samples of
college students ranged from 0.73 to 0.86, with test-retest validity specifically for the
trait scale being reported at 0.73 for males and 0.77 for females. Concurrent validity


20
important if healthcare providers are to fine-tune and improve their already efficacious
treatment.
Quality of Life and the ICD
Incidence. While the efficacy of the ICD in reducing mortality is well established
(AVID Investigators, 1997; Buxton, 1999; Moss et al., 1996), the quality of life of ICD
recipients has not been as well studied. Studies that have assessed quality of life
outcomes indicate that ICD recipients report equal or better quality of life than patients
receiving anti-arrhythmic drugs (Arteaga & Windle, 1995; Herbst, Goodman, Feldstein,
& Reilly, 1999; Sears & Conti, 2002). One exception, the CABG-Patch study, reported
significantly worse mental and physical quality of life outcomes for patients with ICDs
when compared to those without ICDs following coronary artery bypass surgery
(Namerow, Firth, Heywood, Windle, & Parides, 1999). Further analysis of the data
revealed equivalent quality of life for nonshocked ICD and no-ICD patients, indicating
that receipt of ICD shock may be the key factor in diminishing the quality of life of these
patients.
Recent research assessing the views of attending physicians and nurses (N = 261)
regarding specific ICD patient outcomes and common daily problems indicated that
health care providers estimate that 10% to 20% of ICD recipients experience
significantly worse quality of life and emotional and family functioning post ICD-
implantation (Sears et al., 1999). Research specifically designed to address the concerns
of younger ICD recipients suggests that recipients > 50 years of age have a more difficult
time adjusting to their ICD. Their concerns focus on the appearance of the ICD, physical
activity limitations, sexual relations, social interactions, and driving restrictions (Sears,
Bums, Handburg, Sotile, & Conti, 2002; Vitale & Funk, 1995).


21
Measurement. An empirical comparison of instruments for assessing quality of life
in ICD patients is not available in the current research literature. However, an extensive
literature review conducted by Sears et al. (1999) revealed the use of several protocol-
specific rationally-derived measures. From their review, Sears and colleagues concluded
that disease-specific instruments used to measure quality of life for ICD patients should
include a focus on symptoms of cardiac illness and/or symptoms of cardiac arrhythmias.
Implications. Since the introduction of the ICD as a treatment recommendation for
life-threatening ventricular arrhythmias, research has indicated that a significant minority
of patients experience some psychological complications following ICD implantation. In
their review of the literature on the psychosocial impact of the ICD, Sears et al. (1999)
reported diagnosable levels of anxiety for ICD recipients ranging from 13% to 38%, and
depressive symptoms prevalence rates ranging from 24% to 33%. The reported
incidence of these patient complaints is significant but has varied greatly across studies,
primarily due to variations in research design, device technology, and types of
assessment instruments utilized. Despite these inconsistencies within the literature, a
common theme has emerged, many ICD recipients experience psychological distress at
some point following ICD implantation (Sears et al., 1999). This suggests that the
psychosocial outcomes of the ICD recipient may be undermining the potential quality of
life benefit of the ICD. Consistently and adequately measuring the quality of life of ICD
recipients may allow healthcare providers to establish an index of suspicion for
psychological distress and provide appropriate primary as well as secondary
interventions.


33
between the STAI and IP AT Anxiety Scale and the Taylor Manifest Anxiety Scale
ranged from 0.83 to 0.73 (Spielberger et al., 1983).
Life Orientation Test ('LOT'). The LOT (Scheier & Carver, 1985) is an 8-item, self-
report questionnaire (with 4 filter items) which assesses generalized expectancies for
positive versus negative outcomes. Subjects rate the extent to which they agree or
disagree with each item using a 5-point scale. Half of the items are phrased in the
positive direction (e.g., In uncertain times, I usually expect the best). The scores for
the negative items are reversed, and all items are summed to yield an overall
dispositional optimism score. The LOT has a reported reliability coefficient of 0.76 and
test-retest reliability of 0.79 (Scheier & Carver, 1985). Although researchers have
suggested that the measurement of dispositional optimism (using the LOT) overlaps with
neuroticism or negative affectivity (see Smith, Pope, Rhodewalt, & Poulton, 1989),
recent analyses conducted with the data from 4,309 subjects have demonstrated that
associations between optimism and outcome variables remain significant even when the
effects of neuroticism, trait anxiety, self-mastery, and self-esteem are statistically
controlled (Scheier, Carver, & Bridges, 1994). The authors conclude that overall, the
LOT has good predictive validity, and dispositional optimism (as measured by the LOT)
is distinguishable as an independent construct, as compared to constructs of neuroticism
and negative affectivity.
Positive and Negative Affect Scales (PANAS). The PANAS (Watson, Clark, &
Tellegen, 1988) is a 20-item self-report measure comprised of two separate scales. The
scales are designed to measure an individuals level of positive and negative affect. The
respondent is asked to rate adjectives that they feel describe them over the past week on a
5-point scale ranging from not at all to extremely. Positive affect terms include


75
enrollment of patients from a private practice clinic, as well as, the enrollment of patients
from a major research institution provides further support for the generalization of
findings from the current research.
While the use of participants from different settings may be seen as a strength, the
difference in the overall health of patients in the private sector compared to that of
patients chosen to be a part of a randomized clinical trial is also a weakness of the current
study. The IAD patients in the study were chosen due to their high level of overall health
and may not represent the average drug refractory atrial fibrillation patient. However, as
mentioned above, atrial fibrillation patients who choose to have a device that delivers
uncomfortable electrical shock therapies, may be likely to be in better health than the
average atrial fibrillation patient and may, contrary to the researchers initial hypothesis,
be self-selected to have greater acceptance of the device.
Another relative weakness of the study is the lack of pre-implant information
regarding patients psychosocial functioning. This makes it impossible to determine if
people with pre-implant adjustment difficulties or psychiatric concerns may be more or
less likely to report lower levels of patient acceptance of their device. Previous research
indicates that pre-implant psychological functioning is the best predictor of post-implant
functioning in ICD patients (Wallace et al., 2002). Also a weakness of the study is the
reliance on self-report. While self-report has been shown to be a valid way to assess
physical and psychosocial functioning, the inclusion of clinical data for reliability
comparisons would add to the validity of the findings being reported.
Implications and Conclusions
The strengths and weaknesses of the study are important when considering the
implications for future research and the conclusions drawn from the current study. In


