ASSESSING THE IMPACT OF DEPRESSIVE SYMPTOMS ON THE INTENTION TO USE mHEALTH INTERVENTIONS AMONG PEOPLE LIVING WITH HIV AND PARTICIPATING IN THE FLORIDA HEALTH COHORT By CÃ‰SAR GABRIEL ESCOBAR VIERA A DISSERTATION PRESENTED T O THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2015
Â© 2015 CÃ©sar Gabriel Escobar Viera
I dedicate this dissertation to my parents , Ana and Reinaldo Escobar, my husband , Esteban Paniagua Laconich , and the memory of my grandmother, Ermelinda Viera Colombo . This four year journey would have not been possible without your love, encouragement, and unwaveri ng support.
4 ACKNOWLEDGMENTS I want to express my eternal gratitude to my parents, Ana and Reinaldo Escobar. They encouraged me to pursue my doctorate degree and provided constant confidence and support throughout this process. I owe my deepest thanks you to my husband , Esteban Paniagua Laconich , for his loyal commitment to our dream. This research would not be made possible without the support of my distinguished committee members. Dr. Jeffrey Harman, I could not have asked for a better mentor. Thank you for allowing me the freedom to pursue my own research interests while still providing timely input and feedback to help me attain my goals. Dr. Robert Cook, thank you for sharing my interest in mobile health technologies and for your instrumental suppo rt in conducting this work. My thanks also go to Dr. Allyson Hall and Dr. Paul Duncan, for helping me through the conceptualization phase of this project and for being a critical part of my six years as student at the University of Florida. My appreciati on also goes to Dr. Christa Cook and Dr. Mary Ellen Young , for their timely and valuable guidance throughout the qualitative component of this research. I thank my research assistants, Ashley Force and Corinne Gallet de St Aurin, who worked tirelessly coll ecting data with me during the focus group sessions. I would also like to thank my fellow graduate students and friends, both in the U.S. and in my home country, Paraguay, for their love, support, and patience with me during times of hardship. Finally, I want to extend my sincere and respectful gratitude to the participants of the qualitative stage of this work. Thank you for sharing your life experiences and knowledge with us, it is my hope that these serve as foundation of bigger and better research in t he quest of improving the quality of life of other people living with HIV.
5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ ........ 10 LIST OF ABBREVIATIONS ................................ ................................ ........................... 11 ABSTRACT ................................ ................................ ................................ ................... 13 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 15 Specific Aims ................................ ................................ ................................ .......... 17 Significance ................................ ................................ ................................ ............ 17 2 BACKGROUND AND LITERATURE REVIEW ................................ ....................... 22 mHealth: Fundamentals, Evidence, and Challenges ................................ .............. 22 Mobile Applications for Healthcare: Fundamentals ................................ .......... 22 Short Message Services (SMS) for Healthcare and HIV: Empirical Evidence and Challenges ................................ ................................ ............................. 25 Mobile Applications for Healthcare and HIV: empirical evidence and challenges. ................................ ................................ ................................ .... 28 Mobile apps for HIV prevention and care ................................ ................... 29 Mobile apps for depression ................................ ................................ ........ 36 Depression and HIV: Epidemiology, Comorbidity, and Consequences on PLWH .. 40 Summary ................................ ................................ ................................ ................ 42 3 CONCEPTUAL FRAMEWORK AND HYPOTHESE S ................................ ............. 44 Theoretical Foundations ................................ ................................ ......................... 44 Theory of Reasoned Action / Planned Behavior ................................ ............... 44 Technology Acceptance Model: Three Iterations ................................ ............. 46 The Behavioral Model of Health Care Use ................................ ....................... 52 Behavioral Framework for the Use of mHealth Applications ................................ ... 53 Hypothesis ................................ ................................ ................................ .............. 56 4 DATA AND METHODS ................................ ................................ ........................... 59 Study Design ................................ ................................ ................................ .......... 59 Data Sources ................................ ................................ ................................ .......... 59 Secondary Data: Florida Health Cohort Survey ................................ ...................... 60
6 Measures of Data ................................ ................................ ................................ ... 62 Outcome Variable ................................ ................................ ............................. 62 Independent Variable ................................ ................................ ....................... 63 Covariates ................................ ................................ ................................ ........ 66 Demographic characteristics ................................ ................................ ...... 66 Health status ................................ ................................ .............................. 67 Enabling variables ................................ ................................ ...................... 67 Statistical Analyses ................................ ................................ .......................... 69 Sample size ................................ ................................ ............................... 70 Aim I ................................ ................................ ................................ ........... 70 Aim II ................................ ................................ ................................ .......... 71 Primary Data: Small Focus Groups with Participants of the Florida Health Cohort ................................ ................................ ................................ .................. 71 5 RESULTS ................................ ................................ ................................ ............... 77 Quantitative Analyses ................................ ................................ ............................. 77 Descriptive Statistics ................................ ................................ ........................ 77 Bivariate statistics ................................ ................................ ...................... 80 Multivariate statistics ................................ ................................ .................. 8 1 Multivariate analysi s using depressive symptoms as a three level categorical variable ................................ ................................ ................. 83 Multivariate analysis using depressive symptoms as a binary variable ...... 84 Qualitative Analysis ................................ ................................ ................................ . 85 Focus Group Demographics ................................ ................................ ............. 85 Main Themes Emerged during Focus Groups ................................ .................. 86 6 DISCUSSION AND CONCLUSIONS ................................ ................................ .... 144 Discussion ................................ ................................ ................................ ............ 144 Limitations ................................ ................................ ................................ ............. 147 Implications and future research ................................ ................................ ........... 148 APPEN DIX A THE FLORIDA HEALTH COHORT ENTRY SU RVEY ................................ .......... 150 B PRELIMINARY FOCUS GROUP GUIDE ................................ .............................. 194 C DEMOGRAPHIC QUESTIONNAIRE PREVIOUS TO FOCUS GROUP SESSIONS ................................ ................................ ................................ ........... 196 LIST OF REFERENCES ................................ ................................ ............................. 199 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 213
7 LIST OF TABLES Table page 2 1 Main characteristics of different types of mobile applications ............................. 24 4 1 List of Variables ................................ ................................ ................................ .. 68 5 1 Characteristics of participants of the Florida Health Cohort (N=310) ................ 108 5 2 Depressive symptoms among participants of the Florida Health Cohort (N=310) ................................ ................................ ................................ ............ 110 5 3 Characteristics of participants of the Florida Health Cohort by PHQ 8 depressive symptom level ................................ ................................ ................ 111 5 4 Indicators of intention to use mHealth applications among participants of the Florida Health Cohort (N=310) ................................ ................................ ......... 113 5 5 Indicators of intention to use mHealth applications among participants of the Florida Health Cohort by severity of depressive symptoms according the Patient Health Question naire, PHQ 8 (N=310) ................................ ................. 114 5 6 Characteristics of participants by intention to use a phone app to "identify health services r elevant to you" ................................ ................................ ........ 115 5 7 Characteristics of participants by intention to use a phone app to "track changes in your mood and e motions" ................................ ............................... 117 5 8 Characteristics of participants by intention to use a phone app to provide tips to "improve your health, b ased on information about you" ................................ 119 5 9 Characteristics of participants by i ntention to use a phone app to "track and manage alcohol and drug use behavior" ................................ .......................... 121 5 10 Characteristics of participants by intention to use a phone app to "communicate with your doctor or clinic" ................................ .......................... 123 5 11 Characteristics of participants by intent ion to use a phone app to "remember to take your medication" ................................ ................................ ................... 125 5 12 Characteristics of participants by intention to use a phone app to "engage in social networking with other people who live with HIV" ................................ .... 127 5 13 Association between presence of depressive symptoms, population characteristics and intention to use a phone app to "identify health services relevant to you" ................................ ................................ ................................ . 129
8 5 14 Association between presence of depressive symptoms, population characteristics and intention to use a phone app to "track changes in your mood and emotions" ................................ ................................ ......................... 130 5 15 Association between presence of depressive symptoms, population characteristics and intention to use a phone app to "improve your health, based on information about you" ................................ ................................ ...... 131 5 16 Association between presence of depressive symptoms, population characteristics and int ention to use a phone app to "track and manage alcohol and drug use behavior" ................................ ................................ ........ 132 5 17 Association between presence of depre ssive symptoms, population characteristics and intention to use a phone app to "communicate with your doctor or clinic" ................................ ................................ ................................ . 133 5 18 Association between presence of depressive symptoms, population characteristics and intention to use a phone app to "remember to take your medication" ................................ ................................ ................................ ....... 134 5 19 Association between presence of depressive symptoms, population characteristics and intention to use a phone app to "engage in social networking with other people who live with HIV" ................................ .............. 135 5 20 Association between severity of depressive symptoms, population cha racteristics and intention to use a phone app to "identify health services relevant to you" ................................ ................................ ................................ . 136 5 21 Association between s everity of depressive symptoms, population characteristics and intention to use a phone app to "track changes in your mood and emotions" ................................ ................................ ......................... 137 5 22 Association between severity of depressive symptoms, population characteristics and intention to use a phone app to "improve your health, based on information about you" ................................ ................................ ...... 138 5 23 Association between severity of depressive symptoms, population characteristics and intention to use a phone app to "track and manage alcohol and drug use behavior" ................................ ................................ ........ 139 5 24 Association between severity of depressive symptoms, population characteristics and intention to use a phone app to "com municate with your doctor or clinic" ................................ ................................ ................................ . 140 5 25 Association between severity of depressive symptoms, population characteristi cs and intention to use a phone app to "remember to take your medication" ................................ ................................ ................................ ....... 141
9 5 26 Association between severity of depressive symptoms, population characteristics and intention to use a phone app to "engage in social networking with other people who live with HIV" ................................ .............. 142 5 27 Characteristics of focus group participants by presence of depression risk according to the Patient Health Questionnaire, PHQ 2 (N=24) ........................ 143
10 LIST OF FIGURES Fig ure page 3 1 Elements of Theory of Reasoned Action ................................ ............................ 46 3 2 The Technology Acceptance Model, 1989 ................................ ......................... 47 3 3 The Technology Acceptance Model, Version 2, 2000 ................................ ........ 48 3 4 The Unified Theory of Acceptance and Use of Technology, 2003 ...................... 50 3 5 Behavioral Model of Access to Medical Care, 1995 ................................ ........... 52 3 6 Behavioral Framework for the Use of mHealth Applications, 2015 ..................... 53 3 7 A Conceptual Model to Assess the Intention to Use mHealth among PLWH, 2015 ................................ ................................ ................................ ................... 56
11 LIST OF ABBREVIATIONS ADAP AIDS ART CBT CDC CHAMPUS EMA FDA FHC FL GPS HIPAA HIV I RB MHealth MSM PHQ 8 PHQ 9 PTSD RCT SF 8 SMART SMS TAM AIDS Drug Assistance Program Acquired Immunodeficiency Syndrome Antiretroviral Therapy Cognitive Behavioral Therapy Centers of Disease Control Civilian Health and Medical Program of the U niformed Services Ecologic Momentary Assessment Food and Drug Administration Florida Health Cohort Florida Global Positioning System Health Insurance Portability and accountability Act of 1996 Human Immunodeficiency Virus Institutional Review Board Mobile Health Males who Have Sex with Males Patient Health Questionnaire 8 Patient Health Questionnaire 9 Post traumatic Stress Disorder Randomized Controlled Trial Short Form 8 Sequential Multiple Assignment Randomized Trial Short Message Service Technology Acc eptance Model
12 TAM 2 TPB TR A Technology Acceptance Model 2 Theory of Planned Behavior Theory of Reasoned Action UD US UTAUT Unipolar Depression United States Unified Theory of Acceptance and Use of Technology VA Veterans Administration
13 Abstract of Dissertat ion Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ASSESSING THE IMPACT OF DEPRESSIVE SYMPTOMS ON THE INTENTION TO USE mHEALTH INTERVENTIONS A MONG PEOPLE LIVING WITH HIV AND PARTICIPATING IN THE FLORIDA HEALTH COHORT By CÃ©sar Gabriel Escobar Viera December 2015 Chair: Jeffrey S. Harman Major: Health Services Research High rates of depression and anxiety have been consistently found associate d with poor treatment adherence and outcomes among pe ople living with HIV/AIDS (PLWH ) and this is worrisome when taking into account that previous research has found that only 19% of PLWH in the US currently meet the criteria for suppressed virus. Given th e pervasiveness of mobile devices in the last few years (smartphones, cell phones, tablets, wearable devices), as well as their decreasing cost to the public, mHealth (mobile health) has been proposed as an intervention to improve outcomes of chronic disea ses that rely on medical regimens or health behavior modification. Despite limited availability of rigorous evaluation methods and scarce evidence regarding efficacy, the number of apps available in the marketplace with either health, fitness, or medical purposes have recently increased to a staggering 97,000 and counting. However, few US based research have focused on developing mHealth interventions to improve care of pe ople living with HIV (PLWH ) so far. Of these, the majority have targeted either on im proving ART treatment adherence, infection prevention, and comorbidity with substance abuse .
14 The main goal of this investigation was to serve as starting point in the development process of a mobile application that will help to manage HIV related conditio ns among PLWH by: a) identify ing individual characteristics associated with the intention to use mHealth to manage HIV related mental health conditions, b) determin ing its relationship with screening positive for depression and anxiety and 3) assess ing bar riers and facilit ators to use mHealth among PLWH .
15 CHAPTER 1 INTRODUCTION Mobile health (mHealth) can be broadly defined as the use of mobile technologies, such as smartphones, flip phones, tablets, or wearable devices, both at individual and population level, that support prevention of health problems, monitoring or management of chronic conditions, provision of personalized and on demand health interventions, reduction of healthcare visits, and strengthen provider knowledge (Kumar et al. ., 2013). Studi es from mid 2013 estimated that 91% of adults living in the U.S. owned a cellular phone, and over 61% indicated they owned a smartphone (Albrecht & von Jan, 2014; Link et al. ., 2014). Also, 67% of cell phone owners check their phone for messages, alerts, or calls, even when they notice their device did not ring or vibrate and almost 45% sleep with their phone next to them because they do not want to miss any calls, text messages, or alerts (Pew Research Center, 2014) . Given this pervasiveness over the las t few years, as well as their decreasing cost to the public, smartphones and mHealth applications, which will be defined and reviewed in other section of this proposal, have been found to be potentially useful to improve the outcomes of chronic conditions that rely on medical regimens or health behavior modification (Kirk, Himelhoch, Westergaard, & Beckwith, 2013). Unipolar depression (UD) is the single most prevalent chronic psychiatric disorder associated with HIV infection (Arseniou, Arvaniti, & Samakou ri, 2014). Prevalence of UD ranges from 20 to 30% of people living with HIV (PLWH), but some investigators have reported rates of up to 80% of depressive symptoms. High rates of depression were found to be consistently associated with both poor treatment a
16 living with PLWH. This is worrisome when taking into account that previous research has found that currently only 19% of PLWH in the US meet the criteria for suppressed also found to be associated with an increase in risky sexual behaviors among people living with HIV . Despite limited availability of rigorous evaluation methods and scarce evidence regarding effectiveness, the number of apps available in the marketplace with either health, fitness, or medical purposes have recently increased to a staggering number of 9 7,000 earlier this year, most of them with poor, if any empirical research support. Also, of the limited U.S. based research that have focused on developing mHealth applications to improve health care of PLWH, results have shown modest levels of short term efficacy, especially with text messages, but we still have not found a way to extend these benefits in the long term. Moreover, research findings have consistently shown that PLWH report high levels of depression and suicidal behavior as well as increased rates of unmet mental health needs, concerns about confidentiality and less likelihood to use mental health services. Thus, we need to develop novel strategies that manage day to day variation of HIV related menta l health conditions such as depression, that are acceptable to them and also feasible to implement. mHealth applications might be a part of such strategies. Nevertheless, in order to tailor design, development, features, and resources, we need to know what characteristics predict intention to use mHealth applications among
17 PLWH. Also, we need to better understand the impact of depression, if any, on intention to use mHealth applications among this population. Specific Aims The specific aims of this st udy are to: 1. Examine the association between socio demographic characteristics of PLWH with the intention to use mHealth applications to manage HIV related conditions and its significance 2. Determine the relationship between depressive symptoms and the intent ion to use mHealth applications to manage HIV related conditions among PLWH 3. Engage PLWH in qualitative assessments to identify barriers and facilitators to use mHealth applications, as well as preferred components for a mobile application to help manage HI V related conditions. Significance This research will address a gap in academic literature by assessing the relationship between individuals living with HIV w ho screen positive for depressive symptoms and their intention to use mobile health applications to manage HIV related conditions. In spite of the accelerated pace with which mobile applications for a number of health conditions have been developed and marketed over the last few years, we still know very little about their efficacy and effectiveness. However, there is a growing body of efficacy trials that showed promise in management of chronic conditions like diabetes, obesity, and cardiovascular disease (Stephens & Allen, 2013). In the field of mobile health for mental health there has also been a s mall number of feasibility and efficacy trials involving mobile applications to manage specific mental disorders like depression, anxiety, eating disorders, bipolar disorder, and schizophrenia (Ben Zeev, Brenner, et al. ., 2014; Gustafson et al. ., 2014; Rei d et al. ., 2009). In addition, our knowledge about how characteristics of specific populations such as people living with HIV might impact their use of mobile health interventions is still very limited.
18 Another aspect in need of research is how to improv e adherence to mobile health applications themselves. Previous research has found that, confused by the perplexing diversity of options, consumer and clinicians have had a hard time discerning safety and effectiveness of apps, thus being inconsistent in us ing apps or avoiding them altogether (Mohr, Burns, Schueller, Clarke, & Klinkman, 2013). In addition, even though data from larger trials are still needed for mHealth, previous research has made consistent note of attrition rates for other electronic healt h resources, such as web based interventions (eHealth) either because of non use of the intervention or poor retention to follow up (Murray et al. ., 2013). Moreover, a recent systematic reviews has raised questions regarding whether effectiveness of mHealt h interventions is influenced by participant demographics and other individual level characteristics, as well as what kind of mobile application functions are the most effective (Free, Phillips, Galli, et al. ., 2013). This study aims to be a significant co ntributi on to the literature as it expand s our knowledge about how demographic and clinical characteristics (unipolar depression) of PLWH related conditions, which in tu rn can be used to drive the development process of said application. Despite the progress we made with antiretroviral therapy (ART) since the nineties, PLWH with depression continue to have worse health outcomes than PLWH without depression. Therefore, if we develop mobile health applications that are less likely to be used by individuals with depression within this population, we will only exacerbate the difference in outcomes between PLWH with and without depression. The significance of this research can be summarized in the following: first, this study will use data coming from the Florida Cohort to Monitor and Improve Health Outcomes (to
19 Florida has the second highest pre valence rate of HIV infection in the country and considering the diverse population make up of the state, this data will provide a valuable insight of ethnic minorities among PLWH. A handful of previous studies have focused on understanding how to use mobi le health applications to improve prevention of HIV infection among racial and ethnic minorities (Brown et al. ., 2013; Muessig et al. ., 2013). However, these studies did not include PLWH in their samples, and despite of recognizing the role that depression may play in increasing the risky sexual behavior, neither included participants with depressive symptoms. There is a need to expand the input source to include persons living with HIV in order to gather more accurate information that can help further deve lopment of mobile health technologies. The Florida Cohort Survey allows access to this type of information, providing the unique opportunity to explore the impact of depression and other conditions frequently associated with HIV on the intention to use mob ile health technologies among a cohort of PLWH. Secondly, this study asks a question that has not been asked before: what is the impact, if any, of the occurrence of depressive symptoms on the intention to use a mobile application that might help to manage HIV associated conditions among PLWH? This question is raised because of the high comorbidity rates of depression among PLWH and the significant impact of different severity levels of depression on behaviors, such as risky sexual behavior, lack of interes t, and even the impact on cognitive impairment (Arseniou et al. ., 2014; Dal BÃ³ et al. ., 2013). Also, given the high rates of people living with HIV who have a less than optimal treatment adherence, as well as the
20 relationship between beh avior and treatment adherence, we need to ask ourselves hoe can we tailor and modify the design of these mobile applications in order to boost their use, and thus improve treatment adherence, and ultimately health outcomes. This becomes even more important if this study find s indeed an effect of depressive symptoms on intent to use. Further, to our knowledge there is only one previous study t hat incorporate d participants living with HIV into qualitative assessments tha t helped to identify preferences for mobile health applica tions for self monitoring and self management (Ramanathan, Swendeman, Comulada, Estrin, & Rotheram Borus, 2013) . While Muessig, Pike, Fowler et al. (2013) conducted a survey and a round of focus groups among black males who have sex with other males (MSM) in order to gather information for developing a mobile app, participants were not PLWH. In fact, as shown in the following literature review, a majority of studies that collected data from PLWH did it by sampling mostly from MSM rather than both genders. T his study will use a larger data source and all participants are individuals living with HIV. Finally, in order to develop a conceptual model this research will combine elements of the Unified Theory of Acceptance and Use of Technology (UTAUT), a major th new technology (Venkatesh, Morris, Davis, & Davis, 2003) with elements of the Behavioral Model of Access to Medical Care (R. M. Andersen, 1995). Even though both models t ry to explain behaviors, the main outcome in UTAUT is the use of a particular technology, while for the Andersen model one of the main outcome is use of medical services. Most of the currently available literature on mHealth lacks an accepted
21 conceptual or theoretical framework to explain the relationship between demographic or clinical characteristics of the potential user and the intention to use a mobile health application. By combining these two widely accepted frameworks this research will test whether need factors like depression have a particular impact in the intention to use mobile technology.
22 CHAPTER 2 BACKGROUND AND LITERATURE REVIEW mHealth: Fundamentals, Evidence, and Challenges mHealth started to develop just over a decade ago as a comp onent of what is initial definitions of mHealth were only a bit more narrowed, specify ing that those electronic processes and communication had to take place through mobile technologies, first with regular cell phones (e.g. flip phones), and more recently since 2007, with the use of smartphones (e.g. Blackberry, iPhone, Android phones), whi ch are mobile phones with more sophisticated computer and connectivity capabilities than regular mobile phones (Nusca, 2009). However, an additional aspect of mHealth definitions is that from its inception, mobile health technologies were presented as a po tential alternative to improve public health outcomes, given the ever increasing market penetration of mobile phones (Free, Phillips, Watson, et al. ., 2013). Mobile Applications for Healthcare: F undamentals Mobile applications (apps) are self contained p rograms or pieces of software that perform specific functions and are built for specific mobile platforms (e.g. iOS, Android). Mobile apps control functions and features such as the camera, GPS, accelerometer, microphone, calendar, as well as others (Chris tensson, 2009). Apps have become widely popular; most of the time people currently spend in digital media is spent on apps, as opposed to desktop browsers or mobile browsers. Moreover, over one third of smartphone users download between one and eight apps each month (Lella & Lipsman, 2014). The same authors noted that mobile platform app audience
23 breakouts by age to be as follows: 16 20% are persons of 18 24 years of age, 23% are 25 34, 20 22% are 35 44, 16 19% are 45 54, and 20 12% are 55 64 years old. These findings suggest an evenly distributed app audience by age categories. There are different types of mobile applications, and their differences lie not only on the technical aspects of each one, but also in the scope of applicability and the functi ons available (Budiu, 2013; Muessig et al. ., 2013; Summerfield, 2013). Table 2 1 illustrates the main characteristics of each type of app. Native apps have access to the full variety of phone features (e.g. accelerometer, advanced geolocation services) in a way none of the remaining app types can; these are primarily developed for a specific smartphone platform (e.g. iOS for iPhones, Android, Blackberry), and in order to be to develop but do not require Wi Fi access to be able to work, they can only be downloaded from the app stores (e.g. iTunes or Google Play), and in order to do so a third party approval (usually the app store itself) is required. Medical and health apps th at claim to have diagnostic, or treatment capabilities will have to go through the U.S. Food and Drug Administration (FDA) approval process as well (Cortez, Cohen, & Kesselheim, 2014). Mobile web apps are the cheapest to develop but they are not real apps, they are simply web sites with a quick access button on the phone home screen; the use of HTML5 (a computer language) has helped to make mobile web apps look like real apps, but with very poor access to all phone features; they are good for providing info rmation, however most web apps cannot work without Wi Fi access. Most app being pushed out of the app stores in favor of native and hybrid apps. Finally, hybrid
24 apps hav e an intermediate cost between the first two, they are also developed using HTML5 which gives them the advantage of being compatible across platforms and devices. Hybrid apps may not grant access to all phone features as native apps do, and several of thei r functions need Wi Fi access to be able to work. Table 2 1. Main characteristics of different types of mobile applications Characteristic Native App Mobile Web App Hybrid Use of device features +++ + ++ Compatibility + + Wi Fi requirement + + Avai lability in app stores +++ + ++ 3 rd party approval Yes No Yes Development cost $$$ $ $$ Mobile applications potential is also underscored by the high rate of smartphones market penetration. Indeed, as of January 2014, over 60% of adults owned a s martphone, with no significant differences between gender, and an ever decreasing gap in terms of race/ethnicity (Pew Research Center, 2014). However, certain demographic differences in adoption remain, as smartphone users were younger, more educated, with higher income, and lived either in urban or suburban areas. Moreover, according to the Pew Research Center, 81% of adults use their smartphone to receive or send text messages, 60% to access the internet, 52% to send or receive emails, 50% to download app s, and over 49% to get directions, recommendations, or other location based information. Only 21% used their phone to participate in a video call or video chat (2014).
