This item has the following downloads:
1 THE DIGITAL HEALTH DIVIDE: EVALUATING HEALTH TECHNOLOGY ACCESS AND USE AMONG OLDER ADULT POPULATIONS FOR IMPROVED HEALTH OUTCOMES AND MEDICAL DECISION MAKING By AMANDA KEMPTON HALL A DISSERTATION PRESENTED TO THE GRADUATE S CHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2013
2 2013 Amanda Kempton Hall
3 Dedicated to my p arents, Donna and Matthew Hall with lots of love and gratitude In memoriam of Kempton Bishop Hall, my grandfather, 1916 2013.
4 ACKNOWLEDGEMENTS There are a number of people to thank and all have been vital throughout this process. First, I thank my committee members, Drs. Jay M. Bernhardt, Virginia J. Dodd, Joyce Stechmiller, and Don Chaney for their support, guidance, and encouragement. I am honored that each agreed to serve on my committee and contributed significantly to my professional growth. I am grateful for their advice, t ime, and knowledge. I appreciate the mentorship provided by my committee chair, Dr. Jay M. Bernhardt throughout my doctoral training and am extremel y thankful for all he taught me; in addition to all the opportunities, support, advice, patience, brainstor ming sessions, emails, wisdom, confidence, meetings, etc., I am forever grateful and cannot thank him enough for being a visionary and inspiring me to be the best researcher I can be. I thank Dr. Virginia Dodd for her qualitative wisdom, guidance, counseli ng, confidence, humor, dedication, and passion for research and teaching, truly inspirational. I thank Dr. Joyce Stechmiller for her love, unwavering support, knowledge, and for being my research mother throughout this process. I thank Dr. Don Chaney for c hallenging me to look at research from all perspectives and pushing the limits of intellect for increased insight and understanding of health behavior. I thank Dr. James Algina for his statistical advice and acting as a committee member advisor. In additi on to my committee members, a number of people have made important contributions. I thank Dr. Laura Colton, Kara McArthur, Amy Harris, Dr. Cliff Dacso, Dr. Sheri Dacso, Nithin Rajan, and Laura Putkey for their support, advice, wisdom, and the opportunity t o conduct research with an outstanding and knowledgeable team during the summer research training fellowship program at the Methodist Hospital Research
5 Institute and the Abramson Center for the Future of Health in Houston, Texas. I thank Dr. Beth Chaney, M anny, and Vasana for their support, edits, and contributions. I thank Morgan for his help with statistics. I thank Alexis, Tony, Julia, and Bethany for their encouragement. I thank Dr. Barbara Rienzo for her support, first for my masters and later for my P hD. Finally, I thank my paren ts for their patience, love advice, and support.
6 TABLE OF CONTENTS page LIST OF TABLES ................................ ................................ ................................ ............ 9 LIST OF FIGURES ................................ ................................ ................................ ........ 10 LIST OF DEFINITIONS ................................ ................................ ................................ 11 ABSTRACT ................................ ................................ ................................ ................... 12 CHAPTER 1 GENERAL INTRODUCTION AND INTRODUCTION TO LITERATURE REVIEW ................................ ................................ ................................ .................. 14 General Introduction ................................ ................................ ............................... 14 Introduction to Literature Reviews ................................ ................................ .......... 18 2 HEALTH BENEFITS OF DIGITAL VIDEO GAMES FOR OLDER ADULTS: A SYSTEMATIC REVIEW OF THE LITERATURE ................................ .................... 19 Summary ................................ ................................ ................................ ................ 19 Introduction and Literature Review ................................ ................................ ......... 20 Materials and Methods ................................ ................................ ............................ 22 Search Procedures ................................ ................................ ........................... 22 Data Extraction ................................ ................................ ................................ 24 Results ................................ ................................ ................................ .................... 24 Summary of Findings on Significant Key Health Outcomes ............................. 25 Summary of Findings on Study Fea tures ................................ ......................... 27 Summary of Findings on Digital Video Games and Platforms .......................... 28 Discussion ................................ ................................ ................................ .............. 28 Conclusion ................................ ................................ ................................ .............. 32 3 TO MANAGE DISEASE SYMPTOMS ................................ ................................ .... 42 Summa ry ................................ ................................ ................................ ................ 42 Introduction and Literature Review ................................ ................................ ......... 43 Heart Failure ................................ ................................ ................................ ..... 43 Tec hnology ................................ ................................ ................................ ....... 44 Materials and Methods ................................ ................................ ............................ 46 Participants and Measures ................................ ................................ ............... 47 Interviews ................................ ................................ ................................ ......... 48 Analysis ................................ ................................ ................................ ............ 48 Results ................................ ................................ ................................ .................... 49 Managing Heart Failure Sym ptoms ................................ ................................ .. 49
7 Technology Use and MHFS ................................ ................................ ............. 50 Medical Devices and Telemonitoring ................................ ................................ 52 Discussion ................................ ................................ ................................ .............. 53 Limitations ................................ ................................ ................................ ............... 55 Conclusion ................................ ................................ ................................ .............. 56 4 ASSESSING THE DIGITAL HEALTH DIVIDE: DIFFERENCES BETWEEN OLDER ADULT USERS AND NONUSERS OF ONLINE AND OFFLINE HEALTH INFORMATION SOURCES AND MEDICAL DECISION MAKING .......... 60 Summary ................................ ................................ ................................ ................ 60 Introduction and Literature Review ................................ ................................ ......... 62 Health Information Sources, Medical Decision Making, and Self Efficacy ........ 62 Reliance and Medical Decisions ................................ ................................ ....... 65 Digital Divide and Older Adults ................................ ................................ ......... 67 Health Information Technology Eng agement and Older Adults ........................ 68 Digital Health Divide ................................ ................................ ......................... 71 Methods ................................ ................................ ................................ .................. 73 Measures ................................ ................................ ................................ ................ 74 Dependent Variables ................................ ................................ ........................ 74 Decision self efficacy ................................ ................................ ................. 74 Rel iance ................................ ................................ ................................ ..... 75 Computer self efficacy ................................ ................................ ............... 76 Independent Variables ................................ ................................ ..................... 77 Demo graphics and characteristics ................................ ............................. 77 Health information sources ................................ ................................ ........ 78 Analysis ................................ ................................ ................................ .................. 78 Results ................................ ................................ ................................ .................... 79 Participant Characteristics ................................ ................................ ................ 79 Health Information Sources and the Digital Health Divide ................................ 80 Decision Self Efficacy, Computer Self Efficacy, and Reliance ......................... 83 Discussion ................................ ................................ ................................ .............. 85 Li mitations ................................ ................................ ................................ ............... 90 Conclusions ................................ ................................ ................................ ............ 91 5 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS ................................ .. 99 Summary and Conclusions ................................ ................................ ..................... 99 Recommendations for Future Research ................................ ............................... 101 APPENDIX A DEMOGRAPHIC QUESTIONS ................................ ................................ ............. 103 B THE OSLO 3 ITEM SOCIAL SUPPORT SCALE ................................ .................. 105 C EUROPEAN HEART FAILURE SELF CARE BEHAVIOUR SCALE ..................... 106
8 D SYSTEM USABILITY SCALES ................................ ................................ ............. 107 E BLUE SCALE USABILITY SURVEY ................................ ................................ ..... 108 F FOCUS GROUP DISCUSSION GUIDE ................................ ................................ 109 G HEALTH INFORMATION AND TECHNOLOGY SURVEY ................................ ... 110 H DECISION SELF EFFICACY SCALE ................................ ................................ ... 118 I COMPUTER SELF EFFICACY SCALE ................................ ................................ 120 REFERENCES ................................ ................................ ................................ ............ 122 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 137
9 LIST OF T ABLES Table page 2 1 Matrix category and reason for selection ................................ ............................ 35 2 2 Gaming for health in older adults literature review mat rix ................................ ... 36 2 3 Matrix of game features ................................ ................................ ...................... 39 3 1 Baseline characteristics of participants ................................ ............................... 57 3 2 Self care and home device use past 30 days ................................ ..................... 58 4 1 Demographic and characteristic information of users and nonusers of online health information ................................ ................................ ............................... 92 4 2 Health information source use between users and nonusers of online health information by age ................................ ................................ .............................. 93 4 3 Technology access and use between users and nonusers of onlin e health information by age ................................ ................................ .............................. 94 4 4 Additional use of technology for health ................................ ............................... 95 4 5 Overall confidence to get health information online and perceived helpfulness of online health information between users and nonusers of online health information ................................ ................................ ................................ .......... 96 4 6 Decision self efficacy scale, reliance scale, and computer self efficacy diff erences between users and nonuser of online health information ................. 97 4 7 Regression models for decision self efficacy scale, reliance scale, and computer self efficacy ................................ ................................ ......................... 98
10 LIST OF FIGURES Figure page 2 1 Literature review flow diagram ................................ ................................ ............ 34 3 1 Assessment Items for telemonitoring intervent ions for heart failure patients ...... 59
11 LIST OF DEFINITIONS CHRONIC DISEASE / CONDITION An illness that lasts for three months or more, that is generally incurable. (Adapted from U.S. National Center for Health Statistics; Flor ida Department of Health) DIGITAL DIVIDE The gap in the access and use of information and communication technologies among individuals, households, businesses and geographic areas. (Adapted from Understanding the Digital Divide, OECD) EHEALTH The trans fer of health resources and health care by electronic means of health information, to improve public health practices in health systems management. (Adapted from World Health Organization) GAMES FOR HEALTH Video games or interactive games designed to prov ide a health benefit for individuals who engage in game play HEALTH DISPARITY Differences among diseases and adverse health conditions among specific population groups in the United States related to their incidence, prevalence, mortality, and burdens. ( Adapted from NIH Strategic Research Plan and Budget to Reduce and Ultimately Eliminate Health Disparities) HEALTH INFORMATION TECHNOLOGY (HIT) Use of computer hardware and software that permits the storage, access, sharing, and use of health information, data, and knowledge for communication and decision making. ( Adapted from The Office of the National Coordinator for Health Information Technology) TELEMONITORING The use of telecommunications and electronic information processing technologies to monitor p atient status, such as vitals, at a distance. ( Adapted from Institute of Medicine)
12 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doct or of Philosophy THE DIGITAL HEALTH DIVIDE: EVALUATING HEALTH TECHNOLOGY ACCESS AND USE AMONG OLDER ADULT POPULATIONS FOR IMPROVED HEALTH OUTCOMES AND MEDICAL DECISION MAKING By Amanda Kempton Hall August 2013 Chair: Jay M. Bernhardt Major: Health and H uman Performance Older adults represent the fastest growing segment of the U.S. population and most live with one or more chronic conditions. An estimated three quarters of all U.S. healthcare spending is attributed to chronic conditions. Innovations in H ealth Information Technology (HIT) provide an opportunity to reduce healthcare spending, improve quality of care, and improve health outcomes for older adults. Yet, concerns over a digital health divide persist. The present work focuses on three types of H IT applications and examines benefits and use in older adult populations Investigations extend the current literature and address gaps in the literature. The first study provides the first system atic literature review to report on interventions using digi tal video games and health benefits in older adults. The second study presents research findings on patien ts with chronic diseases perceptions and current use of technology, with a focus on telemonitoring technology to manage heart failure symptoms The t hird study provides findings on the differences between older adult users and nonusers of the Internet for health information and factors related to medical decision making.
13 Overall, the findings from these studies show positive health effects of HIT use by older adults. Findings on digital video games found positive health benefits of game play in older adults on social, physical, and mental factors. Findings on the use of technology to manage heart failure symptoms, found all study participants use d a h ome monitoring device; however, while HIT was positively perceived, t he majority of participants did not access onli ne resources for additional information related to their condition Significant findings on differences between older adult users and nonuse rs of online health information and medical decision making contribute to our understanding of factors related to benefits of HIT use, health behavior, and recommendations for future research. In the future, practitioners may integrate platforms and incorp orate findings from these three independent studies into intervention efforts to promote HIT engagement among older adults for improved health outcomes and medical decision making.
14 CHAPTER 1 GENERAL INTRODUCTION AND INTRODUCTION TO LITERATURE REVIEW General Introduction Life expectancy in the United States (U.S.) has increased substantially in the 21 st century, due, in part, to the advancements in medical sciences and technology (Olshansky, Goldman, Zheng, & Rowe 2009). Currently, Americans aged 65 a nd older account for 13% of the U S population (Anderson, 2002; US Census, 2012). This trend is ex pected to continue and reach 20 percent by the year 2030 (Anderson, 2002; U S Census, 2012). Older individuals represent the fastest growing segment of the U S population (Transgenerational, 2012). Additionally, adults 65 years of age and older are at an increased risk of developing more than one chronic disease and this risk increases with age (Anderson, 2002; CDC, 2012). Of this group, nearly 84 percent l ive with one or more chronic diseases (Anderson, 2002). Moreover, chronic disea ses tend to affect their health related quality of life (HRQOL) factors more deeply than other population segments (Anderson, 2002). HRQOL includes the quality of physical, ment al, and social attributes Ahern, Gold, & Heller, 2002; Hennessy, Moriarty, Zack, Scherr, & Brackbill, 1994; Moriarty, Zack, & Kobau 2003). A pproximately 75 percent of U.S. health care costs are due to chronic diseases (CDC, 201 2). However, many of these chronic diseases can be prevented (i.e., through diet, exercise, etc.) or efficiently self managed through changing health behaviors, thus, preserving or improving HRQOL factors. Recent U.S. healthcare reform changes, in the Af fordable Care Act, mandate the increased use of Health Information Tec hnology (HIT) to control health care spending
15 and improve the quality and safety of care. These recent policy changes have, in part, fueled rapid growth in the eHealth technology market ( i.e., delivery of health information and health resources through the Internet) (WHO, 2012). Also occurring is an explosive growth in HIT tools and devices. An ever expanding array of technology is available for both the prevention and self management of c hronic diseases (i.e. medical devices, websites, and video games). The availability of HIT provides increasing opportunities to engage and empower patients to participate in their health care (Hall, Stellefson, & Bernhardt, 2012; Martin, 2012). Growth in th is market is supported by healthcare providers who perceive HIT and eHealth as tools that can directly improve patient provider communication, especially in the areas of patient care and education, compliance to treatment regime n s, and patient access to se rvices. While many market sectors see promi se in the use of HIT other sectors are concerned that widespread use will exacerbate the digital divide present between users and nonusers of HIT (Kieschnick & Raymond, 2011). For HIT to succeed among older adult s, it is critical that this population perceive s technological tools and devices as accessible, acceptable, and useful when compared to standard care practice. Because older adults tend to be a vulnerable population, they are more likely than their younger cohort groups to deal with many disabilities and health disparities that limit their access to health care services and information (i.e., low health literacy, injuries, lack of social support, cognitive decline, decreased sensory and motor capabilities, e tc.) (Jimison, et. al., 2008; Kieschnick & Raymond, 2011). Many factors limiting older adults' access to digital solutions are described in the literature,
16 however, there is a paucity of research about how older adults use and perceive benefits related to technology use (Jimison et al., 2008). A number of initiatives currently address concerns voiced in the digital divide and eHealth inequality debate. These initiatives are headed primarily through efforts of Healthy People 2020 unication and Health Information (Healthy People 2020, 2012). A stated goal of Healthy People 2020 and he alth information technology to improve populat ion health outcomes and health goal is to be accomplished nationally, local community action and support will be required. The rapid market growth and provider ado ption o f HIT creates an urgent need to understand older adults perceived benefits and current use of technology for health purposes. Research findings will provide the empirical evidence to guide future planning, development, and use of HIT driven health b ehavior interventions. Additionally, while this information will be valuable for understanding older adults' use of technology for health, it will also contribute to early assessment of the digital divide and other unintended effects that may present with widespread use of HIT by those who care for older adults. Therefore, given the many types of HIT available to consumers, the author has chosen to focus the scope of this research on three areas of technology, specifically, video games, medical devices a nd the Internet to ultimately address digital divide con cerns and technology use among older adults. The research focuses on three
17 independent studies to assess three different types of technology applicat ions used, and perceived benefits of HIT use in old er adult populations Chapter 1 presents a general introduction to the literature reviews presented in Chapters 2 through 4. This research was developed into manuscripts and submitted for publication in scholarly journals. The research conducted in Chapte r 2 is a systematic review of the peer reviewed literature to report on interventions using digital video games to promote healthy behaviors among adults 65 years of age and older and conduct an analysis on health outcomes related to mental, physical, and/ or social attributes associated with digital video game pla y The investigation in Chapter 3 explored patients with chronic diseases, specifically those with heart failure, perceptions and use of technology to manage heart failure symptoms, assessed patien ts current telemonitoring 1 The research in Chapter 4 examined older adu reliance for medical decisions, and self efficacy for medical decision making bet ween users and nonusers of online health information. Additionally, characteristics between older adults who use the Internet and those who do not were analyzed to identify determinants of Internet use fo r health information and health related quality of l ife. Finally, Chapter 5 presents a synthesized discussion of major findings in each of th e three studies and proposes recommendations for future research. 1 ight, blood pressure, or oxygen saturation ) at a distance and transmits the data either over a tele phone line or a wireless broadband to a healthcare provider. Telemonitoring devices are commonly used in interventions for older adults with chronic diseases for adherence to self care protocols.
18 Introduction to Literature Reviews The literature reviews for each of the independent studies focused on three different types of technological applications used among older adult population s: video games, medical devices, and the Internet. The systematic literature review cond ucted in Chapter 2 provides the first published review of video game interventi ons and health outcomes of game play among older adults. The literature review conducted in Chapter 3, on medical devices, specifically telemonitoring technology, focused on telemonitoring intervention research, addressing the lack of research findings on and actual use of technology to manag e chronic diseases, particularly for heart failure patients. Finally the literature review in Chapter 4 evaluated r ecent research on older tion, thereby examining the digital divide. The review focused on the use of the Internet and its potential as a usefu l decision aid tool for medical decision making. The review also evaluated characteristic differences between Internet users and nonusers. Efficacy Theory provide the theoretical framework for Chapter 4 (Bandura, 1997; Davis, 1989).
19 CHAPTER 2 HEALTH BENEFITS OF DIGITAL VIDEO GAMES FOR OLDER ADULTS: A SYSTEMAT IC REVIEW OF THE LITERATURE Summary This is a systematic review of the research literature on digital video games played by older adults and health outcomes associated with game play. Findings from each study meeting the inclusion criteria were analyzed a nd summarized into emergent themes to determine the impact of digital games in promoting healthy behaviors among older adults. A systematic review of the literature was conducted through multiple academic databases for works, published between the years 20 00 and 2011, looking at digital game interventions with adults 65 and older. Multiple combinations of search terms and Boolean operators relevant to digital video games and older adults were queried A criteria matrix was created to code and evaluate studi es. Thirteen studies met criteria for inclusion and were analyzed in the final review. Significant mental, physical and social health factors, type of digital game platform, study design, and measurements are among emergent themes summarized from the revie wed literature. Significant mental health outcomes of digital game interventions were found in the majority of the reviewed studies followed by physical and lastly social health outcomes in older adults. A majority of the studies revealed significant posit ive effects on health outcomes associated with digital game play among older adults. With current advancements in techno logy, including advanced motion sensing, digital game platforms have significant potential for positive health impact among older popula tions. R eprinted with permission from Hall, A. K., Chavarria, E., Maneeratana, V., Chaney, B., Bernhardt, J. M. (2012). Health benefits of digital videogames for older adults: A systematic review of the literature. Games for Health Journal, 1(6), 402 41 0. doi:10.1089/g4h.2012.0046.
20 More robust and rigorous research designs are needed to increase validity and reliability of results and establish stronger causal relationships on the health benefits of digital game play for older adults. Introduction and Literature Review Life expectancy in the United States (U S ) has increased substantially in the 21 st century, owing, in part, to the advancements in medical sciences and technology (Olshansky et al., 2009). As a result, older individuals represent the fastest growing segment of the U.S. population (Transgenerational, 2012). Currently, Americans 65 years of age and older account for 13 percent of the U.S. population (Anderson, 2002; U.S. Census Bureau, 2010). This trend is expected to continue and reach 20 percent by the year 203 0 (Anderson, 2002; U.S. Census Bureau, 2010). Additionally, adults 65 years of age and above are at an increased risk of developing more than one chronic disease, and this risk increases with age (Anderson, 2002; Center for Disease Control and Prevention, 2012). Of this group, nearly 84 percent live w ith one or more chronic diseases (Anderson, 2002). Moreover, chronic disea ses tend to affect their health related quality of life (HRQOL) factors more deeply than other population segments (Anderson, 2002). HRQ OL includes the quality of resulting in decreased HRQOL (Dominick et al., 2002; Hennessy et al., 1994; Moriarty et al., 2003). Therefore, activities and interventions that promote healthy behaviors are imperative for fostering healthy aging, preventing chronic disease, and improving HRQOL.