59
Table 4-15.
Device group frequencies and means
on demographic
variables
Group
N
Demographic
n
M
SD
Pacemaker
81
Age
75.09
8.13
Gender
Male
37
Female
40
Marital status
Married
53
Unmarried
29
ICD
56
Age
Gender
72.00
9.60
Male
36
Female
18
Marital status
Married
42
Unmarried
13
IAD
92
Age
Gender
62.15
11.75
Male
66
Female
26
Marital status
Married
80
Unmarried
16
Table 4-16. Analysis of variance between groups on demographics
Source
df
MS
F
P
Age
Between groups
2
3586.522
29.024***
.000
Within groups
226
123.572
Gender
Between groups
2
1.234
5.483**
.005
Within groups
220
.227
Marital Status
Between groups
2
.779
4.265*
.015
Within groups
230
.183
***p < .001, **p < .005, *p < .05


14
ICD to the ventricle), and the other is a pacing discharge delivered to the ventricle. The
pacing discharge is delivered in the event that the defibrillation discharge results in a
bradycardic response. A major, and significant, difference between the IAD and the
other devices described, is that it can be programmed to discharge automatically when
symptoms of atrial fibrillation occur; or it can be used in a patient- or
physician-activation mode (Wellens et al., 1998). As with the ICD defibrillation
discharge, the patient experiences a quick kick in the chest when the IAD delivers the
intervention necessary to return the patient to sinus rhythm (Gold, Sulke, Schwartzman,
Mehra, & Euler, 2001). A striking difference between patients with atrial fibrillation and
those with ventricular fibrillation is the lack of immediate threat of death. For LAD
patients, this important difference may increasethe psychological distress associated with
receiving shock.
Quality of Life
Health-Related Quality of Life
Definition. Health-related quality of life has emerged as an end-point of interest
because it reflects a patients ability to function in a variety of life domains (including
physical, social, emotional, and work-related). Some researchers believe that it is
embodied in the approach of the biopsychosocial model of health (Engel, 1977) in which
biological, psychological, and social functioning are interdependent. While an
operational definition of quality of life has not been universally agreed upon, there is a
general consensus that quality of life is a multidimensional construct. Ware (1993)
contends that the primary source of information for understanding health-related quality
of life is standardized patient surveys. During the past 10 years, a number of generic
health-related quality-of-life patient surveys have been developed. The term generic is


7
Chronic bifascicular and trifascicular block (block of specific pacemaker cell foci)
Sinus node dysfunction (tachycardia-bradycardia syndrome)Hypersensitive carotid
sinus syndrome
Pacing in children
Detailed descriptions of each of these implications may be found elsewhere
(Dreifus et al., 1991). However, an important common characterisitie of each of these
implications is the experience of symptoms such as dizziness, shortness of breath, and
loss of consciousness most often associated with bradycardia (abnormally slow heart
rate).
Description. Pacemaker systems consist of two basic parts: the pulse generator and
leads. The pulse generator is located in a pacemaker can that is made of titanium. The
can is placed in a pectoral pocket on the nondominant side of the patients chest. The
pulse generator contains lithium batteries that are light and that have very gradual voltage
decay. The pacemakers electronic circuitry, responsible for sensing spontaneous
intracardiac electrical impulses and providing discharges of electricity to the heart, are
also a part of the pulse generator. To test the functioning of the pacemaker, a reed switch
is also contained within the pulse generator. This switch allows the pacemaker to be
disabled or reprogrammed using a magnet. The pacemakers leads sense intracardiac
signals and deliver the pulse generator charge of electricity to the walls of the heart. The
leads of the pacemaker are metal conductors that are covered with polyurethane or
silicone to insulate them. Leads can be epicardial (attached to the outside of the heart) or
endocardial (attached to the inside of the heart), most contemporary pacemaker leads are
endocardial for ease implantation (does not require a thoracotomy or open-heart surgery).
An endocardial lead can be placed transvenously (threaded through the subclavian;
cephalic; or internal or external jugular veins) under local anesthesia. Placement of an


MEASURING PATIENT ACCEPTANCE OF BIOMEDICAL DEVICES
IN CARDIAC PATIENTS
By
JASON L. BURNS
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL AT THE
UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2003


26
Table 2-1. Initial items and hypothesized factors of the patient acceptance measure
Factor Item
Return to life I am concerned about resuming a normal social schedule.
I am confident about my ability to return to work if I want to.
I have continued my normal sex life.
I have returned to a full life.
I am concerned about resuming my daily physical activities.
I am afraid to drive because of my device.
When I think about the device I avoid doing things I enjoy.
Device-related I avoid my usual activities because I feel disfigured by my
distress device.
Thinking about the device makes me depressed.
When I think about the device I become anxious.
It is hard to function without thinking about my device.
I wear different clothes to cover my device because it makes
me feel self-conscious.
I do not feel disfigured by the medical device.
I feel that others see me as disfigured by my device.
I feel less attractive because of my device.
I keep calm because getting excited or upset may affect my
device.
Positive appraisal I feel that I have no control over the device.
I would recommend this device to a friend.
My device was my best treatment option.
I am safer from harm because of my device.
The positive benefits of this device out-weigh the negatives.
My family feels more secure because of my device.
I need my device to live normally.
I have fewer symptoms because of my device.
My device creates unnecessary pain for me.
I would receive this device again.
My device hurts me.
I am confident that my device will work when I need it to.
The healthcare costs related to my device are unreasonable.
I am not pleased with the technology of this device.
Social issues I am not able to do things for my family the way I used to.
I feel that my device frightens children.
I am careful when hugging or kissing my loved ones.
Other people are more concerned about me overdoing it than
I am.
My family is knowledgeable about how my device works.
People question me about the device.