25 Sh ort Message Services (SMS) for H ealthcare and HIV: E m pirical Evidence and C hallenges The first type of mobile health intervention was the short message service (SMS), also called text message (Fiordelli, Diviani, & Schulz, 2013). Reminders of medical appointments, reminders for medication taking, were among the first kind of text message s ervices tested and implemented. Evidence of efficacy and effectiveness of SMS interventions started to appear around 2008, with an important number of controlled observational studies and a less significant group of randomized controlled trials (Fiordelli et al. ., 2013). Mobile health interventions based on SMS rapidly multiplied not only in developed countries but also in developing nations (Bastawrous & Armstrong, 2013; Guy et al. ., 2012). Likewise, effectiveness evaluations, in the form of meta analyses or systematic reviews started to be conducted on the evidence of available studies. In a systematic review, Kannisto, Koivunen and VÃ¤limÃ¤ki (2014) reviewed the use of mobile text messaging in health care services. Out of 60 studies included in their final sample, they found about one third (21/60) were randomized controlled trials; the remaining papers included pre post tests, clinical trials, cross sectional studies, cohorts, and some qualitative investigations. Among the groups of participants, patients w ith chronic conditions such as PLWH (9/60, 15%) were the most frequent, followed by diabetes, asthma and schizophrenia. The majority of the reminders were related to improving medication adherence (38/60, 63%), followed by reminders of coming medical appoi ntments, either to increase attendance or to reduce non attendance to visits (22/60, 37%).
26 In the same review, an important 77% (46/60) reported improved outcomes, and the highest improvement was reported for treatment adherence, followed by improvements in both attendance and non attendance. Other improved outcomes included attitude of patients toward medication, treatment interruption, and medication missing doses. However, an important drawback reported was the fading overall effect of prompting once th e intervention ceased. Moreover, privacy concerns were expressed by patients who feared losing their cell phones and other people gaining access to their messages. Kannisto, Koivunen and VÃ¤limÃ¤ki (2014) concluded that rates of improved outcomes were enough to recommend the continued use of SMS in health care services. However, they noted the relative small number of RCTs, the lack of focus on studies. The effectiveness o f SMS to improve treatment adherence in chronic conditions, and more specifically treatment adherence to antiretroviral therapy (ART) against HIV was also reviewed. A review of SMS effectiveness to improve adherence in chronic conditions included a final s ample of 13 studies, either randomized controlled trials or controlled clinical trials (Marcia Vervloet et al. ., 2012). Included studies covered a number of chronic conditions: asthma, HIV infection, glaucoma, and hypertension. Only one of the studies had a follow up longer than six months. Eight studies reported an these improvements were reported in studies that included both HIV patients and with hypertension. The author s concluded that reminders sent via SMS showed evidence of improving short time treatment adherence for chronic conditions. Again, concerns
27 remained about two facts: first, only one of the studies looked at effects after six months of implementation, provi ng that more long term evaluation is needed, and also, all 13 studies kept automatically sending reminders to patients regardless of whether they were treatment adherent. They feared that in these cases, SMS reminders might become a routine resulting in cr eating habit but not improving adherence. This brings to mind one of the key advantages of mobile applications, which is the expanded possibility to tailor interventions to patient specific characteristics. Other systematic review and meta analysis specifi cally looked at the effectiveness of SMS reminders at increasing patient attendance to clinical visits (Guy et al. ., 2012). They included a variety of studies conducted both in developed and developing countries, in primary care as well as outpatient clini cs, and with design both observational and experimental. Their findings support the conclusion that SMS reminders increase the odds of attendance to clinical visits by 50% compared to no appointment reminders. With a smaller population in mind, two other reviews looked at the impact of SMS reminders in treatment adherence among PLWH. The first one included randomized controlled trials that tested the effect of mobile phone text messaging to promote adherence to ART among PLWH (T. Horvath, Azman, Kennedy, & Rutherford, 2012). Only 2 studies satisfied the inclusion criteria and they were conducted in Kenya, a developing country. Both studies measured ART adherence, one of them at six and twelve months, while the other did it every twelve week period over the course of forty eight weeks. The authors concluded that the reviewed trials provided good evidence of the efficacy of weekly SMS reminders for improving ART adherence among PLWH. Their concerns included the lack of more quality data from other RCTs and the scarce
28 number of studies conducted in developed countries. Moreover, the ability to assess the effectiveness of SMS reminders in real world settings is limited due to the lack of observational studies in this population. Finally, Finitsis, Pellowski, and Johnson (2014) also conducted a meta analysis of randomized controlled trials to assess whether SMS reminders have any efficacy improving ART adherence among HIV patients. Eight RCT were included and they were conducted in different settings: U.S., Brazil, and Cameroon. The main result of the meta analysis was that, indeed, text message interventions help to improve HIV treatment adherence. Authors noticed that interventions had worked better when bidirectional communication was supported and when intervent ions were personalized to the patient. Finally, one clinical trial assessed the effect of personalized SMS text messages on ART treatment adherence among 20 PLWH who were methamphetamine users (Moore et al. ., 2013). While the control group only received te xt messages about methamphetamine use, the intervention group received both those messages as well as ART adherence messages. Both groups received text messages every day for a 30 day trial period. In addition to find the intervention satisfactory, accepta ble and helpful, almost 82% of the intervention group endorsed taking their ART medication. However, their sample consisted mostly of White, middle aged, college educated, male participants. Mobile Applications for H ealthcare and HIV: empirical evidence a nd challenges. In a way to make it relevant to this study, the literature around mobile apps for healthcare will be presented in two subsections: mobile apps for HIV prevention and care and mobile apps for depression.
29 Mobile a pps for HIV prevention and ca re To discuss the literature in this area, the following outline will be followed: unmet needs will be presented followed by descriptive studies that assessed attitudes, preferences, and acceptability of potential interventions. These will be followed by a review of clinical trials that used either enhanced text messages or actual mobile applications. Finally the available experimental studies will be described. Along with public health concerns related to improving treatment adherence conducive to viral suppression among PLWH, another main driver for researchers, organizations, and industry to keep pushing for innovative interventions to reach out to HIV patients, are the consistent findings of a number of unmet needs for either supportive or treatment ca re in this population. Studies conducted in urban and suburban areas of the U.S. have found that most PLWH with at least one unmet need for services were more likely to be Latino/Hispanic, white, male, 30 years of age and older, and gay/bisexual (Holtgrave et al. ., 2014; Wohl et al. ., 2011; Young, Sullivan, Bogart, Koegel, & Kanouse, 2005). Lack of information (47%), agency barriers (33%), financial/practical barrier (18%), and difficulty to access to housing programs as well as mental health services were among the highest unmet needs. According to these studies, African American men, those who made less than $10,000, and gay/bisexual men were more likely to report at least one unmet need. Published papers focusing on acceptability, preferences, or feasibi lity of mHealth interventions among population at risk of risky sexual behavior and HIV infection (e.g. MSM, adolescents, young adults) are somewhat frequent in the literature. In their systematic review of a 10 year period of mHealth research for HIV trea tment and prevention, Catalani, Philbrick, Fraser, Mechael, and Israelski (2013), found two studies
30 that conducted surveys among MSM looking at the feasibility of collecting behavioral assessment data on risk factors for HIV using mobile devices (cell phon es and PDAs). Both studies found that data collection was effective and informative. They also reported twelve studies that looked at either the reliability, acceptability and/or feasibility of implementing the use of cell phones or PDAs in order to provid e education videos, collect behavioral data, receive reminders, and counseling for smoking cessation. All studies reported that participants found the interventions to be feasible and acceptable. A dditionally, another online survey assessed acceptability o f a smartphone application based HIV prevention among 195 young MSM recruited via a social et al. ., 2013). Over 80% of participants reported willingness to participate in a smartphone app bas ed HIV prevention program. Authors did not analyze association of any of the demographic or smartphone utilization variables with the willingness to participate. All in all, only three of these studies were conducted in North America and with participants who were PLWH. Also, while these three studies explored acceptability of an intervention, they did not investigate personal preferences or user attitudes toward intervention characteristics (Catalani et al. ., 2013; Shacham, Stamm, & Overton, 2009). One m ore cross sectional survey is of interest for this research because it specifically targeted PLWH. Miller and Himelhoch (2013a) conducted a survey about the acceptability of mobile phone technology to improve ART medication adherence. A hundred consecutive HIV patients at an urban clinic were recruited to take the survey. Most participants were middle age, male, African American, over 70% had high school education or less, and income less than $10,000. Interestingly, 96% reported owning
31 cell phone. Of those who owned cell phones, 48% owned a second phone as well, and 25% reported owning a smartphone. Among the main reasons participants gave for using their phones were to call or receive calls (92%), take pictures or video (64%), send or receive text messages (59%), s et al. arms (47%), and access the internet (34%). Although authors reported asking about using phones to download apps, the results are not reported. Almost 70% of respondents reported they were likely to use their cell phone for reminders to take also more likely to send or receive text messages, more likely to use their phone seven days a week, and more likely to use phone alarms. Although this study over sampled African American participants, resp ondents of Hispanic/Latino ethnicity were not as well represented. The importance of group tailoring and personalization of mobile health interventions was highlighted in previous paragraphs related to text messages interventions (Guy et al. ., 2012; T. Ho rvath et al. ., 2012; M. Vervloet et al. ., 2012; Marcia Vervloet et al. ., 2012). Qualitative assessments often provide rich information that, when complemented with observational methods, is able to inform tailored design of health interventions (Creswell, 2007) . In 2012, Muessig, Pike, Fowler, Legrand (2013), et al. conducted a series of focus groups with twenty two Black men who have sex with men with the objective of informing the development of a mobile phone based HIV intervention. Several specific them es emerged through the interviews: men said access the internet. They also discussed ways in which men use their mobile phones to communicate, contents they would like to have in an app and features for HIV related
32 apps, and preferences and confidentiality concerns. Phones were used as a fundamental way to socialize, looking up information, and going through daily life activities. Apps were widely used for a wide range of p urposes. Phones were found to increasingly replace laptops for these activities. Content preferences included information about sexually transmitted diseases and HIV testing places, drug and alcohol use, safer sex, sexuality and relationships, resources to find gay friendly providers, and support groups and groups for HIV positive men. Most participants showed little concern about the privacy of storing health information on their cell phones. Findings of this study clearly underline the importance of gath ering quality data from intended users of a potential mHealth application. However, by not included Hispanic, or not specifically targeting PLWH, some important theme s may not have been discussed. Other study reported findings from focus groups with PLWH r egarding preferences for mHealth applications for self monitoring and self management (Ramanathan et al. ., 2013) . Reminders, surveys, goal monitoring, were the most accepted features. Privacy protection was a main concern and data sharing was acceptable as long as sensitive data was protected in some way. More recently the focus of studies has moved to clinical trials that investigate attitudes and preferences of participants and/or test the feasibility of more sophisticated interventions using smartphone s. One of the approaches used for this purpose is called ecological momentary assessment (EMA). EMA is a behavioral assessment strategy that asks respondents to participate in self reported questionnaires, in real time, real world environments, several tim es a day. EMA decreases the likelihood of participant
33 bias to occur (e.g. recall bias, desirability bias) (Granholm, Ben Zeev, Fulford, & Swendsen, 2013; Kirk, Linas, et al. ., 2013). Smartphones and apps can take advantage of this strategy in unique and co nvenient ways, such as active (e.g. surveys) or passive (e.g. tracking location) data collection from the user (Cohn, Hunter Reel, Hagman, & Mitchell, 2011; Nilsen et al. ., 2012). A recently published clinical trial tested the effect of educational videos and ecological momentary assessment on HIV risk reduction education delivered through smartphones to a group of 26 individuals under treatment for substance abuse (Phillips et al. ., 2013). The intervention was associated with higher scores in HIV risk re duction questionnaire, and it was found acceptable by participants, even when compared to other mediums. Researchers did not find significant changes over time after the intervention, but thought this might be caused by the short length of the intervention itself. A more extensive clinical trial in which ecologic momentary assessment (EMA) was included, looked at the implementation, feasibility, and acceptability of real time data collection in a cohort of substance users who had comorbidity with HIV (Kirk, Linas, et al. ., 2013). For example, when an emotional stress situation would occur, the user could complete a questionnaire about the trigger and his/her current emotions and feelings, and get appropriate feedback or suggestions to cope with the si tuation. This study conducted a series of four trials of four weeks each over a period of five years. Each trial consisted of a group of 30 participants who reported recent use of heroin and/or cocaine. Trials 3 and 4 added HIV infection as inclusion crite ria. Participants were handed PDAs for trials 1 3 but these were switched to smartphones for trial 4. The data collection measures and instruments remained the same. Participants were
34 delivered prompts five times a day assessing current location, activity, social environment, and humor. A total of 109 participants were included in the analyses. One participant characteristic important to this study is that, in each group, between 18 29% reported either moderate or severe symptoms of depression. Across all f our trials most participants were African American, male, middle aged, with less than high school education, uninsured, with income less than $5,000. Feasibility measures showed a consistent decrease of answer to prompts, going from 81% in trial one to 70% in trial 4. None of the demographic factors, except education level, were associated with an increase response to EMA prompts. Because of changes in inclusion criteria for trials 3 and 4, and the resulting small sub sample size, authors were not able to a ssess whether any of the associated factors (e.g. depression) were related to the lower number of responses in trials 3 and 4. In terms of good quality evidence, the Institute of Medicine has helped to establish a set of standards research communities shou ld follow for developing interventions to be considered efficacious and effective, and worthy of dissemination (Tomlinson, Rotheram Borus, Swartz, & Tsai, 2013). These include: two high quality efficacy trials (e.g. RCT or other randomized interventions), two high quality effectiveness trials (e.g. quasi experimental interventions in real life conditions), and dissemination research that accounts for a faithfully replicable delivery of the intervention. In the last few years mHealth researchers have argued that for a number of reasons related to the technology itself, the rapid change and update of mobile devices, and the constant software actualizations (which sometimes are out of the hands of researchers), it is not likely for mHealth research to fulfill t he requirements of
35 traditional randomized controlled trials (Collins, Murphy, & Strecher, 2007; Kumar et al. ., 2013; Nilsen et al. ., 2012). These investigators propose alternative designs, such as the interrupted time series, or the sequential multiple ass ignment randomized trial (SMART). Nevertheless, research literature testing mHealth interventions for HIV prevention or treatment is notoriously scarce. Indeed, during the period 2001 2013 only seven RCTs that evaluated the impact of SMS text messages on ART treatment adherence among PLWH were conducted and their results were published (da Costa et al. ., 2012; Hardy et al. ., 2011; Lester et al. ., 2010; Mbuagbaw et al. ., 2012; Pop Eleches et al. ., 2011; Safren, Hendriksen, Desousa, Boswell, & Mayer, 2003; Simoni et al. ., 2009). Three of these were focused on unidirectional reminders via SMS text messages and did not use smartphones nor mobile apps (da Costa et al. ., 2012; Pop Eleches et al. ., 2011; Safren et al. ., 2003). The remaining four studies looked at the impact of bidirectional SMS text messages between patient and provider (Hardy et al. ., 2011; Lester et al. ., 2010; Mbuagbaw et al. ., 2012), and only one evaluated the impact of peer support by testing bidirectional SMS text messages between participa nts (Simoni et al. ., 2009). All trials found a significant improvement in ART adherence in treatment groups. Notwithstanding that a number of smartphone features and capabilities could prove useful to improve treatment adherence, few randomized trial have tested these alternatives so far. In a recently published paper, researchers randomized 28 patients on ART treatment to two versions of the same smartphone app: a standard version with usual text reminders and an augmented version that contained visual imagery providing real time information about plasma level of ART medication. After three months of trial,
36 patients who received the augmented version of the app had a statistically significant increase in medication adherence and a decrease in viral load (Perera, Thomas, Moore, Faasse, & Petrie, 2014). Even so, a number of capabilities remain to explore. For example, cameras could be used to take picture of pills right before taking them, in order to keep track of treatment adherence. Location services, i f enabled, could trigger on time on the spot suggestions or reminders to the user in case the device detects a location where a sexual encounter might be expected (e.g. clubs, bathhouses, or sex clubs). Video calls might serve to immediate contact for supp ort, either from peers or health workers in situations of emotional distress. Also, as we have already seen with EMA, smartphones allow real time data collection. Evaluation research that looks at these potential interventions will need not only careful st udy design, but also a meticulous collection of data about potential user preferences and user characteristics, if any, that make individuals more likely to engage and use a mobile application with enhanced capabilities. Mobile a pps for depression To di scuss the literature related to mobile applications for depression, a similar outline to the previous section will be followed. First, we will present descriptive studies that assessed attitudes, preferences, and acceptability of potential interventions. T hese will be followed by a review of clinical trials that used either enhanced text messages or actual mobile applications. Finally, the available experimental studies will be revised. Literature on mHealth and mental and/or substance abuse disorders can be found for a number of conditions. A good number of mobile applications are being developed, tested, and in some cases their implementation is being evaluated, in order
37 to improve health outcomes for disorders such as PTSD (Kuhn et al. ., 2014), alcoholis m (Gustafson et al. ., 2014), bipolar disorder (P. A. Prociow & Crowe, 2010; P. Prociow, Wac, & Crowe, 2012), anxiety (Burns, Montague, & Mohr, 2013a), depression (Burns et al. ., 2011; Burns, Montague, & Mohr, 2013b; Harrison et al. ., 2011; Kauer et al. ., 2 012; Ly, Carlbring, & Andersson, 2012; Meglic et al. ., 2010; Reid et al. ., 2009, 2011, 2012, 2013) , and schizophrenia (Ben Zeev, 2012; Ben Zeev, Brenner, et al. ., 2014; Ben Zeev, Kaiser, et al. ., 2013; Ben Zeev, Schueller, et al. ., 2014; Ben Zeev, Davis, Kaiser, Krzsos, & Drake, 2013a, 2013b; Ben Zeev, Frounfelker, Morris, & Corrigan, 2012; Ben Zeev, McHugo, Xie, Dobbins, & Young, 2012; Depp et al. ., 2010; Granholm et al. ., 2013; Palmier Claus et al. ., 2013). Because of its high comorbidity among PLWH (Ars eniou et al. ., 2014), the focus of this section will be on mHealth for depression. A multi phase needs assessment was conducted in 2009 in Australia with the objective to investigate community attitudes toward the use of mobile phones for monitoring and m anaging of depression (Proudfoot et al. ., 2010). Using a mixed methods approach, researchers conducted online surveys, focus groups, and interviews. All interventions assessed current mobile phone use and attitudes toward using mobile phones for mental hea lth interventions. Survey participants (n= 525) reported using their phones mostly for making and receiving calls, sending and receiving SMS, go online (either for music downloading, log on Facebook, video downloading or streaming, and accessing email), ca mera, alerts, and alarms. Similar findings were reported at focus group participants and interviewees (n= 67). About 76% of survey participants reported to be highly or moderately interested in using a mobile phone for a program to manage their mood. Resea rchers found an association with
38 presence of depressive symptoms and more likelihood of being interested in the mHealth option. However, a particular finding among this subgroup is worthy of mention. Of the 45 respondents with current symptoms of depressio n, a significant proportion (68.9%) were not interested in a mobile phone intervention, and they indicated not believing that using a mobile phone to manage moods or track their symptoms could help to improve their symptoms. Focus groups and interviews mir rored survey results. The authors discussed the need to more in depth study to better understand the apparent lack of understanding about the benefits of self monitoring to manage and improve depression outcomes. A cross sectional survey among psychiatri c patients attending outpatient clinics assessed smartphone ownership and interest in mobile apps to monitor symptoms (Torous, Friedman, & Keshavan, 2014). Researchers reported that 64% (n= 100) of their participants were female, 76% were younger than 60 y ears of age. 97% of respondents declared owning a mobile phone, and 72% owned a smartphone. Those younger than 45 years old had the highest number of installed apps (20 30), healthcare apps (0 1), and number of downloaded apps in the last month (2 3.5). Ov erall, about 60% of participants younger than 60 reported interest in receiving text messages with health content, being able to access healthcare information on their phone, and willingness to download and use an app to track mental health conditions, eve n on a daily basis. Of note, more patients were interested in using a mobile app than text messages. Authors proposed this finding could indicate a perception of less third party monitoring and more self empowerment.
39 Overall, four clinical trials that tes ted feasibility of mHealth interventions for depression have their results published. In the first trial, researchers aimed at pilot adolescents of 14 17 years of ag e from mid and high school levels in an urban area in Australia were invited to participate; of those, 29 (20 females and 9 males) obtained parental consent and participated (Reid et al. ., 2009). The intervention comprised an ecological momentary assessmen t methodology assessing current mood, current activity, stress level, and alcohol and cannabis use over the course of 7 days. While only 72.5% of those who were invited ended up participating, findings showed the intervention was acceptable and feasible to participants. In Slovenia, Meglic, Furlan, Kusmanic, Kozel et al. (2010) conducted a pilot test of an intervention that delivered cognitive behavioral therapy (CBT) both via web based (on laptops) and mobile phones for depression care, over a 6 month pil ot. A total of 46 adult patients in treatment for depression agreed to participate (40 females and 6 male). Over 80% of participants found the intervention acceptable. However, 33% reported drawbacks, mainly due to usability issues. A similar study of feas ibility was conducted in Australia (Harrison et al. ., week period among 47 adult patients with mild moderate depression. Results in terms of acceptability were similar to the pr evious study. However, a remarkable 36 % were lost to follow up. Authors recognized a low adherence problem with the intervention. Some users mentioned lack of motivation to use or complete application, and authors mentioned that whether this was due to me ntal health status, personality characteristics,
40 assessment based mobile application to improve depression outcomes. Eight adult patients (7 female and 1 male) with unipolar depr ession (UD) were enrolled to pilot for eight weeks and test the feasibility and patient satisfaction with the app (Burns et al. ., 2011). Participants reported the intervention was satisfactory and feasible to complete. To our knowledge, there is only one s tudy with experimental design that looked at mHealth for depression and which results have been published (Kauer et al. ., 2012). trial. One hundred and eighteen patients 14 24 years of age with early stages of depression were randomized to the program for 2 4 weeks: 68 were able to use all app daily life and mental health ) while daily life only enhanced EMA group would increase emotional self awareness and, in doing so, there would be a decrease in depressive symptoms compared to the basic EMA group. Using structural equations to examine inter and intrapersonal changes, trial results supported more of a regular program than a real applica tion. It did not have enhanced capabilities like providing location services, camera/video camera, social networking, and several other phone features that are included in current mobile applications. Depression and HIV: Epidemiology, Comorbidity, and Cons equences o n PLWH According to estimations from the Centers for Disease Control and Prevention (CDC), about 1.2 million individuals of 13 years of age and old are living with HIV in the U.S (Centers for Disease Control and Prevention, 2013). Regardless of race and ethnicities, gay, bisexual, and other men who have sex with men (MSM) remain the
41 most affected population. In this regard, increased disparities are seen among both Blacks and Hispanics when compared to Whites. The state of Florida ranks third in the country for highest prevalence of HIV infection, with 95,187 PLWH (Florida Department of Health, 2013). Healthcare costs associated with HIV have seen a consistent increase as lifespan of PLWH has increased over the years (Krentz & Gill, 2014). Moreove r, after assessing the association between state based federal funding and HIV epidemiology, Oglesby, Smith and Alemagno (2014) concluded that funding resource allocation for HIV prevention is not as aligned as for HIV treatment. They argue their findings signal the need to revisit federal funding for HIV prevention. The prevalence of depression among PLWH is 3.1 times greater than in the general population. Even when controlling for income, the excess burden of unipolar depression (UD) among this populat ion was 1.5 times larger (Do et al. ., 2014; Ferrari et al. ., 2013). Clinical features of UD among PLWH are, in general, the same clinical manifestations as among the general population: anhedonia, low/irritable mood, insomnia/polysomnia, changes in weight, psychomotor changes, and memory and cognitive impairment. However, unlike the general population, higher prevalence among females is not consistently seen among PLWH (Arseniou et al. ., 2014; Dal BÃ³ et al. ., 2013). Additionally, some UD features may be mor e conspicuous among PLWH, including sleep and eating disorders, problems with decision making, and cognitive impairment (Arseniou et al. ., 2014). The impact of UD on cognitive skills among PLWH has been studied before and a number of findings show cognit ive impairment among this population, even when viral suppression levels are attained (Simioni et al. ., 2010). Almost 50% of PLWH report
42 cognitive impairment (Atkins et al. processes, are frequently affected among HIV infected individuals (Burns et al. ., 2013b). This executive dysfunction places PLWH who suffer depression at risk for poor decision making processes (Thames et al . ., 2012) that could affect situational acceptance and coping abilities (McIntosh, Seay, Antoni, & Schneiderman, 2013), antiretroviral adherence (Wagner et al. ., 2011), adherence to mobile app interventions (Harrison et al. ., 2011), physician visits (Vance , 2013), and high risk sexual behavior (Alvy et al. ., 2011). Indeed, higher rates of unprotected sex have been consistently reported among PLWH who also had depressive symptoms (Gerbi, Habtemariam, Tameru, Nganwa, & Robnett, 2012). Finally, UD among PLWH has been found to be a et al. ., 2012), ineffective patient provider communication (Jonassaint et al. ., 2013), overall higher rates of mental health needs, more concerns about patient confidentiality, and les s likelihood of using mental health services (K. A. Williams & Chapman, 2011). Summary mHealth interventions seem to have the potential to improve access to all phases of healthcare for a number of people living with chronic conditions whose treatmen t is based on long term medication regimens and regular tracking of symptoms. People living with HIV fit into this category. When comorbidity with UD occurs, this adds an extra layer of responsibility related to medication adherence. Paradoxically, UD can also hinder adherence, both for HIV and depression treatment. Over the last ten years or so, research, although still insufficient, has been able to show the efficacy of SMS messages, a particular type of mHealth, to improve attendance to clinical visits a nd
43 antiretroviral adherence. These interventions work better when they are bidirectional, when there are follow up messages, when messages are tailored to the individual, and when content, frequency and wording are relevant to the patient. The literature f or mobile applications related to depression, either with or without HIV, is similarly scarce but it shows some promise in terms of improving self management and patient satisfaction. Further evidence is still needed. It is crucial to deepen our knowledge about potential user needs and preferences in order to tailor development processes. Given that some of the evidence supports the idea that potential users accept and find feasible to use a mobile app to help in their mental health, there seems to be a gr eat potential for mHealth among this population. However, considering the high comorbidity of HIV with depression, and given what we know about how cognitive impairment associated with depression and HIV may hinder interest to adhere to available options o f treatment, it is important to determine user needs to design effective mHealth apps for PLWH. Also, previous research did not clarify whether demographics could also play a role in intention to use mHealth among this population. Finally, a theoretical fr amework that incorporates health services factors, specifically need factors, could help to better assess how individuals decide to use a mobile app as part of their treatment.
