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Complementary and Alternative Medicine

Material Information

Title:
Complementary and Alternative Medicine Current Trends and Predicting Future Use
Creator:
Erenguc, Filiz Naz
Place of Publication:
[Gainesville, Fla.]
Florida
Publisher:
University of Florida
Publication Date:
Language:
english
Physical Description:
1 online resource (154 p.)

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Health and Human Performance
Health Education and Behavior
Committee Chair:
CHEN,WEI W
Committee Co-Chair:
STOPKA,CHRISTINE BOYD
Committee Members:
JAMES,DELORES CORINNE SUZETTE
SCARPACE,NIHAL TUMER
Graduation Date:
8/9/2014

Subjects

Subjects / Keywords:
Complementary therapies ( jstor )
Educational research ( jstor )
Email ( jstor )
Health education ( jstor )
Health promotion ( jstor )
Health status ( jstor )
Health surveys ( jstor )
Modeling ( jstor )
School surveys ( jstor )
Universities ( jstor )
Health Education and Behavior -- Dissertations, Academic -- UF
cam
City of Indian Rocks Beach ( local )
Genre:
bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
Health and Human Performance thesis, Ph.D.

Notes

Abstract:
According to the National Health Statistics Report roughly 40% of U.S. adults have used some form of complementary and alternative medicine (CAM). Not only is CAM used to promote overall wellbeing it is also used to help treat conditions. CAM is used to treat back and joint pain, the common cold, anxiety/depression, indigestion, and sleep disturbances. In addition to the growing popularity of CAM, it is reported that the U.S. population spent $36 to $46 billion on CAM and that roughly $12.2 billion of that was paid for out-of-pocket. Education, household income, sex, race/ethnicity, health status, and age are all suggested determinants of CAM use by the literature. It is also suggested that university settings are great means for health-promotion programs due to the diverse population of students, faculty, and staff. Additionally more research on CAM related worksite health-promotion programs have demonstrated greater efficiency at the workplace while promoting healthy behaviors to the individual employee. With the growing demand of CAM and an increase in worksite health promotion programs relating to CAM, a university setting, which consists of diverse populations, is a great location for CAM specific health promotion programs. This study explored both the determinants of CAM while using the Behavioral Intention Model (BIM) to predict future CAM use. In comparing group means of CAM use Cronbach's alpha and p-values were calculated to determine significance. Of all the variables tested (education, household income, sex, race/ethnicity, health status, and age) only sex was significant. Additionally, in testing the BIM both p-values, correlation coefficients, and F-statistic were used to confirm significance. Results from this study confirm that sex is a determinant of CAM use and that the BIM can be used to predict future behavior. Over 86.4% of the respondents indicating they have used some form of CAM, which is far higher than the national average. These findings suggest that this population is receptive to engaging in CAM opportunities. Lastly, this university setting would be a great setting for CAM related worksite health-promotion programs. ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ph.D.)--University of Florida, 2014.
Local:
Adviser: CHEN,WEI W.
Local:
Co-adviser: STOPKA,CHRISTINE BOYD.
Electronic Access:
RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2015-08-31
Statement of Responsibility:
by Filiz Naz Erenguc.

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Embargo Date:
8/31/2015
Resource Identifier:
968786205 ( OCLC )
Classification:
LD1780 2014 ( lcc )

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COMPLEMENTARY AND ALTERNATIVE MEDICINE: CURRENT TRENDS AND PREDICTING FUTURE USE By FILIZ NAZ ERENGUC A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2014

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© 2014 Filiz Naz Erenguc

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To my family without their encouragement, support, and most of all love, this dream would not have been realized

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4 ACKNOWLEDGMENTS I thank him for all of his guidance and sage wisdom. His continued encouragement and suggestions throughout the doctoral process helped me grow as a student and as a profess ional. I thank my Dad. Without his unconditional love and support (and patience) I would not have been able to achieve this lifetime goal of earning my PhD. Everything from re teaching me statistics, to being a soundboard for ideas, and proofing all of m y awkwardly lengthy sentences helped me get to the light at the end of the tunnel. I thank my Mom for her words of encouragement and optimism. Even when I felt it would be easier to walk away from this, she helped me stay on course. I appreciate everything she has done to help me get to where I am today. She is my role model! I thank my husband, Skylar, for understanding on the many nights I stayed up to same roof who is always thinking about school or work. I thank him for being so honeymoon. I thank my sisters Arzu and Candan. Throughout the process they offered great encouragement and continuo can resume our guilt free shopping trips and manicures/pedicures. I thank my supervisory committee members (Drs. Delores James , Christine S topka, and Nihal Tumer Scarpace ) for their patience, enc ouragement, and unwavering support. I am grateful for the attention shown to me, time devoted to me, and advice shared with me.

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5 I also thank two other professors not on my committee who offered invaluable ass istance. Drs. Alan Cooke and Pekin Ogan were wil ling to answer questions or provide advice. Dr. Cooke was instrumental in explaining the Behavioral Intention Model and how I could apply it to my topic of CAM and health education. I also thank to my Health Education and Behavior friends: Holly Moses, K im Holton , Peter Gryffin , J ulia Varnes, Kelly Jamison, Sam Evans, Taryn Buckley, Melissa Naidu, and Gungeet Joshi. This group of amazing scholars provided stress relief and were available whenever I needed emotional support or brainstorming. I cannot tha nk each of them enough for all their words of encouragement along the way. I also appreciate all the support of my friends. I thank them for their words of encouragement and the countless times I discussed my topic, classes, and frustrations with them. Tha nk you Chrissy Brown Wujick, Christopher Ferraro, Christos Drakos, and Bobbi Knickerbocker. Another group I must mention is my work colleagues. My work family also helped me through stressful times and I sincerely appreciate the countless offers I receiv ed from coworkers to take my survey or proof my papers. I am grateful for my Aca demic Advising Center (AAC) and Master of Business Administration (MBA) Family!!!

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 9 LIST OF FIGURES ................................ ................................ ................................ ........ 11 LIST OF ABBREVIATIONS ................................ ................................ ........................... 12 ABSTRACT ................................ ................................ ................................ ................... 13 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 15 Purpose of the Study ................................ ................................ .............................. 19 Research Questions ................................ ................................ ............................... 20 Hypotheses ................................ ................................ ................................ ............. 21 Key Terms ................................ ................................ ................................ .............. 23 Assumptions ................................ ................................ ................................ ........... 26 Limitations ................................ ................................ ................................ ............... 26 Delimitations ................................ ................................ ................................ ........... 26 Significance of the Study ................................ ................................ ........................ 27 2 LITERATURE REVIEW ................................ ................................ .......................... 28 Complementary and Alternative Medicine (CAM) Overview ................................ ... 28 Trends of CAM Use among the U.S. Population ................................ ..................... 29 Worksite Health Promotion and CAM ................................ ................................ ..... 32 Determinants and Predictors of CAM Use ................................ .............................. 34 Health Status ................................ ................................ ................................ .... 34 Age ................................ ................................ ................................ ................... 36 Education ................................ ................................ ................................ ......... 3 8 Sex ................................ ................................ ................................ ................... 39 Race ................................ ................................ ................................ ................. 40 Income ................................ ................................ ................................ .............. 43 Behavioral Intention Model ................................ ................................ ..................... 43 Summary ................................ ................................ ................................ ................ 46 3 METHODOLOGY ................................ ................................ ................................ ... 49 Research Desig n ................................ ................................ ................................ .... 49 Setting ................................ ................................ ................................ ..................... 50 Participants ................................ ................................ ................................ ............. 50 Instrumentation ................................ ................................ ................................ ....... 52

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7 Subjectivity Statement ................................ ................................ ............................ 53 Data Collection ................................ ................................ ................................ ....... 54 Data Analysis ................................ ................................ ................................ .......... 54 Summary ................................ ................................ ................................ ................ 56 4 RESULTS ................................ ................................ ................................ ............... 60 Demographics ................................ ................................ ................................ ......... 60 Trends of CAM Use ................................ ................................ ................................ 63 Hypothesis 1 ................................ ................................ ................................ ........... 65 Hypothesis 2 ................................ ................................ ................................ ........... 66 Hypothesis 3 ................................ ................................ ................................ ........... 67 Hypothesis 4 ................................ ................................ ................................ ........... 67 Hypothesis 5 ................................ ................................ ................................ ........... 68 Hypothesis 6 ................................ ................................ ................................ ........... 69 Hypothesis 7 ................................ ................................ ................................ ........... 70 Hypothesis 8 ................................ ................................ ................................ ........... 70 Hypothesis 9 ................................ ................................ ................................ ........... 70 Additional Findings ................................ ................................ ................................ . 71 Summary ................................ ................................ ................................ ................ 75 5 DISCUSSION, CONCLUSIONS, & RECOMMENDATIONS ................................ ... 87 Discussion ................................ ................................ ................................ .............. 87 Limitations ................................ ................................ ................................ ............... 93 Implications and Recommendations ................................ ................................ ....... 95 For Future Research ................................ ................................ ........................ 95 For Health Educators & Health Education ................................ ........................ 96 Summary and Final Thoughts ................................ ................................ ................. 98 APPENDIX A SURVEY INSTRUMENT PRIOR TO EXPERT PANEL REVIEW ......................... 100 B SURVEY INSTRUMENT WITH EXPERT PANEL SUGGESTIONS (CHEN, COOKE, LANE, LEITE, & YOON) ................................ ................................ ........ 109 C FINAL SURVEY INSTRUMENT ................................ ................................ ........... 128 D IRB APPROVAL LETTER ................................ ................................ ..................... 139 E EMAIL 1: REQUESTING SURVEY RESPONSE ................................ .................. 140 F REMINDER EMAIL 1: FOR THOSE WHO MAY NOT HAVE RECEIVED THE FIRST REQUEST ................................ ................................ ................................ . 142 G FINAL REMINDER EMAIL ................................ ................................ .................... 144

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8 REFE RENCES ................................ ................................ ................................ ............ 146 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 153

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9 LIST OF TABLES Table page 3 1 Independent Variables versus Dependent Variables ................................ .......... 57 3 2 Hypotheses with Corresponding Statistical Tests ................................ ............... 58 3 3 Timeline for Data Collection & Data Analysis ................................ ..................... 59 4 1 Age of Survey participants ................................ ................................ .................. 76 4 2 Sex of Survey participants ................................ ................................ .................. 76 4 3 Race/Ethnicity of Survey participants ................................ ................................ . 76 4 4 Born in US ................................ ................................ ................................ .......... 76 4 5 Marital status of Survey participants ................................ ................................ ... 77 4 6 Highest Level of Education Achieved ................................ ................................ . 77 4 7 Employment Classification ................................ ................................ ................. 77 4 8 Household Income Range ................................ ................................ .................. 78 4 9 Reasons for Using CAM ................................ ................................ ..................... 78 4 10 Frequency of Consultation with Physician about CAM Use ............................... 78 4 11 Types of Natural Products Used ................................ ................................ ......... 78 4 12 Results of Chi square test and descriptive statistics for educational levels ........ 79 4 13 Results of Chi square test and descriptive statistics for household income ranges ................................ ................................ ................................ ................ 79 4 14 Results of Chi square test and descriptive statistics for sex ............................... 80 4 15 Results of Chi square test and descriptive statistics for racial/ethnic groups ..... 80 4 16 Results of Chi square test and descriptive statistics for health status ................ 81 4 17 Results of Ch i square test and descriptive statistics for age groups ................... 81 4 18 Coefficients for each independent predictor in separate simple li near regression models ................................ ................................ .............................. 81 4 19 M ultiple linear regression model ................................ ................................ ......... 82

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10 4 20 ANOVA for multiple linear regression model ................................ ...................... 82 4 21 Results of Chi square test and descriptive statistics for employment classifications ................................ ................................ ................................ ..... 82 4 22 Results of Chi square test and descriptive statistics for location of birth ............ 83 4 23 Results of Chi square test and descriptive statistics for alcohol consumption frequency ................................ ................................ ................................ ............ 83 4 24 Results of Chi square test and descriptive statistics for tobacco consumption frequency ................................ ................................ ................................ ............ 83 4 25 Results of Chi square test and descriptive statistics for physical activity levels .. 84 4 26 Results of Chi square test and descriptive statistics for BMI ranges .................. 84 4 27 Results of Chi square test and descriptive statistics for previous diagnosis of health condition ................................ ................................ ................................ .. 85

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11 LIST OF FIGURES Figure page 2 1 Behavioral Intention Model ................................ ................................ ................. 48 4 1 CAM Use Lifetime and Last 12 Months ................................ .............................. 86

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12 LIST OF ABBREVIATIONS Acct Attitudes BI Behavioral intent BIM Behavioral intention model CAM Complementary and alternative medicine MC Motivation to comply NB Normative beliefs SN Subjective norms

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13 Abstract of Dissertation Presented to the Gra duate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy COMPLEMENTARY AND ALTERNATIVE MEDICINE: CURRENT TRENDS AND PREDICTING FUTURE USE By Filiz Naz Erenguc August 2014 Chair: William Chen Major: Health and Human Performance According to the National Health Statistics Report roughly 40% of U.S. adults have used some form of complementary and alternative medicine (CAM). Not only is CAM used to promote overall wellbeing it is also used to help treat conditions. CAM is used to treat back and joint pain, the common cold, anxiety/depression, indigestion, and sleep disturbances. In addition to the growing popularity of CAM, it is reported that the U.S. population spent $36 to $46 billion on CAM and that roughly $12.2 billion of that was paid for out of pocket. Education, household income, sex, race/ethnicity, health status, and age are all suggested determinants of CAM use by the literature. It is also suggested that university settings are great means for health promotion programs due to the diverse population of students, faculty, and staff. Additionally more research on CAM related worksite health promotion programs have demonstrated greater efficiency at the workplace while promoting healthy behaviors to the individual employee. With the growing demand of CAM and an increase in worksite health promotion programs

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14 relating to CAM, a university setting, which consists of diverse populations, is a great location for CAM specific health promotion programs. This study explored both the determinants of CAM while using the Behavioral Intention Model (BIM) to predict future CAM use. In comparing group means of CAM values were calculated to determine significance. Of all the variables tested (education, household income, sex, race/ethnici ty, health status, and age) only sex was significant. Additionally, in testing the BIM both p values, correlation coefficients, and F statistic were used to confirm significance. Results from this study confirm that sex is a determinant of CAM use and t hat the BIM can be used to predict future behavior. Over 86.4% of the respondents indicating they have used some form of CAM, which is far higher than the national average. These findings suggest that this population is receptive to engaging in CAM oppor tunities. Lastly, this university setting would be a great setting for CAM related worksite health promotion programs.

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15 CHAPTER 1 INTRODUCTION According to the National Center for Complementary and Alternative Medici ne (NCCAM), Complementary and Alternative Medicine (CAM) is medical and health care systems, practices, and products that are not presently Alternative Medicine, paragraph 1). Complementary medicine is used in conjunction with conventional medicine, whereas alternative medicine is used in lieu for conventional medicine. As CAM therapies are being integrated with conventional medicine, an evolution of th eir boundaries are merging (NCCAM, 2013). According to surveys done in 2004 and 2007, roughly four of ten US adults used some form of CAM. The most common reasons people turn to CAM, according to Barnes, Powell Griner, Division of Health Interview Statis tics (DHIS), McFann, Nahin, NCCAM, & NIH, (2004) was to help with arthritis, back pain, colds, joint pain, and neck pain. The next set of reasons the US population is turning to CAM is to relieve anxiety/depression, headaches, indigestion, and sleep distu rbances. CAM is gaining popularity among the US population, this was determined by the Center for Disease use of CAM among US adults (Barnes, Powell Griner, McFann, & Na hin , 2004) and then again in a follow up survey conducted in 2007 (Barnes, Bloom, DHIS, National Center for Health Statistics, Nahin, & NCCAM, 2007) yielding support for the growing increase of CAM use. Barnes et al. (2004) found that o lder adults (those 3 0 years of age and older) were more likely, than younger adults to use CAM. The National Health Statistics Report (2008, pg.6) also found that While the prevalence of many individual

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16 therapies was similar between 2002 and 2007, acupuncture, deep breathin g exercises, The National Health Statistics Report (NHSR) by Barnes et al. (2004) additionally explored whether and why participants used some variations of CAM. Roughly 75% of the adult population reported using CAM (including prayer and megavitamins) within the last 12 months, but when researchers excluded prayer and megavitamin intake, the percentage dropped to approximately 40%. Excluding prayer and megavitamin consumptio n, chiropractic care and acupuncture were the most commonly used modalities of CAM. Also, most individuals using CAM therapies are using them in conjunction with conventional medicine, rather than in place of it. Another study, by Eisenberg et al. (2001) , discovered that as many as 80% of those engaging in CAM practices think the synergistic effects it has with conventional medicine are far greater than the individual effects of one or the other independently . Lastly, another explanation for the rise in CAM is suggested to be attributed by growing dissatisfaction with conventional medicine and belief that it is too expensive, ineffective, and disease centered rather than patient centered (Astin, 1998). The increasing dissatisfaction of conventional medici ne also comes with a high price. Americans are spending a substantial amount of money on CAM therapies and treatments. For example, the Barnes et al. (2004) and Eisenberg et al. (1998) estimated that overall the US population spent roughly $36 to $46 bil lion on CAM and of that at least $12.2 billion was paid out of pocket. US adults spent the majority of this money on acupuncture, chiropractic care, and massage therapy. In 2007, Nahin et al. forecasted the out of pocket CAM expenditures to increase to $ 33.9 billion among Americans. Although many of the costs were associated with visiting practitioners of

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17 body based therapies and manipulation therapies (such as acupuncture, chiropractic, and massage), 44% of total CAM expenditures were on vitamins, miner als, and other herbal/biologically based supplements. With so much money being spent on CAM, an office was needed to maintain information for health consumer, healthcare providers, and Health Education Specialists. In response to the growing U.S. accept ance and use of CAM, the National Institute of Health (NIH) created the Office of Alternative Medicine (OAM) in 1992. The purpose of the OAM is to serve as an information clearinghouse, to support research and educational programs for health care provider s, and to guide evaluations of alternative therapies. The White House Commission on Complementary and Alternative Medicine Policy (WHCCAMP) was created in 2000 (White House Commission on Complementary and Alternative Medicine Policy, 2002). In 1998 the OAM was elevated to Center status and the National Center for Complementary and Alternative Medicine (NCCAM) was established. The NCCAM is responsible for providing reliable information on CAM, serving as a source for research initiatives, policy developm ent, making recommendations for use, and locating practitioners. NCCAM originally divided CAM into 5 subdivisions: biologically based practices, energy medicine, manipulative and body based practices, mind body medicine, and the whole medical system. It has since been reduced to three divisions: natural products, mind body medicine, and other complementary approaches. Establishing this Center is one step toward bridging the gap between knowledge and CAM usage and meeting the growing demand for CAM thera pies. Having a better idea of trends in CAM usage will help health education policymakers determine what other resources are needed to provide the US population with adequate education about CAM so they are informed

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18 consumers of the non conventional thera pies. In an effort to bridge the gap of the unknown to evidence based science, NCCAM initiated their Third Strategic Plan 2011 2015 to establish priorities for future CAM research with the following strategic objectives: 1. Advance research in mind body int erventions, practices, and disciplines. 2. Advance research on CAM natural products 3. its integration into health care and health promotion 4. Improve the capacity of the field to carry ou t rigorous research 5. Develop and disseminate objective, evidence based information on CAM interventions In addition to the growing research on CAM and increasing use of CAM, researchers are now exploring how CAM can have a positive impact on workplace a key business fact contributors to diminished workplace productivity levels are the aging population and more stress and unhealthy behaviors. And although over 75% of US employers offer health promotion programs , Kristen (2010) suggests that more worksite health promotion programs can be used to reduce sick leave and absenteeism. Absence due to sickness imposes both direct (e.g., lost productivity) and indirect costs (e.g., higher health care cost) to employers (Goetzel, Long, Ozminkowski, Wang, & Lynch, 2004; Michie and Williams, 2003). Therefore, absence due to sickness can be a tool for examining the economic return associated with workplace health promotion programs (Chapman, 2012).