21 In recent years, the digital video game ind ustry has expanded and integrated itself into the health market with games such as PA), and other games to engage p th promoting activities (Shubert, 2010). Consequently, interactive games (i.e. exergames) a nd health based games have emerged to utilize gaming as a strategy to change health behavior (Shegog, 2010). According to the Pew Internet & American Life Project, 23 percent of adults 65 years of age and older actively play digital video games, and this rate is increasing (Lenhart, Jones, & Macgill, 2008; McGonigal, 2011). Moreover, older adults play digital video games more often than any other segment of the popul ation (Lenhart, et al., 2008). Additionally, a recent study conducted and funded by the LEAGE (LEAring Games for Older Europeans) project found that older adults, across three European countries, enjoyed the challenge and social interaction components of d igital video games, which met their demands for fun leisure activities (Diaz Orueta, Facal, Nap, & Ranga, 2012). Therefore, because of the increased availability of interactive digital video games on the market and the percentage of adults engaging in game play, researchers have begun to examine the health benefits of using digital video games (Bowers & Bowers, 2010). A literature review by Baranowski, Buday, Thompson, and Baranowski (2008) on digital games and health behavior change reported positive heal th gains through game play. Game design features, such as health related dialogue, knowledge gains, and goal oriented tasks, resulted in health behavior changes (Baranowski et al, 2008). Other
22 studies have found diverse health behavior outcomes of digital game play that increased physical activity among various population studies (Papastergiou, 2009; Sallis, 2011). A large number of these studies and reviews primarily focused on health outcomes of digital game interventions among younger populations, but li ttle is still known about similar outcomes among populations 65 years of age and older. Therefore, given the aging of the U.S. population, the prevalence of chronic diseases among older adults, the large number of health based digital video games available to consumers, and the increasing percentage of older adult gamers, there is a need for a critical examination into the current state of research on HRQOL benefits (i.e. mental, physical, and social health benefits) of digital video game play among adults 65 years of age and older. This systematic review of the peer reviewed literature ( 1) reports on interventions using digital video games to promote healthy behaviors among adults 65 years of age and older, and (2) analyzes health outcomes related to mental physical, and/or social attributes associated with digital video game play among adults 65 years of age and older. Materials and Methods Search Procedures The literature surveyed were peered reviewed, English language publications and included subjects r elated to evaluating health outcomes associated with digital video game play exclusively among an older adult population. The scope of the review included studies with male and female participants with a mean age of 65 years and older, participating in dig ital video game play in all locales, and studies that measured mental, physical, or social benefits of digital video game play. Literature databases included PubMed, CINAHL, PsycINFO, Ageline (EBSCOhost web), SPORTDiscus, and
23 Web of Science. Only articles published from 2000 to current day were included. The literature search was performed in the winter of 2011. Key terms used in an array of arrangements with Boolean operators to conduct searches were: terms were used in PubMed. The thesaurus available in CINAHL, PsycINFO, Ageline SPORTDiscus was used to ensure that relevant key words were properly searched in each databas e. Initially, broad search terms were used in favor of more specific terms (i.e. health terms) to ensure studies were properly assessed based on inclusion or exclusion criteria. Every article compiled through this primary exploration and monitor procedure (n = 194) was included for further investigation. One hundred sixty four articles were excluded after the screen of titles and abstracts. This initial large number of exclusions was due to the broad search terms utilized. Additionally, many of these 194 a rticles and studies were not interventions but reports, commentaries, or studies involving clinical procedures and findings. Further exclusion included the removal of studies that did not include both male and female participants in their intervention an d those that did not solely use digital video game play in their intervention. The latter relates to the rationale that of sex or in results being attributed to inter vention methods other than digital video game play. After these initial exclusions, 30 studies underwent a full text assessment. From these 30 studies, 17 were excluded for a variety of reasons, including failing to explicitly measure and report participan
24 & Stern, 2010; Hwang, Hong, & Hao, 2011; Kizony, Weiss, Shahar, & Rand, 2006; Rand, Kizony, & Weiss, 2008; Schoene, Lord, Verhoef, & Smith 2011; Sekuler, McLaughlin, & Yotsumoto, 2008; Yamada et al., than 65 years (n = 7) (Graves et al., 2010; Guderian et al., 2010; Merians et al., 2006; Mouawad, Doust, Max, & McNulty, 2011; Saposnik et al., 2010; Yavuzer, Senel, Atay, & Stam, 2008; Yong et al., 2010), case stud ies, acting as opinion or editorial pieces (n = 2) (Fenney & Lee, 2010; Weybright, Dattilo, & Rusch, 2010), or serving only as secondary sources of information, where full results were not reported within the scope of the study (n = 1) (Lamoth, Caljouw, & Postema, 2011). After accounting for conditions outlined by the above exclusion criteria, 17 studies were not summarized for this review. The final 13 journal articles were research studies assessing health outcomes of digital video game play on adults 65 years of age and older. These 13 studies were fully analyzed, and the gathered data were organized. Emergent themes were recognized and summarized, and a matrix is described under the d ata extraction section. Figure 2 1 delineates t he systematic review pro cess Data Extraction Utilizing the methodology described by Garrard (2010), a matrix was constructed using data extracted and compiled from the selected 13 studies. Each study in the final selected sample of 13 was coded on several categories. Additional ly, two researchers independently coded each study to test fo r inter rater agreement. Table 2 1 describes the categories chosen for coding and analysis, along with reasons. Results After the systematic inclusi on process described in Figure 2 1 was impleme nted, in total, 13 studies met all inclusion and exclusion criteria. Table 2 2 includes the findings
25 of the systematic review of the research literature. Although the search parameters included years 2000 through 2011, all of the selected studies fitting t he inclusion and exclusion criteria were published between the years 2008 2011. Summary of Findings on Significant Key Health Outcomes Significant key health outcomes of digital video game interventions reviewed varied considerably. The majority of studie s addressed multiple health domains. Measures of studies with reported significant p values at the p Significant mental health positive outcomes (e.g., working memory, depression) were concluded in 10 of the 13 reviewed studies. Sign ificant physical health positive outcomes (e.g., balance, mobility) were measured in six of the 13 studies. Significant social health positive outcomes (e.g., social interaction, social support) were noted in two of the 13 studies. Taken together, statist ically significant positive health outcomes were found on multiple measures of physical, mental, and/or social variables. When compared with baseline, significant mental health positive outcomes, such as cognitive improvement, were reported in multiple dig ital video game interventions, which used measures such as working memory, focused attention, fluid intelligence, scales for dementia, scales for depression, information processing, enjoyment of physical exercise, and balance confidence to assess cognitive improvement. The most frequently reported significant health outcome among digital game interventions for older adults were mental health outcome factors. Table 2 2 describes all mental health outcome findings. Physical health outcome measures were the se cond most common factors reported by researchers in the reviewed studies. The review of the research literature found that several studies reported significant improvements on balance, physical
26 mobility, strength, and pain. Most studies assessing physical health outcomes used the Nintendo Wii console, which can incorporate the Wii balance board to assess balance. Table 2 2 describes all physical health outcome findings. The review of the selected research literature found that few studies (two out of 13) me asured social health outcomes. Both studies reported significant increased social interaction of digital video game play assessed with measures such as perceived feelings of social participation and social support. Social health outcomes were measured less frequently than mental and physical health outcomes. Table 2 2 describes all significant social health outcomes. In summary, nearly all studies found statistically significant positive health outcomes, in their respective hypothesized directions, affirmi ng change in at least one significant key health outcome. The exception was Bainbridge Bevans, Keeley, and Oriel (2011), who hypothesized significant physical and mental health outcomes associated with digital video games; however, the particular study fo und no statistically significant key health outcomes. Bainbridge et al. surmised that their lack of finding statistically significant results was due to a small sample size (n = 6), the limited capacity of the Wii system to store participant information, a nd the absence of a control group in the study design (Bainbridge et al., 2011). Although a few of the participants did reduce their fall risk as measured by the Berg Balance Scale, overall statistical findings did not reach statistical significance (Bainb ridge et al., 2011). It is notable that Brainbridge et al., Yamaguchi, Maki, & Takahashi (2011), and Bell et al. (2011) used total or group sample sizes of less than 10 participants to run the data analyses and evaluate findings (Bainbridge et al., 2011). The validity of findings using sample sizes of
27 less than 10 subjects is not considered statistically relevant, and future replications of these study designs with increased sample sizes can adequately verify findings. Summary of Findings on Study Features The review of the selecte d research literature found widely varying study design among the 13 selected digital video game interventions. The majority of studies used a pretest and post test design, and two were pilot studies (Rosenberg et al., 2010; Willi ams et al., 2011). Furthermore, only one study reviewed conducted a randomized control trial (Szturm, Betker, Moussavi, Desai, & Goodman, 2011). A theoretical foundation for the reviewed interventions was only found in one study (Szturm, et al., 2011). The settings for the studies were fairly similar with a few exceptions. The most common research setting was a facility specializing in elderly care; however, each study had a slight variation in the term used to describe its facility depending on the type of services provided to the residents. The three exceptions were a laboratory, a college sports center, and a hospital outpatient center. Although the studies incl uded participants of both sexe s, a highly disproportionate number of females participated in al l of the reviewed studies. The number of participants in each study ranged from six to 121 participants, and the mean number of participants was 32.31 (standard deviation (SD) = 28.67). The mean age of participants across the studies in the selected resear ch literature was 75.38 (SD = 6.84) years. Duration of interventions ranged between one to 12 weeks, with a mean of 7.13 weeks (SD = 3.30). The frequency of the interventions ranged between one to seven times per week, with a mean of 2.56 (SD = 1.57). Dura tion of game play interventions ranged from 20 to 90 minutes, with a mean of 38.50 (SD = 20.55) minutes of play across all studies in the selected research literature. Three of the reviewed studies did
28 not specify the number of minutes of game play of thei r intervention. Table 2 2 describes the study features associated with the selected research literature. Summary of Findings on Digital Video Games and Platforms The review of the selected research literature found that game titles used by studies widely varied, yet the Nintendo Wii was the most commonly used console as well as computer games Most games used in the reviewed literature were related to scoring due to elicited physical movement or scoring based on completing cognitive tasks such as problem solving through memory usage. The review of the selected research literature found only one study that incorporated a game that was specifically (Studenski et al., 2010). One exc eption was the Nintendo DS handheld console used to test mental health effects in patients (Brem et al., 2010). One study used a unique three dimensional virtual reality platform called Phantom Omni, which was designed for use in stroke rehabilitation (Bro eren et al., 2008). Besides the latter study, the majority of studies did not incorporate participant tailored games and instead incorporated games designed for a general audience. Discussion The aging of the U.S. population and the prevalence of chronic h ealth conditions both spur a market built on finding innovative solutions, such as digital health initiatives to counteract the rising health care costs and deficiencies (Anderson, 2002; Hanson, 2011). Digital health projects include the refinement and crea tion of interactive digital video game platforms for promoting healthy activities and ultimately increasing HRQOL. The latter contributes to the need and opportunity to asse ss and review the current state of studies of health outcomes of digital video game play by older adults
29 From 2008 to the present, researchers have just begun to empirically question the health benefits that digital games can present to an aging population. Because studies varied in purposes, measures, and instruments used to assess he alth outcome measures, future researchers should seek to use consistent measures to assess physical, mental and social attributes of game play in older adults to facilitate better cross study effect comparisons. Digital and virtual games are successfully being used for rehabilitation therapies. For example, Broeren et al. (2008) reported that healthcare providers are using virtual reality technology to create simulated environments in controlled settings to treat stroke patients. These environments simula te real life tasks and provide feedback, which helps stroke patients regain upper arm extremity functionality (Saposnik et al., 2010). Additionally, digital games are being studied as effective ways to improve physical strength in patients with issues such as balance weakness or upper extremity dysfunction (Hsu et al., 2011; Shubert, 2010). However, only one of the reviewed studies looked at video games as an intervention to promote physical activity, and more studies are needed to confirm these findings in older adults (Studenski, 2010). Furthermore, game platforms are being tested as possible fall risk assessment tools by therapists (Graves et al., 2010; Schoene et al., 2011). Preliminary evidence points to the physical health benefits associated with the use of digital video game play by older adults and as rehabilitation tools used by healthcare providers. The use of digital video games to improve cognitive processes of older adults has also been investigated. Clinical findings support the notion that af ter 65 years of age, the brain experiences variable structural and cognitive functional declines (Basak
30 et al., 2011). Thus, digital video games may prove to be an effective treatment to sharpen mental processes as people age (Basak et al., 2011; Nacke, Na cke, & Lindley, 2009). Rosenberg et al. (2010) found significant positive changes in depressive symptoms of older adults with subsyndromal depression. Overall, the reviewed studies found positive effects of game play on mental health status. Research on i ncreased social interaction and social support was pursued by a small number of researchers (Bell et al., 2011; Hsu et al., 2011). However, these studies showed promising results for increasing verbal communication and expression of positive feelings, whic h are supported by the literature as key elements of social interaction (Bell et al., 2011; Halton, 2008). Positive outcomes relating to social support were found among groups formed and meeting regularly during digital video game play interventions (Hsu e t al., 2011). The latter study suggests that the phenomenon be further investigated through future studies. Therefore, future studies in the area of digital video game play by older adults should incorporate a construct for assessing social support, to con firm current findings and to test for validity of results stemming from research on social support. Only a single study based its research on a theoretical foundation (Szturm et al., 2011). Evidence based research supports the use of behavioral theory t o inform health promotion programs for changing health behavior (Shegog, 2010). Therefore, the current state of research on health based games demonstrates a need to include behavioral determinants utilizing a model for health behavior prediction. Addition ally, more comprehensive and thorough experimental designs are also needed to increase reliability and validity of results stemming from the research.
31 To decrease variations between studies, intervention times can be aligned based on frequency and durati on of intervention. From our findings, we suggest the norm of intervention duration to be in the vicinity of seven weeks and frequency of intervention at around two or three times per week. Because the majority of p articipants studied were female parallel studies related to gender are necessary in order to draw definitive conclusions on the various classes of health outcomes associated with digital video game play in the older generation. The reviewed studies varied in the number of participants, and many studies experienced high participant attrition rates. Therefore, researchers looking to study the effects of gaming on older adults in future studies should plan for a greater number of participants to strengthen statistical significance of their findings and to counter high attrition rates. The Nintendo Wii console was found to be the most chosen digital video game platform on which games were played for interventions. The Nintendo Wii works primarily on motion sensing and has been the industry leader in s ales for several years. Microsoft (Redmond) (Allen, 2006; Pham, 2009; Sinclair, 2010). The newer consoles have more accurate motion sensing and require more realistic physical movement in order to be recognized by the gaming system (Uno, 2012). From this, it is recommended that future studies incorporate as well as compare and contrast curre nt and newer technologies in motion sensing digital video games. Furthermore, the Nintendo Wii console gives older adults a platform that encourages social interaction and simultaneously stimulates mental and physical
32 activity. The majority of studies wer e conducted in facilities specializing in geriatric care, which implemented the Nintendo Wii console. This finding suggests that such games can be played indoors and at the leisure of the users, making them appealing to an older adult population. Consequen tly, the Nintendo Wii was found to be a popular health promotion tool among staff members who care for the elderly (Higgins, Horton, Hodgkinson, & Muggleton, 2010). However, it is recommended that future studies test current and newer game consoles in othe r settings, such as in households of older adults, to see if similar health outcomes hold up in settings lacking continuous social support from staff members and researchers. There is a paucity of available research on the types of games preferred by old er adults and which games have the greatest health benefits. Only one of the reviewed studies asked participants to rate their enjoyment of the various games they played on a Likert scale (Rosenberg et al., 2010). Additionally, another study gave participa nts a choice of games to play but failed to report what games were commonly chosen or which were linked to better health outcome measures (Williams et al., 2011). Future studies should look at game preferences for sustained use and types of games that prov ide the greatest health benefits among an older population. Conclusion Findings from this systematic review of the literature describe the current state of research on HRQOL indicators of older adults associated with interactive digital video game play. Digital games for older adults have been studied for use in rehabilitation treatments, physical activity promotion, mental acuity exercises, and increased social exchanges. The vast majority of reviewed studies revealed positive health outcomes for older a dults associated with digital video game play, especially related to mental and
33 physical health benefits. Evidence suggests that future research on health outcomes associated with digital video game play by older adults can benefit from more rigorous exper imental study designs, an increase in the number of study participants, more equal gender distribution among study participants, and an alignment of trials in regards to frequency and duration of interventions. Strengthening study designs will help increas e the amount of valid and reliable empirical and statistically relevant evidence of the possible health benefits due to digital video game play by older adults. In addition, researchers should test recent innovations and advancements in motion sensing digi tal video game platforms to increase the precision of future research. Lastly, we look forward to the clinical benefits derived from studies incorporating the previously stated suggestions and technologies.
34 Figure 2 1. Literature review flow diagra m
35 Table 2 1. Matrix category and reason for selection Category Reason for Selection Author(s), Title, Journal To search for any recurring authors or journals in which the type of research is found. Year of Study To search for particular years in whi ch the type of research initiated or was popular. Theoretical Foundations To look for any particular theories used by the majority of researchers in the topic. Purpose of Study To determine what purposes guide most Measures of Study To determine what measures do most researchers utilize in conducting their study on the topic. Study Design To determine whether a particular type of study design was used most by researchers. Setting of Study (Context) To determine if a particula r setting was most used by researchers. Content (Physical, Mental, Social) Used as a reference to determine whether particular studies found significant results on the content reported. Description of Sample Sample Size Mean Age (SD) Gender (%) To assess the characteristics of participants and total number of participants used most by researchers to of Medicare eligibility. Game Information Game Title Purpose of Game Tailored (Yes/No) Platform Duration of Play To evaluate the types of games and platforms used across studies to determine if certain games were more effective than others in linking game play to health outcome measures. Significant Key Health Outcomes (physical, mental, social) To examine and compa re statistically significant findings on instruments and assessments used. Duration of Intervention (Weeks) To assess the length of interventions with study findings. Frequency To assess the dose of interventions with study findings. SD, standard de
36 Table 2 2. Gaming for health in older adults literature review m atrix Author(s), Title, Journal Year of Study Theoretical Foundations Purpose of Study Measures of Study Study design Setting of Study (Context) Content (Physical, Mental, Social) Description of Sample Sample Size Mean Age (yrs.) Gender (%) Broeren et al. 2008 Not Stated Virtual reality (VR) system used to assess and promote performance in persons affected by stroke Perceptio n of VR, Manual ability (BBT & ABILHAND), Executive Function and Attention (TMT B), Kinematics(UE test) Pre /post test design with a control group Facility for community dwelling persons Physical, Mental 22 (11 Control, 11 Treatment) 68.0 Female (78%) an d Male (22%) Basak, Boot, Voss, & Kramer 2008 Not Stated Assess executive control processes of older adults Cognitive ability, executive control, and visuospatial skills Pre /post test design with a control group Laboratory Mental 39 (20 Control, 19 Tre atment) 69.1 Control, 70.1 Treatment Female (74%) and Male (26%) Rosenberg et al. 2010 Not Stated Assess feasibility, acceptability, and short term efficacy and safety of exergames (combined game play with exercise) Mood (QIDS), Health Related QoL (MOS, S F 36), Cognitive Functioning (RBANS), Adherence (personal logs). Pilot, follow up study Residential facility, Senior Centers Physical, Mental 19 (All Treatment Participants) 78.7 Female (68%) and Male (32%) Studenski et al. 2010 Not Stated Assess efficac y of adapted interactive video dance game Vital signs (Pulse, BP, BMI), physical function (performance test), self reported quality of life Single group, pre /post test design Senior Centers Physical, Mental 25 (All Treatment Participants) 80.1 Female (83 %) and Male (17%) Brem et al. 2010 Not Stated Assess cognitive performance due to video games Central information processing (Kurtest fur Allgemeine Intelligenz), health related quality of life (12 item short form health survey), Mental stability (Neuroti cism Extroversion Openness Five Factor Inventory) 10 day longitudinal study Hospital (Bedside) Mental 32 (16 Control, 16 Treatment) 66.1 Treatment, 68.9 Control Female (62%) and Male (38%) Hsu et al. 2011 Not Stated Assess effects of adding simulate game play to standard exercise regimen for residents of long term care facility Physical Performance (Nursing Home Physical Performance Test), Physical Activity Enjoyment (Physical Activity Enjoyment Scale (PACES)), Pain Intensity and Pain Bothersomeness (Numer ic Rating Scale(NRS)), Active Range of Motion (AROM), and Perceived Change (Global Pe rceived Rating of Change (GPRC) Randomized, single blind crossover trial Long Term Care Facility Physical, Mental 34 (19 Group I, 15 Group II) 80.0 Female (71%) and Male ( 29%)
37 Table 2 2. Continued Author(s), Title, Journal Year of Study Theoretical Foundations Purpose of Study Measures of Study Study design Setting of Study (Context) Content (Physical, Mental, Social) Description of Sample Sample Size Mean Age (yrs.) Gender (%) Peretz et al. 2011 Not Stated Assess whether cognitive training can result in cognitive gains for older adults Cognitive ability (measured by Neuropsychological Examination CogniFit Personal Coach (N CPC)) and Nexade Randomize d double blind interventional study Hospital outpatient facility Mental 121 (66 Control, 55 Treatment) 67.8 Female (67%) and Male (33%) Torres 2011 Not Stated Assess effects of video games on the elderly pertaining to cognitive ability, self concept, and quality of life Cognitive ability Cognitive portion of Alzheimer Disease Assessment Scale (ADAS Cog), Self Concept Clinical Self Concept Inventory (ICAC), Quality of Life World Health Organisation Quality of Life Questionnaire (WHOQOL) Pre /post test, 3 groups (experimental, passive and active control groups) design Residential homes for elderly Mental 43(15 Treatment, 17 Passive Control, 11 Active Control) 78.3 Female (77%) and Male (23%) Bell et al. 2011 Not Stated Assess effects of Wii on quality o f life, social relationships, and confidence in the ability to prevent falls Quality of Life Control, Autonomy, Self realization, Pleasure 19 (CASP 19), Social relationships via the Social Provisions Scale (SPA), and Confidence to Prevent Falls via the Mo dified Falls Efficacy Scale (M FES) Repeated Measures Design Assisted living facilities Physical, Mental, Social 21 (8 Group I, 6 Group II, 7 Control) 80.8 Female (76%) and Male (24%) Williams et al. 2011 Not Stated Assess benefits Wii Fit may have on bal ance for older adults Balance is measured via the Berg Balance Scale Single group pre /post test design Independent Retirement Communities or Skilled Nursing Facilities Physical 22 83.9 Female (82%) and Male (18%) Bainbridge et al. 2011 Not Stated Assess whether Wii Fit Balance Board leads to improvements in balance in older adults with perceived balance deficit. Balance, Balance Confidence and Limits of Stability were assessed via Berg Balance Scale, Activities specific Balance Confidence Scale, Multi Di rectional Reach Test (MDRT) and Center of Pressure (COP) Excursion Measurements Prospective, cross sectional pilot study, pre /post test design College Sport Center Physical, Mental 6 75.0 Female (87.5%) and Male (12.5%)