44 CHAPTER 3 CONCEPTUAL FRAMEWORK AND HYPOTHESES This chapter will introdu behavioral intention and to model technology acceptance for mHealth. The theory of reasoned action / planned behavior (TRA / TPB) was selected as foundational theory to he technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) provide a framework for understanding technology adoption and they will be discussed here. Finally, the Behavioral Model of Access to Medical Care helps to understand individual and population factors that impact on the access of health services. Collectively these frameworks are used to formulate hypotheses for assessing the impact of depression on the intention to use mHealth interventions among PLWH wi ll be presented. Theoretical Foundations Theory of Reasoned Action / Planned Behavior The theory of reasoned action / planned behavior (TRA / TPB) is one of a making proc esses. It was introduced in 1991 by Icek Ajzen, as a modification of an earlier one called theory of reasoned action (TRA). We will briefly discuss this early version before going more in depth into the TRA / TPB. Over the last 50 years, social psychologi sts have developed several theories with the common overarching goal of explaining health behaviors. These health behaviors theories have evolved through time by building the new theory on top the previous one, in a sort of incremental process. TRA is one of these theories. Developed by Fishbein
45 component that was lacking from previous theories (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975). The original formulation of TR A states that the outcome of behavioral approved or not by social groups that are important to him/her (subjective norms). The performs the behavior and his/her evaluation of whether this outcome will be good or bad. Subjective norms are the combinati think about the behavior and his/her motivation to conform to these norms (Edberg, 2007). Further development of TRA eventually evolved into the theory of planned behavior (TRA / TPB) (Ajzen, 1991). This e volution was mainly due to the increasing number of behavioral experiments with TRA in which behavior was inaccurately predicted. Ajzen proposed then, that behavioral intentions were the resulting outcome of not two, but three main factors: attitude toward the behavior, subjective norms, and perceived behavioral control (Figure 3 1). This new factor was, in turn, the result of both behavior and the amount of power the perso n beliefs he/she has over performing the behavior.
46 Belief toward an outcome ATTITUDE TOWARD BEHAVIOR BEHAVIORAL INTENTION BEHAVIOR PERFORMANCE Evaluation of the outcome Belief of what others think SUBJECTIVE NORM Motivation to conform Co ntrol beliefs PERCEIVED BEHAVIORAL CONTROL Perceived power Figure 3 1. Elements of Theory of Reasoned Action Technology Acceptance Model: Three I terations One of the major extensions/modifications of TRA / TPB is a theory called technology accep tance model (TAM) (Figure 3 2) . TAM was developed in the late 80s within the field of information systems in order to predict acceptance of new information technology in order to improve performance at their jobs (Davis, 1989). Even though the main outcome s were still behavioral intention and behavior performance (under the name of actual system use), the first iteration of TAM replaced some of TRA factors with usefulness was defi perceived usefulness is one that the user believes there is a positive relationship between use and performance (Davis, 1989).
47 Figure 3 2. The Technology Acceptance Model, 1989 conditions, the easie r to use that a new technology looks to potential users, the more likely it is to be accepted by these users (Davis, 1989). TAM also provides a factor called external factors that influence belief, attitude, and use intention. This impact occurs via mediat ed effects on both perceived usefulness and perceived ease of use (Park, 2009). External factors may encompass four different categories: individual characteristics, system characteristics, social context, and organizational characteristics . T AM 2 (Figure 3 3) was developed in the early 2000s as an extension to TAM in regard to social influence (Venkatesh & Davis, 2000). This model was tested using
48 information systems in four different types of companies (manufacturing, financial services, accounting, and investment and banking), but none of them were health organizat ions. Nevertheless, TAM 2 came closer to the original TRA / TPB by incorporating subjective norms among other factors that would exert impact on both, perceived usefulness and intention to use. This relationship would be mediated by increasing user experience and whether this use is voluntary. Researchers found that this new iteration accounted for about 50% of the variance in usefulness perceptions and about 45% of the variance in intentions of use. Among a number of limitations noted, authors acknowledged the importance for future research to adapt user acceptance theories to more current individual level performance related factors (Venkatesh & Davis, 2000). Figure 3 3. The Technolog y Acceptance Model, Version 2, 2000
49 A third iteration built on TAM and TAM 2 was formulated in recent years (Figure 3 4) . This new approach sought to improve previous theories by including individual level factors that were proposed to be related to intent ion of use. This theory/model is called the Unified Theory of Acceptance and Use of Technology (UTAUT), and it comprises four core determinants of both use intention and actual use, performance w technology will positively impact his/her likelihood to make gains in work performance (perceived usefulness from TAM and TAM 2 pertains to this determinant), effort expectancy, or the degree of ease related to the use (related to perceived ease of use f rom TAM and TAM 2), social ical infrastructure to support use of the new technology (Venkatesh et al. ., 2003). Four moderating factors, gender, age, experience, and voluntariness of use, were also proposed in this model (Venkatesh et al. ., 2003). Social influence can be broken down into three different processes: compliance, when use of the technology is based only on expected rewards or an attempt to avoid punishment, identification, when the person accepts the influence of another individual or group with which he or she wants to establish or maintain a relationship, and internalization, when influence is 1999). It could be argued that patient physician relationships, especially those between patients wh o live with chronic conditions such as PLWH and their doctors, allow for identification processes to occur in terms of acceptance of treatment options, disease
50 management, and other aspects of wellbeing. However, this theory was developed to predict intent After testing the model in four organizations (none of them related to health), authors reported th at UTAUT was able to explain 70% percent of the variance related to both intention to use and use behavior. Moreover, authors found that performance expectancy, effort expectancy, and social influence were good predictors of intention to use, with performa nce expectancy being the strongest predictor. Age and gender were identified as strong moderators, and when all four moderators were taken into account, the effect of social influence was non significant (Venkatesh et al. ., 2003). Figure 3 4. The U nified Theory of Acceptance and Use of Technology, 2003
51 Despite being developed based on a health behavior theory (TRA / TPB), none of the presented theories/models were specifically created either to be used in health utilization of technology based health services. A to seek and use health services. Some of these factors lie on the individual level and recently were also found to have an impact on smartphone ownership in the U.S. (Smith, 2013; U.S. Department of Health and Human Services, 2014). For example, recent results from surveys conducted by the Pew Research Center (2013) have found smartphone owners are more likely to be males, adults of 18 54 years of age, Blacks or Hispanics, have at least some level of college education, have annual income over $30,000, and be from urban or suburban areas. Even more interesting are the results of looking at the use of health related smartphon e applications. Despite limited available data, persons who had an app on their phone to help them track or manage their health were more likely to be female, adults of 18 29 years of age, Whites or Blacks, with some college education or more, and from hou seholds with income over $30,000 (U.S. Department of Health and Human Services, 2014). These differences suggest there might be more demographic factors applications. In additio use health services is the need component. Medical need for services is not addressed in any of the three iterations of the technology acceptance theories we have revised. In order to underst and how depression might impact on the intention to use mHealth
52 applications among PLWH, we need to integrate an additional model that takes account of the aforementioned factors. The Behavioral Model of Health Care Use This study will draw from the And utilization (R. Andersen & Davidson, 2007; R. M. Andersen, 1995) to integrate a number of predisposing, enabling, and need factors into the conceptual model (Figure 3 5) . This will help to adjust the association betwe en individuals with or without depression and their intention to use mHealth applications to manage HIV related conditions. Based on the literature, some of the classic population characteristics such as predisposing, enabling and need factors will be sele cted to influence the outcome indicators. Figure 3 5. Behavioral Model of Access to Medical Care, 1995
53 Behavioral Framework for the Use of mHealth Applications The framework developed for this study draws from TRA / TPB in that it looks at how p ersonal beliefs impact on the intention, and ultimately the behavior of using a mobile health application (Figure 3 6). Two main types of belief influence mHealth intention: perceived usefulness and perceived ease of use. Both of these beliefs influence ea ch other, since when a mobile application is perceived as easy to use is also perceived as useful (Venkatesh & Davis, 2000) . Figure 3 6. Behavioral Framework for the Use of mHealth Applications , 2015 Technological factors such as design or engineer ing of the app will exert a direct effect on the perceived ease of use. As we saw both with in TAM 2 and UTAUT, demographic factors, social influence, and facilitating conditions influence either on perceptions or the intention to use technology. This is w hy we propose to use the
54 research incorporates need factors. Since none of the previous theories / models was specifically developed for health related services, the core a spect of medical need to seek services, either perceived or evaluated, was left out of said theories and models. Based on the literature about the effects of unipolar depression among PLWH that was previously presented in chapter 2 (Arseniou et al. ., 2014; Atkins et al. ., 2010; Burns et al. ., 2013b; Dal BÃ³ et al. ., 2013; Gerbi et al. ., 2012; McIntosh et al. ., 2013; Simioni et al. ., 2010; Thames et al. ., 2012; K. A. Williams & Chapman, 2011) the inclusion of need characteristics as a potential factor to exer t influence on the perceived usefulness of a mobile health application was added. A Conceptual Model to Assess Intention to U se mHealth among PLWH . With the model proposed below (Figure 3 7), and before getting to the stage of developing an actual mHeal th application, we seek to gauge factors that may impact related mobile applications among people living with HIV (PLWH), a very specific target population. Thus, this study will use a conceptual model lacking the techn ological factors related to both design and application engineering. Consequently, this research will not assess the impact of perceived ease of use on the intention of using mHealth. However, by assessing perceived usefulness (also called performance expe ctancy in UTAUT) which has been shown to be the strongest predictor of use intention (Venkatesh et al. ., 2003), this research will provide guidance to future development of mHealth technology for this population. Predisposing fa ctors include gender, age, race, ethnicity, education status, and sexual orientation. As previously stated, there is limited research on whether
55 demographic factors impact the decision to use mHealth among PLWH (Krishnan et al. ., 2014; Kumar et al. ., 2013; Muessig et al. ., 2013). M oreover, in the last few years, there is an increased technology appropriation among racial and ethnic minorities, thus the disappear (Krishnan et al. ., 2014; Lella & Lipsman, 2014). These findings, along with previously presented models, suggest the inclusion of gender, age, and race/ethnicity as important factors. While some studies have found that income had no impact on smartphone ownership among males who have sex with males (MSM) (Muessig et al. ., 2013), national reports have found an increase of smartphone ownership as both income and education level increase (Lella & Lipsman, 2014; Smith, 2013). Thus, by including both smartphone ownership and education level, in come does not seem to be an important predisposing factor to include. Previous findings showed that MSM are more likely to be early adopters of new technology (Krishnan et al. ., 2014). Finally, s ince this study will look at PLWH in general, it was decided to include sexual orientation as a potential variable that could influence perceived usefulness. It will be interesting to see whether this variable also exert an influence in the adoption of mobile technology (which could eventually become part of the tre atment) among this population. Enabling variables will include: insurance status and cell phone ownership. According to Andersen (1995), insurance status included as enablers to access services (e.g. a doctor who could prescribe or suggest the use of an app). Finally, depressive symptoms and general health status are also thought to influence on perceived usefulness. The logic behind this decision comes from the previously discussed literature that found that depression symptoms may hinder treatment
56 adher ence because of both disorder symptoms and cognitive impairment (Arseniou et al. ., 2014; Atkins et al. ., 2010; Burns et al. ., 2013b; Dal BÃ³ et al. ., 2013; Gerbi et al. ., 2012; McIntosh et al. ., 2013; Simioni et al. ., 2010; Thames et al. ., 2012; K. A. Willi ams & Chapman, 2011). Figure 3 7. A Conceptual Model to Assess the Intention to Use mHealth among PLWH, 2015 Hypothesi s Study hypothesis correspond ing to specific aims 1 and 2 was developed based on prior empirical evidence and the Concep tual Model to assess Intention to u se mHealth among PLWH. Hypothesis was formulated to evaluate the impact of
57 depressive symptoms on the behavioral intention to use mobile health applications to manage HIV related conditions among PLWH. Aim 1 proposes to examine the association between socio demographic characteristics of PLWH with the intention to use mHealth applications to manage HIV related conditions and its significance. Based on previous arguments by Muessig et al. . (2013), Krishnan et al. . (2014), and Kumar et al. . (2013), we expect that there will be variation in the indicators of intention to use mobile health applications by predisposing and enabling factors. Based on previous empirical findings, it is expected that more variation will be found i n demographic variables such as age, gender, and sexual orientation. Among the enabling variables, it is expected that usual source of care (the one most closely related to the social influence process of identification) will show the most variation. Aim 2 seeks to determine the relationship between screening positive for depressive symptoms and the intention to use mHealth applications to manage HIV related conditions among PLWH. Empirical evidence previously discussed showed how symptoms of depression ma y negatively impact HIV treatment outcomes, such as adherence to antiretroviral therapy, in addition to increase likelihood of risky sexual behavior, and failure to attend to clinical visits. Previous research posits that this negative impact may be due to depressive symptoms themselves as well as a cognitive impartment associated with depression. Given that mHealth applications continue to evolve and might well become an additional way to provide health care services in the near future, the same mechanisms that interfere in adherence to other types of treatment may also hinder intention to use apps, therefore decreasing likelihood of
58 adherence. However, previous findings have also found no relationship between depression and acceptance of mHealth technology among HI V positive males who have sex with males (Krishnan et al. ., 2014) . With these contradictory findings we do not know whether a relationship exists and what the direction of this association might be. Thus: Hypothesis : Respondents with depressive symptoms will differ in their intent to use a mobile health application from respondents without depressive symptoms .
59 CHAPTER 4 DATA AND METHODS This section discusses the data source and variables used in this study, as well as the methods for analyzing the impact of depression on the intention to use mHealth among PLWH. First, we will describe the study design and then introduce the data source. The next step will be defining the variables that were measured. Finally, this chapter will discuss the metho ds that were used for statistical analyses. All data used for this research are descriptive in nature and we used it to assess the intention of using mobile health applications among PLWH. Study Design This study was carried out using a mixed methods app roach. First, an observational cross sectional quantitative analysi s was conducted using secondary data , which was collected over a period of four months for the Florida Health Cohor t entry s urvey. Secondly, small focus groups with PLWH were conducted in o rder to gather and analyze primary, qualitative data. While the unit of analysis for the quantitative analysis was the (survey) participant, the unit of analysis for the qualitative aim was the experience of the person living with HIV using a cell phone an d/or a mobile app for addressing HIV related conditions . Data Sources The purpose of this study wa s to determine whether people living with HIV who have depressive symptoms have a decreased intention to use mobile health applications to manage HIV related conditions than PLWH without depressive symptoms. The study will use both secondary and primarily collected data.
60 Secondary data will be used to complete Aims I and II and it will be extracted from the Florida Health Cohort Intake Survey, disseminated th rough a period of five moths (from November 2014 to February 2015). Data coming from this survey will then be manipulated to generate the required datasets to conduct a set of statistical analyses designed to assess the effect of depressive symptoms on sev en different measures of intention to mHealth to manage HIV related conditions among PLWH. Primary data will be collected for Aim III by conducting small focus groups among participants of the Florida Health Cohort Survey. These qualitative data will then be processed in order to produce transcripts that will be analyzed looking for codes and themes with the aim of identifying barriers and facilitators to use mobile applications among this population. These analyses will also be used to assess preferred co mponents for a mobile application to help manage HIV related conditions. Secondary Data: Florida Health Cohort Survey The Florida Health Cohort (FHC) is an observational, longitudinal, prospective cohort study that is being conducted to identify factors that impact or influence a diverse set of health outcomes among people living with HIV in the state of Florida. This cohort study will assess how factors at individual, clinical, and community level influence both the access and utilization of healthcare s ervices as well as HIV clinical outcomes (e.g. viral load and CD4 cell count). Among clinical factors, the Florida Cohort looks especially at the influence of substance use and mental health treatment on HIV health outcomes. Participants of the Florida Health Cohort were recruited from five different county health departments across the state: two urban (Hillsborough and Orange), two rural (Columbia and Sumter), and one urban/rural (Alachua). Participants were also
61 recruited from the University of Flori da Health Infectious Disease Clinic in Gainesville, FL, and other community based organizations affiliated with the university. The study aims at recruiting a total of 1,700 individuals. Of these, 1,500 will be PLWH currently receiving treatment at any of the participating clinics, and 200 will be HIV negative persons. Individuals seeking care at any of the participating clinics were consecutively approached by clinical staff about the study, and after providing them with information they were invited to en roll as participant. Written informed consent was collected from all interested individuals. Enrolled participants were allowed to refer their colleagues or friends to the study. Additionally, before providing consent, potential participants were informed that they could be approached for additional studies linked to the FHC during the remaining of the research project. Nevertheless, they were also informed that providing consent as participant of the FHC did not mean they would have to agree to participate in all additional studies. FHC participants completed a 30 45 minute entry survey. Respondents were given the choice to respond to the survey online, using a secure laptop computer, or on paper format, instead. Participants are asked to respond to a foll ow up survey six months after first enrollment. As part of the study, survey data will be linked with databases at the Florida Department of Health as well as electronic medical records maintained by each clinic. A complete version of the entry survey is a vailable in the Appendix A of this study.
62 Measures of Data This section outlines the operationalization of the variables that w ere used in the study model, including the outcome measures, independent variables, and control variables. The Florida Hea lth Cohort entry survey is divided into 15 sections: demographics and socioeconomic status, general health status, emotional support, general use of health services, HIV history and treatment, use of HIV related health services, mental health (depression, anxiety, PTSD, and stress), cognitive function, bodily pain, stigma, substance use, sexual behavior, gynecological history, incarceration history, and m obile technology utilization. Table 4 1 summarizes the measurement and operationalization of the study v ariables. Outcome Variable Intention to use mHealth applications . The intention to use mobile health applications was assessed using seven items included in the FHC entry survey. Questions for the mobile technology section were in part adapted from previou s surveys developed by the Pew Research Center. The 7 items related to intention to use mHealth apps were developed adapted for PLWH from similar questions used in previous research (K. J. Horvath et al. ., 2013; Katz & Rice, 2009; Miller & Himelhoch, 2013b ; Muessig et al. ., 2013; Proudfoot et al. ., 2010; Torous et al. ., 2014). Identify health services relevant to you Track changes in your m ood and emotions Providing tips to improve your health, based on information about you Track and manage alcohol and drug use behavior
63 Communicate with your doctor or clinic Remember to take your medication Engage in social networking with other people who live with HIV Respondents were asked to use a direct estimation adjectival scale (Streiner & Norman, 2008) with five choices in order to provide their answer. Choices included: 1 Categories were recoded as a binary variable for the purpose of this study, as well as to avoid the unreasonable assumption that the original numerical codes for the categories as 0=No, while Independent Variable The primary explanatory variable for this research was the participant depression status. FHC entry survey used the Patient Health Questionnai re 8 (PHQ 8), a screening instrument modified from the PHQ 9, to assess the occurrence of depressive syndrome among respondents. The PHQ 9 is part of a larger questionnaire, the Patient Health Questionnaire (PHQ), developed in the late 1990s, which is comp rised by a set self administered questions that asses the most common types of mental disorders occurring in general population: depression, general anxiety disorder, panic disorder, social anxiety, post traumatic stress disorder, and somatization and soma toform disorder (Kroenke, Spitzer, & Williams, 2001). PHQ 9 has been used to identify depressive symptoms in a wide variety of patients with chronic conditions, including diabetes, stroke, end stage kidney disease, and other psychiatric disorders (Abdel Ka der, Unruh, & Weisbord, 2009; Ackermann et al. ., 2005; Acton, Prochaska, Kaplan,
64 Small, & Hall, 2001; L. S. Williams et al. ., 2005). In the last few years it has also been validated for implementation among patients infected with HIV (Cholera et al. ., 2014 ; Edwards et al. ., 2014; Marc et al. ., 2014; Monahan et al. ., 2009; Pence, Gaynes, et al. ., 2012). PHQ 8 contains the same questions that PHQ 9 has, except for the very last item, which is related to suicide ideation. When responding to PHQ 8 respondents are asked to state the degree to which a total of nine situations have happened to him or her over the course of the last two weeks. They would use a direct estimation adjectival have you been bothered by any of the following problems? (please check one box for each question): Little interest or pleasure in doing things Feeling down, depressed, or hopeless Trouble falling or staying asleep, or sleeping too much Feeling tired or having little energy Poor appetite or overeating Feeling bad about yourself or that you are a failure or have let yourself or your family down Trouble concent rating on things, such as reading the newspaper or watching television Moving or speaking so slowly that other people could have noticed? Or the opposite being so fidgety or restless that you have been moving around more than usual
65 PHQ 8 scoring system follows the same guidelines as PHQ 9 scoring system. A score of 0 3 points is then assigned to each question, according to the scale provided counted if present at all). P HQ 8 total possible score goes from 0 to 24 , with cut off points that allow cat egorization of participants in five different grades of severity: 0 4 minimal depression, 5 9 mild depression, 10 14 moderate depression, 15 19 moderately severe depression, and 20 27 severe depression. For the purposes of this study, mild and moderate de pression scores were combined into a single category: moderate . Likewise, moderately severe and severe were conflated into a single category: severe. Thus, we used a three level depression variable: mild (reference group), moderate, and severe. This approa ch is consistent with clinical criteria associated to PHQ 8, whereby mild depression may not require treatment, moderate depression needs to be carefully evaluated in order to determine need of treatment, and severe depression warrants treatment for depres sion, either antidepressant, psychotherapy, or both. Additionally, in an attempt to simplify the final model, we conducted a variety of sensitivity analysis by creating a dummy variable of categories into a single 0=No (based on the clinical criteria that may not require treatment, even moderate depression). Severe depression was recoded as 1=Yes (presence of significant depressive symptoms).