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19 Purpose of the Study Th e purpose of this study was to explore CAM use among university employees and determine which of the following factors are most highly correlated to the use of CAM: educational level, income, sex, race, or health status. In addition to exploring the above factors the researcher aimed to predict future health behaviors (CAM use) by using the Behavioral Intention Model (BIM). Previous studies explored factors contributing to CAM use (such as educational level, income, health status, race/ethnicity, age, and sex). The researcher sought to better understand the relationships among these factors and how to explain attitudes and subjective norms impact behaviors. This tool will enable researchers to better forecast how increasing demand for holistic mind body therapies will impact the US healthcare system. It will also allow Health Education Specialists to create health promotion programs to meet consumer needs. Additionally, the literature suggested that the trend of Americans using CAM is increasing and wil l only continue to increase (Barnes, et al., 2008). Therefore since it is also proven that one of the greatest predictors of CAM use is education and age, more staff. Th ey are more likely to use CAM and they may benefit most from these complementary treatments. Also, as they age, they are faced with increasing health concerns that can be mitigated or controlled with CAM. When considering the need to fill the educational gap between CAM users and informed health consumers, Health Education Specialists can use results from the present study to create interventions and programs to meet the needs of these two populations. Research also suggests (Barnes et al., 2004) that ma ny CAM therapies have been proven to assist in a variety of health

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20 benefits such as reducing allergy symptoms, anxiety, blood pressure, and chronic pain; improving concentration, managing diabetes, lowering heart rate, reducing stress, and assisting in ove rall wellness. Additionally, it is important to the health and wellness of the patient to maintain open communication with conventional medical practitioners, to ensure the safety of both treatment plans. However, a study by Konefal (2002) determined tha t many conventional health providers (such as physicians) do not have adequate knowledge or preparedness to discuss CAM therapies with their patients. Wetzel, Kaptchuk, Haramati, & Eisenberg (2003) said CAM topics should be more integrated into medical ed ucation to meet growing demands of CAM users. Higher competency to discuss CAM with patients will ensure greater patient/physician disclosure, lest patients avoid disclosing CAM use or physicians have inadequate knowledge to answer the questions the patie nt may ask. This rise in CAM requires physicians to increase their knowledge about CAM. It is also imperative that health education specialists must also be trained to discuss CAM, especially since it can be used to prevent disease, promote wellness, and improve quality of life (Versnik, 2005). Ultimately a person considering using CAM therapies should have all the resources needed to be an informed health consumer. Research Questions RQ1 nd CAM use? RQ2 RQ3 RQ4 RQ5 . Is there a RQ6

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21 RQ7 . Is attitude toward CAM use significant in explaining behavioral intent? RQ8 . Are subjective norms significant in explaini ng behavioral intent? RQ9 . Are attitude toward and subjective norms about CAM use jointly significant in explaining behavioral intent? Hypotheses Ho1. There is no significant difference in the proportions of CAM users with differing educational levels in th e population, where <8 the proportion of CAM users in the population with less than or equal to 8 years of education <8+ the proportion of CAM users in the population with more than 8 years of education but did not complete high school =H the pro portion of CAM users in the population who are high school graduates or have their GED =A degree or professional license =B the proportion of CAM users in the population who have a b =M =D the proportion of CAM users in the population who have a doctorate degree =P the proportion of CAM users in the population who have a professional degree. Ho : <8 =
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22 =50k the proportion of CAM users in the population with a household income greater than or equal to $50,001 but less than $65,000 =65k the proportion of CAM users in the population with a household income greater than or equal $65,001 but less than $80,000 = 80k the proportion of CAM users in the population with a household income greater than or equal to $80,001 but less than $95,000 = 95k the proportion of CAM users in the population with a household income greater than or equal to $95,000. Ho : <20k = =20k = =35k = =50k = =65k = 80k = 95k Ha : Not Ho Ho 3. There is no significant difference in the proport ions of CAM users with differing sex in the population, where =Male the proportion of CAM users in the population who are male = Female the proportion of CAM users in the population who are female. Ho =Male = Ha : Not Ho Ho 4. There is no significant difference in the proportions of CAM users with differing race/ethnicities in the population, where =White the proportion of CAM users in the population who are White/Caucasian =Black the proportion of CAM users in the po pulation who are Black/African A merican =Hispanic the proportion of CAM users in the population who are Hispanic/Latino =Asian the proportion of CAM users in the population who are Asian or Pacific Islander =Am the proportion of CAM users in the population who are American Indian or Alaskan Native =Other the proportion of CAM users in the population who are Other. Ho : =White = =Black = =Hispanic = =Am = =Other Ha : Not Ho 5. There is no significant difference in the proportion of CAM users with differing self reported health status in the population, where

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23 Excellent the proportion of CAM users in the population who identified as being in excellent health v Very good the proportion of CAM users in the popul ation who identified as being in very good health Fair the proportion of CAM users in the population who identified as being in fair health Poor the proportion of CAM users in the population who identified as being in poor health Ho : =Excellent = =Very Good = =Fair = =Poor Ha : Not Ho Ho 6. There is no significant difference in the proportion of CAM users of different age groups in the population, where =Agei denotes the proportion of CAM users in the population who are in the i th Ho : =Age1 = =Age2 = =Age3 =Age(n) Ha : Not Ho Ho 7 . Attitude toward use of CAM is not a significant behavioral intent to use CAM. Alternatively, H 0: W 0 =0, Ha: W 0 Ho 8 . Subjective norms toward the use of CAM is not a significant factor in H 0: W 1 =0, Ha: W 1 Ho 9 . Neither attitude toward CAM nor subjective norms about CAM contribute significantly to prediction of behavioral intent (BI) to use CAM. Alternatively, H 0: W 0 =W 1 = 0, Ha : at least one of the two parameters (W 0 , W 1 ) is not equal to zero. Key Terms A CUPUNCTURE . This form of CAM is based on the premise that hea lth can only be obtained by balance of energy (chi or qi), which is thought to be innate. This energy is constantly flowing throughout the body via meridians which are energy pathways. Acupuncture aims to stimulate the energy pathways by inserting of needl es along the meridian pathways to restore the flow of energy and therefore health . A DULT . I ndividuals age 18 and older.

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24 A LTERNATIVE MEDICINE . N on mainstream approach to health used instead of or in addition to conventional medicine. A LTERNATIVE PROVIDER OR PRACTITI ONER . A knowledgeable about alternative therapies who provides care. A TTITUDES . evaluation of the behavior can influence behavioral intent. A YURVEDA . A c omprehensive system of medicine developed more than 5,000 years ag o in India, equally emphasizing body, mind, and spirit. The ultimate goal is to restore harmony of these three parts. Ayurvedic treatments may include: dietary changes, exercise, herbals, meditation, massage therapy, and yoga. B EHAVIORAL INTENTION . The p erceived likelihood of performing a behavior. Behavioral intention the most important determinant of behavior. B ELIEFS . A n individual's belief about the consequences of a particular behavior. B IOFEEDBACK . A treatment method that trains clients to consc iously regulate typically unconscious bodily functions (e.g., breathing, heart rate, blood pressure), using an electronic device, to improve health. C OMPLEMENTARY MEDICIN E . U ses non mainstream techniques in conjunction with conventional medicine. C HELATION THERAPY . The practice of injecting a binding (chelating) agent to remove and eliminate toxic metals and wastes from the bloodstream. C HIROPRACTIC CARE . A djusting the spine and joints to elicit and activate the defense mechanisms to improve overall health and reduce pain. D EEP BREATHING . S lowly inhaling through the nose then slowly exhaling. The process is used to quite the mind and focus on breath. E NERGY HEALING THERAP Y /R EIKI . This treatment method helps the body heal itself by focusing on flow and healing energy. Healing energy is guided through the balance and health. H EALTH B ELIEF M ODEL (H BM) . A psychological model for explaining and predicting health behaviors. H EALTH STATUS ( INDIVIDUAL ). The emotional/mental well H OMEOPATHY . A lso known as homeopathic medicine: is a system of medical practices based on the theory that any substance that can produce symptoms of disease or illness in a healthy person can also cure those symptoms in a sick

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25 person. Remedies are derived from many sources such as minerals, metals, and plants. H YPNOSIS . A n altered state of consciousness characterized by increased responsiveness to suggestions. The hypnotic state is attained by relaxing the body, then shifting the attention toward an object or idea suggested b y the practitioner, to access levels of the mind, to promote positive changes in health behaviors. M ASSAGE . The practice of p ressing, rubbing, manipulating muscles and soft tissues to promote relaxation and relieve pain. M EDITATION . This practice is the act of calming the mind and relaxing the body. M IND BODY MEDICINE . Evaluates and looks at interaction among brain, body, mind, and behavior. Examples are acupuncture, deep breathing, chiropractic, massage, medication, and yoga. M OTIVATION TO COMPLY . T he motivation to comply with referents about performing a behavior. N ATUROPATHY . A system of medicine based on the theory that the body is a self regulating mechanism with an innate ability to maintain a health and wellness. Treatments may include herbal medicine, homeopathic treatment, massage, or dietary supplements. N ATURAL PRODUCTS . H erbal medicine and botanicals, vitamins, minerals, probiotics, and other natural and dietary supplements taken orally. N ORMATIVE BELIEF . B elief that a referent (e.g. friends, family, or physician) thinks a certain behavior should or should not be per formed. O THER COMPLEMENTARY H EALTH APPROACHES . Comprise of natural products and mind body practice that do not fit into either of the two groups above. Examples include Ayurvedic medicine, homeopathy, naturopathy, traditional Chinese medicine, and tradit ional healers. S UBJECTIVE NORM . A combination of normative beliefs and motivation to comply. T AI C HI . A Chinese self defense discipline using low intensity and low impact exercise to maintain/restore health. Tai chi exercises include a set of forms, wi th each form consisting of a series of body positions connected by one continuous movement. Q I G ONG . A n ancient Chinese discipline that combines movements, focus, and deep breathing to connect mind, body, and spirit, while stimulating the flow of vital life energy (qi).

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26 Y OGA . The combination of breathing exercises, postures, and meditation, used to not only calm the nervous system and to maintain balance among body, mind, and spirit. A ssumptions Participants represent the larger population of univer sity employees. Participants completed the survey in its entirety . Sample size is large enough for adequate data analysis. Limitations Self reported surveys may lead participants to provide responses, they believe to be socially desirable. Findings from t he present study cannot be generalized to all university employees. Results from participants may not represent all fringe benefit receiving employees at the University of Florida. Findings of the present study are dependent on authenticity of participants' responses. Outcomes of the present study are limited to data collected from participants at one point in time (Spring 2014). Delimitations This study was conducted at a large University in the Southeastern portion of the United States. Partic ipants included university employees receiving fringe benefits. Data were collected through online survey software (Qualtrics). Study variables were measured using an expert panel, validity tests, and were piloted prior to the execution of the data collect ion. Data were self reported by participants. The present study used a number of statistical tests and correlational research to describe the statistical association among multiple variables. Findings were also validated by an external statistician

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27 Signif icance of the Study The role of a health education specialist is to teach about disease prevention and to encourage health promotion. According to Glanz, Rimer, & Lewis (2002), health education specialists are tasked with the goal of disseminating empiric al and theory if the literature suggests a growing percentage of the US population is turning to CAM, then health education specialists and health professionals need to be more aware of how these complementary and alternative health modalities can be beneficial. Benefits of CAM are seen at the individual level and can also have a positive impact on workplace productivity levels. The Behavioral Intention Model (BIM) was u sed as a prediction tool to determine how likely a person is to use CAM. Therefore this model can be used to create health promotion programs geared toward those already using CAM. For those not currently using CAM, the model can be used to teach them abo ut being informed health consumers and how beneficial CAM can be to their overall health and wellbeing. Because BIM is not a model regularly used among health education and health promotion programs, evidence is given to demonstrate how this model can pro ve to be parsimonious in predicting behavior.

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28 CHAPTER 2 LITERATURE REVIEW This chapter provides a review of the literature related to topics including 1) an overview of CAM 2) the trends of CAM use 3) how CAM can be used to increase workplace productivity, 4) determinants of CAM use among the US population 5) education as a pred ictor of CAM use 6) sex as its relationship to CAM use 7) race/ethnicity predictors 8) CAM use and its impact with age and 9) the Behavioral Intention Model to explain how health behaviors can be predicted. Complementary and Alternative Medicine (CAM) Ov erview Complementary and Alternative Medicine (CAM) is a mix between ancient medical practices and new age approaches used to promote good health, prevent various health conditions, and treat various diseases (Barnes et. al, 2008). Complementary therapies are used with conventional medicine, whereas alternative medicine is used in lieu of conventional medicine (NCCAM, 2008). People who turn to CAM do so for various reasons: some to improve their health and overall wellbeing (Astin, Pelletier, & Haskell, 2 000), others to relieve pain and other symptoms caused by chronic or terminal conditions and to alleviate side effects caused by conventional medical treatments (Shen et al., 2002 and Humpel and Jones, 2006). Others prefer the holistic nature of CAM, sinc e it provides a stronger sense of self efficacy and control of health and well being (Astin, 1998). According to Astin et al. (1998), and Druss and Rosenheck (1999) CAM patients are using these non conventional medical approaches alongside conventional me dicine demonstrating the CAM is used collaboratively instead of strictly in place of conventional medicine (Ni, Simile, & Hardy 2002). Of the CAM modalities used in 2002, the most common forms, according to Tindle, Davis, Phillips, &

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29 Eisenberg (2005) wer e herbal medicine (18.6%), relaxation techniques (14.2%), and chiropractic care (7.4%). Although CAM has been used for many years (with its roots in Traditional Chinese Medicine and Ayurvedic medicine) there continues to be a somewhat hostile relationship between CAM and conventional medicine (Kaptchuk and Eisenberg, 2001). Konefal (2002) said CAM practices and practitioners are undermined because of the lack of scientific evidence to support treatment effects. However, the pendulum has now started to swi ng the other direction as patients are propelling the resurgence of CAM (Ernst, 2000). Rafferty, McGee, Miller, & Reyes (2002) found the majority of CAM users are not only satisfied with the therapies they engage in but they also think they are beneficial to health outcomes. Richardson, Sanders, Greisenger, & Singletary (2000) found that patients used CAM as a means to prolong their life expectancy. Regardless of the historical perspective there is an increasing trend of CAM users in the US. Trends of CA M Use among the U.S. Population The most current survey implemented to determine the use of CAM among US adults and children was done in 2007. Barnes et al. (2008) evaluated the data collected from the 2007 National Health Interview Survey (NHIS) conducte d by the Centers for determine estimates of CAM use among the US population. Their national survey determined that roughly 40% of US adults used some form of CAM therap y within the last 12 months. The most common form of CAM used was consuming natural products (not including vitamin and mineral use), which had a usage rate of 17.7%; the next most used CAM modality was deep breathing exercises, 12.7% (Barnes et al., 2008 ).

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30 This study also found that one in nine children have used CAM within the last 12 months with the most common CAM modality was natural products (3.9%) and chiropractic or osteopathic manipulation (2.8%). Children of parents who used CAM were also five times more likely to use CAM than those whose parents did not. Barnes et al. (2008) did a follow up from the initial 2002 study evaluating CAM use among US adults. However, in the earlier study, prayer was considered a form of CAM and it was the mos t common form of CAM used. Prayer was followed by the same top two found in the updated version of their study: natural product consumption and deep breathing (Barnes et al., 2004). Of the literature available and cited on CAM trends (Eisenberg et al., 19 98; Eisenberg et al. 2001) surveys are the gold standard and most frequently cited studies. Eisenberg et al. (1998) conducted a follow up study adapted from a 1990 survey documenting CAM prevalence and costs in the United States. The initial survey had 1 ,539 participants; the follow up survey in 2007 had 2,055 participants. Results indicated increased CAM usage (from 33.8% to 42.1%). The CAM therapies evaluated in their study that increased the most in usage rate compared to the original (1990) energy h ealing, folk remedies, herbal medicine, homeopathy, massage, megavitamins, and self help groups. Findings suggested that the most common uses are for chronic conditions such as headaches, back problems, anxiety, and depression. Although out of pocket co sts did not rise significantly, it is still estimated that over $21.2 billion was spent towards CAM. Of this, $12.2 billion was out of pocket (this could be a result of insurance companies including CAM in their coverage). Considering the amount of money being invested in complementary and alternative medicine, it is evident more

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31 studies and practitioner licensing are needed in order to meet the growing demand for CAM. In another study by Eisenberg et al. (2001) to determine why adults use CAM. They asked 813 participants who use both conventional medicine as well as non allopathic medicine why they turned to CAM and the order of care initially sought. Of the participan t population, 79% believed that the combination of complementary medicine and western medicine was superior compared to just one or the other. Only 15% first saw a complementary therapist prior to seeing their primary care physician. According to Eisenbe rg et al. (2001), complementary medicine usage spawns from dissatisfaction with allopathic care and from patients wanting to have a more holistic approach to medical management. The primary reason reported for using CAM was for chronic conditions (specifi cally, headaches and neck and back pain). It should be noted that although there has been an increase in CAM usage among the US population (Barnes et al., 2008) the majority of CAM users are using the non conventional modalities in conjunction with their allopathic physicians. Druss and Rosenheck (1999) said that only 1.8% of their study participants used only unconventional therapies, though their survey had lower CAM rates than the national level. Of the unconventional (CAM) therapies used, the most c ommon was chiropractic, medicine followed by massage, then herbal medicine. The most important finding of their study was those who used both conventional (Western/allopathic) medicine and unconventional (CAM) therapies were more likely to engage in preve ntative health measures, with the exception of mammograms.

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32 Worksite Health Promotion and CAM As employee health is becoming a larger focus for employers, given the economic benefits to having healthier employees (Kristen, 2010) more literature is surfacing about worksite health promotion programs. A specific area that is most directly related to productivity levels is health related absenteeism (Goetzel et al., 2004 and Michie & Williams, 2003). Therefore, according to Chapman (2012), absences due to sic kness can be used to measure economic impact and the benefit of workplace health promotion programs to improve health for employees. Additionally, according to Bhandari et al., 2012) health problems such as stress, obesity, back problems, cardiovascular issues, and digestive problems are fairly common in corporate environments and have a negative impact on the overall health and wealth of the corporate world. Although historically companies have focused solely on economic return for employees, the focus i s shifting toward a more holistic and biomedical focus to maximize employee return of investment (Robinson, 2004). Two CAM modalities that have been reported as having a positive impact on worksite health promotion are meditation and yoga. There has been an upsurge in marketing to promote use of CAM in the corporate setting (Bhandari et al., 2012). According to Palumbu, Wu, Shaner McRae, Rambur, & McIntosh (2012), all of the studies that measured the impact of CAM on the workplace reported reduced abse nteeism. Additionally, they measured time off hours of those who participated or did not participate in a tai chi program participants reported no unscheduled time off. Non participants (the control group) reported 49 hours of unscheduled time off during the 15 week study.

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33 Another study looked at neck and back pain reduction with chiropractic treatments among professional bus drivers found that those in the intervention group had a decrease in absenteeism (Almog, Lamond, & Gosselin, 2004). In their stu dy 22 participants received in the range of 2 14 chiropractic treatments. Absenteeism due to spinal pain was then compared to a control group. Researchers found that compared to the control group, treatment group the average absenteeism decreased signifi cantly (from 1.55 days to 0.16 days). In addition to chiropractic care and yoga, mindfulness based stress reduction (MBSR) techniques are also proven to assist in the reduction of stress. Klatt, Buckworth, and Malarky (2008) observed how perceived stress levels were altered with a 6 week worksite MBSR intervention. Stress levels were measured at the beginning of the program and then measured every week for the duration of the program, by measuring salivary cortisol levels. Compared to the wait list grou p, those in the treatment group not only improved their levels of stress they increased mindfulness and reported improved sleep. According to Girdano, Dusek, and Everly (2005), negative health effects can be reduced or even eliminated in the workplace by r educing stressors, creating a healthier workplace and implementing stress reduction programs. They also suggest some guidelines to facilitate worksite stress reduction. These guideline are achieved by creating job functions which are designed to give empl oyees enough control to decrease their frustration and overload, while providing adequate social support with cohesive teams. It is also important to articulate policies employees are expected to adhere to, while giving employees enough flexibility with t heir schedules. Decreasing organizational stress and high turnover rates, and appropriate matching of skills is also

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34 crucial to improving the overall health and wellbeing of employees. Lastly, Girdano et al. (2005) also reinforce the idea that worksite s tress reduction programs can assist in corporate savings. Determinants and Predictors of CAM Use Although a large proportion of the US population uses CAM (Barnes et al., 2008), suggested the determinants for usage may vary. A secondary analysis from the 1996 Medical Expenditure Panel Survey (MEPS) demonstrated that age, level of education, sex, geographic location, and race were statistically significant predictors of CAM use ( Bausell, Wen Lin, & Berman, 2001). Below, each of the predictors are examined in more depth. Health Status Barnes et al. (2008) considered other medical conditions prompting US adults to turn to CAM. They discovered that in addition to using these therapies to treat musculoskeletal problems such as arthritis, back pain, joint pain or stiffness, and neck pain, CAM has been used to help treat anxiety and stress, ADD/ADHD, head or chest colds, high cholesterol, and other musculoskeletal problems. Their study also determined that those with private health insurance were more likely tha n those with public or no health insurance to use CAM. Regardless of health insurance status, Barnes et al. (2008) discovered that costs of conventional medicine treatment also affected use of CAM. Patients were more likely to use CAM when they worried about costs of conventional medicine and when they could not afford conventional treatment. Not only does CAM offer health benefits but a study by Nahin et al. (2007) found that those who engage in CAM are also more likely to have other positive health

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35 be haviors. Such health behaviors include not smoking (for those who were previous smokers), infrequent alcohol consumption, having a normal weight, and receiving the flu vaccine. Nahin et al. (2007) suggested that those who take more control over their own health are also more likely to be CAM users, independent of their health status, access to health care, and socio demographic factors. A study by Astin (1998) assessed why patients use alternative medicines (AM). Dissatisfaction with conventional medicin e, need for greater control over health care decisions, and holistic philosophical congruence were found to be factors for use. The outcome of their study suggests that although dissatisfaction does not significantly impact CAM use, it demonstrated that p ersonal control and philosophical congruence significantly influence the use of CAM. Their study also assessed various factors predicting CAM use. These factors included greater educational attainment and poorer health status. In exploring why people tu rn to CAM, Bausell et al. (2001), studied whether that most who turned to CAM had chronic, co morbid, non life threatening health issues. Lastly, they were able to narrow th e health problems most associated with CAM to mental, metabolic, and musculoskeletal disorders. Another group of researchers, Unutzer et al. (2000), focused strictly on CAM use among those with mental disorders. Those who suffered from panic disorders and major depression were significantly more inclined to use CAM than were those without mental disorders. Additionally, those who thought they needed help with emotional or substance abuse problems also reported higher use of CAM compared to those not repor ting a need for help.