38 Table 2 2. Continued Author (s), Title, Journal Year of Study Theoretical Foundations Purpose of Study Measures of Study Study design Setting of Study (Context) Content (Physical, Mental, Social) Description of Sample Sample Size Mean Age (yrs.) Gender (%) Yamaguchi et al. 2011 Not Stated Assess effects on cognitive function due to use of enjoyable video sports games General Cognitive function Dementia Scale revised (HDS R), Visuospatial and constructive function measured using Kohs block des ign tests (Kohs), and Behavioral changes were measured using the Multidimensional Observation Scale for Elderly Subjects (MOSES) Single group pre /post test design Nursing Home Physical, Mental, Social 9 88.9 Female (66.6%) and Male (33.3%) Szturm et al 2011 Biofeedback Training Assess feasibility and benefits of physical therapy via interactive video game paradigm specific Balance Confidence Scale, Clinical Test of Sensory Interaction and Balance, al l used to assess reactive balance controls and environmental interaction Randomized Control Trial Geriatric Hospital Physical, Mental 27 (13 Control, 14 Treatment) N/A; Median given, inclusion criterion 65 85 yrs. Female (70.4%) and Male (29.6%)
39 Table 2 3. Matrix of game features Author(s), Title, Journal Game Information Significant Key Health Outcomes (physical, mental, social) Duration of Intervention ( Weeks) Frequency Game Title Purpose of Game Tailored (Y/N) Platform Durat ion of Play Broeren et al. Not Specifically Stated To manipulate virtual objects by using upper extremities Yes Phantom Omni 45 minutes Physical: Time and Hand Path Ratio for VR task P < 0.05. 4 3 times/ week Basak, Boot, Voss, & Kramer Rise of Natio ns Using strategy to control land No PC computers and eMAC 1.5 hours Mental: Ability to quickly switch between two tasks (Sessions 2 and 3) F(1, 34)= 6.78, P = .01, improved focus of attention in the visual short term memory task F(2, 16)= 4.02, P = 0.04, and memory load cost decreased (Session 3 versus Session 2) F(1, 36)= 5.49, P = 0.02. 4 5 15 sessions Rosenberg et al. Wii Sports Physical movement to simulate actual games ( Bowling, Baseball, Tennis, Golf and Boxing ) No Nintendo Wii 35 minut es Mental: Quick Inventory of Depressive Symptoms 16, t= 3.24, P =0.004, SF=36 Mental Health Composite Score t= 2.57, P = 0.014, and Cognitive Functioning (RBANS) t= 2.41, P = 0.028. 12 2 times/ week Studenski et al. Pressing correct arrow to the beat of music Yes Screen and dance pad 30 minutes Physical: Narrow walk time P = 0.0260 Short Physical Battery Performance P = 0.0071. Mental: Self reported health P = 0.02, SF36 Mental Component P = 0.0180, Self reported balance confidence P = 0.01 29. 12 2 times/ week Brem et al. Brain Training: How Old is Your Brain? Using cognitive ability to correctly perform tasks No Nintendo DS handheld console 45 minutes Mental: Working memory increased P = 0.004, Fluid intelligence increase d P = 0.006, Information processing increased P = 0.03. 1 7 sessions
40 Table 2 3 Continued Author(s), Title, Journal Game Information Significant Key Health Outcomes (physical, mental, social) Duration of Intervention ( Weeks) Frequency Game Title Purp ose of Game Tailored (Y/N) Platform Duration of Play Hsu et al. Wii Bowling part of Wii Sports Swing to knock down pins, simulated bowling No Nintendo Wii 20 minutes Physical: Nursing Home Physical Performance Test P < 0.001, pain bothersomeness P = 0.015, Active Range of Motion P = 0.007. Mental: Physical Activity Enjoyment Scale, enjoyment of activity P = 0.014. 8 (4 using Wii) 2 times/ week Peretz et al. Complete 21 different training tasks for points Yes PC computers 20 30 minutes Mental: Focused attention P = 0.01, sustained attention P = 0.01, memory recognition P = 0.02, and mental flexibility P = 0.05. 12 3 times/ week Torres QBeez, Super Granny 3, ZooKeepe Pingyn Problem So lving, using work memory and recognition No PC computers Not Specified (unlimited play) Mental: Cognitive portion of Alzheimer Disease Assessment Scale t(14)= 3.505, P = 0.003, r = 0.68. 8 1 time/ week Bell et al. Wii Bowling part of Wii Sports Simu late Bowling game No Nintendo Wii Not Specified (Until 10 frames Completed) Social: Control, Autonomy, Self realization, Pleasure 19, Group 2 item 8 P = 0.030, Social Provisions Scale, Group 3 item 1 P = 0.030, Group 2 item 3 P = 0.045, Group 1 item 14 P = 0.048 Mental: Modified Falls Efficacy Scale, Group 2 item 8 P = 0.047. 8 1 time/ week Williams et et al. Wii Fit Balance Board Completing balance activities by moving forward, backwards and side to side on Wii balance board No Nintendo Wii 20 minutes P hysical: Berg Balance Scale t (21) = 9.861, P < .01. 4 3 times/ week
41 Table 2 3 Continued Author(s), Title, Journal Game Information Significant Key Health Outcomes (physical, mental, social) Duration of Intervention ( Weeks) Frequency Game Title Purpose of Game Tailored (Y/N) Platform Duration of Play Bainbridge et al. Wii Fit Balance Board Completing balance activities by moving forward, backwards and side to side on Wii balance board No Nintendo Wii 30 minutes Physical and Mental outcome m easures were not found to be statistically significant for Berg Balance Scale and Activities specific Balance Confidence Scale (3 subjects decreased their fall risks by 6 12%) 6 2 times/ week Yamaguchi et al. XaviX Hot plus Grab coin when appears to fl oat out of screen or Moving legs to music both to score points Yes Television Screen and tangible accessories such as soft bowling balls Not specified Physical: Visuospatial and constructive function measured using Kohs block design tests P = 0.02, Mental : revised P = 0.002. Social: Multidimensional Observation Scale for Elderly Subjects P = 0.054. 10 1 time/ week Szturm et al. Under Pressure, CMemory Match, Balloon Burst Move weight to get object directed in right spot, o r User shift weight in order to select correct card, or User shift weight in order to burst balloon Yes FSA Pressure mat connected to Laptop computer 45 minites Physical: Berg Balance Scale P = 0.001, Clinical Test of Sensory Interaction and Balance, Loss of Balance sponge surface P = 0.007. Mental: Activities specific Balance Confidence Scale P = 0.02. 8 2 times/ week
42 CHAPTER 3 MANAGE DISEASE SYMPTOMS Summary Technology use for symptom mana gement is beneficial for both patients and physicians. Widespread acceptance of technology use in healthcare fuels continued development of technology with ever increasing sophistication. While acceptance of technology use in healthcare by medical professi onals is evident, less is known about the perceptions, preferences, and use of technology by patients. This study explores symptoms (MHFS). A qualitative analysis of in depth in dividual interviews using a constant comparative approach for emerging themes was conducted. Fifteen participants (mean age 64.43) with heart failure were recruited from hospitals, cardiology clinics, and community groups. All study participants reported u se of a home monitoring device, such as an ambulatory blood pressure device or bathroom scale. The majority of participants reported not accessing online resources for additional MHFS information. However, several participants stated their belief that tech nology would be useful for MHFS. Participants reported increased access to care, earlier indication of a worsening condition, increased knowledge, and greater convenience as potential benefits of technology use while MHFS. For most participants financial c ost, access issues, satisfaction with current self care routine, mistrust of technology, and reliance on routine management by their current healthcare provider precluded their use of technology use for self care and better understanding of issues associated with technology access can aid in the development of effective health behavior interventions
43 for individuals MHFS and may result in increased compliance, better outcomes, and lo wer healthcare costs. Introduction and Literature Review Heart Failure Congestive heart failure, commonly referred to as heart failure (HF), is a chronic disease leading to disability and death (Rogers et al., 2000). HF is a major public health problem affecting 5.8 million Americans, resulting in approximately 300,000 deaths and 670,000 newly diagnosed cases each year (Lloyd Jones et al., 2009; National Institutes of Health, 2013). After age 65, the population incidence of HF nears ten per 1000 (Roger e t al., 2012). As a result HF is among the main causes of hospitalization for hospitalized within 6 months (Fonarow, 2004). As such, the American Heart Association estimated the 2009 costs of U.S. HF related in cidents at $39.2 billion (Lloyd Jones et al., 2010). and medications to optimize symptom control and prevent acute decompensation (Rogers et al., 2000). Proficient patient s elf management skills are important to MHFS on a daily basis (West, Miller, Parker, Senneca, & Ghandour, 1997). Diet, vital signs (e.g., weight and blood pressure), and medications need to be monitored regularly to prevent exacerbations, hospitalizations, and ultimately reduce healthcare costs (Meystre, 2005). Many HF patients have poor knowledge of their disease process, lack skills to manage their condition, have insufficient access to healthcare providers, lack social support, and experience little motiv ation to comply with prescribed self care regimens (Mueller, Vuckovie, Knox, & Williams, 2002; Strmberg, 2005). Furthermore, daily weighing is a cornerstone of outpatient HF self management. Yet, not more than
44 40% of HF patients weigh daily, despite near universal implementation of daily weighing by hospitals and outpatient facilities as recommended by clinical guidelines for education and planning (De Caterina et al., 2008; Hunt et al., 2005; Strmberg, 2005; Van Der Wal, Jaarsma, & Veldhuisen, 2005). Tec hnology Emerging technologies provide opportunities to decrease both the high demands and burdens associated with self care, as well as the MHFS associated healthcare costs. The literature offers multiple studies citing the benefits of telemonitoring techn ology when used to remotely monitor vital signs of HF patients. Current interventions using technology such as telemonitoring devices to remotely monitor and mortality, as well as MHFS associated healthcare costs (Clark, Inglis, McAlister, Cleland, & Stewart, 2007; Inglis et al., 2010; Mller et al., 2010). Findings from a systematic review and meta analysis by Clark et al. (2007) support favorable clinical outcomes when remotely m onitoring HF patients. In fact, meta analysis results point to reductions in both hospitalization and mortality rates by 21% and 20% respectively (Clark et al., 2007). While the literature offers much research supporting the benefits of telemonitoring, oth er randomized controlled trials report no significant differences between telemonitoring intervention and comparison groups on outcome measures such as number of emergency room visits and hospitalizations (Chaudhry et al., 2010; Takahashi et al., 2012). Th ese results may stem from the complexity of MHFS, including learning to use new technology, which can be challenging for elderly patients (Joyce & Loe, 2010; Marziali, 2009; Sanders et al., 2012). As stated by Suter, Suter,
45 like all tools, is only as effective as the skill level of technology along with understanding how to manage their newly diagnosed heart failure symptoms. Rahimpour, Love ll, Celler, and McCormick (2008) conducted a qualitative study findings reveal major patient concerns including anxiety, low self efficacy, provider support, and user friendl iness of the technology. Moreover, study conclusions point to high self telemonitoring technology (Rahimpour et al., 2008). HF telemonitoring technology devices are likely to assume a larger role in MHFS. Improvements in the technology are enabling the provision of more specific information to healthcare professionals, information that is useful for early detection of HF exacerbations (Bodenheimer, Lorig, Holman & Grumback, 200 2; Darkins et al., 2008; McDonald, 2010; Pollonini, Rajan, Xu, Madala, & Dacso, 2012). While technology advances are encouraging, findings from a review by Schmidt, Schuchert, Krieg, and Oeff (2010) indicate potential problems not in the acceptance of remo te home monitoring interventions (90% acceptance rate), but in the actual willingness of HF patients to pa rticipate in the interventions. These findings indicate a technology acceptance rate of 90% in contrast to the intervention acceptance rate of 50% (Sc hmidt et al., 2010). Sustainability of telemonitoring interventions is also of concern since Chaudhry et al. (2010) report adherence rates to a telemonitoring intervention as attenuating from 90% in week 1 to almost 55% by week 26.
46 Without a clear u nderstanding of the factors influencing both acceptance and use of technology by patients for MHFS, continued technological improvements are unlikely to affect healthcare outcomes. Although literature exploring the use of telemonitoring technology in manag ing chronic diseases is expanding, studies specifically addressing care purposes are lacking. Health information technology (e.g., home monitoring devices) will not benefit patients unt il they are willing to accept, use, and perceive the technology as beneficial to their health, particularly when compared to usual or standard care methods (Or & Karsh, 2009). The purpose of this study is two fold (1) to explore and u technology for health. Materials and Methods Much research indicates patient uncertainty regarding health information technology use as a barrier to regular and sustained use of telemo nitoring equipment (Jimison et al., 2008). As a result, health technology researchers continue ef forts aimed such device is the Blue Scale (Blue Box, Inc.). The Blue S cale device tracks multiple vital sign readings important for HF patient home monitoring while the patient grasps handles which are large and attached to a reinforced base for adequate support and fall prevention. For the purposes of this study, the Blue S cale device was employed to symptoms.
47 Participants and Measures Initially, 21 eligible adults were identified and recruited for participation through hospitals, card iology clinics, and community groups in the greater Houston metropolitan area. All 21 participants consented to participate in the study. For varied reasons, one participant voluntarily withdrew from the study and five were ineligible and not included in t he final analysis. The study sample consis ted of 15 adults (age range, 45 82 years, M = 64.43. SD = 10.28) (see Table 3 1 for demographics). Study participants were compensated with a $25 gift card. Fourteen participants reported receiving a formal medical diagnosis of congestive heart failure (New York Heart Association functional class I or above) from a physician. One participant self reported a diagnosis of congestive heart failure; medical confirmation was not available. The sample was 66% male and 33% female. The racial composition was 60% White and 26.7% Black; 13.3% of participants were of Hispanic/Latino origin. The study was reviewed locally and received Institutional Review Board approval. To assess self care knowledge and practice, participants were given the European Hear t Failure Self Care Behavior 12 item Scale (EHFScBS), Usability Scale, and the Olso three item Social Support Scale (Ben, Dalgard, & Bjertness, 2012; Brook, 1986; Jaarsma, Strmberg, Martensson, & Dracup, 2003; Schmidt, Sheikza deh, Beil, Patten, & Stettin, 2008). Two questions on the EHFScBS refer to units of measure not commonly used in the U.S. To avoid misunderstanding, these items were converted to reflect common units relevant to study participants (e.g., kilograms to pound s and liters to cups). Also collected was information related to past 30 day device use, vital sign
48 self recording behavior (e.g., wrote down blood pressure or weight in past 30 days), and experience with the Blue Scale device for HF patients. Table 3 2 pr ovides additional information. Interviews Fifteen individual semi structured interviews lasting from 10 to 30 minutes were conducted by a member of the research team in locations conducive to private conversation. Each interview was audio taped. Prior to the interview participants were asked to step on and off the Blue Scale device. Participants then completed a demographic questionnaire and each of the quantitative measures. The interview was initiated following completion of the survey instruments. Inter view questions ranged from al signs. Because a constant comparative approach was employed, data were collected and analyzed concurrently. Use of this approach allows for integration of emergent themes and ideas into subsequent interviews (Creswell, 2012). Analysis Audiotapes were tr anscribed verbatim by a trained medical transcriptionist. Following transcription the verbatim transcripts were compared to the audio recording to ensure accuracy. The verbatim transcripts were organized and coded for initial themes. To establish reliabili ty, three independent researchers individually coded each transcript, and then met to discuss identified codes. Differences were discussed and the final coding scheme emerged. All qualitative data was analyzed using NVivo10 software
49 (QSR International, Inc .). Quantitative data was analyzed using SPSS version 19 (IBM SPSS Statistics). Results Study participants varied in knowledge related to sel f care of HF (EHFScBS range, 16 50 on a 1 5 Likert scale). However, mean self care scores (M = 29.21. SD = 9.62) (Schmidt et al., 2010) decipher, so for one response the two circled numbers, one and four on the five point Likert scal e, were averaged and for the missed response, the average of the scale was imputed. All participants reported use of a device in their homes sometime in the past 30 days to measure a vital sign (e.g., blood pressure or weight) and most reported recording t he data either in a daily log, on a piece of paper, or stored in a home ambulatory device. This sample reported a high level of social support. The descriptive data analysis complements and provides further insight into the qualitative find ings. Table 3 2 offers more information on these findings. Managing Heart Failure Symptoms When asked about self care for MHFS, participants most frequently mentioned medications, followed by diet and social support, either from friends, family, doctors, or nurses. Part icipants also emphasized the importance of maintaining a daily routine, including either staying on a consistent plan or increasing compliance with recommended self care behavior so as to avoid HF complications that could result in an emergency visit to th e hospital or unplanned visit to the doctor. When asked to describe decreased daily activity, and an increased need to relax or rest; symptoms which
50 caused participants t through their normal daily activities. While participants varied in their perceptions of how HF affected them physically, some defined the effects of HF in terms of emotions such as fear of pain or death. In fact, a number of participants listed fear as compromising not only their willingness and ability to seek additional health information, but also their willingness to take an active approach to MHFS. Technology Use and MHFS When asked about MHFS and technology use, reliance on physicians emerged as a common theme. Most participants reported seeking advice and information on HF self care from their doctor. One participant stated, ion. But the chance you While participants reported asking their doctor for advice on MHFS, compliance with the recommendations for MHFS, such as recording of daily vital signs, often depended on whether the partic ipants expected their doctor to ask for their recorded blood pressure and/or weight readings during office visits. However, participants acknowledged the importance of recording their vital signs between office visits, and expressed their preference for te All participants responded positively towards the general concept of using technology to aid in improving or managing their health in general and f or MHFS. While some participants tempered this positivity with a discussion of being overwhelmed by technology and concerned about keeping up with technological advancements, others liked the ability to obtain objective data.
51 u get from the machine; wh very Also present were technology related themes such as increased access to healthcare services that would facilitate more communication with doctors and sharing of real time health information, which would provide early indicators for worsening conditions, convenience, increased knowledge, and decreased healthcare costs. ld have without you hav ing to call in and tell him. Um, kind of instant information, which is good. In th e ion to your doctor faster than y ou could. And, um, I mean, how good is that to be able to i on in your body, you know, so you can do something about it instead of waiting three or four days to get to the doctor However, apart from a high prevalence of over the counter device use and positive attitudes towards technology, most study participants did not use other types of technology. Approximately one third of participants mentioned use of a computer and the Internet. Common onl ine activities included emailing, finding information on medications and/or health information, and searching for doctors and locations for medical tests. Participants described accessing health information by means of Internet sources such as, WebMD (most common), Google search, disease specific websites reported verifying information found online with their physicians. Overall, participants describe their physicians as their predominant and most reliable source for health information.
52 b is wrong rather take if from someone or an organization, a Overall, soci al media use for gaining health information was not widely accepted among participants. However, a few participants reported using Facebook for social reasons. In general, participants much preferred telephone conversations to social media use. t want to email you or talk with you over Facebook where 9,000 Health applications (apps) were positively perceived, but not often downloaded or used. Barriers to technology use included privacy concerns, mistrust of online information, low com puter use self efficacy, low eH ealth literacy, and the associated financial costs. Medical Devices and Telemonitoring Participants were asked to define telemonitoring, telehealth, and telemedicine. Most pa telemedicine. Responses varied from no understanding to the following, device and somehow you connect it and communicate with somebody Interestingly, among these participants use of over the counter monitoring devices was ov erwhelmingly common (see Table 3 2 ). Blood pres sure devices were most frequently and regularly used; some participants expressed preference for portable blood pressure devices capable of storing blood pressure readings for a short period of time. Participants were also familiar with the following devic es: defibrillator, glucometer, Life Vest, Prothrombin Time/International Normalized Ratio (PT/INR)
53 device, pulse oximeter, scale, Continuous Positive Airway Pressure (CPAP) machine, and hematology machine (complete blood count). While most participants re ported monitoring their blood pressure regularly, few monitored their weight daily. This was consistent with findings from question one on the iscussion with many participants revealed a lack of knowledge regarding the association betw een daily weighing, fluid build up or fluid gain, and prevention of HF exacerbation. Participants were aware of the importance of daily weighing, but offered reason s for not weighing such as forgetfulness, travel, broken or lack of a scale at home, time, not wanting to know their weight daily, or depending on other indicators such as edema for MHFS. Discussion current technology and the association of this use with their experiences and acceptance of technology use for MHFS. All participants in this study r eported using at least one over the counter technology related device, while some mentioned using more than one. However, even though many participants were managing more than one chronic condition, the most often reported and consistently used technological device was a blood pressure monitor. While blood pressure is an important measure and indicator of hea lth status, participants were less compliant with daily weighing. Because the healthcare industry is recognized for promoting regular blood pressure monitoring as essential to maintaining good health, it is likely older adults fail to perceive the importan ce of daily weight monitoring to health maintenance. The topic of daily weighing sparked emotional
54 responses from participants who associated daily weighing with being overweight or ent their blood pressure readings for monitoring volume status. This finding represents an opportunity for healthcare professionals and device manufacturers to work with HF patients to design devices for measuring weight that do not provoke negative weig ht related emotional responses. Devices that report weight whether to view their numerical weight or transmit it blindly to their caregivers may be more acceptable. F requent use of technology by thes e HF participants included over the counter devices, followed by use of the Internet. Study participants mentioned various barriers to access and limitations to use of Internet health information, and in the end, most depen ded on their physicians for health information or to validate information found online. Therefore, it is critical that clinicians recommend appropriate and credible websites to patients who tend to gather health related information online. The American Hea rt Association website includes a list of online patient resources. general, they are positive about the use of technology for MHFS. Therefore, acceptance and use of telemon itoring technology was favorable among study participants, particularly if it is physician initiated. However, patient acceptance and use of telemonitoring technology depends somewhat on their experiences with general technology. As a result, prior to pres cribing telemonitoring technology for patient use,
55 preferences, use, and experiences with technology. Consideration of these issues will help in prescribing appropriate tech nology for patient use, resulting in improved adherence to MHFS. Based on information retrieved from the literature and findings from this study a list of suggested assess ment items are provided in Figure 3 1 Finally, among these participants, social m edia use was not popular. Therefore, recommendations for social media use are guarded. Patients who are comfortable with social media should be advised to limit their interactions to professional sites such as the American Heart Association, and to avoid F acebook and sites offering comments and advice lacking references and credibility. Furthermore, findings suggest that the reluctance of HF patients to use technology in order to take a more active approach to MHFS may stem from fears of disability or dea th associated with their inabilities to appropriately process complex, technology generated information about their condition. Future studies should examine ways to d related fears. For example, healthcare providers and medic al device manufacturers, working with HF patients, could design user centered devices that allow patients to customize features. Customizing would allow for increased compliance with daily monitoring since fears associated with HF should decrease. Limita tions perceptions and use of technology to manage disease symptoms. Findings from this patient sample augment the literature in the areas of technology interventions. Despite the r ich data emerging from the qualitative study, limitations exist. We employed a self selected convenience sample of older adults recruited at hospitals, cardiology clinics,
56 and community groups in the greater Houston metropolitan area; participant stage of heart failure was unknown. Findings from this study may not be generalizable to other and acceptance of technology use for health interventions. The accuracy of these self reported data is unknown, and use of individual interviews to collect data may have caused some participants to offer responses perceived as sociall y desirable to the interviewer. Conclusion for MHFS. incidence and healthcare costs associated with HF in the U S coupled with the proliferation of new technologies to MHFS present an opportunity for healthcare pr ofessionals to leverage technology to increase self care compliance and improve health outcomes for HF patients. However, new technology, no matter how innovative, will not change outcomes if patients are not willing or able to use the devices correctly an d consistently. Therefore, determining user needs and experiences with medical devices is essential. More empirical research is needed to verify findings from this qualitative study. Findings from this study provide formative research for healthcare profes sionals in the development of health behavior interventions to more appropriately match HF patients with technology that will facilitate needs related to MHFS and optimize healthcare resources for increased quality of care.