66 Covariates Control variables are important in order to avoid bias due to an omitted variable. Missing variables can cause this problem when they exert some unaccounted influence whether in the main explanatory or the main outcome variables, thus modifying the true relationship between the two. In this research include covariates included : age, gender, race, ethnicity, education, employment status, sexual orientation, self reported health status, insurance status, and cell phone ownership . Demographic characteristics Demographic variables used for this research include d : age, gender, race, ethnicity, education level, employment status, and sexual orientation. The majority of these variables was manipulated as categorical variables. Age was used as a categorical variable. For gender this research use d th e question , which was broken down in two categories: male and female (reference group) . The following categories were used for race: White (reference group), Black/African American, and other. Ethnicity i s a binary variable and in dividuals of H ispanic origin were used as reference group. Education level was divided into three categories: high school graduate some college or technical/trade school, and college or trade school graduate / graduate degree or professional degree after graduating coll ege. FHC collected the following categories for employment status : employed for wages, self employed, out of work for more than 1 year, out of work for less than 1 year, homemaker, student, retired, and unable to work/retired. For this research, these cate gories will be collapsed into a binary variable of whether the individual is currently employed, with those who currently are un employed used as reference group. For sexual orientation , the following categories will be included: heterosexual or straight (r eference group), gay or lesbian,
67 bisexual, asexual, and other. Bisexual, a sexual and other will be collapsed into a single Health status Participant self reported health status was assessed using the following question from the SF past 4 weeks : excellent, very good , good, fair, poor, and very poor. (reference group), Enabling variables Enabling characteristic s included: smartphone ownership and insurance type. Both of these are categorical variables. Smartphone ownership was ass essed with the accompanying the question and the following options: I have a smartphone, I have a cell phone but it is not a smartphone (reference group), and I do not c urrently have a cell phone. private insurance, Medicaid, AIDS drug assistance program (ADAP ), Tricare or uninsured, other (please describe), and I am not sure. For this research , insurance type was used as a dummy variable of whether respondents had any type of ins urance, with those uninsured as the reference group .
68 Table 4 1 shows the variables that were included in the statistical analyses. Name, definition, type and categories of each varia ble are presented. All data were extracted from the Florida Health Cohort (FHC) entry survey. Table 4 1. List of Variables Variable Definition Type Category Explanatory variable Depressive symptoms Severity grades of depression Categorical 0=Mild 1=Moderate 2=Severe Presence of depressive symptoms Binary No=0 Yes=1 Outco me variables Identify relevant health services never, rarely, about once a week, a few times a week, daily Binary 1=Yes 0=No Track changes in mood or emotions never, rarely, about once a week, a few times a week, daily B inary 1=Yes 0=No Provide tips to improve health, based on personal information never, rarely, about once a week, a few times a week, daily Binary 1=Yes 0=No Track and manage alcohol and drug use never, rarely, about once a week, a few times a week, daily Binary 1=Yes 0=No Communicate with physician or clinic never, rarely, about once a week, a few times a week, daily Binary 1=Yes 0=No Medication taking reminder never, rarely, about once a week, a few times a week, daily Binary 1=Yes 0=No Social network ing with other PLWH never, rarely, about once a week, a few times a week, daily Binary 1=Yes 0=No Control variables Age Age in years Categorical 0=18 34 1=35 44 2=45 54 3=55 and over Gender male, female, transgender, other Categorical 0=Female 1=Male
69 Table 4 1. Continued Variable Definition Type Category Control variables Race White, Black, Native American, Asian, Multi racial, Other Categorical 0=White 1=Black 2=Other Ethnicity (Hispanic) Yes, No Binary 0=No 1=Yes Education level elementary scho ol or below, some high school, high school graduate, some college, college graduate, graduate or professional degree Categorical 0=High school 1=Some college, 2=College graduate or graduate or professional Employment status employed, unemployed Binary 0=E mployed, 1=unemployed Sexual orientation heterosexual, gay or lesbian, bisexual, other Categorical 0=Heterosexual, 1=Homosexual, 2=Other Health status excellent, very good, good, fair, poor, and very poor Categorical 0=Excellent, 1=Good 2=Poor Cell phon e ownership smartphone, regular cell phone, no cell phone Categorical 1=Smartphone, 2=Flip phone 3=No cell phone Insurance type private, public, uninsured, and other Binary 0=No insurance 1=Any insurance Statistical Analyses FHC data were collected an d managed using REDCap electronic data capture tools hosted at the University of Florida (Harris et al. ., 2009). REDCap (Research Electronic Data Capture) is a secure, web based application designed to support data capture for research studies, providing: 1) an intuitive interface for validated data entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4)
70 procedures for importing data from e xternal sources. De identified FHC survey data was then imported into StataCorp. 2011. Stata Statistical Software: Release 12 . College Station, TX: StataCorp LP, which was used to generate the output of this research. Sample size Statistical analyses for Aims I and II of this research were conducted before the Florida Health Cohort study completes recruitment of all 1,700 participants. Thus, it was important to determine the minimum sample size required to carry out the analyses in a way that enhances our probabilities of finding actual group differences. On the other hand, and especially for Aim II, these analyses had the goal of comparing PLWH that screen positive for different severity grades of depression in terms of their intention of using a mobile he alth application to help them manage HIV related conditions. Previous research has found the effect size of depression on intention to use technology to be rather small (Krishnan et al. ., 2014). Desired s ample size was then calculated using its relationshi p effect size, statistical power, and statistical significance criterion that is used is ~ 393. Approximately 400 surveys were required to carry out the statistical analys es. Aim I Descriptive statistics were used to determine means, distribution, standard deviations, and r anges of all measures . TABULATE and PROP commands were used in this step. All variables were selected based on both theoretical and empirical basis thoroughly described in the preceding chapter. Next, we tested the association of all socio demographic variables included in this study with the each of the indicators of intention to use mHealth applications to manag e HIV related conditions were tested u sing bivariate statistics, as well as the significance of this association. Chi Square tests
71 were conducted using TABULATE and CHI2 commands, and significance was set at p . Covariates that met significance level were then used in m ultivariate analyse s . Aim II Study hypothesis specified that participants with increasing severity of d epressive symptoms will show a difference in their intention to use mHealth a pplications when compared to respondents with mild depressive symptoms . Here we s ought to dete rmine the relationship between screening positive for depression and the intention to use mHealth applications to manage HIV related conditions among PLWH. Outcome variables were the seven indicators of intention to use mHea lth and all these indicators wer e binary variables. For this reason , multinomial logistic regression s were us ed to estimate the odds of being in category versus usage intention. LOGIT and XI: LOGIT commands were used for these procedures. Models control led for: age, gender, race, ethnicity, education level, employment status, sex ual orientation, health status, smartphone ownership, and insurance status. However, depending on findings from Aim 1, variables that were not statistically significantly associated with the intention of use mHealth applications were dropped from models for Aim 2. The general empirical model is: Log ( Indicator of intention to use mHealth ) ( yes, no 0 + 1 2 * Prima ry Data: Small Focus Groups with P articipants of the Florida Health Cohort Aim III of this study proposed to contextualize the findings from the previous aims by engaging PLWH in qualitative assessments to identify barriers and facilitators to use
72 mHealth applications, as well as pr eferred components for a mobile application to help manage HIV related conditions. This part of the research sought to help in understanding not only the experience that PLWH have in terms of using new technology (mobile health technology) to manage health conditions , but also what are likely deterrents and what are preferred features that will make PLWH more likely to use and engage with a mobile health application . Data for this study was collected by conducting semi structured focus group sessions with p eople living with HIV. Inductive thematic analysis was conducted in order to find patterns in the data as well as to interpret these patters. T he unit of analysis was the interaction between the individual and his/her mobile technology device. IRB 01 appro val was obtained in order to conduct this portion of the study. Thematic analysis is a descriptive approach to qualitative data analysis that was common threads that run acr (Braun & Clarke, 2006) , and it has been consistently used in the last few years with participants who were either males who have sex with males (MSM) or people living with HIV. Some of the attitudes and bel iefs that were assessed using this approach include: acceptability toward pre exposure prevention (PreP) (PÃ©rez Figueroa, Kapadia, Barton, Eddy, & Halkitis, 2015) , barriers and facilitators to effectively scaling up HIV prevention interventions (Kegeles, R ebchook, Tebbetts, Arnold, & TRIP Team, 2015) , beliefs about illicit drug use in sexual settings and HIV transmission risk behavior among gay men (Bourne, Reid, Hickson, Torres Rueda, & Weatherburn, 2015) , and refinement of an
73 personalized mHealth interven tion to promote medication adherence among PLWH (Montoya et al. ., 2014) . Thematic analysis seeks to examine the description materials about life experiences. According to Braun and Clark, thematic analysis is a flexible instrument, able to provide mainly q ualitative, complex, detailed, and rich details about the data (2006) . Further, when conducting a thematic analysis, the researcher can interpret data in two ways: inductive interpretation is used when few, or no previous studies have dealt with the phenom enon. Meanwhile, deductive interpretation is more suitable when there is interest in testing previously developed theory in a different situation (Vaismoradi, Turunen, & Bondas, 2013) . Participants were recruited from three sources: individuals already en rolled in the Florida Health Cohort were invited to via telephone calls. Flyers and word of mouth were used for recruitment at a local community based support group for PLWH. Individuals interested in participating made contact with the researcher in order to determine their eligibility. Finally, participants were also recruited via telephone calls from Healthstreet, a community engagement program at the University of Florida that aims to reduce research disparities by linking individuals to opportunities t o participate in research that is relevant to them. Inclusion criteria comprised: being 18 years of age or older, HIV positive, and owning a cell phone. Focus groups were conducted in a series of up to 6 or until saturation of themes was achieved. Saturat ion was defined as the stage of the project at which nothing new was coming up (Richards, 2015) . Each participant w as invited to only one of the focus
74 groups. Each focus group would consist of 4 8 participants and would have a duration of 60 90 minutes. Pa rticipants were compensated with a $25 gift card for their time. Results from Aims I and II serve d as guide and shape d the question guide that was used in the focus groups. Focus group guide was flexible in content. As data was collected and new questions appeared, these questions were added, and questions that prompted repetitive answers across groups were removed. In general, focus group discussions explore d the experience of participants using their cell phones, and more specifically smartphone applica tions to manage health conditions related to HIV, as well as their interest, pros and cons, and feelings about the features and capabilitie s of said apps. Focus group guide is available in this research in the Appendix B. Before starting the session partic ipants were asked to complete a short demographic survey that helped to characterize participants. This survey is available in this study in the Appendix C. Participants were also asked to make up a name (e.g. an alias) for themselves and use name tags dur ing the sessions. Since focus groups were digitally recorded , this served a double purpose: it helped them to engage in active conversation and exchanges while at the same time their true identities was protected (given the use of digital recorders). Resea rch team for each session consisted of one facilitator and two research assistants. Research assistants were in charge of taking notes of both non verbal language and leading sentences associated with the appropriate alias. This helped to tag each part of the transcription with the corresponding alias of the participant who made that specific comment. Analysis process started with transcription of digital recordings. One of the research assistants created a Microsoft Word document with a verbatim transcr iption of
75 the recording. Each transcription was also combined with notes of non verbal language and appropriate aliases were assigned to each one of the sentences and text chunks. In order to identify any potentially skipped section of the audio during the first round, another research assistant then proceeded to listen the recording for a second time while reading the corresponding transcription and adding/editing as needed. Finally, the researcher went through each of the recordings and transcriptions for a third time. This time only minor inconsistencies needed to be edited. Qualitative analyses were then conducted using NVivo qualitative data analysis software; QSR International Pty Ltd. Version 10, 2012 . Finalized transcriptions were imported into the software. Inductive thematic analysis were conducted following several steps. Also, analysis procedures were conducted following the evaluative criteria to establish trustworthiness for qualitative research (Lincoln & Guba, 1985) First, the researcher sta rted with data familiarization with the transcription number one by creating codes (nodes) and labeled relevant text sentences or chunks, which then became initial codes. Research assistants then used these initial codes to label and create codes from tran scriptions number two and three. The researcher continued coding transcriptions four and five. With the purpose of establishing credibility research assistants and researcher held several meetings to discuss, revise, and resolve discrepancies related to th ese primary codes (analyst triangulation). Moreover, primary an analytical sessions for the purpose of exploring aspects of the inquiry that might otherwise remain impl (Lincoln & Guba, 1985)
76 Next, primary nodes were then categorized into themes. This resulted in a five main parent nodes (themes) with three of this having a variable number of child nodes (primary codes). Further refine ment was conducted through indexing, charting, and interpretation of themes and codes. Then, confirmability and dependability were attained by including a researcher who was not involved in the research process (external auditor) to examining both the proc ess and the product of the study. Accuracy of codes, themes, were then evaluated, as well as whether interpretations and conclusions were supported by the data.
77 CHAPTER 5 RESULTS The results from this study are presented in two differe nt sections. Secondary data analyses will be presented first, both descriptive statistics of the sample as well as inferential analyses, including bivariate and multivariate analysis. The subsequent section will introduce the results from the qualitative, primary collected data. Quantitative Analyses Descriptive Statistics The total number of respondents to the Florida Health Cohort entry survey during the time of this study was 310. Table 5 1 summarize s the population characteristics of the sample. During the time lapse used for this analysis, a majority of respondents were males (55.2%) while 43.2% were females. In terms of age distribution, 54.5% were in the 45 54 years of age bracket, while the other age groups were more evenly distributed (18 34 were 16 .8%, 35 44 represented 19.7%, and 55 and over were 18.1%). The majority of respondents were Black (69.4%), while Whites were 25.5%, and other races represented 5.2%. Only 8.4% of the sample was of Hispanic origin during the time of this analysis. Moving o n, almost three quarters of the sample had at least a high school degree (73.2%), while 17.4% completed some level of college education, and 9.4% had a full college or graduate degree. While a sizeable majority was unemployed or unable to work (83.9%), onl y a minority of the participants were employed, either part or full time (16.1%). Regarding sexual orientation, 57.4% of participants were heterosexual, 27.7% were homosexual, and 11.3% declared other sexual orientation (e.g. bisexual, asexual). Most par ticipants reported they were in good health status (49.4%), while 39% reported
78 an excellent health status, and 8.1% said their health was poor. A sizeable majority of participants had some type of health insurance (90.6%) and only 9.4% was uninsured. Final ly, in terms of mobile phone ownership, 40.6% of participants reported to own a smartphone, while 45.5% owned a feature phone (non smartphone), and 11.6% did not have a mobile phone. Table 5 2 displays the distribution of the independent variable of intere st among participants of the Florida Health Cohort. As previously explained in chapter 4, depressive symptoms were coded in two ways. First, a binary variable of whether significant depressive symptoms were present (Yes/No) showed that 62.3% of the sample reported they were having significant symptoms at the time, while 29.7% reported they did not. In a way to provide further sensitivity analysis, and following the PHQ 8 scoring system guidelines, depressive symptoms were categorized in three levels of seve rity: mild, moderate and severe. This treatment of the independent variable showed that 29.7% had only mild symptoms (which by PHQ 8 scoring guidelines do not require to seek treatment). Meanwhile, 45.8% of participants had moderate symptoms, and 16.5% had severe symptoms of depression. PHQ 8 recommendations for respondents falling in these latter categories advice to go through a confirmatory diagnosis and get appropriate treatment. Table 5 3 summarizes the population characteristics of the sample by lev el of depressive symptoms using PHQ 8: mild, moderate, and severe. Across all three levels of depressive symptoms most of the participants were male (59.8%, 51.3%, and 52.9% respectively), between the ages of 45 and 54 (51.1%, 36.2%, and 39.2%), Blacks (69 .6%, 63.2%, and 64.7% respectively), and largely non Hispanic (94.6%, 84.2%, and
79 92.2%). Also, a significant majority of respondents completed at least high school education across all three categories of severity of depression symptoms (67.4%, 70.4%, and 72.5%). However, they were largely unemployed (78.3%, 77.6%, and 96.1%). Most respondents in all three level of severity (mild, moderate, and severe) were heterosexual (58.7%, 55.9%, and 49.0%). Regarding their self reported health status, only those who f respectively). A significant majori ty of the sample across all three levels of depressive symptoms severity had some type of health insurance (94.6%, 87.5%, and 80.4%). smartphones. However, among those with modera te and severe symptoms, a slight majority reported to own smartphones (42.1% and 51.0% respectively). Table 5 4 reports the frequency distribution of all seven outcome indicators of intention to use a mobile health application among participants of the Fl orida Health Cohort. A majority of the sample will use, if available and free, a mobile health help
80 alcohol Table 5 5 displays proportions of all seven indicators of intention to use a mobile health application by levels of depressive symptoms. Columns 2 and 3 show these proportions based on the binary transformation of PHQ 8, that is by whether significant symptoms of depression are present. Results show there was increase in the proportion of respondents that had the intention to use a mobile health application across all outcome indicators when significant symptoms of depression were p resent. Likewise, when considering columns 3 to 5, and as severity of symptoms increased, there was also a steady increase in the percentage of respondents that will use a mobile health application, and this increase showed in all but one of the outcome in dicators. The respectively for mild, moderate, and severe levels of depressive symptoms. Bivariat e s tatistics Bivariate analyses were conducted using Chi square Tests in order to assess the association between all seven outcome indicators of intention to use a mobile health application and all candidate control variables. Results of these analyses are reported in Table 5 6 through T able 5 12. These results indicate a statistically significant positive association between the outcome indicator and the candidate covariate of interest, and for this analysis significance level was set at p value<0.1. These variables were significantly associated (p value<0.1) with the intention to and cell phone ownership. Meanwhile, age, education level, employment, sexual
81 orientation, and cell phone ownership were significantly associated with intent to use the employment, and cell phone ownership were significantly associated with intention to use a m value<0.1) with education level, employment, se xual orientation, and cell phone ownership. The same covariates were significantly a with other people who Multivariate st atistics All the outcome indicators used in this study were binary variables constructed based on reported intention to use a mobile health application to access different types of health services that could be of interest for pe ople living with HIV among participants of the Florida Health Cohort. Given that depressive symptoms were coded in two ways (first, as a binary variable and then, as a 3 level categorical variable), multivariate analyses resulted in two sets of seven diffe rent logit models. Each model was adjusted according the results from Chi Square tests conducted during the bivariate analysis
82 doctor or clinic, track changes in your Significance level for all 14 logit models were set up at p value <0.1. The main hypothesis of this study is that intention of using a mobile applicati on for health purposes will be different between two groups: individuals with and without depressive symptoms (two sided hypothesis). Since no direction is being proposed as alternative hypothesis it is reasonable to forego the traditional significance lev el of p value<0.05, because increasing the probability of type I error will not dramatically affect the ability of this study to identify true differences between the two (significant symptoms and non significant symptoms) or three groups (mild, moderate, and severe). Also, decisions about significance level must account for potential of injury or harm of increasing type I error (Peck & Devore, 2008) . Results of this study are not immediately associated with potential harm or injury to people living with HI V. Thus, lowering the significance level for this study is acceptable for two reasons: it is unlikely that ability to detect true differences among groups will be affected, and it is ethically acceptable since the outcome of this analysis does not carry a potential risk of harm. Goodness of fit for all fourteen models was assessed using Hosmer Lemeshow Chi Squared diagnostic test. All seven logit models that used depressive symptoms in three level categories carried chi squared between 0.2 and 0.8 with the Hosmer Lemeshow test, which provided a reasonable evidence of linearity. Likewise, all seven logit models that used depressive symptoms coded as a binary variable carried chi squared between 0.2 and 0.8 with the same test, providing evidence of linearity.
83 Multivariate analysis using depressive symptoms as a three level categorical variable Table 5 20 shows that controlling for age, employment status, and cell phone ownership, respondents with severe depressive symptoms had almost two times the odds of u with mild depressive symptoms (p<0.08). Employed respondents had 1.9 times the odds of using a phone app for this purpose than those who were unemployed (p<0.07), and those who own a smartphone had 1.8 times the odds of doing it (p<0.05). Table 5 21 shows that controlling for age, education level, employment status, sexual orientation, and cell phone ownership, respondents with severe depressive symptoms had almost three times Respondents that completed some college education had 3.3 times the odds of using a mobile app for this purpose than those who only completed high school (p<0.002). Finally, employed respondents had 2 times the odds of using a phone app for this purpose than those who were unemployed (p<0.06). Table 5 22 displays that controlling for age, education level, employment status, and cell phone ownership, individuals with severe depressive symptoms had almost two Respo ndents that completed some college education had 2.3 times the odds of using a mobile app for this purpose than those who only completed high school (p<0.01). Finally, employed respondents had 3.8 times the odds of using a phone app for this purpose than t hose who were unemployed (p<0.001).
84 In T able 5 23 we see that controlling for education level, employment status, sexual orientation, and cell phone ownership, respondents with severe depressive ck and manage alcohol however were over the limit of significance (p<0.13). Table 5 24 shows that when controlling for employment status, sexual orientation, and cell ph one ownership, respondents with severe depressive symptoms had also 2.5 times the odds of using a phone app for this purpose than those who were unemployed (p<0.01). Table 5 25 shows that controlling for age and cell phone ownership, respondents with severe depressive symptoms had almost 1.9 times the odds of using a phone app symptoms (p<0.09). Finally, T able 5 26 displays that controlling for sexual orientation and cell phone ownership, respondents with moderate depressive symptoms had almost 1.8 times the odds sexual orientation than homosexual had two times the odds of using this feature than heteros exual persons (p<0.07). Multivariate analysis using depressive symptoms as a binary variable All but one out of seven logit models that used depressive symptoms as a binary variable carried non statistically significant results. Only the association b etween
85 presence of significant depressive symptoms and intention to use a phone application to with significant symptoms had 1.7 times the odds of using a mobile health app for this purpose than persons with no significant symptoms of depression (p<0.05). Persons of other sexual orientation than homosexual had again two times the odds of using this feature than heterosexual persons (p<0.08). Full results of these multiva riate analyses can be found in Table 5 13 through Table 5 19. Qualitative Analysis This section presents the results from a thematic analysis of focus group sessions conducted to address Aim III of this study. A total of five focus groups were conducted during May and June 2015. Having reached saturation of data at this point, data collection was stopped. Focus groups lasted between 60 and 75 minutes. Participant demographics will be described first. Then, main themes emerged during the focus groups will be presented in different sub sections, accompanied with quotes that illustrate most common responses for each themes. Focus Group D emographics Thirty nine individuals were originally screened for participation, resulting in 29 persons who were eligible to participate in the focus groups. Five people did not meet the inclusion criteria, one had moved out to another city, and 4 were not available to attend at any of the focus group sessions. Five out of 29 potential participants did not attend any of the s essions. Thus, the final sample is comprised of a total of twenty four participants. Table 5 of depression risk. 29.1% (n=7) of participants were at risk of depression at the time of the foc
86 group, most participants were female, Black, in the range of 45 to 54 years of age, with some college education, and heterosexual. The majority of participants in the group with school, and heterosexual. Participants in both groups were largely Non Hispanic and all of them had some type of health insurance. Main Themes Emerged during Focus G roups Five main overarching themes were identified: self care strategies, treatment barriers and difficulties, reactions to long term treatment, potential use of mobile health apps for people living with HIV and concerns related to usage, and desired app functio ns and features. These themes, the concepts associated to them, and supporting quotes are presented below. Section 1: Self care strategies . People living with HIV live with a chronic condition that requires them to engage in a variety of approaches to take care of their own health. Focus Group participants pictured two main group of activities they have to engage in order to stay healthy: clinical care (which included taking their medications, going to medical appointments, and keeping their laboratory test s up to date), and wellness activities (e.g. eat well, having a support group, exercise). y and I got a large container I go for like two weeks and then I have my calendar so I can uh check things cause I forget a lot and I have my phone I have it set so it'll ring if it time tell me in the morning and at night cause I ars of age)
87 positive people around me to continue to stay healthy. And like Ms. Cindy said, I take arted exercising as on a variety of medications um that I need to take which I do go and have billed so I guess for me taking care of myself wo uld be um taking my medication you know at the right times when I need to take it, keep up with my appointments and ah pretty much For some participants, taking care of their heal th also included comorbid conditions, such as diabetes. Some participants also described the challenge of keeping positive and with a good mental health, for some of them that was the hardest part of their health strategies. m ok I um I take care of my health usually like my pills and stuff I keep them in a case you know left them in a case, every Sunday morning I get up and do a week or a week and I check sugar daily and I keep to my Insulin I am not directly on Insulin per say but if it get too high y ou know I always have some in the refrigerator and I just I just write
88 Section 2: Treatment barriers and difficulties . Participants identified a number of challenging situations and conditions that make their treatment more difficult than they think it should be. Most of these barriers were related to their interaction with physicians and the health system. gets redundant having to go two times, having to go here, go over there, go here, over all the time. I mean I went 6 months without my pills at one point because I just got ne of the problems, some doctors have different requirements than the government programs, the assistance for the drugs or medical care. For example, my doctor, uh, at this point only needs to see me or wants to see me every 9 months to 11 months. But when I did that then I fell out of compliance with the Drug Assistance program because they mandate that you see a doctor every 6 months. So their requirements are out of line with a lot of the medical opinions, so, you know, the system creates more visits tha n necessary. You know the doctor only wants to see you,
89 (Eric, 44 years of age) the way I work 45 years of age) Several participants talked about a general, sometimes strong feeling of mistrust, or lack of trust in the health system, the government, pharmaceutical companies, and the system at large. The reason behind this lack of trust was explain ed by these participants. they want us to pay all this money for the medicine for the rest of our lives because they gonna die. So what if they do have that too because like you know, okay you got this basketball player that he got the drugs and we all got the same thing? But, you know, like magic Johnson, you
90 these insurance companies and you know you making money off us. And we like, you age) (Anna, 64 years of age) re to comply with appointments, either with their doctor or their laboratory tests was also a barrier to keep up with treatment. appointment on the 20 th More often than not, failing to remember of taking their medication seemed to be a difficulty to att ain compliance of treatment. over there let me go get some water reminder just like my daughter say mom your mind is too busy and I try to remember and I got a four year old grandson that is a busy body
91 in the of age) I gotta go back home and take my medication because I d For some participants, multiple medications and side effects combined with either problems to communic ate with their doctor or failure to take their medication, makes up for another treatment barrier. l like I am so, you to take these new medicines and I have stashes of the old medic
92 rs of age) r illness we are now taking medications for things that have been side effects from the illness. So um like my muscle relaxers for example, if I took that this morning and came out here and ain when I get home I have to remember, I keep everything in a little blue bag and if the bag is closed that a day, um so it becomes a pain. I also have to take injections so if I could have everything combined in one big needle and just do it and get have morning meds, I have evening meds, and I have noon meds so with all that stuff it just, it just gets frustrating you know, and so a lot of times I miss lots of my doses which is bad because then it incurs other issues and so then I end up getting more meds for those iss Section 3: Reactions to long term treatment . Participants described a series of feelings and actions related, not only t o the requirement of life long treatment, but also to a combination of other factors such as frustration with the system and feelings of loss.
