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1 EHEALTH LITERACY AND SOCIAL MEDIA USE FOR HEALTH INFORMATION AMONG OLDER ADULTS By BETHANY L. TENNANT A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2013
2 2013 Bethany L. Tennant
3 To my number one supporters Jeff and Sue Tennant
4 ACKNOWLEDGMENTS My experience as a graduate student at the University of Florida has been an opportunity for me to grow both personally and professionally. I attribute this, in large part, to the many talented and supportive people I have had the privilege of working with and learning from while here. First and foremost, I could not have succeeded in accomplishing this project without the guidance, patience and support of my chair, Dr. Michael Stellefson Dr. Stellefson went above and beyon d I am indebted to his insightful feedback that kept me focused on my objectives He served as a remarkable advisor, boss, mentor and role model. I feel so very fortunate to have worked with him during this journey. I want to thank my committee, Dr. Be th Chaney, Dr. Don Chaney, and Dr. Virginia Dodd for their feedback and contributions to helping me complete this process. I also want to thank Dr. Jane Em me ree, who unknowingly taught me so much about research methodology and data analysis. Personally, I would like to my family and friends for their unyielding support I would also like to express my sincere and utmost gratitude to my parents for their unwavering, encouragement, support, advice, and friendship. I appreciate d and cherished the Saturday lunches more than I ever let on. Thank you to my cohort who traveled alongside me, especially, Julia, Mandi, without their companionship. I also want to acknowledge all of my friends for their support, insight, conservations, and for helping me have fun along the way I also want to thank Dan, for standing by me, making me laugh, and for always listening, ev
5 story. I never would hav e predicted this time in my life but in the end it was right, and I had the time of my life.
6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURES ................................ ................................ ................................ .......... 9 ABSTRACT ................................ ................................ ................................ ................... 10 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 12 Statement of the Problem ................................ ................................ ....................... 17 Purpose of the Study ................................ ................................ .............................. 18 Research Questions ................................ ................................ ............................... 18 Definition of Terms ................................ ................................ ................................ .. 19 Significance of the Study ................................ ................................ ........................ 20 2 REVIEW OF THE LITERATURE ................................ ................................ ............ 22 Chroni c Disease Prevalence ................................ ................................ ................... 22 Multimorbidity ................................ ................................ ................................ ... 23 Risk Factors for Chronic Disease ................................ ................................ ..... 24 Digital Health Communication ................................ ................................ ................. 25 Olde r Adult Internet Use for Health Information ................................ ................ 29 Chronic Disease and Internet Use ................................ ................................ .... 29 Health Literacy and eHealth Literacy ................................ ................................ ...... 30 Social Media and Health Information Seeking ................................ ........................ 34 Theoretical Explanations for Health Information Seeking ................................ ....... 39 Self Efficacy Theory ................................ ................................ ......................... 39 Technology Acceptance Model ................................ ................................ ........ 42 Structural Influence Model of Health Communication ................................ ....... 43 Conclusion ................................ ................................ ................................ .............. 45 3 METHODS ................................ ................................ ................................ .............. 48 Research Design ................................ ................................ ................................ .... 48 Research Variables ................................ ................................ ................................ 49 Instrumentatio n ................................ ................................ ................................ ....... 51 Data Collection ................................ ................................ ................................ ....... 52 Study Population ................................ ................................ ................................ ..... 53 Data Analysis ................................ ................................ ................................ .......... 54 Research Qualifications and Bias ................................ ................................ ........... 55 Delimitations ................................ ................................ ................................ ........... 56 Limitations ................................ ................................ ................................ ............... 56
7 4 RESULTS ................................ ................................ ................................ ............... 59 Demographic Profile of Respondents ................................ ................................ ..... 59 General Health Status of Respondents ................................ ................................ .. 61 Research Questions ................................ ................................ ............................... 61 Research Question #1 ................................ ................................ ...................... 61 Research Que stion #2 ................................ ................................ ...................... 62 Research Question #3 ................................ ................................ ...................... 64 Research Question #4 ................................ ................................ ...................... 65 Research Question #5 ................................ ................................ ...................... 65 Research Question #6 ................................ ................................ ...................... 66 Research Question #7 ................................ ................................ ...................... 67 Research Question #8 ................................ ................................ ...................... 67 Research Question #9 ................................ ................................ ...................... 67 Research Que stion #10 ................................ ................................ .................... 69 Summary ................................ ................................ ................................ ................ 72 5 DISCUSSION ................................ ................................ ................................ ......... 79 Discussion of Findings ................................ ................................ ............................ 79 Demographics ................................ ................................ ................................ .. 79 Factors Associated with eHealth Literacy ................................ ......................... 80 eHealth literacy and demographic, device factors ................................ ...... 81 eHealth literacy and chronic disease/health status ................................ .... 83 Factors Associated with Social Media Use for Health Information ................... 85 Demographic, device factors, and social media use ................................ .. 85 Social media use and chronic disease/health status ................................ .. 88 Perceived Usefulness and Perceived Ease of Use ................................ .......... 89 Relationship between eHealth Literacy and Social Media Use ......................... 90 Health as an Outcome of Communication ................................ ........................ 91 Implications ................................ ................................ ................................ ............. 92 Recommendations for Future Research ................................ ........................... 92 Implications for Health Educators ................................ ................................ ..... 94 Limitations of the Study ................................ ................................ ........................... 95 Conclusion ................................ ................................ ................................ .............. 97 APPENDIX A EHEALS AND SOCIAL MEDIA SURVEY ITEMS ................................ ................... 99 B FLORIDA CONSUMER CONFIDENCE SURVEY 2013 ................................ ....... 102 LIST OF REFERENCES ................................ ................................ ............................. 123 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 134
8 LIST OF TABLES Table page 3 1 Proposed research variables ................................ ................................ .............. 57 4 1 Demographic characterist ics of study participants. ................................ ............. 74 4 2 Devices used to look for health or medical information ................................ ...... 75 4 3 General health status of study participants. ................................ ........................ 75 4 4 Means and standard deviations for eHEALS items among older adults in sample ( n =283). ................................ ................................ ................................ 76 4 5 Social media use for health information among older adults in sample ( n =283). ................................ ................................ ................................ .............. 76 4 6 Multiple Linear Regression Predicting eHealth Literacy ................................ ..... 77 4 7 Logistic Regression Predicting Use of Social Media for Health Information ....... 78
9 LIST OF FIGURES Figure page 2 1 eHealth Literacy Lily Model. ................................ ................................ ............... 46 2 2 Technology Acceptance Model. ................................ ................................ .......... 46 2 3 The Structural Influence Model of Health Communication. ................................ 47
10 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy EHEALTH LITERACY AND SOCIAL MEDIA USE FOR HEALTH INFORMATION AMONG OLDER ADULTS By Bethany L. Tennant August 2013 Chair: Michael Stellefson Major: Health and Human Performance Background The majority of older adults suffer from chronic conditions, and need health information about multiple diseases Older adults with chronic disease s are already turning to the Internet and social media for health, and it is important to understand their eHealth literacy to effectively deliver online information to this underserved audience. Purpose R esearch was needed to investigate the interrelation ships between socio demographic characteristics, devi ce use, perceived health status eHealth literacy, and social media use for health information. Thus the purpose of this study was to examine eHealth literacy and social media use among older adults in t he state of Florida. Methods. Telephone surveys were conducted with 283 Internet using adults over the age of 50 living in the state of Florida. The eHealth Literacy Scale, social media use, technology acceptance factors, and demographic items were used to measure respective constructs.
11 Results. Study participants consisted of men and women aged 50 91 years, who were primarily White and well educated. More than two thirds of the sample reported suffering from a variety of chronic diseases. The majority of respondents used the Internet for health information, and believed the Internet was useful for helping make decisions about their health. eHealth literacy was influenced by age, education, desktop computer use, and laptop computer use The health variables were not related to eHealth literacy. O ver one third of participants (35.7%) indicated that they used some form of social media to locate or share health information in the last year Social media use for health information was influenced by age, sex, edu cation, and laptop computer use. Chronic disease status, or number of chronic disease was not related to social media use, however a negative association was found between social media use and health status. Conclusion The Internet, including social media applications, has the potential to improve health outcomes for older adults with chronic disease. Yet, much research is still needed to further understand the full influence of online health information seeking and social media on health behaviors and dec ision making in the older adult population.
12 CHAPTER 1 INTRODUCTION According to the Centers for Disease Control and Prevention (CDC 2009 ), chronic diseases such as heart disease, cancer, stroke, diabetes and arthritis are the leading causes of death and disability in the United S tates and are responsible for 78% percent of total U.S. health care expenditures These diseases cause significant functional limi tations for over 44 million Americans or approximately one quarter of all persons living with a chronic illness (CDC, 2009) N early half of all adults in the United States are currently living with at least one chronic health condition (Bodenheimer et al., 2009), and almost o ne in four Americans are managing multiple chronic conditions (Anderson, 2010 ) The number of adults with chronic conditions increase s with age ; at least 80% of older adults age 65 and older are liv ing with one chronic disease This tr end is expected to continue as t he median age of Americans continues to increase B y 2030 adults aged 65 and older are expected to represent almost 20% of the U.S. population (Administration on Aging, 2011). As the baby boomers (i.e. adults born between the years 1946 and 1964) begin to celebrate their 65 th birthdays, it is projected that each day 10,0 00 people will turn 65 and the trend will continue for the next 20 years (Alliance for Aging Research, 2011). Older adults with chronic disease often report physical limitations, decreased quality of life, and an increased need for costly long term health care ( Anderson, 2010 ). Analysts predict that by 2030 t he aging U.S. population will increase health care spending by 25% ; this i s without taking into account inflation or the higher costs of new technologies (CDC, 2007). Given that older adults are living longer than ever before and the number of older adults living with chronic conditions is on the
13 rise, there will likely be even more strain on our already overburdened health care system and some believe the burden and the cost of treating these chronic conditions is not sustainable for future generations (Partnership to Fight Chronic Disease, 2009). Because of this, new measures a re required to lessen the continuing impact of chronic diseases on patient care One avenue for expanding the scope of care for the increasing number of individuals with chronic disease is through the use of internet mediated health information and commun ication technologies (ICTs). These t echnolog ies are changing the way people obtain, evaluate, and communicate health information. For many Americans, t he Internet has become a common source of health information ( Fox, 2012 ). Since 2012, 81% of American adults use the Internet and, of those, 72 % have looked for online for health information in the past year (Fox & Duggan, 2013 ). Online websites offer health consumers a convenient way to ac cess health information that can improve decision making, health be haviors, and health outcomes ( Jones & Goldsmith, 2009 ). One of the major advantage s of the Internet as a source for health information is the elimination of environmental barriers to seeking health advice, which is especially problematic for individuals un able to travel due to disability (Smarr et al., 2011). Among I nternet users with chronic disease, over 86% have looked online for health information at least one time in their lives (Fox, 2007). Research indicates that the Internet acts a source of healt h information, therapy, and support for individuals with chronic conditions Internet mediated health ICTs can enhance individual self management capabilities and thereby improve important outcomes such as health related quality of life (Wagner et al., 200 4; Jones & Goldsmith, 2009; Solomon et al. 2012).
14 H ealthcare organizations, health advocates, and researchers have used the Internet to deliver healthcare services and information efficient ly and cost effective ly (Crabb, Rafie, & Weingardt, 2010 ; Rideout et al., 2005; Wagner et al., 2004; Wright & Hill, 2009). A great deal of private and public investment has gone into web based health interventions and the development of health and medical websites ( Crabb et al., 2012) As compared to traditional in pers on treatments, w eb based interventions addressing different types of chronic disease s have the potential to reach a broader population of patients for extended periods of time (Solomon et al. 2012). F or older adults who typically have high health informat ion and service needs (Taja, Sharit, & Czaja, 2009), the Internet is especially useful for easing the b urden associated with chronic disease management (Xie, 2011). Compared to previous generations, aging baby boomers have relatively high education leve ls. They also tend to possess experience with c omputers and navigating the Internet (Gilbert, 2000). Compared to adults in younger age groups, older adults are less likely to use the Internet although the proportion of older adults using the Internet is r apidly grow ing (Jones & Fox, 2009). B etween 2005 and 2008, the rate of Internet use among adults aged 70 75 rose from 26% to 45% (Jones & Fox, 2009). Recent data shows that the majority (53%) of adults age 65 and older now use the Internet In fact, a mong older Internet users, 70% access the Internet daily (Zickuhr & Madden, 2012). Researchers have noted that o lder adults seem receptive to the idea of using the Internet to enhanc e their own healthcare (Crabb et al., 2011) Up to 95% of older adults believe the Internet is a valuable tool for finding health information (Miller & Bell, 2012).
15 Habitual Internet use is expected to increase dramatically as the baby boomer generation ages, and their health care needs increase (Rideout et al., 2005 ). For older ad ults, preventing complications associated with chronic illness is inherently important for maintaining overall health status (Sinden & Wister 2008 ). O lder adults living with chronic disease may benefit from accessing and using high quality online health i nformation for decision making S ocial media or online platform s for participation, conversation, community and connectedness offer s opportunities to impact elder health and can keep older patients connected and informed (Chou et al 2009; Hall, Stellefson, & Bernhardt 2012). Online communities, blogs, and social networking websites, such as Facebook and Twitter are examples of social media outlets. While older adults lag behind all other groups in terms of social media use almost half (47%) of all I nternet users age s 50 64 report having used social networking sites at least once and 20% report using social networking sites on a daily basis (Madden, 2010) The increasing numbers of older adults gravitation to social media use is evidenced by a r ecent report indicating use of social medial among Internet users 65 years and older increased 13% to 33% between the years 2009 to 2011 (Fox, 2012). Social media websites have become innovative social channels for delivering and receiving health inform ation and advice, in addition to connecting people with similar health concerns (Chou et al., 2009) S ocial media provides new prospects for health communication as well as potential opportunities to decrease health related communication gaps and inequali ties (Gibbons et al. 2011). Research suggests that social media may improve health related quality of life, reduce feelings of social
16 isolati on and depression, and increase social support for ad ults with chronic care needs ( Chou et al., 2009; Kontos et al. 2010; Pulman, 2010 ). For older adults, e ffectively using social media as a health communication strategy has the potential to address health disparities and may have important public health implications due to its low cost and wide reach (Kreps & Neuhauser, 2010) Nonetheless, it has been noted that t he full potential of the Internet and social media to support healthy aging has yet to be realized Progress in this research area has been slow, and many questions remain unanswered (Chou et al., 200 9) There are a variety of reasons that may explain why advances have been slow to occur in this arena. Equal access to the Internet still remains an issu e for older populations (Zickuhr & Madden, 2012). Although a ccess to the Internet and social media do es not ensure that individuals can locate, understand, appraise and apply the health information that they find Older adults are also less trusting of the Internet as a source of health information (Miller & Bell, 2012). Many eHealth services intended for public consumption also cannot be fully used because the general public may not have the reading or Internet skills to benefit from their use (Gottlieb & Rogers, 2004) To have product ive interactions with technology based health tools participants gene rally need a particular set of knowledge, skills abilities and other attributes (KSAOs) ( Chan & Kaufman, 2011). The evolution of more interactive and complex web 2.0 technologies has made identifying and using credible eHealth information sources even mor e important (Sinden & Wister, 2008). For example, health consumers must be able to determine whether health information is from reputable medical sources (e.g. Centers for Disease Control and Prevention, National Institutes of Health, American
17 Cancer Socie ty), and they must be able to understand the difference between biased and unbiased claims that may or may not be evidenced based (Stellefson et al. 2011; Van Deuresen & van Dijk, 2011). Statement of the Problem Among older adult populations, r esearchers have focused on assessing health literacy rather than eHealth literacy (Ivanitskaya, primarily because eHealth literacy skills are a moving target as technologies are constantly changing (Xie, 2011) While eHealth literacy has been previously measured among adolescents, college students, and the general adult population (Hanik & Stellefson, 2011; Neter & Brainin, 2012; Norman & Skinner, 2006 b ), there is limited information on eHealth literacy in aging populations with chronic diseas e. Currently, however, i t is largely unknown whether older adults with chronic disease have the capability or the confidence to seek, find, understand and apprai se health information from electronic sources and apply that knowledge to a health problem (Nor man & Skinner, 2006 a ) Without sufficient KSAOs, older adults are at heightened risk for consuming non credible online health information ; information that could lead to both intentional and unintentional health risk behaviors (Rice, 2006) Some research suggests that elderly persons with poor health are the least likely subgroup in the United States to use the Internet (Wright & Hill, 2009). Today, it is especially important to investigate the eHealth literacy of older adults experiencing ch ronic disease, because evidence suggest s that this population may especially benefit from what the Internet has to offer from onlin e activities that can support health literacy and promote healthy behaviors (Czaja et al., 2006) Programs and interventions designed to improve eHealth literacy aim to empower individuals to be come active participants in their own health care (Norman &
18 Skinner, 2006 a ; Hou et al. 2010). While potential improvements in eHealth literacy rates may improve the quality, capacity, an d efficiency of healthcare systems (IOM, 2009), l ow or unknown eHealth literacy rates may halt policy efforts necessary to increase health system efficiencies, decrease costs, improve health outcomes and decrease disparities (Collins et al. 2012) In add ition, little research has been done on the effect that social media has had on eHealth literacy rates The use of social media brings new opportunities to impact population health by enhancing information sharing capability and increasing connectivity mor e so than static, non interactive health websites For individuals with chronic disease, social media provides a new and valuable channel for facilitating social support and patient engagement (Madden, 2010). Currently, online health c onvers ations are bein g driven by the availability of social media tools and the increased desire for using this new form of media especially for people living with chronic conditions who wish to connect with like individuals (Fox, 2011). Thus, social media use among older adults, especially among older adults with chronic disease, warrants further inquiry. Purpose of the Study The purpose of this study wa s to investigate eHealth literacy and social media use among older adults with one or more chronic disease ( s ) in the state of Florida Research Questions The following research questions directed this study: 1. What is the extent to which older adults living in the state of Florida believe that the Internet is a useful resource for health information ? 2. What are th e eHealth literacy scores of older adults living in the state of Florida?
19 3. What is the extent to which older adults in the state of Florida use social media to locate or share health information? 4. Do older adults living with one or more chronic disease(s) e xhibit higher eHealth literacy than older adults without chronic disease(s)? 5. Do older adults living with one or more chronic disease(s) exhibit greater social media use for health information than older adults without chronic disease(s)? 6. Does the use of social media for health information predict overall eHealth literac y in older adults living in the state of Florida? 7. What is the extent to which eHealth literacy is associated with perceived h ealth status among older adults living in the state of F lorida? 8. What is the extent to which using social media for health information is associated with perceived health status among older adults living in the state of Florida? 9. What are the socio demographic, chronic disease, and Internet device use factors that predict eHealth literacy among older adults in the state of Florida? 10. What are the soci o demographic, chronic disease, Internet device use, and technology acceptance factors th at predict use of social media for health information among older adults in the state of Florida? Definition of Terms Chronic disease: an illness lasting 3 months or more, including, but not limited to: arthritis, cardiovascular disease, cancer, diabetes, obesity, and COPD. eHealth: health information or services delivered throug h the Internet or related web technologies (Eysenbach, 2001). eHealth literacy: ability of an individual to seek, find, understand and appraise health information from electronic sources and apply that knowledge to a health problem (Norman & Skinner, 2006 a ). Health communication: the study and use of communication strategies to inform and influence individual decisions that enhance health (CDC) Healthy People: A federal interagency workgroup that establishes national population health goals. Older adults: adults aged 50 or older (World Health Organization, 2012) Social media: Online media such as social networking, blogging, and/or online support groups that advances conversation and allows participants to contribute to the creation or development of the information delivered (Eysenbach, 2008).
20 Web 2.0: Web enabled applications that are built around user generated or user manipulated content, such as wikis, blogs, podcasts and social networking sites Pew Internet & American Life Project, 2011). Significance of the Study Given that Internet mediated health ICTs could potentially reach broader audiences with health information, Healthy People (2020) has called for the proportion of online health information seekers who report being able to easily access online health information (Health Communication and Health Information Technology Objective #9). The Healthy People 2020 goal is to have 41.0% (up from 37.3% reported in 2007) online health information seekers report being able to e asily accessing health information. To track progress toward this national public health objective, w e must determine whether populations perceive online health information to be useful and if they are eHealth literate. This study will measure older adults with chronic disease in the state of Florida perceived usefulness of the Internet for health information as well as their perceived ease of use of the Internet for health information which can be compared to the national data. Findings can enable health professionals (e.g. physicians, nurses, health educators) to be tter recommend and prescribe eHealth resources to older adults with chronic disease If eHealth resources are to improve public health while avoiding the perpetuation of further social inequa lities, the divergence between what eHealth is provided and what individuals can access / use must be resolved (Norman & Skinner, 2006 b ). It is important to focus this study on older adults because these groups have much to gain from online health informatio n seeking and social media as they typically have greater needs for health services and information and often are faced with additional barriers to meet these needs (Miller & Bell, 2012 ; Xie, 2008 ).
21 The findings from this study fill several gaps in this e merging field and provide insight into the eHealth literacy of older adults with chronic diseases. Knowledge of individuals levels of eHealth literacy in this population, allows for creation of tailored eHealth technologies thus allow ing older adults to b ecome more empowered to better self manage chronic conditions and proactively manage their health. The findings from this study will aid in develop ing new strategies for address ing challenges inherent to delivering online self management health interventions to th is growing population. The eHealth literacy, and reveal what factors may predict eHealth literacy, as well as, any associations between eHealth litera cy and perceived health status in this population. In addition, the data from this study will shed light on current social media use for health information among older adults and any relationships between social media use and eHealth literacy and perceived health status among this population.