REFERENCES
American College of Physicians. (1988). Cardiac rehabilitation services. Health and
public policy committee. Annals of Internal Medicine. 109. 671-673.
Arteaga, W. J., & Windle, J. R. (1995). The quality of life of patients with life
threatening arrhythmias. Archives of Internal Medicine. 155. 2086-2091.
Antiarrhythmics Versus Implantable Defibrillators Investigators. (1997). A comparison
of anti-arrythymic-drug therapy with implantable defibrillators in patients
resuscitated from near-fatal ventricular arrhythmias. New England Journal of
Medicine. 337. 1576-1583.
Aydemir, O., Ozman, T., Kuey, L., Kultur, S., Yesil, M., Postaci, N., & Bayata, S.
(1997). Psychiatric morbidity of depressive symptomatology in patients with
permanent pacemakers. Pacing and Clinical Electrophysioloev. 20. 1628-1632.
Bennet, S.J. (1993). Relationships among selected variables and coping effectiveness in
myocardial infarction patients. Research in Nursing and Health. 16. 131-139.
Bergner, M., Bobbit, R.A., Carter, W.B., & Gilson, B.S. (1981). The Sickness Impact
Profile: Development and final revision of a health status measure. Medical Care.
19,787-805.
Block, M. & Breithardt, G. (1999). The implantable cardioverter defibrillator and
primary prevention of sudden death. The Multicenter Automatic Defibrillator
Implantation Trial and the Coronary Artery Bypass Graft (CABG)-Patch Trial.
American Journal of Cardiology. 83. 74D-78D.
Bosworth, H. B., Siegler, I. C., Olsen, M. K., Brummett, B. H., Barefoot, J. C., Williams,
R. B., Clapp-Channing, N. E., & Mark, D. B. (2000). Social support and quality of
life in patients with coronary artery disease. Quality of Life Research. 9, 829-839.
Brookings, J. B., & Bolton, B. (1988). Confirmatory factor analysis of the Interpersonal
Support Evaluation List. American Journal of Community Psychology. 16. 137-147.
Burke, L. J. (1996). Securing life through technology acceptance: The first six months
after transvenous internal cardioverter implantation. Heart and Lung. 25. 352-366.
81


29
Most of the sample was male (62.2%) with a mean age of 69.3 years (range: 30 to
90). Most of the participants were married (74.6%) and retired (68.3%). Table 3-1
provides a summary of the general demographic characteristics of the study sample.
Ethnicity and education information was not gathered from IAD patients participating in
the PASSAT Study.
Table 3-1. General characteristics of study participants
Characteristic
n
Percent
Device type
IAD
96
40.3%
ICD
58
24.4%
Pacemaker
84
35.3%
Patient age
Under 50
17
7.3%
50-59
29
12.5%
60-69
51
22.0%
70-79
101
43.5%
80 and above
34
14.7%
Sex
Male
140
62.2%
Female
85
37.8%
Marital status
Married
176
74.6%
Unmarried
60
25.4%
Employment status
Full-time
36
15.0%
Part-time
19
7.9%
Not employed
21
8.8%
Retired
164
68.3%
Ethnicity*
Caucasian
121
95.3%
African-American
4
3.1%
Other
2
1.6%
Education*
Less than high school
28
20.6%
High school
54
39.7%
Greater than high school
54
39.7%
* Characteristic not reported for PASSAT Study (IAD) participants.


13
with atrial fibrillation do not experience any symptoms. The other two-thirds experience
fatigue, heart palpitations, dizziness, and presyncopal episodes that often interfere with
their daily functioning. Most of these patients are successfully treated with
antiarrhythmic drugs and/or dual chamber pacemaker therapy. A significant minority of
patients have recurring symptoms and must be externally cardioverted. It is this minority
of patients who may be helped by the IAD. The IAD alleviates symptoms but does not
cure the underlying disorder. Disorders that may result in atrial fibrillation include
ischemic heart disease, rheumatic heart disease, hypertension, sick sinus syndrome,
cardiomyopathy, and other structural abnormalities. Atrial fibrillation can be
paroxysmal or sustained; however, most patients have paroxysmal atrial fibrillation (Lip,
2001). Currently, there are no indications for the implantation of an IAD; however, the
patients involved in clinical trials using the IAD have paroxysmal atrial fibrillation that is
symptomatic drug-refractory. Patients have also been excluded if they have atrial
fibrillation from a reversible cause; have Wolff-Parkinson-White Syndrome; have had a
myocardial infarction in the last year, have cardiac ischemia and resultant angina; or have
a history of thromboembolic events, congestive heart failure, and/or left ejection fraction
of-< 40% (Wellens et al., 1998).
Description. The IAD consists of the atrioverter can and three leads. As with the
pacemaker and the ICD, the IAD can contains the circuitry and power supply. There are
three leads connected to the can. The leads are placed transvenously: one in the coronary
sinus, one in the right atrium, and one in the right ventricle. The can is pectorally
implanted.
Function. The IAD is capable of delivering two types of discharges: one is a
defibrillation shock for the atrium (similar to the defibrillation discharge delivered by the