93 It just gets old, its old big time. I mean, in order for me to, I m ean 33 years um I am undetectable, my T cells are very good but it just gets old having to go two, three times a month trying to keep up with you bills, with your house that you own, with your animals that you have and with your clients that you have to de al with since I own my own business. It just gets old, gets old you know got no one by me One, medicines are expensive so I wish the cost would decrease, of course. Um, struggle and then I need the meds but some of some of those meds are extremely impo rtant so I have to often distinguish whether I have to pay this bill or take care of my end up neglecting the bills and then having to try to struggle to get the bi ll paid or have to
94 sometimes I even go without the meds that I need because I afford to let something go, you know in order to take care of healthcare of age) so tired. After 22 years, 22 years, medicatio n, and only been switched like 3 times out of Section 4: Use of phones and mobile health apps for people living with HIV and concerns related to its use . Appreciation for cell phones among participants was varied. For older participants, use of the phone was equated to talking on the phone, as opposed to texting or using mobile a pplications. they are for those that like them and appreciate them. They are for t hose that have, you know how to use them but I just use them I call my ex wife me and her are still friends
95 55) years of age) On th e other hand, younger participants (those under 50 years old) were much appreciative of cell phone capabilities, including texting and mobile applications. phones. So, you know, it might be, you know, just part of my normal routine. Ooh gotta ot of apps on my phone that I use *laughing*. One of my favorites is Facebook, another one is iheart for music, um, another one that I use is Google chrome, I like Google chrome, and I like google chrome better than internet explorer. Um, what else I have? I have a lot of game apps *laughs* lots of games; pet rescue, pp, a fitness app, you know, track your fitness, what you eat, that kind of thing b ut I got (laughs) I got off of that because it was too much to keep up with like you were saying. I
96 8 years of age) you to go to the calendar and to set appointment dates into your phone and um to also set an alarm on it, you know, and so I pretty much go in and I set it up f or, you know, to pharmacy to renew my prescriptions, you know those types of things, all appointments Some particip ants specifically mentioned the variables age, time passed since HIV diagnosis as a factor to be taken into account when thinking abAout people who might use mobile health applications. right now, we hether then.. the younger kids, 20 years, 30 years younger than us that might come up with these chronic illnesses, it might something new ge) I think the app would probably be useful for um, newly diagnosed probably. with
97 know that has issues about remembering stuff, I mean that would be, you know, this would their stuff going on, this is not a needed tool. But there is a use for this tool for some folks. Another barrier for utilization of mobile applications mentioned by some participants was related to the learning curve associated with modern technology. My, my, disability daughter that, you know, she the one that still knows, knows do it then I go to your friend, I will speak for myself savvy to get it going and learn i t, how can that person learn it without potentially giving up some of their personal health information. I would consider using an app. I'm not going to say yes but you know I would consider but I'm not what you I would say compu
98 age) A few participants raised questions regarding the hidden costs associated to using any type of mobile application. They mentioned the tradeoff of personal d the lack of knowledge on how this information could be used. to have to take that (Douglas, 45 years of age) for the service, sometimes even then, I mean ther fee or some flat fee, there is money being made somewhere and it has to be on their of age) Safety and privacy concerns were the most consistently mentioned issue related to adopting a mobile health application among these focus group participants. use those company soliciting me based on my health pattern that they see. You know if they see that maybe thei
99 much of my information is it looking at how much of my notes and stuff is it looking at insu rance company so they could tailor your new rates if you ever got insurance or whatever. So if I did, I would just be like, to be totally confident, you know I have to have a lock on it and all this other stuff, yep I d want them looking on my phone and seeing CD4 count such and such and such, you this and lymph node that, you know they going to apps I have to flash notification either to make a certain sound or display certain messages. Some apps you know depending on what you want to hear you can set them
100 to not do, but I would be a little leery about just sitting at this meeting and all the sudden what pops up on my screen is a message from the app you know would it be a ringtone or would it be an actual.. a lot of apps display the full message like hey blah blah blah of age) Section 5: Usefulness of mobile health apps and desired app functions and features . W hen asked about how they thought a mobile health application could be useful for PLWH, most participants endorsed different types of reminders, medication refill a ssistance, communication with providers, resource finder, and help with managing comorbid conditions. Some apps are super secure like CVS App if I sign bank, Florida credit union, once I If I log on, if I log on to their app and I have all my information here but someone come back and look at my banking. Now with CVS they can. So I mean maybe privacy involved, y ou can go in there and you have to respond to a question of any sort in that app it actually helps it gives you texting secure messages back and forth to your
101 and wade through, wade through all the automated stuff to get to just talk to a person t o s a lot faster and a lot easier than having to actually go over there and make a phone call. And the same thing is also with the pharmacy, you have medications that you need to refill, you go right there to the page and hit refill, refill, refill, hit send age) Communication with the doctor, or even the clinic would definitely be useful, especially if you have, um, 24 hour 7 day a week access to someone who can assist, um, there are a lot of times things come up, um a nd chronic especially having chronic illness, some days are better than others, some days are worse than others, so if you have that app and have the ability to chat with the physician or even the nurse or triage nurse or whoever, it helps. of age) because often times when they go on the internet because I am academic and if 2014? I think the app would be good and it depends on who wants to use this app, stages, e verybody is at different levels, everybody, some of us just recently been with this virus, so what would be the purpose of this app, what would I use this app for?
102 Would it be reminding me to take my medication because I just found out that I was Participants provided ideas on the features and characteristics a mobile health app for PLWH should have in order to help managing their health and healthcare. Appointments, medication taking reminders, medication refill and pickup reminders, symptom tracking for some mental health concerns (e.g depression), resource provision, current r esearch, ability to monitor medication side effects, and ability to directly communicate, if possible with their own doctor, or other health professional at their usual source of care (e.g. using videoconference). Finally, focus group participants provided specific design suggestions for a potential mobile application for PLWH. They suggested the app should look simple, colorful, vibrant, and easy to learn, with explicit statements about privacy and safety and some login requirements. They mentioned the imp ortance of HIPAA compliance in this regard. My appointments and my medicines and my pick ups. Just using it as a scheduling calendar If I use an app for a reminder I would not want to have to click 10 times I took Tivicay, I too k this one, I took Truvada I took this one I would like an option to say, you inf ormation. But historically he can go back and look the past 3 months or 6 months or 9 reason whether he forgot to take them, but have an option where instead of i t asking you
103 years of age) n app but when you just to wake up. So I think that would be something good *snapping fingers*. I think that would So many drugs cause weight loss, weight gain. And, um, you know, I over eat a something and say oh wow you know in 2013 you know September that month I started this drug, and this drug, and in October my weight went to this, you know and I could see ge) yeah an app like that would definitely help you know, reduce time and the headache and the frustration of going through mes s and with your regular provider he already knows you, he knows what your blood work has been like, unless he has to order new tests or anything like that, he knows what , 43 years of age) time, so. And I believe it would probably, for me it would probably would probably lessen
104 my visits too, you know from going to the doctor all the time, th Well some apps I know, some of these apps you pull up you tube and they talking they have video on there tell you about and they are actually talk ing to you about talking to you to go walking especially health things tell you to walk every morning tell you to go walking what they do reduces your blood pressure walking reduced stress what they do encouraging to have somebody see their face and talkin g Oh there you go, there you go, and I was just getting ready to say that. The one thing that I would enjoy that app for is, because a lot of times at my doctor office, you So if it would make it easier for me to set appointments, cancel appointments, get refills, find out about referrals, that would be great, now that would be helpful to me, absolutely helpful, but other than that no, no but that is a great idea years of age) Yes if I was able to see my doctor on the app would be important because um, having chronic illness, I recently had the flu and pneumonia at the same time and I had to on numerous amount of times I had to go back and forth to the emergency r oom to only end up waiting for hours and hours ok once or twice they want to hospitalize me which I s and this. Well if I had e possibly prescribed the
105 time and travel cause once I think, once I had to pay for a ride to get to the hospital and the next time I rode in the ambulance and the ambulance r ide is over six hundred dollars, stand at a bus stop and wait for the, connect with my physician or even a triage nurse who can relay something to my physician so that he could get right back to me. Yes, I would pay for it. I would pay for it (Pam, 43 years of age) Say f or instance say I have to go to doctor the app you talking about say for instance I had to go to another doctor and whatever they told me what they said it can go h ave to worry about taking no papers with me I have to go see the Dr so and so for such 47 years of age) go where the jobs are and people constantly moving now, uh I could see social networking resources being (Anna, 64 years of age) I think it would help, it depends on where you are so I am from couple of ranger completely before psychology you know, it teaches you that when you go to do counseling, things like that.
106 ranger than with someone that you know. whoever is sitting in that corporate office, that icon means something to them. I mean (Eric, 44 years of age) a guarantee that my information is not going to be shared wit h anybody outside of this Any personal information should be encrypted, that you would see the lock sign on it that says this, this part of this session is secured, u m, something that would inform you that you could proceed without having your information splattered all over the place and one that would possibly like say you lose you lose your phone or something and somebody sees the app on your phone and they trying t o access it that it would ask you, you know, you leave your Facebook open, you leave your um twitter open, things like that so that they would be the only o nes to get that information because that information
107 et system because of the HIPA A law, you know years of age) a real driver license from a fake one. I think we should be able to tell the app the same way. Um, whether it has some kind of hieroglyphic or something, or set it up where we, you know like how banks have it where you can set up your own image behind it, something like that that no one can, a pattern or something that you would know to do to get into the app. Um, I definitely think it should be bold and colorful, I like colors, I like vibrant, brilliant colors. Um and most of all, again I have to agree with mister Poopsie I get to. Ask me a few doctors, and send me straight to the doctor. I want something simple. Simple, secure,
108 T able 5 1. Characteristics of participants of the Florida Health Cohort (N=310) Respondent Characteristic N % Gender Male 171 55.2 Female 134 43.2 missing 5 1.6 Age 18 34 52 16.8 35 44 61 19.7 45 54 169 54.5 55 and up 56 18.1 missing 8 2.6 Race White 79 25.5 Black 215 69.4 Other 16 5.2 Ethnicity Hispanic 26 8.4 Non Hispanic 282 91.0 missing 2 0.6 Education level High school 227 73.2 Some college 54 17.4 College or graduate schoo l 29 9.4 Employment Employed 50 16.1 Unemployed 260 83.9 Sexual orientation Heterosexual 178 57.4 Homosexual 86 27.7 Other 35 11.3 missing 11 3.5
109 T able 5 1. Continued Respondent Characteristic N % Health status Excellent 121 39.0 Good 153 49.4 Poor 25 8.1 missing 11 3.5 Insurance Any insurance 281 90.6 No insurance 29 9.4 Cell phone ownership Smartphone 126 40.6 Feature phone (flip phone) 141 45.5 No cell phone 36 11.6 missi ng 7 2.3
110 T able 5 2. Depressive symptoms among participants of the Florida Health Cohort (N=310) N % PHQ 8 depressive symptoms three level scoring Mild 92 29.7 Moderate 142 45.8 Severe 51 16.5 missing 25 8.1 PHQ 8 depressive symptoms binary transformation Significant depressive symptoms 193 62.3 No significant depressive symptoms 92 29.7 missing 25 8.1
111 T able 5 3. Characteristics of participants of the Florida Health Cohort by PHQ 8 depressive symptom level Mild (n=92) Moderate (n=142) Severe (n=51) Respondent Characteristic N % N % N % Gender Male 55 59.8 78 51.3 27 52.9 Female 36 39.1 61 40.1 24 47.1 missing 1 1.1 3 2.0 1 2.0 Age 18 34 9 9.8 29 19.1 11 21.6 35 44 13 14.1 30 19.7 14 27.5 45 54 47 51.1 55 36.2 20 39.2 55 and up 23 25.0 21 13.8 5 9.8 missing 0 0.0 7 4.6 1 2.0 Race White 24 26.1 38 25.0 15 29.4 Blac k 64 69.6 96 63.2 33 64.7 Other 4 4.3 8 5.3 3 5.9 Ethnicity Hispanic 5 5.4 14 9.2 4 7.8 Non Hispanic 87 94.6 128 84.2 47 92.2 missing 1 1.1 0 0.0 0 0.0 Education level High school 62 67. 4 107 70.4 37 72.5 Some college 17 18.5 23 15.1 10 19.6 College or graduate school 13 14.1 12 7.9 4 7.8 Employment Employed 20 21.7 24 15.8 2 3.9 Unemployed 72 78.3 118 77.6 49 96.1 Sexual orientatio n Heterosexual 54 58.7 85 55.9 25 49.0 Homosexual 27 29.3 40 26.3 17 33.3 Other 7 7.6 13 8.6 8 15.7 missing 4 4.3 4 2.6 1 2.0
112 T able 5 3. Continued Mild (n=92) Moderate (n=142) Severe (n=51) Responden t Characteristic N % N % N % Health status Excellent 52 56.5 53 34.9 5 9.8 Good 37 40.2 73 48.0 33 64.7 Poor 0 0.0 12 7.9 12 23.5 missing 3 3.3 4 2.6 1 2.0 Insurance Any insurance 87 94.6 133 87 .5 41 80.4 No insurance 5 5.4 9 5.9 10 19.6 Cell phone ownership Smartphone 31 33.7 64 42.1 26 51.0 Feature phone (flip phone) 46 50.0 63 41.4 16 31.4 No cell phone 13 14.1 14 9.2 6 11.8 missing 2 2.2 1 0.7 3 5.9
113 T able 5 4. Indicators of intention to use mHealth applications among participants of the Florida Health Cohort (N=310) If available and free, how often would you use a phone app to help N % Identify health services relevant to you 140 45.2 missing 3 1.0 Track changes in your mood and emotions 92 29.7 missing 5 1.6 Provide tips to improve your health, based on information about you 126 40.6 missing 4 1.3 Track and manage alcohol and drug use behavior 52 16.8 missing 6 1.9 Communicate with your doctor or clinic 164 52.9 missing 5 1.6 Remember to take your medication 144 46.5 missing 4 1.3 Engage in social networking with other people who live with HIV 124 40.0 miss ing 5 1.6
114 T able 5 5. Indicators of intention to use mHealth applications among participants of the Florida Health Cohort by severity of depressive symptoms according the Patient Health Questionnaire, PHQ 8 (N=310) Significant depressive sym ptoms Severity of depressive symptoms in three level category No (n=92) Yes (n=193) Mild (n=92) Moderate (n=142) Severe (n=51) If available and free, how often would you use a pho ne app to Identify health services relevant to you 35.9 48.2 35.9 45.1 56.9 Track changes in your mood and emotions 21.7 31.6 21.7 26.8 45.1 Provide tips to improve your health, based on information about you 32.6 43.0 32.6 40.6 49.0 Track and manage alcohol and drug use behavior 12.0 16.6 12.0 14.1 23.5 Communicate with your doctor or clinic 46.7 55.4 46.7 52.8 62.8 Remember to take your medication 34.8 49.7 34.8 47.9 54.9 Engage in social networking with other people who live with HIV 29.4 45.1 29.4 45.8 43.1
115 T able 5 6. Characteristics of participants by intention to use a phone app to "identify health services relevant to you" Respondent Charac teristic Yes (n=140) No (n=167) Chi Square P value Gender 2.355 0.671 Male 50.71 58.68 Female 47.86 39.52 missing 1.43 1.80 Age 22.232 0.005 18 34 24.29 10.78 35 44 22.86 17.37 45 54 37.86 46.11 55 and up 11.43 23.9 5 missing 3.57 1.80 Race 0.011 0.994 White 25.71 25.75 Black 69.29 69.46 Other 5.00 4.79 Ethnicity 2.006 0.735 Hispanic 8.57 8.38 Non Hispanic 91.43 90.42 missing 0.00 1.20 Education level 2.171 0.338 High school 69.29 76.65 Some college 22.86 13.17 College or graduate school 7.86 10.18 Employment 5.613 0.060 Employed 21.43 11.98 Unemployed 78.57 88.02 Sexual orientation 3.719 0.445 Heterosexual 59.29 55. 69 Homosexual 30.00 25.75 Other 9.29 13.17 missing 1.43 5.39
116 T able 5 6. Continued Respondent Characteristic Yes (n=140) No (n=167) Chi Square P value Health status 0.414 0.981 Excellent 38.57 39.52 Good 49.29 49.10 Poor 8.57 7.78 missing 3.57 3.59 Insurance 0.801 0.670 Any insurance 89.29 91.62 No insurance 10.71 8.38 Cell phone ownership 74.158 <0.001 Smartphone 53.57 30.54 Feature phone (flip phone) 36.43 53.89 No cell phone 9.29 13.17 missing 0.71 2.40
117 T able 5 7. Characteristics of participants by intention to use a phone app to "track changes in your mood and emotions" Respondent Characteristic Yes (n=92) No (n=213) Chi Square P value Gender 3. 029 0.553 Male 56.52 54.93 Female 40.22 44.13 missing 3.26 0.94 Age 20.328 0.009 18 34 22.83 14.55 35 44 25.00 17.84 45 54 35.87 44.60 55 and up 10.87 21.60 missing 5.43 1.41 Race 2.605 0.272 White 31. 52 23.47 Black 63.04 71.83 Other 5.43 4.69 Ethnicity 2.115 0.715 Hispanic 6.52 9.39 Non Hispanic 93.48 89.67 missing 0.00 0.94 Education level 10.194 0.006 High school 60.87 78.40 Some college 29.35 12.68 College or graduate school 9.78 8.92 Employment 6.494 0.039 Employed 23.91 13.15 Unemployed 76.09 86.85 Sexual orientation 7.099 0.131 Heterosexual 50.00 60.56 Homosexual 39.13 23.00 Other 9.78 11.74 missing 1 .09 4.69
118 T able 5 7. Continued Respondent Characteristic Yes (n=92) No (n=213) Chi Square P value Health status 3.649 0.456 Excellent 29.35 42.72 Good 56.52 46.48 Poor 9.78 7.51 missing 4.35 3.29 Insurance 1.456 0.48 3 Any insurance 88.04 91.55 No insurance 11.96 8.45 Cell phone ownership 46.765 <0.001 Smartphone 56.52 34.74 Feature phone (flip phone) 32.61 51.64 No cell phone 9.78 11.74 missing 1.09 1.88
119 T able 5 8. Cha racteristics of participants by intention to use a phone app to provide tips to "improve your health, based on information about you" Respondent Characteristic Yes (n=126) No (n=180) Chi Square P value Gender 0.930 0.920 Male 55.56 55.00 Fem ale 42.06 42.89 missing 2.38 1.11 Age 25.191 0.001 18 34 24.60 11.67 35 44 23.81 17.22 45 54 34.13 47.78 55 and up 12.70 22.22 missing 4.76 1.11 Race 0.395 0.821 White 27.78 24.44 Black 67.46 70.56 O ther 4.76 5.00 Ethnicity 3.183 0.528 Hispanic 6.35 10.00 Non Hispanic 93.65 88.89 missing 0.00 1.11 Education level 7.216 0.027 High school 65.08 78.89 Some college 25.40 12.22 College or graduate school 9.52 8. 89 Employment 16.144 <0.001 Employed 26.19 9.44 Unemployed 73.81 90.56 Sexual orientation 4.298 0.367 Heterosexual 54.76 58.89 Homosexual 32.54 24.44 Other 11.11 11.67 missing 1.59 5.00
120 T able 5 8. Cont inued Respondent Characteristic Yes (n=126) No (n=180) Chi Square P value Health status 8.242 0.083 Excellent 34.13 42.78 Good 54.76 45.56 Poor 7.94 8.33 missing 3.17 3.33 Insurance 1.093 0.579 Any insurance 88.89 91.67 No insurance 11.11 8.33 Cell phone ownership 55.514 <0.001 Smartphone 53.17 32.78 Feature phone (flip phone) 36.51 52.22 No cell phone 9.52 12.78 missing 0.79 2.22
121 T able 5 9. Characteristics of participants by i ntention to use a phone app to "track and manage alcohol and drug use behavior" Respondent Characteristic Yes (n=52) No (n=252) Chi Square P value Gender 0.635 0.959 Male 57.69 54.37 Female 40.38 44.05 missing 1.92 1.59 Age 11.091 0.197 18 34 28.85 14.68 35 44 25.00 18.25 45 54 32.69 44.84 55 and up 11.54 19.44 missing 1.92 2.78 Race 3.831 0.147 White 13.46 28.17 Black 80.77 67.06 Other 5.77 4.76 Ethnicity 25.498 <0.001 Hispanic 9.62 7.94 Non Hispanic 90.38 91.67 missing 0.00 0.40 Education level 3.887 0.143 High school 65.38 75.40 Some college 25.00 15.48 College or graduate school 9.62 9.13 Employment 3.215 0.200 Employed 23.0 8 15.08 Unemployed 76.92 84.92 Sexual orientation 19.227 0.001 Heterosexual 51.92 58.73 Homosexual 32.69 26.98 Other 15.38 10.71 missing 0.00 3.57
122 T able 5 9. Continued Respondent Characteristic Yes (n=52) No (n =252) Chi Square P value Health status 5.889 0.208 Excellent 30.77 41.27 Good 55.77 47.62 Poor 9.62 7.94 missing 3.85 3.17 Insurance 1.769 0.413 Any insurance 86.54 91.27 No insurance 13.46 8.73 Cell phone owne rship 28.995 <0.001 Smartphone 44.23 40.87 Feature phone (flip phone) 42.31 46.43 No cell phone 11.54 11.11 missing 1.92 1.59
12 3 T able 5 10. Characteristics of participants by intention to use a phone app to "communicate with your doctor or clinic" Respondent Characteristic Yes (n=164) No (n=141) Chi Square P value Gender 1.906 0.753 Male 52.44 58.87 Female 45.73 39.72 missing 1.83 1.42 Age 10.431 0.236 18 34 21.34 12.06 35 44 21.95 17.73 45 54 38.41 46.81 55 and up 15.85 20.57 missing 2.44 2.84 Race 2.179 0.336 White 22.56 29.79 Black 72.56 65.25 Other 4.88 4.96 Ethnicity 2.699 0.609 Hispanic 7.93 8.51 Non Hispanic 90.85 91.49 missi ng 1.22 0.00 Education level 1.147 0.564 High school 70.73 75.89 Some college 21.34 13.48 College or graduate school 7.93 10.64 Employment 4.371 0.112 Employed 20.12 11.35 Unemployed 79.88 88.65 Sexual orie ntation 7.280 0.122 Heterosexual 61.59 52.48 Homosexual 28.66 26.24 Other 8.54 14.89 missing 1.22 6.38
124 T able 5 10. Continued Respondent Characteristic Yes (n=164) No (n=141) Chi Square P value Health status 2.710 0.607 Exc ellent 34.15 44.68 Good 52.44 45.39 Poor 9.15 7.09 missing 4.27 2.84 Insurance 0.818 0.664 Any insurance 90.24 91.49 No insurance 9.76 8.51 Cell phone ownership 42.015 <0.001 Smartphone 48.17 33.33 Feature p hone (flip phone) 40.24 52.48 No cell phone 10.98 11.35 missing 0.61 2.84
125 T able 5 11. Characteristics of participants by intention to use a phone app to "remember to take your medication" Respondent Characteristic Yes (n=144) No (n=162) Chi Square P value Gender 3.440 0.487 Male 52.08 57.41 Female 45.14 41.98 missing 2.78 0.62 Age 12.687 0.123 18 34 22.22 12.35 35 44 20.14 19.75 45 54 39.58 45.06 55 and up 13.89 21.60 missing 4.17 1 .23 Race 3.292 0.193 White 20.83 30.25 Black 74.31 64.81 Other 4.86 4.94 Ethnicity 3.483 0.480 Hispanic 6.94 9.88 Non Hispanic 91.67 90.12 missing 1.39 0.00 Education level 1.671 0.434 High school 71.53 75.31 Some college 20.14 14.81 College or graduate school 8.33 9.88 Employment 0.807 0.668 Employed 15.97 16.67 Unemployed 84.03 83.33 Sexual orientation 4.268 0.371 Heterosexual 59.72 55.56 Homosexual 29. 17 26.54 Other 9.72 12.35 missing 1.39 5.56
