Abstract
Our objective was to describe mobile telephone and Internet use and assess the correlation of Internet use for health care engagement (HCE) purposes and HIV clinical outcomes among HIV-positive individuals. We conducted a national survey using online social media to examine mobile telephone and Internet use, self-reported HIV viral load (VL; detectable versus undetectable), and antiretroviral adherence rating (excellent versus <excellent). Participants (N= 1,494) were asked about their Internet use for HCE purposes (including emailing health care providers, refilling medications online, or making medical appointments online). Approximately 95% of participants accessed the Internet nearly daily or daily in the past month (mean hours on Internet use per day= 5.2 hours) and 55.5% used the Internet for HCE purposes. Those who used the Internet for any HCE purposes had a 1.52-fold odds of reporting an undetectable VL (p= 0.009) and a 1.49-fold odds of reporting excellent adherence (p= 0.001). Although Internet access and use were similar across racial/ethnic, educational, and socioeconomic groups, disparities existed with the use of the Internet for HCE purposes among racial/ethnic minorities, those with low to moderate financial stability, lower education, and history of incarceration. Our data reveal that among HIV-positive users of online social media, use of the Internet for HCE purposes is associated with better self-reported virologic and adherence outcomes.
Key words or phrases: HIV, Internet, mobile telephone, health care engagement, online social media
Introduction
In the United States, it is estimated that 1.1 million people aged 13 years and older are living with HIV (PLWH) (CDC, 2013) and approximately 50,000 new cases of HIV occur each year (CDC, 2012a). Greater than 50% of those diagnosed with HIV are not engaged in medical care and only 25% of PLWH have suppressed virus (CDC, 2012b; Gardner, McLees, Steiner, Del Rio, & Burman, 2011). Poor engagement in HIV care has been associated with delayed initiation of antiretroviral (ARV) therapy and ARV non-adherence (Horstmann, Brown, Islam, Buck, & Agins, 2010; Mugavero, Amico, Horn, & Thompson, 2013), which is one of the strongest predictors of progression to AIDS and death (Bangsberg et al., 2001; Garcia de Olalla et al., 2002; Hogg et al., 2002).
Much research has been devoted to the development and evaluation of interventions designed to improve engagement in HIV care and ARV adherence (Amico, Harman, & Johnson, 2006; Simoni, Pearson, Pantalone, Marks, & Crepaz, 2006). However, due to the high cost, need for trained personnel, and limited reach and scalability of clinic-based interventions, researchers are increasingly examining mobile technologies and the Internet for behavioral interventions aimed at improving engagement in HIV care. The Internet is a major source of HIV-related information and PLWH are increasing using the Internet to access this information (Courtenay-Quirk et al., 2010). Prior research has shown that PLWH who used the Internet for health-related purposes were significantly less likely to be non-adherent to their ARV regimen in the past week (Kalichman et al., 2005) and Internet use has been associated with greater confidence in adhering to ARV medications (Kalichman, Benotsch, Weinhardt, Austin, & Luke, 2002).
Several studies have presented the importance of involvement of patients in their own care and the value of an informed patient (Greenfield, Kaplan, Ware, Yano, & Frank, 1988; Joosten et al., 2008; Maly, Bourque, & Engelhardt, 1999; Perestelo-Perez, Gonzalez-Lorenzo, Perez-Ramos, Rivero-Santana, & Serrano-Aguilar, 2011). Additionally, individually-tailored self-care technology-based methods have the potential for improving engagement in care and enhanced ARV adherence (Saberi & Johnson, 2011). Prior research in integrated health care systems has shown that the use of patient electronic personal health records was associated with higher pharmacy-based refill adherence (McInnes et al., 2013; Silverberg et al., 2013). Given the rapid evolution of technology and the lack of data on use of the Internet for the purpose of engaging in health care (e.g., emailing health care providers, refilling medications online, and making medical appointments online) outside of a healthcare system, we conducted a survey to describe how PLWH are currently using mobile telephones and the Internet for health-related purposes and to examine the association between their use of the Internet for health care engagement (HCE) purposes and self-reported HIV clinical outcomes.