48
(r = -A25,p< .001). The STAI State Scale score was also negatively correlated with the
RTL and PA subscale scores (r = -.345,/? < .001; r = -.366, p < .001, respectively) and
positively correlated with the DRD and BIC subscale scores, (r = .311 ,p< .001; r = .256,
p = .004, respectively). It was negatively correlated with the F-PAS total score (r = -
447,/? < .001). The LOT was positively correlated with the RTL subscale score (r =
.309,/? = .001) and negatively correlated with the DRD subscale score, (r = -.199,/? =
.030). It was positively correlated with the F-PAS total score (r = .270,/? = .004).
The PANAS Positive Affect Scale score was positively correlated with the RTL
and PA subscale scores (r = .430,/? < .001; r = .235,/? = .011, respectively) and
negatively correlated with the DRD subscale score, (r = -.227,/? = .016). It was
positively correlated with the F-PAS total score (r = .372,/? < .001). The PANAS
Negative Affect Scale score was negatively correlated with the RTL and PA subscale
scores (r = -.303,/? = .001; r = -.319,/? = .001, respectively) and positively correlated
with the DRD and BIC subscale scores, (r .329,/? < .001; r .233, /? = .013,
respectively). It was negatively correlated with the F-PAS total score (r = -.384,
p < .001). The MCMQ Avoidance Scale score was negatively correlated with the RTL
subscale score (r = -.183,/? = .006). See Table 4-8 for all correlations between the F-
PAS and the measures of the psychological component of the biopsychosocial model of
illness.
F-PAS Correlations with Social Component Measures
Finally, Pearson correlation coefficients were also calculated to determine if the
F-PAS total score and subscale scores were correlated with measures of the social
component of the biopsychosocial model of illness. The IIRS was negatively correlated
with the RTL and PA subscale scores (r = -.592,/? < .001; r = -.335,/? < .001,


Table 4-1. Obtained scores for total sample
37
Measure
N
M
SD
Range
Quality of life
SF-36
Phy function
215
57.19
28.56
0-100
Phy role
214
49.67
42.15
0-100
Bodily pain
212
63.62
24.76
12-100
Gen health
214
56.86
22.14
0-100
Vitality
216
48.70
22.09
0-100
Social
217
76.59
24.65
0-100
Emotion role
215
68.99
40.51
0-100
Mental health
216
76.46
17.18
16-100
Biological Component
AFSS
217
26.52
12.90
1-61
Psychological component
CES-D
218
10.44
8.74
0-47
STAI
State
131
32.77
10.89
20-65
Trait
233
33.57
10.33
20-70
LOT
123
16.37
4.86
5-27
PANAS
Positive
117
33.52
7.36
17-50
Negative
116
17.09
5.99
10-36
MCMQ
Confrontation
229
18.91
2.08
12-26
Avoidance
232
14.30
2.23
9-20
Accept-Resign
232
10.06
1.00
7-13
Social component
IIRS
177
27.18
15.06
13-82
ISEL
125
10.28
5.58
0-16
Scale was significantly lower than the mean score obtained by the healthy sample, t (301)
= 3.079, p < .01 and significantly higher than the mean of scores obtained by the
congestive heart failure sample, t (301) = 4.389, p< .001. The current sample obtained a
mean score of 48.70 (SD = 22.09) on the Vitality Scale. This mean score is significantly
lower than the mean score of the healthy sample, t (302) = 6.126, p < .001 and
significantly higher than congestive heart failure patients, t (301) = 1.969, p < .05. The


TABLE OF CONTENTS
page
ACKNOWLEDGMENTS ii
LIST OF TABLES v
ABSTRACT vii
CHAPTER
1 INTRODUCTION 1
2 LITERATURE REVIEW 4
Cardiac Arrhythmias 4
Treatments 5
Implantable Cardiac Biomedical Devices 6
Quality of Life 14
The Construct of Patient Acceptance 23
Study Purposes and Hypotheses 24
3 METHOD 28
Participants 28
Procedure 30
Instruments 30
4 RESULTS 36
Assessment Measures 36
Health-Related Quality of Life 36
Biopsychosocial Model 39
Development of the F-PAS 40
Hypothesis 1: Factor Analysis 41
Hypothesis 2: Convergent and Discriminant Validity of the F-PAS 44
Device Group Comparisons 58
Hypothesis 3: Group Differences on SF-36 Scale Scores 58
Hypotheses 4 and 5: Differences in Patient Acceptance 65
in


Table 4-10. Summary of hierarchical regression model for variables predicting SF-36 physical functioning scale scores using F-
PAS subscale scores as a measure of patient acceptance
Variable
B
SEB
P
t
P
R2
R2A
Step 1
Age*
-.585
.188
-.238
-3.104
.002
Gender
-5.551
4.287
-.094
-1.295
.197
Marital status
-3.590
4.679
-.055
-.767
.444
.113
.113
Step 2
ARSS*
-.584
.178
-.264
-3.280
.001
.306
.192
Step 3
CES-D
4.034E-02
.372
.012
.109
.914
STAI trait
-.190
.281
-.069
.677
.500
.339
.033
Step 4
IIRS
-.286
.190
-.151
-1.504
.135
.364
.025
Step 5
RTL*
.273
.099
.251
2.759
.007
DRD
.110
.116
.075
.950
.344
PA
6.067E-02
.112
.039
.540
.590
BIC
.151
.120
.094
1.263
.209
.408
.044
*Predictor of unique variance
K>




(PA), and Body Image Concerns (BIC). The four factors were internally consistent, and
a reliable higher-order factor also was found. The validity of the measure was addressed
using a battery of instruments that assessed each component of the biopsychosocial
model of illness. In between-group comparisons, the patient acceptance measure
revealed that, overall, device acceptance was significantly greater for pacemaker patients
then for ICD patients. Device-related distress was lower for pacemaker patients than for
either ICD or IAD patients. Concerns about returning to their former lives are fewer for
pacemaker and IAD patients than for ICD patients. The patient acceptance measure is
the first empirically investigated device-specific instrument to provide a common metric
used to study this unique group of patients. The information obtained has important
implications for the development and implementation of psychosocial interventions that
address the distress associated with the implantation of a cardiac device.
Vlll