57 Table 3 1. Baseline characteris tics of p articipants Variables Frequency (%) M SD Age 64.43 10.28 Gender Male 10 (66) Female 5 (33) Race White 9 (60) Black 4 (26.7) Hispanic 2 (13.3) Education 3 (20) GED 1 (6.7) High School 2 (13.3) Some College 5 (33.3) 1 (6.7) 2 (13.3) Graduate Degree 1 (6.7) Chronic Diseases Hypertension 11 (73.3) Diabetes 3 (20) COPD 1 (6.7) CAD 4 (26.7) Note. SD, Standard Deviation; M, Mean; COPD, Chronic Obstructive Pulmonary Disease; CAD, Coronary Artery Disease
58 Table 3 2. Self care and home device use past 30 d ays Variables Frequency (%) M SD EHFScBS 29.21 9.62 Device Measures Blood Pressure 13 (86.7) Weight 12 (80) Sugar Level 2 (13.3) Oxygen Sat 3 (20) Other 2 (13.3) Record Measures 11 (73.3) Social Support Help from Neighbors Very Easy 4 (26.7) Easy 4 (26.7) Possible 5 (33.3) Difficult 1 (6.7) Very Difficult 1 (6.7) People to Count On None 1 (6.7) 1 2 5 (33.3) 3 5 1 (6.7) 5+ 8 (53.3) People Concerned A lot 9 (60) Some 3 (20) Uncertain 3 (20) Note. EHFScBS, European Heart Failure Self Care Behavior Scale; Sat, Saturation; SD, Standard Deviation; M, Mean
59 Over the Counter Device Use Blood Pressure Machine Scale Pulse Oximeter Other Record Vitals Access to Technology Landline Cordless Phone Wireless Technology Use Computer Email Internet Personal Health Information Health Website Smartphone Health Applications (Apps) Text Messaging Internet iPad Health Applications (Apps) Health Website Other Social M edia Use Facebook Twitter Other Home Environment Social Support: Technology Support at Home Location for Technology: Bathroom Bedroom Other Flooring: Carpet Tile Wood or Laminate Other Outlets: Few (2 3) Multiple (5+) Figure 3 1 Assessment Items for telemonitoring interventions for heart failure p atients
60 CHAPTER 4 ASSESSING THE DIGITAL HEALTH DIVIDE: DIFFERENCES BETWEEN OLDER ADULT USERS AND NONUSERS OF ONLINE AND OFFLINE HEALTH INFORMATION SOURCES AND MEDICAL DECISION MAKING Summary The Internet is increasingly being used as a source of health information for improved patient provider communication and medical decision making. However, concerns over limited access to Health Information Technology (HIT), such as the Internet by older patients who cou ld benefit the most, fuel the digital divide debate. Older adults ten d to be a vulnerable population and are more likely than their younger cohort groups to deal with many disabilities and health disparities that limit their access to healthcare services a nd HIT. However, once barriers to technology access are reduced, issues surrounding a digital health divide ensue. HIT benefits must be actualized by older adults compared to other sources of access to healthcare information and services. Yet, more researc h has been conducted on barriers to HIT access and use by older adults and less on factors that promote engagement. Therefore, this study evaluated a potential digital health divide present among older adults, accessed characteristics between older adult u sers and nonusers of online health information, and examined relationships between factors that promote HIT engagement in older adults. A cross sectional survey study design was conducted on a randomly selected sample of 225 older adults (age range 50 92, M = 68.9, SD = 10.4). were conducted between April and May of 2013. The Decision Self Efficacy Scale, Computer Self Efficacy Measure, and Reliance Scale were used to measu re relationships promoting HIT engagement between users (n = 105) and nonusers (n =
61 119) of online health information. Pearson chi square tests compared user and nonuser groups. T tests and Ordinal Least Square regression models were used to compare group differences and correlates on the Decision Self Efficacy Scale, Computer Self Efficacy Measure, and Reliance Scale. Seventy six percent of all participants had Internet access Technology access was also high among participants; 79% reported having cells p hones 65.6% desktop computers and 58.6% laptop computers or netbooks Overall, the user group reported significantly more devices in their homes in all categories of technology compared to the nonuser group. While user and nonuser groups were significant ly different on Internet and technology access, a large percentage of nonusers had Internet access (56.3%), desktop computers (55.9%), and laptop computers or netbooks (43.2%). The user and nonuser groups were significantly different on age (M = 66.29 vers us M = 71.13), education and prior exposure to healthcare Approximately 75% of participants sought information regarding health from healthcare professionals followed by the Internet (46.9%), however, users and nonusers differed in frequency and types of sources sought. Users of online health information used more offline sources of health information compared to nonusers. Statistically significant differences were found. Users of online health information had higher mean scores on the Computer Self Effic acy Measure than nonusers, t (159) = 7.29, p <.0001. Users of online health information preferred a self reliant approach and nonusers a physician reliant approach to involvement in medical decisions on the Reliance Scale t (218) = .3.09, p = .001. No sign ificant differences were found between users and nonusers on the Decision Self Efficacy Scale. Computer Self Efficacy and tablet computers were positively associated with Decision Self Efficacy and other sources of
62 health information were negatively associ ated. Internet or World Wide websites were associated with a self reliant approach to medical decisions on the Reliance Scale. Finally, overall confidence to get health information online and other sources of health information were positively associated w ith Computer Self Efficacy; age and desktop computer were negatively associated. This study is the first to examine Decision Self Efficacy, Computer Self Efficacy, Reliance, and evaluate the digital health divide in an older adult population. This study fo und significant differences between users and nonusers of online health information and examined factors of HIT engagement. More empirical research is needed to verify these findings and explore other factors that promote HIT engagement among older adult p opulations for improved health outcomes and medical decision making. Introduction and Literature Review Health Information Sources, Medical Decision Making and Self Efficacy nform their h ealthcare decision making and increase their healthcare knowledge and management is well known (Benbassat, Pilpel, & Tidar, 1998; Flynn et al., 2006). However, prior to the development of the World Wide Web, access to health information was limited. Tradit ionally, patients obtained information from healthcare providers, mass media sources, or trusted local community members deemed knowledgeable about health issues (Cotton & Gupta, 2004). The Internet now offers patients a convenient way to access a wide var iety of health information for health prevention, disease management, and to assist with medic al decision making (Ybarra & Suman, 2006). The Internet is now being used as a medium to improve patient provider communication and quality of care, through such applications as personal health records, email, and transmission of
63 medical data from home based devices (Kruse, et al., 2012). Recently, the Institute of Medicine embraced these innovations in digital access to health information for improved healthcare d elivery and patient involvement in decision making in the form of Patients are now taking a more active approach to managin g their health by using technology. Chronic disease patients use the Internet to seek information to improve their condition and make health decisions (Fox, 2007). Approximately 75 percent of all patients with a chronic disease who use the Internet reporte d that a previous health information search contributed to a decision about how to treat an ailment, and 69 percent stated the information prompted new questions to ask doctors (Fox, 2007). While significant increases in Internet use for health information have been noted in the last decade, little is known about how access to health information on the Internet affects health related decisions made by older adults (Fox, 2007; Taha, Sharit, Czaja, 2009; Zickuhr & Madden, 2012). Many medical and health relat ed decisions involve numerous options that lack an optimal clear choice (Frosch et al., 2008). Since older adults tend to deal with chronic diseases and use healthcare services at a much greater rate than younger adults, they frequently are faced with maki ng many more medical decisions (Anderson, 2002; Taha et al., 2009; Xie, 2009). The Internet is increasingly seen as a useful decision aid (i.e., a tool that provides unbiased information about the advantages and disadvantages of a specific choice) (Stacey et al., 2012; Frosch et al., 2008).
64 A recent Cochrane review found that decisio n aids, such as videos or web based tools, increased patient knowledge a nd their engagement in decision making, improved understanding of medical health outcomes, and ultimatel y increased patient decision satisfaction (Stacey et al., 2012). A randomized controlled trial conducted by Frosch et al. (2008) found reductions in d ecisional conflict and knowledge differences between intervention and cont rol groups on use of Internet de cision support sites for men contemplating pr ostate cancer screening A survey of 2 575 adult Internet users and nonusers (ages 40) rated a doctor as their primary source of information for medical decisions. Among Internet users, the Internet was liste d as the second most popular source (Couper et al., 2010). Decisions related to surgeries, medications, and cancer screening s were cited most often by adults who referred to the Internet for help with medical decisions (Couper et al., 2010). The Internet i oices; i n fact, only 28 percent of respondents in this sample reported making a speci fic medical decision using information found online (Couper et al., 2010). However, while this study sheds li or informing medical decisions discussed with a doctor, it is limited and lacks an investigation of the relationship between older use of the Internet for health information and their ability or confidence to e ngage in medical decisi on making (Couper et al., 2010). few studies have attempted to explore the relationship between older adults who use the Internet for health information an d their ability or self efficac y to engage in medical decision making. Self efficacy measures have been used to predict behaviors such as
65 technology use and ability to make informed medical decisions (Chu, Mastel Smith, 2010; Cranney et al., 2002; Czaja et al. 2006). Self efficacy has been widely used to particular task or behavior (Bandura, 1997). Low self efficacy has been shown to predict less engagement in a behavior an d higher levels of self efficacy predict more engagement (Bandura, 1997; Lorig, 2001). Self efficacy for medical treatment decisions information about treatment options and con vey their concerns to make more informed two computer education intervention studies conducted on older adults found increased perceptions of computer self efficacy for h eal th information retrieval pre post training sessions (Chu, Hube r, Mastel Smith, & Cesario, 2009 ; Chu, Mastel Smith, 2010). Furthermore, Czaja et al. (2006) found that use of technology was predicted by computer self efficacy. Self efficacy is a widely re searched construct and a proven measure to predict health behavior; furthermore, self efficacy beliefs show stability with advancing age (Bandura, 1994). Reliance and Medical Decisions Technology has changed the U.S. healthcare system from provider centric to a shared o r informed approach to decision making (Xie, 2009). Therefore, patients are now expected to take a more active ro le in their healthcare decision making (McNutt, 2004). Predictive factors correlated to low invo lvement by patients in decision m aking include severity of illness, culture, patient role, sociodemographic status, and personality (Xie, 2009). Yet, despite these predictive factors, less is understood about why patients still search for health information, what benefits they obtain from the
66 information, or how access to online health information impacts patient and healthcare Burnett 2004; Bylund, Sabee, Imes, & Sanford, 2007; Xie, 2009). Most patients want information related to their specifi c illne ss or treatment, yet differ in their preferences for involvement in m edical decision making (Makoul, 1998). Makoul (1998) developed a two item scale to test this concept. A physician reliant patient is characterized as someone who 1) relies on a doctor to make their medical decisions and 2) is typically uninterested in talking about treatment options. In contrast, a self reliant patient is defined as an individual who prefers a mutual participation approach to medica l decisions (Makoul, 1998 ). The scale mea sures these constructs in two separate statements, consisting of six Likert scaled responses. Makoul developed and tested these constructs prior to the popularity of the Internet as a source of health information. Less is understood about reliance as a con struct for understanding involvement in medical decisions and use of the Internet for health information. Health information found online by patients can greatly affect patient and healthcare 2007). Yet, only one study tested these constructs among patients who used the Internet for health information. Findings revealed minimal differences between self reliant and physician reliant patient preferences for discussing Internet health informatio n with a healthcare provider (Bylund, et al., 2007). The authors noted v arious limitations to the generalizability of their findings, such as use of a convenience sample and a sample of individuals who reported only using the Internet for health informatio n (Bylund et al., 2007).
67 Digital Divide and Older Adults The digital divide is defined as the gap between those who have access to information and communication technologies and those who do not (Bernhardt, 2000) However, as broadband access becomes ubi quitous, a more pressing concern is a potential digital health divide (i.e., a gap between those who access and use health information technology and those who do not), especially among vulnerable populations. Older adults are typically among the most vuln erable because they tend to deal with many disabilities and health disparities that limit their access to healthcare services and technology, such as an increased prevalence of chronic diseases, lack of experience using technology, and other limiting facto rs compared with younger adult cohorts (Anderson, 2002; Jimison et al., 2008). Recent healthcare reform and healthcare IT adoption incentive programs show promise in reducing barriers of access to technology and healthcare among older adults. Programs and initiatives such as Healthy People 2020 the American Reinvestment and Recovery Act, meaningful use regulations (i.e., electronic health record), and accountable care organizations promote the development of electronic methods to engage patients in their healthcare (Kieschnick & Raymond, 2011; Monsen et al., 2012; Shrewbury, 2002). In addition to government initiatives and programs, many private and nonprofit sectors, such as SeniorNet and Microsoft, have joined the cause to close the digital divide. Senio rNet is a nonprofit organization that promotes computer and Internet usage among older adults through education programs, discounts on technology products, and other similar offerings; and Microsoft supports IT efforts to increase computer use among older adults (SeniorNet; Shrewbury, 2002).
68 A recent report on broadband and Internet access from the National Telecommunications and Information Administration (NTIA) found that 2010 nationwide adoption rates for broadband was 68.2 percent and nearly 72 percen t of Americans used the Internet (NTIA, 2011). Most people accessed the Internet from home followed Frequent reasons reported for not connecting to broadband at home we ethnicity, and disability are predictive factors of broadband use at home (NTIA, 2011). Whil e adults between the ages of 18 24 lead in broadband use, older adults show ed the largest growth rate from the previous year; however, overall, in adults 55 years of age and older, rates of broadband use at home equaled 50.1 percent compared to 80.5 percent of younger adults (NTIA, 2011). NTIA findings are consistent with other national findings. Recent Pew Internet & American Life Project data on Internet use by older adults reported that 53 percent of adults 65 years of age and older use the Internet or email, and 70 percent of those who access the Internet use it daily (Zickuh r & Madden, 2012). This percentage decreases significantly after age 75. Among adults 75 years of age and older, 34 percent use the Internet and only 21 percent reported home broadband use (Zickuhr & Madden, 2012). Health Information Technology Engagement and Older Adults Technology and mobile device use among older adults is increasing. Surprisingly, 69 percent of adults 65 years of age and older report ed owning a mobile phone; however, information on mobile Internet access was not reported (Zickuhr & Ma dden, 2012). Furthermore, 48 percent own desktops, 32 percent own laptops, 11
69 percent own e readers, and eight percent own tablets; notable, for adults 76 years of age and older, 56 percent own cell phones, 31 percent own desktops, 20 percent own laptops, five percent own e readers, and three percent own tablets (Zickuhr & Madden, 2012). However, once older adults have access to technology (i.e., through mobile devices or a computer) and the Internet, issues related to acceptance and usefulness of HIT ari se. The Technology Acceptance Model (TAM) has been used to predict technology use in adults. TAM constructs have been tested in studies predicting use of the Internet and HIT. The TAM proposes that perceived ease of use and perceived usefulness of technolo gy predicts behavioral intention to use and leads to actual technology use adoption (Davis, 1989; Venkatesh, Morris, Davis, & Davis, 2003). Lederer, Maupin, Sena, and Zhuang (2000) surveyed 163 adults and found that ease of use and usefulness predicted Int ernet and Web site acceptance and use. Another study surveyed 294 patients on acceptance of an Internet patient physician portal, and found that usefulness had a positive direct effect on behavioral intention to use the system (Klein, 2007). Moreover, a sy stematic review of patient acceptance of health information technology fou nd that most studies testing the TAM confirmed ease of use and usefulness as predictors of technology acceptance (Or & Karsh, 2009). Furthermore, older adults must perceive access to health information through the Internet as beneficial to their well being when compared to other modes of access to health information, such as from healthcare providers or print sources. Recent trend reports show that the number of adults who access heal th information online is increasing (Zickuhr & Madden, 2012). Approximately 74 percent of U.S. adults 18 years
70 of age and older use the Internet and of those who use the Internet, 80 percent looked online for health information, according to a report by th e Pew Internet & American Life Project (Fox, 2011a). However, fo r adults between the ages of 66 to 74, the percentage who access the Internet for health information drops to 63 percent, and for adults aged 75 years and older it drops to 49 percent (Rainie, 2012). Adults reported going online to look for information related to a particular disease or treatment among a number of other health topics (Fox, 2011b). Approximately 35 percent of adults reported they went online to self diagnose a medical condition they or someone they know might have; additionally, 70 percent obtained health information from healthcare professionals, 60 percent from friends and family, and 24 percent from other individuals with similar medical conditions (Fox & Duggen, 2013). Adult s who are more likely to access the Internet for health informa tion are between the ages of 18 to 49, and also more likely to be white, female, a caregiver, college educated, and of high socioeconomic status (Fox, 2011). Whereas, adults who are less likely to access the Internet for health information are 65 years of age and older, have a disability, are non white, possess a high school education or less, and are of low socioeconomic status (Fox, 2011). Pew Internet & American Life Project data findings ali gn with other research findings in the area of Internet use for health information. In a sample of 713 primary care patients, Kruse et al. (2012) found that 78 percent used the Internet. Predictors of Internet use were age, income, socioeconomic status, he alth status, and lack of chronic conditions; however, age was the strongest predictor of Internet use among patients with chronic diseases. In a sample of 385 participants Cotton and Gupta (2004) found that age, income, education, and health
71 were defining characteristics between online and offline health information seekers. Health information seeking was defined in this study as the search for knowledge or advice that helps lessen uncertainty and increase understanding about health status (Cotton and Gupta 2004). A study conducted with 2007 Health Information National Trends Survey (HINTS) data found that education, age, and home Internet access were significantly associated with use of the Internet for health information ( Lustria, Smith, Hinnant, 2011). G ender, education, and age were factors significantly associated with searching for health information from any source (Lustria, et al., 2011). Additionally, another study conducted with 2009 HINTS data reported age, education, users of health services, ge nder, health status, income, and ethnicity as predictor factors of HIT use (Choi, 2011). Interestingly, one study asked participants if they used the Internet to keep track of personal health information in the last 12 months (i.e., test results) and nea rl y 12 percent responded affirmatively (Lustria et al., 2011). Fu rthermore, a survey of younger (n = 430 aged 18 28 ) and older (n = 251 aged 65 90 ) adults on computer/Internet experience and use found significant differences between older and younger adult s o f older adults reported experience with a compu ter and less than 50 percent reported u se of the Internet for more than five years compared to 99 percent and 90 percent of younger adults respectively (Olson et al., 2011). Digital Health Divide A digital divide persists between younger and older adults. However, as government, nonprofit, and private initiatives continue to close this gap, a more pressing
72 concern is a di gital health divide. As the number of older adults who access the Internet continues to rise, it appears they will increasingly access the Internet as a source of health information. However, for HIT to benefit older adults and positively affect their heal th outcomes, it is critical they perceive technology as being useful for their health when compar ed to other sources of health information. While many factors were found to limit or predict technology engagement for health purposes (i.e., age, health statu s, and education) less is known about the benefits of HIT use and differences between older adult users and nonusers of online and offline health inform ation sources. For HIT to improve health outcomes in older adults, it is first necessary to understand h ow this population currently interacts with and perceives online h ealth information, including the benefits and drawbacks. Given that no study has investigat ed health information sources, Computer Self Efficacy, R eliance for m edical decisions, and Decisio n Self Efficacy for medical decision making between older American users and nonusers of online health information, the purpose of this study is (1) to assess the relationship between users and nonusers of online health information and sel f efficacy for me dical decision making (2) to examine the relationship between users and nonusers of onlin e health information and their R eliance, either self reliant or physician reliant, for medical decisions (3) assess the re lationship between users and nonusers of onli ne health information and computer self efficacy, and (4) to evaluate the digital health divide among older adults. In addition, the following hypotheses will be tested: (H1) there will be no significant difference in Decision Self E ffi cacy between users a nd nonusers of online heal th information; (H2) users of online health information will be more self reliant and nonusers will be more
73 physician reliant on the Reliance Scale ; (H3) users of online health information will have higher Computer Self E ffi cacy t han nonusers of online heal th information (H4 ) there will be no relationships between demographic variables, computer self efficac y, self reported health status chronic disease(s), technology use, and use of health information sources on D ecision Self Eff icacy; (H5 ) there will be no relationships between d emographic variables, level of Computer Self E fficac y, self reported health status chronic disease(s), technology use, and use of health inform ation sources on Reliance; and (H6) there will be no relatio nships between demographic variables, self reported health status chronic disease(s), technology use, overall confidence to obtain health information on the Internet, and use of health information sources on Computer Self Efficacy. Methods We conducted a survey of 225 English speaking Florida residents 50 years of age or older. The Bureau of Economic and Business Research (BEBR) at t he University of Florida conduct ed random digital dialing (RDD) of landl ine telephone numbers of part icipants 50 years of a ge and older in the state of Florida Landline phone numbers occur in banks of 100. For example, the telephone number 352 392 2908 is part of a bank of phone numbers beginning with 352 392 2900 and ending with 352 392 2999. The sample included Florida phon e number banks containing at least one listed phone Institutional Review Board. Trained telephone interviewers collected all data. To account for gender non response bias, the interviewer script asked to speak with a male household resident 50 years old or older (Hu, Pierannunzi, & Balluz, 2011). If a male fitting the criteria was not