126 T able 5 11. Continued Respondent Characteristic Yes (n=144) No (n=162) Chi Square P value Health status 0.971 0.914 Excellent 35.42 41.98 Good 52.08 46.91 Poor 9.03 7.41 missi ng 3.47 3.70 Insurance 2.157 0.340 Any insurance 88.19 92.59 No insurance 11.81 7.41 Cell phone ownership 47.338 <0.001 Smartphone 47.22 35.80 Feature phone (flip phone) 41.67 50.00 No cell phone 10.42 11.73 mis sing 0.69 2.47
127 T able 5 12. Characteristics of participants by intention to use a phone app to "engage in social networking with other people who live with HIV" Respondent Characteristic Yes (n=124) No (n=181) Chi Square P value Gend er 4.240 0.374 Male 57.26 53.59 Female 39.52 45.86 missing 3.23 0.55 Age 10.331 0.243 18 34 22.58 13.26 35 44 17.74 20.99 45 54 37.90 45.30 55 and up 17.74 18.78 missing 4.03 1.66 Race 0.212 0.900 W hite 26.61 24.86 Black 69.35 69.61 Other 4.03 5.52 Ethnicity 2.382 0.666 Hispanic 7.26 9.39 Non Hispanic 92.74 89.50 missing 0.00 1.10 Education level 2.341 0.310 High school 68.55 76.24 Some college 22.58 1 4.36 College or graduate school 8.87 9.39 Employment 3.768 0.152 Employed 20.97 12.71 Unemployed 79.03 87.29 Sexual orientation 8.921 0.063 Heterosexual 52.42 60.77 Homosexual 31.45 24.86 Other 15.32 8.84 mi ssing 0.81 5.52
128 T able 5 12. Continued Respondent Characteristic Yes (n=124) No (n=181) Chi Square P value Health status 4.334 0.363 Excellent 32.26 43.65 Good 55.65 44.75 Poor 8.06 8.29 missing 4.03 3.31 Insurance 2 .775 0.250 Any insurance 87.90 92.82 No insurance 12.10 7.18 Cell phone ownership 39.786 <0.001 Smartphone 49.19 35.91 Feature phone (flip phone) 40.32 49.17 No cell phone 9.68 12.71 missing 0.81 2.21
129 T able 5 13. Association between presence of depressive symptoms, population characteristics and intention to use a phone app to "identify health services relevant to you" Respondent Characteristic OR Std. Error p value 95% Confidence Interval Depressive symptoms Yes 1.312 0.377 0.345 0.747 2.305 No Reference Age 35 44 0.569 0.239 0.180 0.250 1.298 45 54 0.438 0.168 0.032 0.206 0.930 55 and up 0.280 0.135 0.008 0.109 0.719 18 34 Reference Employment Em ployed 1.781 0.648 0.113 0.873 3.633 Unemployed Reference Cell phone ownership Smartphone 1.818 0.523 0.038 1.034 3.196 No cell phone 0.975 0.432 0.955 0.409 2.324 Feature phone (flip phone) Reference
130 T able 5 14 . Association between presence of depressive symptoms, population characteristics and intention to use a phone app to "track changes in your mood and emotions" Respondent Characteristic OR Std. Error p value 95% Confidence Interval Depressive symptom s Yes 1.503 0.510 0.230 0.773 2.921 No Reference Age 35 44 1.227 0.546 0.646 0.513 2.937 45 54 0.829 0.339 0.647 0.372 1.849 55 and up 0.340 0.200 0.067 0.107 1.079 18 34 Reference Education level Some college 3.340 1.267 0.001 1.589 7.023 College or graduate school 1.575 0.776 0.356 0.600 4.136 High school Reference Employment Employed 1.779 0.696 0.141 0.827 3.831 Unemployed Reference Sexual orientation Homos exual 1.277 0.433 0.471 0.657 2.483 Other 0.744 0.395 0.577 0.263 2.106 Heterosexual Reference Cell phone ownership Smartphone 1.644 0.567 0.149 0.836 3.231 No cell phone 1.752 0.903 0.277 0.638 4.812 Feature phone (flip phone) R eference
131 T able 5 15. Association between presence of depressive symptoms, population characteristics and intention to use a phone app to "improve your health, based on information about you" Respondent Characteristic OR Std. Error p value 95% Confidence Interval Depressive symptoms Yes 1.385 0.423 0.286 0.761 2.522 No Reference Age 35 44 0.724 0.308 0.447 0.314 1.667 45 54 0.466 0.182 0.051 0.216 1.004 55 and up 0.346 0.172 0.032 0.131 0.914 18 34 Reference Education level Some college 2.364 0.843 0.016 1.175 4.756 College or graduate school 1.288 0.588 0.579 0.526 3.154 High school Reference Employment Employed 3.529 1.340 0.001 1.677 7.426 Unemployed Re ference Cell phone ownership Smartphone 1.441 0.444 0.236 0.788 2.638 No cell phone 1.102 0.508 0.833 0.447 2.721 Feature phone (flip phone) Reference
132 T able 5 16. Association between presence of depressive symptom s, population characteristics and intention to use a phone app to "track and manage alcohol and drug use behavior" Respondent Characteristic OR Std. Error p value 95% Confidence Interval Depressive symptoms Yes 1.442 0.567 0.352 0.667 3.118 No Reference Education level Some college 1.632 0.707 0.259 0.698 3.817 College or graduate school 1.379 0.786 0.573 0.451 4.216 High school Reference Employment Employed 1.404 0.628 0.449 0.584 3.374 Unemployed Reference Sexual orientation Homosexual 1.299 0.515 0.510 0.597 2.827 Other 2.255 1.134 0.106 0.842 6.044 Heterosexual Reference Cell phone ownership Smartphone 1.004 0.385 0.991 0.474 2.127 No cell phone 1.320 0.756 0.628 0.429 4.057 Feature phone (flip phone) Reference
133 T able 5 17. Association between presence of depressive symptoms, population characteristics and intention to use a phone app to "communicate with your doctor or clinic" Respondent Characteristic OR Std. Error p value 95% Confidence Interval Depressive symptoms Yes 1.436 0.395 0.188 0.838 2.461 No Reference Employment Employed 2.244 0.840 0.031 1.078 4.672 Unemployed Reference Sexual orientation Homosexual 0.753 0.218 0.327 0.427 1.328 Other 0.436 0.187 0.052 0.189 1.008 Heterosexual Reference Cell phone ownership Smartphone 1.566 0.429 0.101 0.916 2.679 No cell phone 1.363 0.572 0.460 0.600 3.101 Feature phone (flip phone) Reference
134 T able 5 18. Association between presence of depressive symptoms, population characteristics and intention to use a phone app to "remember to take your medication" Respondent Characteristic OR Std. Error p value 95% Confidence Interval Depressive symptoms Yes 1.502 0.414 0.140 0.875 2.578 No Reference Age 35 44 0.461 0.188 0.058 0.207 1.027 45 54 0.488 0.179 0.050 0.238 1.001 55 and up 0.418 0.190 0.056 0.171 1. 021 18 34 Reference Cell phone ownership Smartphone 1.292 0.366 0.366 0.741 2.252 No cell phone 1.021 0.434 0.962 0.443 2.350 Feature phone (flip phone) Reference
135 T able 5 19. Association between presence of depr essive symptoms, population characteristics and intention to use a phone app to "engage in social networking with other people who live with HIV" Respondent Characteristic OR Std. Error p value 95% Confidence Interval Depressive symptoms Yes 1.740 0.490 0.049 1.001 3.023 No Reference Sexual orientation Homosexual 1.290 0.367 0.371 0.738 2.255 Other 2.104 0.889 0.078 0.919 4.816 Heterosexual Reference Cell phone ownership Smartphone 1.414 0.387 0.205 0.827 2.416 No cell phone 0.710 0.318 0.444 0.295 1.707 Feature phone (flip phone) Reference
136 T able 5 20. Association between severity of depressive symptoms, population characteristics and intention to use a phone app to "identify health services relevant to you" Respondent Characteristic OR Std. Error p value 95% Confidence Interval Depressive symptoms Moderate 1.154 0.348 0.634 0.640 2.082 Severe 1.971 0.783 0.088 0.905 4.294 Mild Reference Age 35 44 0.565 0.239 0.176 0.246 1.293 45 54 0.444 0.172 0.036 0.208 0.947 55 and up 0.284 0.137 0.009 0.110 0.733 18 34 Reference Employment Employed 1.911 0.699 0.077 0.933 3.914 Unemployed Reference Cell phone owne rship Smartphone 1.762 0.510 0.051 0.998 3.108 No cell phone 0.949 0.423 0.906 0.396 2.275 Feature phone (flip phone) Reference
137 T able 5 21. Association between severity of depressive symptoms, population characteristics and i ntention to use a phone app to "track changes in your mood and emotions" Respondent Characteristic OR Std. Error p value 95% Confidence Interval Depressive symptoms Moderate 1.190 0.427 0.627 0.590 2.404 Severe 2.915 1.296 0.016 1.219 6.967 Mild Reference Age 35 44 1.192 0.539 0.698 0.491 2.891 45 54 0.853 0.353 0.702 0.379 1.922 55 and up 0.343 0.205 0.074 0.106 1.109 18 34 Reference Education level Some college 3.304 1.272 0.002 1.553 7.027 C ollege or graduate school 1.565 0.774 0.365 0.593 4.126 High school Reference Employment Employed 2.144 0.866 0.059 0.972 4.731 Unemployed Reference Sexual orientation Homosexual 1.189 0.409 0.614 0.607 2.332 Oth er 0.678 0.365 0.470 0.236 1.945 Heterosexual Reference Cell phone ownership Smartphone 1.566 0.547 0.199 0.790 3.105 No cell phone 1.657 0.864 0.333 0.596 4.603 Feature phone (flip phone) Reference
138 T able 5 22. Association between severity of depressive symptoms, population characteristics and intention to use a phone app to "improve your health, based on information about you" Respondent Characteristic OR Std. Error p value 95% Confidence Interval Depressi ve symptoms Moderate 1.235 0.395 0.509 0.660 2.311 Severe 1.995 0.825 0.095 0.887 4.487 Mild Reference Age 35 44 0.723 0.309 0.448 0.313 1.670 45 54 0.475 0.187 0.058 0.220 1.026 55 and up 0.354 0.177 0.038 0.133 0.944 18 34 Reference Education level Some college 2.353 0.845 0.017 1.164 4.758 College or graduate school 1.271 0.581 0.599 0.519 3.114 High school Reference Employment Employed 3.793 1.456 0.001 1.788 8.048 Unemploy ed Reference Cell phone ownership Smartphone 1.402 0.435 0.276 0.763 2.577 No cell phone 1.080 0.499 0.868 0.436 2.671 Feature phone (flip phone) Reference
139 T able 5 23. Association between severity of depressive sy mptoms, population characteristics and intention to use a phone app to "track and manage alcohol and drug use behavior" Respondent Characteristic OR Std. Error p value 95% Confidence Interval Depressive symptoms Moderate 1.246 0.518 0.597 0.5 52 2.813 Severe 2.152 1.079 0.126 0.806 5.749 Mild Reference Education level Some college 1.599 0.697 0.259 0.680 3.759 College or graduate school 1.369 0.781 0.573 0.447 4.190 High school Reference Employment Employed 1.551 0.709 0.449 0.633 3.800 Unemployed Reference Sexual orientation Homosexual 1.245 0.497 0.510 0.569 2.722 Other 2.098 1.066 0.106 0.775 5.680 Heterosexual Reference Cell phone ownership Smartphone 0 .969 0.374 0.936 0.455 2.064 No cell phone 1.282 0.737 0.666 0.416 3.954 Feature phone (flip phone) Reference
140 T able 5 24. Association between severity of depressive symptoms, population characteristics and intention to use a phone a pp to "communicate with your doctor or clinic" Respondent Characteristic OR Std. Error p value 95% Confidence Interval Depressive symptoms Moderate 1.232 0.353 0.467 0.702 2.161 Severe 2.461 0.991 0.025 1.118 5.417 Mild Reference Employment Employed 2.501 0.949 0.016 1.189 5.261 Unemployed Reference Sexual orientation Homosexual 0.710 0.209 0.243 0.399 1.262 Other 0.390 0.170 0.031 0.166 0.916 Heterosexual Reference Cell phone owners hip Smartphone 1.502 0.415 0.141 0.874 2.583 No cell phone 1.314 0.556 0.519 0.573 3.011 Feature phone (flip phone) Reference
141 T able 5 25. Association between severity of depressive symptoms, population characteristics and int ention to use a phone app to "remember to take your medication" Respondent Characteristic OR Std. Error p value 95% Confidence Interval Depressive symptoms Moderate 1.391 0.402 0.254 0.789 2.452 Severe 1.889 0.717 0.094 0.898 3.976 Mild Ref erence Age 35 44 0.455 0.186 0.054 0.204 1.015 45 54 0.487 0.179 0.050 0.237 1.000 55 and up 0.419 0.191 0.056 0.171 1.024 18 34 Reference Cell phone ownership Smartphone 1.270 0.361 0.401 0.727 2.218 No cell phone 0.998 0.426 0.997 0.432 2.306 Feature phone (flip phone) Reference
142 T able 5 26. Association between severity of depressive symptoms, population characteristics and intention to use a phone app to "engage in social networking wit h other people who live with HIV" Respondent Characteristic OR Std. Error p value 95% Confidence Interval Depressive symptoms Moderate 1.833 0.541 0.040 1.028 3.268 Severe 1.490 0.574 0.300 0.701 3.170 Mild Reference Sexual or ientation Homosexual 1.305 0.373 0.351 0.745 2.285 Other 2.163 0.920 0.070 0.940 4.980 Heterosexual Reference Cell phone ownership Smartphone 1.430 0.392 0.192 0.835 2.449 No cell phone 0.722 0.324 0.468 0.300 1.740 Featur e phone (flip phone) Reference
143 T able 5 27. Characteristics of focus group participants by presence of depression risk according to the Patient Health Questionnaire, PHQ 2 (N=24) Depression Risk (n=7) No Depression Risk ( n=17) Participant Characteristic Gender Male 2 10 Female 5 5 Transgender 0 2 Age 18 34 0 1 35 44 2 5 45 54 3 7 55 and up 2 4 Race White 2 5 Black 5 11 Other 0 1 Ethnicity Hispanic 0 1 Non Hispanic 7 16 Education level Less than high school 0 7 High school 2 5 Some college 3 2 College or graduate school 2 3 Sexual orientation Heterosexual 4 8 Homosexual 1 5 Other 2 4 Insurance Any insurance 7 17
144 CHAP TER 6 DISCUSSION AND CONCLUSIONS Discussion This was the first study conducted in the Southeastern region with the primary objective of determining the relationship between depressive symptoms and intent of using mobile health applications to manage HIV re lated conditions among people living with HIV. Main findings from this study suggest there is a positive association between severity of depressive symptoms and intention to use a mobile health application among PLWH in the Florida Health Cohort, meaning t hat the PLWH with the highest need (those severely depressed) are the most willing to use mHealth apps. Moreover, cell phone ownership was the only factor consistently associated with all seven indicators of intend to use a mobile health application in thi s sample. Finally, mistrust in in the system and privacy concerns were important deterrents to using a mobile health app among focus group participants. Ability to communicate with health provider or clinic and reminder features functions were the most des ired features, but communication capabilities was the single most sought after feature, even at the expense of some information sharing or some costs associated with the service. While this study indicate that PLWH with severe depressive symptoms are mor e likely to use a mHealth app for helping self monitoring of HIV related conditions, Krishnan et al. did not find an association between depression and acceptance of communication technology and mHealth (2014) . However, that study was conducted in the cou ntry of Peru, including only males who had sex with other males, and some cultural differences may explain help seeking behavior among PLWH. Also, Krishnan and collaborators used depression as a binary variable for their analyses. When we
145 used depressive s ymptoms recoded as binary no relationship was present either, Our results stem from a sensitivity analysis approach, whereby depressive symptoms was used as a three l evel variable, which also might explains the difference in findings. Three findings from our multivariate analyses warrant further discussion. Two indicators of intention to use a health app showed strong relationship with severe depressive symptoms: " track changes in your mood and emotions" and "communicate with your doctor or clinic" . These results highlight particular needs that some PLWH may have when they have a co occurring depression. Tracking mood changes is useful to depressed individuals in or der to assess how treatment is working, and communication with the doctor seems understandable given that severe depression may trigger or exacerbate help seeking behaviors already present in PLWH. Additionally, the intention to use a phone app to "engage in social networking with other people who live with HIV" had a strong relationship with moderate depressive symptoms, as opposed to all other 6 indicators, which a significant relationship only with severe symptoms. This finding that may seem counterintui tive, is really not. Persons dealing with severe depression tend to isolate themselves, and rarely seek to socialize or interact with other people. For this reason is reasonable that PLWH and moderate depression are more likely to use social networking fea tures. Thus, given the high comorbidity of depression with HIV, it is poignant to mention that, while moderately depressed PLWH may still engage in social networking, the ones with severe depression may not, but these severely depressed individuals might s till want to seek for help by communicating directly with their doctors, providers, or clinic.
146 Another interesting finding of this study is that while age, employment status, education level, and sexual orientation, and cell phone ownership were associate d with the outcome indicators in bivariate analyses, gender, race, and ethnicity did not. This seem to support findings from previous studies regarding the progressive (Krishnan et al. ., 2014; Lella & Lipsman, 2014) . Th is also may suggest additional research is needed regarding the appropriateness of some modifying factor impacting on use behavior of consumer health technology (Venkatesh et al . ., 2003) . The qualitative findings of this study confirm and extend those of Ramanathan et al. (2013) . Safety and privacy concerns remain the main barrier for adoption of mobile health technology. A general mistrust of anything that is made via the inter net was expressed several times by participants. This distrust mostly comprised the information sharing process and involved mistrust in both government agencies and insurance/pharmaceutical companies. Information sharing was much more accepted when includ A novel contribution of this study compared to current literature in this area is the intense interest in a mobile health application that allow communication with viders, in several different ways. Two way SMS was proposed in order to schedule, reschedule, or cancel appointments, set up medication pick up time, ask about prescription renewals, and medication side effects. Data sharing between laboratory clinics, pat ient, pharmacy, and doctors was found desirable in order to avoid delays in the process of sharing information that could be valuable for
147 app, and then results sent ou t to the doctor via app as well). Videoconference was also proposed as a way to improve communication with doctors. It was observed that this type of feature, while highly desirable, will require a previously existing, ongoing patient doctor relationship, which will help to establish mutual trust and respect. Participants mentioned how useful this resource could be for people with issues of transportation, or financial problems that might impede them to travel to the clinic. Likewise, given the high freque ncy of visits to the clinic (or sometimes to the emergency room) that PLWH make, several participants noted that this format of communication via app could help to reduce the number of unnecessary visits. Limitations Several study limitations are to be not ed . First, data was collected using self reported surveys on the intent to use a mobile health application. Despite being a good indicator of actual use, intent to use overlooks an entire side of technology acceptance models, which is the actual technology and its characteristics. However, intent to use is able to provide guidelines and serve as groundwork for actual development of consumer health technology. Second , the Florida Health Cohort is a made of a convenience sample. Participants are recruited onl y from participating clinics and are individuals with current contact with the health system. This fact limits the representativeness of the sample. However, most recruiting sites are located in local Health Departments and distributed both in rural and ur ban areas across the state of Florida. Third , the study is cross sectional in nature. While this precludes us to look at causal relationships between depression and the intention of using mHealth among
148 PLWH, it serves the purpose of informing further desig n and developing processes of a potential mobile application which then should be piloted and its efficacy tested am ong this population. Fourth, the survey data represented information from 310 respondents, which limited the power of the statistical analys is. Finally, participants of the focus groups include very few younger individuals. While this might have hurt the ability to gather information about how privacy concerns impact on younger people, it will still help to contextualize survey findings, thus providing information for the next step of development of a mobile application for PLWH. Implications and future research The empirical con text of this study was the Florida Cohort to Monitor and Improve Health Outcomes, a cohort study of PLWH in the stat e of Florida. The Florida Cohort is being conducted by a multidisciplinary team of researchers from the departments of Epidemiology, Health Services Research, Management and Policy, Biostatistics, Clinical and Health Psychology at the College of Public Hea lth and Health Professions, as well as from the College of Nursing and the Veterans Affairs Hospital of Gainesville. Participants are recruited from both urban and rural areas across the state, from five health departments located in Alachua, Columbia, Hil lsborough, Orange and Sumter counties. We know that PLWH have one of the lowest treatment adherence among chronic et al. ., 2012). Indeed, of the estimated 1.1 million infected in the U S, only 19 to 25% get treatment and adher e to it, thus achieving successful control of the virus. We also know that mobile health applications have been found to help improve treatment adherence for other chronic conditions (Stephens & Allen, 2013). However, dropout rates of health mobile applica tions is a concern for
149 researchers (Marcano Belisario, Huckvale, Greenfield, Car, & Gunn, 2013). This study suggests t he answer may lie in appropriate personalization and tailoring of apps to very specific needs of different group of patients. Persons who live with a chronic health condition in general, and people living with HIV in particular, often face comorbidity with other chronic condition. Depression is the single most frequent mental health comorbidity for PLWH and it is the main reason for less th an optimum treatment adherence (Pence, Gaynes, et al. ., 2012) . If the findings from this study prove to be correct, then current research focused on developing mobile health tools targeted to people living with HIV needs to address the concerns, preference s and desires in terms of safety, privacy, and features that these results showed. Future research could use t he results of this study in order to design and develop a tailored mobile application to be used by people living with HIV accounting for the char acteristics of these individuals that may impact their intention to use the app. Once these characteristics are accounted for, the mobile health app could be used to improve access to care by providing a number of services, such as, reminders to improve me dication adherence, medication refill management, managing medical appointments, health information requests, self managing HIV related mental conditions like depres sion itself , communication with healthcare providers, symptom tracking, either of depressiv e symptoms or medication side effects, that ultimately may impact HIV outcomes.
150 APPENDIX A THE FLORIDA HEALTH COHORT ENTRY SURVEY ID # R A Study S i t e F l or i da Cohort S urv e y T h a n k y o u f o r t a k in g th e ti m e t o f i l l o u t t h i s s u r v e y ! T h ere a re n o w r o n g o r r i g ht a ns w e r s , s o w e ho p e t h at y o u wil l fe e l c o m f o r t a b l e a n s w er in g e a ch q u e s ti o n as h o n e s t l y as p os s i b l e . T h a nk s a g a i n , T h e F l o r id a C o h o rt S t u d y T e a m
151 Sect i o n A: To get started, we are going to ask several questions that help us to describe the participants in our survey. 1 . A re yo u o f H is p a n i c / L a t i n o o r i g i n o r d e s cent? Y es No 2 . Wh a t i s y o ur r a c e ? (Please check one) W hite B l a c k/ A f r i c a n A m er i can N a t i v e A m er i can A si a n M u l t i ra c i a l (please describe) : ____________________ O th e r (please describe) : ____________________ 3 . Were y o u b o rn i n t h e U n i ted S t a t e s ? Y es No 4 . Wh a t i s t h e h i g h e s t g r a de o r y e a r o f s c h o o l y o u h a v e c o m p l e ted? Elementary s chool or below Some high school (grades 9 12) High school g raduate or GED Some college or technical/trade school C o l l e g e or trade school g r a du a t e G ra d u a t e d e g ree o r p r o f e s s i o n a l d e g r ee a f t e r graduating c o l l e ge 5 . Wh a t i s y o ur current m a r i t a l s t a t u s? Marr i ed D i v o rc e d W ido w ed Se p a r a t e d N e v er Ma r r i e d / Single L i v i n g wi t h a l on g t e r m p ar t n er 6 . Wh a t sex were you assigned at birth ? Ma l e Fe m a l e 7 . Wh a t is your current gender identity ?