47
F-PAS Correlations with Biological Component Measure
Pearson correlation coefficients were calculated to determine if the F-PAS total
score and subscale scores were correlated with the AFSS, a measure of the biological
component of the biopsychosocial model of illness. The AFSS was negatively correlated
with the RTL subscale score (r = -.424, p < .001) and the F-PAS total score, (r = -.241,
p = .001). See Table 4-7 for all correlations between the F-PAS and the measures of the
biological component of the biopsychosocial model of illness.
Table 4-7. Pearson product moment correlation coefficients for F-PAS and biological
component measure
F-PAS
RTL
DRD
PA
BIC
Total F-PAS
AFSS
424***
.065
-.039
-.047
241**
**p < .01; ***/?<.001
F-PAS Correlations with Psychological Component Measures
Pearson correlation coefficients were also calculated to determine if the F-PAS total
score and subscale scores were correlated with measures of the psychological component
of the biopsychosocial model of illness. These measures were the CES-D, STAI, LOT,
PANAS and MCMQ. The CES-D was negatively correlated with the RTL and PA
subscale scores (r = -.510, p < .001; r = -.317, p < .001, respectively) and positively
correlated with the DRD and BIC subscale scores, (r = .289,/? < .001; r = .212,/? = .002,
respectively). The CES-D was negatively correlated with the F-PAS total score,
(r = -.513,/? < .001). The STAI Trait Scale score was negatively correlated with the RTL
and PA subscale scores (r = -.403,/? < .001; r = -.239, p < .001, respectively) and
positively correlated with the DRD and BIC subscale scores, (r = .278,/? < .001; r = .169,
p = .011, respectively). It was negatively correlated with the F-PAS total score


87
Smith, T. W., Pope, M. K., Rhodewalt, F., & Poulton, J. L. (1989). Optimism,
neuroticism, coping, and symptom reports: An alternative interpretation of the Life
Orientation Test. Journal of Personality and Social Psychology. 56. 640-648.
Spielberger, C. D., Gorsuch, R. L., Lushene, R., Vagg, P. R., & Jacobs, G. A. (1983).
Manual for the State-Trait Anxiety Inventory ('Self-Evaluation Questionnaire: Form
Y). Palo Alto, CA: Consulting Psychologist Press.
Spitzer, R. L., Endicott, J., & Robins, E. (1978). Research diagnostic criteria. Archives of
General Psychiatry. 35. 773-782.
Stewart, A. L., Hays, R. D., & Ware, J. E. (1988). The MOS short-form general health
survey. Reliability and validity in a patient population. Medical Care. 26. 724-735.
Stofmeel, M. A., Post, M. W., Kelder, J. C., Grobbee, D. E., & van Hemel, N. M. (2000).
Quality of life of pacemaker patients. Pacing and Clinical Electrophvsiology. 23.
946-952.
Stofmeel, M. A., Post, M. W., Kelder, J. C., Grobbee, D. E., & van Hemel, N. M.
(2001a). Psychometric properties of the Aquarel. A disease-specific quality of life
questionnaire for pacemaker patients. Journal of Clinical Epidemiology. 54. 157-165.
Stofmeel, M. A., Post, M. W., Kelder, J. C., Grobbee, D. E., & van Hemel, N. M.
(2001b). Changes in quality-of-life after pacemaker implantation: Responsiveness of
the Aquarel questionnaire. Pacing and Clinical Electrophvsiology. 24. 288-295.
Turk, D. C., & Okifuji, A. (1994). Detecting depression in chronic pain patients:
Adequacy of self-report. Behavior Research and Therapy. 32. 9-16.
Vitale, M. B., & Funk, M. (1995). Quality of life in younger persons with an implantable
cardioverter defibrillator. Dimensions of Critical Care Nursing. 14. 100- 111.
Wallace, R. L., Sears, S. F., Lewis, T. S., Griffis, J. T., Curtis, A., & Conti, J. B. (2002).
Predictors of quality of life in long-term recipients of implantable cardioverter
defibrillators. Journal of Cardiopulmonary Rehabilitation. 22.278-283.
Ware, J. E., Snow, K., Kosinski, M., & Gandek, B. (1993). SF-36 Health Survey:
Manual and interpretation guide. Boston, MS: Nimrod Press.
Watson, D., Clark, L.A. & Tellegen, A. (1988). Development and validation of brief
measures of positive and negative affect: The PANAS scales. Journal of Personality
and Social Psychology, 54. 1063-70.
Wellens, H. J., Lau, C. P., Luderitz, B., Akhtar, M., Waldo, A. L., Camm, A. J.,
Timmermans, C., Tse, H. F., Jung, W., Jordaens, L., & Ayers, G. (1998). Atrioverter:
An implantable device for the treatment of atrial fibrillation. Circulation. 98.1651-
1656.


38
current samples mean score on the Social Functioning Scale (M = 76.59, SD = 24.65)
was not significantly different from the healthy samples or the congestive heart failure
samples. On the Emotional Role Scale the current samples obtained mean score of
68.99 (SD = 40.51) was significantly lower than the healthy samples obtained mean
score, t (262) = 3.873, p < .001, but not significantly different than the mean score
obtained by congestive heart failure patients. The current samples mean score on the
Mental Health Functioning Scale (M = 76.46) was not significantly different from the
healthy samples or the congestive heart failure samples. For a summary of the mean
comparison statistics for normative samples on the SF-36 scales, see Table 4-2.
Table 4-2. Obtained scores on the SF-36 and statistics for comparison between
normative samples and current sample
Normative Sample
MSD
df
Welchs t
Healthy 65 to 75 year olds (n = 442)
Phy function
69.38
26.26
279
5.268***
Phy role
64.54
41.30
293
4.264***
Bodily pain
68.49
26.42
312
2.303*
Gen health
62.56
22.42
301
3.079**
Vitality
59.94
22.12
302
6.126***
Social
80.61
25.63
314
1.942
Emotion role
81.44
34.56
262
3.873***
Mental health
76.87
18.08
316
0.283
Congestive heart failure patients (n =
Phy function
216)
47.54
31.00
279
3.361***
Phy role
34.37
39.72
293
3.873***
Bodily pain
62.67
30.97
312
0.352
Gen health
47.05
24.17
301
4.389***
Vitality
44.29
24.41
302
1.969*
Social
71.31
33.06
314
1.883
Emotion role
63.67
43.00
262
1.322
Mental Hh
74.68
21.29
316
0.956
*p < .05; **p < .01; ***p < .001