74 available the interviewer asked to speak with a female 50 years of age or older. This method i s a standard process used during telephone based surveys. Once an eligible participant was reached the interviewer read participants the verbal informed consent script. Once eligible participants verbally consented, they were read the survey questions and response options. We pilot test ed the survey to check for item clarity The first 29 completed surveys were analyzed for missing data or item response biases. The script, questions, and answer choices passed the pilot stage and were included in the final analysis. No identifiable or confidential information was collected or recorded from participants. A total of 4,524 landline telephone numbers were dialed during the data collection process during six weeks between April and May of 2013. Phone numbers w ere originally dialed to a maximum of seven attempts, but due to budget constraints the maximum attempts per phone number were reduced to three. A total of 957 of the 4,524 calls eventually reached a potential participant, however, of those 957, 159 were d eemed ineligible based on the inclusion criteria. A total of 573 of the 957 were deemed eligible but refused to participate. Therefore, the final sample of 225 eligible respondents represented a response rate of 28.2% of eligible participants, which was th e total that the BEBR was hired to recruit. Measures Dependent Variables Decision self e fficacy The Deci sion Self Efficacy Scale, comprised of 11 questions, was used to measure self The Deci sion Self E fficacy Scale was developed to measure older adults ability or confidence in making
75 informed decisions The scale measures three constructs of decision making, 1) ability to obtain information (questions 1 4), 2) ability to ask questions (questions 5 7), a nd 3) ability to make an informed choice (questions 8 11) in relation to medication decisions ( Dy, 2007; self efficacy, and research on decision aids (Dy, 2007; T he Decision Self Efficacy Scale is a va lid and reliable scale tested among separate populations of 2002). The three response version of the Decision Self Efficacy Scale wa s chosen over the five response scale for ease of administration over the phone. Answer categories to statements related to confidence for making choices and obtaining information about Reliance Reliance for involvement in medical decisions was measured using two single statements each with a four = 4 Lower s cores on this scale correspond to a more physician r eliant orientation and higher scores correspond with a more self reliant orientation in medical decision making. The original scale developed by Makoul (1998) used a six core of one or two was associated with a greater self reliant orient ation and a score of five or six with a greater physician r eliant orientation for medical care related decision making. Makoul summed responses from the two statement questions, and then divided them by two; a score of five or six relate d
76 to a physician r eliant orientation preference and a score of one or two relate d to a self reliant orientation preference (Makoul, 1998). A correlation of r = 0.55 with a sample size of N = 269 at p < .001, with a U shaped distribut reported between the two reliance item s (Makoul, 1998). Makoul (1998) tested this on a range of patients in a primary care setting (n = 855, age range 0 87) and found that older adults tend to be more physician reliant than younger adults. This scale was later modified by Bylund et al. (2007). They used a five studies used this scale for RDD data collection, it was modified to a four item Likert answer scale for administration over the phone. Additionally, the modified statements Computer self efficacy The Comput er Self Efficacy Measure was used to measure older adults c onfidence in using computers, the Internet and searching for health information This instrument was initially tested and used in a population of ol der women (Campbell, 2004). The instrument was lat er modified and tested for validity and reliability by Chu, Huber, Mastel Smith, and Cesario (2009 ) in an older adult population. The modified version by Chu et al., (2009) was used because it uses a four item response Likert scale instead of the original five item Likert response scale, however, for this study the
77 Independent Variables Demographics and characteristics D emographic and characteristic measures include d age, gender, ethnicity, education, chronic disease, perception of helpfulness or harmfulness of online health information, self reported health sta tus, and prior exposure to healthcare taken from a combination of nationally valid and reliable open source survey items. Self reported answer categories were excellent, ve ry good, good, fair, or poor. Prior exposure to illnesses, injuries, or other health issu are there any other ways you have been exposed to dical advice or health information found on the follow Additionally drawn from nationally available open source survey items, were questions related to Internet and technology access. Technology access was measured by reading participants a list of items and asking participants to indentify each item
78 available in their home (i.e., a desktop computer, a cell phone, etc.). Participants who phones to look up health information, and if they used any software applications (apps) you access the Internet or World Wide Web at home, work or from any other locatio Health information s ources Health information sources were measured using the following combination of yes/no questions, during the past 12 months have you sought information regarding a health concern or me dical problem from (1) healthcare professionals (2) friends or family members (3) Internet or World Wide websites (4) magazines, brochures, or books (5) newspaper articles (6) television or radio, or (7) other (Cotton & Gupta, 2004). Participants reporting questions related to frequency of use (i.e., daily, weekly, monthly, or more than monthly), what health information website they liked, and if they had used the Internet to keep track of perso nal health information in the last 12 months. Lastly, participants were Analysis BEBR supplied a file of all recorded data from completed surveys. First, we responses as deletion was used to account for any missing items in the analysis. Reverse coding was
79 conducted on the Computer Self Efficacy Measure and the Decision Self Efficacy Scale for ease of interpretation of the results Efficacy Scale and Efficacy Measure. Three new variables were created, (1) age = 50 64, 65 74, and 75+, (2) chronic2 = without a chronic disease, one or more chronic disease(s) (3) race = non minority, minority. A confirmatory factory analysis for latent variables was conducted on the Decision Self Efficacy Scale and the Computer Self Efficacy Measu re. M plus 7.1 (Muthen & Muthen, Los Angeles, CA) was used to test factors and model fit on each scale; each scale met the goodness of fit Approximation (RMSEA) < .05. We also condu cted tests of reliability for internal Efficacy Measure, = .91, Decision Self Efficacy Scale, = .83, and Reliance = .67. We conducted univariate analysis to examine frequency and distribution of study variables; and bivariate analysis to test H1, H2, H3, and Pearson chi square ( 2 ) tests on independent variable differences between users and nonusers of online health information. Finally, ordinary least squares (OLS) regression was used to test H4, H5, and H6. All univariate, bivariate, and regression analyses were conducted with SAS 9.3 (SAS Institute Inc., Cary, NC). Results Participant Characteristics Table 4 1 shows that participants consisted of N = 225 older adults (age range 50 92, M = 68.9, SD = 10.4); 45.8 percent were male and 54.2 percent female; and
80 87.6 percent were White, 6.7 percent Black, and 6.3 percent Hispanic. Overall, the majority of participants had some college education or greater, while approximately 22 percent had a high school education or less. Most reported living with one or more ds or family members in the medical field and 44.4 percent reported taking a health related course or emergency training (i.e., CPR). Users (n = 105) and nonusers (n = 119) of online health information did not differ significantly on self reported health status race gender or chronic disease The two groups differed significantly on education 2 (5, N = 222) = 11.47, p = .04, age 2 (2, N = 220) = 16.65, p = .0002, and healthcare exposure courses or emergency traini 2 (1, N = 224) = 4.79, p = .03. Users of online health information tended to be younger (M = 66.29 versus M = 71.13) and more educated (87.6% versus 69.2% had some college education or more) compared to nonusers. Health Information Sources and the Dig ital Health Divide Table 4 2 shows sources of health information participants accessed for information concerning a health or medical issue. Overall, the majority of participants sought information regarding health from healthcare professionals (75.6%) fol lowed by the Internet or World Wide websites (46.9%). In addition to the use of the Internet or World Wide websites for health information, online users also reported more offline use of health information sources compared to nonusers: healthcare professio nals 2 (1, N = 224) = 26.07, p < .0001, friends or family members 2 (1, N = 224) = 20.11, p < .0001, magazines, brochures, or books 2 (1, N = 224) = 29.84, p < .0001, newspaper articles
81 2 (1, N = 224) = 7.08, p = .008, and television or radio 2 (1, N = 223) = 4.15, p = .04. Among the online user group the most cited sources of health information, following the Internet and healthcare professionals were magazines, brochures, books and fr iends or family members Among the nonuser group, television or r adio and newspaper articles were the most cited sources, following healthcare professionals Participants mentioned additional health information sources from journal articles, medical bulletins, special agencies that supply information on diseases, and Hu mana health insurance and home care. Table 4 nonusers of online health information. Surprisingly, 76 percent of all participants reported access to the Internet. However, 99.1 percent of onli ne health information users and only 56.1 percent of nonusers reported Internet access 2 (1, N = 224) = 56.43, p < .0001. The majority of participants had cell phones (79%), desktop computers (65.6%), and laptop computers or netbooks (58.6%) in their home s; fewer participants had tablet computers (34.8%), electronic book devices (29.6%), iPods or other MP3 players (29.5%), or game consoles (17.9%). Overall, the online user group reported significantly more devices in their homes across all types of technol ogy compared to the nonuser group: desktop computers 2 (1, N = 223) = 11.13, p = .001, laptop computers or netbooks 2 (1, N = 221) = 25.45, p < .0001, tablet computers 2 (1, N = 223) = 14.70, p = .0001, electronic book devices 2 (1, N = 222) = 5.65, p =. 017, cell phones or mobile devices 2 (1, N = 223) = 5.19, p = .023, iPods or other MP3 players 2 (1, N = 223) = 21.90, p < .0001, and game consoles 2 (1, N = 223) = 4.65, p = .031. Yet, despite differences between user and nonuser groups on Internet and technology
82 access, 56.3 percent of the nonuser group reported Internet access and 55.9 percent reported having a desktop computer Table 4 who reported use of the Internet or Worl d Wide websites for health information or had a cell phone were asked additional questions about their use of technology for health. Online users reported use of the Internet, daily (4%), weekly (18%), monthly (48%), and more than monthly (30%). Furthermor e, 45.7 percent had a preferred health website and 33.3 percent kept track of personal health information online. The most preferred health website mentioned by participants was WebMD followed by Mayo Clinic. Among g a cell phone 15.3 percent reported using a cell phone in the past 12 months to look up health or medical information, 2 (1, N = 176) = 9.45, p = .002, and 12.1 percent reported using a health app to track or manage their health. Finally, Table 4 5 shows participants overall confidence to retrieve health information online and perceived helpfulness or harmfulness of online health information (29.2%) that they could get advice or information about healthcare or medical topics on the Internet if they needed it. However, significant differences were found between user and nonuser groups, 2 (3, N = 223) = 36.47, p < .0001, on overall confidence t o get health information online; whereas, 26.5 percent of percent of online users and 24.1 percent of nonusers reported that they or someone
83 they knew had been helped by following any medical advice or health information found on the Internet, 2 (1, N = 221) = 47.62, p < .0001. Decision Self Efficacy, Computer Self Efficacy, and Reliance Tables 4 6 shows results of the t tests for the Decision Self Efficacy Scale, Reliance Scale, and Computer Self Efficacy Measure between users and nonusers of online health i nformation H1 predicted no significant difference in Decision Self E ffi cacy between use rs and nonusers of online heal th information. Decision Self Efficacy scores were not statistically significant between users (M = 28.93, SD = 3.41) and nonusers (M = 28.79, SD = 4.17) of online health information, t ( 201) = .25, p > .05, therefore we fail to reject H1. H2 predicted that users of online health information would be more self reliant and nonusers would be more physician reliant on the Reliance Scale; Reliance score differences were found to be statistically significant between users (M = 4.41 SD = 1.68) and nonusers (M = 3.68, SD = 1.78) of online health information, t (218) = 3.09, p = .0011, d = .42, where lower scores on the Reliance Scale correspond to self reliant and higher scores correspond to physician reliant preference for involveme nt in medical decisions. Therefore, we reject H2. H3 predicted that users of online health information would have higher Computer Self E ffi cacy than nonusers of online heal th information. Computer Self Efficacy Measures were found to differ significantly between users (M = 17.39, SD = 2.39) and nonusers (M = 13.43, SD = 5.19) of online health information t (159) = 7.29, p = <.0001, d = .98. Higher mean scores on the Computer Self Efficacy Measure are associated with higher levels of self efficacy. Therefor e, we reject H3.
84 Table 4 7 shows regression models on the Decision Self Efficacy S cale, Reliance Scale, and the Computer Self Efficacy Measure. H4 predicted no relationships between demographic variables, Computer Self Efficacy, self reported health sta tus, chronic disease(s), technology use, and use of health information sources on Decision Self Efficacy. All predictor variables (gender, Internet access, health information sources, categories of technology, overall confidence to get health information o nline, Computer Self Efficacy Measure, self reported health status, chronic2, age, education, race, and exposure to healthcare) for H4 were entered simultaneously in the regression model to check for significance at p < .05. Significant parameter estimates at p < .05 were retained and the model was rerun until only significant parameters at p < .05 remained (i.e., backward elimination). The final model of statistically significant parameter estimates for Decision Self Efficacy was Computer Self Efficacy, ot her category of health information sources, and tablet computers, R2 = .08, F(3, 196) = 5.72, p = .001. Therefore, we reject H4. H5 predicted no relationships between demographic variables, Computer Self Efficacy, self reported health status, chronic dise ase(s), technology use, and use of health information sources on Reliance scores. All predictor variables (gender, Internet access, health information sources, categories of technology, overall confidence to get health information online, Computer Self Eff icacy Measure, self reported health status, chronic2, age, education, race, and exposure to healthcare) for H5 were entered simultaneously in the regression model to check for significance at p < .05. Significant parameter estimates at p < .05 were retaine d and the model was rerun until only significant parameters at p < .05 remained. The final model with statistically significant
85 parameter estimates included only the Internet or World Wide websites, R2 = .05, F(1, 205) = 10.47, p = .001. Therefore, we reje ct H5. H6 predicted no relationships between predictor variables (gender, Internet access, health information sources, categories of technology, overall confidence to get health information online, self reported health status, chronic2, age, education, ra ce, and exposure to healthcare) and Computer Self Efficacy. Again, all parameter estimates were entered simultaneously in the regression model to check for significance at p < .05. Significant predictor variables at p < .05 were retained and the model was rerun until only significant parameters at p < .05 remained. The final model of statistically significant parameter estimates for H6 included desktop computer overall confidence to get health information online, other sources of health information, and ag e R 2 = .62, F (6, 202) = 52.40, p < .0001. Therefore we reject H6. Discussion Many previous studies assessed the presence of a digital divide between older and younger adults and characteristic differences between these groups and use of online health in formation (Cotton & Gupta, 2004; Kieschnick & Raymond, 2011 ; Kruse et al., 2012; Olson et al., 2011; Ybarra & Suman, 2005; Zickuhr & Madden, 2012). Much research has also been conducted on adult online searches for medical topics and barriers to use (Coupe r et al., 2010; Fox & Duggan, 2013; Gatto & Tak, 2008; Jimison et al., 2008; Schwartz, et al., 2006). Most previous studies addressed barriers and limitations to HIT use by older adults and fewer focused on factors that promote HIT engagement (Wagner, Bund orf, Singer, & Baker, 2005; Kiel, 2005). This study examined differences between older adult users and nonusers of online and offline
86 health information sources and investigated factors of HIT engagement for medical decision making. As expected, this rand Internet access, particularly for participants 65 years of age and older, were higher in almost every category compared to national trend data from 2012 (Table 4.3) (Zickuhr & Madden, 2012). Additionally a higher percentage of participants reported use of the Internet to keep track of personal health information online compared to previous findings ( Lustria et al., 2011) This suggests that digital divide initiatives have been successful in increasing ac cess to technology in older adult populations. However, significant differences were found between users and nonusers of health information on various factors. Users tended to be younger and more educated, which is consistent with previous surveys (Fox & D uggan, 2013). A significant factor not measured in previous studies on differences between users and nonusers was healthcare exposure ; users reported more exposure to health related courses compared to nonusers. Users also reported more access to the Int ernet and technology than nonusers. Yet, over half of nonusers reported access to desktop computers and the Internet, and close to 80 percent had cell phones (Table 4.3). This indicates a potential digital health divide between users and nonusers of online health information, when nonusers have access to HIT, but fail to access health information. More research is needed to confirm the presence of and factors contributing to a digital health divide. This study found significant differences between user and nonuser groups on offline sources of health information. Consistent with findings in the literature on health information seeking behavior both users and nonusers most frequently sought health
87 information from healthcare professionals (Cotton & Gupta, 200 4; Couper et al., 2010 ). Unlike previous findings the user group sought more sources of offline health information compared to nonusers (Table 4 2) (Cotten & Gupta 2004; Taha et al., 2009). Users also differed on types of offline information sources sought Nonusers second most frequently reported source of health information was television or radio whereas users reported the Internet or World Wide websites Given that over half of nonusers reported Internet access and desktop computers and use of televis ion or radio as sources of health information, future research should be directed at investigating audio and video health information sources. More research is needed to verify differences between this study and prior study findings. This was the first st udy to look at differences between older adult users and nonusers of online health information on the Decision Self Efficacy Scale, Computer Self Efficacy Measure, and Reliance Scale. While valid and reliable scales were used, a few modifications were made The Reliance Scale response items were modified from a six item Likert response scale to four and the modified three item version of the Decision Self items have a tendency to produce hi gher mean scores (Dawes, 2008). However, significant differences were found. Computer Self Efficacy to search for health information was statistically significant (Table 4 6). Users had higher mean scores than nonusers, indicative of higher levels of self efficacy. This finding is not surprising, since self efficacy is self efficacy has been shown to predict less engagement in a behavior and higher levels
88 of self efficacy predict more engagement (Bandura, 1997; Lorig, 2001). Therefore, a factor for use of HIT is higher self efficacy relating to use of online search tools, online searches for health information, and managing personal healthcare via the Internet. Previously, the Computer Self Efficacy Measure was used to measure pre post self efficacy in elderly Internet training programs and this is the first time it was tested and used in a cross sectional survey study design (Campbell 2004; Chu et al., 2009; Chu & Mastel S mith, 2010). Significant predictors associated with Computer Self Efficacy were age desktop computers overall confidence to obtain health information online, and other sources of health information. While other sources of health information and overall c onfidence to obtain information online were positively associated with Computer Self Efficacy, age and desktop computer where negatively associated with Computer Self Efficacy (Table 4 7). Future studies are needed to explore the contributions made by othe r sources of information made by Computer Self Efficacy as well as reasons for the indirect association between desktop computers and Computer Self Efficacy when searching for health information. Study findings also revealed statistically significant resu lts between users and nonusers of health information on the Reliance Scale. Users tended to have higher Reliance means than nonusers, which indicates a greater self reliant preference for involvement in medical decisions compared to nonusers who tended to be more physician reliant. Self reliance is associated with a shared approach to involvement in medical decisions that may lead to increased access to healthcare and improved health outcomes (Makoul, 1998). The only significant predictor of Reliance was In ternet or World Wide websites Reliance is not a widely used construct; future research is
89 needed to interpret findings using a continuous scale. However, Reliance was determined as a factor of HIT engagement. No significant differences were found between users and nonusers of health information on the Decision Self Efficacy scale. Most participants in this study used multiple sources of health information. It is challenging to determine if Decision Self Efficacy for medical decision making among older adu lts was solely dependent on Internet use. A previous study on Internet users and nonusers found that participants who used other sources of health information based their health decisions more often on familiar sources of offline instead of online health i nformation (Taha et al., 2009). However, predictors of Decision Self Efficacy included Computer Self Efficacy, tablet computers and other sources of health information. Computer Self Efficacy and tablet computers were positively associated with Decision S elf Efficacy and other sources of health information were inversely associated. This was the first study to test the Decision Self Efficacy Scale in a large sample of older adult users and nonusers of online health information. Measuring the constructs of and challenging constructs to measure (Couper, 2007). Few scales are available to measure these constructs, especially for medical decision making in older adult populat ions (Dy, 2007; Sung et al., 2010). As the availability of print sources of health information, such as newspapers and magazines diminish and the Internet becomes the preferred platform for the distribution of and access to health information, the Internet will increasingly be used more often as a source of health information. Benefits and barriers mentioned by older adults about use of the Internet for health information in
90 previous studies, included ease of use, improved knowledge, feelings of connectedne ss, mistrust, and frustration (Gatto & Tak, 2008; Kiel, 2005; Taha et al., 2009; Wagner et al., 2005). Therefore, research is needed to determine what types of health information sources patients use to make medical decisions. Research should also be direc ted at developing scales to measure medical decision making and Internet based decision aid tools to determine how better to advise and direct patients to useful online decision tools. Finally, the Theory of Self Efficacy and TAM formed the theoretical fra mework for this study (Bandura, 1997; Davis, 1989). Future research will analyze relationships between study findings and theory constructs. Limitations This study used a random digit dialing sample to collect participant data, however, findings from thi s study are generalizable only to similar older adults, 50 years and older, in the state of Florida. The sample includes participants wh o own landline telephones. This limits findings and results to only landline owners and excludes partici pants who only o wn cell phones; there is a continued trend seen in declines in number of landline only households and increases in number of households with cell phones (Blumberg & Luke 2012; Hu et al., 2011). Data were s elf reported by participants and measures are limit ed to the honesty of participant interpretation of questions, which could diff er from the intended construct. Furthermore, p articipants may have respond ed to questions in a social ly desirable manner by providing responses to the interviewer assumed to be favorable. The results of this study are limited to the time in which the survey was administered and only provide information from participa nts at this one point in time.
91 Conclusions Results of this study provide further insight into the differences between older adult users and nonusers of online health information and the potential presence of a digital health divide. Additionally, results provide updated trend data on technology and Internet access of Florida residents. More e mpirical research is needed to verify these findings and explore other factors promoting HIT engagement in older adult populations.