22 CHAPTER 2 REVIEW OF THE LITERATURE This chapter provides a review of the literature on : 1) chronic disease prevalence ; 2) digital health communication; 3) health literacy and eHealth literacy ; 4) social media and he alth information seeking; and 5 ) the theoretical explanations for health information seeking. Chronic Disease Prevalence Chronic diseases are non communicable illnesses characterized by prolonged duration that are rarely cured completely (CDC, 2009) These types of diseases are prevalent in the United States and are a leading public health concern. Chronic diseases have surpassed infectious diseases as the leading cause of death and disability in the United States, causing seven out of every 10 deaths (CDC, 2009 ), with five chronic disease s -heart disease, cancer, stroke, chronic obstructive pulmonary disease (COPD) and diabetes account ing for more than two thirds of all deaths (CDC, 2007 ). The incidence of chronic disease in the United States is also steadily increasing. In 2009, 145 million Americans -almost 1 out of every 2 adults -had a t least one chronic illness B y 2020, this number is expected to grow to 157 million (Anderson, 2010). This increase in chronic disease incidence and preval ence has put additional financial and delivery system burden s on an already strained healthcare system. People with chronic conditions more frequently use h ealth care services, including hospitalization s office visits, home health care, and prescription drugs (Anderson, 2010). The overwhelming majority (99%) of Medicare expenditures are for beneficiaries with at least one chronic disease (Anderson, 2010). The Medical Expenditure P l an
23 Survey (2006) reported that 97% of home health care visits and 93% of pr escription drugs are for individuals with chronic conditions. Between the years 2002 and 2009 the percentage of health care spending on individuals with chronic conditions increased from 78% to 84% ; this increase in health care expenditures is estimated t o be costing the economy more than a trillion dollars annually (Anderson, 2010 ; DeVol & Bedroussian, 2007 ). Under the new Patient Protection and Affordable Care Act, the current Medicare system for Americans 55 and older is being restructured. The Act (201 0) proposes to reduce Medicare spending by $716 billion over 10 years which could lead to cuts in benefits and services for some seniors. Therefore, it is imperative to identify and utilize alternative methods of health management to help adjust the rising healthcare costs Multimorbidity Unfortunately, most adults experience multimorbid ity or the coexistence of multiple chronic diseases or conditions Multimorbidity a ffects almost half of all people with chronic conditions (Tinnetti, Fried, & Boyd, 2012; Anderson, 2010). Among health care recipients almost 75% of adults aged 65 and older are affected by multimorbidity (Tinnetti, Fried, & Boyd, 2012 ; Anderson, 2010 ). Older adults with multiple chronic conditions are the major users of health ca re services with utilization paralleling the number of chronic conditions M ore than three fifths of health care spending is attributed to individuals wi th multiple chronic conditions (Anderson, 2010). Individuals with multiple chronic conditions are far more likely to : (a) be hospitalized, (b) fill more prescriptions, (c) receive more physician and home health care visits, and (d) spend more for inpatient hospital care than individuals with one or no chronic conditions (Anderson, 2010). For example, a typical individual with just one chronic condition fills an average of 7.3
24 prescriptions per year, while an individual with five or more chronic conditions fill s an average of 57.4 prescriptions per year (Anderson, 2010). Anderson (2010) further described that ealth care spending for a person with one chronic condition is almost three times greater than spending for someone without any chronic condition, while spending is about 17 times (p.15). As the U.S population ages, t he proportion of individuals diagnosed with chronic conditions is growing exponentially. In 2010, individuals age s 65 and over represented 13.1% of the population ; this figure is expected grow to 19.3% (72.1 million older persons) by 20 30 due to the aging baby boomer generation (Administration on Aging, 2011). Adults over age 85, a subgroup of older adults, suffer the highest levels of multimorbidity ; levels projected to reach 21 million in 2050, representing an increase of more than 15 million people when compared to 2005 levels. These projections predict a future U.S. healthcare workforce incapable will not be capable of treating the increased number of older adults who are managing high cost multimorbidity (Bodenhelmer, Chen, & Bennett 2009). Risk Factors for Chronic Disease The increased prevalence of chroni c disease is multi factorial. The major drivers of this increase in prevalence are the aging population coupled with a rise in risk factors such as obesity and alcohol use (Bodenhe lmer, Chen, & Bennett, 2009). While a dvances in medical science and a marked increase in screening for and diagnosing chronic conditions has improved treatment outcomes and mortality rates the growing incidence of diabetes, ca rdiovascular disease and stroke threaten s to cancel out any gains that have recently been made (Anderson, 2010; DeVol & Bedroussian, 2007)
25 even though most chronic diseases are generally preventable. Tobacco use, lack of physical activity, poor eating habits, and excessive alcohol use are four common yet modifiable behaviors that are responsible for much of the illness, disability, and premature death associated with chronic disease (CDC, 2009). From the patient perspective, chronic disease is viewed as a disruption and an unce rtainty (Bury, 1982). While the effects of chronic conditions have varying levels of severity (Anderson, 2010), a ctivity limitation a ffects more than a quarter of Americans with chronic conditions ( CDC, 2009 ). C hronic conditions can cause many functional l imitations that result in an inability to maintain normal daily activities (e.g. walking, bathing, and dressing). Over time, living with a c hronic condition can lead to isolation, depression and physical pain which affect mental health, social involvement and employment status. Individuals managing multimorbidity are at greater risk for disability and activity limitations (Anderson, 2010) and therefore have a unique task to cope with multiple conditions through adherence to a variety of self care approache s and practices (Ayers & Kronenfeld, 2007). Individuals with multiple chronic conditions are in need of resources to help with the complex task of self managing multimorbidity. In are avai lable to help adults with chronic disease better self manage multiple conditions. Digital Health Communication The Internet has changed the way people communicate and is a powerful and important resource for information. While access to the Internet remai ns unequal, its overall use has grown rapidly Today, 82% of U.S. adults use the Internet, represent ing a 300% increase sinc e 2008 (Fox, 2012). The rise in Internet usage has resulted in a concurrent increase in the amount of available online health inform ation. The amount of
26 Internet usage and other Internet activities are also consistent factors explaining online health seeking (Rice, 2006). Health has been noted as the sixth largest content area on the I nternet (Ayers & Kronenfeld, 2007). At present thousands of health related websites covering a wide variety of content areas such as: fitness, prescription drugs disease management, medical treatments, al ternati ve medicine, and doctor reviews can be accessed online. The Internet provides a forum for captur ing archiv ing, and retriev ing a vast quantity of current information regarding health, health care and disease specific pathology (Powell et al. 2011). Research suggests that web based applications can be used to promot e healthy lifestyle s and supp ort self care (Nijland et al. 2011 ). The Internet is an effective tool for sharing health information since i ndividuals can anonymously search the Internet for relevant health information at their convenience free from the limitations of location, time and social judgment In turn, providers attempting to curb rising health care costs have increasingly begun to see the Internet as an effective and efficient deliver y system for health information and services (Zajac et al., 2012). By connecting users to l ine services such as banking, shopping, library borrowing, and social chat groups the Internet serves to enable instrumental activities of daily living (Zajac et al., 2012). However additional research is needed to determine if the Internet c an c ontribute positively to long term healthcare benefits for patients with chronic disease. Within the past 10 years, p ublic percep tions of the importance of the I nternet as a source for health information ha ve risen dramatically among adults in the U.S. ( Fox, 2012 ). Individuals are using the Internet to supplement traditional sources of health
27 information (e.g., healthcare providers, family) for the purpose of improv ing their own health and the health of those whom they care for ( Ayers & Kronenfeld, 2007; Fox & Purcel l, 2010 ). Couper and colleagues (2010) found that, among Internet users, health information gathered from the Internet has surpassed health information from television, magazine, books and newspapers This work cited the Internet as second only to health care providers as a source important to making health decisions. Among U.S adults who use the Internet 80 % have searched online for health related information at least one time ( Fox, 2012). This online health information searching is not limite d to traditional desktop computers, but includes 31% of the 85% U.S. adult cell phone owners who report having used their cell phone to look for health or medical information online (Fox & Duggan, 2013). While older adults lag behind younger generations in terms of using the Internet, as the baby boomers age and their health needs increase t he proportion of older adults who use the Internet is expected to increase dramatically (Sinden & Wister, 2008). With the immense amount of health informatio n and resou rces available on the Internet, the World Wide Web has the capacity to empower patients to improve their health behaviors and to become more active participant s in their own healthcare. Findings from a 2006 meta analysis of seven major datasets from the Pe w Internet and American Life Project revealed that of adults who use the Internet for health information, 91% reported they had learned something new about their health, had improved their health, and /or their level of medical information (Rice, 2006) P revious r needs and conditions In fact, gaining knowledge via the Internet has been shown to
28 relate very closely to the formal health care system s since individuals can check for prescription drug side effects, they can manage their chronic diseases and can compare their symptoms to a diagnosis (Ayers & Kronenfeld, 2007). In addition to being a resource for health information, the Internet has become a significant source of social support The use of the Internet has been found to enhance health related quality of life through improving communication with family and friends (Wagner et al., 2004) Virtual communities (e.g. online support groups) and the anonymity that is often associated with asynchronous communication on the Internet allows for increased social interaction among peer groups (Powell et al., 2011 ; Wangberg et al., 2007) The Internet has also become a common way to obtain assistance for coping with medical condi tion s through emotional and practical support ( Ayers & Kronenfeld, 2007). Internet users with chronic disease are more likely to go online to interact with other individuals experiencing their illness than Internet users with no chronic conditions (Fox, 20 11). A recent survey indicated that o ne in four I nternet users living with a chronic illness say they have gone online to find others with similar health conditions (Fox, 2011 ). Online social networking can provide information, support, acceptance, a sense of immediate understanding, and can a lleviate loneliness and alienation that often result s because of age related health issues ( Nayak, Priest & White, 2010 ). An increase in social interactivity has the potential to increas e social capital when individu als are able to communicate with friends, family, or others with similar health conditions who they may not have otherw ise been able to communicate with (Nayak, Priest & White, 2010; Wangberg et al. 2007 ). In addition, the Internet has the potential to change the way patients communicate with the ir physicians. Research
29 has shown that health information individuals receive from the Internet enables patients to ask their physicians new questions, making them feel more emp owered and better able to make healthcare decisions (Rice, 2006). Older Adult Internet Use for Health Information Elderly persons with poor health remain the least likely to use computers and the Internet (Wright & Hill, 2009). A common fear is that eHeal th technologies may only reach those who need them the least (ceiling effect) and will fail to reach individuals who need health information the most. Older adults with multiple chronic diseases are one population not currently be ing reached by eHealth (Ni jland et al. 2011). Numerous barriers in this population limit widespread adoption of the Internet. Research suggests that older adults underutilize the Internet as a tool for obtaining health information because they (a) lack confidence in their computer abilities, (b) have concerns about the privacy of health information transmitted over the Internet, (c) only trust health information provided by clinicians, and (d) have difficulty understanding health informati on (Miller & Bell, 2012; Zulman et al., 20 11). Other widespread concerns have also been raised about the following related issues: credibility of health information on the Internet (Habel, Liddon, & Stryker, 2009), the potential for unhelpful peer to peer interactions (Boulos, 2012), and excluding individuals who experience barr iers to Internet access (Powell et al. 2011). A Pew Internet study found that 76% of Internet users with chronic conditions do not consistently check the source and date of the health information they locate (Fox, 2007). Ch ronic Disease and Internet Use While the actual propo rtion of older adults with chronic disease who use the Internet may be low ( Miller & Bell, 2012 ) the general consensus is that the number of
30 users is growing rapidly (Wright & Hill, 2009 ; Zajac et al., 2012 ). The 2010 Pew Internet Research Survey Chronic Disease and the Internet reported that 83% of Internet users with chronic disease (s) have looked online for health information (Fox & Purcell, 2010) Res earch indicates that individual Internet use for health information retrieval increase with the number of chronic conditions diagnosed (Ayers & Kronenfeld, 2007). The complex management of multiple chronic conditions prompts patients to seek new resources which can be quickly access ed for quality health information to cope with multimorbidity (Crabb et al., 2012). Still, there remains limited data on how and why information, especially among older populations (Powell et al. 2011; Wright & Hill 2009). It is unclear whether health status influences use of eHealth technologies. A meta analysis conducted by Rice (2006) found that Internet use for health information seeking was higher in individuals with poorer health; however Cotton and Gupta (2 004) and Fox and Purcell (2010) findings differ since in their studies they found adults who reporting better health use d the Internet more often Health Literacy and eHealth Literacy Health literacy is vital to making appropriate health decisions and pl ays an The Affordable Care Act of 2010 operationally defined health literacy as and understand basic health information and services needed to make appropriate healt National Ins titutes of Health (2012 ) further explained health literacy in the following way : Similar to our traditional understanding of literacy, health literacy incorporates a range of abilities: to read, comprehend, and analyze informa tion; decode instructions, symbols, charts, and diagrams; weigh
31 risks and benefits; and, ultimately, make decisions and take action. The concept of health literacy extends to the materials, environments, and challenges specifically associated with disease prevention and health promotion (n.p.). T he 2003 National Assessment of Adult L iteracy found that only 12% of US adults and 3% of US older adults are proficient (i.e., can understand and use health information effectively) in health literacy ( Kutner et al., 200 6 ). The Institute of Medicine (IOM) (2004) reported that hal f of the U.S. population find s it challenging to understand health information; and most people will have difficulty understanding health information at some point in their lives. Rese is associated with higher rates of hospitalization, more use of emergency services, and billions of dollars spent on helping individuals recover from preventable health conditions (IOM, 2004). Older adults are th e largest group with limited general and health literacy skills (White, 2008); thus the population group with the highest prevalence of chronic disease and the greatest need for health care has the least ability to comprehend information needed to protect and maintain their health (Aspinall, Bechnett & Ellwood, 2012 ) S ince i mprov ing health literacy has the potential to help address issues of health care access, quality and cost it has become the objective of several national policy initiatives including the National Action Plan to Improve Health Literacy and Healthy People 2020 Several studies ( Aspinall, Bechnett & Ellwood, 2012 ; Federman et al. 2009 ) have determined that health literacy is correlated with health outcomes in adults. A ccording to the American Medical Association Foundation (AMA, 1999) health literacy skills are a stronger predictor of a person's health than age, income, employment status, education level and race Individuals with low levels of health literacy have less health knowled ge,
32 worse self management of chronic disease, lower use of preventive services and poorer overall health (Baker et al. 2007). The increas ed use of ICTs in healthcare presents new challenges and opportunities for population health literacy M any eHealth services intended for public consumption cannot be fully used because the public does not have the skills to use such resources or services (Gottlieb & Rogers, 2004). B eing health literate in a technological world requires additional set s of skills above a nd beyond being able to access to the Internet Because of the emerging use of electronic resources to access health information the construct of eHealth literacy has been studied to determine whether individuals have the ability to seek find, understand, and appraise health information with electronic re sources and apply that knowledge to a health problem (Norman & Skinner, 2006 a ). According to Norman and Skinner (2006 a ), eHealth literacy is a foundational skill set that combines many differen t literacy skills that extend beyond health literacy and numeracy. Norman and Skinner (2006 a ) categorized the following six forms of literacy as fundamental analytic and context specific skills needed to effectively use ICTs to locate and evaluate health i nformation : The six components operate as part of a learning system and are not easily amenable to subdivision (Norman, 2011). T o optimize eHealth literacy it is believed that the individual must develop each set of skills equally For instance, if an individual has the ability to use computers but does not have the skills to read and understand health information, then they would not be considered eHealth literate.
33 Analytical skills 1. Traditional literacy the basic ability to read and understand written passages 2. Informational literacy the ability to understand how knowledge is organized and how to find answers and teach others 3. Media literacy ability to critically assess the media, and political context and to consider issues such as the marketplace, audience relations, and how media forms in (Norman & Skinner, 2006 a p. 3). Content Specific skills 4. Health literacy ability to read and understand information in the health care environment 5. Scientific literacy the nature, aims, methods, applications, limitations, and politics of creating knowledge in a ( Norman & Skinner, 2006 a p. 4) 6. Computer literacy ability to use computers to solve problems Norman & Skinner (2006 a ) conceptualized eHealth literary by depicting it as a lily model Within the model, t here are six independent literacy skills (or petals) that work together and overlap to influence eHealth literacy which is the cor e of the pistil ( Figure 2 1). At the time of each health information search, eHealth literacy is influenced by health status, motivation, educatio n le vel, and the particular technology being used eHealth litera c y is not static ; rather it is a process that evolves and consistently has to be developed and evaluated based on literacy concepts and technologies that change over time (Norman & Skinner, 2006 b ).
34 Numerous research studies have evaluated the reading grade level and / or readability of information on the Internet and uniformly found the information too difficult to be understood by the average adult, let alon e those with the most lim ited literacy skills (Baur, 2008). Older adults generally have low health and computer literac y making it challenging for them to function well in the eHealth era where technology is increasi ng ly being used in health care (Xie, 2011). Older adults tend to have poor navigational skills, an inability to distinguish between sponsored and non sponsored web links, and difficulty explaining ( in their own words ) the information they find on the Internet (Baur, 2008). They are often overwhelmed by the sheer volume of different online methods, and often lack of awareness and confidence in learning Internet skills ( Sheaves et al., 2011 ). However, the literature has suggested that people who spend more time online will generally acquire more knowledge about the Intern et and develop better online skills with experience ( Norman & Skinner, 2006 b ; Van Deursen & Van Dijk, 2011). Social Media and Health Information Seeking Coinciding with the surge in Internet use, there has also been a dramatic increase in participative I nternet among adults in the United States Participative Internet is frequently referred to as Web 2.0 which is refer to a new era of Web enabled applications that are built around user generated or user manipulated conte (Pew Internet & American Life Project, 2011, para 2 ). The main attributes of Web 2.0 are s ocial media and social networking Social media allows user s to add information or content to the Internet while enabling interaction, information sharing and collaboration ( Eysenbach, 2008; Gibbons et al. 2011). Social media has emerged as the leading
35 channel for political and consumer matters and for primary sources for health, healthcare, and s cience based information ( Eysenbach, 2008; Jones & Fox, 2009) Participation in social networking (e.g. Facebook, Twitter, MySpace) has more than quadrupled from 2005 to 2009 (Chou et al. 2009) currently reaching four out of five U.S. Internet users (Bou los, 2012). It is important to note that this increase has not just been observed among young adults. A 2011 Pew Internet survey revealed that from 2009 to 2011 growth in the use of social networking rose 150% among Internet users ages 65 and older (Madden & Zickuhr, 2011). This has led some to describe this migration as Social media has the potential to reduce the financial and overcrowd burden s currently being experienced by conventional heal thcare systems. The social 2.0 nature of the Internet has transformed health communication patterns (Chou et al., 2009). As ilability of content and might influence for health communication allows personalized health information to be disseminated rapidly and expands opportunities for peer t o peer support (Fox, 2011). For example, a randomized web based walking program conducted by Richardson et al., (2010) found that participants who had access to the online community (could post and read messages with other participants) were more likely to stay engaged in the walking program over a longer period of time compared to the control group that did not have access to the online community.
36 Among social networking users, a growing number (22%) report sharing and receiving health information from fr iends within their online social networks (Fox & Jones 2009). A recent national survey found that 96% of Americans used Facebook to gather information about health care, and 40% noted that or ct their future health care decisions (Fuscaldo, 2011). P ublic health communication practitioners have beg u n utilizing social media sites for a number of health education programs, interventions, and outreach efforts (Kontos et al. 2010). As a result, t here has been a sub stantial migration of public health campaigns (e g. The social media and social networking web sites ( Kaiser Family Foundation & MTV, 2012; Long et al., 2010 ). Accordingly, the CDC has issued guidelines for best pract ices for using social media in health promotion and disease prevention programs (CDC, 2012 ). Through enhanced information sharing capabilities and increased connectivity among adults, social media offers new opportunities for impact ing the population health S ocial networking integrates people into virtual communities and build s support channels, social capital and trust that obtain, and (2) process and understand health information and services needed to ( Boulos, 2012, p.3 ). For individuals managing chronic disease social media provides a new and valuable channel for facilitating social support and patient engagement (Madden, 201 0). Social media provides the potential for individuals to connect with others who have similar interests and health concerns. A cancer support group on Face book has the ability to easily bring cancer patients and caregivers together to share ideas, concerns and support It has been suggested t hat
37 Internet mediated so cial networking may have a beneficial impact on both perceived social support and psychological wellbeing (Chou et al., 2009). Social media promotes patient engagement in their own care by involving the individual to monitor their health behaviors (Gibbons et al. 2011) Social media also ability to obtain process and understand health information and services by increasing the number of options that patients have to access and communicate with th eir healthcare team (Boulos, 2012). Through monitoring real world events and public health issues, social media has the potential to provide the public health community valuable and inexpensive real time statistics, the opportunity to study patient health behaviors, and gather data on health topics not normally included in public health data (Dredze, 2012). Despite the many potential benefits of using social media in public health, there are several disadvantages inherent when used as a source of health i nformation. These barriers include, but are not limited to, blind authorship, lack of source citation, and presentation of opinion as fact (Vance, Howe, & Dellavalle, 2009) Given that social media often allows for an open and unrestricted forum for health information sharing there is increased risk for rapid disseminati on of non credible and potential ly erroneous health information (Boulos, 2012; Chou et al., 2009). In addition, the lack of face to face contact between health professionals and patients ca n lead to confusion and contribute to misdiagnosis (Pulman, 2010). Social media use patterns vary by age; however, no racial, ethnic education or he alth status differences have been reported among social media users. I n fact reverse trends have been noted. In a study conducted by Chou and colleagues (2009), it was
38 found that, among Internet users, African American s were more likely than non Hispanic whites to use a social networking site ( odds ratio [ OR ]=1.51, 95% CI = [ 1.01 2.24 ] ). Researchers have even suggested that media penetrate [s] the population regardless of education, race/ethnicity or health care The statistically non significant group differences also hold when considering the use of social networking sites for health. Fox and Jones patients of various levels of education, whites, African Americans, Latinos all are equally likely, once they are using social networking services, social media may offer public health practitioners the opportunit y to eliminate health disparities, reduce health communication inequalities, and reach underserved populations. The use o f social media has become an efficient way to reach target population s regardless of socioeconomic and health related characteristics Social media web sites have the potential to become a powerful tool to attract and engage a large pro portion of Internet users (Eysenbach, 2008) Given that the use of social media will likely continue to grow (Madden, 2010) these tools can be used to maximiz e the reach and impact of health communication and eHealth interventions. Among older adults with chronic disease, soc ial media offers forums for patient enga gement and social su pport (Hall et al., 2012; Madden, 2010). Because of its ease of use, low cost, and wide reach using social media as a health communication strategy for older adults has great potential to improve public health.