152 Male Female Transgender Other (Please Specify): _______________ 8 . What do you consider your sexual orientation or preference? H e t e r os e x u al o r st r a i g h t G ay o r L e s b i a n B i s e x u a l Asexual Other (please describe): ____________________ 9 . Please check all the t y pes o f h e al t h i n s u r a n ce y o u currently h a v e ? Private Insurance Medicaid AIDS Drug Assistance Program (ADAP) Tricare or CH AMPUS Veterans Administration (VA) Coverage Uninsured Other (please describe): _____________________ I am not sure 10 . Check all of the places that you have lived in the past 12 months Own apartment or house Rented room, apartment, or house Stayed with family or friends Housing Options for People with AIDS (HOPWA) Substance abuse treatment facility Psychiatric facility Jail, prison, or detention facility Homeless shelter Hospital Emergency shelter (such as a domestic violence shelter, church, or motel voucher) Car, street, or abandoned building 1 1 . How many people live in your household, including yourself? Number of Adults: ______
153 Number of Children under 18 years of age: ______ I do not currently hav e a household. 1 2 . Please check all of the following types of employment you currently have ? E m plo y ed f o r w a g es Se l f e m p l o y ed O u t o f w o r k f o r m o re th a n 1 y ear O u t o f w o r k f o r l e s s t h a n 1 y ear A ho m e m a k er A st u d e nt Re t i r ed U n a b l e t o wo rk / Disabl ed 1 3 . What is your total household income per month (this is the total income of all of those who live in your household before taxes from a job or money received through federal assistance )? $_________________
154 Sect i o n B : T h i s n ext s e t o f q u e s ti o n s w i l l a s k how you feel ab o u t y o u r h e a lt h . 1. O v e r a ll , h o w w o u l d yo u r a t e y o ur h e a l th i n the p a s t 4 w e e k s ? E x ce l l e nt V ery good G o od Fa i r P o or V ery poor 2. D ur i ng t he p a st 4 w ee k s , h o w m uch d i d ph y s i c a l he a l th p r o b l e m s li m i t y o ur u s u a l p h y s i c a l a c t i v i t i e s ( s u c h a s wal k i ng o r c l i m b i ng s t a i r s ) ? N o t a t a l l V ery lit t le S o m e w h at Q u i t e a l o t C o u l d n o t d o p h y si c a l ac ti v i t i es 3. D ur i ng t he p a st 4 w ee k s , h o w m uch d i f f i cu l ty d i d y o u h a v e d oi n g yo u r d a i l y w o r k , b o th a t h o m e a nd a w a y f r o m the h o m e, bec a u s e o f y o ur ph y si c a l he a l th? N on e a t a ll A li t t l e b i t S o me Q u i t e a l o t C o u l d n o t d o d a i l y wo rk 4. H o w m uc h b o d i l y p ai n h a v e y o u h a d i n the p a st 4 w ee k s ? N one V ery m ild M i l d M o d er a t e Se v e r e V ery Se v ere
155 5. D ur i ng t he p a st 4 w ee k s , h o w m uch en e r g y d i d yo u h a v e ? V ery m u ch Q u i t e a l o t S o me A li t t l e N one 6. D ur i ng t he p a st 4 w ee k s , h o w m uch d i d yo u r ph ys i c a l h e a l th o r e m o t i o n a l pr o b l e m s li m i t y o ur u s u a l s o c i a l a c t i v i t i e s wi th f a m il y o r f r i e nd s? N on e a t a ll V ery lit t le S o m e w h at Q u i t e a l o t C o u l d n o t d o s o c i al a c t iv i t i e s 7. D ur i ng t he p a st 4 w ee k s , h o w m uch h av e yo u b een b o ther e d by e m o t i o n a l pr o b l e m s ( s uch a s fe e li n g a n xi o u s , de p r e ss e d o r i r r i t a b l e )? N o t a t a l l S l ig h t l y M o d er a t e ly Q u i t e a l ot E xt re m e ly 8. D ur i ng t he p a st 4 w ee k s , h o w m uch d i d per s o n a l o r e m o t i o n a l p r o b l e m s k eep yo u f r o m d oi n g yo u r u s u a l w o r k , s ch o o l o r o t h er d ai l y a c t i vi t i e s ? N o t a t a l l V ery lit t le S o m e w h at Q u i t e a l ot C o u l d n o t d o d a i l y a c t i v it ie s
156 Sect i o n C : P e o p l e s o m e ti m e s loo k t o o t h e r s f o r c o mpa n io n s h i p , as s i s t a n ce, o r o t h e r t y p es o f s up p o rt. Some have lots of support and others have no support. H o w o f t en is each o f t h e f ollo w ing k inds of s upport availab l e f o r y ou if y ou need i t ? Please check one box for each question. No n e of t h e t i me A li tt le of t h e t i me S o me of t h e t i me M ost of t h e t i me All of t h e t i me 1. So m eo n e y ou c an count on to li s ten to y ou when y ou n e ed to talk 2. So m eo n e to give y ou i n f o r mation to help y ou u n de r s tand a s ituat i on 3. So m eo n e to give y ou g o od advice a b out a c r i s is 4. So m eo n e to co n f ide in o r talk to about y ou rs elf or y our p r obl e ms 5. So m eo n e wh o s e adv i ce y ou r eal l y want 6. So m eo n e to s h a r e y our m o s t p r ivate wo rr ies and f e a r s with 7. So m eo n e to tu r n to f or s ugge s tio n s about how to deal with a pe r s o nal p r oblem 8. So m eo n e who un d e r s t a nds y our p r obl e m s 9. So m eo n e to help y ou if y ou we r e co n f ined to a bed 10. So m eo n e to take you to the doctor if y ou needed it 11. So m eo n e to p r epa r e y o u r m ea l s if you we r e unable to do it y ou rs elf 12. So m eo n e to help with d aily c ho r es i f y ou we r e s ick
157 Sect i o n D : In t h i s n e x t s e c t i o n , w e a r e g o in g t o a s k s e v e r a l qu e s t io n s r e la t ed t o how you receive t he h e a l t h c a r e s e r v i c es y o u n e e d. P l e a s e c i r c l e y ou r re s p on s e t o in di c a t e t h e nu m ber o f t i m es d u r i n g t h e l a s t 6 m o nths y o u h ad t o d o ea c h o f th e f o l l o wi n g : Circle one number for each question N u m ber o f t i m es 1. S t ay at a h os p i t a l for at least one night 0 1 2 3 + 2. G o t o an e m er g e n cy r oo m o r u r g e n t care ce n t er f o r m e di cal c a r e 0 1 2 3 + 3. V i s i t a m e nt al h e a l t h p r o vi d e r (like a p s y c hi a t r is t, ps y c h ol o gi st , s o c i al wo r k e r) 0 1 2 3 + 4. Visit a dental care provider (like a d e n t is t , d e n t a l or oral s u r g e o n , o r th o do n t i s t ) 0 1 2 3 + 5. Be t a k e n care o f b y a fr i e n d o r fa m ily m e m b er b ec a us e y o u w ere i l l 0 1 2 3 + 6 . H a v e y o u e v er b e en te s ted f o r H ep a t i t i s C (Hep C )? Y es re su l t w a s p o s i t iv e Y es re su l t w a s n e g a t i ve No N o t s u re 6 b. D i d yo u rece i v e a n y tr e a t m ent for Hepatitis C? I did no t n e ed a n y t rea t m e nt I n e e d e d t r e a t m e nt , b u t h a v e n e v e r rec e i v e d i t I st a r t e d t re a t m e n t, b u t d i d n o t f i n i s h I c o m pl e t e d t re a t m e n t, a n d i t w o r k e d ( T h e h e p a ti t i s C v i r u s i s g on e) I c o m pl e t e d t re a t m e n t , b u t i t d i d n o t w o r k ( I s t i l l h a v e T h e Hepatitis C vi r us) N o t S u re Continue to Next Page
158 7. H a v e y o u e v er b e en d i a g n o s ed wi th tu b e rcu l o si s (TB), o r been t ol d yo u h a v e a p o s i t i v e s k i n te s t (so m et i m es c all e d a PPD) or a positive tuberculosis blood test (called a Quantiferon Gold or T spot test) ? Y es No N o t s u re
159 Sect i o n E: T h i s n ext s e c t io n asks a b ou t y ou r H IV c a r e a n d t r e at m e n t . Some people have good experience s with HIV care and others do not . 1. H o w do y o u th i nk yo u c o nt r a cted H I V ? Sexually Rape or sexual assault I n t ra v e no u s d r u g us e o r n ee d l e sh a r in g B l oo d t r a n s f u s i o n Bo r n wi t h H I V (Perinatally) Occupationally (accidental needle stick) I d o n o t k n ow 2 . What year do y o u th i nk yo u got H I V ? (Your best guess is ok) Year __________ 3. What year did you first test positive for HIV? Year __________ 4. When you first tested positive for HIV, at what type of facil ity were you tested? Primary care clinic or community health center Health department Labor/delivery setting OBGYN or family planning clinic Emergency room Inpatient hospital ( not labor/delivery or emergency room) HIV counse ling and testing site STD clinic HIV/AIDS infectious disease clinic Mobile test site Correctional facility Blood donation facility Substance abuse treatment center Insurance or employee clinic Military or VA facility Other (Describe: _____________ ___________________________________) Refuse to answer
160 5 . A fter y o u f i r s t tested p o si t i v e for H I V , h o w l o n g did it take to get your first m ed i c a l c a re visit f o r H IV ? L e s s th a n 1 m ont h 1 5 m on t h s 6 12 months 1 4 y ears 5 9 y ears 1 0 2 0 y ears M o re t h an 2 0 years 5a. W care worker for HIV medical care within 6 months of testing positive for HIV? __________________________________________________________ ___ _______________________________________________________ ____ 6 . A re yo u c urren t l y t a k i ng H I V a nt i v i r a l m ed i c a t i o n ? Y e s No 6 a. If not, why not? My own choice (please describe) ________________________________ I have no one to prescribe to me Cost Other (please describe) ________________________________ Skip to Section F , Page 13 7 . I n the la s t 3 0 d a y s , o n h o w m a n y d a y s d i d yo u m i s s a t l e as t o ne d o s e o f a n y of yo u r H I V m ed i c i ne? W r it e i n n u m b er o f d a y s : ______________ (0 30) 8 . I n the la s t 3 0 d a y s , h o w well did you do at remembering to take all your prescribed HIV medication ? E x ce l l e nt V ery good G o od Fa i r P o or V ery poor
161 9 . I n the la s t 3 0 d a y s , h o w o f t en d i d y o u t a k e yo ur H I V medication as directed? A l w a y s A l m os t a l w a y s U su a lly S o m e ti m es Rare l y N e v er 10 . D ur i ng t he p a s t 3 0 d a y s , how often did you have s i de eff ec ts from your HIV medication ? A l w a y s M o s t o f t h e t i m e A b o u t h a l f o f t h e t i m e Rare l y N e v er 11 . H o w s u r e a r e y o u t h a t y o ur m ed i c a t i o n w i l l h a v e a p o si t i v e effect o n y o ur he a l th? V ery su re S o m e w h at s u re N o t a t a l l s u re
162 Section F: In t h i s n e x t s e c t i o n , w e a r e g o in g t o a s k s e v e r a l qu e s t io n s r e la t ed t o g e tt i n g h e a l t h c a r e th a t i s r e l at e d t o y o u r H IV i nf e c t io n . 1. D ur i ng t he p a s t 6 m o nt h s , wa s t h ere o ne u s u a l p l a c e, li k e a d o c t o r s o f f i ce or c li n i c, w here y o u w e nt f o r m os t o f y o ur H I V health c a re? Y es No Go to Question 3 2. Have you missed any scheduled HIV health care appointments in the past 6 months ? Y es No 3. What are some of scheduled appointment in the past 6 months ? Felt good Initial CD4 count and viral load were good enough money or health insurance Had other responsibilities such as child care or work Experienced homelessness Was drinking or using drugs Felt sick Forgot to go Missed appointment(s) Moved or out of town Unable to get transportation Facility is inconvenient (location, facility hours, wait time) Unable to get earlier appointment Not a United States Citizen Other (Describe:________________________________________________ __)
163 4. D o y o u h a v e a c a s e m a n ag er r e l a ted t o yo u r H I V / A I D S c a r e? ( A c a se m a n ag er is a so c i a l w o r k er, nur s e, A I D S s e r vi c e o r ga n i z a t i o n s t a f f m e m ber, s t a ff, o r a n y o ne e l s e w h o i s a s s i g ned t o he l p yo u obtain medical c a re). Y es No Not sure 5 . Check all of the k i nds o f tr a n s p o r t a t i on that you u s u al l y use t o get to y o ur H I V p r o v i de r o f f i c e ? I w a l k I bike I d r i v e m y o w n car/vehicle My p ar tn e r / f r i e n d/family d r i v es m e P u bl i c t r a ns po r t a t io n 6 . H o w l o ng d o e s i t t a k e to g et t o HIV provide way ? L e s s th a n 3 0 m in u t e s 3 0 6 0 M i n u t e s 1 2 Ho u rs 2 4 Ho u rs M o re t h an 4 ho u rs 7 . I n the past 1 2 m o nth s , did you have a p r i m a ry c a re provider (PCP) , nurse, or c li n i c f o r a ny o n g o i n g he a l th i s s u e s o t her th a n H I V care ? I receive p rimary care from someone different than my HIV care provider I receive primary care from my HIV care provider I do not have a primary care provider
164 Sect i o n G : People experience all types of emotions throughout their life. Here we ask several questio ns about the types of feelings or emotions that you may have experienced . Over the last 2 weeks , how often have you been bothered by the following problems? Check one answer for each question Not at all S e v er al d ays Ov er h alf t he d ays N e a r ly e v ery d ay 1 . L it t l e i n t e r e s t o r p l ea s u re i n d o i n g t h i ng s 2. Fee l i n g do w n , d e p r e s s e d , o r ho p e l e s s 3. T r o u b l e fa ll i n g o r s t a y in g a s l e e p , or sl e e pi n g t o o m u ch 4. Fee l i n g t i r e d o r h a v i n g l i t t l e e n er gy 5. P o o r a p p e t i t e o r o v e r ea t i n g 6. Fee l i n g b ad a b o u t y ou r s e l f o r t h at y o u a r e a fa i lu r e o r h a v e l e t y ou r s e l f o r y ou r fa m ily do w n 7. T r o u b l e c o n c e n t r a t in g o n t h in gs , s u ch a s rea d i n g th e n e ws p a p e r o r w a t c h i n g t e l e v i si o n 8. M o v i n g o r sp e a ki n g s o s lo w l y th at o t h er p e o p l e c o ul d h a v e n o ti c e d . O r Being s o f i dg e t y o r re s tl e s s t h at y o u h a ve b e e n m ovi n g a r o u n d a l o t m o re th a n us u al
165 O ver t h e la st 2 we e k s , ho w o f t e n h a ve y o u b e e n b o t h e r e d b y t h e f o l l o w i n g p ro bl e m s? Check one answer for each question Not at all S e v er al d ays Ov er h alf t he d ays N e a r ly e v ery d ay 9. Fee l i n g n e r v o us , a n x i o u s , o r o n e d g 10. N o t b e in g a b l e t o s to p o r c o nt r o l w o rr y i ng 11. W o r r y in g t o o m u ch a b ou t d i ffe r e n t t h in gs 12. T r o u b l e re l a xi ng 13. Be i n g s o r e s t l e s s t h a t it s h ard t o s i t s t i l l 14. Bec o m in g e a s il y a n n o y ed o r i r r i t a bl e 15. Fee l i n g af r a i d as i f so m e th in g a w f u l m ight h a pp e n 16. Have you ever had any experience that was so frightening, horrible, or upsetting that in the past 30 days : a. You had nightmares abou t it or thought about it when you did not want to? Ye s No b. You tried hard not to think about it or went out of your way to avoid situations that reminded you of it? Ye s No c. You were constantly on guard, watchful, or easily startled? Ye s No d. Felt n umb or detached from others, activities, or your surroundings? Ye s No
166 17. In the past 12 months , h o w o f t en h a v e y o u fe l t s tr e s s a t h o m e (re l a t i ng to f a m i l y or s i g n i f i c a nt o th e r)? N e v er S o m e ti m es M o s t o f t h e t i m e A l l o f t h e t i m e 18. In the past 12 months , h o w o f t en h a v e y o u fe l t s tr e s s a t w o rk (re la t i ng to c o w o r k er s, c a s e lo a d, o r j o b s e c ur i t y ) ? N e v er S o m e ti m es M o s t o f t h e t i m e A l l o f t h e t i m e 19. In the past 12 months , ho w often have you felt stress due to your financial situation (such as the amount of money you have or the bills that you have to pay) ? N e v er S o m e ti m es M o s t o f t h e t i m e A l l o f t h e t i m e 20. H a v e y o u taken a n t i d e pr e s s a nt m ed i c a t i o n to treat a mental hea lth diagnosis in the past 6 months ? Y es a n d c u rr e n t l y t a k i n g i t Y es b u t n o t t a ki n g i t no w Go to Question 22 N o Go to Question 24 2 1 . I n the p a s t 6 months , h o w o f ten did y o u t a k e yo ur a n t i de p r e s s a nt m ed i c a t i o n s ? Pl e a s e p la c e a d a rk X a n y w here o n the f o llo wi n g li ne f r om 0 % ( o r n o ne o f t h e t i m e) to 1 00 % ( a l l o f the t i m e) th a t re p re s ents h o w o ften y o u w e re t a k i ng y o ur a nt i depr e s s a nt . 0% None of the time 25% Little of the time 50% Some of the time 75% Most of the time 100% All of the time
167 22 . Check all of the reasons why you may have stopped taking your a n t i d e pre s sa n t m ed i c a t i o n in the past 6 months ? Me d i c in e t o o e x p e nsi ve or not covered by insurance Me d i c a t io n did not help me I just d i d no t w a n t t o t a k e a n y m o re Health care provider re c o m m e nd ed that I stop taking the medication S i d e effe c t s s e x u al S i d e effe c t s no n s e x u al Other (please descr ibe): ___________________________________ None of the above I have not stopped taking my anti depressant medication in the past 6 months 23 . Check all of the providers that have pr e s c r i b e d y o ur a nt i depr e s sa nt m ed i c a t i o n in the past 6 months ? H IV h ea l t h care p r ovi d er Pr i m a r y care p rovider Mental health provider Other s p e c i a l i s t ph y s i c i a n ( e . g . n e u r o lo g i st , c a r di o lo g i st , p a i n sp e c i a l i s t) O th e r : 24. D id y o u receive a n y c o u n s e l i n g , t al k ther a py, or psychotherapy f o r d e pre s s i o n/anxiety in the past 6 months ? Y es N o Go to Next Page 25. Check all of those that p r o v i d e t h i s this c o u n s e l i n g , talk therapy, or psychotherapy ? H IV h ea l t h care p r ovi d er Pr i m a r y care p rovider Mental health provider Other s p e c i a l i s t ph y s i c i a n ( e . g . n e u r o lo g i st , c a r di o l o g i st , p a i n sp e c i a l i s t) Pa s to r o r c o m m unit y m e m b er G o o d fr i e n d o r c o l l e a g ue O th e r (please describe) : ________ __________________________
168 Section H: Everyone experiences memory function differently. We want to know how you experience memory, and if you have noticed changes in your memory. P l e a s e r a t e t h e d e gr e e t o w h i c h e a ch s t at e m e n t d e s c ri b es y o u r t y pi c a l o r u s ua l b e ha v i o r d u r i n g t h e past 7 days . Please check one answer for each question. N e v er Ra re ly S o m e t i m e s O f ten V ery O f ten 1. I a m c u rre n tl y e x p er i e n c i n g th i nk i n g or m e m o r y p r o b l e m s. 2. I p u t d o w n t h i n g s ( g l a s s e s , k e y s, w a ll e t , p u r s e, p a p e r s ) a n d h a v e t r o u b le f i nd i n g t h e m . 3. I f o r g et r i g h t a w ay wh at p e o p l e s ay to m e. 4. W h en w a l ki n g o r d r i v in g , I f o r g e t h ow I v e g o t t en f r o m on e pl a ce t o a n ot h er. 5. I f o r g et t o p ay bi ll s , rec o r d c h e c ks , or m a i l l e t t er s.