92 Table 4 1. Demographic and characteristic information of users and nonusers of online health i nformation Demographic / Characteristics All (N = 225) Users of Online Health Information (n = 105) Nonuser of Online Health Information (n = 119) p Age (yrs, M, SD) 68.9 (10.4) 66.29 (9.2) 71.31 (10.9) .0002 Gender (%) .3780 Male 45.8 42.9 48.7 Female 54.2 57.1 51.3 Education (%) .0427 5.8 3.8 7.7 High School/GED 16.1 8.6 22.2 Some College 24.7 26.7 23.1 10.3 9.5 11.1 18.4 21.9 15.4 Graduate Degree 24.7 29.5 20.5 Chronic Disease(s) (%) .6337 Diabetes 21.1 19.2 22.9 High Blood Pressure 50.5 48.1 52.9 Asthma, bronchitis, emphysema, or other lung condition 16.1 15.4 16.8 Heart disease, heart failure or heart attack 18.8 15.2 21.9 Cancer 8.4 8.6 8.4 Other chronic h ealth problem 32.3 39.8 26.1 Health Status (%) .8724 Excellent 16.0 18.1 14.3 Very Good 36.9 37.1 37.0 Good 27.6 25.1 28.6 Fair 11.6 12.4 11.0 Poor 8.0 6.7 9.2 Race/Ethnicity (%) .6586 Hispanic 6.3 2.9 9.2 White 87.6 86.7 88.2 Black 6.7 7.6 5.9 Asian or Pacific Island 0.4 0.0 0.8 Other 3.6 1.9 5.0 Exposure to Healthcare (%) Had significant illness or injuries requiring extended medical care 39.6 42.9 37.0 .3693 Been employed in a healthc are facility 22.7 23.8 21.9 .7269 Close friends/relatives/roomm ates in a medical field 67.1 71.4 63.9 .2281 Taken health related courses or emergency training 44.4 52.4 37.9 .0286 Other ways you have been exposed to healthcare 35.6 40.0 31.9 .2086 Not e. M, Mean; SD, Standard Deviation; %, percent; yrs, years ; *pearson chi square
93 Table 4 2. Health i nformation source use between users and nonusers of online health information by age Health Information Sources Users of Online Health Information (n = 10 5) Nonuser of Online Health Information (n = 119) Total Users (N = 225) p Online Health Information 100 46.9 50 64 (yrs) 44.8 58.7 65 74 (yrs) 37.1 54.9 75+ (yrs) 18.1 27.5 Healthcare Professionals 91.4 62.2 75.6 <.0001 50 64 (yrs) 91.5 54.6 76.2 65 74 (yrs) 92.3 65.6 80.3 75+ (yrs) 89.5 66.0 71.4 Friends or Family Members 49.5 21.0 34.2 <.0001 50 64 (yrs) 51.1 18.2 37.5 65 74 (yrs 46.2 18.8 33.8 75+ (yrs) 53.6 24.0 31.4 Magazines, brochures, or books 53.3 18.5 34.7 <.0001 50 64 (yrs) 46.8 15.2 33.7 65 74 (yrs) 53.9 18.8 38.0 75+ (yrs) 68.4 22.0 34.3 Newspaper articles 38.1 21.9 29.3 .0078 50 64 (yrs) 31.9 6.1 21.2 65 74 (yrs) 41.0 18.8 30.9 75+ (yrs) 47.4 43.0 37.1 Television or radio 39.1 26.3 32.1 .0417 50 64 (yrs) 40.4 15.2 30.0 65 74 (yrs) 46.2 34.4 40.8 75+ (yrs) 21.1 28.6 26.1 Other 7.6 10.1 9.3 .5185 50 64 (yrs) 8.5 9.1 8.8 65 74 (yrs) 10.3 15.6 12.7 75+ (yrs) 0.0 8.0 7.1 Note. %, percent; *pea rson chi square
94 Table 4 3. Technology access and use between users and nonusers of online health information by age Users of Online Health Information (n = 105) Nonuser of Online Health Information (n = 119) Total Users (N = 225) p Access to Internet (%) 99.1 56.3 76.0 <.0001 50 64 (yrs) 100 75.8 90.0 65 74 (yrs) 100 56.3 80.3 75+ (yrs) 94.7 46.0 58.6 Technology Use Desktop computer 77.1 55.9 65.6 .0009 50 64 (yrs) 72.3 66.7 70.0 65 74 (yrs) 76.9 51.6 65.7 75+ (yrs) 89.5 52.0 61.4 Laptop computer or netbook 76.7 43.2 58.6 <.0001 50 64 (yrs) 87.0 68.8 79.5 65 74 (yrs) 73.7 56.3 65.7 75+ (yrs) 57.9 20.0 30.0 Tablet computer (iPad) 48.1 23.5 34.8 .0001 50 64 (yrs) 56.5 39.4 49.4 65 74 (yrs) 53.9 25.0 40.8 75+ (yrs) 15.8 14.0 14.3 Electronic book device 37.5 22.9 29.6 .0174 50 64 (yrs) 39.1 30.3 35.4 65 74 (yrs) 41.0 31.3 36.6 75+ (yrs) 26.3 14.0 17.1 Cell phone or mobile device 85.6 73.1 79.0 .0228 50 64 (yrs) 87.0 78.8 83.5 65 74 (yrs) 89.7 78.1 84.5 75+ (yrs) 73.7 70.0 71.4 iPod or other MP3 player 44.8 16.1 29.5 <.0001 50 64 (yrs) 61.7 24.2 46.2 65 74 (yrs) 41.0 21.9 32.4 75+ (yrs) 10.5 8.0 8.6 Game console (xbox or Play Station) 23.8 12.7 17.9 .0311 50 64 (yrs) 34.0 33.3 33.7 65 74 (yrs 23.1 9.4 16.9 75+ (yrs) 0.0 2.0 1.4 Note. %, percent; *pearson chi square
95 Table 4 4. Additional use of technology for health Users of Online Health Information (n = 105) Cell Phone Users (n = 177) p Frequency of Use of Online Health Information Daily 4.0 Weekly 18.0 Monthly 48.0 More than monthly 30.0 Preferred Health Website 45.7 Keep Track of Personal Health Information Online 33.3 Use Cell Phone to Look Up Health Inform ation 15.3 .0021 Health Apps on Cell phone 12.1 .1093 Note. %, percent; Apps, applications; *pearson chi square between users and nonusers of online health information
96 Table 4 5. O verall confidence t o get health information online and perceived helpf ulness of online health information between users and nonusers of online health information Users of Online Health Information (n = 105) Nonuser of Online Health Information (n = 119) Total Users (N = 225) p Overall Confidence to Get Health Info rmation Online: <.0001 Very confident 26.5 13.9 40.4 Somewhat confident 14.4 14.8 29.2 A little confident 4.5 7.6 12.1 Not confident at all 1.8 16.6 18.4 Have you or anyone you know ever been helped or Harmed by health information found on line: <.0001 Helped 70.5 24.1 45.9 Harmed 1.9 1.7 Ho w Helpful was the online health information: .0576 Very helpful 51.6 50.0 Somewhat helpful 44.6 32.1 A little helpful 4.1 17.9 Note.*pearson chi square
97 Table 4 6. Decisi on self efficacy scale, reliance scale, and computer self efficacy differences between users and nonuser of online health information Users of Online Health Information Nonusers of Online Health Information Measure M SD LL UL M SD LL UL p Co d a Decision Self Efficacy 28.93 3.41 28.2*** 29.6*** 28.79 4.17 27.9*** 29.6*** .7991** b Reliance Scale 4.41 1.68 4.1*** 4.7*** 3.68 1.78 3.4*** 4.0*** .0011 ¤ .42 c Computer Self Efficacy 17.39 2.39 16.9*** 17.9*** 13.43 5.1 9 12.5*** 14.4*** <.0001** ¤ .98 Note. CI = confidence interval; LL = lower limit; UL = upper limit. M, mean; SD, standard deviation; ** Satterthwaite p value reported for unequal variances. a Higher scores correspond with higher levels of self efficacy for medical decision making. b Lower scores correspond to being more physician reliant and higher scores correspond to being ***95% confidence interval more self reliant. c Higher scores correspond with higher levels of computer self efficacy. ¤ p < .05, one tailed
98 Table 4 7 Regression models for decision self efficacy scale, reliance scale, and computer self efficacy Model Outcome Predictor b t R2 Decision Self Efficacy 0.08 Intercept 26.26** 19.37 ****Other Sources of Healt h Information 1.88* 2.15 Computer Self Efficacy 0.22** 3.42 ****Tablet Ownership 1.45* 2.51 Reliance 0.05 Intercept 4.37** 25.49 ****User or Nonuser of Online Health Information 0.77** 3.24 Computer Self Efficacy 0.62 Intercept 14.68** 8.46 age 0.09** 4.43 Desktop Ownership 1.60** 3.77 ****Other Sources of Health Information 1.67* 2.51 Overall Confidence to Search for Health Information Online Very confident 8.15** 13.30 Som ewhat confident 6.34** 10.13 A little confident 6.02** 7.68 ***Not confident at all Note. p <.05. ** p <.01.***comparison group for overall confidence to search for health information online predictor variable with four levels. ****dummy c
99 CHAPTER 5 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS Summary and Conclusions The aforementioned research included three independent yet related studies designed to assess three different types of technology applicat ions used, and the benefits gained for Health Information Technology ( HIT ) use in older adult populations The author chose to focus the scope of this research on three areas of techno logy, specifically, video games, medical devices and the Internet to ultimately address digital divide con cerns and technology use among older adults. Three investigations extend the current literature and address gaps in previous research. The research presented in Chapter 2 was the first systematic literature review on interventions using digital video ga mes among adults 65 years of age and older and to report on health outcomes related to mental, physical, and/or social attribut es associated with game pla y Findings on digital video games found positive health benefits of game play in older adults on social, physical, and mental factors. Study findings added to the current literature on the growing field of games for health research. The article titled Health benefits of digital videogames for older adults: A syst Games for Health Journal December 2012. The research presented in Chapter 3 explored patients with chronic diseases, specifically those w ith heart failure, perceptions and use of technology to manage heart failure symptoms (MHFS). This study found that all study participants use d a home monitoring device, however, while HIT was positively perceived, t he majority of participants did not acce ss onli ne resources for additional information related to their
100 condition current use and preferences for technology to manage heart failure symptoms. A checklist of recommendations for practitioners to consider prior to prescribi ng telemonitoring technology to patient s was presented, such as access, preferences, use, and experiences with technology. C o nsiderations of these issues were presented to help pra ctitioners prescribe appropriate technolo gy for patient use for improved adherence to MHFS. The manuscript based on this research, titled symptoms Telemedici ne and e Health Journal (revisions for resubmission are in progress) The study presented in Chapter 4 examined a potential digital health divide present among older adults, accessed characteristic differences between users and nonusers of online and offli ne health information sources, and examined relationships between factors promoting HIT engagement in an older adult population. This investigation extended current research in the area of the differences between older adult users and nonusers of online an d offline health information sources and medical decision making. The Decision Self Efficacy Scale, Computer Self Efficacy Measure, and Reliance Scale were used to measure relationships serving to promote HIT engagement between users and nonusers. This stu dy found significant differences between users and nonusers of online health information and factors influencing HIT engagement. Significant differences were found on the Reliance Scale and Computer Self Efficacy Measure between users and nonusers of onlin e health information. No significant differences were found between users and nonusers on the Decision Self
101 Efficacy Scale. Finally, multiple predictor factors were significantly associated with Decision Self Efficacy, Reliance, and Computer Self Efficacy for searching for health information using the Internet. In conclusion, findings from this line of research show overall positive health effects of HIT use by older adults. This research advances the science regarding use of HIT for health education, heal th promotion, and health prevention in older adult populations. The author foresees various components of these three areas of technology, specifically, video games, medical devices and the Internet will be amalgamated into new technology designed specifi cally to improve the health of older adults. Eventually, practitioners may incorporate findings from these three independent studies into intervention efforts to promote HIT engagement among older adults for improved health outcomes and medical decision ma king. Recommendations for Future Research This dissertation has contributed to a clearer understanding of HIT use in older adult populations for health education and behavior. Furthermore, it extended the current literature in the areas of game play, spec ifically the health benefits, and factors associated with Internet use in older adults. Additionally, this research addresses gaps in the literature on use of technology by heart failure patients to MHFS and factors associated with HIT engagement. However, due to current study findings and limitations presented in previous chapters, future research and recommendations are warranted. Research on video games for health is a fairly new and growing field, especially in older adults. Future research should inv estigate preferences for types of games that provide the greatest health benefits for older adults. Additionally, more theory based games need to be developed and tested using more rigorous experimental study
102 designs with larger, more gender equitable samp les. Alignment of trials in regard to frequency and duration of interventions is also recommended. Also recommended are testing recent innovations and advancements in motion sensing digital video game platforms More research is needed in the area of tech nology use among heart failure patients to MHFS, particularly ways to match or prescribe the most appropriate recommendations for research include testing the assessment item checklist presented in Chapter 3; developing and testing patient customizable features on monitoring devices technology for MHFS. Finally, more empirical research is recommended t o verify the qualitative findings presented in Chapter 3. Future research is needed to examine findings from Chapter 4 on differences between users and nonusers of online health information. Future research focusing on factors associated with the digital h ealth divide and investigating other sources of health information use for medical decision making is recommended. To aid in this research a scale should be developed and tested to measure constructs of medical decision making and Internet based decision a id tools Finally, structural equation modeling methods are recommended to test the proposed variables and theoretical constructs of the Theory of Self Efficacy and the Technology Acceptance Model (Bandura, 1997; Davis, 1989).
103 APPENDIX A DEMOGRAPHIC QUEST IONS 1. What is your age? 2. What is your race? (Check all that apply) White/Caucasian Hispanic Black or African American Asian Native Hawaiian/other Pacific Islander Any other race ____________________ 3. What is your hig hest level of education? (Check one) 12 th grade or less, no diploma GED High school diploma Some college Graduate or professional degree 4. What is your gender? (Check one) Female Male 5. W hat stage or classification of heart failure do you have (New York Heart Association)? (Check one) I (Stage 1) II (Stage 2) III (Stage 3) IV (Stage 4) 6. Date of your heart failure diagnosis __________________ Response 7. Have you been diagnosed with any of the following? (Check all that apply)
104 Hypertension Diabetes Mellitus COPD (Chronic Obstructive Pulmonary Disease) Chronic Renal Failure/ Chronic Kidney Disease Coronary Artery Disease None of the above D 8. In the past 30 days have you used a device in your home to measure any of the following, (Check all that apply) Blood pressure Weight Sugar level Oxygen saturation Other ____________________ None of the above onse Note: Proceed to 8a only if you used a device in your home in the last 30 days, skip to question 9 if you did not use a device in your home in the last 30 days. 8a. In the past 30 days, did you write down or record the measurement(s) in a daily log or on a piece of paper when you used a device in your home? Yes No
105 APPENDIX B THE OSLO 3 ITEM SOCIAL SUPPORT SCALE 1. How easy can you get help from neighbors if you should need it? ( Check one ) Very Easy Easy Possible Difficult Very Diffic ult 2. How many people are so close to you that you can count on them if you have serious problems? ( Check one ) None 1 2 3 5 5+ 3. How much concern do people show in what you are doing? ( Check one ) A lot Some Uncertain Little No
106 APPENDIX C EURO PEAN HEART FAILURE SELF CARE BEHAVIOUR SCALE how would you answer the following statements: ( Circle one number per row. ) Question: I Completel y Agree agre e at all 1. I weigh myself every day 1 2 3 4 5 2. If I get short of breath, I take it easy 1 2 3 4 5 3. If my shortness of breath increases, I contact my doctor or nurse 1 2 3 4 5 4. If my feet/legs become more swollen than usual, I contact my doctor or nurse 1 2 3 4 5 5. If I gain 4.4 lbs in 1 week, I contact my doctor or nurse 1 2 3 4 5 6. I limit the amount of fluids I drink (not more than 6 8.5 cups/day) 1 2 3 4 5 7. I take a rest during the day 1 2 3 4 5 8. If I experience increased fatigue, I contact my doctor or n urse 1 2 3 4 5 9. I eat a low salt diet 1 2 3 4 5 10. I take my medication as prescribed 1 2 3 4 5 11. I get a flu shot every year 1 2 3 4 5 12. I exercise regularly 1 2 3 4 5 (Jaarsma Stromberg, Martensson & Dracup, 2003)
107 APPENDIX D SYSTEM USABILITY SCALES On a scale from 1 to 5, one being Strongly Disagree and 5 being Strongly Agree how much do you disagree or agree with the following statements: (Circle one number per row.) Question: Strongly Disagree Strongly Agree 1. I think that I would like to us e this system frequently 1 2 3 4 5 2. I found the system unnecessarily complex 1 2 3 4 5 3. I thought the system was easy to use 1 2 3 4 5 4. I think that I would need the support of a technical person to be able to use this system 1 2 3 4 5 5. I found the variou s functions in this system were well integrated 1 2 3 4 5 6. I thought there was too much inconsistency in this system 1 2 3 4 5 7. I would imagine that most people would learn to use this system very quickly 1 2 3 4 5 8. I found the system very cumbersome to us e 1 2 3 4 5 9. I felt very confident using the system. 1 2 3 4 5 10. I needed to learn a lot of things before I could get going with this system 1 2 3 4 5 John Brooke, 1986
108 APPENDIX E BLUE SCALE USABILITY SURVEY On a scale from 1 to 5, one being Very Dif ficult and 5 being Very Easy, how would you rate your experience doing the following: (Circle one number per row.) Question: Very Difficult Somewh at difficult Neither easy nor difficult Somewh at easy Very easy know or not sure a. Stepping on the B lue Scale 1 2 3 4 5 0 b. Stepping off the Blue Scale 1 2 3 4 5 0 c. Positioning your index finger in the holder 1 2 3 4 5 0 d. Holding the bars of the Blue Scale 1 2 3 4 5 0 e. Viewing the brightness or screen contrast on the screen 1 2 3 4 5 0 f. Reading the text size on the screen 1 2 3 4 5 0 g. Reading the font on the screen 1 2 3 4 5 0 2. On a scale from 1 to 5, one being Strongly Disagree and 5 being Strongly Agree how much do you disagree or agree with the following statements: (Circle one number per row.) Question: Strongly Disagree Strongly Agree a. Using the Blue Scale once a day would be easy for me to do. 1 2 3 4 5 b. Using the Blue Scale twice a day would be easy for me to do 1 2 3 4 5 c. I would feel comfortable if people outside my family saw the Blue Scale in my home 1 2 3 4 5
109 APPENDIX F FOCUS GROUP DISCUSSION GUIDE 1. Please tell me your thoughts about using the Blue Scale to manage your heart failure symptom s. 2. Tell me about your home environment, how would Blue Scale fit into your home? 3. In your opinion, what makes your home a good or bad place for the Blue Scale? (i.e. what did you like or dislike about using the Blue Scale? and/or would you use the B lue Scale in your home every day?) 4. What do you do (e.g. self care methods: medications, low sodium diet, support from friends and family) to manage your heart failure? 5. Tell me about your heart failure, how does it affect your daily activities (i.e. activities of daily living, getting dressed, running errands, cooking, etc.)? 6. What do the terms telemonitoring, telehealth, or telemedicine mean to you? 7. Do you use technology to improve/monitor your health or to find information about health to pics? (i.e. medical devices, websites, apps, etc.) If so, what do you use, if not, why? 8. Show each reading one at a time this mean to Member Check 1. Now I will summarize some of the main points to make sure I captured what we 2. Are there any additional comments or suggestions about the Blue Scale device, heart failure, self care, or the home environment that you would like to include that were not covered in the previous question s?
110 APPENDIX G HEALTH INFORMATION AND TECHNOLOGY SURVEY INTRO: We are conducting research about how Florida residents access health care information. Have I reached you on your home phone? Q: First I need to know if you are 50 years old or older? If no: QA: May I please speak to someone 50 years old or older that lives there? Skip back to HELLO if person passes phone. Schedule callback if at least one person in household is 50 years old or older, but nobody home now 50 years old or older. Code case as no eligible respondent if nobody lives there 50 years old or older. If yes: accomplish this, th ink about the adult males FIFTY YEARS OLD OR OLDER living in speak to me now. (INT: We need to speak to the youngest male adult 50 years old or older that lives there and is currently available. If this person refuses, code as refusal) If person sounds male: QC: Would that be you? If yes, skip to IC. If no, skip to QD If person sounds female, or not sure whether male or female: QD: May I please speak with him? If pe rson on the phone indicates they are that person, skip to IC. If person on phone passes phone, skip back to HELLO If nobody living in household available now is a male age 50 or older, ask QE QE: Then please think about the adult females FIFTY YEARS OLD O R OLDER living in speak to me now. If person on the phone indicates they are that person, skip to IC. If person on phone passes phone, skip to QF If person on phone in dicates no female 50 or older is available now, schedule callback
111 conducting research about how Florida residents access health care information. andomly select someone within each household. Please think about the adult females FIFTY YEARS OLD OR OLDER living in your household. Out of Would that be you? If yes, skip to IC If no, QG: May I please speak to her? If person on the phone indicates they are that person, skip to IC. If person on phone passes phone, skip to QF If person on phone indicates no female 50 or older is available now, schedule callback The gender ca n be surmised from answers to the screening questions. Informed Consent: Before we begin, I want to let you know that your phone number was selected at random by computer and we won't be recording your name or connecting it in any way with your responses You do not have to answer any questions you do not wish to answer. Participation is completely voluntary and you may withdraw your consent at anytime without penalty. There are no direct benefits, compensation, or risks to you for participating. This cal l may be recorded for quality control purposes. We appreciate you answering the survey questions to the best of your ability. Please do not provide any additional information about specific conditions or health during the survey. If you have any questions or concerns about this survey please feel free to contact Amanda Hall, Graduate Student, at 352 514 3976 or Jay Bernhardt, Professor and Chair, at 352 294 1800, at the University of Florida, Department of Health Education and Behavior; if you have questio ns related to your rights as a participant please contact the Institutional Review Board office at the University of Florida at 352 392 0433. The survey should take about 15 minutes. The first question is: 1. Do you access the Internet or World Wide Web at home, work or from any other location? (Source: Modified from Pew) 1. Yes 2. No 4. Refused regarding a health concern or a medical problem in the past 12 m onths. For each of the
112 source. (Source: Modified, Cotton & Gupta, 2004) During the past 12 months have you sought information regarding a health concern or medical pro Health care professionals? Friends or family members? Internet or World Wide websites? Magazines, brochures, or books? Newspaper articles? Television or radio? Any other? _______________ Reponses: 1. Yes 2. No 9 Refused SKIP qu estions 2a 5 only if participant replied NO to use of the Internet or World Wide websites as a source of health information used in the past 12 months PROCEED to questions 2a 5 if participant replied YES to use of the Internet or World Wide websites as a s ource of health information used in the past 12 months 2a. During the past 12 months, on average how often did you seek information regarding a health concern or medical problem on the Internet or the World Wide Web, daily, weekly, monthly, or more than m onthly? (Source: Modified from Pew) 1. daily 2. weekly 3. monthly 4. more than monthly 9 Refused 3. Is there a health or medical information website that you especially like? (Source: HINTS) 1. Yes 2. No 9 Refused SKIP 4 if participant said No to 3 and Proceed to 5 PROCEED to 4 if participant said Yes to 3 4. Which website do you especially like as a source of health information? ______________ (Source: Modified from HINTS)
113 5. During the past 12 months, have you used the Internet to keep track of personal health information such as care received, test results, or any upcoming medical appointments? (Lustria, Smith, & Hinnant, 2011) 1. Yes 2. No 9 Refused The next couple of questions ask about your healthca re provider. I am going to read you a list of statements. Please tell me if you agree or disagree with each statement. 1. Strongly Agree 2. So mewhat Agree 3. Somewhat Disagree 4. Strongly Disagree 9 Refused y 1. Strongly Agree 2. Somewhat Agree 3. Somewhat Disagree 4. Strongly Disagree 9 Refused 8 18 Decision Self efficacy scale 11 19 23. Computer self efficacy measure 5 items (Campbell, 2004; Ch u et al., 2009) (See Appendix I) For each item, please tell me YES if the item exists in your home, or NO if the item does (Do you have:) 1. A desktop computer 2. A laptop computer or netbook 3. A tablet computer such as an iPad 4. An electronic book device or eBook reader, such as Kindle or Sony Digital Book
114 5. A cell phone or mobile phone, such as Android, iPhone or other device that i s also a cell phone 6. An iPod or other MP3 player 7. A game console such as an Xbox or Play Station Game1: During the past 12 months have you played any games on an electronic device such as a smartphone, computer, or video game console? 1. Yes 2. No 8 9 Refused If respondent replied Yes to Game1 proceed to Game2 and Game3. If respondent replied No to Game1 skip Game2 and Game3. Game2: On what system did you play electronic games during the past 12 months? 1. Smartphone 2. iPad or tablet 3 Computer 4. Video Game Console 5. Other 9 Refused Game3: What game did you most recently play? ________ Getting back to your use of the Internet. SKIP questions 25 and 26 if participant replied NO to cell phone as an item in question 24 PROCEED to questions 25 and 26 if participant replied YES to cell phone as an item in question 24. 25. Do you ever use a cell phone to look up health or medical information? (Source: modified from Pew) or other device that can act as a cell phone.) 1. Yes 2. No 9 Refused help you track or manage your health, or no t?