39 Theoretical Explanations for Health Information Seeking There are three conceptual frameworks that informed the research project that is described herein First, the S elf Efficacy Theory will be used to explain the origins of eHealth lite influences his or her eHealth literacy. Second, the Technology Acceptance Model (TAM) will be used to explain and predict health ICT us e among older adults with chronic disease. F inall y, the Structura l Influence Model of Health Communication (SIMHC) will explore the effects of different individual level factors on communication outcomes. Self Efficacy Theory The definition of eHealth literacy by Norman and Skinner was built upon the Self Efficacy T heory which proposes that self confidence is a precursor to behavior change and skill development (2006 a ). As a central construct oci al Cognitive T heory (1997) s elf efficacy is n his or her capability to perform a particular behavior when faced with a variety of challenges choices, and motivation regarding behavior change are determined in part ; by how effective they believe they can be (Bandura, 1982). A key principle of the Self Efficacy Theory is that individuals are more likely to engage and put forth more effort and persistence in activities when they have higher feelings of efficacy and le ss likely to engage in those activities if they do not ( Van der Bijl & Shortridge Baggett, 2002). Research has shown that self efficacy has an indirect but positive relationship to overall and condition specific health outcomes (Sue, 2012) High s elf effi cacy is important for the initiation and maintenance of behavior change including the management of chronic
40 conditions P atients with higher self efficacy have better problem solving skills and exhibit better self care (Sue, 2012). Regarding Internet use, high self efficacy is positively associated with (a) willingness to choose and participate in computer based activities, (b) expectations of success of computer use (c) perseverance when faced with computer use difficulties and (d) compu ter based performance (Eachus & Cassidy, 2006 ). The concept of self efficacy is derived from four major sources of information: (1) mastery experience, (2) vicarious experience or modeling, (3) emotional or physiological arousal, and (4) verbal persuasion (Bandura, 1997). The most influential source of information comes from mastery experience. Mastery experience refers to when an individual succeed s through a series of gradual steps that solicit the performance of desired behaviors often through incremental goal set ting ( McAlister, Perry & Parcel, 2008). When specific tasks are completed successfully, perceived self efficacy is ; however when repeated failure occurs, feelings of mastery are diminished For example, w hen teaching older adults how to properly search the Internet to learn about chronic disease self management participants should be presented with easily mastered steps (e.g. Internet basics, using a mouse, evaluating a website while working with an instr uctor) to reinforce mastery experience. The second source of efficacy, vicarious experience or social modeling is less influential than mastery experience (Chu et al., 2009). Vicarious experience occurs when learning is achieved through observing behavi or performed by others. According to Bandura (1994) s eeing people similar to oneself succeed by sustained effort raises
41 p.80 ). The impact of peer modeling on perceived self efficacy is subject to the perceived similarity those acting as models I f individuals see themselves as very different from the models they are observing, then they will not be influenced to a high degree avior (Bandura, 1994). For older adults with chronic disease, it may be necessary to have their peers demonstrate effective eHealth literacy rather than a younger more tech savvy individual (Chu & Chu, 2010) Efficacy arousal is another source of informatio n gathered through improving physical and emotional states. This includes responses such as anxiety, stress and fatigue. Bandura (1977) theorizes that arousal in judging their anxiety and vulnerability to str p.82 ). Older adults with chronic disease may believe they can accurately perform Internet searches to help self manage their disease ; however, those who do not believe they can navigate the Internet to find health information related to their disease (s) may experience high anxiety arousal regarding their perceived deficiencies (Chu et al., 2009). The fourth source of efficacy information is acquired through verba l persuasion. Verbal persuasion includes suggestions, positive appraisal and social encouragement Feedback should be corrective and framed in a positive way to create higher self efficacy (Bandura, 1977) Verbal persuasion is more than positive appraisals ; it also includes structuring situations in ways that are set up for su ccess to avoid situations that are likely to result in failure (Bandura, 1994). For older adults, social support is important in forming self efficacy (Chu & Chu, 2010; Woodgate, Brawley, & Shields 2007) ; it has been suggested that social media could serv e as a positive source of
42 social support and persuasion for improving eHealth literacy among older adults with chronic diseases (Hall, Stellefson, & Bernhardt, 2012). Technology Acceptance Model The Technology Acceptance Model (TAM) (Davis, 1989) is an in tention based model developed specifically to explain and/or predict user acceptance of computer technology. The TAM was adapted from the Theory of Reasoned Action (TRA) (Ajzen & Fishbein, 1980 ) for modeling user acceptance of information technology with t he aim of explaining future behavioral intention to use the system (Lede rer et al., 2000). TRA posits that perceived ease of use and perceived usefulness can predict attitudes toward technology that can then pre dict technology usage (Lederer et al., 2000) According to TAM be a determinant of whether or not he /she actually uses it. The depende nt variable, actual usage, is typically a self reported measure or frequency of e mp loying the application (Lederer et al., 2000). Following the rationale of TRA, behavioral intention to use the system is a measure of the likelihood a person will employ the application which is ultimately sing the system. of two major determinants: perceived usefulness and perceived ease of use (Holden & Karsh, 2010; Davis, 1989) ( Figure 2 2 ). It has been asserted that perceived usefulness and perceived ease of use are especially important variables that influence system use, because they can be viewed as independent constructs without the need to model what exp lains or predicts them (Kukafka et al., 2003). Perceived usefulness is referred to as the degree to which a person believes that using technology will enhance his/her performance. In contrast,
43 perceived ease of use is the degree to which a person believes that using a particular techn ology would be free of effort (Davis, 1989). The importance of perceived ease of efficacy (Davis, 1989). The TAM suggests that perceived ease of use is central to explaining the variance in perceived useful ness. According to Davis (1989), a system perceived to be easy to use is also likely to be useful by users. It could be argued that if older adults with chronic disease perceive the Internet to be easy to use, they will likely perceive it to be useful. Th e TAM model has gone through a number of modifications over time, and some authors have considered additional relationships. Some researchers have ignored intention to use or attitude toward use, and instead focused on the effect of perceived ease of use d irectly on usage, since research has shown inconsistent findings regarding the effect of attitude and intention on actual use (Holden & Karsh, 2010; Lederer et al., 2000). The current research will examine the relationship between perceived ease of use and usefulness, as well as, the levels perceived usefulness among older adults with chronic disease who use the Internet for health information. Structural Influence Model of Health Communication The SIMHC is an emerging framework that draws the connection be tween social determinants and health outcomes through a range of interpersonal communication factors (Viswanath Ramandahan & Kontos, 2007). The model identifies the role of communication in linking social determinants with health outcomes It is founded on the idea that media communications can influence health by raising awareness, focusing attention, highlighting issues, providing information, and reinforcing knowledge, attitudes, and behaviors. Furthermore, t he SIMHC is based on the assertion that cont rol of communication is power and who ever has the capacity to generate, access, use,
44 and distribute information benefits from it (Viswanath Ramandahan & Kontos, 2007) This model suggests that differences among social and racial group s in the use of co mmunication channels, such as social networking sites, could result in both an indirect and direct effect on health, which could ultimately lead to an exacerbation of existing health disparities among vulnerable groups (Kontos et al. 2010). SIMHC acknowle dges that different forms of mass media and different genres within a medium such as the using the Internet for email, social media, or just as a search engine may differentially influence behaviors (Ackerson & Viswanath, 2009). The model posits that the different communication outcome s, in turn may affect health outcomes, including health behaviors, comprehension and quality of life (Viswanath Ramandahan & Kontos, 2007). While previous research has shown differences among demographic groups when consid ering the use of social networking sites for health ( Chou et al. 2009 ; Fox & Jones, 2009), this study will examine if eHealth literacy and/or social media use is associated with perceived health status. The SIMHC will also be used to help determine whether social determinants, such gender, and age are predictive of different communication outcomes, such as us ing the Internet and social media to find health information and if those health communication outcomes affect their perceived health outcomes ( Figure 2 3). In factors influence Internet and social media use and eHealth literacy and their corresponding perceived health status.
45 Conclusion eHealth applications are d eveloping rapidly; h owever, they are only useful if the user has adequate eHealth literacy. To date, Internet use among older adults with chronic disease has not been widely examined. W e still have much to learn about how the Internet and social media is b eing used by various populations to harness the explosion of social networking that is being used to inform health decisions (Kreps & Neuhauser, 2010). It is highly likely that t he full social potential of the Internet for health promotion and disease mana gement has not yet been met in this population If older adults become empowered to access the Internet and use social media to obtain and evaluate health information they may benefit from the social support and sense of empowerment. It could be the case that o lder adults who have exposed themselves to social media have higher eHealth literacy than other older adults with no such experience Therefore it is important to understand the association between social media use and eHealth literacy
46 Figure 2 1. eHealth Literacy Lily Model. Figure 2 2. Technology Acceptance Model. Perceived Ease of Use Perceived Usefulness Actual Use Behavioral Intention Attitude Toward Use
47 Figure 2 3 The Structural Influence Model of Health C ommunication. Social Determinants Education Income Employment Marital Status Mediating/ moderating conditions Age Gender Race/ethnicity Health Communication outcomes eHealth literacy Social media use Health Outcomes Chronic disease status Health status
48 CHAPTER 3 METHODS The purpose of this study wa s to investigate eHealth literacy and social media use among older adults with one or more chronic disease ( s ) Chapter 3 describes the research methods used to examine associ ations between various demographic variables chronic disease presence, number of chronic diseases, types of chronic diseases, eHealth literacy, social media use, perceived ease of use and usefulness of the Internet for health information, and perceived he alth status This chapter includes a description of the research design, independent and dependent variables, the study population, the telephone survey instrum ent, data collection procedures, the data analys i s procedures and study limitations Data was c ollected from participating adults age 50 and older in the state of Florida during the s pring of 2013. Research Design The approach selected for this research study was quantitative, specifically descriptive research, in which adults in the state of Florida were surveyed by telephone. An advantage of using a quantitative approach in research is the ability to generalize to a larger population, as well as collect a large amount of data with relative ease, in a relatively short period of time ( Neutens & Rubinson, 2002). A cross sectional survey design was implemented in this research study. Cross sectional design refers to gathering data all at one point in time, as opposed to a longitudinal survey design, which collects data multiple times over a given period of time (Dooley, 2001). This cross sectional study record ed describe d analyze d and interpret ed conditions and trends that presently exist ( Neutens & Rubinson, 2002) by examining the association between eHealth literacy and various non manipulated varia bles (i.e., demographic variables,
49 chronic disease status, perceived health status). Survey research is conducted by gathering information from a small sample of people in order to identify trends in characteristics, attitudes, opinions or behavior of a population (Creswell, 2012). To ensure that inferences ar e as accurate as possible, it is important to reduce both coverage and sampling error (Creswell, 2012). This study address ed these considerations by selecting a large sample from a representative target population of older adults. Research Variables This study investigate d associations between the following variables: eHealth literacy, social media use, the number of chronic conditions, chronic disease status, perceived usefulness, perceived ease of use, perceived health status, and demographic variables, including age, gender, income, education, and marital status. Each measure was categorized as either an independent or dependent variable, as appropriate for the research question ( Table 3 1 ). Participation in the study was limited to Florida residents aged 50 + years who self reported Internet use. R esearch question #1 seeks to de termine the extent to which older adults living in the state of Florida believe the Internet is a useful resource for health information. For this question perceived usefulness was measured through use of a supplemental item R esearch questions #2, 4, 6 7 and 9 involved measuring eHealth literacy This was accomplished using a composite score composed of items from eHEALS, a validated instrument. R esearch question s #3 5, 6, 8, and 10 focus on social media use which was measured by participants self repo rt ed use of social networking, blogging or online support group s for locating or sharing health information. R esearch questions #2
50 and 3, compare different generations by age groups Respondents were placed in one of three age groups b ased on their self reported age. Age grouping were necessary for comparisons to detect differences between age groups previously labeled by (Strauss & Howe, 1992) as Baby B oomers (50 64 year s ), t he S ilent G eneration (65 74 year s ) and the G.I. G eneration (75 years and older ) To answer research question #4 and 5 a dichotomous independent variable chronic disease status measure chronic conditions Dependent variables were eHealth literacy and social media, respectively. A binary independent variable (use of social networking, blogging or online support groups) was used in the analyses for research question #6 with the dependent variable eHealth literacy For research questions #7 and 8 eHealth literacy and social media use was represented as the independent variables respectively with health status serving as the dependent variable. Health status was reported as excellent, very good, good, fair or poor. For resea rch questions #9 and 10 the demographic variables of gender, age, income, education, marital status, number of chronic diseases and type s of Internet device used, represent ed the independent variables. Age w as measured as a self reported number and analyzed as a continu ous variable Gender was reported as either male or female. Race was reported as White, Black/African America, Asian or Pacific Islander, American Indi an or Alaska native, other, or m ulti racial or mixed race. Participants' self identification as of Spanis h or Hispanic origin indicate d their ethnicity. Education was reported by the highest grade of school or year in college completed.
51 Participants' self report as currently married, separated, divorced, widowed, or never been married indicate d their marital status. Income was The number of chronic diseases was constructed by adding the total number of chronic conditions a respondent reported having out of eight possible chronic illnesses or conditions (diabetes, high blood pressure, asthma, heart disease, cancer, stroke, arthritis or other). Participants reported what type of Internet devices (desktop computer, laptop computer, cell phone, mobile handheld device like an e reader or tablet) they used to look for health or medical information. eHealth literacy and social media use for health information represented the dependent variables respectively. For research question #10 technology acceptance factors were added as a dditional independent variables. They included perceived usefulness as measured by a supplemental eHEALS item and perceived ease of use measured using an average of items from the eHealth literacy scale. Instrumentation Data collection employed the eHealt h Literacy Scale ( eHEALS) (Appendix A items 5 12). The scale was comfort, and perceived skills at finding, evaluating, and applying electronic health 06 b p. 1). The scale was designed to be simple and easy to administer and can be used alone or in conjunction with other measures of health (Norman & Skinner, 2006 b ). The main scale has eight items ( Appendix A items 5 12) with 1 to 5 point Likert scale re sponse options using the following anchors: 1) strongly disagree; 2) disagree; 3) undecided; 4) agree; 5) strongly agree. Psychometric testing on the eHEALS has revealed high internal consistency
52 retest reliabilit y ( r = .68) (Norman & Skinner, 2006 b ). The instrument is a reliable and easy to use self report tool which has been used to measure eHealth literacy in several studies with diverse populations (Knapp et al., 2011, Norman & Skinner, 2006 b ; van der Vaart et al., 2011, Xie, 2011). Norman and Skinner (2006 b ) do not provide guidelines for the interpretation of scores other than the higher score of the summation of responses indicates a higher level of eHealth s were recoded as undecided in this study. Data was collected on Internet use, Internet use for health information and social media use for health information by items adapted from the Health Information National Tren ds Survey (HINTS) 2012 ( Appendix A items 1 3). The participants responded to Data Collection An application was submitted to the University of Florida Institutional Review Board 02 (UFIRB 02 ) prior to beginning any portion of this study. A pproval from the UFIRB 02 indicate d that the study was judged to be ethical in its proposed treatment of participants and that was acceptable to begin data collection. Data was collected as part of a larger telephone survey During February 2013 the Florid a Consumer Confidence Index (CCI) Survey (Appendix B conducted by t he Survey Research Center in the Bureau for Economic and Business Research (BEBR) at the University of Florida was contracted to administer the additional items and eHEALS as part of the CCI Items measuring Internet use for health information, social media use for health information, eHEALS, and an item regarding chronic disease status were appended to the end of the CCI. The employees of BEBR undergo rigorous training in
53 professional con duct during telephone interviews and surveys. Th is training includes ongoing monitoring with respect to confidentiality, as well as training specific to each survey that is f ielded. Telephone survey administrators understand (and sign) a code that document s the consequences of failing to comply with standards for confidentiality For any set of survey items fielded as part of the CCI a series of specific steps occur. First, the researcher meets with BEBR senior staff, including the director and survey center supervisor to discuss the survey, population, and research design. Second, several iterations of survey development occur that include programming the survey, testing the survey and item sequences, and finally pilot testing the survey among the int erviewers. Third, once the item programming is complete, a series of training sessions are held among the survey center supervisor and surveyors to go over issues related to the survey and population The training sessions also cover ed issues involving sen sitivity and confidentiality and practice time for survey administ ration. Finally, when the survey is officially launched, the survey center supervisor obs erves the interviewers and randomly listens in on selected telephone surveys to ensure adherence to t he research protocol. Study Population A minimum of f our hundred households in Florida were surveyed via the telephone The sample used for the monthly administration of the CCI constitute s the sample for this study. During February 2013, surveys were cond ucted between 9am and 9pm Monday through Friday, between 12pm and 6pm on Saturdays and between 3pm and 9pm on Sundays. The CCI survey length is typically twelve minutes. It is estimated that the appended survey items for this study add ed an extra 8 minutes to the average length of time necessary for participants to complete the survey.
54 Data Analysis SPSS version 21.0 was used to compu te both descriptive and inferential statistics. Descriptive statistics were used to summarize demographi c characteristics and examine frequencies and means according to group, and thereby answer ed res earch question #1; results are presented with the mean and standard deviation. Inferential statistics were used to an swer research questions # 2 10 Research q uestions #2, 4 and 6 were answered using a composite score of the eight items from the eHEALS. To ensure the reliability of eHEALS data collected in this sample, internal consistency statistics were computed. For research question #2 descriptive analyses w ere performed and a n analysis of variance was conducted to more fully explore variations in eHealth literacy across the Baby B oomers, the S ilent Generation, and the G.I. Generation For research question #3 descriptive analyses were performed and a chi square test was conducted to determine if social media use differed between the Baby Boomers, the Silent Generation, and the G.I. Generation For research question #4 a n independent t test was used to determi ne if older adults with chronic disease exhibit higher levels of eHealth literacy than older adults wit hout chronic disease. Research question #5 was answered by conducting a chi square test for independence to explore the relationship between the two cate gorical variables (Pallant, 2010). Research question #6 an independent t test was used to determine if older adults who use social media for health information exhibit higher levels of eHealth literacy than non social media users. Research question #7 was answered using a bivariate correlation to determine if eHealth literacy is associated with perceived health status. In this analysi s, perceived
55 health status was treated as a continuous variable. Each health status was assigned a corresponding numeric val ue ; poor=1, fair=2, good=3, very good= 4 excellent=5 Research question #8 health status was again treated as a continuous variable and was answered using a t test to determine if social media use for health information is associated with perceived health status. Research question #9 was answered using multiple linear regression, chosen because it is designed to determine if the independent variables can predict the dependent variable (Thomps o n, 2008). Research question was #10 answered using logistic regression due to the binary nature of the dependent variable. To conduct the logistic regr ession three variables were recoded to adjust for small sample sizes in categories; education (categorized as less than high school, high sc hool, some college college graduate, and post graduate), income (categorized as household income less than $20,000, $20,000 $49,999, $50,000 $99,999, and over $100,000), and race (dichotomized as white and nonwhite) Research question #10 was answered using a composite score of some of items within the eHEALS. Perceived ease of use was calculated by averag ing the scores from three eHEALS items; (1) I know where to find helpful health resources on the Internet, (2) I know how to find helpful resources on the Internet, and (3) I know how to use the Internet to answer questions about my health. Perceived usefulness of the Internet to find health information was measured with an eHEALS supplemental item. R esearch Qualifications and Bias This study was condu cted by a single researcher as part of her dissertation. The researcher has previous experience assisting in the design, implementation and analysis of large sample health surveys as part of her job at a university health
56 education and promotion department The researcher has taken numerous graduate level quantitative statistics courses, survey development courses, as well as a chronic disease epidemiology course. The researcher has also assisted in drafting several manuscripts regarding eHealth literacy an d the self management of chronic disease. The researcher conduct ed the project with some bias since she has an outsider perspective and does not have personal experience with chronic disease management. She is already proficient in using eHealth technologi es. In addition, data was collected by an outside agency ( BEBR ) and the researcher was not able to control the validity of telephone survey implementation. Delimitations This study utilize d a cross sectional, random digit dialing telephone survey research design. This study focuses on perceived eHealth literacy only. Data was collected in February 2013. Participants were able to understand the directions, questions and response options necessary to complete the survey. The eHEALS instrument was used to assess eHealth literacy. Limitations The use of self report surveys may have lead participants to provide responses that they believe are socially desirable. Findings in this study cannot be generalized to other populations. Data collected during February 2013 may differ from data collected during other time periods. Data collected from this cross sectional survey reflects responses from participants at a specific point in time and cannot establish causation. Demographic inf ormation obtained may not include all pertinent information about participants.