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36 Not only does having mental disorders increase the probability of CAM use. In addition, CAM has also been proven to be used at a higher rate among those with chronic health conditions (arthritis, cancer, cardiovascular disease, and l ung disease) (Saydah & Eberhardy, 2006). Researchers found that those with arthritis reported the highest use of CAM (59.6%) followed by those with cancer and/or lung disease (55%), and finally patients with diabetes (41.4%). Further examination of the d ata confirmed that those with chronic diseases are more likely to use CAM. Saydah and Eberhardy (2006) also determined that less than 30% of the patients consulted their healthcare professional about use of CAM. Research is somewhat conflicting some sugg est those with perceived health status have a stronger correlation to CAM use. Other studies, including those highlighted above, demonstrate that chronic conditions and mental disorders predict stronger use of CAM. A meta analysis by Bishop and Lewith (2 008) suggests patients turn to CAM one of three reasons. Patients are dissatisfied with conventional medicine, they believe it can benefit their conventional treatment plan, or they are desperate to try Age Another pred ictor the literature focuses on is age. Although there is no consensus on how age is related to CAM use, one such study by Kessler et al. (2001) examined how many adults (18 or older) in the U.S. used some variation of 20 complementary and alternative med icine (CAM) therapies, and how often respondents used them. They divided the participants of the study in a telephone survey by age (pre baby boomer, baby boomer, and post baby boomer). Their study had a response rate of 60% (2055 participants): 67.7% of r espondents had used at least one CAM therapy in

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37 their lifetime. Trends of these findings also showed that lifetime CAM use steadily increased over time. In the pre baby boomer population, three out of ten were lifetime users of CAM. In the baby boomer po pulation, half of the respondents were users, and of the post baby boomer population seven out of ten reported using some type of CAM. When considering why the adult population is turning to CAM, Barnes et al. (2002) determined that age impacts the type o f CAM use. Their study examined the following forms of CAM: megavitamin therapy, prayer, biologically based therapies (including and excluding vitamins), mind body therapies, energy therapies, and manipulative and body based therapies. When prayer was incl uded in the CAM definition there was a linear correlation between age and use, demonstrating the highest use of CAM among those 85 and older (when considering mind body therapies). However, when prayer was taken out of the CAM definition, those in the 50 59 age range reported the highest rates of CAM, although the curve of the regression was U shaped (it rose until the age of 50 and then drastically decreased with age). Findings su ggest that although age is generalized as being a strong predictor of CAM use, it also depends on what type of CAM is being measured. Oldendick et al. (2000) conducted a telephone interview of 1,584 South Carolina adults to determine CAM usage and factors associated with usage. They determined that increasing age was positively correlated with CAM use. Specifically, of the participants, those middle aged or older had a higher probability (51%) of using CAM compared to those below the age of 30 (35.7%). Ab out 47% of participants who used CAM did so to maintain health rather than as a treatment plan for a certain health condition. Their study determined that age was the greatest predictor of CAM use for

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38 lifetime use. However, when just looking at CAM use wi thin the last 12 months education was a stronger predictor. Education According to Oldendick et al. (2000) the single greatest predictor of CAM use within the last 12 months was education. Those with more education reported a higher chance of engaging in CAM. Of this population nearly 52% reported using some modality of CAM at least once in their life and 44% had used CAM within the last 12 months. The most common form of CAM was personal therapy (27.5% of the population). The next most common form of C AM was relaxation therapy (25.7%). Roughly half of respondents reported CAM usage as a method of maintaining health rather than treating a specific health condition. In addition, women reported higher rates of usage than men. Also, white respondents wer e more likely to have engaged in some form of CAM than black respondents, which is consistent with the Barnes et al. (2008) study. It is apparent that in almost all of the literature exploring predictive factors for CAM use, education and sex are the two o f the strongest predictors. In a study to examine only one dimension of CAM (herbal supplements use), researchers found that females and those with a college education used herbs at a higher rate, with the exception of the herb Echinacea (Bardia, Nisly, Z immerman, Gryzlak, &Wallace, 2007). Herbal supplements were used mostly among those over age 60, Echinacea was also an exception, to that notion as well since those under the age of 60 consumed it at a similar rate. Their study surveyed more than 30,617 a dults. Nearly 19% indicated they had consumed some herb(s) within the last year. Of those who reported using herbs within the last year, more than half (57.3%) used herbal remedies for health conditions.

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39 e need for healthcare providers to play a more integral role in discussing which herbal supplements patients are taking, to prevent antagonistic interactions from any other medications they are taking. The study by Barnes et al. (2002) also determined that education can have an impact on CAM use. They explored overall rates of CAM and the reasons adults use CAM. They also examined how educational level can affect what type of CAM is used. During data analysis, researchers determined the only exception to the positive linear correlation between educational level and CAM use was when prayer was included in the definition. When diving deeper into what types of CAM those with higher education levels used, once again, when removing prayer from the equation, m ind body therapies were most commonly used, followed by biologically based therapies, then manipulative and body based therapies. Another study examining predictors of CAM use, Rafferty, McGee, Miller, and Reyes (2002), said the most commonly used therapie s were herbal supplements (20.5%), followed by specialized diets (12.6%), and chiropractics (12.2%). Their study also found that higher educational attainment was a strong predictor of CAM use. Although education did have a positive relationship with CAM use, findings suggest that those who graduated college (55% versus 54.5%). Those who had not graduated high school reported a CAM usage rate of 40.6% as opposed to the 44 .2% rate among those who completed high school. Sex Literature suggests that in addition to age and education, another strong predictor of CAM use is sex (Neiberg et al., 2011, Tindle et al., 2005, and McFarland,

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40 Bigelow, Zani, Newsome, & Kaplan, 2002). W omen reported higher usage rates of CAM therapies compared to men. Bishop (2002) believed this may be due to the notion that women tend to seek out healthcare more frequently than men do (Green & Pope, 1999). Below is a review of the literature demonstrati ng sex and its impact on using CAM. Neiberg et al. (2011) found that women are more likely than men to use CAM and to use more than one modality of CAM. When considering age groups, those women in the age range of 46 64, who were white, had the highest us age rates compared to men and those in other age, racial/ethnic, and educational groups. McFarland et al. (2002) determined that ethnic and racial minorities are less likely to be users of CAM. That the most prevalent CAM using group was white women ages of 20 revealed similar results. When analyzing NHIS data, the greatest predictor of CAM use was sex. Females who are non black/non Hispanic with an annual income of mo re than $65,000 were the primary users of CAM. The national survey researched by Barnes et al. (2002) showed that women had higher usage rates for all forms of CAM. They also determined that the largest division of CAM use fell under mind body treatment s, when prayer was included. Similarly, Jain and Astin (2001) determined that the least likely population to use CAM was healthy men. This supports the notion that women make up the majority of the CAM using population. Race Up to this point, the key pred ictors discussed have been health status, educational level, age, and sex; but another predictor of CAM use is race/ethnicity. Barnes et al. (2007) found that CAM rates differ among racial/ethnic groups. Indian

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41 and/or Alaskan Natives (50.3%) had the highe st prevalence of CAM use, followed by white adults (43.1%), then Asian adults (39.1%), and then black adults (25.5%). When researches looked deeper into what modalities each racial/ethnic group used, they determined that rates of use differ by modality. For example, mind body practices (including prayer) were most prominent among black adults (68.3%) followed by white adults (50.1%), and finally Asian adults had the lowest rate at 48.1%. When considering manipulative and body based practices, the group re porting highest use was white adults (12.0%), followed by Asian adults (7.2%), and then black adults (4.4%). Although these results are similar to other studies relating race/ethnicity to CAM rates, Barnes et al. (2007) study was the most comprehensive an d used by much of the other literature to make comparisons. Another group of researchers also tried to understand how race/ethnicity impact CAM use. Ni, Simile, and Hardy (2002) further analyzed the NHIS data and also determined that the domain of CAM mo st used varied by racial/ethnic group. For instance, mind/body medicine was practiced most by those who identified as being black (non Hispanic). Those who mostly used biologically based methods reported non Hispanics had the highest rates of using manipulative body based therapies. The other, non Hispanic, category also had the highest percentage of users in alternative medical systems and energy therapy domains. These results suggest that although whit es show highest prevalence of CAM use this may not be true when considering the various forms of CAM individually. When looking at the trends of CAM use among minority populations, Mackenzie, Taylor, Bloom, Hufford, and Johnson (2003) found no ethnic diffe rences. The major predictors of CAM use were sex (female), being uninsured, and having a high school or

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42 higher level of education. Although CAM usage was equally prevalent among white, African American blacks, Latinos, Asians, and Native Americans the ch aracteristics of use varied individual CAM modality. For example, among Asian Americans and African Americans, herbal medicine was most commonly used; whereas among Latinos, Native Americans, and Whites, home remedies were most common. The 5 CAM groups c ommonly assessed were herbal medicine, acupuncture, chiropractic, traditional healer, and home remedies. A study by Graham et al. (2005) analyzed data from the 2002 National Health Interview Survey (NHIS) to determine which racial and ethnic groups used CA M, and which types of CAM were used. Graham et al. (2005) found significant differences in CAM use among these three racial and ethnic groups (Hispanics, non Hispanic blacks, and non Hispanic whites). Their results were congruent with those of Barnes et al. (2008), which found the highest CAM were non Hispanic whites, followed by Hispanics, then non Hispanic blacks (Asian Americans, Native Americans, and Alaska Natives were not considered for the purpose of their study). Their study also found that herba l medicine, relaxation techniques, and chiropractic care were used more frequently among non Hispanic whites. Survey results showed high CAM use by Hispanics because of the high costs of conventional treatments (more so than for non Hispanic whites and bl acks). Lastly, it was also determined that Hispanics and blacks were less likely to disclose their CAM use with their healthcare providers. Another study focused on the Africa American population (Brown, Barner, Richards, and Bohman, 2007), found that of the total 23,828,268 African American respondents from the National Health Interview Survey (NHIS), 67.8% used CAM (when including prayer in the definition) within the last 12 months. Predictors of CAM use were

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43 the same for this group as for other researc h (for example, higher education, being female, older age yielded higher CAM rates). Beyond prayer (60%) the next two CAM therapies most reported were herbals (14.2%) and relaxation (13.6%). Their study also found that among the African American populati on, those using CAM were doing so as a means to treat a health condition, as opposed to a prevention mechanism. Income When considering the impact of income on CAM use, conflicting says those who are in a higher income bracket tend to use CAM more freq uently, as found in the Barnes et al. (2002) and Rafferty et al. (2002) studies. Even the Brown et al. (2007) study that looked at African American adults also correlated higher income and health insurance is with CAM rates. On the other end of the spectr um, some literature suggests that those who are unable to access conventional medical treatments or afford medical care, or who are uninsured are most likely to use CAM (Avogo, Frimpong, Rivers, & Kim, 2008). Additionally, when comparing income to race/et hnicity, Hsiao. et al. (2006) found that among white participants, those with a higher income reported more CAM use. In other racial/ethnic groups no significance between use and income was found. Therefore suggesting that more research is needed for a be tter understanding of how income impacts the use of CAM. Behavioral Intention Model the t heory and model used to predict behavior for this research is the Behavioral Intention Model that (BIM). Many health behavior theories also conclude that behavioral

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44 intention is a key in predicting actual behaviors. One theory is the Theory of Reason Act ion (TRA), which analyzes the relationship between behavior, beliefs, attitudes, and intentions. According Fishbein and Azjen (1975) the most predictive determinant of behavior is behavioral intention, the likelihood of actually performing a behavior. A p a behavior. Their attitudes are malleable and are dependent on whether people close to them approves of this behavior (or disapproves). This concept is known as subj ective norm (Ajzen). The BIM is the perceived likelihood of performing a behavior and is viewed as the most important determinant of behavior: Attitude: intention. Behavioral beliefs and the evaluation of behavioral outcomes shape Subjective Norm: beliefs of family/peer approval of the behavior. This construct focuses on beha vioral intention being influenced by the intent to gain approval and assesses normative beliefs and motivation to comply. Motivation to Comply: refers to the motivation to comply with referents about performing a specific behavior tention Model (BIM) postulates that behavior can be predicated by the intent to perform a behavior. Additionally, when considering what factors impact behavioral intent (BI) this model divides behavioral influences into two major categories: the personal attitudes and beliefs regarding the behavior and subjective norms surrounding the behavior. Attitude (Acct) likelihood for performing a specific behavior assuming certain consequences and an evaluation of the consequences res ulting in the behavior. The next contributor to BI is the subjective norm (SN) , which can further be broken down into two factors: normative beliefs (NB) , and motivation to comply (MC). NB(s) are those beliefs held by and individual about the behavior an d whether or not those close to them such as such as

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45 family, friends, and in this situation, health care provider or physician perceive that behavior. Finally, MC is the intention of following the advice or meeting the expectations of those meaningful to the individual (Wilson, Mathews, and Harvey, 1975). Estimates for parameters Wº and W¹ are obtained from a multiple linear regression analysis which provide the coefficients a 0 , w 0 and w 1 for the following prediction expression BI= a 0 Although this model can be used as a tool to predict behavior, Glanz (2002) suggests that all other factors such as cultural and environmental operate through the variou s constructs of this health behavior theories and models and do not independently predict behavior. Therefore, when using a model or a theory to plan health education programs or interventions the health education specialist must remember that other facto rs may also be involved. Behavioral intention can be used to predict intention to perform behaviors. Theories and models can be used to predict behaviors (Glanz, 2005) therefore in looking at a specific study which evaluated CAM use, Furnham and Lovett (2 001) observed both behavioral intentions and the actual use of homeopathy. Their findings suggest that intent to change behavior can be used to predict homeopathy use. It also concludes that attitudes, subjective norms, and perceived behavioral control ( an extension of the Theory of Planned Behavior) were significant in predicting behavioral intention to use homeopathy. Furnham and Lovett (2001) said behavioral intention on its own was the most significant in predicting behavior. Their study also explor ed how past behavior impacted

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46 future behavior. Previous use was a strong predictor for both actual behavior and behavioral intention to use homeopathy. In a study of CAM use by Hirai et al. (2008) surveyed 1,100 patients seeking cancer treatment from var ious treatment facilities. Family/social support was the primary predictor for patients using CAM. This also supports the notion of the important of social norms on behavior change. Familial expectations to use CAM had the largest variance in explaining CAM use behaviors. Their study demonstrated that patients alone are not autonomous when making the decisions to use CAM. Patients and families work collaboratively to make decisions about health behaviors. Therefore, not only can theories and models th at include familial and social influence in the theoretical framework be used for treatment outcomes, they should also be considered when creating educational programs and interventions for patients to use CAM. When considering determinants of using CAM, the BIM suggests that with positive attitudes and intentions toward a behavior make a person will be more likely to use it. Honda and Jacobsen (2004) also determined that being open to CAM was positively correlated with the various domains of CAM, except for manipulative body based therapies. Having a friend or a family member support CAM therapies was positively correlated with using mind body therapies, manipulative body based therapies, and alternative medical systems. Findings suggested that, similar to Hirai et in their decision making process. Summary This literature review provided an overview of CAM explaining both what it is and its history, followed by a surv eillance of the trends of using CAM among the US

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47 population. When considering trends of CAM use it is also important to understand the various reasons people use CAM and how CAM is being used (in a complementary fashion with conventional medicine, or inst ead of it). The main predictor of CAM use recurring in the literature is education (beyond high school) and sex. But beyond those two factors, being female, white, dissatisfaction with conventional medicine, and wanting more control over treatment plans can predict the use of CAM. Also, those who believe in the positive health benefits of CAM are more likely to use CAM. This comprehensive literature review examined trends and types of CAM and also the various factors that impact use (such as in the work place increased productivity and costs for companies). This review of the literature also explored the Behavioral Intention Model, as a theoretical perspective. In addition to subjective norms (including social norms and motivation to comply), attitudes, and behavioral intention toward a certain behavior can be strong predictive factors used to guide behavior change. Additionally, Fertman and Allensworth (2010) suggest the university setting as being great locations for health education programs since th e researcher has access to a large number of people who can gain the knowledge and skills to make positive health behavior changes. health of the communities the y end up residing in .

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48 Figure 2 1: Behavioral Intention Model

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49 CHAPTER 3 METHODOLOGY The purpose of this study was to explore the trends among faculty and staff at the University of Florida on CAM use and to examine the following research questions: 1. 2. Is there a 3. 4. 5. h status and CAM use? 6. 7. Is Attitude toward CAM use significant in explaining Behavioral Intent? 8. Are Subjective Norms significant in explaining Behavioral Intent? 9. Are Attitude toward and Subje ctive Norms about CAM use jointly significant in explaining Behavioral Intent? positivism. The methodology is survey research, and the methods used was statistical analysis of da ta obtained through a survey instrument. Research Design For this study the researcher employed a quantitative approach by using a survey instrument. Since the researcher is attempting to describe the CAM trends on a larger scale, this design is the most efficient method of collecting a large number of responses to make generalizations about the specific populations being researched (Creswell, 2008). According to Creswell (2008), cross sectional survey designs are popular because they are able to collect a large amount of data at one point in time. This

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50 method is especially useful when questions about current attitudes are asked, since responses are subject to change over time. Cross sectional designs are best used when trying to determine attitudes and p ractices and community needs. They are useful for program evaluation, and are able to make group comparisons and implement national assessments (Creswell, 2008). Therefore due to the versatility of this research design, the researcher has chosen to use i t to answer the research questions and test the hypotheses. Setting This study was conducted at the University of Florida (UF) campus where there are over 26,000 employees (including 5,000 faculty members) to select from. Participants were selected on a random basis with the only exclusion criteria that they are 18 or olde r. The objective of obtaining participants was to get a wide variety of faculty and staff from the 16 separate colleges and administrative departments. With this cross sectional design the researcher is able to ensure there are no threats to testing or h istory effects that may impact the data. This study employs a web based survey, Qualtrics, which is both descriptive and explorative. Participants and staff directory. Partic ipants who do not wish to have their contact information open to the public can opt out of being listed in the UF directory. Of the 26,000 University of Florida employees, IRB approved 5,000 participants for this study and University Relations provided a list of 15,000 employee names and email addresses. The researched randomly selected 5,000 participants (using online software, www.randomize.org ) from the list supplied. Of the surveys sent out, roughly

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51 300 were ba d email addresses. Based on email correspondence with participants the researcher was notified that many of the emails sent out with the survey link ended up in junk mailboxes. So of the 5,000, it is difficult to tell how many participants did not receiv e the request to complete the survey. Additionally, the survey was optional. Inclusion criteria were that all participants have fringe benefits (faculty, clinical faculty, exempt TEAMS/ USPS, and non exempt TEAMS/ USPS). By completing the online survey, participants automatically gave consent to participate in this study. A series of three emails (indicating the purpose of the study, a call to action with the survey, a link to the survey, and a reminder indicating the closing date/time of the survey) wer e sent to all UF employees who received the survey. In these emails participants were also reminded that they and their responses will remain confidential and anonymous. Sample size is crucial to the statistical power of a study. The larger the sample, the stronger the association between sample findings and population (Creswell, 2008). When determining how many participants need to complete the survey, the researcher conducted a power analysis (using G*Power software) to identify the appropriate sample size for a group comparison by taking into consideration the following three factors: significance level, power, and the effect size. With a significance level set at = .05, a standard power criterion level (1 ) at .8, and an effect size at .15 (typical for social science research, according to Creswell) the number of p articipa nts needed is 68 to test the regression model (F test). In order to obtain an adequate sample size for the hypotheses that use chi square, once again G*Power software was consulted. With the parameter values of = .05, (1 ) = .8, the effect size = 0.30 (middle value) the total sample size would need to be152. The researcher targeted enough survey participants

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52 to have an adequate effect size, which according to Cohen (2 008) was be set to the middle level of .15 for both the t test and F test, and .3 for the Chi squared tests. Instrumentation The instrument used for this study was adapted from the National Health Statistics Survey published in 2007, and the literature. An expert panel (Dr. Chen from the Health Education and Behavior Department, Dr. Cooke from the Marketing Department, Dr. Lane from the Adult and Elderly Nursing Department, Dr. Leite from the School of Human Development and Organizational Studies, and Dr . Yoon, from the Adult and Elderly Nursing Department) were consulted to assist with the creation of the referenced to make sure the survey design, implementation, and ana lysis were done rigorously (Figure 3.2). Institutional Review Board (IRB) the researcher created a Qualtrics online survey to distribute to participants. According to Dillman (2007), there are many benefits to using a web based, online, survey. Such benefits include little to no implementation cost, quick formatting adjustments, quicker data collection, and elimination of human error during data entry. Dillman also said, by letting respondents know upfront the survey will not take much time, followed by multiple reminders can assist in increasing the response rate. Thus, using the Qualtrics software to house my survey and data while helping me manage reminder messages was he lpful in compiling the information needed to run my analyses. After the survey instrument was created in Qualtrics and double checked by three of my five expert panelists, the instrument was ready to be piloted. Before

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53 sending the survey out to 5,000 UF e mployees, a pilot test was sent to 20 individuals in the sample group. Ten of these volunteers agreed to complete the survey twice, once as a pre test and again at a later date as a post test. After completing the pre test the researcher sent out the sur vey instrument two weeks later to the same set of volunteers and their responses were captured as a post test. Of the 20 pretests/posttests analyzed, the p value was significant at 0.000 and the Pearson Correlation was 1.000. The researcher also wanted to ensure consistency and reliability with the Likert scale 0.70 to 0.95 are within an a cceptable range. Tavakol and Dennick also state that low instruments with high reliability are also considered to be valid. Therefore at this point the researcher felt c omfortable sending the instrument to the list of 5,000 university employees. Subjectivity Statement It should be noted that this is not the first study the researcher has done, the topic of CAM has been examined in other studies including student populati ons. However, the population of university employees has not previously been studied. Of the studies previously done by the researcher, both quantitative and qualitative methods were employed; therefore the researcher is familiar with designing and using quantitative methods. In addition to experience the researcher has with quantitative both survey design and data analyses helped ensure the use of rigorous and precise met hodological techniques.