233 FTfsj
02/18/84 34781


30
Procedure
All participants were surveyed once. Data for assessing patient acceptance in IAD
patients were gathered through the PASSAT Study. These patients were consented by
the Jewel AF-AF Only investigators and the questionnaire battery was either mailed or
directly handed to the patient. Surveys were specially coded so that neither the Jewel
AF-AF Only investigator nor the current investigator could identify the patient. The
patients were allotted a 3-week period to complete the battery. Data for assessing patient
acceptance in pacemaker and ICD patients were gathered by having consecutive patients
at the Watson Outpatient Electrophysiology Clinic approached to participate in the study
during routine follow-up appointments. Informed consent was obtained by nurses who
agreed to act as research assistants. Patients were handed the battery of self-report
instruments in an addressed stamped envelope. Participants were asked to complete the
battery at home and mail the completed battery back to the investigator.
Instruments
Patient Acceptance: Florida Patient Acceptance Survey
The F-PAS began as 47 self-report items composed with the help of experts on
psychosocial functioning in device patients, an expert in the field of psychometrics, and a
cardiac electrophysiologist. Information obtained through surveys and interviews were
used as guidelines for item construction. The results from this initial psychometric
investigation are reported in the Results section of this manuscript.
The F-PAS was designed to measure all aspects of the biopsychosocial model of
illness; therefore, validated instruments measuring each component of the
biopsychosocial model were chosen for the purpose of providing convergent and
divergent validity indices. Participants in the PASSAT Study completed all measures.


34
inspired, excited, and attentive and negative affect terms include upset,
distressed, and irritable. The 1-week time frame was used in an effort to yield more
accurate recall of levels of affect that are ongoing. Research has shown that the positive
and negative affect scores produced are minimally correlated and reliable (Cronbachs
alpha in the 0.80 range). It has also been reported that the PANAS has good convergent
and divergent validity with other instruments assessing affect (Watson et al., 1988).
Medical Coping Modes Questionnaire ('MCMOI. The MCMQ (Feifel, Strack, &
Nagy, 1987) is a 19-item questionnaire that assesses three types of coping responses to a
medical situation, including confrontation, avoidance, and acceptance-resignation.
Reliability coefficients range from 0.67 to 0.70; a total score for coping is not obtained.
Social Component Measures
Illness Intrusiveness Rating Scale (IIRST The IIRS (Devins, Orme, & Costello,
1988) is a 13-item self-report measure that asks respondents to rate how much their
illness and consequent treatment interferes with each of 13 life domains. The domains
relate to health, recreation, diet, work, financial situation, self-expression, family
relations, relations with spouse, sex life, social relations, religious expression and
community involvement. A total intrusiveness score and 13 domain scores can be
calculated. Total scores range from 13 (where the illness is not intrusiveness) to 91
(where the illness is extremely intrusive in all domains). It has been reported that the
IIRS has adequate to high internal consistency, reliability, and validity. Patients
appraisals of intrusiveness were corroborated by ratings from significant others (family
and health care professionals). Studies also indicate that intrusiveness varies across
treatment modalities and illnesses (Devins et al., 1993). The construct has been shown to
be associated with a number of QOL endpoints including life satisfaction, mood states,


Table 4-12. Summary of hierarchical regression models for variables predicting SF-36 general health scale scores using the F-PAS
total score as a measure of patient acceptance
Variable
B
SEB
P
t
P
R2
R2A
Step 1
Age
.159
.135
.083
1.175
.242
Gender
5.514
3.158
.121
1.746
.083
Marital status
1.562
3.406
.031
.458
.647
.018
.018
Step 2
ARSS*
o
00
r
.125
-.467
-6.406
.000
.344
.326
Step 3
CES-D
-.418
.271
-.165
-1.541
.125
STAI trait
3.889E-02
.207
.018
.188
.851
.414
.070
Step 4
IIRS
-5.378E-02
.135
-.037
-.399
.691
.424
.010
Step 5
FPAS total*
.350
.123
.229
2.839
.005
.454
.030
* Predictor of unique variance


77
While several conclusions can be drawn from the information obtained, one
important conclusion is that global measures of health-related quality of life such as the
SF-36 may be adequate for assessing pre and post-implantation differences in quality of
life, but they may not have the specificity needed to detect subtle differences and changes
in health status in patients after they have been treated. Current disease specific
measures for cardiac patients also provide adequate information regarding pre and post
treatment changes in cardiac patients. In many cases they are also able to distinguish
between cardiac illness populations and may be able to detect changes in health status in
similar cardiac illness patients. However, neither global measures of quality of life or
current cardiac disease specific instruments are adequate in assessing the unique
psychosocial issues associated with patients acceptance of an implantable cardiac
device. This preliminary investigation of the F-PAS indicates that it is a valid measure
for assessing these issues.
The F-PAS also provides relevant information for the design and implementation of
psychosocial interventions that would increase acceptance of a device and consequently
improve health-related quality of life. For instance, the F-PAS revealed that ICD patients
have a more difficult time returning to life and experience greater device related distress
than other cardiac device patients. While the reasons for these finding are not assessed in
the current study, a reasonable explanation is that ICD patients typically receive a device
after they have experienced ventricular tachycardia or sudden death, and may,
consequently, have mortality-related fears. These fears may be amenable to change via a
psychosocial intervention. Another possible use of the F-PAS is in the area of research
and design of implantable devices. Information obtained from the measure may allow