57 T able 3 1. Pr oposed research v ariables Research Question Independent Variable Dependent Variable 1. What is the extent to which older adults living in the state of Florida believe that the Internet is a useful resource for health information? Age, Residence Perceived usefulness 2. What are the eHealth literacy scores of older adults living in the state of Florida? Age, Residence eHealth Literacy 3. What is the extent to which older adults in the state of Florida use social media to locate or share health information? Age, Residence Social media use 4. Do older adults living with one or more chronic disea se(s) exhibit higher eHealth literacy than older adults without chronic disease(s)? Chronic disease status eHealth Literacy 5. Do older adults living with one or more chronic disease(s) exhibit greater use of social media than older adults without chronic disease(s)? Chronic disease status Social media use 6. Does the use of social media for health information predict overall eHealth literacy in older adults living in the state of Florida? Social networking Blogging Online support group eHealth Literacy 7. What is the extent to which eHealth literacy is associated with perceived health status among older adults living in the state of Florida? eHealth literacy Health status 8. What is the extent to which using social media for health information associated with perceived health status among older adults living in the state of Florida? Social media use Health status
58 Table 3 1. Continued. Research Question Independent Variable Dependent Variable 9. What are the socio demographic, chronic disease, and Internet device use factors that predict eHealth literacy among older adults in the state of Florida? Gender Age Race/Ethnicity Income Education Marital status Number of chronic diseases Internet device used eHealth Literacy 10. What are the socio de mographic, chronic disease, Internet device use, and technology acceptance factors that predict using social media for health information among older adults in the state of Florida? Gender Age Race/Ethnicity Income Education Marital status Number of chronic diseases Internet device used Perceived ease of use Perceived usefulness Social media use
59 CHAPTER 4 RESULTS Chapter 4 presents the results from an investigation surveying 283 older adults in the state of Florida. S ample demographics are discussed, followed by results from data analyses for each of the ten research questions. Study participants were adults in the state of Florida who were 50 years of age or older who ha d some previous experience using the Internet. T rained interviewers working for the Florida Survey Research Center at the University of Florida contacted h ouseholds using a random digit d ialing (RDD) sampling frame. A total of 6695 telephone calls were placed between February 1 st and February 28 th 2013. A minimum of five attempts per household were made From these call attempts, a total of 493 individ uals agreed to complete the telephone survey. Among these respondents 393 (79.7%) were 50 years of age or older meeting the age inclusion criteria for participation in this study Of the se 393 willing participants 283 (72%) responded yes to o you ever go on line to access the Internet or World Wide Web, or to send and receive email ? affirming that they had some previous experience using the Internet R esponses from these 283 records were compiled and analyzed in this study Demographic Profile of Respondents Table 4 1 presents the d emographic characteristics of study participants. Respondents ranged in age from 50 to 91 years with a mean age of 67.46 years ( SD =9.98 years ). The largest group of respondents were adults ages 50 64 yea rs oomer generation ( n =111, 39.2%), followed by the Silent Generation (65 74 yea rs ) ( n =98 34.6% ), and the G.I. Generation (75 years and older ) ( n = 74 26.1% ). The sample was fairly even in distribution with respect to sex with
60 45.2% of study participants being female ( n =128) and 54.8% being male ( n =155). T he vast majority of respondents self identified as White ( n =252; 9 0.3% ) while notably small er percentage s self identified as Black/African American ( n =10; 3.6% ) Multi racial ( n =7; 2.5% ), American Indian or Alaska N ative ( n =3; 1.1% ) Asian or Pacific Islander ( n =1 ; 0.4 % ) and Other ( n =6; 2.2%) The final sample include d 16 participants (5.7%) who self identified as being of Hispanic or Spanish ethnicity. The majority of respondents ( n =186; 66.9% ) reported being married while 16.2 % (n=45) were widowed, 12.9% ( n =36) were divorced or separated, and 4.0% ( n =11) reported never being married. Education levels of the final sa mple were fairly high Ninety three percent ( n =263 ) of participants reported completing at least high school ; 29.1% ( n =82) reported completing some college ; 24.8% ( n =70) reported having year degree ; 22.3% ( n =63) reported completion of graduate school or a graduate/ professional degree ; 17% ( n =48) reported completing a high school education or received their GED ; and only 6.7% ( n =19) reported they did not complete high school. With regard to household income level, t he largest number of respondents fell into the $20,000 to $49,999 household income bracket ( n =82, 32.8%), f ollowed by the $50,000 to $ 99,999 bracket ( n =80, 32.0%), and the $100,000 bracket ( n =58, 23.2%) O ver half the sample ( n = 138, 55.2%) reported a higher household income than the 2011 median household income in the state of Florida ($44,299) (U.S. Census Bureau, 2012). The majority of participants (72%) used the Internet to find health or medical information. Table 4 2 describe s the technological devices use d by study participants when looking for health or medical information Appr oximately half of respondents said
61 they used a desktop computer ( n =143, 50.5%) to access the Internet, followed by 42.4% who used a laptop computer ( n =120) Over one third of the sample reported using a mobile device to look for health information : 20.5% u sed a c ell phone ( n =5 8 ) a nd 14.5% used a tablet ( n =41 ). 59% ( n =124) of the sample reported us ing one device, while 41% ( n =86) reported using two or more devices to locate health information General Health Status of Respondents Table 4 3 describes a summary profile of the health status of study participants. The vast majority of the sample was healthy with 84.3% indicat ing good health ( n =62), very good health ( n =103), or excellent health ( n =62) O nly 14.7% of the sample reported having fair ( n =30) or poor ( n =14) health. M o re than two thirds of respondents ( n =197, 69.6%) reported living with at least one chronic disease Among those who reported reporting living with a chronic condition, over half 54.3% ( n =107) reported multimorbidity, or experiencing two or more chronic conditions concurrently A rthritis or joint problems ( n =109, 38.5%) was the most commonly cited condition, followed by high blood pressure ( n =99, 35.0%), diabetes ( n =46, 16.3%), heart disease ( n =35, 12.4%), asthma or other lung conditions ( n =29, 10.2%), cancer ( n =20, 7.1%), stroke ( n =7, 2.5%), an d other chronic health conditions not specified ( n =17 6% ) Research Questions Research Question #1 What is the extent to which older adults living in the state of Florida believe that the Internet is a useful resource for health information ? The majority of respondents found the Internet to be useful ( n =123; 43.5%) or very useful ( n =53; 18.7%) in helping make decisions about health Sixteen percent of the sample ( n =47)
62 was unsure i f the Internet was helpful, 12.7% ( n =36 ) found the Internet to not be useful and 8.5% ( n =24) found the Internet not to be useful at all in helping make decisions about health. The mean score on the perceived usefulness was 3.51 points ( SD =1.180 points) on a scale of 1 ( not useful at all ) to 5 ( very u seful ). Research Question # 2 What are the eHealth literacy scores of older adults living in the state of Florida? The distribution of the total scores on the eHEALS was examined t o determine the extent to which data met normality assumptions T he Shaprio Wilk test of normality suggested a violation of this assumption ( S W = .964, df =283, p = .000) A n inspection of the normal Q Q plots of total eHEALS scores and s kewness ( .393, SE = .145) and kurtosis (.620, SE= .289) statistics revealed the distribution was not seriously violated. However, t o account for the non normal distribution, non param etric data analysis techniques were used where appropriate (Field 2009). In the present study, t he internal consistency of the data collected using the eHEALS was high ( ) indicating the items making up the eHEALS scale produced similar scores when measuring the underlying eHealth literacy construct. The reliability statistic comput ed in this study is c omparable to alpha values previously reported by Norman and Skinner (2006 b in their studies that utilized the eHEALS to measure eHealth literacy The eHEALS scale was scored on a 5 point Likert scale with total score s being the sum of the 8 items ( with a total possible score ranging from 8 to 40 ) with a higher score suggesting higher eHealth literacy. Item scale correlations ranged from r=.49 to r =.79. Tab le 4 4 lists the mean score and correct item total correlation for each eHEALS
63 item in the current sample In the current study, scores on t he eHEALS ranged from 11 ( n =1) to 40 points ( n =16) with the total mean score being 29.05 points ( SD =5.749 points ) in the sample The average mean score on each item was 3.63 points ( SD = .72 points ) o n a scale from 1 (strongly disagree) to 5 (strongly agree). Additional analyses were run to determine whether eHealth literacy differed by the older adult age generation s specified above (Baby B oomers, Silent Generation and G.I. Generation) A Kruskal Wallis Test was conducted to compare the eHealth literacy scores among the three generations There was a sta tistically significant difference in eHEALS score across the th r ee generations H ( 2, n =283) = 16.354 p =.000. The Baby Boomers (50 64 y ea r s ) recorded the highest median eHEAL S score ( M d n =31.0 n =111 ), followed by the Silent Generation ( 65 74 y ea r s ) ( M d n =30.0 n =98 ), and the G.I. Generation (75 years and older ) ( M d n =27.5 n =64 ) Mann Whitney tests were used as a post hoc test for this analysis to determine which of the generations we re statistically different from one another To adjust for Type I errors, a Bonferroni correction was applied with all effect s reported at a .0167 level of significance eHealth literacy scores among older adults in the Baby Boomer generation were significantly higher than older adults from the Silent Generation ( U = 4395.5, z= 2.4, p =.016, r = .17) and the G.I. Genera tion ( U =2672.0, z= 4.032, p =.000, r = .30); yet, eHealth literacy scores were no t different between the Silent Generation and G.I. Generation ( U = 3129.0, z= 1.543, p = .12 r = .12 ). Thus the Baby B oomers reported significantly higher eHealth literacy sco res than either the Silent Generation or the G.I. Generation with no differences observed between the Silent and G.I. Generation s
64 Research Question # 3 What is the extent to which older adults in the state of Florida use social media to locate or share health information? Table 4 5 describes the distribution of social media use in the sample. Over one third of the sample ( 35.7% ; n =101) indicated using at least one type of social media to locate or share health information in the last 12 months Almost 90% of social media users ( n =90) reported using only one social media tool while only 10 .9% ( n =11 ) reported using two or more type s of social media. Among social media users, most participants used social networking technologies (e .g. Facebook, Twi tter ) to locate or share health information ( n =96, 95%) Using online support groups ( n =11, 10.9%) or writing in an online diary or blog ( n =6, 5.9%) w ere reported far less often Additional analyses were conducted to determine whether social media use differed by age. Approximately 40% ( n =45) of the Baby B oomers (50 64 year s ), 39.85% ( n =39) of the Silent Generation (65 74 years ), and 23.0% ( n =17) of the G.I. Generation (75 year s and older ) reported using social media to locate or share health informati on. A chi square test was conducted to compare social media use in these three age generations. The analysis indicated a statistically significant difference between generations regarding social media use 2 (2, n =283) = 7.072, p V =.158 Additional between groups ch i square test s were conducted as a post hoc test with Yates Continuity Correction to adjust for Type 1 error (Field, 2009) to check for statistical significance between groups No statistically significant diff erences were found between the Baby B oomers and the Silent Generation ( 2 (1, n =209) = .00 p =1.0, phi = .008) ; however, Baby B oomers were more likely to use social media than the G.I. Generation ( 2 (1, n =185) = 5.387 p =0.20, phi = .182) Older adults in the Silent
65 Generation were also more likely to use social media than the G.I. Generation ( 2 (1, n =172) = 4.695 p =.030, phi = .178). These results indicate d that social media use for locating and sharing health information also decreased with advanced age The decreased use in social media was especially evident in the adults over seventy five years of age, and not as apparent between the Baby Boomers and the Silent Generation. Research Question # 4 Do older adults living with one or more chronic disease(s) exhi bit higher eHealth literacy than older adults without chronic disease(s)? T o determine whether older adults living with one or more chronic disease had higher eHealth literacy than older adults without chronic disease(s) an independent sample Mann Whitney U test was conducted. The test revealed n o statistically significant difference s in eHealth literacy when comparing older adults with ( Mdn = 30.0, n =197) and without ( Mdn = 30.0, n =86) chronic disease chronic disease, U = 8470.5, z = .001, p = .99, r =.00 This finding suggests that chronic disease status did not influence eHealth literacy in this sample of older adults Research Question # 5 Do older adults living with one or more chronic disease(s) exhibit greater use of social media for healt h information than older adults without chronic disease(s)? A Chi square test for independence with Yates Con tinuity Correction was conducted to determine if older adults with chronic disease were more likely to use social media for health information than older adults without chronic disease. The analyses indicated no significant association between social media use and the presen ce of chronic disease (s) 2 (1, n =283) = 1.279, p =.258 phi =.075 These results
66 imply that having a t least one chronic disease does not influence an older adult decision to use social media for locating and sharing health information. Research Question # 6 Does the use of social media for health information predict overall eHealth literacy in older adults living in the state of Florida? A Mann Whitney U Test was conducted to determine if social media use was associated with eHealth literacy. As expected, eHealth literacy scores of older adults who used social media ( Mdn = 31.0 points n = 101) were significantly higher than eHealth literacy scores of older adults who did not report social media use for health information ( Md n = 29.0 points n= 1 82), U = 7001.5, z = 3.330, p = .001, r = .20. S everal additional Mann Whitney U Tests were conducted to determi ne whether use of specific social media outlets ( e.g. social networking, online support groups, or online blogs) w as associated with overall eHealth literacy. eHealth literacy levels of older adults who used social networking sites ( Mdn = 31.0 points, n = 96 ) were statistically significantly higher than eHealth literacy levels of older adults who did not use social networking sites ( Mdn = 29.0 points, n= 187 ), U = 7174.0 z = 2.774 p = 006 r = .17 eHealth literacy levels of older adults who used online support groups ( Mdn = 32.0 points, n =11) were also statistically significantly higher than eHealth literacy levels of older adults who did not use online support groups ( Mdn = 30.0 points, n =272), U = 937.5, z = 2.106, p = .035, r = .13. Interestingly though, eHealth literacy levels of older adults who used online diaries or blogs ( Mdn = 31.5 points, n = 6 ) did not differ to a statistical ly significant degree compared to older adults who did not use online diaries or blogs ( Mdn = 30.0 points, n = 277 ), U = 528.5 z = 1.530 p = 126 r = 09
67 Research Question # 7 What is the extent to which eHealth literacy is associated with perceived health status among older adults living in the state of Florida ? coefficient between eHealth literacy and perceived health status was examined to determine the relationship between the two variables. The analysis revealed the relationship between perceived health status and eHealth literacy was not statistically significant ( r s =.01 6, n =280 p =.785 ) indicating that eHealth literacy was not related to health status in this sample of older adults Research Question # 8 What is the extent to which using social media for health information is associated with perceived health status among older adults living in the state of Florida? A n independent t test was conducted t o assess differences in perceived health status among o lder adults who use d so cial media ( n = 100 ) and those who did not use social m edia ( n =180) to locate and share health information On average, older adults who were non users of social media reported slightly higher perceived health status ( M = 3.71, SD = 1.082 ) when compared to social media users ( M = 3.42, SD =1.103). T his difference was statistically significant t (278)= 2. 102, p = 0.03 ; however, the effect size of this statistically significant difference was small d = 0.267 Research Question # 9 What a re the socio demographic, chronic disease, and Internet device use factors that predict eHealth literacy among older adults in the state of Florida? To answer this research question, a multiple linear regression was conducted to determine whether covariates included in the model [i.e., gender, age, income, ethnic group, educational background, marital status, Internet device use (deskt op, laptop, cell phone,
68 tablet), chronic disease status and number of chronic diseases] predicted overall eHealth l iteracy. Prior to conducting the multiple linear regression analysis, inter correlations were computed between all independent variables to determine if multicollinearity would bias the model making it difficult to assess the importance of a n individual pr edictor (Field 2009). Multicollinearity may arbitrarily e xplain more variability in the dependent variable by allocating predictive credit to two or more predictor variable s at the same time ( Thompson, 2006 ) After examining intercorrelations between the independent variables c hronic disease status was removed from the regression model because it was highly correlated with number of chronic diseases ( r =.72, p <.01). After removing the chronic disease status variable from the model collinear it y diagnostics between the independent variables indicated collinearity was not present. Both the variance inflation factors (VIF) ( 1.483) and tolerance statistics ( 0.887) were compliant with the recommended cut off points of less than 10 and greater than 0.10 respectively (Pallant, 2010) which suggested that the model would not be influenced by co l linearity All other variables were simultaneously entered into the regression model which accounted for 18.2% of the variance in eHEALS scores which was st atistically significant ( R 2 =.182, R 2 adj = .139, F (12, 228) = 4.241, p <.001) Table 4 6 presents a summary of the regression coefficients generated by this analysis. The s tatistically significant predictors of eHealth literacy were age, education, desktop computer use, and laptop computer use. T he beta weight of the independent variable s suggested that all four statistically significant predictors made relatively large contributions to the regression model.
69 The model showed that as age ( b = .105) inc rease d by one year, the total eHEALS score decreased by .105 points A s educati on level ( b = .934) increase d the total eHEALS score increase d by .934 points. The model also showed that use of different Internet device s h ad a statistically significant effect on eHealth literacy. T he use of a desktop computer ( b = 2.231) increased tot al eHEALS score s by 2.231 points and the use of a laptop computer ( b = 1.725) increased total eHEALS score s by 1.725 points holding all other factors in the model constant Gender, marital status, race, ethnicity, income, cell phone use, tablet use and total number of chronic diseases were not statistically s ignificant predictors in the model Research Question #1 0 What are the socio d emographic, chroni c disease, Internet device use and technology acceptance factors that predict using social media for health information among older adults in the state of Florida? L ogistic regression was conducted to determine factors associated with u se and non use of social media to locat e and share health information Covariates included in the model were: gender, age, income, ethnic group, educatio nal background, marital status, chronic disease presence number of chronic diseases, type of Internet device used, and technology acceptance factors (perceived ease of use, and perceived usefulness of the Internet for health information) Social m to categorize participants who had never used social media for health to categorize participants who had reported us ing some type of social media for health information. Before conducting the logistic regression analysis, inter correlations between independent variables were examined to determine if multicollinearity would bias the model Chronic disease status was removed from the model because it was highly
70 correlated with the number of chronic diseases ( r =.72, p <.01). correlations indicated that perceived usefulness and ease of use variables were correlated ( r s =.45 p =.000), additional collinearity diagnostics were computed to determine if the model would be influenced by collinearity. Both the variance inflation factors (VIF) ( 1.275) and tolerance statistics ( 0.785) met their respective cut off points of less tha n 10 and greater than 0.10 (Pallant, 2010) and thus could be reasonably included in multivariable analyses. A two step h ierarchical logistic regression was performed to evaluate the socio demographic, chronic disease, and Internet device type factors on so cial media use, as well as, the effect of technology acceptance factors on social media use including all other predictors. Specifically, the covariates included in step 1 were: gender, age, income, ethnic group, educational background, marital status, num ber of chronic diseases, and type of Internet device used. At step 1 the model was statistically 2 [ df =19] = 42.973 p =.00 1 ) and explained 22.3% (Nagelkerke R 2 = .223) of the variance. To assess the Technology Acceptance Model technology acceptance factors (perceived ease of use and perceived usefulness) were added in Step 2 to see if they had an effect above and beyond measures included in the first step and to see if their inclusion added significantly to the total ability of the model to predict social media use for health information. Overall, t he full model was also statistically 2 [ df =21] = 50.362 p <.001 ) indicating the model was able to distinguish between those who did and did not use social media for health information. Technological acceptance factors improved the model as the model as a whole explained 25.7 % (Nagelkerke R 2 ) of the variance in social media use T hus the techn ology acceptance factors adde d
71 3 .4% predictive power to the model. The full model at step 2 was able to correctly classif y 71.8% of the cases. T able 4 7 contains the regression coefficients for each variable and their 95% confidence intervals at each st ep. The entire model found five of the independent variables made unique, statistically significant contributions to the final model and predicted social media use ; sex ( b = 1.007), a high school education ( b = 1.564), a post graduate education ( b = 1.575), using a laptop ( b = .876 ) and perceived usefulness of the Internet to find health information ( b = .435) Sex and laptop computer use were statistically significant at each step, although the beta weights became attenuate d after the additional variables w ere added to the model. Race ( b = 1.214) was statistically significant at Step 1, but not in Step 2 after including the technology acceptance factors. The strongest predictor was found to be sex (OR= 2.737, Wald= 8.272 df =1, p =.004 ), with females almost three times more likely to use social media for health information than men, even after controlling for all other factors in the model. Older adults reporting use of a lap top were 2.4 times more likely to use social media for health information than those who did not report using a laptop (OR= 2.402, Wald= 5.929 df =1, p =.015 ), all other factors being equal. High school graduates (OR= .209, Wald= 4.527 df =1, p =.033 ) and individual s with post college graduate education (OR= .207, Wald= 4.706 df =1, p =.030 ) we re less likely to use social media than those who did not graduate high school. Another statistically significant predictor of using social media for health information was perceived usefulness (OR = 1.545, Wald= 6.610 df =1, p =.010 ). The model found that for every one point increase on the perceived usefulness scale, the
72 odds of using social media for health information increased by a factor of 1.5 when controlling for a ll other factors in the model. Perceived ease of use (OR=.915, Wa ld = .126 df =1, p =.722 ) did not predict social media use to a s tatistically significant degree, all factors being equal. Summary Chapter 4 presents telephone survey responses from older adults in the state of Florida regarding eHealth literacy and use of social media to locate or share health information The study participants, aged 50 91 years were primarily white and well educated. While the majority of participants reported being in good health more than two thirds of the sample reported suf fering from a variety of chronic diseases. The present study found that the majority of respondents used the Internet to find health information, and believed the Internet was useful in helping make health decisions Almost one third of the sample used s ome form of social media to locate or share health information eHealth literacy scores and use of social for health information decrease d with advanced age Baby boomers and the Silent generation had higher eHealth literacy scores and social media use as compared to adults 75 and older. Older adults in the sample who used social media, especially those who used social networking sites (e.g., Facebook, Twitter) and online support groups, reported higher eHealth literacy tha n non users of social media. Dat a collected during this study reveal ed no association between either chronic disease status or the number of chronic diseases with eHealth literacy or social media use for health information In addition, b ivar i ate analyses revealed no statistically significant association between eHealth literacy and health status; however, non users of social media reported higher perceived health status than social media users.
73 In multivariable analyses age, education, desktop computer use, and lapt op computer use were si gnificant predictors of eHealth literacy while sex leve l of education, using a laptop and perceived usefulness of the Internet were significant predictors of using social media for health information among this sample of older adu lts In C hapter 5, results will be discussed in depth limitations will be identified, and implications for future health education research and practice will be suggested
74 Table 4 1. Demographic characteristic s of study participants n Valid % Gender Female 128 45.2 Male 155 54.8 Age 50 64 (Baby Boomers) 111 39.2 65 74 (Silent Generation) 98 34.6 75 + (G.I. Generation) 74 26.1 Marital Status Married 186 6 5.7 Widowed 45 15.9 Never Married 11 3.9 Divorced or separated 36 12. 7 No response 5 1.8 Ethnicity Yes Spanish or Hispanic 16 5.7 No Spanish or Hispanic 264 93.3 No response 3 1.1 Race White 252 89.0 Black 10 3.5 Asian or Pacific islander 1 0.4 American Indian or Alaska native 3 1.1 Other 6 2.1 Multi racial or mixed race 7 2.5 No response 4 1.4 Education Less than High school graduate 19 6.7 High school graduate/ GED 48 17.0 Some college/Assoc. degree 82 29.0 College graduate 70 24.7 Postgraduate 63 22.2 No response 1 0.4 Income Less than $19,999 30 10.6 $20,000 to $49,999 82 29.0 $50,000 to $99,999 80 28.3 $ Over 100,000 58 20.5 No response 33 11.7
75 Table 4 2. Devices use d to look for health or medical information n % Device Desktop Computer 143 50.5 Laptop Computer 120 42.4 Cell Phone 58 20.5 Mobile handheld (tablet, e reader) 41 14.5 None 73 25.8 Note: Participants could select more than one device Table 4 3 General health status of study participants. n % Health Status Excellent 62 21.9 Very Good 103 36.4 Good 71 25.1 Fair 30 10.6 Poor 14 4.9 Refused to answer 3 1.1 Chronic Disease Status Yes 197 69.6 No 86 30.4 Total Number of Chronic Diseases 0 86 30.4 1 90 31.8 2 67 23.7 3 27 9.5 4 or more 13 4.7 Type of Chronic Disease Arthritis or joint problems 109 38.5 High blood pressure 99 35.0 Diabetes 46 16.3 Heart disease 35 12.4 Asthma or other lung conditions 29 10.2 Cancer 20 7.1 Stroke 7 2.5 Other chronic health condition 17 6.0
76 Table 4 4 Means and standard deviations for eHEALS items among older adults in sample ( n =283) Mean SD Item Total Correlations I know what health resources are available on the Internet. 3.61 .906 .494 I know where to find helpful health resources on the Internet. 3.76 .855 .741 I know how to find helpful health resources on the Internet. 3.80 .862 .716 I know how to use the Internet to answer questions about my health. 3.82 .876 .702 I know how to the health information that I find on the Internet to help me. 3.81 .850 .767 I have the skills I need to evaluate the health resources I find on the Internet. 3.72 .933 .787 I can tell high quality health resources from low quality health resources on the Internet. 3.35 1.063 .682 I feel confident in using information from the Internet to make health decisions. 3.19 1.088 .594 Mean eHEALS score 3.63 .72 Note: Items rated on a 5 point Likert scale (1= Strongly Disagree, 2= Disagree, 3= Undecided, 4= Agree, 5= Strongly Agree Table 4 5 S ocial media use for health informati on among older adults in sample ( n =283) n % Any form of Social Media No 182 64.3 Yes 101 35.7 Social Networking Sites No 187 66.1 Yes 96 33.9 Online Support Group No 272 96.1 Yes 11 3.9 Online Blogs No 277 97.9 Yes 6 2.1
77 T able 4 6 Multiple Linear Regression Predicting eHealth Literacy Model B SE B Constant 30.116 4.567 Gender 1.117 .729 .099 Age .105 .038 .187** Marital status .265 .360 .049 Ethnicity .276 1.430 .012 Race .047 1.230 .002 Education level .934 .313 .197** Income .069 .427 .012 Desktop computer use 2.231 .739 .199** Laptop computer use 1.725 .787 .152* Cell phone use .880 .995 .064 Tablet use .014 1.106 .001 Total # of chronic diseases .038 .307 .008 Note. R 2 = .182, R 2 adj =.139. p <.05, ** p <.01.