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54 Data Collection Data collection occurred during the s pring 2014 semester. Additionally, the researcher avoided sending out the survey at the beginning and very end of the semester to ensure a time when faculty and staff were more engaged and present on campus. The total length of the data collection took place over the span of two weeks (9 24 April 2014). As the participants submitted their responses to the survey information automatic thank you messages were prompted and data w ere captured in a database which was, subsequently used for data analysis. Additionally those who did not fill out the survey were sent follow up requests three days afterwards and then again the following week, in order to increase response rates. Given the nature of this study, participants had the flexibly of responding to survey questions at their convenience and therefore data were continually collected throughout the duration of the study. In order to increase response rates, the researcher sent out a series of emails explaining the purpose of the study and what was being asked of them. Additionally, two separate email reminders, (roughly one week apart) were sent to those who had not yet completed the survey, to maximize the response rate while min imizing response bias. Data Analysis Data were analyzed using the SPSS ® version 21 software package, G*Power, and Excel. In addition to analysis software statistical analysis textbooks were referenced to ensure accurate interpretation of the data. While the principal investigator performed the data analysis, an external statistician was consulted to determine congruence in the analyses. The initial analysis conducted was descriptive statistics, which provided summaries and frequencies of the sample popul ation. Once descriptive statistics were

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55 run, the researcher undertook the task of answering the research questions by testing the hypotheses posed. Since many of the research questions involved comparisons between more than two groups (e.g. age ranges, i ncome levels, and race/ethnicity), Chi square tests were run to test the first 6 hypotheses. Chi square is used to determine if the difference in the population proportions of usage of CAM varied among different groups. An important advantage of using th e Chi square test is it allows for unequal numbers of respondents in each group. Although a z test could be used to run a comparison for two groups (e.g. sex), to be consistent, Chi square tests were run for all six group comparisons. After running the C hi square test for the first set of hypotheses, a multiple regression was run for the purpose of developing a predictive model for estimating the behavioral intent of the population for the use of CAM. The regression model was used to test hypotheses 7 to 9. Hypotheses 7 and 8 pertain to the significance of the regression coefficients for the two independent variables, namely, attitudes and subjective norms. The last hypothesis involved using the F test to evaluate the coefficient of determination (R 2 ) a nd to determine the explanatory power of the regression model. The BIM is given by the following expression Where estimates for parameters Wº and W¹ were obtained from a multiple linear regression analysis that provided the coefficients a 0 , w 0 and w 1 for the following prediction expression: BI= a 0

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56 Lastly, in using the BIM, in order to determine predicted BI the researched summed the total raw scores from each of the three Acct questions (ple ase refer to survey instrument items 5 and 6) for each sample unit, this constituted the which is the sum of both Normative Beliefs and Motivation to Comply scores multipl ied, please refer to survey instrument item 7 in Appendix C) for each respondent using the Summary Chapter 3 describes the methodology for this study. The intention of this section of the study is to give enough d etail about the study so that if other researchers wanted to duplicate it they could. In this chapter the researcher discusses the research design, the population, the sample and how it was acquired, the instrument used to conduct this study and how it wa s established, the data collection procedures, as well as the procedures and statistical tests used for data analysis.

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57 Table 3 1. Independent Variables versus Dependent Variables Independent Variables Dependent Variables Age CAM use within last 12 months Sex CAM use within lifetime Race/Ethnicity Behavioral Intent Household income Educational level Health Status Attitudes Subjective norms (normative beliefs + motivation to comply)

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58 Table 3 2. Hypotheses with Corresponding Statistical Tests Hypotheses Statistical test 1. H o <8 =
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59 Table 3 3. Timeline for Data Collection & Data Analysis Task Time Expert Panelist 20 28 February 2014 IRB application 18 31 March 2014 Pilot test April 7, 2015 Data Collection 9 25 April 2014 First letter indicated purpose of study with survey April 9, 2014 1 st Follow up letter April 14, 2014 2 nd Follow up letter April 22, 2014 Data analysis & write up April 26 May 15th

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60 CHAPTER 4 RESULTS This study explores the use of Complementary and Alternative (CAM) among University of Florida employees. The goal of this study was not only to explore which CAM modalities are used more frequently among this population but also aimed investigate the determinants of the behavior that leads to CAM use and develop a model to predict future behavior. As previously stated the research questions driving nal level e a explaining Behavioral Intent? 8. Are Subjective Norms significant in explain ing Behavioral Intent? 9. Are Attitude toward and Subjective Norms about CAM use jointly demographic attributes are presented below. Demographics A list of 15,000 University of Florida employee email addresses was provided by the Office of University Relations. From this list a random sample of 5,000 potential participants were selected. The online survey tool used was Qualtrics which enabled the researcher to send out the sur vey, collect and store the data, and run basic statistics on the participants. A total of 613 participants started the survey. Of those, only 595 completed it (97%) resulting in an attrition rate of 3%. This attrition rate may be due to

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61 individuals who did not meet the eligibility criteria but yet started the survey. Upon realization of their ineligibility they simply discontinued the survey. Another impact on the survey response rate may be attributed to the fact that the maximum number of surveys s tudents are allowed to send through Qualtrics is 100. However, after consulting the office in charge of the Qualtrics license, they agreed to increase the limit to 1,000. This meant that there were still 4,000 remaining email addresses to whom the survey needed to be sent. An attempt was made to use a system but rather through an auto address anonymous. Within the firs t 48 hours, a number of respondents notified the researcher that emails including the survey link were going to their junk/spam folders. Because there was no way to track this, a request was made to submit the survey to all 5,000, excluding those who had a lready responded to the survey. The request was personal email address to hopefully reach those who may not have received the original request due to it being placed in a spam folder. The reason for mentioning this is twofold. First, it could not be determined how many people did not receive the survey due to it being undeliverable; second, Qualtrics was able to give certain statistics on the open rate, start rate, and completion rate of the survey. Of those who received the Qualtrics generated email, only 25% opened it. Of that 25%, 49% started the survey; and finally of those who started the survey, 91% completed it. Consequently, data from 595 respondents (a respons e rate of 11.9%) were used for data analyses. According to Nulty (2008), although incentivizing the potential respondents could increase the

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62 response rate, it is typical for online surveys to yield lower response rates compared to face to face or telephon e surveys. Nulty also indicated that it is not unusual to have response rates between 10% and 20%. In looking at who completed the survey, 55.4% of respondents were classified as TEAMS employees and 36.9% were faculty (this includes 9, 10, and 12 month f aculty and clinical faculty). Also, when comparing the educational levels of participants, the had doctorate degrees (23.9%), then the third highest educational level w as of those degree as their highest obtained degree comprised 9.6%, followed by 8.1% who h aving either graduated high school or less consisted of 6.1% of the sample. Of the respondents 62.5% were female compared to 37.3% identifying as male. Additionally, 79.2% of the sample identified themselves as being White/Caucasian, 5.0% Black/African A merican, 4.9% Hispanic/Latino, 3.0% Asian or Pacific Islander, and only 0.5% indicated they were American Indian or Alaskan Native, and finally 1.8% selected There was a relatively even distribution among the various age ranges. Of all respondents, 22% were between 18 and 35; 21.7% between 36 and 45, 24.4% between 46 and 55 and finally the largest group with 31.4% consisted of those older than 56. A complete breakdown of the demographic and descriptive statistics ar e given in Tables 4 1 through 4 7.

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63 Trends of CAM Use Out of the 595 survey responses, 86.4% (n= 514) indicated that they have used some form of CAM. Of those who indicated they had used CAM at some point in their lives, 81.5% responded that they have als o used CAM within the last 12 months. Figure 4 1 shows the frequencies of the types of CAM used both lifetime and within the last 12 months. The most common forms reported were massage therapy (65%) followed by homeopathic medicine (43.4%), then chiropra ctic care (43.2%), and deep breathing was used by 41% of the participants. Interestingly, the four highest forms of CAM use within the last 12 months differed slightly from lifetime use. The most common form used within the last 12 months was still massa ge therapy (40.7%), followed by homeopathic medicine (27.2%), then deep breathing (26.7%), and then yoga (20.5%). Additionally, when asked the main reason for using CAM, participants could n of health problems or used CAM to treat a health condition, while 32.4% used CAM preventatively. The majority used CAM for their overall well being (60.3%). Although this is somewhat similar to what the literature suggests, that CAM is used as secondary or tertiary care, it also tells us that the primary reason for CAM use is for prevention and for maintaining balance and well being. Survey participants were encourage d to also indicate why they use CAM. Some of the responses: improve sleep quality, using CAM feels good, helps with hurt back, stress management, spiritual well being, recovery, the social aspect of group CAM (such as with yoga) and to increase fertility.

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64 when asked how often they discuss their CAM use with their physicians were also looked at. The number of participants who indicated that they consult with their physicians abou t their CAM use was 148 (24.9%). In the meanwhile 153 of the participants (25.7%) selected that they sometimes disclose their use of CAM to the groups at 294 (28.4%). The National Center for Complementary and Alternative Medicine recognizes natural products as one of the two subgroups of CAM (the other subgroup being mind body practices), which led the researcher to investigate which forms of natural products are being used in this sample. The options that the participants were allowed to select from were the following (participants were able to select all responses that applied): herbs/botanicals, vitamins/minerals, non vitamins/non minerals (i.e. fish oil and Omega 3s ), probiotics, and other. Vitamin consumption was the most common response with over 68% indicating having used vitamins/minerals. The next most frequently used product was non vitamins/non minerals (31.8%) and then probiotics (24.7%). Respondents were natural products they consume. Most of what was filled in would fall into one of the would not apply Next, the results of the hypothesis tests are presented. For each test, the test statistic value along with the corresponding p value will be stated. The conclusion of

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65 Hyp othesis 1 Ho 1. There is no significant difference in the proportions of CAM users with differing educational levels in the population, where <8 denotes the proportion of CAM users in the population with less than or equal to 8 years of education
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66 categories. Therefore, since the expected frequency for ea ch of the first three groups was less than 5 (those who had less than or equal to 8 years of education, those who did not complete high score, and those who either graduated high school or received their GED) these were combined into one group. These adju stments yielded the Chi square and p values given in Figure 4 12. Hypothesis 2 Ho 2. There is no significant difference in the proportions of CAM users with differing family incomes in the population, where <20k denotes the proportion of CAM users in the population with a family income less than $20,000 =20k denotes the proportion of CAM users in the population with a family income greater than or equal to $20,000 but less than $35,000 =35k denotes the proportion of CAM users in the population wit h a family income greater than or equal $35,000 but less than $50,001 =50k denotes the proportion of CAM users in the population with a family income greater than or equal to$50,000 but less than $65,000 =65k denotes the proportion of CAM users in the population with a family income greater than or equal $65,000 but less than $80,000 = 80k denotes the proportion of CAM users in the population with a family income greater than or equal to $80,000 but less than $95,000 = 95k denotes the proportion of CA M users in the population with a family income greater than or equal to $95,000 Ho: <20k = =20k = =35k = = 50k = =65k = = 80k = 95k Ha: Not Ho In order to test the difference in CAM use among those with differing income levels, both a Chi square test was performed and a p value was calculated (Figure 4 13). Additionally, similar to the previous case, since not all groups had expected frequencies of five or more, some of the groups were combined. Those who reported

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67 having a family inc ome of less than $35,000 were placed into one group while all other 2 = 4.39 and p = 0.49. Based on these values, the null hypothesis that there is no significant difference between the p roportions of CAM users among different income groups could not be rejected. Hypothesis 3 Ho 3. There is no significant difference in the proportions of CAM users with differing sex in the population, where =Male denotes the proportion of CAM users in the population who are male =Female denotes the proportion of CAM users in the population who are female Ho: =Male = =Female Ha: Not Ho The Chi square test statistic and p 2 = 19.38 and p = 0.000, (Figure 4 14). With these results it is concluded that gender does influence the use of CAM. More specifically females have a higher usage rate compared to males. Hypothesis 4 Ho 4. There is no significant difference in the propo rtions of CAM users with differing race/ethnicities in the population, where: =White denotes the proportion of CAM users in the population who are White/Caucasian =Black denotes the proportion of CAM users in the population who are Black/African Am erican =Hispanic denotes the proportion of CAM users in the population who are Hispanic/Latino =Asian denotes the proportion of CAM users in the population who are Asian or Pacific Islander =Am denotes the proportion of CAM users in the population who are American Indian or Alaskan Native

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68 =Other denotes the proportion of CAM users in the population who are Other Ho: =White = =Black = =Hispanic = =Am = =Other Ha: Not Ho Asian or Pacific Islander, American Indian or Alaskan Native, and those who 2 value of 3.310 and p value of 0.35 (Figure 4 15). With these results the null hypothesis that there is no si gnificant difference between the proportions of CAM users among different racial/ethnic groups could not be rejected. Hypothesis 5 Ho 5. There is no significant difference in the proportions of CAM users with differing self reported health status in the population, where Excellent denotes the proportion of CAM users in the population who identified as being in excellent health Very good denotes the proportion of CAM users in the population who identified as being in very good health Fair denotes the proportion of CAM users in the population who identified as being in fair health Poor denotes the proportion of CAM users in the population who identified as being in poor health Ho: =Excellent = =Very Good = =Fair = =Poor Ha: Not Ho Literature on CAM use also suggests that those who have poor health status are more likely to use CAM. This hypothesis compares the difference in the proportions of CAM users between those who self reported as being either in excellent, very good, good, fair, or poor health. Figure 4 16 provides details on both the observed and expected frequencies and observed proportions for each cross tabulation.

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69 The alternative hypothesis was not supported by the Chi 2 = 5.78) and p values (p = 0.12) that were calculated. Therefore, in this case too, the null hypothesis could not be rejected. These results demonstrate that there is no significant difference in the proportions of CAM users with differing self reported health statuses. Hypothesis 6 Ho 6. There is no significant difference in the proportions of CAM users between different age groups in the population, where: denotes the proportion of CAM users in the population who are in the i th Ho: =Age1 = =Age2 = =Age3 =Age(n) Ha: Not Ho The survey instrument used for this study allowed respondents to fill in their age. Therefore res ults included a large range of ages that spanned from 22 to 77. In order to test this hypothesis the ages needed to be organized so a group comparison could be made. Those who indicated they were 35 or younger were grouped together (22 35) to make up the first group. The age range of 36 45 made up the second group. Participants in the age group 46 55 were grouped together to make the third category. Those who reported being between the ages 56 65 were categorized as the fourth group. Finally, anyone 6 5 and older were placed in the fifth group. All groups and their frequencies can be seen in Figure 4 17. Once participants were grouped by age range the following values of Chi square = 0.46 and p value = 0.98. Based on these re sults it was concluded that the null hypothesis cannot be rejected. The proportions of CAM users do not differ between the considered age ranges.

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70 Hypothesis 7 Ho 7. Attitude toward the use of CAM is not a significant factor in determining 0: W 0 =0, Ha: W 0 The objective of this hypothesis was to determine strength of the Attitude variable in explaining BI. In order to test this both a simple and a multip le linear regression model were used. For the simple linear regression, F statistic and p value were 806.146 and 0.000, respectively indicating that the regression was significant. The resulting R 2 value was 0.612 indicating that 61.2% of the variance in BI was explained by the simple regression model. Furthermore, with a very small p value and a very large t statistic (t = 28.4) the null hypothesis was rejected. See Figures 4 18 and 4 19 for more details. Hypothesis 8 Ho 8. Subjective Norms toward the use of CAM is not a significant factor in 0: W 1 =0, Ha: W 1 Similar to Hypothesis 7, both a simple and a multiple linear regression model was used. The objective of this hypothesis was to determine how much of the variance in BI is explained by the variable Subject Norm. For the simple linear regression, F statistic and p value were 205.350 and 0.000, respectively indicating that the regression was significant. The resul ting R 2 value was 0.286 indicating that 28.6 % of the variance in BI was explained by the simple regression model. Furthermore, with a very small p value and a large t statistic (t = 14.330) the null hypothesis was rejected. See Figures 4 18 and 4 19 for more details. Hypothesis 9 Ho 9. Neither attitude toward CAM (Acct) nor subjective norms (NBCMC) about CAM contribute significantly to the prediction of Behavioral Intent (BI) to use CAM.

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71 Alternatively, H 0: W 0 =W 1 = 0, Ha: at least one of the two param eters (W 0 , W 1 ) is not equal to zero. In order to test this hypothesis, the F statistic was examined. A multiple regression including all three variables of the prediction mode: attitudes, subjective norms, and behavioral intent were used to obtain the f ollowing results (see Figure 4 20). F = 421.710, R= .789, an d R 2 = 0.623. What these outcomes demonstrate is that the multiple regression model is significant and 62.3% of the variance in behavioral intention is explained with this complete model. Furthe rmore, with t values of 21.347 (attitudes) and 3.885(subjective norms) and p values of 0.000 in both cases, the null hypothesis was rejected. See Figure 4 20 for details. The resulting prediction model is given by 0.885 + 0.348Acct + 0.008SN Addi tional Findings In addition to the nine hypotheses, previously stated, a number of other exploratory analyses were conducted. CAM rates were compared for the following variables: employment classification, country of birth, alcohol use, tobacco use, physi cal activity levels, Body Mass Index (BMI), and the previous diagnosis of a health condition. The final statistics that were analyzed involved the Behavioral Intention scores of those who indicated that they have never used any form of CAM. Although empl oyment classification was not one of the original hypotheses, the researcher was interested in seeing if there was a difference in CAM use between those who are classified as faculty (either clinical, or 9, 10, or 12 month) and as staff (this is defined as 2 = 5.091 and p value=

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72 0.405, was that there is no significant difference in the proportions of CAM users between the two employment classifications (Figure 4 21). According to Fertman and Allensworth (2010), university environments can have very diverse populations. Not only can the student body be diversified, so can the employees including the faculty and staff. So in addition to looking at whether race/ethnicity and employment classification may impact C AM use, the researcher was also curious if those who were born outside the United States would skew my results. Therefore, a comparison of CAM use between those who were born in the US compared and those who were not was made. Although either a z test or Chi Square for these results could have been used (since the comparison is with two groups) for 2 = 0.770 and p= 0.68 it is concluded that there is no difference in CAM u se between the proportion of those who were born in the US and those who were not (Figure 4 22). The relationship between tobacco use at different levels (regular, occasional, and never) and CAM use was also explored using the Chi square test with the fol lowing 2 greater than 0.058, then the null hypothesis that proportions of CAM users are not significantly different between the considered tobacco user levels will be rejecte d. Alternatively, level of tobacco use and CAM use are not independent. Those with higher level of tobacco use tend to be less inclined to use CAM. The groups comprised of those who selected either everyday use, two to six times a week use, once a week,

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73 However, due to low frequencies groups one and two were combined and categorized as regular smokers; groups three, four, and five are combined to make up the occasional smok ers and the last group consisted of nonsmokers (Figure 4 24). The alcohol consumption options were exactly the same as those for tobacco consumption: everyday, two to six times a week, once a week, only on weekends, on = 0.462, and a very large p = 0.794, the null hypothesis that proportions of CAM users are not significantly different between the considered alcohol user levels cannot be rejected (Figure 4 23). Hypothesis 5 explored how perceived heal th status is related to CAM use. Although this hypothesis was not significant and therefore the null hypothesis could not be rejected, the researcher wanted to see if other health factors impacted CAM use. Chi square tests were run to determine if CAM us e was related to, previously diagnosed health conditions as well as physical activity levels and Body Mass Index. It was discovered that none of these relations were significant. For details see Figures 4.25 to 4.27. For those who had been diagnosed wit h one of the following conditions: obesity, diabetes, heart disease, high cholesterol, hypertension, stroke, cancer, and other (participants could fill in the blank) the Chi 2 = 1.083 and p = 0.29. Using the National Institute f or Health (NIH) BMI calculator, BMIs were calculated for all participants who indicated both their weight and height. Once BMI values were calculated, each respondent was put into a group. The five

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74 = 6.617 and p = 0.157. The next exploration was the relationship between self reported physical activity levels and CAM use. Respondents were classified into four groups (very active, active, moderately active, and not active). The Chi square 2 = 3.81 and p = 0.433 indicating that the proportions of CAM users do not differ significantly between the self reported activity levels. Therefore, based on the results from the exploratory questions about diagnosis, BMI, and physical activity levels, the survey data did not provide support to the alternate hypotheses that CAM use and each of these variables are independent. The final exploratory analysis that was done to better understand the dynamics of the participants who indicat ed they had never used any form of CAM before and what that meant for their Behavioral Intent score. The notion behind the BIM is that behavior and behavioral intent are very strongly correlated. It can be assumed with this model that even if a behavior h as not been acted upon, if there is a high level of behavioral intent then there is very high predictability that the actual behavior will follow soon score was de rived from question eight which asked how likely they were to use CAM in the next 12 months. The seven Likert scale response options ranged from very unlikely to very likely. Of the 81 participants that indicated they did not use any form of CAM, 75 comp leted the behavioral intention questions yielding each respondent a BI score. Although there were no non CAM users who answered as being likely or very likely to use CAM within the next 12 months, about 13.3% indicated that they were somewhat

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75 likely to try CAM. Roughly 9.3% responded that they were neither likely nor unlikely to attempt any form of CAM within the next 12 months. The majority of participants answered that they were somewhat unlikely to very unlikely to use CAM. Summary This chapter present ed the findings of the hypotheses concerning the relation between CAM use and certain demographic attributes of the employees of University of Florida. In addition the multiple regression model for predicting behavioral intent was shown to have a signific ant explanatory and predictive power. Findings suggested a significant relationship between sex and CAM use. It should also be noted that there was incongruence between the results from the literature and those of this study. The only variable for which both this study and the literature found significant relationship with CAM use was sex. Unlike the results from the literature, the findings in this study did not support the relations between the following variables and CAM usage as significant: educati onal levels, family income, race/ethnicity, health status, and age. It is perhaps noteworthy that of all these variable only health status came close to being significant with a p value of 0.12. Also included in this chapter are the exploratory analyses conducted to see if there were any other variables not covered in the literature that are also significant in explaining CAM use. Other variables considered included frequency of alcohol consumption and physical activity levels, BMI, and previous diagnos is of a health condition. Chapter 5 will review the findings of the various statistical analyses conducted and will also provide conclusions that can be drawn from these analyses. Implications of the study will also be discussed and the last section of t he subsequent chapter will highlight recommendations for future research and practice.