44
frequencies were examined. Item number 17, I have continued my normal sex life,
was the most frequently unanswered item on the measure (18 participants did not respond
to the question) and the modal item response was neither agree nor disagree. A
Cronbachs Alpha of 0.89 was obtained when the item was removed from the factor. The
item was consequently dropped from the factor and from further analyses.
A total of 15 items were retained for further validation procedures and Cronbachs
Alpha was computed for the items (a = .83). The four remaining factors were labeled:
(a) Return to Life (RTL), (b) Device-Related Distress (DRD), (c) Positive Appraisal
(PA), and (d) Body Image Concerns (BIC). Scale scores were calculated for each of the
factors. In order to decrease confusion resulting from subscale labels, negatively worded
items in the RTL factor were reverse scored before a scale score was calculated. A
higher order factor analysis procedure, using the factor scores, resulted in a single factor.
The higher order factor allows for the calculation of a total F-PAS scale score; however,
negatively worded items must be reverse scored for the total score to be interpretable.
After calculating the factor scale scores, the negatively worded items were reverse scored
and a Total F-PAS score was calculated for all participants. See Table 4-5 for the final
factor items, labels, reverse scored items, and reliability coefficients.
Hypothesis 2: Convergent and Discriminant Validity of the F-PAS
F-PAS and SF-36 Correlations
In order to determine if the F-PAS possessed convergent validity with the SF-36,
Pearson product-moment correlation coefficients were calculated. The coefficients
revealed that the F-PAS total score was moderately correlated with all eight scales of the
SF-36:


4-12. Summary of hierarchical regression models for variables predicting SF-36
general health scale scores using the F-PAS total score as a measure of
patient acceptance 54
4-13. Summary of hierarchical regression model for variables predicting SF-36
vitality scale scores using F-PAS subscale scores as a measure of patient
acceptance 56
4-14. Summary of hierarchical regression model for variables predicting SF-36
social scale scores using F-PAS subscale scores as a measure of patient
acceptance 57
4-15. Device group frequencies and means on demographic variables 59
4-16. Analysis of variance between groups on demographics 59
4-17. Obtained scores for pacemaker group 60
4-18. Obtained scores for ICD group 61
4-19. Obtained scores for I AD group 62
4-20. Analysis of variance between groups on SF-36 scales 64
4-21. Tukey HSD homogenous subsets of device groups on scales of the SF-36 .... 64
4-22. Analysis of variance between groups on FPAS 66
4-23. Homogenous subsets of device groups on the F-PAS 66
vi


72
ICD patients. However, unexpectedly, IAD patients also have fewer concerns about
returning to their former lives than ICD patients. Pacemaker patients and IAD patients
are not significantly different in this respect. As predicted, pacemaker patients have
much less device related distress than IAD and ICD patients. This is likely due to the
lack of painful and/or unexpected therapies. While preliminary ANOVAs indicated that
pacemaker patients have fewer body image concerns than IAD patients and similar
concerns to ICD patients, when demographic variables are controlled, these differences
disappear. Because no single demographic variable was found to be a significant
covariate, a combination of age, gender, and marital status are assumed to be more
important than the type of device received in determining if body image is an issue. A
review of the significant demographic differences between groups revealed that IAD
patients were younger than both pacemaker and ICD patients, the pacemaker group was
comprised of more women than the IAD group, and pacemaker patients were more likely
to be unmarried than were IAD patients. The F-PAS total score indicates that pacemaker
patients, as expected, have greater patient acceptance than ICD patients.
With regard to hypothesis fiveICD patients will have significantly greater patient
acceptance of their device than IAD patientsthe hypothesis was not supported. On the
RTL subscale of the F-PAS, significant differences between ICD and IAD patients were
found. Implantable cardioverter defibrillator patients had more return to life concerns
than IAD patients. A finding that contradicts what was hypothesized. While controlling
for differences in demographic variables did not result in the eradication of this
difference, it is important to note that selection criteria for IAD patients included the
absence of a life-threatening condition. Implantable cardioverter defibrillator patients
are, by definition, dealing with a life-threatening condition. Two explanations are


61
Table 4-18. Obtained scores for ICD group
Measure n
M
SD
Range
Quality of life
SF-36
Phy function
54
47.69
28.34
0-100
Phy role
53
38.21
40.61
0-100
Bodily pain
54
59.67
24.67
12-100
Gen health
54
48.33
21.82
0-92
Vitality
53
44.25
23.56
0-95
Social
54
71.44
26.38
12.5-100
Emotion role
53
64.15
41.79
0-100
Mental health
53
78.49
15.05
36-100
Biological component
AFSS
55
35.09
9.45
18-55
Psychological component
CES-D
54
11.07
8.75
0-38
STAI
State
16
29.19
9.41
20-48
Trait
58
32.88
10.13
20-63
LOT
12
14.08
5.05
8-24
PANAS
Positive
14
29.36
5.75
17-37
Negative
14
18.71
4.60
12-28
MCMQ
Confrontation
54
18.94
1.75
14-23
Avoidance
56
13.95
2.37
10-20
Accept-Resign
55
10.11
1.05
7-13
Social Component
IIRS
25
35.24
19.50
13-82
ISEL
12
2.50
2.61
0-8


Table 4-5. Final factors and reliability coefficients
Factor
46
a
Factor 1: Return to life .89
I am not able to do things for my family the way I used to.**
I am confident about my ability to return to work if I want to.
I am concerned about resuming my daily physical activities.**
I have returned to a full life.
Factor 2: Device-related distress .79
When I think about the device I avoid doing things I enjoy.*
I avoid my usual activities because I feel disfigured by my device.*
It is hard for me to function without thinking about my device.*
Thinking about the device makes me depressed.*
I am careful when hugging or kissing my loved ones.*
Factor 3: Positive appraisal .82
The positive benefits of this device out-weigh the negatives.
I would receive this device again.
I am safer from harm because of my device.
My device was my best treatment option.
Factor 4: Body image concerns .74
I feel less attractive because of my device.*
I feel that others see me as disfigured by my device.*
Total F-PAS .83
* Items must be reversed before calculating a F-PAS total score.
**Items must be reversed scored before calculating subscale score and F-PAS total score.
Table 4-6. Pearson product moment correlation coefficients for F-PAS and SF-36
F-PAS
RTL
DRD
PA
BIC
Total F-PAS
SF-36
Phy funct
436***
-.013
.147*
.067
.260***
Phy role
479***
-.102
.159*
.028
.356***
Bodily pain
223***
-.012
.079
-.071
.206**
Gen health
.601***
-.203**
.185**
-.017
470***
Vitality
.500***
-.070
.178**
-.079
359***
Social
479***
-.054
.146*
.029
290***
Emotion role
.334***
-.204**
.099
-.099
321***
Mental
387***
_195**
.151*
-.162*
.351***
*p < .05; **p < .01; ***/? < .001