78 Table 4 7 Logistic Regression Predicting Use of Social Media for Health Information Step 1 Step 2 B SE B Exp ( ) 95% CI B SE B Exp ( ) 95% CI Constant .948 1.465 2.580 .121 1.861 .886 Age .033 .018 .968 [.934, 1.003] .025 .019 .976 [.940, 1.012] Sex 1.062 .340 2.893* [1.485, 5.635] 1.007 .350 2.737** [1.378, 5.435] Ethnicity .528 .635 .590 [.170, 2.047] .550 .635 .577 [.166, 2.004] Race 1.214 .574 .297* [.096, .915] 1.034 .594 .355 [.111, 1.138] Marital status W idowed .356 .502 .700 [.262, 1.874] .342 .513 .710 [.260, 1.940] Never married .120 .794 1.128 [.238, 5.345] .059 .811 .943 [.192, 4.619] Divorced or separated .535 .477 1.707 [.670, 4.350] .631 .492 1.880 [.718, 4.927] Education High school graduate 1.309 .695 .270 [.069, 1.054] 1.564 .735 .209* [ .050, .884] Some college .808 .630 .446 [.130, 1.534] 1.145 .678 .318 [.084, 1.202] 4 years of college .625 .645 .535 [.151, 1.896] .826 .690 .438 [.113, 1.692] Post graduate 1.150 .666 .317 [.086, 1.168] 1.575 .726 .207* [.050, .859] Income $20,000 to $49,999 .855 .568 2.351 [.772, 7.160] .909 .579 2.481 [.798, 7.714] $50,000 to $99,999 .700 .612 2.014 [.607, 6.685] .719 .621 2.052 [.608, 6.931] Over $100,000 .940 .667 2.561 [.692, 9.472] .918 .677 2.504 [.664, 9.439] Desktop computer .350 .329 1.420 [.744, 2.707] .156 .343 1.169 [.597, 2.289] Laptop computer 1.013 .347 2.753 ** [1.393, 5.439] .876 .360 2.402* [1.186, 4.862] Cell phone .064 .422 1.066 [.410, 2.145] .080 .428 1.084 [.398, 2.137] Tablet .287 .465 .751 [.536, 3.312] .346 .473 .708 [.559, 3.571] # of chronic disease s .193 .137 1.213 [.927, 1.588] .183 .140 1.201 [.913, 1.580] Perceived Usefulness .435 .169 1.545** [1.109, 2.154] Perceived Ease of Use .089 .250 .915 [.560, 1.494] R 2 .223 .257 R 2 .034 p < 0.05, ** p < 0.01
79 CHAPTER 5 DISCUSSION Older adults in the United States, especially those with chronic disease(s) are a growing population in need of support for disease self management. Given that t he Internet is a readily available source for health information new online health resources have emerged to improve older adult health Therefore the purpose of the present study was to explore eHealth literacy and social media use for health information seeking among older adults in the state of Florid a. To date the literature lack studies examining factors among older adults especially those with one or more chronic disease(s ) that predict or explain eHealth literacy and use of s ocial media for obtaining health information To contribute to the indentified gap in the literature, research using a cross sectional survey sampled older adults to investigate the interrelationship s between socio demographic characteristics, device use, perceived health status chronic disease status, eHealth literacy, and social media use for health information This chapter discusses study finding s, along with acknowledged study limitations and recommendations for future health education research and p ractice in the field of health literacy research. Discussion of Findings Demographics The s tudy population consisted of mostly White (89%) male s (54.8%), aged 50 91 years The majority of the sample (93%) had at least a high school degree. Most participants (67%) were married, with 29% reporting being widowed or divorced T hus almost one third of the sample within this study reported not having a spouse
80 Approximately 84% of part icipants described their overall health as good, which is comparab le to nation al data, indicating 75.6 % of U.S. adults 55 and older report good or better health (CDC, 2011). In spite of this, m ore than two thirds of the sample reported suffering from a variety of chronic diseases and over half reported living with more than one chronic disease Given that the presence and number of chronic disease increase s with age, this was expected due to the age restriction of the study examining adults over the age of 50 Over one third of participants reported living with arthritis (38.5%) and high blood pressure (35%) followed by diabetes (16.3%) heart disease (12.4%) asthma or other lun g conditions (10.2%) cancer (7.1%) and stroke (2.5%) Th is data is similar to 2011 national data of adults 55 years and older in whic h that almost half of older adults report ed living with high blood pressure (54.1%) and/or arthritis (45.9%) (CDC, 2011). Factors A ssociated with eHealth Literac y Almost three quarters of this sample ( 72%) reported using the Internet which is a lso similar to findings from a nationally representative study of Americans reporting that 71 % of adults 50 64 and 58 % of older adults 65 and older use the Internet (Zickuhr & Madden 201 2 ) A mong Internet users in this sample nearly three fourths ( 72 %) repor ted having used the Internet for health or medical information in the past 12 months Interestingly, this finding i s identical to findings reported in a larger national survey investigating th information (Fox, 2012). eHEALS score s ( M =3.34, SD=.88) reported in this sample are slightly higher, yet comparable to the eHEALS scores of older adults reported by Choi and DiNitto (2013) ( M =3.22, SD =.85) and Neter
81 and Brainin (2012) ( M =3.34, SD=.88). Overall, respondents in the current study were confident in their ability to use the Internet to find health resources. However, this confidence did not extend to their ability to evaluate the quality of online health information or use online health information to make health decisions These results mirror recent findings reported by Manafo and Wong (2012). L ack of confidence when using online health information suggests a need for effective eHealth lit er acy interventions focus ing on increasing eH ealth literacy capabilities and confidence in older adults (Miller & Bell, 2012; Xie, 2011) as opposed to addressing only computer access and technological skills (Manafo & Wong, 2012). A ccess and availability of the Internet are not the sole determinant s of older ability to use the Internet effectively for health information (Zajac et al., 2012). If o lder adults have inadequate skills to discern which websites can be trusted then there is a need for in tervention s and educational programs that teach older populations to search for online health information that is relevant, accurate, and tailored to the ir health needs and skills. eHealth literacy and d emographic, device factors In this study, eHealth lit eracy in older adults was found to be influenced by age, education, and the use of desktop and laptop computer s. These findings support the notion that demographics, educational background, and use of specific technologies all influence health literacy ( IOM, 2004) and eHealth literacy (Norman & Skinner, 2006a). Not surprisingly, b ivariate and multivari able analys e s found an indirect relationship between eHealth literacy and advanced age The Baby B oomers had significantly higher e Health literacy scores th an both other older generations ; however there was no significant difference between the Silent Generation and the G.I. Generation This suggests that eHealth literacy scores may begin to declin e after 65 years of age This
82 may also be explained by the fa ct that abnormal cognition increases with age, which has also been found to be significantly associated with inade quate health literacy (Federman et al. 2009). Older adults in this age group may also have significantly less Internet experience and exposur e E ducation level was another statistically significant predictor of eHealth literacy, with higher education level predicting higher eHEALS scores. This is not surprising considering education level has been shown to be a significant influence on both health literacy (IOM, 2004) and eHealth literacy (van der Vaart et al., 2013) Although having a higher level of education has been associated with more frequ ent health related Internet use ( Neter & Brainin, 2012; Powell et al., 2011) previous research ha s shown that higher education does not necessarily predict better Internet skills ( van Deursen et al., 2011; Neter & Brainin, 2012) Therefore, more research is needed to determine whether educational level and eHealth literacy are associated. D esktop and laptop computer use were significantly associated with increa se d eHealth literacy among older adults in this sample As previously stated eHealth literacy is thought to be comprised of six core literacies : traditional, health, information, scientific, media and computer (Norman & Skinner, 2006 a ) The notion that c omputer literacy is Skinner, 2006 a p. 3 ) is partially supported and explained by the res ults from this cross sectional study F indings from this study suggest c omputer literacy may be a larger perhaps even the of eHealth literacy in older adults H owever, it is likely the developers of eHealth literacy Lily Model would argue should not be examine d in parts (Norman, 2011). Regardless, an updated definition of
83 eHealth literacy or eHealth literacy 2.0 term is needed to add to account for the changing literacies and evolving participative online social context of health information (Norman, 2011). Another interesting finding among the older adults in this study relates to the lack of association between certain socio demographic variables and eHealth literacy Findi ngs reveal a lack of association between eHealth literacy and race, ethnicity, and income which suggests factors influencing eHealth may be those that traditionally have influence d health literacy ( i.e. age, education) and not necessarily factors historically believed to negatively influence the digital divide ( i.e. race, ethnicity, income) These rather on the knowledge gap logy is creating a new social inequality, rather than leveling out social discrepancies p.2 ). Data from this study demonstrate the need for health p rograms and interventions that promote additional engagement, practice, and educati on regarding Internet health information for older adults rather than just promoting access to the Internet (Choi & DiNitto, 2013; Manafo & Wong 2012 ). eHealth literacy and c hronic disease/health status H aving a chronic disease appears to promote Internet use for health information yet none of the health related variables in this study were associated with eHealth literacy. eHealth literacy was not related to the presence of chronic disease, the number of chronic diseases a respondent reported liv ing wit h, or self reported perceived health status. & Skinner, 2006a, p.3 ). Pre vious research indicates that the
84 frequency of seeking health information on the Internet increases with the number of specific health problems ( Rice, 2006 ; Ayers & Kronenfeld, 2007; Choi & DiNitto, 2013 ) It has been suggested th & Skinner, 2006a) and individual skills and health status are constantly changing, thus additional longitudinal studies are needed to assess the relationship between eHealth literacy and health outcomes. I t is somewhat surprising that eHealt h literacy did not influence health status, especially given that previous research has shown limited health literacy leads to worse overall health status and higher mortality ra tes among older adults (Berkman et al., than any other factor, including education, income, employment or race (AMA, 1999). Given that eHealth literacy is influenced by many of the same socio demo graphic factors of health literacy one may expect that they the health consequences of health literacy and eHealth literacy would be similar of using the Internet for health purpose s may extend the traditional outc omes of health literacy (Neter & Brainin, 2012, p. 2) Therefore, additional research is needed to determine the health outcomes of eHealth literacy, opposed to just the health outcomes of health literacy. Chronic disease status has also been previously a ssociated with eHealth literacy. Neter and Brainin (2012) found that participants (independent of age) who were chronically ill had significantly lower eHealth literacy scores as compared to respondents with no reported chronic illness even though percei ved health did not vary with eHealth literacy Another study utilizing the eHEALS instrument found negative
85 associations between chronic disease (depression diagnosis) and eHEALS scores (Choi & DiNitto, 2013). Data from the current study revealed no relati onship between chronic disease status and eHealth literacy. These contradictory findings warrant a dditional research exploring relationship s between chronic disease, health status, health literacy, and eHealth literacy Factors A ssociated with Social M edia Use for Health I nformation O ver one third of participants (35.7%) indicated that they used some form of social media to locate or share health information in the last 12 months This finding is in line with prior research of U.S. adults, in which 40% of t he sample indicated that or ct their future health care decisions (Fuscaldo, 2011 ). Among social media users t he vast majority (90%) reported using only one form of social media, namely social networking techn ologies such as Facebook and Twitter This is similar to findings from a 2011 Pew Internet survey which found among Internet users ages 50 and older 42% had used online social networking tools in the past year (Madden 2010 ). Demographic, device factors, and social media use S ocial media use for health information was found to be influenced by age, sex, education and laptop computer use. Bivar i ate analyses showed social media use differed among generations with use among older adults decreasing with age with use especially declining after the age of 75. This is not surprising since, Kontos and colle ag ues (2010) also social networking use amon g adults, with use decreasing with age Interestin gly, there were no statistically significant diff erences between the Baby B oomers (50 64 yea rs )
86 and the Silent Generation (65 7 4 yea rs ) in terms of social media use, yet the Silent Generation respondents were more likely to use social media than the G.I. Generation (75 and older ) While social media use declines with age, t his finding suggests that the use of social media for health information does bridge some generational gaps and its use extend s beyond just Baby B oomers. These findings provide evidence of and that it may be an opportune time to utilize social media as a health promotion tool for order adults. Females were almost three times more likely to use social media for health information than men Relatively few studies have explored gender differences in regards to social media use. Cho and colleagues (2009) found gender was not associated with general social media, while Elki n (2008) found men were more likely than women to use online social media to research health and wellness issues However, a national survey found that since 2009 women have been more likely than men to use social networking sites in general A December 2 012 study found that women use social media 9% more than men do (Duggan & Brenner, 2013 ) In this study, it was also noted that women us e social media for health information almost 22% more than men. (health) Now, in the age of social media researchers should consider developing social media applications that caregivers. Additional research is needed to determine the role gender plays in social media use for health information.
87 I nterestingly, r ace and ethnicity w ere not statistically significant predictor s of socia l media use for health information among older adults This finding matches trends in larger surveys which note d that W hites, African Americans, an d Latinos are equally likely (once online) to use social networking sites (Fox & Jones, 2 009 ). Kontos et al. (2010) found reverse trends in social networking site use with higher frequencies of use seen among racial/ethnic minorities and those with lower education and income levels. In fact, Chou and colleagues (2009) found non white American s Internet users were more likely to use social media than white Internet users. In the current study r ace and ethnicity may not have emerged as significant predictors due to the age restriction and lack of diversity in the sample. Nevertheless s ocial media is consistently used regardless of many socio demographic characteristics (Kontos et al. 2010) and thus should be leveraged as a health promotion tool for older adults. Surprisingly using a laptop computer was a statistically significant predictor of older adults using social media for accessing health information However, o ver one third of the sample (35%) reported using mobile devices (cell phones, tablets, etc) to look for health information and a recent Nielsen study found that 40% of social me dia consumption was via a mo bile application or the mobile web (Nielsen Company, 2012) Thus i t will be interesting to observe changes in mobile technology use in next several years as mobile Internet usage is projected to overtake conventional desktop I nternet use by 2015 ( Charlton, 2012 ) The type of Internet device used for health information continues to change rapidly as there are a growing number of seniors who own cell phones (Zickuhr & Madden, 2012). Currently, 12% of adults age 65+ and 32% of th ose ages 50 64 years old own a S martphone ( Fox 2012) E vidence suggests minority
88 populations may have a n even higher uptake of mobile technology (Gibbons et al., 2011). Smartphones and their apps are rapidly transforming healthcare, especially the care of patients with chronic conditions (Boulos, 2012). Therefore, a dditional research is needed to see how the adoption of mobile technology affects social media use for health information. Of note, income was not a significant predictor of social media use fo r health information in the multivaria te analyses. This challenges recent research which found income to be a significant factor predicting the use of social media for health information among older adults H ousehold s earning $75,000 or more were found to be more likely to turn to social media for health care information than households with smaller incomes (Fuscaldo, 2011). Additional empirical studies are needed to determine which socio demographic and device use factors predict social media use for healt h information in order to develop and promote health via social media Social media use and chronic disease/health status In this study, older adults who did not use social media had better perceived health status than those who did use social media. P rev ious research found that older adults with poorer self reported health status had higher rates of using online support groups (Chou et al. 2009). S ocial media may be a good source of outreach and support among those with lo wer self reported health status. Additional research is needed to understand the benefits from online health promotion programs and support groups that target individuals with lower health status. D ata obtained in this study reveal no statistically significant association between social media use and the presence of chronic disease. This finding contradicts the review of the National Health Information National Trends Survey data which showed
89 individuals with a history of cancer had a 43% lower odd s of using social networking sites compared to individuals with no history of cancer even after controlling for age (Kontos et al., 2010). However, a mong the sample living with a chronic disease did not predict an l media f or health information. These findings contrast with Pew Internet data which showed that living with chronic disease inc reases the probability that an I nternet user will blog or contribute to an online discussion or group forum about health (Fox & Purcell, 2010). Additional research is needed to determine how social media use can influence perceived health status an d a ffect self management of a variety of chronic diseases. P erceived Usefulness and Perceived Ease of U se The Technology Accepta nce Model (TAM) posits that perceived ease of use and perceived usefulness are fundamental determinants predict ing application usage (Lederer et al. 2000). Perceived usefulness and perceived ease of use are independent constructs that influence technology usage behavior (Kufafka et al., 2003) Models of technology acceptance recognize demographic factors and perceptions play a role in & Bell, 2012). This study used the principle of the TA M to explore individual s perceived ease of use and usefulness of the Internet for health information to determine whether these perceptions were associated with the use of social media for health information. R esults of this study are in part consistent w ith TAM The majority of respondents (62.2%) found the Internet to be useful or very useful in helping to make decisions about their health However, 37% of Internet users w ere unsure or did not find the Internet to be useful in helping with health decisio n making T hese results are compar abl e with other studies such as Manafo and Wong (2012) who found that 67 % ( n =32 of 48) of
90 older adults found the Internet useful or very useful in helping to make health decisions In another study of Medicare website usability ( Czaja, Sharit, & Nair, 2008 ) 95% of older adults reported that the Internet was a valuable tool for finding health information. In the present study, perceived ease of use was significantly correlated with perceived usefulness of the Internet to find health information A logistic regression analysis found that only perceived usefulness predict ed social media use for health information among older adults; however eas e of use of the Internet for health information was not statistically significant. This finding is comparable to other TAM studies that noted eas e of use did not significant ly impact usage ( Nayak, Priest, & White 2010; Martinez Torres et al. 2008). Previous researchers suggest that once an older adult begins use of the Internet for health information, they may not diversify to other activities. Older adults may become proficient in using simpler Internet activities (email, using search engines) and stick to those activities as opposed to using more adv ance d technologies, such as social media and thus not be affected by the ease of use ( Nayak, Priest, & White, 2010 ) Earlier research also suggests of use may be a causal antecedent to perceived usefulness, as opposed to a parallel, di This may explain why ease use of not a significant predictor of social media use for health information in this study. F or older adults to fully embrace and benefit from social media use social media applications should focus more on user friendliness in the design of the application to increase user acceptance of platforms that promote health information on chronic disease Relationship between eHealth Literacy and Social Media Use As expected, o lder adults who report ed using social media also had higher eHealth literacy scores especially among those who reported use of social networking
91 and online support group application s However, access to the Internet and Web browsing does not guarantee that an individual will be able to properly understand and evaluate online health information and make competent decisions based on this information (Knapp et al., 2011; Stellefson et al., 2011). Although data from this study suggests th at using social media may increase eH ealth literacy among older adults Older adults who utilize social media for health information may develop greater computer and media literacy skills than those who do not use social media Previous eHealth literacy research indicates that dividual uses technology, the & Ski nner, 2006b, p. 3). Additional longitudinal research is needed to examine the frequency of social media use, the different types of social media tool s that are used, and how social media use may influence on eHealth literacy among older adults over time Health as an Outcome of Communication The Structural Influence Model of Health Communication (SIMHC) is based on the premise that communic ation is the critical link between social determinants and health outcomes. One of the key dimensions of the model is that health outcomes are affected by not only access to computers and the Internet, but also by the ability to properly navigate the Inter net (Kontos, Bennett, & Viswanath, 2007). Among individuals with chronic disease in this study there was no association found between eHealth literacy or social media use for health information and health status. In fact, among the entire sample o lder ad ults who did not use social media reported slightly higher perceived health status than those who did use social media. Therefore eHealth
9 2 literacy and social media use for health information may not affect health status of older adults as much as might be suggested by the SIMHC I mplications Although eHealth literacy is a s much a process as an outcome and requires a p.5), the findings from this study highlights some of the opportunities and challenges that the public health community faces in moving forward with the design implementation, and evaluation of online applications that improve self management adherence among older adults with chronic disease. Recommendations for Future Research More empirical studies are needed to understand how and why older adults with chronic disease (s) use the Internet as well as how the Internet affects health related decision m aking The data presented in this study clearly illustrates the importance of furthering research that examines the use of social media by older adults seeking for health in formation There is much to learn about how social media can enhance health as ther e are a limited number of empirical studies that document the effects of social media use on health among older populations In this study, m ore than a third of older adults used social media to locate or share health information Older adults reporting lower perceived health status were more likely to turn to social media It is expected that these trends will continue to grow; thus, additional research is needed to determine what particular social media applications are being used by older adult to loc ate and evaluate health information Health education researchers need to know how, when, where, why, and what social media is being used by older adults with chronic disease
93 Future research should also focus on designing and validating programs to increase eHealth literacy especially those aimed at improving health outcomes among older adults with chronic disease. Few would argue that being eHealth literate requires a skill set all its own (Norman & Skinner, 2006 a ). However, while eHealth literacy has been defined as a skill set that consists of six literacies that work together, data from this study highlighted a that two type s of literacies (computer and media) may have a stronger impact on eHealth literacy than the other dimensions Futu re resear ch should assess how eHealth literacy is measured in older adults to determine whether all six literac y types are important to consider In addition, limited research has been conducted on the connection between disparities in health outcome s regarding e H ealth literacy or social media use for health information While the access gap is narrowing research is need ed to explore other dimensio ns of communication inequality (Kontos, Bennett, & Viswanath, 2007). Many of the known factors associated with low hea lth literacy (income, race, health status, chronic disease status) were not associated with lower levels of eHealth literacy in this study This may be because a new social inequality revolves around the knowledge gap in the skills associated with eHealth literary and not the digital divide (Neter & Brainin, 2012 ) eHealth literacy may constitute a second divide (Neter & Brainin, 2012) hence it is important for researchers to fully understand and measure eHealth skills in order to prevent further health in equalities Furthermore, more research is need ed to identify the role of eHealth literacy and social media use play in affecting overall health outcomes. The lack of studies in this particular area may be due to the relative infancy of investigating eHealth literacy and social media use in older adults
94 Implications for Health Educators data for health education professionals who utiliz e health information technology to improve older adult health particularly in chronic disease prevention and management The utilization of health information technology to improve health outcomes is a national health priority (Healthy People 2020) which has shift ed focus from the digital divide to concerns about digital usage and associated online skills (Neter & Brainin, 2012) H ealth educators need to focus their efforts on awareness, measurement, and education with regard to improving eHealth literacy skills for older adults and those with chronic disease ( s ) eHealth litera cy decisions informed by eHeal & Skinn er, 2006a, p.3). It is imperative for health educators to develop interventions and programs to increase eHea lth literacy, especially among older adults with chronic disease who have much to gain from online health information resources As findings from this study suggest, older generations have already begun to adopt Internet techn ologies for health informatio n ; thus health educators need to go where the people are. With almost half of older adults report living with high blood pressure and/or arthritis (CDC, 2011 ), it seems critical to have online resources for managing these conditions. I t is important that health educators and health providers collaborate at multiple levels (e.g., patients, nurses, health educators, doctors, web developers ) to develop applications designed to meet the skill levels of older adults In addition, d octor offices libraries, senior centers, offer online eHealth literacy courses to improve eHealth literacy skills of older adults and those with chronic diseases. In addition to skill sets, as findings
95 from this study reveal, it is also important to consider age, educ ation level and gender when developing elder health resources on the Internet. When developing online applications, researchers should know their target audience and use plain language, large fonts, and clear messages to address the needs of older adults with different levels of education ( CDC, 2012). When planning research studies, investigators should also recognize that women are more likely than men to use social media for health information It is important to explore different ways to attract men to using social media as a health promotion tool. Limitations of the Study Although the current study extend s prior research, study limitations must be acknowledged First, because data were collected over the telephone, the types of items that could be included in the survey were somewhat restricte d (i.e. more exploratory, open ended questions were not able to be asked) However, this method did allow the researcher to reach a large sample of older adults throughout the state of Florida. Second, the c ross sectional design of the study does not allow for the formulation of causal conclusions. Also, due to limitations in the number of items that could be included in the telephone survey, this study did not account for participants Internet access or fre quency of Internet use for health information. Previous research has found that significantly more frequent access to computer s and the Internet resulted in higher eHealth literacy (Neter & Brainin, 2012; Choi & DiNitto, 2013) and positive health behavior changes (Ayers & Kronenfeld, 2007). F requency of Internet use and type of Internet access could interact with the variables under consideration in this study.
96 The fourth limitation involves use of the eHEALS instrument to assess eHealth literacy The eHEALS instrument of personal eHealth literacy skills and knowledge (van Deursen & van Dijk, 2011) rather than actual eHealth literacy skills R ecent research has question ed the validity of the eHEALS tool ( C han & Kau fman 2011; van der Vaart et al., 2011). When eHEALS was developed social media was still in its infancy W hile the eHEALS is a valuable instrument for assessing Web 1.0 skills, it is unclear how it fits with Web 2.0 technologies (Norman, 2011) A more co mprehensive research instrument is needed to assess frequency of use, types of information sought, and goals of online health information seeking, especially considering the new relevance of social media use (Miller & Bell, 2012). Another limitation is re lated to the landline sampling frame. This sampling frame excluded a large proportion of the population who only own mobile phone. In 2011, m odel estimate s found over a third (34.4%) of adults in the state of Florida only had wireless telephone and did not hav e household landlines (Blumberg et al., 2012). These individuals may have been more eHealth literate and social media savvy. In addition, given that all participants in the present sample reported having some pr ior Internet experience, it may have rendered some of the variables non significant. Finally, the sixth limitation has to the do with t he demographic makeup of the sample. R elatively few older adults from diverse racial and ethnic backgrounds were include d in the sample and the minority representation was somewhat lower than what would be expected in the state of Florida. In Florida 15% of adults over the age of 50 are of Hispanic or Latino ethnicity (U.S. Census Bureau, 2011). In this study, only 5.7% s elf identified as being Hispanic or Latino ethnicity Alternative, non landline telephone
97 survey, methods are needed to reach the this population given that Hispanic adults are more likely than non Hispanic white adults to be living in households with only wireless telephones (Blumberg & Luke, 2012). Consequently, the findings from this study may not be representative of the older population as a whole and t he results should be generalized with caution Conclusion Over half of older adults suffer from multiple chronic conditions, and need health information about multiple diseases/disorders. Older adults with chronic disease (s) are already turning to the In ternet and social media for health information, and it is important to understand their skill set s to find and evaluate online health information among this underserved audience Despite limitations, t he present study is the first to provide a detail descr iption of eHealth literacy and social media use for health information among older adults. This study represents a systematic effort to examine eHealth literary and social media use for health information among older adults with chronic disease. The present investigation revealed a link between using social media for health information and health status The findings of this study also emphasize the importance of clarify ing the role of health and socio demographic factors as covariates explaining and/or predicting eHealth literacy and social media use for health information among older adults Social media has become a leading communication platform and will continue to attract adult users across all segments ; thus it is important to understand so cial information seeking in older adults. The Internet, including social media applications, has the potential to improve health outcomes for older adults with chronic disease. Yet much research is still
98 needed to further understa nd the full influence of online health information seeking and social media use on health behaviors and decision making in the older adult population.