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76 Table 4 1. Age of Survey participants Age Frequency Percent 18 35 123 22.04 36 45 121 21.68 46 55 136 24.37 56 65 151 27.06 65+ 27 4.3 Table 4 2. Sex of Survey participants Frequency Percent Female 348 62.6 Male 208 37.4 Table 4 3. Race/Ethnicity of Survey participants Frequency Percent White/Caucasian 123 22.04 Black/African American 121 21.68 Hispanic or Latino 136 24.37 Asian or Pacific Islander 151 27.06 American Indian or Alaskan Native 27 4.3 Other Table 4 4. Born in US Frequency Percent Yes 348 58.5 No 208 35.0

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77 Table 4 5. Marital status of Survey participants Frequency Percent Single/Separated 114 19.2 Married or domestic partnership 392 65.9 Divorced 47 7.9 Widowed 8 1.3 Table 4 6. Highest Level of Education Achieved Frequency Percent Eighth grade or less 0 0 Did not finish high school 1 .2 High school graduate/GED 35 5.9 Associates degree or professional license 48 8.1 109 18.3 167 28.1 Doctoral degree 142 23.9 Professional degree (i.e. MD, JD, PharmD, VMD, & etc.) 57 9.6 Table 4 7. Employment Classification Frequency Percent Faculty (9, 10, or 12 months) 187 31.4 Clinical Faculty 33 5.5 Exempt TEAMS/USPS 237 39.8 Non Exempt TEAMS/USPS 93 15.6 Other 9 1.5

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78 Table 4 8. Household Income Range Range Frequency Percent 1 0.2 $20,001 $35,000 32 5.4 $35,001 $50,000 63 10.6 $50,001 $65,000 62 10.4 $65,001 $80, 000 53 8.9 $80,001 $95,000 53 8.9 282 47.4 Table 4 9. Reasons for Using CAM Reason Frequency Percent Treatment of health problem or condition 296 49.7 Prevention of health problem or condition 193 32.4 Personal well being 359 60.3 Table 4 10. Frequency of Consultation with Physician about CAM Use Frequency Percent No response 125 21.0 Yes 148 24.9 Sometimes 153 25.7 No 169 28.4 Table 4 11. Types of Natural Products Used Type Frequency Percent Herbs/botanicals 119 20.0 Vitamins/minerals 406 68.2 Non vitamins/Non minerals 189 31.8 Probiotics 147 24.7

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79 Table 4 12. Results of Chi square test and descriptive statistics for educational levels High School/GED Associates Degree Degree Degree Doctoral Degree Professional Degree Used CAM (Observed) 32 (88.9%) 42 (87.5%) 96 (88.1%) 145 (86.8%) 120 (84.5%) 47 (82.5%) Used CAM (Expected) 31.0 41.5 94.2 144.3 122.7 49.2 Did not use CAM (Observed) 4 (11.1%) 6 (12.5%) 13 (11.9%) 22 (13.2%) 22 (15.5%) 10 (17.5%) Did not use CAM (Expected) 4.8 6.5 14.8 22.7 19.3 7.8 Note . 2 = 0.92, df = 5, p = 0.97. Numbers in parentheses indicate column percentages. *p > .05 Table 4 13. Results of Chi square test and descriptive statistics for household income ranges $35,000 $35,001 $50,000 $50,001 $65,000 $65,001 $80,000 $80,001 $95,000 $95,001 Used CAM (Observed) 26 (78.8%) 55 (87.3%) 54 (87.1%) 49 (92.5%) 48 (90.5%) 237 (84%) Used CAM (Expected) 28.4 54.1 53.3 45.5 45.5 242.2 Did not use CAM (Observed) 7 (21.2%) 8 (12.7%) 8 (12.9%) 4 (7.5%) 5 (9.5%) 45 (16%) Did not use CAM (Expected) 4.7 8.9 8.7 7.5 7.5 39.8 Note . 2 = 4.39, df = 5, p = 0.50. Numbers in parentheses indicate column percentages. *p > .05

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80 Table 4 14. Results of Chi square test and descriptive statistics for sex Female Male Used CAM (Observe) 317 (91.1%) 162 (77.9%) Used CAM (Expected) 300.6 179.7 Did not use CAM (Observed) 31 (0.9%) 46 (22.1%) Did not use CAM (Expected) 47.4 28.3 Note . 2 = 19.4, df = 1, p = 0.00. Numbers in parentheses indicate column percentages. *p < .05 Table 4 15. Results of Chi square test and descriptive statistics for racial/ethnic groups White/Caucasian Black/African American Hispanic Other Used CAM (Observed) 405 (86%) 26 (86.7%) 28 (96.6%) 26 (81.3%) Used CAM (Expected) 406.5 25.9 25 27.6 Did not use CAM (Observed) 66 (14%) 4 (13.3%) 1 (4.4%) 6 (18.7%) Did not use CAM (Expected) 471 30 29 32 Note . 2 Islander, American Indian or Alaskan Native, and Other. *p > .05

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81 Table 4 16. Results of Chi square test and descriptive statistics for health status Excellent Very Good Good Fair/Poor Used CAM (Observed) 89 (79.5%) 243 (88.4%) 121 (86.4%) 35 (89.7%) Used CAM (Expected) 96.6 237.1 120.7 33.6 Did not use CAM (Observed) 23(20.5%) 32 (11.6%) 19 (13.6%) 4 (10.3%) Did not use CAM (Expected) 15.4 37.9 19.3 5.4 Note . 2 = 5.78, df = 3, p = 0.12. Numbers in parentheses indicate column percentages. *p > .05 Table 4 17. Results of Chi square test and descriptive statistics for age groups 36 45 46 55 56 65 Used CAM (Observed) 106 (86.2%) 103 (85.1%) 117 (86%) 132 (87.4%) 24 (88.9%) Used CAM (Expected) 106.2 104.5 117.5 130.4 23.3 Did not use CAM (Observed) 17 (13.8%) 18 (14.9%) 19 (14%) 19 (12.6%) 3 (11.1%) Did not use CAM (Expected) 16.8 16.5 18.5 20.6 3.7 Note . 2 = 0.46, df = 4, p = 0.98. Numbers in parentheses indicate column percentages. *p > .05 Table 4 18. Coefficients for each independent predictor in separate simple linear regression models B Standard error t Significance R R square Attitudes .385 .014 28.393 .000 0.782 0.612 Subjective Norms .033 .002 14.330 .000 0.535 0.286 *p < .05

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82 Table 4 19. M ultiple linear regression model B Standard error t Significance Attitudes .348 .016 21.347 .000 Subjective Norms .008 .002 3.885 .000 Constant .885 .229 3.855 .000 *p < .05 Table 4 20. ANOVA for multiple linear regression model Sum of squares df Mean Square F Significance R R square Regression 1234.129 2 617.064 421.710 0.000 0.789 0.623 Residual 747.717 511 1.463 Total 1981.846 513 *p < .05 Table 4 21. Results of Chi square test and descriptive statistics for employment classifications Faculty (9, 10, & 12 month) Clinical Faculty Exempt TEAMS/USPS Non Exempt TEAMS/USPS Other Used CAM (Observed) 154(82.4%) 29 (87.9%) 207 (87.3%) 83 (%) 89.2 (%) Used CAM (Expected) 161.5 28.5 204.7 80.3 7.9 Did not use CAM (Observed) 33 (17.6%) 4 (12.1%) 30 (12.7%) 10 (%) 0 (%) Did not use CAM (Expected) 25.5 4.5 32.3 12.7 1.2 Note . 2 = 5.091, df = 5, p = 0.405. Numbers in parentheses indicate column percentages. *p > .05

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83 Table 4 22. Results of Chi square test and descriptive statistics for location of birth Born in U.S. Born outside U.S. Used CAM (Observed) 420(86.2%) 63 (85.1%) Used CAM (Expected) 420.7 63.9 Did not use CAM (Observed) 67 (13.8%) 11 (14.9%) Did not use CAM (Expected) 66.3 10.1 Note . 2 = 0.770, df = 2, p = 0.68. Numbers in parentheses indicate column percentages. *p > .05 Table 4 23. Results of Chi square test and descriptive statistics for alcohol consumption frequency Regular Occasional Never Used CAM (Observed) 131(86.2%) 265 (86.8%) 91 (84.3%) Used CAM (Expected) 131 262.9 93.1 Did not use CAM (Observed) 21 (13.8%) 40 (13.1%) 17 (15.7%) Did not use CAM (Expected) 21 42.1 14.9 Note . 2 = 0.462, df = 2, p = 0.79. Numbers in parentheses indicate column percentages. *p > .05 Table 4 24. Results of Chi square test and descriptive statistics for tobacco consumption frequency Regular Occasional Never Used CAM (Observed) 19(76%) 22 (100%) 446 (86.1%) Used CAM (Expected) 21.5 19.0 446.5 Did not use CAM (Observed) 6 (24%) 0 (0%) 72 (13.9%) Did not use CAM (Expected) 3.5 3.0 71.5 Note . 2 = 5.71, df = 2, p = 0.058. Numbers in parentheses indicate column percentages. *p > .058

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84 Table 4 25. Results of Chi square test and descriptive statistics for physical activity levels Very active Active Moderately active Not active Used CAM (Observed) 59(88.1%) 151 (82.5%) 204 (88.7%) 74 (86%) Used CAM (Expected) 57.9 158.1 198.7 74.3 Did not use CAM (Observed) 8 (11.9%) 32 (17.5%) 26 (11.3%) 12 (14%) Did not use CAM (Expected) 9.1 24.9 31.3 11.7 Note . 2 = 3.808, df = 4, p = 0.433. Numbers in parentheses indicate column percentages. *p > .05 Table 4 26. Results of Chi square test and descriptive statistics for BMI ranges Underweight Thin for height Healthy weight Overweight Obese Used CAM (Observed) 5 (%) 6 (%) 195 (85.6%) 159 (88.8%) 100 (0.8%) Used CAM (Expected) 4.3 5.1 194.8 153.6 107.2 Did not use CAM (Observed) 0 (0%) 0 (0%) 32 (14.4%) 20 (11.2%) 25 (0.2%) Did not use CAM (Expected) 0.7 0.9 32.2 25.4 17.8 Note . 2 = 6.617, df = 4, p = 0.157. Numbers in parentheses indicate column percentages. *p > .05

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85 Table 4 27. Results of Chi square test and descriptive statistics for previous diagnosis of health condition No diagnoses Previous diagnosis(ses) Used CAM (Observed) 173 (84.8%) 278 (88%) Used CAM (Expected) 176.9 274.1 Did not use CAM (Observed) 31 (15.2%) 38 (12%) Did not use CAM (Expected) 27.1 41.9 Note . 2 = 1.08, df = 1, p = 0.30. Numbers in parentheses indicate column percentages. *p > .05

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86 Figure 4 1. CAM Use Lifetime and Last 12 Months 0 50 100 150 200 250 300 350 400 450 Acupunture Ayurveda Biofeedback Chelation Therapy Chiropractic Care Deep Breathing Energy Health Therapy Herbal Medicine Homeopathy Hypnosis Massage Meditation Naturopathy Tai Chi Qi Gong Yoga Lifetime Last 12 Months

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87 CHAPTER 5 DISCUSSION, CONCLUSIONS, & RECOMMENDATIONS Discussion As previously stated, this study explored the use of CAM among University of Florida employees. The goal of this study was not only to explo re which CAM modalities are used more frequently among this population but also investigate the determinants of the behavior that leads to CAM use and develop a model to predict future behavior. The following research questions guided the data analyses: 1. Is there etween Behavi oral Intent? 8. Are Subjective Norms toward CAM use significant in explaining Behavioral Intent? 9. Are Attitude toward and Subjective Norms about CAM use jointly significant in explaining Behavioral Intent? In addition to using these research questions , comparisons of CAM usage rates and what CAM modalities are most frequently used were also evaluated. Of the nine hypotheses that correspond to the above research questions, the first six were focused on attempting to confirm what the literature states a s being determinants of CAM use. The relationships to CAM use of the following variables were tested using the Chi square test and the significance levels were reported with p values: educational level, family income, sex, race/ethnicity, health status an d age.

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88 Of these six variables only sex was shown to be a determinant of CAM use at a significance level of 0.05. This result is not in agreement with the current literature in that all of these six variables were found to be significant in independent st udies. Although more research would need to be done to determine why the results for the population studied vary from other findings, one very likely factor is the homogeneity of this population. The National Health Interview Survey (NHIS) consisted of 2 9,266 participants that represented individuals from all across the U.S. Therefore not only was the NHIS sample diverse it can also be said that the respondents were heterogeneous (Barnes et al., 2008). The average educational level of University of Flori da employees was higher than the national average due to the fact that many of the positions require higher education as job pre requisites. According to the 2013 US Census Report, roughly 45% of the population held a high school degree or less, whereas for those who completed the current survey this number was only 6.1%. The educational level of those who completed the NHIS who had the highest rates of CAM use were those who had graduate degrees (masters, doctoral, or professional degrees) followed by b Respondents who had less than a high school degree reported the lowest CAM usage rates compared to those with higher educational levels (Barnes et al., 2008). Because of the above average educational attainment o f the current sample when a group comparison of CAM use was investigated to verify if educational levels impact CAM use, findings indicated that educational level was not a predictor of CAM use among the University of Florida employee population.

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89 Income l evels were another source of discrepancy between the data sampled and the national averages. Those who completed the survey had significantly higher household incomes than the national average. According to the Census Bureau (2013), in 2012 the national average household income was $51,017, whereas the average Previous literature suggested income as a determinant of CAM use (Barnes et al., 2013, Rafferty et al., 2002, and Brown et al., 2007). Hypothesis two explored if CAM use was independent of income levels and resulted in a p= 0.50. Accordingly, the independence hypothesis could not be rejected indicating that for the population studied, household income was not a determinant of CAM use. Of the first six hypotheses, which were suggested by the literature, only sex had an impact on CAM use, with p = 0.000. The conclusion of this study is in congruence with the previous findings (Neiberg et al., 2011, Tindle et al., 2005, and McFarland, B igelow, Zani, Newsome, Kaplan, 2002, Bishop, 2002, Green & Pope, 1999), which stated that women have a higher CAM use rate than men. According to Brett & Burt (2001), an explanation for this difference between men and women may be attributed to the belief that women are the primary health decision makers for both their families and themselves, and women were more likely to use health care services including CAM. those investigate d in the literature, as presented in C hapter 2, the average use of CAM among different races and ethnicities was not found to be significantly different. The literature suggests that race/ethnicity does impact CAM use. Research by Barnes et al. (2005) sai

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90 whereas in the current study not only were frequencies calculated but a Chi square tests was also run to determine if race/ethnicity impacts CAM use. versus the national population, be cause of varying degrees of access to health care. All participants of this study were full time employees receiving fringe benefits including health insurance. The NHIS (Barnes, 2008) reported that when people with no health insurance, public health insu rance and private health insurance were compared. Individuals with public health insurance reported the lowest CAM use rates followed by those who have no health insurance, and then those with private health insurance. Lafferty et al. (2006) speculated th at as private health insurance companies are being required to cover evidence based CAM modalities, demand for CAM use will increase. Kessler et al. (2001) and Oldendick et al. (2000) both found that age and CAM use were directionally correlated: as age i ncreased so did CAM use. In addition to those two studies, Barnes et al. (2005) also found CAM use to be related to age. However, after further sifting through their data, they concluded that the age/CAM use relationship displayed an inverse shaped curve, indicating that the oldest and the youngest respondents had lower CAM use rates. It should also be noted that the age range for this study was larger (18 85+) than the population of University of Florida employees. Since all respondents in this study wer e within a working age range, again the hypothesis of independence between age and CAM use could not be rejected.

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91 The following three hypotheses (7, 8, and 9) were focused on testing the Behavioral Intention Model. When p values and F statistic were eval uated all three were significant in explaining the behavioral intent to use CAM. The first variable tested was attitudes and yielded an R 2 value of 0.612 suggesting that 61.2% of the variance of BI was explained by this variable. Subjective Norms toward CAM use were also tested to see how much of the variance in BI was explained by them and the R 2 value indicated that subjective norms accounted for 28.6% of the variance in BI. What this tells us is that when focusing of the two components that make up th is prediction model, attitudes have a stronger weight compared for Subjective Norms. Ho 7 . Attitude toward the use of CAM is not a significant factor in 0: W 0 =0, Ha: W 0 Ho 8 . Subjective Norms toward the use of CAM is not a significant factor in 0: W 1 =0, Ha: W 1 Ho 9 . Neither attitude toward CAM (Acct) nor subjective norms (NBCMC) about CAM contribute significantly to the prediction of Behavioral Intent (BI) to use CAM. Alternatively, H 0: W 0 =W 1 = 0, Ha: at least one of the two parameters (W 0 , W 1 ) is not equal to zero. In the current study, 86.4% of the respondents indicated tha t they have used at least one form of CAM within their lifetime. This is greater than the roughly 75% of Americans who responded they had used CAM sometime within their lifetime. This higher level of CAM usage rate may be attributable to the determinants working holistically rather than independently and to the homogeneity of the population studied in this study. NHIS survey included prayer in its definition of CAM whereas prayer was excluded in the current survey. In addition, NHIS reported that the mos t common forms

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92 of CAM used were natural products, followed by deep breathing, meditation, chiropractic care, yoga, and massage. For the population that responded to the NHIS, the common reasons for using CAM were: conventional medicine not being effectiv e, conventional medicine being too expensive, CAM combined with conventional medicine was more effective than either independently, physician recommendation, and the desire to try CAM. Although the literature suggests that CAM is used as secondary or tert iary care, results from the source for disease prevention and overall well being. Of the UF employee respondents to the CAM survey, 32.4% indicated they used CAM as a preven tion mechanism to diminish health problems or conditions. 60.3% responded saying they use CAM for personal well being purposes. Beyond the proposed research questions and hypotheses, additional exploratory hypotheses were tested. The first set of these hypotheses tests used Chi square and p values to test group comparisons based on the following variables: employment classification, country of birth, alcohol use, tobacco use, physical activity levels, BMI, previous diagnosis of a health conditi on. None of these hypothese s were significant at at an What this indicates is that those who do not smoke are more inclined to use CAM as opposed to those who d o smoke. Although I did not ask specifically about previous tobacco use, Barnes et al. (2008) found that former smokers had the highest e is concerned, a direct comparison with this study

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93 and the national data cannot be made, since the questions asked were not similar. One explanation for the difference in CAM use between the tobacco consumption groups could be that those who are more hea lth conscious tend to choose healthier behaviors, such as abstaining from smoking. In addition, the higher rates of CAM usage among former smokers included in the NHIS study may also be a result of them taking more initiative to engage in healthy behavior s. The final exploratory hypothesis was examining BI scores from non CAM users. Of the 85 participants who indicated they had never used CAM, roughly 13% responded that they were very likely to use CAM within the next 12 months. This finding sugges ts that even for non CAM users the BIM can be used to predict future behavior and it can also guide programs that can positively impact personal attitudes towards CAM. As previously stated, the BIM postulates that behavioral intent is strongly related to actual behavior. Therefore even for a population of non users this model can be utilized to change behaviors by focusing on attitudes and beliefs about CAM. Limitations Adequate survey response rate is critical for the success of data collection and ana lyses. Without meeting the effect size it is difficult to make generalizations about a population (Creswell, 2008). One negative impact on the survey response rate of this study may be attributed to the fact that the maximum number of surveys are allowed to send through Qualtrics is 100. However, after consulting the office in charge of the Qualtrics license, they agreed to increase the limit to 1,000. This meant that there were still 4,000 remaining email addresses to whom the survey needed to be sent. An attempt was made to use a Qualtrics tool which allows the researcher to send surveys

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94 generated email address to Within the first 48 hours a number of respondents notified me that emails, including the survey link, were going to their junk/spam folders. Because there was no way to track this, a request was made to submit the survey to all 5,000 potential participants excluding those who had already res ponded to the survey. The request was approved and this time the participants were sent the survey through researcher personal email address to hopefully reach those who may not have received the original request due to it being placed in a spam folder. personal email was to minimize undeliverable messages. A downside of using personal opportunity to obtain certain statistics on th e open rate, start rate and completion rate of the survey. Of those who received the Qualtrics internal email only 25% opened it and of that 25%, 49% started the survey, and finally of those who started the survey 91% completed it, resulting in 111 respon ses. However, by combining the responses to the survey automatically sent by Qualtrics and those sent through personal email, a total sample of 595 respondents were achieved. This represents a response rate of 11.9%. According to Nulty (2008), although incentivizing the potential respondents could increase the response rate, it is typical for online surveys to yield lower response rates compared to face to face or telephone surveys. Nulty also indicated that it is not unusual to have response rates betw een 10% and 20%. Another limitation of this study is its length. In an effort to make the survey concise to limit the time burden of the respondents and to minimize attrition rates, some