115 or other device that can act as a cell phone.) 1. Yes 2. No 9 Refused 27. Overall, how confident are you that you could get advice or information a bout healthcare or medical topics on the Internet if you needed it? Would you say you are Very confident, Somewhat confident, A little confident, or Not confident at all? (Source: HINTS) 1. Very confident 2. Somewhat confident 3. A little confident 4. Not confident at all 9 Refused 28. Have you or has anyone you know ever been HELPED by following any medical advice or health information found on the Internet? (Source: Pew) 1. Yes 2. No 9 Refused 28a. IF YES: Would you say t his medical advice or information (found on the internet) was very helpful, somewhat helpful or a little helpful 1. Very helpful 2. Somewhat helpful 3. A little helpful 29. Have you or has anyone you know ever been HARMED by following any medical advice or health information found on the Internet? (Source: Pew) 1. Yes 2. No 9 Refused 29a. IF YES: Would you say this medical advice or information (found on the internet) was very harmful, somewhat harmful or a little harmful 1. Very harmful 2. Somewhat harmful 3. A little harmful The next questions are about the way you make medical decisions.
116 30. When making a medical decision, do you prefer to make the final decision yourself or do y ou prefer that your doctor (or healthcare provider) m ake the final decision for you? (Sung, Raker, Myers, Clark 2010) 1. Myself 2. My doctor (or healthcare provider) 3. My doctor (or healthcare provider) and I make the final decision together 4. No preference/Not applicable 9 Refused going to read three statements about making medical decisions. Please tell me which one most applies to you. (Sung, Raker, Myers, Clark 2010) 1. You prefer to make the final decision yourself after seriously considering your 2. You prefer that your doctor (or healthcare provider) and you share responsibility for the decision 3. You prefer that your doctor (or healthcare provider) make the dec ision after he/she seriously considers your opinion 32. In general, how would you rate your own health? Would you say it is excellent, very good, good, fair, or poor? (Source: Pew, self reported quality of life question) 1. Excellent 2. Very Good 3. Good 4. Fair 5. Poor 9 Refused 33. Are you now living with any of the following health problems or conditions (Insert; randomize 1 6, Ask 6 last). (Source: Pew, Chronic Disease Question) 1. Diabetes or sugar diabetes 2. High blood pressure 3. Asthma, bronchitis, emphysema, or other lung conditions 4. Heart disease, heart failure or heart attack 5. Cancer you. 34. What is the highest level of education you yourself have completed? (INT: READ CHOICES IF NECESSARY) 1. 12 th grade or less, no diploma 2. High School graduate or GED
117 3. Some college 6. Graduate or prof essional degree 9 Refused 35. Are you of Spanish or Hispanic origin? (INT: READ CHOICES IF NECESSARY) 1 Yes (Spanish or Hispanic) 2 No (Not Spanish or Hispanic) 36. What race do you consider yourself? (INT: READ CHOICES IF NECESSARY. SELECT ALL THAT APPLY.) White (Caucasian) Black (African American) Asian or Pacific Islander American Indian or Alaska native Other 37. What is your age? ______________ re to health care. During your life, you have had significant illnesses, injuries, or other health issues requiring extended medical care. You have been employed in a healthcare facility. You have close friends, relatives, family members, or roommates that have worked in a medical field. You have taken health related courses or emergency training (e.g. CPR with Red Cross, etc.) Are there any other ways you ha ve been exposed to healthcare? (specify)
118 APPENDIX H DECISION SELF EFFICACY SCALE The next 11 questions relate to statements about making informed choices about PILLS OR MEDICATIONS. Please tell me how confident you would feel in doin g each of these things when taking pills or medications. The answer choices are: Very confident, A little confident, or Not confident at all. 1. available to you? For this st 1 Very confident 2. A little Confident 3. Not confident at all 1. Very confident 2. A little Confident 3. Not c onfident at all 1. Very confident 2. A little Confident 3. Not confident at all nformation about each medication 1. Very confident 2. A little Confident 3. Not confident at all 1. Very c onfident 2. A little Confident 3. Not confident at all 1.Very confident 2. A little Confident
119 3. Not confident at all 7. (The statement is:) 1. Very confident 2. A little Confident 3. Not confident at all 1. Very confident 2. A little Confident 3. Not confident at all 1. Very confident 2. A little Confident 3. Not confident at all office staff know what 1. Very confident 2. A little Confident 3. Not confident at all 1. Very confident 2. A little Confident 3. Not confident at all
120 APPENDIX I COMPUTER SELF EFFICACY SCALE (Campbell, 2004; Chu et al., 2009) Please answer the next set of questions to the best of your ability regardless of whether you use the Internet or not. There are no right or wrong answers, so please choose the answer that best describes your feelings towards the following statements 1. Strongly Agree 2. Somewhat Agree 3. Somew hat Disagree 4. Strongly Disagree 1. Strongly Agree 2. Somewhat Agree 3. Somewhat Disagree 4. Strongly Disagree 3. (The statement is:) You are confident that you can use information on the Internet. engines like Google, Bing, Yahoo, etc.) 1. Strongly Agree 2. Somewhat Agree 3. Somewhat Disagree 4. Strongly Disagree 4. Strongly Disagree rch engines like Google, Bing, Yahoo, etc.) 1. Strongly Agree 2. Somewhat Agree 3. Somewhat Disagree 4. Strongly Disagree 1. Strongly Agree
121 2. Somew hat Agree 3. Somewhat Disagree 4. Strongly Disagree
122 REFERENCES Allen, D. (2006 November 17 ). PC world A closer look at the nintendo wii. Retrieved from http://web.archive.org/web/20080205074335/http://www.pcworld.com/article /id,127859 page,1/article.html American Association of Retired Persons (AARP). (2012). Retrieved from http://www.aarp.org/about aarp/ Anderson, G. (2002). Chronic Conditions: Making the Case for Ongoing Care Princeton, NJ: Robert Wood Johnson Foundation. Retrieved from (2002 report is no longer available online only 2010) http://www.rwjf.org/content/dam/farm/ reports/reports/2010/rwjf54583 Bagley Burnett, C. (2004). Measuring information seeking behaviors and decision making preferences. Instruments for Clinical Health Care Research 455 471. Bainbridge, E., Bevans, S., Keeley, B., & Oriel, K. (2011). The effects of the nintendo wii fit on community dwelling older adults with perceived balance deficits: A pilot study. Physical & Occupational Therapy in Geriatrics 29 (2), 126 135. doi:10.3109/02703181.2011.569053 Bandura, A. (1994). Self Efficacy, In: Ramachaudran VS, ed. Encyclopedia of Human Behavior, Vol 4 ., New York; Academic Press, 71 81. Bandura, A. (1997). Self efficacy: The exercise of control New York: W .H. Freeman. Bandura, A. (2004). Health promotion by social cognitive means. Health Education & Behavior, 31 (2), 143 1 64. Baranowski, T., Buday, R., Thompson, D.I., Baranowski, J. (2008). Playing for real: Video games and stories for health related behav ior change. American Journal Prevention Medicine 34 (1), 74 82. Basak, C., Boot, W. R., Voss, M. W., & Kramer, A. F. (2008). Can training in a real time strategy video game attenuate cognitive decline in older adults? Psychology and Aging, 23 (4), 765 777. doi:10.1037/a0013494 Basak, C., Voss, M. W., Erickson, K. I., Boot, W. R., & Kramer, A. F. (2011). Regional differences in brain volume predict the acquisition of skill in a complex real time strategy videogame. Brain and Cognition, 76 (3), 407 414. doi:10. 1016/j.bandc.2011.03.017 Bell, C. S., Fain, E., Daub, J., Warren, S. H., Howell, S. H., Southard, K. S., . Shadoin, H. (2011). Effects of nintendo wii on quality of life, social relationships, and confidence to prevent falls. Physical & Occupational Th erapy in Geriatrics, 29 (3), 213 221.
123 Benatar, D., Bondmass, M., Ghitelman, J., & Avitall, B. (2003). Outcomes of chronic heart failure. Archives of Internal Medicine, 163(3), 347 352. Benbassat, J., Pilpel, D., & Tidhar, M. (1998). Patients' preferences for participation in clinical decision making: A review of published surveys. Behavioral Medicine, 24 (2), 81 88. Bernhardt, J. M. (2000). Health education and the digital divide: building bridges and filling chasms. Health Education Research 15 (5), 527 53 1. Miller, L. W. (2005). Evaluation study of congestive heart failure and pulmonary artery catherization effectiveness: The ESCAPE trial. Journal of the American Med ical Association 294, 1625 1633. Blumber, S. J. & Luke, J., V. (2012). Wireless substitution: Early release of estimates from the national health interview survey, January June 2012. Retrieved from http://www.cdc.gov/nchs/data/nhis/earlyrelease/wireless201212.pdf Blumen, H. M., Gopher, D., Steinerman, J. R., & Stern, Y. (2010). Training cognitive control in older adults with the space fortress game: The role of training ins tructions and basic motor ability. Frontiers in Aging Neuroscience, 2 145 doi:10.3389/fnagi.2010.00145 Bodenheimer, T., Lorig, K., Holman, H., & Grumbach, K. (2002). Patient self management of chronic disease in primary care. Journal of the American Medic al Association, 288(19), 2469 2475. Ben, H., Dalgard, O. S., & Bjertness, E. (2012). The importance of social support in the associations between psychological distress and somatic health problems and socio economic factors among older adults living at h ome: A cross sectional study. BMC Geriatrics, 12 (1), 27. doi:10.1186/1471 2318 12 27 Bowers, C.A., Cannon Bowers, J.A. (2010) Serious Game Design and Deve lopment: Technologies for Training and Learning Hershey, PA: Information Science Reference. Brem, M., H., Lehrl, S., Rein, A., K., Massute, S., Schulz Drost, S., Gelse, K., . Gusinde, J. (2010). Stop of loss of cognitive performance during rehabilitation after total hip arthroplasty -prospective controlled study. Journal of Rehabilitation Research & Development, 47 (9), 891 898. doi:10.1682/JRRD.2010.01.0011 Broeren, J., Claesson, L., Goude, D., Rydmark, M., & Sunnerhagen, K. S. (2008). Virtual rehabilitation in an activity centre for community dwelling persons with stroke. the possibilities of 3 dim ensional computer games. Cerebrovascular Diseases (Basel, Switzerland), 26 (3), 289 296. doi:10.1159/000149576
124 Brooke, J. (1986). SUS A quick and dirty usability scale. Retrieved from http://hell.meiert.org/core/pdf/sus.pdf Bunn, H., O'Connor, A. (1996). V alidation of client decision making instruments in the context of psychiatry. Canadian Journal of Nursing Research, 28 13 27. Bylund, C. L., Sabee, C. M., Imes, R. S., & Sanford, A. A. (2007). Exploration of the construct of reliance among patients who ta lk with their providers about internet Information. Journal of Health Communication, 12 (1), 17 28. Campbell, R. (2004). Older woman and the internet. Journal of Women and Aging 16, 161 174. Cardoz o, L., & Steinberg, J. (2010). Telemedicine for recen tly di scharged older patients. Telemedicine and e Health 16(1), 49 55. doi: 10.1089=tmj.2009.0058 Centers for Disease Control and Prevention (CDC). (2012). National Center for Chronic Disease Prevention and H ealth Promotion. Retrieved from www.cdc.gov/chronic disease/index.htm Chaudhry, S. I., Mattera, J. A., Curtis, J. P., Spertus, J. A., Herrin, J., Lin, Z., . Krumholz, H. M. (2010). Telemonitoring in patients with heart failure. N ew Engl and J ournal of Med edicine 363 (24), 2301 2309. doi:10.1056/NEJMoa1010029 Choi, N. (2011). Relationship between health service use and health information technology use among older adults: Analysis of the US national health interview survey Journal of Medical Internet Research, 13 (2), e33. doi: 10.2196/jmir.1753 Chu, A., Huber, J., Maste l Smith, B., & Cesario, S. (2008 ). "Partnering with seniors for better health": Computer use and internet health information retrieval among older adults in a low socioeconomic community. Journal of the Medical Library Association: JMLA, 97 (1), 12 20. doi: 10.3163/1536 5050.97.1.003 Chu, A., & Mastel Smith, B. (2010). The outcomes of anxiety, confidence, and sel f efficacy with internet health information retr ieval in older adults: A pilot study. Computers, Informatics, Nursing: CIN, 28 (4), 222 228. doi: 10.1097/NCN.0b013e3181e1e271 Clark, R. A., Inglis, S. C., McAlister, F. A., Cleland, J. G. F., & Stewart, S. (2007). Telemonitoring or structured telephone sup port programmes for patients with chronic heart failure: Systematic review and meta analysis. British Medical Journal, 334, 942 doi : 10.1136/bmj.39156.536968.55 Cohen, J. (E d.) (1988). Statistical power analysis for the behavioral sciences (2 nd ed.). Ne w Jersey: Lawrence Earlbaum Associates.
125 Cotten, S. R., & Gupta, S. S. (2004). Characteristics of online and offline health information seekers and factors that discriminate between them. Social Science & Medicine, 59 (9), 1795 1806. Couper, M. P., Singer, E., Levin, C. A., Fowler, F. J.,Jr, Fagerlin, A., & Zikmund Fisher, B. J. (2010). Use of the internet and ratings of information sources for medical decisions: Results from the DECISIONS survey. Medical Decision Making : An International Journal of the Soc iety for Medical Decision Making, 30 10 6S 114S. doi: 10.1177/0272989X10377661 Cranney, A., O'Connor, A. M., Jacobsen, M. J., Tugwell, P., Adachi, J. D., Ooi, D. S., . Wells, G. A. (2002). Development and pilot testing of a decision aid for postmenopau sal women with osteoporosis. Patient Education and Counseling, 47 245 255. Creswell, J. (Ed.). (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Boston, MA: Pearson Education, Inc. Czaja, S J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S. N., Rogers, W. A., & Sharit, J. (2006). Factors predicting the use of technology: Findings from the center for research and education on aging and technology enhancement (CREATE). Psychology and Aging, 21 (2), 333 352. doi: 10.1037/0882 79220.127.116.113 Darkins, A., Ryan, P., Kobb, R., Foster, L., Edmonson, E., Wakefield, B., & Lancaster, A. E. (2008). Care coordination/home telehealth: The systematic implementation of health informatics, home telehealth, a nd disease management to support the care of veteran patients with chronic conditions. Telemedicine and e Health, 14 (10), 1118 1126. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptanc e of information technology. Managemen t Information Systems Quarterly 13 (3), 319 339. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science 35 (8), 982 1003. Dawes, J. (2008). Do Data Character istics Change According to the number of scale points used? An experiment using 5 point, 7 point and 10 point scales. International Journal of Market Research 51 (1). Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2013613 De Caterina, R., Dean, V., Dickstein, K., Filippatos, G., Tendera, M., Widimsky, P., & Zamorano, J. L. (2008). ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 20 08. European Journal of Heart Failure, 10 933 989.
126 Diaz Orueta, U., Facal, D., Nap, H. H., & Ranga, M. (2012). What is the key for older people to show interest in playing digital learning games? initial qualitative findings from the LEAGE project on a mul ticultural european sample. Games for Health Journal, 1 (2), 115 123. doi:10.1089/g4h.2011.0024 Dominick, K. L., Ahern, F. M., Gold, C. H., & Heller, D. A. (2002). Relationship of health related quality of life to health care utilization and mortality among older adults. Aging Clinical and Experimental Research, 14 (6), 499 508. Dy, S. M. (2007). Instruments for evaluating shared medical decision making: A structured literature review. M edical Care Research and Review : MCRR, 64 (6), 623 649. doi:10.1177/107755 8707305941 Eddy, J. M., Bibeau, D. L., Glover, E. D., Hunt, B. P., Westerfield, R. C., & Eddy, J. (1989). Wellness perspectives part 1: History, philosophy and emerging trends. Wellness Perspective: Research, Theory, and Practice, 6 3 19. European Society of Cardiology (ESC). (2008). Guidelines for the diagnosis and treatment of acute and chronic heart failure. European Heart Journal, 29 2388 2442. doi:10.1093/eurheartj/ehn309 Fenney, A., & Lee, T. D. (2010). Exploring spared capacity in persons with dem entia: What WiiTM can learn. Activities, Adaptation & Aging, 34 (4), 303 313. Flynn, K. E., Smith, M. A., & Freese, J. (2006). When do older adults turn to the internet for health information? findings from the wisconsin longitudinal study. Journal of Gener al Internal Medicine, 21 (12), 1295 1301. Fonarow, G. C. (2004). Heart failure disease management programs: Not a class effect. Circulation, 110 (23), 3506 3508. Fox, S. (2007 October 8 ). E patients with a disability or chronic disease. Pew Internet and Am erican Life Project. Retrieved from http://www.pewinternet.org/ Reports/2007/Epatients With a Disability or Chronic Disease.aspx Fox, S. (2011a May 12 ). The social life of health information. Pew Internet and American Life Project. Retrieved from http://pewinternet.org/~/media //Files/Reports/201 1/PIP_Social_Life_of_Health_Info.pdf Fox, S. (2011b February ). Health topics. Pew Internet and American Life Project. Retrieved from http://pewinternet.org/~/media //Files/Reports/2011/PIP_ Health_Topics.pdf Fox, S. & Duggan, M. (2013, January 15). Health Online. Pew Internet and American Life Project. Retrieved from http://www.p ewinternet.org/~/media//Files/ Reports/PIP_HealthOnline.pdf
127 Frosch, D. L., Bhatnagar, V., Tally, S., Hamori, C. J., & Kaplan, R. M. (2008). Internet patient decision support: A randomized controlled trial comparing alternative approaches for men consideri ng prostate cancer screening. Archives of Internal Medicine, 168 (4), 363 365. doi:10.1001/archinternmed.2007.111 Garrard, J. (Ed.). (2010). Health sciences literature review made easy (3rd ed.). Sudbury, MA: Jones & Bartlett Publishers. Gatto, S. L., & Tak S. H. (2008). Computer, Internet, and e mail use among older adults: Benefits and barriers. Educational Gerontology 34 (9), 800 811. Graves, L. E., Ridgers, N. D., Williams, K., Stratton, G., Atkinson, G., & Cable, N. T. (2010). The physiological cost an d enjoyment of wii fit in adolescents, young adults, and older adults. Journal of Physical Activity & Health, 7 (3), 393 401. Guderian, B., Borreson, L. A., Sletten, L. E., Cable, K., Stecker, T. P., Probst, M. A., & Dalleck, L. C. (2010). The cardiovascula r and metabolic responses to wii fit video game playing in middle aged and older adults. Journal of Sports Medicine and Physical Fitness, 50 (4), 436 442. Healthy People 2020. (2013, April 10 ). Health communication and health information technology health y people. Retrieved from http://www.healthypeople.gov/2020/ topicsobjectives2020/overview.aspx?topicid=18 Hall, A. K. Chavarria, E., Maneeratana, V., Chaney, B., Bernhardt J. M. (2012 ). Health benefits of digital video games for older adults: A systematic review of the literature. Games for Health Journal 1 (6), 402 410. doi:10.1089/g4h.2012.0046 Hall, A. K., Stellefson, M., & Bernhardt, J. M. (2012). Healthy aging 2.0: The potential of new media and technology. Preventing Chronic Disease, 9 E67. http://dx.doi.org/10.5888/pcd9.110241 Halton, J. (2008). Virtual rehabilitation with video games: A new frontier for occupational therapy. Occupational Therapy Now, 10 (1), 12 14. Hanson, W. (2011). Smart medicine: How the changing role of doctors will revolutionize health care. New York: Palgrave MacMillan. Hennessy, C. H., Moriarty, D. G., Zack, M. M., Scherr, P. A., & Brackbill, R. (1994). Measuring health related quality of life for public health surveillance. Public Health Reports (Washington, D.C.: 1974), 109 (5), 665 672. Higgins, H. C., Horton, J. K., Hodgkinson, B. C., & Muggleton, S. B. (2010). Lessons learned: Staff perceptions of the nintendo wii as a healt h promotion tool within an aged care and disability service. Health Promotion Journal of Australia : Official Journal of Australian Association of Health Promotion Professionals, 21 (3), 189 195.