99 A PPENDIX A EHEALS AND SOCIAL MEDIA SURVEY ITEMS eH ealth literacy and social media items added to the Florida Consumer Confidence Survey: For adults age 50+ 1. Do you ever go on line to access the Internet or World Wide Web, or to send and receive e mail? 1 Yes 2 No (Go to Question 14) 9 Refu sed 2. In the past 12 months, have you used the Internet on any of the following devices to look for health or medical information for yourself? (Check all that apply) 1 Desktop Computer 2 Laptop Computer 3 Cell phone 4 Mobile handheld device like an, e reader, or tablet 8 9 Refused 3. In the last 12 months, have you used the Internet for any of the following reasons to locate or share health information? (Check all that apply) 1 Participated in an on line support group? 2 Used a social networking site like Fac ebook, Twitter, or LinkedIn.com ? 3 Wrote in an online diary or blog? 8 9 Refused Now, I would like to ask you for your opinion and about your experience using the Internet for health information. For each statement, tell me which response best reflects your opinion and experience right now. 4. How useful do you feel the Internet is in helping you in making decisions about your health? 1 Not useful at all 2 Not useful 3 Unsure 4 Useful 5 Very Useful 9 Refused
100 5. I know what health resources available on the Internet. 1 Strong Disagree 2 Disagree 3 Undecided 4 Agree 5 Strongly Agree 9 Refused 6. I know where to find helpful health resources on the Internet. 1 Strong Disagree 2 Disagree 3 Undecided 4 Agree 5 Strongly Agree 9 Refused 7. I know how to find helpful health resources on the Internet. 1 Strong Disagree 2 Disagree 3 Undecided 4 Agree 5 Strongly Agree 9 Refused 8. I know how to use the Internet to answer questions about my health. 1 Strong Disagree 2 Disagree 3 Undecided 4 Agree 5 Strongly Agree 9 Refused 9. I know how to use the health information that I find on the Internet to help me. 1 Strong Disagree 2 Disagree 3 Undecided 4 Agree 5 Strongly Agree 9 Refused
101 10. I have the skills I need to evaluate the health resources I find on the Internet. 1 Strong Disagree 2 Disagree 3 Undecided 4 Agree 5 Strongly Agree 9 Refused 11. I can tell high quality health resources from low quality health resou rces on the Internet. 1 Strong Disagree 2 Disagree 3 Undecided 4 Agree 5 Strongly Agree 9 Refused 12. I feel confident in using information from the Internet to make health decisions. 1 Strong Disagree 2 Disagree 3 Undecided 4 Agree 5 Strongly Agree 9 Refused The next question is about chronic health conditions that may affect you. 13. Are you now living with any of the following health problems or conditions? [CODE ALL THAT APPLY] [PROBE: Any others?] 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 6 Stroke 7 Arthritis or joint problems 9 Refused
102 APPENDIX B FLORIDA CONSUMER CONFID ENCE SURVEY 2013 Q u es t ion H E L L O Hell o m y n a m e i s I'm call in g f r o m t h e U n i v e r s i t y o f Florida. ( T h is is n o t a s ales ca l l.) ( W e re c o n d u ct i n g resea r c h re g a rd ing t h e s tate o f t h e ec o n o my a n d o t h er i s s u es i n Florida.) ( T h is is n o t a s al e s call.) (I N T : P R ESS 1 T O C O N TINUE SU R VEY P R ESS 3 IF T HIS IS A S P AN I SH SU R VE Y ) P R ESS C T R L /END T O END SU R VEY Hell o t h is i s f r o m t h e U n i v e r s i t y o f F lo rida. T h is is n o t a s al e s call. (I N T : T HIS C ALL C OU L D B E A P A R T IA L C OM P L E T E) (I N T : P R ESS 1 T O C ON T I NUE S U R V E Y P R ESS 3 IF T HIS I S A S P A NISH SU R VE Y ) [IF ( A NS = 1 ) S KI P T O HOME] Q u es t ion L A NG ( I N T : YOU C ODED T HIS SU R VEY AS A S P A NI S H C ASE. IF T HIS I S N O T C O R R E C T USE MOUSE T O C L I C K ON B A C K KEY IN T HE L O W ER L EFT H A N D OF T HE S C R EEN AND RE C O DE T HIS C ASE C O R R E C T L Y ) (I N T : P R ESS 1 T O C ONTINUE W I T H SU R VEY IN S P A NI S H P R ESS 2 T O END SU R VE Y ; CODE C A SE A S C AL L B A C K) [IF ( A NS = 1 ) A L T E R N A T E s p a n i s h ] [IF ( A NS = 2 ) C T R L END] Q u es t ion I R B 1 T h e U n i v e r s i t y is c o n d u cti n g resea r ch a bo u t ec o n o m ic c o n d itio n s a n d o t h er i ss u e s i n Florida a n d w e w o u ld l i k e y o u r op i n io n ( W e re c o n d u cti n g resea r ch re g a rd ing t h e s tate o f t h e e c o n o m y a n d o t h er i ss u e s in Florida.) ( T h is is n o t a s ales ca l l.) First, I n eed to kn o w i f y o u a r e 1 8 y ears o ld o r o ld er ? (I N T : R E A D C HO I C E S IF N E C ES S A R Y )
103 1 YES, 1 8 YE A R S O L D O R O L D E R 2 NO, UND E R 1 8 YE A R S O L D [IF ( A NS = 1 ) S KI P T O M A L E ] Q u es t ion A D L T M a y I s p eak to s o m e o n e 1 8 y e a r s o ld o r o l d er w h o li v es t h e r e? (IN T : P R ESS 1 IF P E R SON P A S S ES T HE P HONE P R ESS 2 IF E L IGI B L E A D U L T IS NOT HOME, A SK F O R A C A L L B A C K D A T E & T IME A ND C O DE C A SE A S C AL L B A C K" P R ESS 3 T O END SU R VEY IF NO E L IGI B L E A D U L T S L IVES T HE R E A N D C ODE C A SE A S NO E L IGI B L E R E S P OND E N T ) [IF ( A NS = 1 ) S KI P T O HE L LO] Q u es t ion M A L E A cc o r d ing to t h e resea r ch m e t h o d b ei n g u s ed b y t h e u n i v e r s it y I n eed to ask s o m e q u est i o n s o f t h e Y OUNGEST MA L E a g e 1 8 o r o lder w h o is c u rre n t l y h o m e a n d l i v es t h e r e M a y I s p eak w i t h h i m ? ( T h e U n i v e r s i t y is c o n d u ct i n g resea r ch a bo u t ec o n o m ic c o n d itio n s a n d o t h er i s s u es i n Florida a n d w e w o u ld li k e y o u r op i n io n ) (IN T : P R ESS 1 T O C ONTINUE W I T H YOUNGEST M AL E 18 + W HO IS C U R R EN T L Y H O ME P R ESS 2 IF YOUNG E ST MA L E NOT HOME OR NO ADU L T MA L E L IVES T HE R E P R ESS 3 IF P E R SON P A S S E S T HE P HONE P R ESS 4 IF YOUNG E ST MA L E IS HOME B UT UN A V A I L A B L E, ASK F O R A C AL L B A C K D A T E & T IME A N D C ODE C A SE A S C AL L B A C K P R ESS 5 IF YOUNG E ST MA L E O R P E R SON R E FUSE S OR YOUNGEST MA L E IS P HYSI C A L LY/M E N T AL L Y U N A B L E. C O DE C A SE A PP R O P R I A T E L Y ) [IF ( A NS = 1 ) S KI P T O I R B2 ] [IF ( A NS = 2 ) S KI P T O FE M A L E] [IF ( A NS = 3 ) S KI P T O HE L LO] Q u es t ion FE M A L E
104 M a y I s p eak w i t h t h e O L DE S T FEM A L E a g e 1 8 o r o l d er t h at is c u rre n t l y h o m e a n d l i v es t h e r e ? ( T h e U n i v e r s i t y is c o n d u ct i n g resea r ch a bo u t ec o n o m ic c o n d itio n s a n d o t h er i s s u es i n Florida a n d w e w o u ld li k e y o u r op i n io n ) (IN T : P R ESS 1 T O C ONTINUE W I T H YOUNGEST F EM AL E 1 8 + W HO IS C U RR EN T L Y H OME P R ESS 2 IF P E R SON P A S S E S T HE P HONE P R ESS 3 IF T HE R E IS NO A DU L T A GE 1 8 OR O L D E R A V A I L A B L E, A SK F OR C A LL B A C K D A T E & T IME A ND C ODE C A SE A S C AL L B A C K" P R ESS 4 IF O L DEST FE M A L E OR P E R S ON R E FUSES, OR O L DEST FEMA L E IS P HYSI C AL L Y/M E N T AL L Y U N A B L E. C ODE C A SE A PP R O P R I A T E L Y ) [IF ( A NS = 1 ) S KI P T O I R B2 ] [IF ( A NS = 2 ) S KI P T O HE LL O] Q u es t ion I R B 2 Y o u r p h o n e nu m b er w a s s ele c ted at ra n do m b y c o m p u ter, a n d o n l y y o u r f ir s t n a m e w ill b e u s ed to e n s u re c o nf ide n ti a li t y Y o u d o n o t h a v e to a ns w er a n y q u es t ion y o u d o n o t w i s h to a n s w er a n d I w a n t y o u to kn o w t h i s call m a y b e rec o r d ed f o r q u ality c o n tr o l ( s u r v e y s h o u ld t a k e le s s t h a n 1 0 1 5 m i n u tes) Ha v e I reac h ed y o u o n y o u r H OME tele p h o n e? (IN T : W E C A N C OND U C T I N T E R VI E W S W I T H HOME B A SED B USINE S S ) (IN T : P R ESS 1 IF Y E S, HOME P HONE o r HOME B A SED B U S INESS P R ESS 2 T O END SU R VEY A N D C ODE C A SE A PP R O P R I A T E L Y P R ESS 3 IF R ESP SAYS W E'VE R E A C HED T HEM ON THEIR C E LL P HON E ) [IF ( A NS < > 3 ) S KI P T O R N A M] Q u es t i o n C ALLF W D Is t h is p h o n e nu m b er b ei n g f or w a rd ed f r o m y o u r h o m e l a n d l i n e tele p h o n e? (IN T : P R ESS 1 IF Y E S P R ESS 2 IF NO; CODE C A SE A S C ELL P HON E ) [IF ( A NS = 2 ) C T R L END]
105 Q u es t ion R N A M M a y I h a v e y o u r f ir s t n a m e? (IN T : T Y P E N A ME T HEN P R ESS EN T ER T O C ON T IN U E ) (O n l y y o u r f ir s t n a m e w i ll b e u s e d to e n s u re c o nf id e n tiali t y ) Q u es t ion ISEX R E C O R D SEX O F R ES P O N DENT (IN T : A SK I F N E C ES S A R Y) A re y o u (IN T : RE A D C HO I C ES I F N E C ES S A R Y) 1 Male 2 Fe m ale Q u es t ion A GE A n d w h at is y o u r a g e? ( 18 1 1 0 ) 9 R e fu s ed Q u es t ion C U R FIN W e a r e i n terested in h o w p e op le a r e g etting al on g fi n a n cial l y t h ese d a y s Wo u ld y o u s a y t h at y o u ( a n d y o u r f a m il y l i v ing t h e r e) a re b etter o f f o r w o r s e f i n a n ci a l l y t h a n y o u w e r e a y ear a g o ? 1 B etter o f f 2 Sa m e 3 Wo r s e o f f 8 D o n t K n o w 9 R e f u s ed Q u es t ion FU T FIN N o w lo o k ing a h ead d o y o u t h i n k t h at a y ear f r o m n o w y o u (a n d y o u r f a m i l y living t h e r e) w i l l b e b etter o f f f i n a n c i al l y o r w o r s e o f f o r j u s t a bo u t t h e s a m e as n o w ?
106 1 B etter o f f 2 Sa m e 3 Wo r s e o f f 8 D o n t kn o w 9 R e f u s ed Q u es t ion U S FUFI N o w t u r n ing to b u s i n e s s c o n d itio n s i n t h e c o u n t r y as a w ho le d o y o u t h i n k t h at d u r i n g t h e n e x t 1 2 m on ths w e ll h a v e g oo d ti m es f i n a n cia ll y o r b ad t i m e s o r w h at ? 1 G o o d ti m es 2 G o o d w i t h q u al i f i catio n s 3 U n certai n ; G oo d a n d B ad 4 B ad ti m es 5 B ad w i t h q u al i f i c ati o n s 8 D o n t kn o w 9 R e f u s ed Q u es t ion USN E X5 L oo k i n g a h ea d w h ich w ou ld y o u s a y is m o re li k e l y t h at i n t h e c o un t r y as a w ho le w e ll h a v e c on tin u ou s g oo d t i m e s d u ring t h e n e x t f i v e y ears o r s o o r t h at w e w ill h a v e p e r io d s o f w i d espread un e m p l o ym e n t o r d e p res s io n o r w h at ? 1 G o o d ti m es 2 G o o d w i t h q u al i f i catio n s 3 U n certai n ; G oo d a n d B ad 4 B ad ti m es 5 B ad w i t h q u al i f i c ati o n s 8 D o n t kn o w 9 R e f u s ed Q u es t ion G B T IME A bo u t t h e b ig t h in g s p e o p le bu y f o r t h eir h o m e s -s u c h as fu r n it u re, re f r i g e r at o r s s to v es, tel e v i s i o n s a n d t h i n g s l i k e t h a t. Ge n e r ally s p e a k in g d o y o u t h i n k n o w is a g oo d o r a b a d t i m e f o r p e op le to b u y m a j o r h o u s e h o ld it e m s ? 1 G o o d ti m e 2 U n certain 3 B ad ti m e 8 D o n t kn o w 9 R e f us ed Q u es t ion Z I P C OD
107 T h e n e x t f e w q u e s tio n s a re a bo u t li v ing i n Florida W h at is y o u r Z ip C od e in Florida ( 5 d i g it) ? ( 3 2 00 0 3 5 00 0 ) 8 D o n t K n o w 9 R e fu s ed Q u es t ion SNO WB D Is Florida y o u r u s u a l p lace o f reside n ce? B y u s u al re s i d e n ce I m ean y o u r p r i m a r y reside n ce, o r t h e p lace w h e r e y o u l i v e a n d s leep m o s t o f t h e ti m e. (I N T : RE A D C HO I C E S I F N E C ES S A R Y) 1 Yes 2 No 8 D o n t K n o w 9 R e fu s ed [IF ( A NS = 1 ) SKI P T O P C OUN T ] [IF ( A NS < 1 ) SKI P T O UN L S T ] Q u es t ion F L S NOHS W h at k i n d o f h ou s ing u n it d o y o u l i v e i n w h e n y o u a r e IN Florida? Is it a m ob ile h o m e o r traile r a o n e f a m il y h ou s e d etac h ed f r o m a n y o t h er h o u s e, a o n e f a m il y h o u s e atta c h ed to o n e o r m o re h o u s es o n o n e o r m o re s ides, an a p a r t m e n t b u ild i ng h o t el / m o tel, o r o t h e r (I N T : I F R ES P S A YS ONE F A M I LY 'U N A TT A C H ED YOU MAY S E L E C T ONE F A M I L Y D E T A C HED ). (I N T : RE A D C HO I C ES IF N E C ES S A R Y) 1 M o b ile h o m e o r trailer 2 O n e f a m i l y d etac h ed 3 O n e f a m i l y atta c h ed 4 B u ilding w i t h 2 4 a p a r t m e n ts o r c o n do s 5 B u ilding w i t h 5 o r m o re a p a r t m e n ts o r c o n do s 6 H o tel/ m o tel 7 Va n / R V 8 Bo at 9 Ot h er 8 D o n t k n o w 9 R e fu s ed Q u es t ion F L S NO W N D o y o u o r y o u r f a m i l y o w n t h at h o u s i n g u n it?
108 (I N T : RE A D C HO I C ES IF N E C ES S A R Y) 1 Yes 2 No 8 D o n t k n o w 9 R e fu s ed Q u es t ion P C OUN T SB I n cl u d ing y o u r s e l f h o w m a n y p e op le w h o a r e n o t u s u a l r eside n ts o f Fl o rida a r e c u rre n tly l i v i n g i n t h is h ou s ing un i t? ( 1 20 ) 8 D o n t K n o w 9 R e fu s ed Q u es t ion P C N P E R M A re a n y o f t h e p e op le li v i n g w ith y o u p e r m a n e n t r esid e n ts o f Florida? (t h at i s Florida is t h eir u s u al p lace o f resid e n ce ? ) (I N T : RE A D C HO I C ES IF N E C ES S A R Y) 1 Yes 2 No 8 D o n t K n o w 9 R e fu s ed [IF ( A NS < > 1 ) SKIP T O MON T F L ] Q u es t ion P C N P MNU H o w m a n y ? ( 1 20 ) 8 D o n t K n o w 9 R e fu s ed Q u es t ion MON T FL H o w m a n y m o n ths d o y o u i n t e n d to s p e n d in Florida d u ring y o u r c u rre n t v i s it? (I N T : IF L ESS T H A N ONE MON T H ENTER 0 IF MO R E T H A N 1 1 MON T HS E N T ER 12 ( 0 12 ) 8 D o n t K n o w 9 R e fu s ed
109 [IF ( A NS = 12 ) SKIP T O R ES T Y R ] [IF ( A NS < 0 ) SKI P T O R E S TY R ] Q u es t ion MON T H F L W h ich m o n ths w ill y o u b e in Florida? (I N T : IF R ES P OND E NT KNO W S W HEN T HEY W I LL B E IN F L O R I D A B U T R EFUSES T O A NS W E R EN T ER 9 ) 1 R ES P OND E N T GI V ES L IST OF MON T HS 8 D o n t K n o w 9 R e fu s ed [IF ( A NS < > 1 ) SKIP T O R ES T Y R ] Q u es t ion MON T H F L2 ( W h ich m on ths w ill y o u b e in Florida ? ) (I N T : I F R ES P OND E NT KNO W S W HEN T HEY W I LL B E IN F L O R I D A B U T R EFUSES T O A NS W E R GO B A C K T O P R EVIOUS QUES T ION AND E N T ER 9 J A N U A R Y F E B R UARY M A RC H A P R IL M A Y J UNE J U L Y A UGUST S E P T EMBER O C T O B ER NOV E M B ER DE C EM B ER DON T KNOW NO MO R E Q u es t ion R E S T YR W h at is y o u r p r i m a r y s t a te o r c o un t r y o f resid e n ce d u r i n g t h e m o n t h s y o u a r e n o t li v i n g in Florida? (IN T : RE A D C HO I C ES I F N E C ES S A R Y) (IN T : IF I T S NOT A S T A T E IN T HE U.S. J UST R E A D T HE R EST OF T HE L IST) 1 A la b a m a 2 1 M a r y l a n d 4 1 So u th C a ro li n a 9 R e f u s ed 2 A las k a 2 2 Ma ss ac h u s etts 4 2 So u th D a k o ta 3 A riz o n a 2 3 Mic h i g a n 4 3 T e n n es s ee 4 A r k a n s as 2 4 Mi n n eso t a 4 4 T e x as
110 5 C ali f o r n ia 2 5 Mis s i ss i p p i 4 5 Utah 6 C o lora d o 2 6 Mis s o u ri 4 6 Ve r m o n t 7 C o nn ecti c u t 2 7 M o n ta n a 4 7 Vir g i n ia 8 Del a w a r e 2 8 Ne b ras k a 4 8 W as h in g t o n 9 DC 2 9 Ne v a d a 4 9 W est Vir g i n ia 10 Florida 3 0 N e w H a m p s h ire 5 0 W i s c o n s in 11 Ge o r g ia 3 1 New J e rs e y 5 1 W y o m ing 12 Ha w a i i 3 2 New M e x ico 5 2 C a n a d a 13 I d a h o 3 3 New Y o rk 5 3 C e n tral A m e r ica 14 Illi n o is 3 4 N o rth C a ro li n a 5 4 C a r ib b ean 15 I n d ia n a 3 5 N o rth Da k o ta 5 5 E u r op e 16 I o w a 3 6 O h io 5 6 A s ia 17 Ka ns as 3 7 O k la h o m a 5 7 A f rica 18 Ke n t u c k y 3 8 Ore g o n 5 8 So u th A m e r ica 19 L o u i s ia n a 3 9 P e n n s y l v a n ia 5 9 Ot h er 20 Mai n e 4 0 R h od e I s la n d 8 D o n 't K n o w [IF ( SNO WB D = 2 ) SK I P T O C OUN T Y] Q u es t ion P C OUNT I n cl u d ing y o u r s e l f h o w m a n y p e op le u s u al l y reside at y o u r p lace o f reside n ce? I N T E R VIE W E R IF T HE R ES P OND E NT IS C ONF U SED A B OUT W HO C OUN T S AS A USUAL R ESIDEN T R E A D T HE FOL L O W ING IN I T S E N T I R E T Y. P L E A SE IN C LUDE ALL B O A R D E R S, L ODG E R S, I N F A N T S A N D O T HER F AMI L Y M E M B E R S W HO USUAL L Y R ESIDE HE R E, IN C LUD I NG T HOSE W HO A R E TEM P O R A R I L Y A W AY ON V A C A T ION, A B US I NESS T R I P OR IN T HE H O S P I T A L DO N O T IN C L UDE P E R SO N S W HO A R E J UST VISI T ING ( I.E. S T A YING L E S S T H A N 6 MO N T HS) OR W HO A R E A W A Y IN C O L L EGE, M I L I T A R Y SERV I C E, P R ISON OR A L ONG T E R M NUR S I N G HOME. ( 1 20 ) 8 D o n t K n o w 9 R e fu s ed [IF ( A NS < 2 ) SKI P T O B L D T Y P ] Q u es t ion UND E R 1 8 H o w m a n y o f t h e s e a r e u n d er t h e a g e o f 18 ? ( 0 20 ) 8 D o n t K n o w 9 R e fu s ed Q u es t ion P C O V R 6 5 H o w m a n y o f t h e s e a r e o v er t h e a g e o f 65 ?