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95 questions were generalized to all CAM modalities. However, if ques tions were asked specifically about each CAM treatment, more details and more precise data could have ral terms fails to make the distinction between a situation where a person may discuss her/his chiropractic use with her/his physician but may choose not to discuss the use of herbal medicines. Question four is similar in that, it too could have been writ ten to be more specific to the CAM modality since certain forms of CAM may be used for treatment of a health problem or condition but not for preventative purposes. In looking at the BIM questions, although the researcher was more concerned with CAM use o verall, some of the attitudes and subjective norms between the treatments may also vary. Therefore the data presented could be skewed by the difference in attitudes and beliefs about each type of CAM. Implications and Recommendations For Future Research Although this study explored how the BIM can be used to predict future CAM use and what the CAM use landscape looks like in the population studied, future research could explore how BI can be applied to other, more heterogeneous populations to predict futu re CAM use. It would also be interesting to see how the results from this study would vary in other worksite populations. Not only can BI be explored in a health education context, but more national data on CAM attitudes, beliefs, and subjective norms ar e also vital in predicting the demand for non allopathic care. Also, by expanding on the limitations of the study by asking questions related to each CAM treatment as opposed to generalizing all types of CAM into one concept will

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96 paint a far more descrip tive picture of the perceptions about each type of CAM therapy and the utilization rates. Further, results from this study demonstrate that more empirical evidence and scientific studies about the benefits of CAM are needed. For Health Educators & Health Education The implications of the findings of this study appear to be important for the field results not only demonstrate a need for health education programs regarding CAM, but they also lay a pathway to consider using the BIM as a model/theoretical perspective when planning health promotion programs. In fact, many of the major Health Education theories (such as the Health Belief Model, Stages of Change Model, Theory of Planned Behavior, and Social Cognitive Theory) have an overlapping theme with the BIM, namely attitudes and subjective norms (National Cancer Institute, 2005 & Glanz et al, 2002). Therefore this prediction model can be used by itself to evaluate behaviora l intent and future behavior use for any health behavior; not just CAM. It is also suggested that using the BIM in conjunction with one of the other Health Education theories/models could have a synergistic effect of not only changing behavior but also fo recasting it. The primary differentiator of the BIM is that up to this point, none of the current theories or models attempted to predict future behavior. Their main focus was on how to change behavior. This model used for this currents study is able to quantify the likelihood of behavior change and is therefore measurable prior to actual behavior change. Benefits would accrue to health education specialists and health promotion programs by being able to determine the immediate impact of a program. BIM can be used as a pre test and a post test tool to measure the return on investment (ROI) for the

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97 program planner. Evaluation is a key aspect of program planning and should be done at the forefront (Windsor, Clark, Boyd, & Goodman, 2004); therefore using t he BIM can be another tool to shape a need assessment as well. In addition, to maximize efforts in creating health promotion programs around CAM, this model resulted in higher explanatory power with the attitudes variable as opposed to normative beliefs a nd the motivation to comply (subjective norms). This tells us that of the two variables, the one to put the most energy in to have the highest program impact, would be attitudes. Work site health promotion programs are designed to maximize the safety and wellness of employees (CDC, 2013). It is just one of the many ways health education specialists can access a large group of people to promote positive health behaviors. The focus of work site health promotion programs can be on the individual level, orga nizational level, or community level. Approaches such as the Workplace Health Model have been designed to address how to accomplish health promotion programs at various levels. Here at the University of Florida such worksite health promotion programs exis t through its collaboration with UF Health Shands. The vision of this joint committee is to promote wellness initiatives which aid in fostering both a healthy University community and improvements in employee health. Services they offer include: fitness, weight management, nutrition, recreation, and emotional well being, stress reduction, smoking cessation, health services, and various training and information programs. Specifically, under the stress reduction category various forms of CAM are offered: Guided meditations, Tai Chi, and yoga. In addition to these forms of CAM, a six week Mindfulness Bases Stress Reduction (MBSR) seminar series was

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98 offered to teach mindfulness meditation, yoga, and facilitated group dialog about stress reduction techniques (UF Health, 2014). As previously stated the CAM modalities offered to UF employees are meditation, Tai Chi, and Yoga. Based on other common modalities of CAM used by the population studied in this dissertation, UF and UF Health Shands should consider of fering programs that also include chiropractic care, deep breathing, herbal medicine, and massage therapy. Summary and Final Thoughts Education, household income, sex, race/ethnicity, health status, and age are all suggested by the literature to be deter minants of CAM use. According to Upchurch and Chyu (2005) white women who are well educated and who have health insurance make up the demographic with the highest rate of CAM use. While results of this study suggested women reported higher CAM use when c ompared to men, after adjusting for education, income, race/ethnicity, health status, and age, there were no other demographic predictors associated with CAM use beyond sex. Implications for these findings could help health education specialists create C AM health promotion programs. Once again, the homogeneity of this population yielded different results from those studies looking at national determinants. This demonstrates why a needs assessment is so vital for program success. As health promotion progra ms are created, these generalizations based on the literature. As seen through the data analysis, with sex being the only significant determinant of CAM, programs could consi der centering program planning with women as the target population. If the goal is to increase overall

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99 CAM use, this may be a sufficient avenue, since the literature suggests women are the primary healthcare decision makers for their families. In addition to creating health promotion programs catered to women, it would also be of benefit to understand how attitudes about CAM would allow program creation targeting each populatio n with separate messages. In addition to demographic variables, the BIM model was also explored to better understand the determinants of CAM use. Although the only significant demographic variable predicting CAM use in this current study was sex, outcome s of the BIM suggest attitudes, subjective norms, and the complete BI model are all significant in explaining future behavior toward using CAM. Considering the makeup of the population of this study (UF employees), with an above national average CAM use r ate of 86.4%, it is apparent that this population would be receptive to engaging in more CAM opportunities, including worksite health promotion programs. Furthermore, more research could be done to understand the attitudes of CAM users to try to engage no n CAM users, in behavior modification programs, so they too can reap the benefits CAM treatments can offer.

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100 APPENDIX A SURVEY INSTRUMENT PRIOR TO EXPERT PANEL REVIEW Invitation to Participate University Eligibility: To participate you must currently be classified as a full time University of This survey examines your experience and perceptions of compleme ntary and alternative medicine (CAM). For our purposes, we define CAM as a group of diverse medical and health care systems, practices, and products that are not presently considered to be part of conventional (Western) medicine. We will use the survey information you provide to better understand the trends of CAM use among university employees and how future worksite health promotion programs can be marketed and implemented to improve overall health and well being. The survey requires about 10 15 minut es of your time. Please answer all questions completely. We offer you the following safeguards for completing the survey: 1. Your participation in the survey is voluntary. Whether or not you choose to participate, your status at the University of Florida w ill not be affected in any way. 2. Your responses will remain anonymous. Your responses will remain private, and cannot be traced to you. 3. We do not request any personal identification such as name, UFID, Social Security number, etc. 4. Demographic informa tion about your background only will be used to describe the general types and categories of respondents. Your name will not be traced to your responses, and no respondent names will ever be reported. 5. If at any time, now or in the future, you have questi ons or concerns about the survey, you will receive immediate assistance by contacting: Naz Erenguc, Doctoral Candidate University of Florida Dr. William Chen, Professor and Supervisor University of Florida

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101 Thank you for your time and consideration. If you would like to participate in the survey, please indicate your consent by clicking the START SURVEY button below. START SURVEY

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102 Definitions of CAM treatments Acupuncture based on the premise that health can only be obtained by balance of energy, which is thought to be within you. This energy is constantly flowing throughout the body via meridians which are energy pathways. Acupuncture aims to stimulate the energy pathways to help correct or rebalance by the insertion of needles along the meridian pathways to restore the flow of energy and therefore health. Alternative Medicine is a non mainstream approach to health which is used instead of conventional medicine. Ayurveda Comprehensive system of medicine that was developed over 5,000 years ago in India, where there is an equal emphasis on body, mind, the spirit. The ultimate goal is to restore harmony of these three parts. Treatments with this type of medicine include: dietary changes, exercise, herbals, meditation, massage therapy, yoga, and other treatments. Biofeedback Is a treatment method which trains clients how to consciously regulate typically unconscious bodily functions (e.g., breathing, heart rate, blood pressure), using an electronic device, to improve health. Complementary Medicine r efers to uses non mainstream techniques in conjunction with conventional medicine. Chelation Therapy involves injecting a binding (chelating) agent to remove and eliminate toxic metals and wastes from the bloodstream. Chiropractic Care involves adjustin g the spine and joints to elicit and activate the reduce pain. Deep Breathing is the process of slowly inhaling through the nose followed by slow exhalation. It is used to quite the mind and focus on the breath. Energy Healing Therapy/Reiki heal itself by focusing on the flo w and healing energy. Healing energy is guided through balance and health. Herbal medicine/Natural Products herbal medicine and botanicals, vitamins, minerals, pr obiotics, and other natural and dietary supplements taken orally Homeopathic treatment also known as homeopathic medicine is a system of medical practices which is based on the theory that any substance that can produce symptoms of disease or illness in a healthy person can also cure those symptoms in a sick person. Remedies are derived from many sources such as minerals, metals, and plants. Hypnosis is an altered state of consciousness and characterized by an increase of responsiveness to suggestions. T he hypnotic state is attained by relaxing the body, then shifting the attention toward an objects or idea which is suggested by the practitioner to access levels of the mind to promote positive changes in health behaviors. Massage involves pressing, rubbi ng, and/or manipulating muscles and soft tissues to promote relaxation and relieve pain Meditation is the act of calming the mind and relaxing the body Naturopathy is a system of medicine which is based on the theory that the body is a self regulating me chanism with an innate ability to maintain a state of both health and wellness. Treatments usually include: herbal medicine, homeopathic treatment, massage, dietary supplements, and other therapies.

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103 Tai Chi is a Chinese self defense discipline which empl oys low intensity and low impact exercise to maintain/restore health. Tai chi exercises include a set of forms, with each form consisting of a series of body positions connected by one continuous movement. Qi Gong is an ancient Chinese discipline that co mbines movements, focus, and deep breathing to connect the mind, body, and spirit, while stimulating the flow of vital life energy (qi). Yoga is a combination of breathing exercises, postures, and meditation, which is used to not only calms the nervous s ystem but to maintain balance among body, mind, and spirit. CAM Survey Demographical information 1. Age___ 2. Sex: a. Male b. Female 3. If you were not born in the United States, approximately how many years have you lived here? 4. If you were not born in the United States, approximately how many years have you lived here? 5. Which Racial/ethnic group best describes you: a. White/Caucasian b. Black/African American c. Hispanic or Latino d. Asian or Pacific Islander e. American Indian or Alaskan Nat ive f. Other 6. Marital status: a. Single, not in a committed relationship b. Single, in a committed relationship c. Married or domestic partnership d. Divorced e. Widowed f. Separated

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104 7. What is the highest level of education that you have achieved? a. Eight grade or less b. Did not finish high school c. High school graduate/ GED d. Associates degree or professional license e. f. g. Doctorate or professional degree 8. Which category best describes your employment status: a. Faculty (9,10, 12 month) b. Clinical Faculty c. Exempt TEAMS/ USPS d. Non Exempt TEAMS/ USPS (hourly) e. Other: 9. What is your family income range? a. $20,000 and below b. $20,001 $35,000 c. $35,001 $50,000 d. $50,001 $65,000 e. $65,001 $80,000 f. $80,001 $95,000 g. $95, 001 and above Following are some questions relating to your medical history 10. How would you describe your health? a. Excellent b. Very good c. Good d. Fair e. Poor 11. Do you consume tobacco products? a. Yes b. No 12. How would you describe your current alcohol usage? a. Very high b. High c. Moderate d. Low e.

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105 13. Pleas e indicate if you are taking any of the following. CHOOSE ALL THAT APPLY. a. Multivitamin b. Vitamin C c. Vitamin E d. Fish oils e. Protein supplement f. Muscle building supplements g. Other: 14. How would you describe your current physical activity level? a. Very active b. Active c. Moderately active d. Not active 15. How would you describe your weight? a. Underweight b. Just right c. Slightly overweight d. Very overweight e. Extremely overweight 16. Please indicate if you have been diagnosed with any of the following diseases or conditions? CHOOSE ALL THAT APPLY a. Obesity b. Diabetes c. Heart disease d. High cholesterol e. Hypertension (high blood pressure) f. Stroke g. Cancer h. Other

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106 i. Following are some questions relating to Complementary and Alternative Medicine usage 17. Have you EVER used any of the following Complementary and Alternative Medicine (CAM) treatments(select all): a. Acupuncture b. Ayurveda c. Biofeedback d. Chelation therapy e. Chiropractic f. Energy Health Therapy/Reiki g. Herbal Medicine h. Homeopathy treatment i. Hypnosis j. Massage k. Meditation l. Nat uropathy m. Tai Chi n. Qi Gong o. Yoga p. HAVE NEVER USED ANY OF THE ABOVE CAM TREATMENTS 18. Which of the following CAM treatments have you used within the last 12 months ? a. Acupuncture b. Ayurveda c. Biofeedback d. Chelation therapy e. Chiropractic f. Energy Health Therapy/Reiki g. Herbal Medicine h. Homeopathic treatment i. Hypnosis j. Massage k. Meditation l. Naturopathy m. Tai Chi n. Qi Gong o. Yoga

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107 19. During the past 12 months , did you use any (select all that apply) CAM treatment for a health problem or condition? a. Acupuncture b. Ayurveda c. Biofeedback d. Chelation th erapy e. Chiropractic f. Energy Health Therapy/Reiki g. Herbal Medicine h. Homeopathic treatment i. Hypnosis j. Massage k. Meditation l. Naturopathy m. Tai Chi n. Qi gong o. Yoga 20. How frequently do you use any of the above forms of CAM? a. Daily b. Weekly c. Monthly d. Annually e. As needed basis f. Do no use CAM 21. Do you discuss your use of CAM with your physician? a. Yes b. No c. Sometimes 22. Have you used CAM to treat any of the following? CHECK ALL THAT APPLY a. Cold/Flu b. Headaches/Migraines c. Backaches d. Allergies e. Sinus infection f. Other 23. I believe CAM is beneficial to my overall wellbeing (1 being NOT HELPFUL and 7 Being HELPFUL): 1 2 3 4 5 6 7

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108 24. I believe CAM is beneficial in treating a specific health condition (1 being NOT HELPFUL and 7 Being HELPFUL): 1 2 3 4 5 6 7 25. I believe CAM is safe (1 being unsafe and 7 completely safe): 1 2 3 4 5 6 7 26. Most members of my family would recommend using CAM: 1 2 3 4 5 6 7 27. Most of my friends would recommend using CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 28. My primary care physician would recommend using CAM ((1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 29. I intend to follow the advice of my family to use CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 NA 30. I intend to follow the advice of my friends to use CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 NA 31. I intend to follow the advice of my physician to use CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 NA 32. How likely are you to use CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 NA Thank you for participating in the survey.

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109 APPENDIX B SURVEY INSTRUMENT WITH EXPERT PANEL SUGGESTIONS (CHEN, COOKE, LANE, LEITE, & YOON) Invitation to Participate Medicine Use Among University Eligibility: To participate you must currently be classified as a full time University of . This survey examines your experience and per ceptions of complementary and alternative medicine (CAM). For our purposes, we define CAM as a group of diverse medical and health care systems, practices, and products that are not presently considered to be part of conventional (Western) medicine. We will use the survey information you provide to better understand the trends of CAM use among university employees and how future worksite health promotion programs can be marketed and implemented to improve overall health and well being. The survey requir es about 10 15 minutes of your time. Please answer all questions completely. We offer you the following safeguards for completing the survey: 6. Your participation in the survey is voluntary. Whether or not you choose to participate, your status at the University of Florida will not be affected in any way. 7. Your responses will remain anonymous. Your responses will remain private, and cannot be tr aced to you. 8. We do not request any personal identification such as name, UFID, Social Security number, etc. 9. Demographic information about your background only will be used to describe the general types and categories of respondents. Your name will not be traced to your responses, and no respondent names will ever be reported. 10. If at any time, now or in the future, you have questions or concerns about the survey, you will receive immediate assistance by contacting: Naz Erenguc, Doctoral Candidate Unive rsity of Florida

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11 0 Dr. William Chen, Professor and Supervisor University of Florida Thank you for your time and consideration. If you would like to participate in the survey, please indicate your consent by clicking the START SURVEY button below. START SURVEY Definitions of CAM treatments Acupuncture based on the premise that health can only be obtained by balance of energy, which is thought to be within you. This energy is constantly flowing throughout the body via meridians which are energy pathways. Acupuncture aims to stimulate the energy pathways to help correct or rebalance by the insertion of needles along the meridian pathways to restore the flow of energy and therefore health. Alternative Medicine is a non mainstream approach to health which is used instead of conventional medicine. Ayurveda Comprehensive system of medicine that was developed over 5,000 years ago in India, where there is an equal emphasis on body, mind, the spirit. The ultimate goal is to restore harmony of these three parts. T reatments with this type of medicine include: dietary changes, exercise, herbals, meditation, massage therapy, yoga, and other treatments. Biofeedback Is a treatment method which trains clients how to consciously regulate typically unconscious bodily func tions (e.g., breathing, heart rate, blood pressure), using an electronic device, to improve health. Complementary Medicine refers to uses non mainstream techniques in conjunction with conventional medicine. Chelation Therapy involves injecting a binding (chelating) agent to remove and eliminate toxic metals and wastes from the bloodstream. Chiropractic Care involves adjusting the spine and joints to elicit and activate the reduce pain. Deep Breathing is the process of slowly inhaling through the nose followed by slow exhalation. It is used to quite the mind and focus on the breath. Energy Healing Therapy/Reiki heal it self by focusing on the flow and healing energy. Healing energy is guided through balance and health. Herbal medicine/Natural Products herbal medicine and botanica ls, vitamins, minerals, probiotics, and other natural and dietary supplements taken orally Homeopathic treatment also known as homeopathic medicine is a system of medical practices which is based on the theory that any substance that can produce symptoms of disease or illness in a healthy person can also cure those symptoms in a sick person. Remedies are derived from many sources such as minerals, metals, and plants.

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111 Hypnosis is an altered state of consciousness and characterized by an increase of respons iveness to suggestions. The hypnotic state is attained by relaxing the body, then shifting the attention toward an objects or idea which is suggested by the practitioner to access levels of the mind to promote positive changes in health behaviors. Massage involves pressing, rubbing, and/or manipulating muscles and soft tissues to promote relaxation and relieve pain Meditation is the act of calming the mind and relaxing the body Naturopathy is a system of medicine which is based on the theory that the bod y is a self regulating mechanism with an innate ability to maintain a state of both health and wellness. Treatments usually include: herbal medicine, homeopathic treatment, massage, dietary supplements, and other therapies. Tai Chi is a Chinese self defen se discipline which employs low intensity and low impact exercise to maintain/restore health. Tai chi exercises include a set of forms, with each form consisting of a series of body positions connected by one continuous movement. Qi Gong is an ancient Chi nese discipline that combines movements, focus, and deep breathing to connect the mind, body, and spirit, while stimulating the flow of vital life energy (qi). Yoga is a combination of breathing exercises, postures, and meditation, which is used to not on ly calms the nervous system but to maintain balance among body, mind, and spirit. CAM Survey Demographical information 1. Age___ 2. Sex: a. Male b. Female 3. If you were not born in the United States, approximately how many years have you lived here? 4. If you were not born in the United States, approximately how many years have you lived here?

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112 5. Which Racial/ethnic group best describes you: a. White/Caucasian b. Black/African American c. Hispanic or Latino d. Asian or Pacific Islander e. American Indian or Alaskan Native f. Other 6. Marital status: a. Single, not in a committed relationship b. Single, in a committed relationship c. Married or domestic partnership d. Divorced e. Widowed f. Separated 7. What is the highest level of education that y ou have achieved? a. Eighth grade or less b. Did not finish high school c. High school graduate/ GED d. Associates degree or professional license g. Doctorate or professional degree 8. Which category best describes your employment status: a. Faculty (9,10, 12 month) b. Clinical Faculty c. Exempt TEAMS/ USPS d. Non Exempt TEAMS/ USPS (hourly) e. Other: Comment [AC2]: Some professional degrees are the equiva lent to a Masters while some have requirements of or beyond a typical Doctorate. Maybe make this a separate category? Comment [CW3]: Other doctoral professional degrees (e.g., JD, MD or DDS) Comment [AC4]: Are yon not sampling from unemployed, homemaker, a nd student populations?