128 Hsu, J. K., Thibodeau, R., Wong, S. J., Zukiwsky, D., Cecile, S., & Walton, D. M. (2011). A "wii" bit of fun: The effects of adding nintendo wii((R)) bowling to a standard exercise regimen for residents of long term care with upper extremity dysfunction. Physiotherapy Theory and Practice, 27 (3), 185 193. doi:10.3109 /09593985.2010.483267 Hu, S. S., Pierannunzi, C., & Balluz, L. (2011). The Impact of a Mixed mode Data Collection Design on Response and Non response Bias on a RDD Landline Telephone Survey, 5659 5666. American Association for Public Research. Retrieved fr om http://www.amstat.org/Sections/Srms/Proceedings/ y2011/Files/400166.pdf Hunt, S. A., Abraham, W. T., Chin, M. H., Feldman, A. M., Francis, G. S., Ganiats, T. G., . Michl, K. (2005). ACC/AHA 2005 guideline update for the diagnosis and management of chronic heart failure in the adult. Circulation, 112 (12), e154 e235. Hwang, M., Hong, J., Hao, Y., & Jong, J. (2011). Elders' usability, dependability, and flow experien ces on embodied interactive video games. Educational Gerontology, 37 (8), 715 731. doi:10.1080/03601271003723636 Inglis, S. C., Clark, R. A., McAlister, F. A., Ball, J., Lewinter, C., Cullington, D., . Cleland, J. G. (2010). Structured telephone support or telemonitoring programmes for patients with chronic heart failure. Cochrane Database of Systematic Reviews. doi: 10.1002/14651858.CD007228.pub2. Retrieved from http://onlinelibrary.wiley.com.lp.hscl.ufl.edu/store/10.1002/14651858.CD007228.p ub2/asset/CD007228.pdf?v=1&t=hi5gzopj&s=e1a3b36481e1cfbed9751044ac60c 4fd2411b215 Jaarsma, T ., Stromberg, A., Martensson, J., & Dracup, K. (2003). Development and testing of the european heart failure self care behaviour scale. European Journal of Heart Failure, 5 (3), 363 370. Jimison, H., Gorman, P., Woods, S., Nygren, P., Walker, M., Norris, S. & Hersh, W. (2008). Barriers and drivers of health information technology use for the elderly, chronically III, and underserved. Rockville (MD): Agency for Healthcare Research and Quality (US); Nov. (Evidence Resports/Technology Assessment, No. 175). Ret rieved from http://www.nebi.nlm.nih.gov/books/NBK38653. Joseph, A. M. (2006). Care coordination and telehealth technology in promoting self management among chronically ill patients. Telemedicine Journal and e Health : The Official Journal of the American Telemedicine Association, 12 (2), 156 159. Joyce, K., & Loe, M. (2010). A sociological approach to ageing, technology and health. Sociology of Health & Illness, 32 ( 2), 171 180. doi:10.1111/j.1467 9566.2009.01219.x
129 Kaufman, D. R., Pevzner, J., Hilliman, C., Weinstock, R. S., Teresi, J., Shea, S., & Starren, J. (2006). Redesigning a telehealth diabetes management program for a digital divide seniors population. Home Health Care Management & Practice, 18 (3), 223 234. Klein, R. (2007). An empirical examination of patient physician portal acceptance. European Journal of Information Systems 16 (6), 751 760. Kiel, J. M. (2005). The digital divide: Internet and e mail use by the elderly. Informatics for Health and Social Care 30 (1), 1 9 23. Kieschnick, T. & Raymond B. (2011 March 7 ). Can Health IT Promote Health Equity and Patient Centered Care? Health IT Roundtable Background paper, Institute for Health Policy, Kaiser Permanente. Retrieved from http://www.amia.org/sites/ amia.org/files/Roundtable Background Paper 2011.pdf Kizony, R., Weiss, P. L., Shahar, M., & Rand, D. (2006). TheraGame: A home based virtual reality rehabilitation system. International Journal on Disab ility and Human Development, 5 (3), 265 269. Kruse, R. L., Koopman, R. J., Wakefield, B. J., Wakefield, D. S., Keplinger, L. E., Canfield, S. M., & Mehr, D. R. (2012). Internet use by primary care patients. Family Medicine, 44 (5), 342 347. Lamoth, C. J., Ca ljouw, S. R., & Postema, K. (2011). Active video gaming to improve balance in the elderly. Studies in Health Technology and Informatics, 167 159 164. Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the world wide web. Decision Support Systems, 29 (3), 269 282. Lenhart, A., Jones S., Macgill, A.R. (2008 December 7 ). Adults and Video Games. Retrieved from http://www.pewinte rnet.org/Reports/2008/Adults and Video Games.aspx Lloyd Jones, D., Adams, R. J., Brown, T. M., Carnethon, M., Dai, S., De Simone, G., . Gillespie, C. (2010). Heart disease and stroke statistics 2010 update. Circulation, 121 (7), e46 e215. Lloyd Jones, D., Adams, R., Carnethon, M., De Simone, G., Ferguson, T. B., Flegal, K., . Greenlund, K. (2009). Heart disease and stroke Statistics 2009 update A report from the american heart association statistics committee and stroke statistics subcommittee. Circ ulation, 119 (3), 480 486. Lorig, K. (2001). Patient education: A practical approach Thousand Oaks, CA: Sage Publications.
130 Lustria, M. L., Smith, S. A., & Hinnant, C. C. (2011). Exploring digital divides: An examination of eHealth technology use in health information seeking, communication and personal health information management in the USA. Health Informatics Journal, 17 (3), 224 243. doi: 10.1177/1460458211414843 Transgenerational. (2012). Characteristics of Our Aging Population. Retrieved from http://transgenerational.org/aging/demographics.htm Makoul, G. (1998) Perpetuating p assivity: Reliance and reciprocal determinism in physician patient interaction. Journal of Health Communicatio n : International Perspectives 3:3, 233 259. Martin, T. (2012). Assessing mHealth: Opportunities and barriers to patient engagement. Journal mof Health Care for the Poor and Underserved, 23 (3), 935 941. Marziali, E. (2009). E health program for patients w ith chronic disease. Telemedicine and e Health, 15 (2), 176 181. McDonald, K. (2010). Disease management programs for heart failure. Current Treatment Options in Cardiovascular Medicine, 12 578 586. McGonigal J. (2011). Reality Is Broken: Why Games Make U s Better and How They Can Change the World. New York: The Penguin Press. McNutt, R. A. (2004). Shared medical decision making. The Journal of the American Medical Association, 292 (20), 2516 2518. Merians, A. S., Poizner, H., Boian, R., Burdea, G., & Adamo vich, S. (2006). Sensorimotor training in a virtual reality environment: Does it improve functional recovery poststroke? Neurorehabilitation and Neural Repair, 20 (2), 252 267. doi:10.1177/1545968306286914 Meystre, S. (2005). The current state of telemonito ring: A comment on the literature. Telemedicine And e Health 11 (1), 63 69. Monsen, K. A., Westra, B. L., Paitich, N., Ekstrom, D., Mehle, S. C., Kaeding, M., . Ruddarraju, U. K. (2012). Developing a personal health record for community dwelling olde r adults and clinicians: Technology and content. Journal of Gerontological Nursing, 38 (7), 21 25. doi: 10.3928/00989134 20120605 03; 10.3928/00989134 20120605 03 Montano, D. E., Kasprzyk, D. (2008).Theory of reasoned action, theory of planned behavior, and integrated behavioral model. In Glanz, K., Rimer, B., K., & Viswanath, K (eds.), Health behavior and health education theory, research, and practice (pp. 169 188). San Francisco, CA: John Wiley & Sons, Inc.
131 Moriarty, D.G., Zack, M.M., Kobau R. (2003). The Centers for Disease Control and Population tracking of percei ved physical and mental health over time. Health Quality Life Outcomes 1 37. doi:10.1186/1477 7525 1 3 7 Mouawad, M. R., Doust, C. G., Max, M. D., & McN ulty, P. A. (2011). Wii based movement therapy to promote improved upper extremity function post stroke: A pilot study. Jou rnal of Rehabilitation Medicine : Official Journal of the UEMS European Board of Physical and Rehabilitation Medicine, 43 (6), 527 5 33. doi:10.2340/16501977 0816 Mueller, T. M., Vuckovic, K. M., Knox, D. A., & Williams, R. E. (2002). Telemanagement of heart failure: A diuretic treatment algorithm for advanced practice nurses. Heart & Lung: The Journal of Acute and Critical Care, 31 (5 ), 340 347. Mller, A., Schweizer, J., Helms, T. M., Oeff, M., Sprenger, C., & Zugck C. (2010). Telemedical support in patients with chronic heart failure: Experience from the different projects in Germany. International Journal of Telemedicine and Applic ations 8. doi: 10.1155/2010/181806, 1 11 Nacke, L. E., Nacke, A., & Lindley, C. A. (2009). Brain training for silver gamers: Effects of age and game form on effectiveness, efficiency, self assessment, and gameplay experience. Cyberpsychology & Behavior : Th e Impact of the Internet, Multimedia and Virtual R eality on Behavior and Society, 12 (5), 493 499. doi:10.1089/cpb.2009.0013 National Institute of Health (NIH). (2010). National heart lung and blood institute. Retrieved from http://www.nhlbi.nih.gov/health/dci/Diseases/Hf/HF_WhatIs.html National Telecommunications and Information Administration (NTIA). (2011 February ). Digital Nation; Expanding Internet Usage. Retrieve from http://www.ntia.doc.gov/files/ntia/publications/ntia_internet_use_report_february_ 2011.pdf Olshansky, S. J., Goldman, D. P., Zheng, Y., & Rowe, J. W. (2 009). Aging in A merica in the twenty first century: Demographic forecasts from the MacArthur foundation research network on an aging society. The Milbank Quarterly, 87 (4), 842 862. doi: 10.1111/j.1468 0009.2009.00581.x O'Connor, A. M. (1995). User Manual Decision Self Efficacy Scale. Ottawa: Ottawa Hospital Research Institute; 4 p. Retrieved from http://decisionaid.ohri.ca/docs/develop/User_Manuals/UM_Decisio n_SelfEfficacy. pdf technology: Frequency of use for younger and older adults. Ageing International, 36 (1), 123 145.
132 Or, C. K. L., & Karsh, B. T. (2009). A systematic review o f patient acceptance of consumer health information technology. Journal of the American Medical Informatics Association, 16(4), 550 560. Papastergiou, M. (2009). Exploring the potential of computer and video games for health and physical education: A liter ature review. Computer Education 53, 603 622. Peretz, C., Korczyn, A. D., Shatil, E., Aharonson, V., Birnboim, S., & Giladi, N. (2011). Computer based, personalized cognitive training versus classical computer games: A randomized double blind prospective trial of cognitive stimulation. Neuroepidemiology, 36 (2), 91 99. doi:10.1159/000323950 Pham, A (2009 June 1 ). E3: Microsoft shows off gesture control technology for xbox 360. Latimes.com. Retrieved from http://latimesblogs.latimes.com/technology /2009/06/microsofte3.html Pollonini, L., Rajan, N. O., Xu, S., Madala, S., & Dacso, C. C. (2010). A novel handheld device for use in remote patient monitoring of heart failure Pat ients Design and preliminary validation on healthy subjects. Journal of Medical Systems 36 (2), 653 659. doi: 10.1007/s10916 010 9531 y Rahimpour, M., Lovell, N. H., Celler, B. G., & McCormick, J. (2008). Patients' perceptions of a home telecare system. I nternational Journal of Medical Informatics, 77 (7), 486 498. Rainie, L. (2012 September 15 ). Senior citizens and digital technology. Pew Internet and American Life Project. Retrieved from http://www.slideshare.net/PewInternet /senior citizens and digital technology Rand, D., Kizony, R., & Weiss, P. T. (2008). The sony PlayStation II EyeToy: Low cost virtual reality for use in rehabilitation. Journal of Neurologic Physical Therapy: JNPT, 32 (4), 155 163. doi:10.1097/NPT.0b013e31818ee779 Roger, V. L., Go, A. S., Lloyd Jones, D. M., Benjamin, E. J., Berry, J. D., Borden, W. B., . Fox, C. S. (2012). Heart disease and stroke Statistics 2012 update A report from the A merican heart association. Circulation, 125 (1), e2 e220. Rogers, A. E., Addington Hall, J., Abery, A., McCoy, A., Bulpitt, C., Coats, A., & Gibbs, J. (2000). Knowledge and communication difficulties for patients with chronic heart failure: Qualitative stu dy. British Medical Journal 321 (7261), 605 607. Rosenberg, D., Depp, C. A., Vahia, I. V., Reichstadt, J., Palmer, B. W., Kerr, J., . Jeste, D. V. (2010). Exergames for subsyndromal depression in older adults: A pilot study of a novel intervention. The American Journal of Geriatric Psychiatry : Official Journal of the American Association for Geriatric Psychiatry, 18 (3), 221 226. doi:10.1097/JGP.0b013e3181c534b5
133 Sallis, J., F. (2011). Potential vs actual benefits of exergames. Archives of Pediatric Adole scent Medicine 165 667 669. Sanders, C., Rogers, A., Bowen, R., Bower, P., Hirani, S., Cartwright, M., . Hendy, J. (2012). Exploring barriers to participation and adoption of telehealth and telecare within the whole system demonstrator trial: A quali tative study. BMC Health Services Research, 12 (1), 220. Saposnik, G., Mamdani, M., Bayley, M., Thorpe, K. E., Hall, J., Cohen, L. G., . EVREST Study Group for the Stroke Outcome Research Canada Working Group. (2010). Effectiveness of virtual reality ex ercises in STroke rehabilitation (EVREST): Rationale, design, and protocol of a pilot randomized clinical trial assessing the wii gaming system. International Journal of Stroke : Official Journal of the International Stroke Society, 5 (1), 47 51. doi:10.1111 /j.1747 4949.2009.00404.x Schreurs, K. M. G., Colland, V. T., Kuijer, R. G., de Ridder, D. T. D., & van Elderen, T. (2003). Development, content, and process evaluation of a short self management intervention in patients with chronic diseases requiring sel f care behaviours. Patient Education and Counseling, 51 (2), 133 141. Schmidt, S., Schuchert, A., Krieg, T., & Oeff, M. (2010). Home telemonitoring in patients with chronic heart failure. Deutsches Arzteblatt International, 107 (8), 131 138. Schmidt, S., She ikzadeh, S., Beil, B., Patten, M., & Stettin, J. (2008). Acceptance of telemonitoring to enhance medication compliance in patients with chronic heart failure. Te lemedicine Journal and e Health : The Official Journal of the Ame rican Telemedicine Association, 14 (5), 426 433. doi:10.1089/tmj.2007.0076; 10.1089/tmj.2007.0076 Schoene, D., Lord, S. R., Verhoef, P., & Smith, S. T. (2011). A novel video game -based device for measuring stepping performance and fall risk in older people. Archives of Physical Medicine and Rehabilitation, 92 (6), 947 953. doi:10.1016/j.apmr.2011.01.012 Schwartz, K. L., Roe, T., Northrup, J., Meza, J., Seifeldin, R., & Neale, A. V. (2006). study. The Journal of the American Board of Family Medicine 19 (1), 39 45. Sekuler, R., McLaughlin, C., & Yotsumoto, Y. (2008). Age related changes in attentional tracking of multiple moving objects. Perception, 37 (6), 867 876. doi:10.1068/p5923 SeniorNet. (2012). SeniorNet The Organization. Retrieved from http://www.seniornet .com/index.php?option=com_content&task=view&id=42&Itemid=204
134 Shegog R. (2010). Application of behavi oral theory in computer game design for health behavior change. In: Cannon Bowers J, Bowers C, eds. Serious Game Design and Development New York: IGI Global, 196 232 Shubert T. (2010). The use of commercial health video games to promote physical activity in older adults. Annals of Long Term Care 18 27 32. Shrewsbury, C. M. (2002). Information technology issues in an era of greater state responsibilities. Journal of Aging & Social Policy, 14 (3 4), 195 209. Sinclair, B. (2010 March 11 ). Sony reveals what makes PlayStation move tick. News at GameSpot. Retrieved from http://gdc.gamespot.com/story/6253435/sony reveals what makes playstation move tick Smith, M ., Saunders, R., Stuckhardt, L., & McGinnis, J. M. (Eds.). (2012 ). Best Care at Lower Cost: The Path to Continuously Learning Health Care in America National Academies Press. Stacey, D., Bennett, C. L., Barry, M. J., Col, N. F., Eden, K. B., Holmes Rovner M., . Thomson, R. (2011). Decision aids for people facing health treatment or screening decisions. Cochrane Database System Review, 10. doi : 10.1002/14651858.CD001431.pub3 Strmberg, A. (2005). The crucial role of patient education in heart failure The European Journal of Heart Failure, 7(3), 363 369. Studenski, S., Perera, S., Hile, E., Keller, V., Spadola Bogard, J., & Garcia, J. (2010). Interactive video dance games for healthy older adults. The Journal of Nutrition, Health & Aging, 14 (10 ), 850 852. Sung, V. W., Raker, C. A., Myers, D. L., & Clark, M. A. (2010). Treatment decision making and information seeking preferences in women with pelvic floor disorders. International urogynecology journal 21 (9), 1071 1078. Suter, P., Suter, N., & Johnsto n, D. (2011). Theory based telehealth and patient empowerment. Population Health Management 14 (2), 87 92. doi: 10.1089/pop.2010.0013 Szturm, T., Betker, A. L., Moussavi, Z., Desai, A., & Goodman, V. (2011). Effects of an interactive computer game exerc ise regimen on balance impairment in frail community dwelling older adults: A randomized controlled trial. Physical Therapy, 91 (10), 1449 1462. doi:10.2522/ptj.20090205 Taha, J., Sharit, J., & Czaja, S. (2009). Use of and satisfaction with sources of healt h information among older internet users and nonusers. The Gerontologist, 49 (5), 663 673.
135 Takahashi, P. Y., Pecina, J. L., Upatising, B., Chaudhry, R., Shah, N. D., Van Houten, H., . Hanson, G. J. (2012). A randomized controlled trial of telemonitoring in older adults with multiple health issues to prevent hospitalizations and emergency department visits. Archives of Internal Medicine, 172 (10), 773 779. doi: 10.1001/archinternmed.2012.256; 10.1001/archinternmed.2012.256 Torres, A. C. S. (2011). Cognitiv e effects of video games on old people. International Journal on Disability and Human Development, 10 (1), 55 58. doi:10.1515/IJDHD.2011.003 U.S. Census Bureau. (2010 March 2 ). Older Americans Month: May 2010 Retrieved from https://www.census.gov/newsroom/releases/archives/facts_for_features_ special_editions/cb10 ff06.html Uno, R. (2012 May 16 ). Wii vs. kinect vs. move. Retrieved from http://www.buzzle.com /articles/wii vs kinect vs move.html Van Der Wal, M., Jaarsma, T., & Veldhuisen, D. J. (2005). Non compliance in patients with heart failure; How can we manage it? The European Journal of Heart Failure, 7(1), 5 17. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly 425 478. Wagner, T. H., Bundorf, M. K., Singer, S. J., & Baker, L. C. (2005). Free internet access, the digital divide, and health information. Medical care 415 420. West, J. A., Miller, N. H., Parker, K. M., Senneca, D., Ghandour, G., Clark, M., Greenwald, . Debusk, R. F. (1997). A comprehensive management system for heart failure improves clinical outcomes and reduces medical resource utilization. The American Journal of Cardiology, 79 (1), 58 63. Weybright, E. H., Dattilo, J., & Rusch, F. R. (2010). Effects of an interact ive video game Therapeutic Recreation Journal, 44 (4), 271 287. Williams, B., Doherty, N., L., Bender, A., Mattox, H., & Tibbs, J., R. (2011). The effect of nintendo wii on balance: A pilot stud y supporting the use of the wii in occupational therapy for the well elderly. Occupational Therapy in Health Care, 25 (2), 131 139. doi:10.3109/07380577.2011.560627 World Health Organization. (2012). E health Retrieved from http://www.who.int/trade /glossary/story021/en/
136 Xie, B. (2009). Older adults' health information wants in the internet age: Implications for patient provider relationships. Journal of Health Communication, 14 (6), 510 524. Y amada, M., Aoyama, T., Nakamura, M., Tanaka, B., Nagai, K., Tatematsu, N., . Ichihashi, N. (2011). The reliability and preliminary validity of game based fall risk assessment in community dwelling older adults. Geriatric Nursing, 32 (3), 188 194. doi:10 .1016/j.gerinurse.2011.02.002; 10.1016/j.gerinurse.2011.02.002 Yamaguchi, H., Maki, Y., & Takahashi, K. (2011). Rehabilitation for dementia using enjoyable video sports games. International Psychogeriatrics, 23 (4), 674 676. Yavuzer, G., Senel, A., Atay, M. B., & Stam, H. J. (2008). ''Playstation eyetoy games'' improve upper extremity related motor functioning in subacute stroke: A randomized controlled clinical trial. European Journal of Physical and Rehabilitation Medicine, 44 (3 ), 237 244. Ybarra, M. L., & Suman, M. (2006). Help seeking behavior and the internet: A national survey. International Journal of Medical Informatics, 75 (1), 29 41. Yong Joo, L., Soon Yin, T., Xu, D., Thia, E., Pei Fen, C., Kuah, C. W., & Kong, K. H. (2010). A feasibility study usin g interactive commercial off the shelf computer gaming in upper limb rehabilitation in patients after stroke. Journal of Rehabilitation Medicine: Official Journal of the UEMS European Board of Physical and Rehabilitation Medicine, 42 (5), 437 441. doi:10.23 40/16501977 0528 Zickuhr, K., & Madden, M. (2012 June 6 ). Older adults and internet use. Pew Internet and American Life Project. Retrieved from http: //pewinternet.org/~/media//Files/ Reports/2012/PIP_Older_adults_and_internet_use.pdf
137 BIOGRAPHICAL SKETCH Amanda K. Hall was born in 1978 in Philadelphia, Pennsylvania. She grew up in Florida and Kentucky and graduated from Bishop Moore High School in 1997. Amanda attended the University of Florida (UF). In 2001, Amanda earned a Bachelo r of Science degree from UF in business administration with a focus in marketing and a minor in e conomics. After graduation, Amanda enrolled in a graduate prog ram at UF. She degree at UF, Amanda worked as a Graduate Research Assistant for the Florida Traffic and Bicycle Safety Education Program (FTBSEP) in the Department of Urban and Regional Planning at UF. After graduation, Amanda worked as Program Director Assistant for FTBSEP. In 2006, Amanda enrolled at the University of West Florida (UWF). Amanda earned a Master of Science in physical e ducation from UWF in 2009. While pursuing he and Telehealth Coordinator for a local Home Healthcare Agency serving a Medicare/Medicaid patient population. Amanda entered the PhD program in the Department of Health Education and Behavior at UF in 2010. During her first year at UF, Amanda specifically defined her research focus. She developed a line of research investigating technology use among older adults for improved healt h outcomes and medical decision doctoral disser tation includes scientific papers based on her research submitted for publication to scholarly journ als. Amanda received her Doctor of Philosophy in Health and Human Performance with an emphasis in Health Behavior in August 2013. Amanda was awarded a Natio nal Library of Medicine Post Doctoral Trainee Fellowship in
138 Biomedical and Health Informatics in the School of Medicine at the University of Washington in Seattle.