169 Sect i o n I : T h ro u gh o u t ou r l i v e s , m os t o f u s h a ve h a d p ai n f ro m t i m e t o t i m e ( s u ch a s m i n or h e a d a c h e s , s pr a in s , a n d t oo t h a c h e s ). W e ar e go i n g t o a s k so m e q u e s t io n a b ou t h o w m u c h p a i n i m p a c t s y o u r l i f e. 1. H a v e y o u h a d p a i n o ther t h a n m i n o r e v er y d a y k i nds o f p a i n in the past 24 hours ? Y es N o Go to Next Page P l e a s e u s e t he s c a l e below and choose the answers that best describe y o ur p ai n in the past 24 hours . Rate your p ain: 2 . A t i t s W O RST . . . . . . . . . . 0 1 2 3 4 5 6 7 8 9 10 3 . A t i t s LEA ST . . . . . . . . . . . 0 1 2 3 4 5 6 7 8 9 10 4 . O n AVE R AG E . . . . . . 0 1 2 3 4 5 6 7 8 9 10 5 . RI GH T N O W . . . . . . . . . . 0 1 2 3 4 5 6 7 8 9 10 6. Are you taking any treatment or medication for your pain? Y es N o
170 Sect i o n J: P eople living with HIV may experience times when they have felt left out or were treated differently than those without HIV infection. The next questions will ask about these times. Some persons have told many people about their abo ut their HIV status, and some persons have told no one. Have you told any of the following persons in your immediate family that you are HIV positive? Yes No Not Applicable 1. Mother 2. Father 3. Long term partner 4. My children How many of the following groups of people have you told about your HIV status? None or hardly any Some Most or All Not Applicable 5. Relatives besides your immediate family 6. Friends 7. Healthcare professionals (doctors, dentist, counselor) 8. Casual sex partners 9. Employer and/or Co workers
171 P l e a s e t e l l us h o w often y o u h a ve ever felt that the following e x p e ri e nces happened? N e v er O n ce T w i ce 3+ t i me s 10 . A f a m il y m e m b er st o p p ed s p ea ki n g t o m e w h en th ey f o un d o u t I h a ve H IV 11 . I l o s t a f r i e n d w h e n t h ey f o u n d o u t I h a v e H IV 12 . S o m e on e wh o k no w s I h a v e H IV g rew m o re d i s t a nt 13 . S o m e on e di dn t w a n t t o to u c h m e b eca u s e I h a ve H IV 14 . I w as n o t i nv it e d t o a s o c i a l e v e n t b ec a u s e I h a v e H IV 15 . S o m e on e t o l d m b ad p e r s o n b ec a u s e I h a v e H IV 16 . S o m e on e h i t m e, b eat m e, th rew so m e t h i n g at m e, sp i t o n m e, o r th r ea t e n e d m e w it h v i ol e n ce b ec a u s e I h a v e H IV 17 . S o m e on e i n su lt e d o r v e r b a l l y a b u s e d m e b eca u se I h a v e H IV 18 . S o m e on e d i s cr i m in a t ed a g a in s t m e i n a j o b or h ou s i n g b ec a u s e I h a v e H IV 19 . A do c t o r, n u r s e, o r h e a l t h ca r e w o r k er a v oi d e d m e o r ref u s e d t o t a k e care o f m e b ec a us e I h a ve H IV
172 Sect i o n K: Substance use i s c o mmo n i n p e r s on s w i t h H IV i n f e c t i o n . This section will ask about substances that you may u s e t o b e t t e r un d e rs t an d w ha t s e r v i c es a re n e e d ed . Current Drinking 1. In your lifetime did you e v er d r i nk a ny a l c o h o l? Y es a t l e a s t o n e d r i n k i n p a s t y ear Y es bu t n o d r i n k s i n p a s t y ear Go to Question 22 N o n e v er d r a n k a l c o ho l Go to Question 25 Try to answer the next questions In the past 12 months . . . Less than once a month 1 3 times a month (less than weekly) 1 3 times a week 4 6 times a week Every day 2. How often did you have a drink containing alcohol? In the past 12 months . . . 3. How many standard drinks would you ha ve on a typical day when you were drinking? 1 2 3 4 5 6+
173 In the past 12 months . . . Never Less than once a month Monthly Weekly Daily or almost daily 4. When you were drinking regularly, how often did you have 4+ standard drinks (for women) or 5+ sta ndard drinks (for men) on one occasion? 5. D ur i ng t he l a s t 3 0 d a y s ( m o nth) , w h a t i s the l a r g e s t nu m ber o f d r i n k s c o n t ai n i n g a l c o h o l t h a t y o u d r a nk w i th i n a 2 4 h o ur pe r i o d? Less than 1 dr i nk 1 dr i nk 2 dr i n k s 3 dr i n k s 4 dr i n k s 5 to 7 d r i n k s 8 to 1 1 d r i n ks 1 2 t o 1 7 d r i n k s 1 8 t o 2 3 d r i n k s 2 4 t o 3 5 d r i n k s 3 6 d r i n k s o r m o re 6. I n the p ast 12 m o nths , when you drank alcohol how often did you consume the following types of alcohol ? Never Rarely Often Beer Red wine Other wine Liquor or mixed drink
174 He re are a number of events that people sometimes experience when drinking . Read each one carefully, and circle the number that indicates whether this has happened to you in the PAST 12 MONTHS . 7 . I have been unhappy because of my drinking No Yes 8 . Becaus e of my drinking, I have not eaten properly. No Yes 9 . I have failed to do what is expected of me because of my drinking. No Yes 10 . I have felt guilty or ashamed because of my drinking. No Yes 11 . I have taken foolish risks when I have been drinking. N o Yes 12 . When drinking, I have done impulsive things that I regretted later. No Yes 13 . My physical health has been harmed by my drinking. No Yes 14 . I have had money problems because of my alcohol use. No Yes 15 . My physical appearance has been harme d by my alcohol use. No Yes 16 . My family has been hurt by my drinking. No Yes 17 . A friendship or close rela tionship has been damaged by my drinking. No Yes 18 . My drinking has gotten in the way of my growth as a person. No Yes 19 . My drinking has dam aged my social life, popularity, or reputation. No Yes 20 . I have spent too much or lost a lot of money because of my drinking. No Yes 21 . I have had an accident while drinking or intoxicated. No Yes
175 Past Drinking 22. Was there ever a time when you drank 4+ standard drinks (for women) or 5+ standard drinks (for men) on one occasion at least once a week? Y es N o 23 . Check all of the following treatments o r s tr a t e gi e s t o he l p y o u s t o p o r cut b a ck o n y o ur d r i n k i n g that you have ever tried ? Alcoholics Anonymous (AA) Counseling or therapy ing Medication to help reduce drinking None of the above 24. What are you thoughts about cutting back on drinking alcohol? I a m no t i n t er e s t ed i n cutting back on my drinking a t t h i s t i m e. I w o u l d l i k e t o cut back on my drinking s o m e ti m e, bu t not now I a m c u rre n tl y t r y in g t o cut back on my drinking Cigarette Smoking 25 . Have y o u e v er s m o k ed c i g a r ett e s? Y es N o Go to Question 31 26. H o w o l d w ere yo u w hen y o u s t a r t ed to s m o k e? Y e a rs Old 27. D ur i ng y o ur l i fe t i m e, w h a t i s the m os t that y o u have e v er s m o k ed re g u l a r l y? N one Less than 1 0 cigarettes a day (less than Â½ a pack) 10 20 cigarettes a day ( Â½ to 1 pack) 21 30 cigarettes a day ( 1 pack to 1 Â½ pack) 31 40 cigar ettes a day (1 Â½ to 2 packs) > 40 cigarettes a day (over 2 packs) 28. D o y o u currently s m o k e?
176 Y es No, quit within the past 11 months No, quit 1 2 years ago No, quit 3 5 years ago No, quit 6 10 years ago Go to Question 31 No, quit 11 20 years ago No, quit over 20 years ago 29. On average, in the past 12 months h o w m uch do y o u curren t l y s m o k e? Less than 10 cigarettes a day (less than Â½ a pack) 10 20 cigarettes a day ( Â½ to 1 pack) 21 30 cigarettes a day (over 1 p ack to 1 Â½ pack) 31 40 cigarettes a day (over 1 Â½ to 2 packs) 40 cigarettes a day (over 2 packs) 30. Wh a t a re yo u r t h o u g h t s a b o ut q u i t t i ng s m o k i n g? I a m no t i n t er e s t ed i n q u i tt i n g a t t h i s t i m e. I w o u l d l i k e t o q ui t s o m e ti m e, bu t not now I a m c u rre n tl y t r y in g t o q ui t s m ok i ng Marijuana Use 31. I n y o ur li f et i m e , h o w m a ny y e a r s h av e yo u u s ed m a r i ju a n a o n a r e g u l a r b as is ( a t l e a s t o nce per w e e k )? N e v er Go to Question 54 < 1 y ear 1 5 y ears 6 1 0 y ears 11 1 5 y ears G rea t er t h an 1 5 y ears 32. H o w o l d w ere yo u w hen y o u f i r s t u s ed m a ri j u a n a ? 1 0 y ears ol d or less 11 1 5 y ears o ld 16 2 0 y ears o ld 20 3 0 y ears o ld 30 4 0 y ears o ld 4 0 y ears ol d > 40 years old 33. On a v e r ag e, h o w o f t en h a v e y o u u s ed m a r i ju a n a i n the past 3 months ? I did not us e in the past 3 months Go to Question 57
177 L e s s th a n on c e a m onth 1 3 ti m es a m ont h ( l e s s th a n w e e kl y ) 1 3 ti m es a w eek 4 6 ti m es a w eek E v ery d ay 34. I n the p ast 3 m o nth s , o n the d ay s w h e n y o u u s ed m a r i j u a n a , h o w m a n y t i m es d i d y o u u s e on average ? ( N o t e: a t l e a s t o n e h o u r b e t ween e a u s e ) O n ce Tw i ce 3 t i m es 4 t i m es 5 o r m o re ti m es I n the p ast 3 m o nths w h i c h o f t h e s e m eth o ds d i d y o u u s e w h e n c o n s u m i ng m a r i ju a n a ? Never Rarely Often 35. J oi n t s [ m ar i j u a n a c i g are t t e s m a d e w it h r o ll ed p a p e r] 36. B l un t s [ c i g ar w r a p p e r s f i l l ed wit h m ar i j u a n a a n d t o b ac c o ] 37. P i p es [ w a t er p i p e s , b o ng s , h o o k a hs , o n e h i t t e r s , e t c . ] 38. I n g e s t io n [ a s a t ea, o r i n a c o ok i e o r b r o wn i e, e t c . ] 39. V a po r i z i n g d e v i c es 40. O th e r s ( P l e a s e d e s c r i b e ) :
178 I n th e p ast 3 m o nth s , h o w o ften w a s yo u r u s e o f m a r i j u a na f o r the f o llo wi n g re a so n s ? N e v er R a re l y O ften 41. T o i m p r ov e m y a pp e t it e / g a i n w e ig ht 42. T o i nd u ce sl e ep 43. T o re l i e v e n a u s e a / vo m iting 44. T o re l i e v e p a i n 45. T o re l i e v e a n x i e t y /d e p r e s s i on / s t r e ss 46. T o g e t h i g h o r s t o n e d 47. T o f i t i n t o s o c i a l s i tu a t io n s 48. T o i m p r ov e m y s e xu a l p e r f o r m a n c e / l i bi do 49. O th er ( P l e a s e d e s cr ib e ):__________________ Indicate how strongly you agree or disagree with each of the following statements by selecting a number between 1 (Strongly Disagree) and 7 (Strongly Agree). Strongly Disagree Disagree Somewhat Disagree Neutral Somewhat Agree Agree Strongly Agree 50. I could not easily limit how much marijuana I smoked right now. 1 2 3 4 5 6 7 51. I would not be able to control how much marijuana I smoked if I had some here. 1 2 3 4 5 6 7 52. I need to smoke marijuana now. 1 2 3 4 5 6 7 53 . D o y o u sometimes purc h a s e the m a r i ju a n a t h a t y o u u s e ? Y es N o Go to Question 5 7
179 54 . H o w m uch qu a nt i t y o f M a r i j u a n a do y o u t y p i c a l l y pu r ch a s e ? A few j oi n t s o r bl u n t s A ni c k el b a g ( a b ou t 1 g ra m ) A di m e b ag (a bo u t 2 g ra m s) A n e i g ht h (a bo u t 3 . 5 g ra m s) A qu a r t er ( a b o u t 7 g r a m s) A n o un c e ( a b o u t 2 8 g ra m s) M o re t h an a n ou n ce 55 . A b o ut h o w m uch d o es th i s a m o unt o f m a r i ju a n a t y p i c al l y c os t ? $5 $ 1 0 $20 $ 2 5 $50 $ 7 5 $ 100 $ 10 0 $ 1 50 $ 15 0 $ 2 00 $ 20 0 $ 4 00 M o re t h an $ 40 0 56 . A b o ut h o w lo n g w i l l th i s a m o unt o f m a r i ju a n a t y p i c a ll y l a s t? L e s s th a n a d ay A few d a y s A w eek o r t w o A m onth Se v e r al m on t h s o r m o re Injected Drug Use 57 . Have you used drugs that you injected into your body with a needle that were not prescribed to you by a doctor ? Y es, I have used in the past 12 months Yes, but not in the past 12 months Go to Question 60 No, I have never used injection drugs Go to Question 64 5 8 . Check all of the following that describe yo u r u s e o f nee d l e s i n the p a s t 12 months : I a l w a y s us e a c l e a n n ee d l e t h a t n o ot h er p e r s o n h as u s ed I so m e ti m es have shared a n e e d l e with s o m e o n e e l s e
180 59 . Wh a t a re y o ur t h o u g hts a b o ut a n e e d l e e x ch a n g e p r o g r a m ? I us e o n e n ow I w o u l d u s e o n e i f t h e re w as o n e i n m y c o m m unity I w o u l d n o t us e o n e i f i t w as i n m y c o m m un ity I a m no t su r e F o r e a ch o f t he f ol l o w i n g injection dr u g s p l e a s e m ar k t he r e sp o n s e t h a t b e s t describes h o w o f t en y o u u s e d e a c h dr u g in the past 12 months . Never Not in t h e past 12 months L e ss t h an o n c e a m o n t h 1 3 t i me s a m o n t h 1 3 t i me s a w eek 4 6 t i me s a w eek E v ery d ay 60 . Injected H er o i n 61 . Injected C o c a i n e 62 . Injected S timulants (like Methamphetamine) 63 . O th e r
181 F o r e a ch o f t he f ol l o w i n g non injection dr u g s p l e a s e m ar k t he r e sp o n s e t h a t b e s t describes h o w o f t en y o u u s e d e a c h dr u g in the past 12 months . Never Not in t h e past 12 months L e ss t h an o n c e a m o n t h 1 3 t i me s a m o n t h 1 3 t i me s a w eek 4 6 t i me s a w eek E v ery d ay 64 . Snorted Cocaine 65 . Smoked Cr ack C o c a i n e 6 6. Snorted or Smoked Heroin 6 7 (like amphetamines, 68 . Pain medication (like Oxycontin) 69 . Sedatives or Xanax) 70. Ecstasy or Molly 71 . Other 72 . Have you taken any of the following medications as a treatment for a drug or alcohol use problem? N e v er Currently Yes, but not currently Me t h a d o ne B u p r e no r p h i ( S ub o xo n e) N a lt r e x o ne
182 Sect i o n L: P e o p l e e n ga g e i n a whole r a n g e o f s e x u a l b e h a v i o r s . Please answer the following as honestly as possible . 1. I n y o ur li f et i m e, with w h om h a v e yo u e v e r h a d s ex ( i nc l u d i n g o r a l s e x )? Men o n ly Men a n d w o m en W o m en only N e v er h ad s e x i n m y li f e Go to Section M: Incarceration 2 . Have you EVER been diagnosed with the following ? Never Yes, in the past 12 months Yes, over 12 months ago C h l a m y di a, g o n o r r h ea, S y phi l is G e n i t a l h e r p es G e n i t a l W ar ts 3 . D ur i ng t he past 12 months , with w h om h a v e yo u e v e r h a d s ex ( i nc l u d i n g o r a l s e x )? Men o n ly Men a n d w o m en W o m en only N e i t h er Go to Section M , Page 37 Continue to Next Page
183 N e x t w e wa nt t o a s k a b o u t sexual intercourse th a t y ou h a d du r i n g the p a s t 1 2 m o nt h s wi th ce rt ai n t y p e s o f s e x u a l p a rtn e r s . 4 . In the past 12 months , with how many people have you had anal and/or vaginal sex? 0 1 2 3 4 5 10 11 20 21 50 > 50 5 . In the p ast 12 months , with how many people have you had anal and/or vaginal sex without a condom ? 0 1 2 3 5 6 10 11 25 26 50 51 100 >100 6. Please check all of the places that you met any new sexual partner(s) in the past 12 months ? I have no t met any new sexual partners in the past 12 months Work Through a friend Internet Bar or club Phone app (like Tinder or Grinder) Bath house Massage parlor Other
184 In the past 12 months , h a v e y o u h a d a n y anal or vaginal sex w i th a n y o f the f o l lo w i ng t y pes of p a rtn e r s ? No Yes ALWAYS with a condom Yes without a condom at least one time 7 . A main partner (spouse or long term lover) 8 . Any other partner who you knew (friend or acquaintance) 9 . Any other partner whom you did not know (anonymous sex or someone you just met) 1 0 . Any other partner that you met on the internet or cell phone application 1 1 . Any partner who was HIV positive 1 2 . Any partner who was HIV negative 1 3 . Any partner whose HIV statu s was unknown or you were not sure 1 4 . Any partner whom you received money or drugs in exchange for sex 1 5 . Any partner whom you provided money or drugs in exchange for sex
185 ATTENTION: If you current have a penis comple te this section If you currently have a vulva/vagina Continue to Next Page P l e a s e i nd i c a te t he number of partners y o u h a d in the past 12 months f o r e a c h o f the f o llo wi n g t y p e s o f s e x u a l be h a v i o r. Se x u a l a c t i v i t y with women N u m ber o f p a rtne r s 1. O ral s e x 0 1 2 3 4 5 6 + 2. V a g i n a l s e x m y p e n i s i n a w o m a n s v a g i na 0 1 2 3 4 5 6 + 3. Penetrative a n al s e x m y p e ni s i n a w o m a n s a n u s (buttocks ) 0 1 2 3 4 5 6 + Se x u a l a c t i v i t y with men N u m ber o f p a rtne r s 4. O ral s e x 0 1 2 3 4 5 6 + 5. Penetrative a n al s e x m y p e ni s i n a m a n s a nu s (buttocks) 0 1 2 3 4 5 6 + 6. Receptive a n al s e x a m a n s p e n i s i n m y a nu s (buttocks) 0 1 2 3 4 5 6 + 7. H a v e y o u u s e d any p r e s c r i pt i o n d r u g t o i m p r o v e s e x u al p er f o r m a n ce, s u ch a s V i a g r a, C i a l i s , o r L e v it ra in the past 12 months ? Yes No N o t s u re Men
186 8. When was the last time you had had an anal pap smear? An anal pap smear is collecting a sample of cells from the anal area with a small brush or cotton swab to check for signs of an abnormality . Within the past year 1 3 ye ars ago More than 3 years ago Never Not sure
187 ATTENTION: If you currently have a vulva/vagina complete this section If you currently have a penis Continue to Next Page P l e a s e i nd i c a te t he number of partners y o u h a d in the past 12 months f o r e a c h o f the f o llo wi n g t y p e s o f s e x u a l be h a v i o r. Se x u a l a c t i v i t y with men N u m ber o f p a rtne r s 1. O ral s e x 0 1 2 3 4 5 6 + 2. V a g i n a l s e x i n my v a g i na 0 1 2 3 4 5 6 + 3. A n al s e x i n my a n u s (buttocks ) 0 1 2 3 4 5 6 + Se x u a l a c t i v i t y with women N u m ber o f p a rtne r s 4. O ral s e x 0 1 2 3 4 5 6 + 6. Sex with a toy that wa s shared with another woman 0 1 2 3 4 5 6 + 7. When is the last time you had a cervical pap smear? A cervical pap smear involves using a speculum to look inside the vagina and to collect cells to test for signs of an abnormality. Within past year 1 3 ye ars ago More than 3 years ago Never Not sure Wome n
188 8. When is the last time you had a anal pap smear? An anal pap smear is collecting a sample of cells from the anal area with a small brush or cotton swab to check for signs of an abnormality. Within past y ear 1 3 years ago More than 3 years ago Never Not sure 9 . H a v e y o u h a d a h ys t erec t o m y (a surgery to remove all or part of your uterus) ? No Y es N o t s u re 10. What is your history of pregnancy? I have never been pregnant I have been pregnant, b ut no live births I have been pregnant, and had a live birth 11. Are you currently pregnant? No Y es N o t s u re
189 Section M: Some experience problems with the law. We want to know if you have experienced any problem with the law that required you to spend some time in a correctional facility. 1. H ow many times have you e v er gone to a j a i l , pr i s o n, de t ent i o n ce n t er, o r j u v e n ile c o r r ect i o n a l f a c i l i t y ? None 1 2 3 5 6 10 >10
190 Section N: The following section asks about your current use of digital technology to communicate and access information In the last 30 days , h o w o f t en d i d yo u use the Internet with any of the following devices? Never Rarely About once a week A few times a week Daily 1 . My cell phone 2 . My own Computer/La ptop/Tablet at home 3 . Computer at public locations (e.g. library, community centers) 4 . Wh a t i s y o ur cur r e n t ce l l ph o n e s i t u a t io n? I have a s m ar tp h o n e I have a c e l l p h o n e b u t i t i s not a s m ar t pho n e I d o not c u rr e n tl y h a v e a c ell phone Go to Question 22 Wh a t t y pe o f p h o ne s e r vi c e do y o u cu r ren t l y h a v e or most recently had? No n e P ay pe r U s e L i m i te d, and it usually runs out Limited, but it usually lasts the entire month U n li m i te d 5 . T e x t m e ss a g i ng 6 . Ca l l i n g ti m e 7 . Internet on the go or d a t a u s e
191 H o w o f t en d o / d i d yo u do the f o llo wi n g u s i n g a c e l l ph o n e: Never Rarely About once a week A few times a week Daily 8 . Se n d o r r e ce i v e t e x t m e ss a g es (SMS)? 9 . G et d i re c t io n s o r ot h e r lo c a t i o n b a s ed i n f o r m a t i on? 10 . U s e mobile application (app) for medical or health purposes? 11 . C h e c k your Face b o o k / Tw itt e r / Instagram or similar ac c o un t? 12 . H e l p y o u re m e m b er t o t a k e y our m e di c a t io ns? 13 . Use an app to meet potential sex partners online? 14 . Approximately, how many apps do you currently have in your cell phone? None 1 10 11 25 25 50 > 50
192 If available and free, h o w o f t en would you use a phone app to he lp you . . . Never Rarely About once a week A few times a week Daily 1 5 . Identify health services relevant to you 1 6 . Track changes in your mood and emotions 1 7 . Provide tips to improve your health, based on information about you 1 8 . Manage alcohol and drug use behavior 19 . Communicate with your doctor or clinic 2 0 . Remember to take your medication 2 1 . Engage in social networking with other people who live with HIV 2 2 . Are the re questions about HIV or HIV care services that you think are important for researchers to ask, but did not appear to be part of this survey? [Please write in your answer] ______________________________________________________________________ ____________ __________________________________________________________ ______________________________________________________________________ ______________________________________________________________________ __________________________________________________________ ____________ __________
193 Thank you for your time! The Florida Cohort Team
194 APPENDIX B PRELIMINARY FOCUS GROUP GUIDE The following section shows the discussion guide that was used during the focus groups. Consistent with qualitative m ethodologies, wording and order of questions may be modified based on participant lead and ongoing analyses. Engagement Questions Can you tell me about you and how you take care of yourself in terms of health? What are your current concerns about how y our health care is managed? these words, what comes to mind? What are your favorite mobile apps? Have you ever used an app to manage your health, your medications? What is your f avorite app to do this? Do you find yourself using mobile apps to help you manage your health or medications in the future ? Exploration Questions How could a n app be useful to ease your health care management? What features would make you more willing t o use an app to help managing your health ? If I were to build a mobile app to help managing your health, what features would you recommend such an app to have? For example, would you recommend: : (a) tool to help you keep track of your symptoms, (b) sel f monitoring diary to keep track of your mood,
195 (c ) access to p ersonalized treatment resources or tells you what to do if you are experiencing very severe symptoms , (d ) feature to remind you to take your medications, (e ) Features that give you personaliz ed feedback using information provided y you? (f) Features to help you communicate with your doctor ? Communicate with your other people going through similar situation? What items and questions do you feel are most important for the development of a survey to evaluate the use of a mobile app for health care management? Exit Questions Is there anything else you would like to say about the use of a mobile app to facilitate your health care management?
196 APPPENDIX C DEMOGRAPHIC QUESTIONNAIRE PREVIO US TO FOCUS GROUP SESSIONS HEALTH APPLICATION AMONG PEOPLE LIVING WITH A CHRONIC CONDITION IN Demographic Questionnaire T h a n k y o u f o r t a k in g th e ti m e t o f i l l o u t t h i s s u r v e y ! T h ere are n o w r o n g o r r i g ht a ns w e r s , s o w e ho p e t h at y o u wil l fe e l c o m f o r t a b l e a n s w er in g e a ch q u e s ti o n as h o n e s t l y as p os s i b l e . T h a nk s a g a i n , 1. A re yo u o f H is p a n i c / L a t i n o o r i g i n o r d e s cent?
197 Y es No 2. Wh a t i s y o ur r a c e ? (Please check one) W hite B l a c k/ A f r i c a n A m er i can N a t i v e A m er i can A si a n M u l t i ra c i a l (please describe) : ____________________ O th e r (please describe) : ____________________ 3. What is your age? ___________ 4. Wh a t i s t h e h i g h e s t g r a de o r y e a r o f s c h o o l y o u h a v e c o m p l e ted? Elementary school or below Some high school (grades 9 12) High school graduate or GED Some college or technical/trade school C o l l e g e or trade school g r a du a t e G ra d u a t e d e g ree o r p r o f e s s i o n a l d e g r ee a f t e r graduating c o l l e ge 5. Wh a t i s y o ur current m a r i t a l s t a t u s? Marr i ed D i v o rc e d W ido w ed Se p a r a t e d N e v er Ma r r i e d / Single L i v i n g wi t h a l on g t e r m p ar t n er 6. Wh a t is your current gender identity ? Male Female Transgender Other (Please Specify): _______________ 7. What do you consider your sexual orientation or preference? Heterosexual or straight Gay or Lesbian Bisexual
198 Asexual Other (please describe): 8. Please check all the t y pes o f h e al t h i n s u r a n ce y o u currently h a v e ? Priv ate Insurance Medicaid Medicare AIDS Drug Assistance Program (ADAP) Tricare or CHAMPUS Veterans Administration (VA) Coverage Uninsured Other (please describe): _____________________ I am not sure Over the last 2 weeks , how often have you been bothered by the following problems? Check one answer for each question Not at all S e v er al d ays Ov er h alf t he d ays N e a r ly e v ery d ay 9. L it t l e i n t e r e s t o r p l ea s u re i n d o i n g t h i ng s 10. Fee l i n g do w n , d e p r e s s e d , o r ho p e l e s s
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213 BIOGRAPHICAL SKETCH Dr. Escobar Viera received his Ph.D. in health services r esearch in 2015 from the D epartment of Health Services Research, Management, and Policy a t the University of Florida , where he also received his Master of Public Health degree in 2011 . Before coming to the U.S., Dr. Escobar Viera received his Medical Doctor degree at the National University of Paraguay, his home country, where he was also trained as a psychiatrist. In the summer of 2014 he worked as an intern at the Pacific Northwest National Laboratory (Richland, WA) where he teamed with developers and designer s to create a biosu rveillance mobile app lication to predict changes in mosquito populations. His research interests involve the use of health i nformation technology, such as mobile applications and electronic health records to improve mental health services and outcomes, amo ng both people living with HIV (PLWH) and with mental conditions. His dissertation have look ed at the impact of depression on the intention to use mobile health applications among PLWH. In September 2015, he joined the School of Medicine at Stanford Univer sity as a post doctoral associate in the Division of Health Services Research.