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113 9. What is your family income range? a. $20,000 and below b. $20,001 $35,000 c. $35,001 $50,000 d. $50,001 $65,000 e. $65,001 $80,000 f. $80,001 $95,000 g. $95, 001 and above Following are some questions relating to your me dical history 10. How would you describe your health? a. Excellent b. Very good c. Good d. Fair e. Poor sense to distinguish different forms of tobacco? 11. Do you consume tobacco products? a. Yes b. No 12. How would you describe your current alcohol usage? a. Very high b. High c. Moderate d. Low 13. Please indicate if you are taking any of the following. CHOOSE ALL THAT APPLY. a. Multivitamin b. V itamin C c. Vitamin E d. Fish oils e. Protein supplement f. Muscle building supplements g. Other: 14. How would you describe your current physical activity level? a. Very active b. Active c. Moderately active d. Not active

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114 15. How would you describe your weight? a. Underweight b. Just right c. Slightly overweight d. Very overweight e. Extremely overweight onse 16. Please indicate if you have been diagnosed with any of the following diseases or conditions? CHOOSE ALL THAT APPLY a. Obesity b. Diabetes c. Heart disease d. High cholesterol e. Hypertension (high blood pressure) f. Stroke g. Cancer h. Other Following are some questions relating to Complementary and Alternative Medicine usage 17. Have you EVER used any of the following Complementary and Alternative Medicine (CAM) treatments (select all that apply): a. Acupuncture b. Ayurveda c. Biofeedback d. Chelation therapy e. Chiropractic f. Energy Health Therapy/Reiki g. Herbal Medicine h. Homeopathy treatment i. Hypnosis j. Massage k. Meditation l. Naturopathy m. Tai Chi n. Qi Gong o. Yoga p. HAVE NEVER USED ANY OF THE ABOVE CAM TREATMENTS

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115 18. Which of the following CAM treatments have you used within the last 12 months (select all that apply) ? a. Acupuncture b. Ayurveda c. Biofeedback d. Chelation therapy e. Chiropractic f. Energy Health Therapy/Reiki g. Herbal Medicine h. Homeopathic treatment i. Hypnosis j. Massage k. Meditation l. Naturopathy m. Tai Chi n. Qi Gong o. Yoga be conditional on a specific response to #17. 19. During the past 12 months , did you use any (select all that apply) CAM treatment for a health problem or condition? a. Acupuncture b. Ayurveda c. Biofeedback d. Chelation therapy e. Chiropractic f. Energy Health Therapy/Reiki g. Herbal Medicine h. Home opathic treatment i. Hypnosis j. Massage k. Meditation l. Naturopathy m. Tai Chi n. Qi gong o. Yoga

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116 20. How frequently do you use any of the above forms of CAM? a. Daily b. Weekly c. Monthly d. Annually e. As needed basis f. Do not use CAM 21. Do you discuss your use of CAM with your physician? a. Yes b. Sometimes b.c. No c. Sometimes 22. Have you used CAM to treat any of the following? CHECK ALL THAT APPLY a. Cold/Flu b. Headaches/Migraines c. Backaches d. Allergies e. Sinus infection f. Other 23. I believe CAM is beneficial to my overall wellbeing (1 being NOT HELPFUL and 7 Being HELPFUL): 1 2 3 4 5 6 7 24. I believe CAM is beneficial in treating a specific health condition (1 being NOT HELPFUL and 7 Being HELPFUL): 1 2 3 4 5 6 7 25. I believe CAM is safe (1 being unsafe and 7 completely safe): targeted towards some health problem or condition, at least broadly defined? Formatted: Indent: Left:1", No bullets or numbering Comment [AC9]: Seems like some question measuring the severity of medical conditions might be helpful, as well as the efficacy of CAM to treat these problems. here and below. 1 2 3 4 5 6 7 1 2 3 4 5 6 7

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117 27. Most of my friends would recommend using CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 28. My primary care ph ysician would recommend using CAM ((1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 29. I intend to follow the advice of my family to use regarding CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 NA 30. I intend to follow the advic e of my friends to use regarding CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 NA 31. I intend to follow the advice of my physician to use regarding CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 NA 32. How likely are you to use CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 NA Thank you for participating in the survey. conflicting advice. Comment [AC12]: Specify time period?

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118 I nvitation to Participate Eligibility: To participate you must currently be classified as a full time University of must be 18 or older. . This survey examines your experience and perceptions of complementary and alternative medicine (CAM). For our purposes, we define CAM as a group of diverse medical and health care systems, practices, and products that are not present ly considered to be part of conventional (Western) medicine. We will use the survey information you provide to better understand the trends of CAM use among university employees and how future worksite health promotion programs can be marketed and implemen ted to improve overall health and well being. The survey requires about 10 15 minutes of your time. Please answer all questions completely. We offer you the following safeguards for completing the survey: 1. Your participation in the survey is voluntary. W hether or not you choose to participate, your status at the University of Florida will not be affected in any way. 2. Your responses will remain anonymous. Your responses will remain private, and cannot be traced to you. 3. We do not request any personal i dentification such as name, UFID, Social Security number, etc. 4. Demographic information about your background only will be used to describe the general types and categories of respondents. Your name will not be traced to your responses, and no respondent names will ever be reported. 5. If at any time, now or in the future, you have questions or concerns about the survey, you will receive immediate assistance by contacting: Naz Erenguc, Doctoral Candidate University of Florida Dr. William Chen, Professor and Supervisor University of Florida Thank you for your time and consideration. If you would like to participate in the survey, Please indicate your consent by clicking the START SURVEY button below. START SURVEY Comment [sy1]: This purpose does not accur ately reflect what you are asking in the survey. You are asking more than perception and not enough about perception in detail when I read all 32 items. Definitions of CAM treatments Acupuncture based on the premise that health can only be obtained by balance of energy, which is thought to be within you. This energy is constantly flowing throughout the body via meridians which are energy pathways.

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119 Acupuncture aims to stimulate the energy pathways to help correct or rebalance by the insertion of needles along the meridian pathways to restore the flow of energy and therefore health. Alternative Medicine is a non mainstream approach to health which is used instead of conventional medicine. Ayurveda Comprehensive system of medicine that was developed over 5,000 years ago in India, where there is an equal emphasis on body, mind, the spirit. The ultimate goal is to restore harmony of these three parts. Treatments with this type of medicine include: dietary changes, exercise, herbals, meditation, massage thera py, yoga, and other treatments. Biofeedback Is a treatment method which trains clients how to consciously regulate typically unconscious bodily functions (e.g., breathing, heart rate, blood pressure), using an electronic device, to improve health. Complem entary Medicine refers to uses non mainstream techniques in conjunction with conventional medicine. Chelation Therapy involves injecting a binding (chelating) agent to remove and eliminate toxic metals and wastes from the bloodstream. Chiropractic Care involves adjusting the spine and joints to elicit and activate the reduce pain. Deep Breathing is the process of slowly inhaling through the nose followed by slow exhalatio n. It is used to quite the mind and focus on the breath. Energy Healing Therapy/Reiki heal itself by focusing on the flow and healing energy. Healing energy is guided through the hands of a practition balance and health. Herbal medicine/Natural Products herbal medicine and botanicals, vitamins, minerals, probiotics, and other natural and dietary supplements taken orally Homeopathic trea tment also known as homeopathic medicine is a system of medical practices which is based on the theory that any substance that can produce symptoms of disease or illness in a healthy person can also cure those symptoms in a sick person. Remedies are deriv ed from many sources such as minerals, metals, and plants. Hypnosis is an altered state of consciousness and characterized by an increase of responsiveness to suggestions. The hypnotic state is attained by relaxing the body, then shifting the attention to ward an objects or idea which is suggested by the practitioner to access levels of the mind to promote positive changes in health behaviors. Massage involves pressing, rubbing, and/or manipulating muscles and soft tissues to promote relaxation and relieve pain Meditation is the act of calming the mind and relaxing the body Naturopathy is a system of medicine which is based on the theory that the body is a self regulating mechanism with an innate ability to maintain a state of both health and wellness. Tr eatments usually include: herbal medicine, homeopathic treatment, massage, dietary supplements, and other therapies. Tai Chi is a Chinese self defense discipline which employs low intensity and low impact exercise to maintain/restore health. Tai chi exerc ises include a set of forms, with

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120 each form consisting of a series of body positions connected by one continuous movement. Qi Gong is an ancient Chinese discipline that combines movements, focus, and deep breathing to connect the mind, body, and spirit, w hile stimulating the flow of vital life energy (qi). Yoga is a combination of breathing exercises, postures, and meditation, which is used to not only calms the nervous system but to maintain balance among body, mind, and spirit. CAM Survey Demographical information 1. Age___ 2. Sex: a. Male b. Female 3. If you were not born in the United States, approximately how many years have you lived here? 4. If you were not born in the United States, approximately how many years have you lived here? 5. Which Racial/ethnic group best describes you: a. White/Caucasian b. Black/African American c. Hispanic or Latino d. Asian or Pacific Islander e. American Indian or Alaskan Native f. Other 6. Marital status: a. Single, not in a committed relationship b. Single, in a committed relationship c. Married or domestic partnership d. Divorced e. Widowed f. Separated 7. What is the highest level of education that you have achieved? a. Eight grade or less b. Did not finish high school c. High school graduate/ GED

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121 d. Associates degree or professional license g. Doctorate or professional degree 8. Which category best describes your employment status: a. Faculty (9,10, 12 month) b. Clinical Faculty c. Exempt TEAMS/ USPS d. Non Exe mpt TEAMS/ USPS (hourly) e. Other: __________________ Comment [sy2]: 3 & 4 are same. Comment [sy3]: Would it be a matter whether committed or non committed relationship? Comment [sy4]: I would put these two together u nless you think faculty and clinical faculty would practice CAM differently. I would not consider clinical faculty differently from faculty. 9. What is your family income range? a. $20,000 and below b. $20,001 $35,000 c. $35,001 $50,000 d. $50,001 $65 ,000 e. $65,001 $80,000 f. $80,001 $95,000 g. $95, 001 and above Following are some questions relating to your medical history 10. How would you describe your health? a. Excellent b. Very good c. Good d. Fair e. Poor 11. Do you smoke cigarettes or consume any tobacco products? a. Yes b. No 12. How would you describe your current alcohol usage? a. Very high b. High c. Moderate

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122 d. Low 13. Please indicate if you are taking any of the following. CHOOSE ALL THAT APPLY. a. Multivitamin b. Vitamin C c. Vitamin E d. Fish oils e. Protein supplement f. Muscle building supplements g. Other:______________________ a. Very active b. Active c. Moderately active d. Not active /sedentary (???) 15. How would you describe your weight? a. Underweight b. Just right c. Slightly overweight d. Very overweight e. Extremely overweight give more guidanc e to answer this question or change a question to be clearer and meaningful data (answers).Comment [sy6]: How are these selection made? For example, based on literature review, are these supplements the most commonly used? What are the rationales to list t hese since there are many other frequently used month? Any examples for participants to estimate their activity level? So participants can estimate their activity levels (look at some literature).Comment [sy8]: Would you like to know their perception or actually overweight etc? Because, some people may not feel overweight even morbidly obese, while others feel obese even they are in normal BMI. You can ask their height and weigh t at the beginning to calculate their BMI. And leave the #15 here so you can examine the perception about their weight. 16. Please indicate if you have been diagnosed with any of the following diseases or conditions? CHOOSE ALL THAT APPLY a. Obesity b . Diabetes

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123 c. Heart disease d. High cholesterol e. Hypertension (high blood pressure) f. Stroke g. Cancer h. Other:_______________________________________ Fo llowing are some questions relat ing to Complementary and Alternative Medicine usage 17. Have you EVER used any of the following Complementary and Alternative Medicine (CAM) treatments(select all): a. Acupuncture b. Ayurveda c. Biofeedback d. Chelation therapy e. Chiropractic f. Energy Health Therapy/Reiki g. Herbal Medicine h. Homeopathy treatment i. Hypnosis j. Massage k. Meditation l. Naturopathy m. Tai Chi n. Qi Gong o. Yoga p. HAVE NEVER USED ANY OF THE ABOVE CAM TREATMENTS

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124 18. Which of the following CAM treatments have you used within the last 12 months ? a. Acupuncture b. Ayurveda c. Biofeedback d. Chelation therapy e. Chiropractic f. Energy Health Therapy/Reiki g. Herbal Medicine h. Homeopathic treatment i. Hypnosis j. Massage k. Meditation l. Naturopathy m. Tai Chi n. Qi Gong o. Yoga Comment [sy9]: Would you like to add any timeframe unless you Comment [sy10]: Please visit the NIH/NCCAM website to revise your questions related to CAM use (17 21).http://nccam.nih.gov/health/whatiscam Comment [sy11]: A little bit clearer instr uction would be helpful for the participants to differentiate this #18 from#19. 19. During the past 12 months , did you use any (select all that apply) CAM treatment for a health problem or condition? a. Acupuncture b. Ayurveda c. Biofeedback d. Chelation therapy e. Chiropractic f. Energy Health Therapy/Reiki g. Herbal Medicine h. Homeopathic treatment i. Hypnosis j. Massage k. Meditation l. Naturopathy m. Tai Chi n. Qi gong o. Yoga

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125 20. How frequently do you use any of the above forms of CAM? a. Daily b. Weekly c. Monthly d. Annually e. As needed basis f. Do no use CAM 21. Do you discuss your use of CAM with your physician? a. Yes b. No c. Sometimes 22. Have you used CAM to treat any of the following? CHECK ALL THAT APPLY a. Cold/Flu b. Headaches/Migr aines c. Backaches d. Allergies e. Sinus infection f. Other 23. I believe CAM is beneficial to my overall wellbeing (1 being NOT HELPFUL and 7 Being HELPFUL): 1 2 3 4 5 6 7 24. I believe CAM is beneficial in treating a specific health condition (1 being NOT HELPFUL and 7 Being HELPFUL): 1 2 3 4 5 6 7 25. I believe CAM is safe (1 being unsafe and 7 completely safe): 1 2 3 4 5 6 7 prevention or treatment? Because many people use CAM for multiple reasons. For treatment and prevention. Comment [sy13]: Participants may use multiple herbal medicines for different reasons. Therefore, you may not get accu rate answers. Comment [sy14]: If there are multiple answers o question #19, how participant should answer the #20?Please think about it. Comment [sy15]: Based on my study, participants can disclose information selectively, which means they discuss whatever CAM they like to discuss but not all. Comment [sy16]: As a person who constructs such a survey , It seems to be very question? Comment [sy17]: Hereon, questions are diff erent so you should provide introduction and directions what questions are about and how to answer them.

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126 Comment [sy18]: Any CAM, every CAM you listed, or using CAM???? 26. Most members of my family would recommend using CAM (1 being extremely unlikely an d 7 being extremely likely ??????): 1 2 3 4 5 6 7 27. Most of my friends would recommend using CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 28. My primary care physician would recommend using CAM ((1 being unlikely and 7 being very likel y): 1 2 3 4 5 6 7 29. I intend to follow the advice of my family to use CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 NA 30. I intend to follow the advice of my friends to use CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 NA 31. I intend to follow the advice of my physician to use CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 NA 32. How likely are you to use CAM (1 being unlikely and 7 being very likely): 1 2 3 4 5 6 7 NA Thank you for participating in th e survey. Comment [sy19]: Family and relatives??? Comment [sy21]: Not sure about his question. More informatio n is needed to answer this question. (determine) to make changes? (29 31) Comment [sy23]: I think you need to rephrase these sentences for clarity. Comment [sy24]: If I outcomes.

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127 Comments from Expert Panelist Dr. Leite: 1. The stem should be contrasted with the options through the use of bold or a different font color. 2. Check guidelines I gave about question ordering: Demographics should be at the end 3. The options should not have a number after them, such as Male (1). 4. Q6 has a typo for Separated. 5. Section one should be the final section. 6. The answers for Q13 are not comprehensive. For example, six times a week and 2 times a week are not covered. 7. Same problem with Q14 8. For Q15, emphasize natural products and choose all that apply in different ways. 9. I would start the survey with Section 3, which is the least threatening 10. Not sure that you need the definitions on top of Section 3. If they used it, they will know what it is. 11. There is no reason to make the last option of Q21 capital letters. If this is an online survey, program the skip patterns into the survey. Qualtrics does a great job with that. 12. Same comment as previous one. 13. Q24 can be implemented for each question in Qualtrics through display logic. On can create one version of Q24 for each answer in Q22 and program the survey such it will only show the respective Q24 if the person selections it in Q22. 14. There is a type in Q28. It should be How likely.

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128 APPENDIX C FINAL SURVEY INSTRUMENT Invitation to Participate Eligibility: To participate you must currently be classified as a full time University of AND you must be 18 or older. . This survey examines your use and perceptions of complementary and altern ative medicine (CAM). For our purposes, we define CAM as a group of diverse medical and health care systems, practices, and products that are not presently considered to be part of conventional (Western) medicine. We will use the survey information you provide to better understand the trends of CAM use among university employees and how future worksite health promotion programs can be marketed and implemented to improve overall health and well being. The survey requires about 10 15 minutes of your time. Please answer all questions completely. We offer you the following safeguards for completing the survey: 11. Your participation in the survey is voluntary. Whether or not you choose to participate, your status at the University of Florida will not be affec ted in any way. 12. Your responses will remain anonymous. Your responses will remain private, and cannot be traced to you. 13. There is a minimal risk that security of any online data may be breached, but since Qualtrics performs regular vulnerability scans a nd employs high end firewall systems. Also, complete penetration tests are performed yearly. All services have quick failover points and redundant hardware, and complete backups are performed nightly. Additionally data collected will be removed from the ser ver once data analysis is completed. It is unlikely that a security breach of the online data will result in any adverse consequence for you. statement: https://qualtrics.com/security statement/ 14. We do not request any personal identification such as name, UFID, Social Security number, etc.

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129 15. Demographic information about your background only will be used to describe the general types and categories of respondents. Your name will not be traced to your responses, and no respondent names will ever be reported. 16. For information regarding your rig hts as a research participant contact the IRB at 352 392 0433 17. There are no direct benefits, risks, or compensation to you for participating in the study 18. If at any time, now or in the future, you have questions or concerns about the survey, you will recei ve immediate assistance by contacting: Naz Erenguc, Doctoral Candidate University of Florida Dr. William Chen, Professor and Supervisor Thank you for your time and consideration. If you would like to participate in the survey, please indicate your cons ent by clicking the START SURVEY button below. START SURVEY SECTION 1: Following are some questions related to Complementary & Alternative Medicine (CAM) Usage *Note the following definitions can guide your responses to these questions. Acupuncture based on the premise that health can only be obtained by balance of energy, which is thought to be within you. Acupuncture aims to stimulate the energy pathways to help correct or rebalance by the insertion of needles. Alternative Medicine is a non mains tream approach to health which is used instead of conventional medicine. Ayurveda Comprehensive system of medicine where there is an equal emphasis on health among the body, mind, the spirit. The ultimate goal is to restore harmony of these three parts. Biofeedback Is a treatment method which trains clients how to consciously regulate typically unconscious bodily functions (e.g., breathing, heart rate, blood pressure), using an electronic device, to improve health. Complementary Medicine refers to use s non mainstream techniques in conjunction with conventional medicine.

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130 Chelation Therapy involves injecting a binding (chelating) agent to remove and eliminate toxic metals and wastes from the bloodstream. Chiropractic Care involves adjusting the spine and joints to elicit and activate the reduce pain. Deep Breathing is the process of slowly inhaling through the nose followed by slow exhalation. It is used to quite the mind and focus on the breath. Energy Healing Therapy/Reiki heal itself by focusing on the flow and healing energy. Healing energy is guided through in an effort to restore a normal energy balance and health. Herbal medicine/Natural Products herbal medicine and botanicals, vitamins, minerals, probiotics, and other natural and dietary supplements taken orally Homeopathic treatment also known as hom eopathic medicine is a system of medical practices which is based on the theory that any substance that can produce symptoms of disease or illness in a healthy person can also cure those symptoms in a sick person. Remedies are derived from many sources suc h as minerals, metals, and plants. Hypnosis is an altered state of consciousness and characterized by an increase of responsiveness to suggestions. The hypnotic state is attained by relaxing the body, then shifting the attention toward an objects or idea which is suggested by the practitioner to access levels of the mind to promote positive changes in health behaviors. Massage involves pressing, rubbing, and/or manipulating muscles and soft tissues to promote relaxation and relieve pain Meditation is the act of calming the mind and relaxing the body Naturopathy is a system of medicine which is based on the theory that the body is a self regulating mechanism with an innate ability to maintain a state of both health and wellness. Tai Chi is a Chinese self defense discipline which employs low intensity and low impact exercise to maintain/restore health. Tai chi exercises include a set of forms, with each form consisting of a series of body positions connected by one continuous movement. Qi Gong is an ancient Chinese discipline that combines movements, focus, and deep breathing to connect the mind, body, and spirit, while stimulating the flow of vital life energy (qi).

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131 Yoga is a combination of breathing exercises, postures, and meditation, which is u sed to not only calms the nervous system but to maintain balance among body, mind, and spirit. 1. Have you EVER used any of the following Complementary and Alternative Medicine (CAM) treatments? CHOOSE ALL THAT APPLY Acupuncture Ayurveda Biofeedback Chelation Therapy Chiropractic Deep Breathing Energy Health Therapy/Reiki Herbal Medicine Homeopathy treatment Hypnosis Massage Meditation Naturopathy Tai Chi Qi Gong Yoga Have never used any of the above CAM treatments 2. Which of the following CAM treatments have you used within the last 12 MONTHS ? Acupuncture Ayurveda Biofeedback Chelation Therapy

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132 Chiropractic Deep Breathing Energy Health Therapy/ Reiki Herbal Medicine Homeopathy treatment Hypnosis Massage Meditation Naturopathy Tai Chi Qi Gong Yoga Have not used any of the above CAM treatments within the last 12 months 3. If you use CAM, do you discuss your use of with your physician? Yes Sometimes No 4. How would you describe your use of CAM? CHOOSE ALL THAT APPLY Treatment of health problem or condition Prevention of health problems or condition Personel wellbeing Other 5. Please rate the following statements on a scale from Not Very Helpful to Very Helpful

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133 Not Very Helpful Not Help ful Somewhat Not Helpful Neither Helpful or Not Helpful Somewhat Helpful Helpful Very Helpful I believe CAM is beneficial to my overall well being I believe CAM is beneficial in treating specific health conditions I believe using CAM is safe Most members of my family and relatives would recommend using CAM treatments Most of my friends would recommend using CAM treatments My primary care physician would recommend using CAM treatments I intend to consider the advice of the family and relatives regarding CAM I intend to consider the advice of my friends regarding CAM I intend to consider the advice of my physician regarding CAM

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134 6. Please rate the following question below on a scale from Very Unlikely to Very Likely Very Unlikely Unlikely Somewhat Unlikely Neither Likely nor Unlikely Somewhat Likely Likely Very Likely How like are you to use CAM within the next 12 months? SECTION 2: Following are some questions related to your medical history 7. How would you describe your health? Excellent Very Good Good Fair Poor 8. How often do you use tobacco products (cigarettes, e cigarettes, smokeless tobacco)? Everyday 2 6 times a week Once a week Only on weekends On special occasions I don't consume tobacco products 9. How would you describe your current alcohol usage? Everyday 2 6 times a week Once a week Only on weekends On special occasions

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135 I don't drink 10. Please indicate if you are taking any of the following NATURAL PRODUCTS. CHOOSE ALL THAT APPLY Herbs/botanicals Vitamins/minerals Nonvitamins/nonminerals (i.e. fish oil/ omega 3s) Probio tics Other dietary supplements 11. How would you describe your current physical activity level? Very active Active Moderately active Not active 12. How would you describe your weight? Underweight Just right Slight overweight Very overweight Extremely overweight 13. What is your height? 14. How much do you weigh?