25
(b) to investigate the convergent and discriminant validity of the measure; and (c) to
compare patient acceptance and quality of life in pacemaker, ICD and IAD patients via
the biopsychosocial model. The development of a patient acceptance instrument and
comparison of quality of life between device groups provides evidence for the theoretical
construct of patient acceptance, begins the validation process of a patient acceptance
measure, and provides information necessary for designing interventions that will
improve the quality of life of patients that must rely on life sustaining technology.
Hypotheses
Hypothesis 1. It was hypothesized that an exploratory factor analysis of 47 self-
report items designed to measure patient acceptance of implantable biomedical devices
would result in five stable factors. These factors would serve as subscales of a patient
acceptance measure, to be referred to as the Florida Patient Acceptance Survey (F-PAS).
The factors were hypothesized to be Return to Life, Device-Related Distress, Positive
Appraisal, Social Issues, and Technology-Education-Understanding. For a complete
listing of the items and the factors they were hypothesized to measure refer to Table 2-1.
Hypothesis 2. It was hypothesized that the F-PAS would be moderately correlated
with the subscales of the SF-36 and would account for a significant amount of the
variance in scores on the SF-36 that was not accounted for by demographic variables or
affective symptoms. It was hypothesized that all the measures in a biopsychosocial
model of illness battery would be moderately correlated with the SF-36 due to its
purported measuring of physical, mental and social functioning. However, it was
asserted that the F-PAS would show divergence from the SF-36 because of its sensitivity
to subtle changes in the functioning of biomedical device recipients.


....itv rc Pi HR1DA
3 12b' U I Vt-v yj


88
Wiklund, I., Gorkin, L., Pawitan, Y., & Schron, E. (1992). Methods for assessing quality
of life in the cardiac arrhythmia supression trial (CAST). Quality of Life Research. 1.
187-201.
Wood, M. A. (2000). Automated pacemaker function. Cardiology Clinics. 18. 177-191.
Xie, B., Thakur, R. K., Shah, C. P., & Hoon, V. K. (1998). Permanent cardiac pacing.
Emergency Medicine Clinics of North America. 16.419-462.


2
cardiac patients (Carney et al., 1995; Frasure-Smith, Lesperance, Talajic, 1993, 1995;
Ornish et al., 1990).
In the past decade, the goal of medical care providers has moved from merely
decreasing morbidity and mortality to the preservation of functioning and better quality
of life than was experienced before medical treatment (American College of Physicians
[ACP], 1988). Levine (1987) asserted that quality of life should become a primary
criterion for evaluating the efficacy of medical treatments because psychosocial variables
shape healthcare outcomes. They argued that quality of life must be evaluated because
chronic medical conditions are now being managed but not cured and medical
technologies that prolong life result in ethical dilemmas regarding end-of-life care.
These ethical questions resulted in the conceptualization of health-related quality of life.
Health-related quality of life has been defined as how health conditions, treatment
modalities, and prognoses impact patients preferences for treatments and their desire to
continue treatment. Researchers in this area have consistently reported that individuals
would trade more years of life for fewer years of higher quality life (Lawton, Moss,
Hoffman et al., 1999), thus making quality-of-life outcomes even more important than
prolonging life. A major concern for recipients of implantable cardiac biomedical
devices is that the life-saving benefits may be diminished because of reduced quality of
life (Sears, Todaro, Saia, Sotile, & Conti, 1999).
Formidable evidence increasingly shows that psychological distress undermines
health outcomes and quality of life across cardiac populations. Therefore, the importance
of further scientific inquiry into the psychosocial factors affecting recipients of
implantable cardiac biomedical devices should not be underestimated. A theoretical
framework for explaining the unique psychosocial issues experienced by people with


49
respectively) and postively correlated with the DRD and BIC subscale scores, (r = .428,
p <.001; r = .196,/? = .010, respectively). It was negatively correlated to the F-PAS
total score, (r = -.599, p< .001). The ISEL was not significantly correlated with the F-
PAS subscale or total scores. See Table 4-9 for all correlations between the F-PAS and
the social component measures.
Table 4-8. Pearson product moment correlation coefficients for F-PAS and
psychological component measures
F-PAS
RTL
DRD
PA
BIC
Total F-PAS
CES-D
STAI
-.510***
289***
-.317***
.212**
-.513***
State
- 345***
3ij***
-.366***
.256**
.447***
Trait
_ 403***
.278***
- 239***
-.169*
- 425***
LOT
PANAS
.309**
-.199*
.116
-.111
.270**
Pos
.430***
-.227*
.235*
-.064
.372***
Neg
-.303**
329***
-.319**
.233*
-.384***
MCMQ
Con
-.028
.110
.093
.007
-.027
Avoid
-.183**
.095
-.037
.087
-.124
Accpt
-.079
-.097
-.034
.126
-.048
*p < .05; **p<.01;
***p < .001
Table 4-9. Pearson product moment correlation coefficients for F-PAS and social
component measures
F-PAS
RTL
DRD
PA
BIC
Total F-PAS
IIRS
592***
.428***
-.335***
.196*
_ 599***
ISEL
.069
-.062
.091
.003
.113
*p < .05, ***/? < .001
Regression on Scales of the SF-36
Hierarchical regressions were used to determine if the F-PAS predicted health-
related quality of life as measured by the SF-36, when the variance in scores accounted
for by demographics, core battery measures of the biological, psychological, and social
components of the biopsychosocial model had been considered. Separate blocked