111 ( 0 20 ) 8 D o n t K n o w 9 R e fu s ed Q u es t ion B L D T YP H o w w o u ld y o u d escribe t h e b u ild i n g w h e r e y o u a r e livi ng ? Is it a m ob ile h o m e o r traile r a o n e f a m il y h ou s e d etac h ed f r o m a n y o t h er h o u s e, a o n e f a m il y h o u s e atta c h ed to o n e o r m o re h o u s es o n o n e o r m o re s ides, an a p a r t m e n t b u ild i ng o r o t h e r (I N T : I F R ES P S A YS ONE F A M I LY 'U N A TT A C H ED YOU M AY S E L E C T ONE F A M I L Y DE T A C H E D ) 1 M o b ile h o m e o r trailer 2 O n e f a m i l y d etac h ed 3 O n e f a m i l y atta c h ed 4 B u ilding w i t h 2 4 a p a r t m e n ts o r c o n do s 5 B u ilding w i t h 5 o r m o re a p a r t m e n ts o r c o n do s 6 H o tel/ m o tel 7 Va n / R V 8 Bo at 9 Ot h er ( s p ecif y :) 8 D o n t k n o w 9 R e fu s ed Q u es t ion B L D R O Is t h is h o u s e o r a p a r t m e n t o w n ed b y y o u o r s o m e o n e i n t h i s h o u s e h o ld ( w ith o r w i t h o u t a m o r t g a g e) o r re n te d ? (I N T : IF T HIS I S A T I M E S H A R E IT SHOULD B E C OUN T ED AS REN T ED, EVEN IF T HE R ES P OND E NT C ONSIDER T HEMSE L VES A N O W N E R ) 1 O wn ed 2 R e n ted 8 D o n t k n o w 9 R e fu s ed Q u es t ion C OUN T Y In w h at Florida c o un t y d o y o u li v e? 1 A lac h u a 2 2 Gla d es 4 3 Martin 6 4 V o l u s i a 2 B a k er 2 3 G u lf 4 4 M o n r o e 6 5 W a k u lla 3 B ay 2 4 Ha m i lton 4 5 Nas s au 6 6 W alt o n 4 B r a d f o rd 2 5 Ha r d ee 4 6 O k al oo s a 6 7 W ashi n g ton 5 B re v a r d 2 6 He n d ry 4 7 O k eec h ob ee 8 D o n 't K n o w 6 B r o w a r d 2 7 He r n a n d o 4 8 O ra n g e 9 R e f u s ed 7 C al h ou n 2 8 Hi gh l a n d s 4 9 Osce o la 8 C h a r lotte 2 9 Hill s bo r ou g h 5 0 P alm B each
112 9 C itr u s 3 0 H o l m e s 5 1 P asco 10 C lay 3 1 I n d ian R i v er 5 2 P i n ellas 11 C o llier 3 2 J ac k s o n 5 3 Polk 12 C o lu m b ia 3 3 J e ff e r s o n 5 4 P u t n a m 13 Mia m i Da d e 3 4 L a f a y ette 5 5 St. Jo hn s 14 De Soto 3 5 L a k e 5 6 St. Lu cie 15 Di x ie 3 6 L ee 5 7 Sa n ta R o s a 16 D u v al 3 7 L e o n 5 8 Sarasota 17 E s ca m b ia 3 8 L e v y 5 9 Se m i n o le 18 Fla g ler 3 9 L ibe r ty 6 0 S um ter 19 Fra nk lin 4 0 Ma d i s o n 6 1 Su w a nn ee 20 Ga d s d en 4 1 Ma n atee 6 2 T a y lor 21 Gilc h ri s t 4 2 M a rion 6 3 U n ion [IF ( SNO WB D = 2 ) S K I P T O VISI T ] Q u es t ion L IVE F L W h at y ear d id y o u l a s t m o v e to Florida ? 1 A l w a y s l i v ed i n Florida (Nati v e) 2 Mi g ra n t m o v ed h e r e f r o m els e w h e r e 8 D o n t k n o w 9 R e fu s ed [IF ( A NS < > 2 ) SKIP T O C H R IS 4 ] Q u es t ion Y E A R F L R E C O R D Y E A R : ( 190 0 2006 ) 8 D on t K n o w 9 R e f u s ed Q u es t ion B FO RF L F r o m w h i c h s tate o r c ou n t r y d id y o u m o v e? 1 A la b a m a 2 1 M a r y l a n d 4 1 So u th C a ro li n a 9 R e f u s ed 2 A las k a 2 2 Ma ss ac h u s etts 4 2 So u th D a k o ta 3 A riz o n a 2 3 Mic h i g a n 4 3 T e n n es s ee 4 A r k a n s as 2 4 Mi n n eso t a 4 4 T e x as 5 C ali f o r n ia 2 5 Mis s i ss i p p i 4 5 Utah 6 C o lora d o 2 6 Mis s o u ri 4 6 Ve r m o n t 7 C o nn ecti c u t 2 7 M o n ta n a 4 7 Vir g i n ia 8 Del a w a r e 2 8 Ne b ras k a 4 8 W as h in g t o n 9 DC 2 9 Ne v a d a 4 9 W est Vir g i n ia 10 Florida 3 0 N e w H a m p s h ire 5 0 W i s c o n s in 11 Ge o r g ia 3 1 New J e rs e y 5 1 W y o m ing
113 12 Ha w a i i 3 2 New M e x ico 5 2 C a n a d a 13 I d a h o 3 3 New Y o rk 5 3 C e n tral A m e r ica 14 Illi n o is 3 4 N o rth C a ro li n a 5 4 C a r i b b ean 15 I n d ia n a 3 5 N o rth Da k o ta 5 5 E u r op e 16 I o w a 3 6 O h io 5 6 A s ia 17 Ka ns as 3 7 O k la h o m a 5 7 A f rica 18 Ke n t u c k y 3 8 Ore g o n 5 8 So u th A m e r ica 19 L o u i s ia n a 3 9 P e n n s y l v a n ia 5 9 Ot h er 20 Mai n e 4 0 R h od e I s la n d 8 D o n 't K n o w Q u es t ion R E AMOVE W h at w as y o u r p r i m a r y rea s o n f o r m o v ing to Florida ? (I N T : RE A D C H OI C ES IF N E C ES S A R Y) 1 Jo b tra ns f e r rel o catio n n e w j ob b u s i n e s s 2 L oo k i n g f o r a j o b 3 L o w c o s t o f l i v i n g 4 C l i m ate, w eat h er 5 Fa m i l y : m a r ria g e, b e cl o s er to relati v es, m o v ed w it h p a r e n ts 6 Health 7 E n ter/ L ea v e c o lle g e o r s c h oo l 8 Military 9 Po lit i cal r ea s o n s 10 Ot h er 8 D o n t k n o w 9 R e fu s ed Q u es t ion C H R IS4 H o w l i k e l y is it t h at y o u w ill m o v e o u t o f Florida d u ring the n e x t 1 2 m on th s ? 1 Ve r y l i k e l y 2 So m e w h at l i k e l y 3 So m e w h at u n li k el y 4 Ve r y un l i k e l y 8 D on t k n o w 9 R e f u s ed Q u es t ion VISIT In t h e p ast m o n th h a v e y o u h ad a n y o v e r n i g h t v i s itors fr o m ou t s ide t h e s tate? (I N T : RE A D C HO I C ES IF N E C ES S A R Y) 1 Yes
114 2 No 8 D o n t k n o w 9 R e fu s ed [IF ( A NS = 1 ) S KI P T O VIS N UM] [IF ( A NS < > 1 ) S KI P T O P S S 44 ] Q u es t ion VISN U M H o w m a n y o v e r n i g h t v i s itors d id y o u h a v e? ( 1 20 ) 8 D o n t k n o w 9 R e fu s ed Q u es t ion VI S L EN W h at w as t h e l e n g th o f t h eir s t a y ? (I N T : IF MO R E T H A N ONE VISI T O R A SK F OR T HE MOST R E C EN T ) (I N T : RE A D C HO I C ES IF N E C ES S A R Y) 1 L ess t h a n o n e w e e k 2 1 to 2 w ee k s 3 3 to 4 w ee k s 4 1 to 3 m o n t h s 5 3 to 6 m o n t h s 6 M o re t h an 6 m o n t h s 8 D o n t k n o w 9 N o t a v aila b le Q u es t ion U N L ST Is t h is an u n li s ted p h o n e n u m b e r ? (IN T : RE A D C HO I C ES IF N E C ES S A R Y ) (IN T : IF R ES P OND E NT A S KS W HY YOU W ISH T O KNO W T E L L T HEM I T W I L L H E L P US M E A S U R E T HE P O T E N T I A L B I A S O F S A M P L ING M E T HODS W HI C H EX C L U DE UN L IS T ED PH ONE NUM B E R S ) 1 Yes, is u n li s ted 5 N o is n o t u n l i s ted 8 D on t K n o w 9 R e f u s ed Q u es t ion M A RR Y
115 T h e n e x t set o f q u e s tio n s I h a v e w ill h elp u s a n a l y ze y o u r a n s w e r s a l o n g w i t h t h e a n s w e r s o f o t h e r s A re y o u c u rre n t l y m a r rie d s e p a r ate d d i v o rce d w id o wed o r h a v e y o u n e v er b een m a r ried ? ( I N T : RE A D C HO I C E S I F N E C ES S A R Y) 1 N o w m a r ried 2 N o w w id o w ed 3 Ne v er m a r ried 4 Di v o rced o r s e p a r ated 8 D o n t K n o w 9 R e fu s ed Q u es t ion HIS P A N A re y o u o f Spa n i s h o r H i s p a n ic o ri g i n ? (I N T : RE A D C HO I C ES I F NE C ES S A R Y) 1 Yes (Spa n i s h o r Hi sp a n ic) 2 No (N o t Spa n i s h o r Hi s p a n ic) 8 D o n t K n o w 9 R e fu s ed Q u es t ion RR A C E W h at race d o y o u c o n s i d er y o u r s e l f ? (IN T : RE A D C HO I C ES I F N E C ES S A R Y) 1 W h ite ( C a u ca s i a n ) 2 B lack (A f rica n A m e r i ca n ) 3 A s ian o r P aci f ic I s l a nd er 4 A m e r ican I n d ian o r Alas k a n at i v e 5 Ot h er 6 M u lt i racial o r m i x ed race 8 D o n t K n o w 9 R e fu s ed [IF ( A NS < > 5 ) S K I P T O ST B O R N] Q u es t ion A L T R A C E A n d w h at w o u ld t h at b e ? Q u es t ion S T B O R N In w h at s tate o r c o u n t r y w e r e y o u bo rn ?
116 (I N T : RE A D C HO I C E S I F N E C ES S A R Y) (I N T : I F I T S NOT A S T A T E IN T HE U.S. J U S T R E A D T HE R EST OF T HE L IST) 1 A la b a m a 2 1 M a r y l a n d 4 1 So u th C a ro li n a 9 R e f u s ed 2 A las k a 2 2 Ma ss ac h u s etts 4 2 So u th D a k o ta 3 A riz o n a 2 3 Mic h i g a n 4 3 T e n n es s ee 4 A r k a n s as 2 4 Mi n n eso t a 4 4 T e x as 5 C ali f o r n ia 2 5 Mis s i ss i p p i 4 5 Utah 6 C o lora d o 2 6 Mis s o u ri 4 6 Ve r m o n t 7 C o nn ecti c u t 2 7 M o n ta n a 4 7 Vir g i n ia 8 Del a w a r e 2 8 Ne b ras k a 4 8 W as h in g t o n 9 DC 2 9 Ne v a d a 4 9 W est Vir g i n ia 10 Florida 3 0 N e w H a m p s h ire 5 0 W i s c o n s in 11 Ge o r g ia 3 1 New J e rs e y 5 1 W y o m ing 12 Ha w a i i 3 2 New M e x ico 5 2 C a n a d a 13 I d a h o 3 3 New Y o rk 5 3 C e n tral A m e r ica 14 Illi n o is 3 4 N o rth C a ro li n a 5 4 C a r ib b ean 15 I n d ia n a 3 5 N o rth D a k o ta 5 5 E u r op e 16 I o w a 3 6 O h io 5 6 A s ia 17 Ka ns as 3 7 O k la h o m a 5 7 A f rica 18 Ke n t u c k y 3 8 Ore g o n 5 8 S o u th A m e r ica 19 L o u i s ia n a 3 9 P e n n s y l v a n ia 5 9 Ot h er 20 Mai n e 4 0 R h od e I s la n d 8 D o n 't K n o w Q u es t ion ED U C A T W h at is t h e h ig h e s t g ra d e o f s c h oo l o r y ear i n c o lle g e y o u y o u r s e l f c o m p leted ? (IN T : RE A D C HO I C ES I F N E C ES S A R Y) (IN T : IF R ES P S A YS E L E M EN T A R Y ' M I D D L E S C H O OL ' HIGH S C HO O L O R C O L L EG E YOU M A Y PR O B E W I T H T HE R E L EVA N T A NS W ER C HO I C ES I NS T E A D O F EN T I R E L IS T .) 0 N o n e/le s s t h a n 1 st g r a de ....... 1 1 11 th g ra d e 1 1 st g ra d e 1 2 12 th g ra d e/GED/Hi g h s c h oo l d iplo m a 2 2 nd g ra d e 1 3 1 y ear o f c o lle g e 3 3 rd g ra d e 1 4 2 y ears o f c o lle g e / As s o ciate s d e g ree ( A A AS) 4 4 t h g ra d e 1 5 3 y ears o f c o lle g e 5 5 t h g ra d e 1 6 4 y ears o f c o lle g e/ B a c h el o r s d e g ree ( B A B S) 6 6 t h g ra d e 1 7 So m e Gra d u ate S c h oo l 7 7 t h g ra d e 1 8 Gra d u ate/ P r o f es s io n al De g ree : ( Ma s s : M A ; M S, D o ct o rate: P h D; E d D; Me d ici n e/MD; D e n t i s t r y / DD S ; L a w / J D/ JJ /L L B etc ) 8 8 t h g ra d e 8 D o n t kn o w 9 9 th g ra d e 9 R e f u s ed 10 10 th g ra d e Q u es t ion VO T ER A re y o u c u rre n t l y r e g i s tered t o v o te in F l o rida?
117 (IN T : RE A D C HO I C ES I F N E C ES S A R Y) 1 Yes 2 No 8 D o n t K n o w 9 R e fu s ed Q u es t ion P ID Ge n e r ally s p e a k in g d o y o u u s u al l y t h ink o f y o u r s e lf a s a R e p u b lica n a D e m o c r at, an I n d e p e n d e n t, o r w h at? (IN T : RE A D C HO I C ES I F N E C ES S A R Y) 1 R e p u b lican 2 De m o c r at 3 I n d e p e n d e n t 4 Ot h er p a r ty 5 No p re f e r e n ce 8 D o n t k n o w 9 R e fu s ed Q u es t ion EM P L OY A re y o u c u rre n t l y e m p l o y ed o u t s ide t h e h o m e ? (I N T : RE A D C HO I C E S I F N E C ES S A R Y) 1 Yes 2 No 8 D o n t K n o w 9 R e fu s ed [IF ( A NS = 1 ) SKI P T O FU L P R T ] [IF ( A NS < 1 ) SKI P T O HE A L T H 1 ] Q u es t ion HOME B US D o y o u y o u r s e l f op e r ate o r w o r k f o r a b u s i n e s s f r o m yo u r h o m e? (I N T : RE A D C HO I C E S I F N E C ES S A R Y) 1 Yes 2 No 8 D o n t K n o w 9 R e fu s ed [IF ( A NS = 2 ) SKI P T O L OOK W K ] [IF ( A NS < 1 ) SKI P T O HE A L T H 1 ]
118 Q u es t ion F U L P R T A re y o u e m p l o y ed f u l l t i m e o r p a rt t i m e ? (I N T : RE A D C HO I C E S I F N E C ES S A R Y) 1 F u ll t i m e 2 P a r t ti m e 8 D o n t K n o w 9 R e fu s ed [IF ( A NS = 1 ) SKIP T O EM P 1 ] [IF ( A NS < 1 ) SKI P T O HE A L T H 1 ] Q u es t ion P T 1 Wo u ld y o u p re f er to b e e m p l o y ed f u ll t i m e? (I N T : RE A D C HO I C E S I F N E C ES S A R Y) 1 Yes 2 No 8 D o n t K n o w 9 R e f u s ed Q u es t ion EM P 1 A re y o u p rese n t l y w o r k i n g m o re t h an o n e j ob ? (I N T : RE A D C HO I C E S I F N E C ES S A R Y) 1 Yes 2 No 8 D o n t K n o w 9 R e fu s ed [SKIP T O HE A L T H 1 ] Q u es t ion L OOK W K Wo u ld y o u d escribe y o u r s e l f a s U n e m p l o y ed b u t l oo k i n g f o r w o r k ; N o t l oo k i n g f o r w o r k o r R etire d ? (IN T : RE A D C HO I C ES IF N E C ES S A R Y) 1 U n e m p l o y ed b u t l oo k i n g f o r w o rk 2 N o t l oo k i n g 3 R etired 8 D on t K n o w 9 R e f u s ed [IF ( A NS = 3 ) SKI P T O HE A L T H 1 ]
119 Q u es t ion UE1 H o w m a n y w e e k s h a s it b een s i n ce y o u w e r e e m p l o y e d ? (IN T : IF R ESP IS UNSU R E OF T IME IN W EEKS B E C AUSE I T S B EEN YEA R S, YOU M A Y R EM I ND HIM/H E R T H A T 1 YEAR = 5 2 W EEKS.) 1 50 0 ( w e e k s ) 7 Ne v er b een e m p l o y ed 8 D on t K n o w 9 R e f u s ed Q u es t ion UE2 W e re i n terested i n t h e rea s o n s p e op le a r e c u rre n t l y u n e m p l o y e d Wo u ld y o u s a y t h a t 1 Y o u lo s t y o u r j o b (i. e ., laid o ff ; C o m p a n y s h u t d o w n ; te r m i n ate d ) 2 Y o u r te m po ra r y j o b e n d ed ( it w a s s h o rt te r m o r s ea s o n al w o r k ) 3 Y o u le f t t h e j o b v o l u n tari l y 4 Y o u a r e / w e r e d i s a b led 5 Ot h er ( s p ecif y :) 8 D on t K n o w 9 R e f u s ed Q u es t ion H E A L T H1 N o w I w o u ld l i k e to a s k y o u a f e w q u est i o n s a bo u t y o u r h eal t h s ta t u s B eca us e o f a h eal t h c o n d ition t h a t h a s l asted f o r 6 o r m o re m on th s d o y o u h a v e a n y d if f i c u lt y g o i n g o u t s ide t h e h o m e al o n e, f o r e x a m p le, t o s h o p o r v i s it a do ct o r s o f fice? (I N T : RE A D C HO I C E S I F N E C ES S A R Y) 1 Yes 2 No 8 D o n t kn o w 9 R e fu s ed Q u es t ion H E A L T H2 B eca us e o f a h eal t h c o n d ition t h a t h a s las t ed f o r 6 o r m o re m on th s d o y o u h a v e a n y d if f i c u lt y ta k ing care o f p e r s o n al n e e d s s u c h as b at h i ng d res s in g o r g et t i n g a rou n d in s ide t h e h o m e? (I N T : RE A D C HO I C ES IF N E C ES S A R Y) 1 Yes 2 No 8 D o n t kn o w 9 R e fu s ed
120 [IF ( P C OUNT = 1 o r P C OUN T SB = 1 ) SKP HE A L T H 5 ] Q u es t ion H E A L T H3 Ot h er t h an y o u r s e l f d o a n y m e m b e r s o f y o u r h ou s e h o l d h a v e a h eal t h c o n d ition t h at h as la s ted f o r 6 o r m o re m o n t h s t h at m a k es it d i ff i c u lt f o r t h e m to g o o u t s ide t h e h o m e al o n e, f o r e x a m p le, to s h o p o r v i s it a do ct o r s o f fice? (I N T : RE A D C HO I C ES IF N E C ES S A R Y) 1 Yes 2 No 8 D o n t kn o w 9 R e fu s ed Q u es t ion H E A L T H4 Ot h er t h an y o u r s e l f d o a n y m e m b e r s o f y o u r h ou s e h o l d h a v e a h eal t h c o n d ition t h at h as la s ted f o r 6 o r m o re m o n t h s t h at m a k es it d i ff i c u lt f o r t h e m to t a k e care o f p e r s o n al n ee d s s u ch a s b at h i ng d res s i ng o r g ett i n g a ro un d i n s ide t h e h o m e? (I N T : RE A D C HO I C ES IF N E C ES S A R Y) 1 Yes 2 No 8 D o n t k n o w 9 R e fu s ed Q u es t ion H E A L T H5 In g e n e r al, w o u ld y o u s a y y o u r h eal t h is e x cell e n t, v e r y g ood g ood f air, o r p oo r ? (I N T : RE A D C HO I C ES IF N E C ES S A R Y) 1 E x celle n t 2 Ve r y g oo d 3 G o o d 4 Fair 5 Po o r 8 D o n t k n o w 9 R e fu s ed Q u es t ion IN C O M2 N o w c o n s ider y o u r f a m i l y 's h o u s e h o ld i n c o m e f r o m a l l s o u rce s A s I read a li s t, p lease s top m e w h en I g e t to t h e i n c o m e l e v el t h at b est d escribes y o u r h o u s e h o l d i n c o m e in Y YYY ( B e f o re Ta x es)
121 (IN T : P L E A SE R E AD C H OI C ES UN T IL R ES P OND E NT IND I C A T ES A PP R O P R I A T E IN C OME R A NGE.) 1 less t h an $10 00 0 2 $ 10 00 0 t o $ 1 9 9 9 9 3 $ 20 00 0 t o $ 2 9 9 9 9 4 $ 30 00 0 t o $ 3 9 9 9 9 5 $ 40 00 0 t o $ 4 9 9 9 9 6 $ 50 00 0 t o $ 5 9 9 9 9 7 $ 60 00 0 t o $ 7 9 9 9 9 8 $ 80 00 0 t o $ 9 9 9 9 9 9 $ 10 0 0 0 0 to $ 15 0 0 0 0 1 0 O v er $ 150 00 0 8 D o n t K n o w 9 R e fu s ed Q u es t ion R ET A re y o u rece i v ing retir e m e n t i n c o m e? (I N T : RE A D C HO I C E S I F N E C ES S A R Y) 1 Yes 2 No 8 D o n t k n o w 9 R e fu s ed *** *** G OV1 a nd G OV2 r o t a te q u a r t erl y ** ** ** * * Q u es t ion GOV1 O v e r all, d o y o u a pp r o v e o r d i s a pp r o v e o f t h e w a y R ick Sc o tt is h a n d ling h is j o b as G o v e r n o r ? (IN T : RE A D C HO I C ES I F N E C ES S A R Y) 1 A pp r o v e 2 Di s a pp r o v e 3 U ns u re 8 D o n t k n o w 9 R e fu s ed Q u es t ion GOV2 O v e r all, d o y o u a pp r o v e o r d isa pp r o v e o f t h e w a y t h e Florida L e g i s la t u re is h a n d l in g its j ob ? (IN T : RE A D C HO I C ES I F N E C ES S A R Y) 1 A pp r o v e 2 Di s a pp r o v e
122 3 U ns u re 8 D o n t k n o w 9 R e fu s ed Q u es t ion T H A N K YOU T h a n k y o u v e r y m u c h T h at s all I n eed to k n o w P R ESS G T O C ON T INUE DO N O T H I T C T R L / E N T ER OR QU I T !! !!! IF Y O U DO T HIS W I L L NOT B E C OUN T ED A S A C OM P L E T E! !!!
123 LIST OF REFERENCES Ackerson L.K., & Viswanath, K. (2009). The social context of interpersonal communication and health. Journal of Health Communication, 14 (1) 5 17. Administration on Aging. (2011). A profile of older Americans: 201 1. Retrieved from: http://www.aoa.gov/AoARoot/Aging_Statistics/Profile/2011/4.aspx Alliance for Aging Research. (2011). Alliance for aging research home page. Retrieved from: http://www.silverbook.org/browse.php?id=57 American Medical Association. (AMA) (1999). Report on the council of scientific affairs, ad hoc committee on health literacy for the council on scientific a ffairs. Journal of the American Medical Association 281 (6), 552 7. Anderson, G. (2010). Chronic conditions: making the case for ongoing care. Princeton, NJ: Robert Wood Johnson Foundation. Retrieved from: http://www.rwjf.org/content/rwjf/en/research publications/find rwjf research/2010/01/chronic care.html Aspinall E.E., Beschnett, A., & Ellwo od, A.F. (2012). Health literacy for older adults: Using evidence to build a model educational program. Medical Reference Services Quarterly, 31 (3), 302 314. Ayers, S.L., & Kronenfeld, J.J. (2007). Chronic illness and health seeking information on the Internet. health, 11 (3), 327 347. Baker, D.W., Wolf, M.S., Feinglass, J., et al. (2007). Health literacy and mortality among elderly persons. Archives of Internal Medical, 167 (14), 1503 1509. Bandura, A. (1977). Self efficacy: toward a unifying theory o f behavioral change. Psychological Review, 84 191 215. Bandura, A. (1982). Self efficacy mechanisms in human agency. American Psychology, 37 122 147. Bandura, A. (1994). Self efficacy. In V.S. Ramachaudran (Ed.), Encyclopedia of human behavior (pp.71 8 1). New York, NY: Academic Press. Bandura, A. (1997). Self efficacy: The exercise of control New York: W.H. Freeman and Company. Baur, C. (2008). An analysis of factors underlying e health disparities. Cambridge Quarterly of Healthcare Ethics, 17 417 428.