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136 15. Have you ever been diagnosed with any of the following diseases or condition? CHOOSE ALL THAT APPLY Obesity Diabetes Heart disease High cholesterol Hypertension (high blood pressure) Stroke Cancer Other SECTION 3: Demographic Information 16. Age: 17. Sex: Male Female 18. Which racial/ethnic group best describes you? White/Caucasian Black/African American Hispanic or Latino Asian or Pacific Islander American Indian or Alaskan Native Other 19. Were you born in the US? Yes

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137 No years have you lived here? 21. Marital status: Single/Separated Married or domestic partnership Divorced Widowed 22. What is the highest level of education that you have achieved? Eighth grade or less Did not finish high school High school graduate/GED Associates degree or professional license Bachelor's degree Master's degree Doctoral degree Professional degree (i.e. MD, JD, PharmD, VMD, and etc.) 23. Which category best describes your employment status here at UF? Faculty (9, 10, or 12 months Clinical Faculty Exempt TEAMS/USPS Non Exempt TEAMS/USPS (hourly) Othe r

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138 24. What is your household income? $20,000 and below $20,001 $35,000 $35,001 $50,000 $50,001 $65,000 $65,001 $80,000 $80,001 $95,000 $95,001 and above

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139 APPENDIX D IRB APPROVAL LETTER

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140 APPENDIX E EMAIL 1: REQUESTING SURVEY RESPONSE From: Filiz Naz Erenguc < noreply@qemailserver.com > Subject: Survey Link: Please Take Short Survey for Student Research Project Date: April 9, 2014 at 2:48:47 PM EDT Reply To: Filiz Naz Erenguc < naz.erenguc@warrington.ufl.edu > Dear UF Employee, My name is Naz Erenguc, and I am a doctoral candidate in the College of Health & Human Performance Health Education and Behavior Department at the University of Florida. I am currently working on research for my dissertation with Dr. Chen, Professor at the University of Florida and chair of my committee. I received your email address from the office of University Relations. I would like to inv ite you to participate in a research study. The primary purpose of my dissertation study is to assess complementary and alternative medicine (CAM) use and to predict future use among university employees. The hope is that results from this study will aid in creating worksite health promotion programs focused on CAM. Eligibility: To participate you must currently be classified as a full time University of Florida employee AND you must be 18 or older. This survey assesses your use and perceptions of complementary and alternative medicine (CAM). For our purposes, we define CAM as a group of diverse medical and health care systems, practices , and products that are not presently considered to be part of conventional (Western) medicine. We will use the survey information you provide to better understand the trends of CAM use among university employees and how future worksite health promotion p rograms can be marketed and implemented to improve overall health and well being. The survey takes about 10 15 minutes of your time. Please answer all questions completely. We offer you the following safeguards for completing the survey: 1. Your participa tion in the survey is voluntary. Whether or not you choose to participate, your status at the University of Florida will not be affected in any way 2. Your responses will remain anonymous. Your responses will remain private, and cannot be traced to you. 3. Ther e is a minimal risk that security of any online data may be breached, but since Qualtrics performs regular vulnerability scans and employs high end firewall systems. Also, complete penetration tests are performed yearly. All services have quick failover po ints and redundant hardware, and complete backups are performed nightly.Additionally data collected will be removed from the server once data analysis is completed. It is unlikely that a security breach of

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141 the online data will result in any adverse consequ ence for you. Additionally, https://qualtrics.com/security statement/ 4. We do not request any personal identification suc h as name, UFID, Social Security number, etc. 5. Demographic information about your background only will be used to describe the general types and categories of respondents. Your name will not be traced to your responses, and no respondent names will ever be reported 6. For information regarding your rights as a research participant contact the IRB at 352 392 0433 7. There are no direct benefits, risks, or compensation to you for participating in the study, but your participation will help the researcher to recommend better worksite health promotion programs. 8. If at any time, now or in the future, you have questions or concerns about the survey, you will receive immediate assistance by contacting: Naz Erenguc, Doctoral Candidate University of Florida PO Box 1 17152 Gainesville, FL 32611 Dr. William Chen, Professor and Supervisor University of Florida PO Box 118210 FLG 19 Gainesville FL 32611 Thank you for your time and consideration. If you would like to participate in the survey, please indicate your cons ent by following the link to the Survey: Take the Survey Or copy and paste the URL below into your internet browser: https://ufl.qualtrics.com/WRQualtricsSurveyEngine/?Q_SS=3z7wYVU49LWhv13_eX16 1E2HPz64y6p&_=1 Follow the link to opt out of future emails: Click here to unsubscribe

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142 APPENDIX F REMINDER EMAIL 1: FOR THOSE WHO MAY NOT HAVE RECEIVED THE FIRST REQUEST From: Erenguc,Naz Sent: Mo nday, April 14, 2014 2:34 PM Subject: Survey Link: Please Take Short Survey for Student Research Project Dear UF Employee, My name is Naz Erenguc, and I am a doctoral candidate in the College of Health & Human Performance Health Education and Behavior De partment at the University of Florida. I am currently working on research for my dissertation with Dr. Chen, Professor at the University of Florida and chair of my committee. I received your email address from the office of University Relations. I would like to invite you to participate in a research study. The primary purpose of my dissertation study is to assess complementary and alternative medicine (CAM) use and to predict future use among university employees. The hope is that results from this study will aid in creating worksite health promotion programs focused on CAM. Eligibility: To pa rticipate you must currently be classified as a full time University of AND you must be 18 or older. This survey assesses your use and perceptions of complementary and alternative medicine (CAM). For our p urposes, we define CAM as a group of diverse medical and health care systems, practices, and products that are not presently considered to be part of conventional (Western) medicine. We will use the survey information you provide to better understand the t rends of CAM use among university employees and how future worksite health promotion programs can be marketed and implemented to improve overall health and well being. The survey takes about 10 15 minutes of your time. Please answer all questions completel y. We offer you the following safeguards for completing the survey: 1. Your participation in the survey is voluntary. Whether or not you choose to participate, your status at the University of Florida will not be affected in any way 2. Your responses will remai n anonymous. Your responses will remain private, and cannot be traced to you. 3. There is a minimal risk that security of any online data may be breached, but since Qualtrics performs regular vulnerability scans and employs high end firewall systems. Also, co mplete penetration tests are performed yearly. All services have quick failover points and redundant hardware, and complete backups are performed nightly. Additionally data collected will be removed from

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143 the server once data analysis is completed. It is un likely that a security breach of the online data will result in any adverse consequence for you. Additionally, https://qualtrics.com/security statement/ 4. We do not request any personal identification such as name, UFID, Social Security number, etc. 5. Demographic information about your background only will be used to describe the general types and categories of respondents. Your name will not be traced to your responses, and no respondent names will ever be reported 6. For information regarding your rights as a research participant contact the IRB at 352 392 0433 7. There are no direct benefits, risks, or compensation to you for participating in the study, but your participation will help the researcher to recommend better worksite health promotion programs. 8. I f at any time, now or in the future, you have questions or concerns about the survey, you will receive immediate assistance by contacting: Naz Erenguc, Doctoral Candidate University of Florida Dr. William Chen, Professor and Supervisor University of Florida Thank you for your time and consideration. If you would like to participate in the survey, please indicate your consent by following the link to the Survey: https://u fl.qualtrics.com/SE/?SID=SV_eX161E2HPz64y6p

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144 APPENDIX G FINAL REMINDER EMAIL From: Erenguc,Naz Sent: Tuesday, April 22, 2014 4:30 PM Subject: Survey Link: Please Take Short Survey for Student Rese arch Project Dear UF Employee, will be available until Friday, April 25 th . If you meet the eligibility criteria for this survey and have not yet completed it your participation would be greatly appreciated! My name is Naz Erenguc, and I am a doctoral candidate in the College of Health & Human Performance Health Education an d Behavior Department at the University of Florida. I am currently working on research for my dissertation with Dr. Chen, Professor at the University of Florida and chair of my committee. I received your email address from the office of University Relation s. I would like to invite you to participate in a research study. The primary purpose of my dissertation study is to assess complementary and alternative medicine (CAM) use and to predict future use among university employees. The hope is that results from this study will aid in creating worksite health promotion programs focused on CAM. Eligibility: To participate you must currently be classified as a full time University of AND you must be 18 or older. This survey assesses your use and perceptions of complementary and alternative medicine (CAM). For our purposes, we define CAM as a group of diverse medical and health care systems, practices, and products that are not presently considered to be part of conventional (Western) medicine. We will use the survey information you provide to better understand the trends of CAM use among university employees and how future worksite health promotion programs can be marketed and implemented to improve overall health and well being. The survey takes about 10 15 minutes of your time. Please answer all questions completely. We offer you the following safeguards for completing the survey: 1. Your participation in the survey is voluntary. Whether or not you choose to participate, your status at the University of Florida will not be affected in any way 2. Your responses will remain anonymous. Your responses will remain private, and cannot be traced to you.

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145 3. There is a minimal risk that security of any online data may be breached, but since Qualtrics performs regular vulnerability scans and employs high end firewall systems. Also, complete penetration tests are performed yearly. All services have quick failover points and redundant hardware, and complete backups are performed nightly. Additionally data collected will be removed from the server once data analysis is completed. It is unlikely that a security breach of the online data will resul t in any adverse consequence for you. Additionally, https://qualtrics.com/security statement/ 4. We do not request any pers onal identification such as name, UFID, Social Security number, etc. 5. Demographic information about your background only will be used to describe the general types and categories of respondents. Your name will not be traced to your responses, and no respond ent names will ever be reported 6. For information regarding your rights as a research participant contact the IRB at 352 392 0433 7. There are no direct benefits, risks, or compensation to you for participating in the study, but your participation will help the researcher to recommend better worksite health promotion programs. 8. If at any time, now or in the future, you have questions or concerns about the survey, you will receive immediate assistance by contacting: Naz Erenguc, Doctoral Candidate University of Florida Dr. William Chen, Professor and Supervisor University of Florida Thank you for your time and consideration. If you would like to participate in the survey, please indicate your consent by following the link to the Survey: https://ufl.qualtrics.com/SE/?SID=SV_eX161E2HPz64y6p

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146 REFERENCES Ajzen I. The theory of planned behavior. Organizational Behavior and Human D ecision Process . 1991; 50: 179 211. Ajzen I., & Fishbein, M. Understanding attitudes and predicting social behavior . Englewood Cliffs, NJ: Prentice Hall; 1980. Almog, G., Lamond, P. J., & Gosselin, G. (2004). Effects of chiropractic care on spinal symptomatology among professional drivers: A pilot study. Clinical Chiropractic. 7 (3), 114 119. Astin, J.A., Pelletier, K.R., & Haskell, W.L. (2000). Complementary and Alternative Medicine Use Among Elderly Person: One Year Analysis of a Blue Shield Medi care Supplement. J Gerontol A Biol Sci Med Sci . 55 (1), M4 M9. Astin, J.A. (1998). Why Patients Use Alternative Medicine. Journal of American Medical Association (JAMA) . 279 (19), 1548 1553. Avogo, W., Frimpong, J., Rivers, P., & Kim, S. (2008). The effe cts of health status on the utilization of complementary and alternative medicine. Health Education Journal. 67 (4). 258 275. Barnes, P. & Bloom, B. (2008). Complementary and Alternative Medicine Use Among Adults and Children: United States, 2007. Nationa l Health Statistics Reports. 12, 1 24. Barnes, P., Powell Griner, E., McFann, K., & Nahin, R. (2004). Complementary and Alternative Medicine Use Among Adults: United States, 2002. Advanced Data. 343, 1 20. Bardia, A., Nisly, N., Zimmerman, M., Gryzla k, B., & Wallace, B. (2007). Use of Herbs Among Adults Based on Evidence Based Indications: Findings from the National Health Interview Survey. Mayo Clinic Proceedings . 82(5), 561 566. Beemsterboer, W., Stewart, R., Groothoff, J., & Nijhuis, F. (2009). A literature review on sick leave determinants (1984 2004). International Journal of Occupational Medicine & Environmental Health. 22 (2), 169 179. Bishop, F., & Lewith, G. (2010). Who Uses CAM? A Narrative Review of Demographic Characteristics and Health F actors Associated with CAM Use. eCAM . 7(11). 11 28. Bhandari, R. B., Bhandari, C. B., Acharya, B., Pandya, P., Singh, K., Katiyar, V. K., & Sharma, G. D. (2012). Implications of corporate yoga: A review. In G. R. Naik (Ed.), Applied biological engineering: Principles and practice . Rijeka, Croatia: InTech.

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147 Brett, K., & Burt, C. (2001). Utilization of ambulatory medical care by women: United States , 1997 98. Vital and Health Statistics . 13, 1 46. Brown, C., Barner, J., Richards, K., & Bohman, T. (2007). Patterns of Complementary and Alternative Medicine Use in African Americans. The Journal of Alternative and Complementary Medicine. 13(7). 751 758. Centers for Disease Control and Prevention. (2013). 2012 National Health Interview Survey (NHIS ) public use data release: NHIS survey description . Hyattsville, MD: National Center for Health Statistics. Champion, V. L., Skinner, C. S. (2008) The Health Belief Model. In Health Behavior and Health Education: Theories, Research, and Practice. K. Glan z, Rimer, B.K., Viswanath, K. (Eds.) San Francisco, CA: Jossey Bass. Chapman, L. S. (2012). Meta evaluation of worksite health promotion economic return studies: 2012 update. American Journal of Health Promotion. 26 (4), 1 12. Cohen, B. H. (2008). Explai ning Psychological Statistics . Hoboken, New Jersey; John Wiley & Sons, Inc. Creswell, J. W. (2008). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research. Boston, MA; Pearson Education. Crocker, L. & Algina, J. Introduction to Classical & Modern Test Theory. Orlando, FL; Holt, Rinehart, and Winston, Inc. Dillman, D.A., Smyth, J.D., & Christian, L. M. (2009). Internet, Mail, and Mix Mode Surveys: The Tailored Design Method. Hoboken, New Jersey; John Wiley & Sons, Inc. Druss, B.G. & Rosenheck, R.A. (1999). Associations Between Use of Unconventional Therapies and Conventional Medical Services. JAMA . 282 (7). 651 656. Eisenberg, D. M., Kessler, R. C., Foster, C., Norlock, F.E., Calkins, D. R., & Delbanco, T. (2001). Perceptions about Complementary Therapies Relative to Conventional Therapies among Adults Who Use Both. Annals of Internal Medicine . 135 (5), 344 351. Eisenberg, D. M., Davis, R. B., Ettner, S. L., Appel, A., Wilkey, S., Van Rompay, M., & Kessler , R. C. (1998). Trends in Alternative Medicine Use in the United States, 1990 1997. JAMA . 280 (18), 1569 1575. Ernst, E. (2000). Prevalence of Use of Complementary and Alternative Medicine: A systematic review. Bulletin of the World Health Organization. 78(2), 252 257.

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148 Fertman, C. & Allensworth, D. (2010). Health Promotion Programs: From Theory to Practice. San Francisco: Jossey Bass. Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Thousand Oak, California: Sage Publications. Fish bein, M. & Ajzen, I. (1975). Belief, Attitudes, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA; Addision Wesley, Inc. Furnham, A. & Lovett, J. (2001). Predicting the Use of Complementary and Alternative Medicine: A Test of t he Theories of Reasoned Action and Planned Behavior . Journal of Applied Social Psychology . 31(12). 2588 2620. Girdano, D., Dusek, D., & Everly, G. (2005). Controlling Stress and Tension. San Francisco, California: Pearson Education. Glanz K., Lewis F.M., & Rimer B.K. (2002). Health behavior and Health Education: Theory, Research, and Practice . San Francisco: Jossey Bass. (2005). Use of Complementary and Alternative M edical Therapies Among Racial and Ethnic Minority Adults: Results from the 2002 National Health Interview Survey. Journal of National Medical Association . 97(4). 535 545. Green, C., & Pope, C. (1999). Gender, Psychosocial Factors and the Use of Medical Se rvices: a Longitudinal Study. Social Science Med . 481, 1363 1372. Goetzel, R. Z., Long, S. R., Ozminkowski, R. J., Hawkins, K., Wang, S., & Lynch, W. (2004). Health, absence, disability, and presenteeism cost estimates of certain physical and mental healt h conditions affecting U.S. employers. Journal of Occupational & Environmental Medicin. 46 (4), 398 412. Grzywacz, J.G., Suerken, C.K., Neiberg, R.H., Lang, W., Bell, R.A., Quandt, S.A., & Arcury, T.A. (2007). Age, Ethnicity, and Use of Complementary and Alternative Medicine in Health Self Management. Journal of Health and Social Behavior . 48(1), 84 98. Hirai, K., Komura, K., Tokoro, A., Kuromaru, T., Ohshima, A., Ito, Ti., Sumiyoshi, Y., & Hyodo, I. (2008). Psychological and Behavioral Mechanisms Influen cing the Use of Complementary and Alternative Medicine (CAM) in Cancer Patients. Annals of Oncology . 19(1), 49 55. Honda, K., & Jacobson, J.S. (2005). Use of Complementary and Alternative Medicine Among United States Adults: The Influences of Personality, Coping Strategies, and Social Support. Preventative Medicine . 40, 46 53.

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149 Humpel, N. & Jones, S.C. (2006). Gaining Insight Into the What, Why, and Where of Complementary and Alternative Medicine Use by Cancer Patients and Survivors. European Journal of Cancer Care (English). 15(4). 362 368. IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp Jain, N. & Astin, J. (2001) Barriers to acceptance: an exploratory study of complementary/alternative medicine disuse . Jou rnal of Alternative Complementary Medicine. 7(6), 689 696. Kaptchuk, T. J. & Eisenberg, D. M. (2001). Varieties of Healing. 1: Medical Pluralism in the United States. Social Science & Medicine. 135(3), 189 195. Kessler, R.C., Davis, R.B., Foster, D.F., Van Rompay, M.I., Walters, E.E., Wilkey, S.A., Kaptchuk, T.J., & Eisenberg, D.M. (2001). Long Term Trends in the Use of Complementary and Alternative Medical Therapies in the United States. Academic and Clinic: Complementary and Alternative Medicine Series . 135, 262 268. Klatt, M., Buckworth, J., & Malarkey, W. (2009). Effects of Low Dose Mindfulness Based Stress Reduction (MBSR ld) on Working Adults. Health Education and Behavior. 36 (3), 601 614. Konefal , J. (2002). The Challenge of Educating Physicians about Complementary and Alternative Medicine. Academic Medicine . 77(9), 847 850. Kirsten, W. (2010). Making the link between health and productivity at the workplace: a global perspective. Industrial Heal th. 48 (3), 251 255. (2006). Insurance Coverage and Subsequent Utilization of Complementary and Alternative Medical (CAM) Providers. American Journal of Managing Care . 12 (7), 397 404. Mazzola, J. J., Schonfeld, I. S., & Spector, P. E. (2011). What qualitative research has taught us about occupational stress. Stress & Health: Journal of the International Society for the Investigation of Stress, 27 (2), 93 110. McFarlan d, B., Bigelow, D., Zani, B., Newsome, J., & Kaplan, M. (2002). Complementary and Alternative Medicine Use in Canada and the United States. American Journal of Public Health . 92(10), 1616 1618. Michie, S., & Williams, S. (2003). Reducing work related psy chological ill health and sickness absence: A systematic literature review. Occupational & Environmental Medicine. 60 (1), 3 9.

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153 BIOGRAPHICAL SKETCH Filiz Naz Erenguc was born in 1984 in Gainesville, Florida. She grew up and attended school in Gainesville. Upon graduating high school, Naz attended the University of Florida (UF) as an undergraduate in Health Science Education. As a freshman she took her first health education course and from there knew that she was interested in this field. She graduated with a Bachelor of Science in Health Science Education (with a specialization in Community Health), in 2005. Shortly after graduating she decided she wanted to continue her educatio n by pursuing a Master of Science in health education. Upon starting her graduate studies, she was given the opportunity to teach multiple sections of HSC2100 (Personal and Family Health) throughout her graduate studies. , Naz was hired as an undergraduate academic advisor for the College of Liberal Arts and Sciences (CLAS). During her tenure with CLAS, she had the privilege of advising pre professional students looking to join health and allied health professions. She tau ght SLS1102 (Enhanced Freshman Experience) and was again given the opportunity to teach HSC3102 (Personal and Family Health). She also served as Biology Department undergraduate coordinator with the other pre health advisor; and was the laison to the Junio r Honors Medical Program at the UF College of Medicine. In 2011, she changed her career focus to graduate admissions and has been working at the UFMBA program ever since. Educa tion and Behavior department to enroll part time in the Ph.D. program. During her time as a UF doctoral student, she determined her research focus and developed a survey instrument to investigate how the Behavioral Intention Model can be used to

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154 predict fu included significant findings that will lead to several publications in scholarly journals. Naz was granted a Doctor of Philosophy from the College of Health and Human Performance in August 2014.



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This dissertation was written in accordance with APA guidelines