124 Berkman, N.D., Sheridan, S.L., Donahue, K.E., Halpern, D.J., and Crotty, K. (2011). Low health literacy and health outcomes: an updated systematic reviews. Annals of Internal Medicine, 155 (2), 97 107. Blumberg, S.J., Luke, J.V., Ganesh, N., Davern M.E., & Boudreaux, M.H. (2012, October 12). Wireless substitution: State level estimates from the national health interview survey, 2010 2011. Retrieved from: http://www.cdc.gov/nchs/data/nhsr/n hsr061.pdf Blumberg, 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 Bodenheimer, T., Chen, E., & Bennett, H.D. (2009). Confronting the growing burden of chronic disease: can the U.S. health care workforce do the job? Health Affairs, 28 (1), 64 74. doi: 10.1 377/hlthaff.28.1.64 Bond, G.E., Burr, R.L., Wolf, F.M., & Feldt, K. (2010). The effects of a web based intervention on psycholosocial well being among adults aged 60 and older with diabetes. The Diabetes Educator, 36 (3), 446 456. doi: 10.1177/0145721710366758 Boulos, M.N.K. (2012). Using social media for improving health literacy. In: The solid facts Health literacy: Enabling healthier decisions in the 21st century. Copenhagen, Denmark: World Health Organization Regional Office for E urope. Retrieved from: http://www.researchgate.net/publication/230616262_Using_social_media_for_im proving_health_literacy Charlton, G. (2012, November 26). Will mobile Internet replace desktop? Retrieved from: http://econsultancy.com/us/blog/11186 will mobile internet replace desktop infographic Centers for Disease Control and Prevention. (2007). The state of aging and health in America 2007. Retrieved from: http://www.cdc.gov/aging/pdf/saha_2007.pdf Centers for Disease Control and Prevention.(2009). Chronic disease at a glance. Retrieved from: http://www.cdc.gov/chronicdisease/resources/publications/AAG/chronic.htm Centers for Disea se Control and Prevention. (2011). Behavioral risk factor su rveillance system. Prevalence and trends data Retrieved from: http://apps.nccd.cdc.gov/brfss/page.asp?yr=2011&state=UB&cat=HS#HS
125 Centers for Disease Control and media. Retrieved from: http://www.cdc.gov/socialmedia/Tools/guidelines/pdf/GuidetoWritingforSocialMed i a.pdf Chan, C.V., & Kaufman, D.R. (2011). A framework for characterizing eHealth literacy demands and barriers. Journal of Medical Internet Research 13 (4): e94. doi: 10.2196/jmir.1750 Choi, N.G., & DiNitto, D. (2013). The digital divide among low income housebound older adults: Internet use patterns, eHealth literacy, and attitudes toward computer/Internet use. Journal o f Medical Internet Research 15 (5): e93. doi: 10.2196/jmir.2645 Chou, W.S., Hunt, Y.M., Beckjord E.B ., Moser, R.P., & Hesse, B.W. (2009). Social media use in the United States: Implications for health communication. Journal of Medical Internet Research 11 (4): e48. doi:10.2196/jmir.1249 Chu, A., Huber, J., Mastel Smith, B., & Cesario, S. (2009). Partneri ng 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, 97 (1), 12 20. Chu, R.J., & Chu A.Z. (2010). Multi level analysis of peer support, Internet self efficacy and e learning outcomes The contextual effects of collectivism and group potency. Computers & Education, 55, 145 154. Collins, S.A., Currie, L.M., Bakken, S., Vawdrey, D.K., & Stone, P.W. (2012). Health literacy scre ening instruments for eHealth applications: A systematic review. Journal of Biomedical Informatics, 45 598 607. Cotton, S.R. & Gupta, S.S. (2004). Characteristics of online and offline health information seekers and factors that discriminate between th em. Social Science & Medicine, 59 (9), 1795 1806. Couper, M.P., Singer, E., Levin, C.A., et al. (2010). Use of the Internet and ratings of information sources for medical decisions: Results from the DECISIONS survey. Medical Decision Making, 30, 106S. Crabb, R.M., Rafie, S., & Weingardt, K.R. (2012). Health related internet use in older primary care patients. Gerontology, 58 ,164 170. doi:10.1159/000329340 Czaja, S.J., Charness, N., Fisk, A.D., et al. (2006). Factors predicting the use of technology: Fi ndings from the center for research and education on aging and technology enhancement. Psychology and Aging, 21 333 352.
126 Czaja, S.J., Sharit, J., Nair, S.N. (2008). Usability of the Medicare health web site. JAMA 300 (7), 790 792. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13(3), 319 339. De Vol, R., & Bedroussian, A. (2007, October). An unhealthy America: The economic burden of chronic disease. Retrieved from : http://www.milkeninstitute.org/pdf/ES_ResearchFindings.pdf Dredze, M. (2012). How social media will change public health. Intelligent System, IEEE, 27 (4), 81 84. Duggan, M., & Brenne r, J. The demographics of social media users 2012. Retrieved from: http://pewinternet.org/~/media/Files/Reports/2013/PIP_SocialMediaUsers.pdf Eachus, P., & Cassidy, S. Development of the web users self efficacy scale (WUSE). Issues in Informing Science and Information Technology, 3 199 209. Elkin, N. (2008, January 14). How America searches: Health and wellness. Retrieved from: http://www.icrossing.com/sites/default/files/how america searches health and wellness.pdf Eysenbach, G. (2001). What is e health? Journal of Medical Internet Research, 3 (2), e20. doi:10.2196/jmir.3.2.e20 Eysenbach, G. (2008). Medicine 2.0: Social networking, collaboration, participation, apomediation, and openness. Journal of Medical Internet Research, 10 (3), e22. doi:10.2196/jmir.1030 Federman, A.D., Sano, M., Wolf, M.S., Siu, A.L., & Halm, E.A. (2009). Health literacy and cognitive performance in older adults. The American Geriatrics Society, 57 (8), 1475 1480. Field, A. (2009). Discovering statistics using SPSS. Thousand Oaks, CA: Sage Publications, Inc. Fox, S. (2007, Octo ber 8). E patients with a disability or chronic disease Retrieved from: http://www.pewinternet.org/Reports/2007/Epatients With a Disability or Chronic Disease.aspx Fox, S., & Duggan, M. (2013). Health online 2013 Retrieved from: http://pewinternet.org/~/media//Files/Reports/2013/Pew%20Internet%20Health% 20Online%20report.pdf
127 Fox, S. & Jones, S. (2009). The social life of health information Retrieved from: http://www.pewinternet.org/~/media//Files/Reports/2009/PIP_Health_2009.pdf Fox, S., & Purcell, K. (2010, March 24). Chronic disease and the Internet Retrieved from: http://web.pewinternet.org/~/media/Files/Reports/2010/PIP_Chronic_Disease_wit h_topline.pdf Fox, S. (2012). Pew Internet: Health Retrieved from http://pewinternet.org/Commentary/2011/November/Pew Internet Health.aspx Fuscaldo, D. (2011, August 9). More consumers turn to social media for health care information. Retrieved from http://www.foxbusiness.com/personal finance/2011/08/09/more consumers tu rn to social media for health care information/ Gibbons, M.C., Fleisher, L., Slamon, R.E., Bass, S., Kandadai, V., & Beck, J.R. (2011). Exploring the potential of web 2.0 to address health disparities. Journal of Health Communication, 16 77 89. doi:10.10 80/10810730.2011.596916 Gilbert, N.H. (2000). Techno savvy boomers dictate services. Provider, 26 (1), 55 56. Gottlieb, R., & Rogers, J.L. (2004). Readability of health sites on the Internet. The International Electronic Journal of Health Education 7, 38 42. Habel M.A., Liddon, N., Stryker, J.E. (2009). The HPV vaccine: a content analysis of online news stories. (3), 401 407. Hall, A.K., Stellefson, M., & Bernhardt, J.M. (2011). Healthy aging 2.0: the potential of new medi a and technology. CDC Preventing Chronic Disease, 9 ,1 4. Hanik, B., & Stellefson, M. (2011). E health literacy competencies among undergraduate health education students: A preliminary study. International Electronic Journal of Health Education, 14,46 58. Healthy People 2020. (2010). Health communication and health information technology objectives Retrieved from: http://www.healthypeople.gov/2020/topicsob jectives2020/objectiveslist.aspx?topi cId=18 Holden, R.J. & Karsh, B. (2010). The technology acceptance model: its past and its future in health care. Journal of Biomedical Information, 43 (1), 159. Doi:10.1016/j.jbi.2009.07.002 Hou, S. (2010). Health literacy, eHealth, and communication: putting the consumer first. Health Promotion Practice, 11 (3), 303 305. doi:10.1177/1524839909358658
128 Institute of Medicine. (2004). Health literacy: A prescription to end confusion. Washington DC: National Ac ademies Press. Retrieved from: http://books.nap.edu/openbook.php?record_id=10883 Institute of Medicine. (2009 ). Health literacy eHealth, and communication. Washington DC: National Academi es Press. Retrieved from: http://books.nap.edu/openbook.php?record_id=12474&page=R1 competencies o f information age students: results from the interactive online research readiness self assessment (RRSA). Journal of Medical Internet Research, 8 (2), e6. doi:10.2196/jmir.8.2.e6 Jones, R., & Goldsmith, L. (2009). What is the evidence for the benefits and outcomes for digital health services? Final Report to NHS Choices Plymouth : University of Plymouth: 2009. Jones, S., & Fox, S. (2009, January 28). Generations online in 2009 Retrieved from: http://www.pewinternet.org/Reports/2009/Generations Online in 2009.aspx http://www.itsyoursexlife.com/about iysl/ Knapp, C., Madden, V., Wang, H., Sloyer, P., & Shenkman, E. (2011). Internet use and eHealth literacy of low income parents whose children have special health care needs. Journal of Medical Internet Research, 13 (3), e75. doi:10.2196/jmir.1697 Kontos, E.Z., Emmons, K.M., Puleo, E., & Viswanath, K. (2010). Communication inequalities and public health implications of adult social networking site use in the United States. Journal of Health Communication,15 (3),216 235. Kreps, G.L., & Neuhauser, L. (2010). New directions in eHealth communication: Opportunities and challenges. Patient Education and Counseling, 68 329 336. Kukafka, R., Johnson, S.B., Linfante, A., & Allegrante, J.P. (2003). Grounding a new information technology implementation framework in behavioral science: a systematic analysis of the literature on IT use. Journal of Biomedical Informatics, 36 218 227. Kutner, M., Greenberg, E., Jin, Y., & Paulsen, C. (2006). The health literacy of Retrieved from: http://nces.ed.gov/pubs2006/2006483.pdf Lederer, A.L., Maupin, D.J., Sena, M.P., & Zhuang, Y. (2000). The technology acceptance model and the world wide web. Decision Support Systems, 29 269 282.
129 Long, T., Taubenheim, A.M., Wayman, J., Temple, S., & Yu, E. (2010). Using social media to reach women with the heart truth 2009 update. Ca ses in Public Health Communication & Marketing, 4 55 68. Madden, M. (2010, August 27). Older adults and social media Retrieved from: http://pewinternet.org/Reports/2010/Older Adults and Social Media.aspx Madden, M, & Zickuhr, K. (2011, August 26). 65% of online adults use social networking sites Retrieved from: http://pewinternet.org/Reports/2011/Social Networking Sites.aspx Manafo, E., & Wong, S. (2012 ). Assessing the eHealth literacy skills of older adults: A preliminary study Journal of Consumer Health on the Internet ,1 6 (4), 369 381 Martinez Torres, M.R., Toral Marin, S.L., Barrero Garcia, F., et al. (2008). A technological acceptance of e learning tools used in practical and laboratory teaching, according to the European higher education area. Behaviour and Information Technology, 27 (6), 495 505. McAlister, A.L., Perry, C.L., & Parcel, G.S. (2008). How individuals, environments, and health behaviors interact. In K. Glanz, B.K. Rimer, & K. Viswanath (Eds.), Health behavior and health education (pp. 168 188). San Francisco, CA: Josse y Bass. Miller, L.M., & Bell, R.A. (2012). Online health information seeking: the influence of age, information, trustworthiness, and search challenges. Journal of Aging and Health, 24 (3), 525 541. doi:10.1177/0898264311428167 National Institutes of Health. (2012). Health literacy. Retrieved from: http://www.nih.gov/clearcommunication/healthliteracy.htm Nayak, L.U.S., Priest, L., & White, A.P. (2010). An application of the technol ogy acceptance model to the level of Internet usage by older adults. Universal Access in the Information Society, 9 367 374. Neter, E., & Brainin E. (2012). eHealth literacy: Extending the digital divide to the realm of health information. Journal of Me dical Internet Research, 14 (1), e19. doi:10.2196/jmir.1619 Nielsen Company. State of the media: The social media report 2012. Retrieved from: http://www.nielsen.com/content/dam/corporate/us/en/reports downloads/2012 Reports/The Social Media Report 2012.pdf
130 Nijland, N., van Germert Pijnen, J., Kelders, S.M., Brandenburg, B.J., & Seydel, E.R. (2011). Factors influencing the use of web based application for supporting the self care of patients with type 2 diabetes: A longitudinal study. Journal of Medical Internet Research, 13 (3), e71. doi:10.2196/jmir.1603 Norman, C.D., & Skinner, H.A. (2006 a ). eHea lth literacy: Essential skills for consumer health in a networked workl Journal of Medical Internet Research, 8 (4), e27. doi:10.2196/jmir.8.4.e27 Norman, C.D., & Skinner, H.A. (2006 b ). eHEALS: the eHealth literacy scale. Journal of Medical Internet Resea rch, 8 (4), e27. doi:10.2196/jmir.8.4.e27 Pallant, J. (2010). SPSS survival m anual. A step by step guide to data analysi s using SPSS. New York: McGraw Hill. for health advertising and promotion. Journal of Interactive Advertising, 12 (1): 62 77. Partnership to Fight Chronic Disease (2009). Almanac of chronic disease 2009. Retrieved from: http://www.fightchronicdisease.org/sites/fightchronicdisease.org/files/docs/2009Al manacofChronicDisease_updated81009.pdf Pew Internet & American Life Project (2011). Web 2.0 Retrieved from: http://www.pewinternet.org/topics/Web 20.aspx?typeFilter=5 Powell, J., Inglis, N., Ronnie, J., & Large, S. (2011). The characteristics and motivations of online health information s eekers: Cross sectional survey and qualitative interview study. Journal of Medical Internet Research, 13 (1), e20. doi:10.2196/jmir.1600 Pulman, A. (2010). A patient centred framework for improving LTC quality of life through Web 2.0 technology. Health Inf ormatics Journal, 16 (1), 15 23. Rice, R.E. (2006). Influences, usage, and outcomes of Internet health information searching: Multivariate results from the Pew surveys. International Journal of Medical Informatics, 75 8 28. Richardson, C.R., Buis L.R., Janney, A., et al. (2010). An online community improves adherence in an Internet mediated walking program. Part 1: Results of a randomized controlled trial. Journal of Medical Internet Research, 1 2 (4), e71. doi:10.2196/jmir.1338 Rideout, V., Neuma n, T., Kitchman, M., & Brodie, M. (2005). e health and the elderly: how seniors use the Internet for health information ( Report No.7223). Retrieved from the Henry J. Kaiser Family Foundation:
131 http://www.kff.org/entmedia/upload/e Health and the Elderly How Seniors Use the internet for Health Information Key Findi ngs From a National Survey of Older Americans Survey Report.pdf Sheaves, B., Jones, R.B., Williamson, G.R., & Chauhan, R. (2011). Phase 1 pilot study of e mail support for people with long term conditions using the Internet. BMC Medical Informatics and Decision Making, 11 (20), 1 10. Sinden, D., & Wister, A.V. (2008). E health promotion for aging baby boomers in North America. Gerontechnology, 7 (3), 271 278. Smarr, K.L., Musser, D.R., Shigaki, C.L., Johnson, R., Hanson, K.D., & Siva, C. (2011). Online self management in rheumatoid arthritis: a patient centered model application. Telemedicine and e Health, 17 (2), 104 110. doi:10.1089/tmj.2010.0116 Solomon, M., Wagner, S.L., & Goes, J. (2012). Effects of a web based intervention for adults with chronic conditions on patient activation: online randomized controlled trial. Journal of Medical Internet Research, 14 (1), e32. doi:10.2196/jmir.1924 Stellefson, M., Haink, B., Chaney, B., Chaney, D., T ennant, B., & Chavarria, E.A. (2011). eHealth literacy among college students: a systematic review with implications for eHealth education. Journal of Medical Internet Research, 13 (4), e102. doi: 10.2196/jmir.1703 Strauss, W., & Howe, N. (1992). Generations: The history of future, 1584 to 2069. New York, NY: William Morrow and Company, Inc. Sue, V.M. (2012). The use of kp.org and self efficacy to manage chronic conditions. Kaiser Foundation Health Plan, Inc. Retrieved from: http://xnet.kp.org/newscenter/pressreleases/nat/2012/downloads/Self Efficacy_and_KpOrg_Use.pdf Taha, J., Sharit, J., Czaja, S. (2009). Use of and sa tisfaction with sources of health information among older internet users and nonusers. The Gerontologist, 49 (5), 663 673. Thompson, B. (2008). Foundations of behavioral statistics. New York, NY: The Guilford Press. Tinetti, M.E., Fried, T.R., & Boyd, C.M. (2012). Designing health care for the most common chronic condition multimorbidity. Journal of American Medical Association, 307 (23), 2493 2494.
132 U.S. Census Bureau. (2011). 2010 Census Demographic Profile Data. Retrieved from: http://edr.state.fl.us/Content/population demographics/2010 census/data/index.cfm U.S. Census Bureau. (2012) Household income for states: 2010 and 2011. Retriev ed from: http://www.census.gov/prod/2012pubs/acsbr11 02.pdf Van der Bijl, J. J., & Shortridge Baggett, L. M. (2002). The theory and measurement of the self efficacy construct. In E. A. Lentz & L. M. Shortridge Baggett (Eds.), Self efficacy in nursing: Research and measurement perspectives (pp. 9 28). New York: Springer. van der Vaart, R., van Deursen, A., Drossaert C., et al. (2011). Does the eheatlh literacy scale (eHEALS) measure what it intends to measure? Validation of a Dutch version of the eHEALS in two adult populations. Journal of Medical Internet Research, 13 (4 ), e86 doi: 10.2196/jmir. 1840 van der Vaart R., Drossaert, C., de Heus, M., Taal, E., & van de Laar, M. (2013). Measuring actual eHealth literacy among patients with rheumatic diseases: a qualitative analysis of problems encountered using health 1.0 and health 2.0 applications. Journal of Medical Internet Research, 15 (2 ), e27 doi: 10.2196/jmir. 2428 van Deursen A., & van Dijk J. (2011). Internet skills performance tests: are people ready for eHealth? Journal of Medical Internet Research, 13 (2), e35. doi: 10.2196/jmir.1581 van Deursen A., van Dijk J & Peters, O (2011). Rethinking Internet skills: The contribution of gender, age, education, Internet experience, and hours online to medium and content related Internet skills. Poetics, 39, 125 144. Vance, K., Howe, W., & Dellavalle, R.P. (2009). Social internet sites as a source of public health information. Dermatologic Clinics, 27 (2), 133 136. Viswanath, K, Ramanadhan S.R., & Kontos, E.Z. (2007). Mass media and population health: a macrosocial view. In S.E. Galea (ed.) Macroscioal determinants of population health (pp. 275 294). New York: Springer Wagner, T.H., Baker, L.C., Bundorf, K., & Singer, S. (2004). Use of the internet for health information by the chronically ill. Preventing Chronic Disease,1 (4). Wangberg, S.C., Andreassen, H.K., Prokosch, H., et al. (2007). Relations between Internet use, socio economic status (SES), social support and subjective health. Health Promotion International, 23 (1), 70 77.
133 ngs of the national assessment of adult literacy (NAAL). Retrieved from: http://www.ama assn.org/ama1/pub/upload/mm/367/hl_report_2008.pdf Woodgate, J., Brawley, L.R., & Shields, C.A. (2007). Social support in cardiac rehabilitation exercise maintenance: associations with self efficacy and health related quality of life. Journal of Applied Social Psychology, 37 (5), 1041 1059. World Health Organiz ation (WHO). (2012). Defin ition of an older or elderly person: Proposed working definition of an o lder person in Africa for the MDS project. Retrieved from: http://www.who.int/healthinfo/survey/ageingdefnolder/en/index.html Wright, D.W., & Hill, T.J. (2009). Prescription for troub le: Medicare part D and patterns of computer and Internet access among the elderly. Journal of Aging & Social Policy, 21 172 186. Xie, B. (2008 ). O lder adults health information, and the Internet Interactions,15 (4), 44 46. Xie, B. (2011). Effects of a n eHealth literacy intervention for older adults. Journal of Medical Internet Research, 13 (4), e90. doi: 10.2196/jmir.1880 Zajac, I.T., Flight, I.H.K., Wilson, C., Turnbull, D., Cole, S., & Young, G. (2012). Internet usage and openness to internet delivered health information among Australian adults aged over 50 years. Australasian Medical Journal, 5 (5),262 267. Zickuhr K. (2010, December 16 ). Generations 2010. Retrieved from http://pewinternet.org/~/media//Files/Reports/2010/PIP_Generations_and_Tech1 0.pdf Zickuhr, K., & Madden, M. (2012, June 6). Older adults and internet use Retrieved from http://www.pewinternet.org/~/media//Files/Reports/2012/PIP_Older_adults_and_i nternet_use.pdf Zulman, D.M., Kirch, M., Zheng, K., & An L.C. (2011). Trust in the Internet as a health resource among older adults: Analysis of data from a nationally representative survey. Journal of Medical Internet Research, 13 (1), e19 doi: 10.2196/jmir.1552
134 BIOGRAPHICAL SKETCH Bethany Lynn Tennant was born and raised in Gainesville, FL. She earned her dministration from the University of North Carolina Chapel Hill in 2002. Upon graduating, she moved to the East Bay region of n orthern California and worked in the legal field for several years. In 2008, she moved back to Gainesville, FL to pursue her graduate degree in health e ducation at the University of Florida. t at GatorWell Health Promotion Services at the University of Florida. She received her ehavior in the Spring of 2010 and a certificate in Public Health in the Summer of 2011, both from the University of Florida. In the Fall 2010, Bethany began the Ph.D. program in Health and Human Performance at the University of Florida. While progressing through the doctoral program, she worked as a research and teaching assistant in the College of Health and Human Performance an d as a graduate assistant in the Clinical and Translation al Science Institute at the University of Florida. Her dissertation research involves investigating eHealth literacy and use of social media for health information among older adults with chronic dis ease ( s ) Bethany will be granted a Doctor of Philosophy in Health and Human P erformance with an emphasis in health education and b ehavior in August 2013.