Methods
Study design
We conducted a cross-sectional study using an online survey among HIV-positive individuals using online social media to: 1) describe general mobile telephone and Internet use in this population; 2) examine the demographic correlates of those who used the Internet for HCE purposes, including emailing health care providers, refilling medications online, or making medical appointments online; and 3) evaluate the correlation of the use of the Internet for HCE purposes and self-reported ARV adherence and HIV viral load. The University of California, San Francisco (UCSF) Committee on Human Research approved this study in April 2013.
Recruitment and participants
From May through August 2013, we implemented a campaign approach where participants were recruited through online social media, such as Facebook, Twitter, LinkedIn, Craigslist, or Tumblr. For survey programming, we used Qualtrics Research Suite (Qualtrics, Provo, UT), an online survey tool that allows researchers to build, distribute, and analyze online surveys in real-time. This survey was designed to automatically disqualify duplicate Internet Protocol (IP) addresses. Monetary incentives were not offered in order to minimize duplicate and false responses. However, to keep participants engaged, we inserted stimulating health facts, titled “Fun Facts”, throughout the survey and asked an HIV-specific question at the beginning of the survey and provided the answer to the question after survey completion. Details of our research methodology have been previously reported (Yuan, Bare, Johnson, & Saberi, 2014).
Measurement and data collection
Demographics
These data included participants’ age, race/ethnicity, sex at birth, sexual identity, perceived financial stability based on current income (high= “live comfortably”, moderate= “can barely get by”, or low= “cannot get by”), education, school and work status, any history of homelessness or living in a shelter, and any history of incarceration.
HIV clinical outcomes
Participants were asked about the date of their last HIV viral load test and whether it was detectable or undetectable, an assessment approach that has been previously validated with laboratory HIV viral load assay results (Kalichman, Rompa, & Cage, 2000). Participants’ ability to adhere to ARVs over the past 30 days was assessed using a validated adherence rating scale (Lu et al., 2008) and categorized as “excellent” versus “less than excellent”. This single item has been linked to more objective ARV adherence estimates, such as medication event monitoring systems (MEMS) (Lu et al., 2008).
Internet usage
Questions pertaining to Internet and mobile telephone usage were developed specifically for this study and were informed by the Pew Research Internet Project’s (www.pewinternet.org) poll questions on mobile telephones and the Internet, as well as in consultation with collaborators working in the field of HIV and technology. Participants were asked about the frequency of Internet access, mean number of hours per day spent on the Internet using any device, and whether they used the Internet to: 1) send/receive emails to/from their health care providers, 2) refill medications, 3) schedule, cancel, or change an appointment, 4) look for health or medical information, 5) search for information about HIV or sexually transmitted diseases, 6) sign up to receive email updates or alerts about health or medical issues, 7) read someone else's commentary or experience about health or medical issues on an online news group, website or blog, 8) watch an online video about health or medical issues, 9) find others who might have health concerns or medical questions similar to theirs, 10) track their weight, diet or exercise routine online, 11) track any other health indicators (such as CD4+ cell count and HIV viral load) online, or 12) post comments, questions, or information about health or medical issues on a social networking site, an online discussion (such as a listserv or other online group forum), or a blog. We asked participants about their trust in health information on the Internet and prior use of the Internet to ever find a sex partner.
Mobile telephone usage
Participants were asked about mobile telephone access or ownership and smartphone ownership. Among those with access to a mobile telephone, participants were asked if they ever used their mobile telephone to: 1) send/receive text messages to/from their health care providers, 2) remind them to take their medications, 3) remind them to keep their medical appointments, and 4) keep a list of questions to ask their medical providers. These health-related uses of the mobile telephone were designed to be complementary to the Internet usage list. Frequency of breaks in mobile telephone service in the past six months and the details of mobile telephone plans (i.e., limited/unlimited data/minutes) were examined. Participants were asked about the types of applications (“apps”) they used on their mobile telephone. Examples of these apps include exercise/fitness, diet/weight management, HIV/STDs prevention and treatment, medication management (i.e., alerts, tracking, etc.), and general wellness (e.g., sleep, mood, smoking cessation, etc.).
Analysis
Initially, we used descriptive statistics to characterize our study sample’s demographics, self-reported HIV clinical outcomes (ARV adherence, HIV viral load, and CD4+ cell count), and use of mobile telephones and the Internet. The HCE variable was created by summing responses to questions regarding Internet use for sending/receiving emails to/from health care providers, refilling medications online, and scheduling, canceling, or changing an appointment online. Next, using bivariate logistic regression, we examined the demographic correlates of those who used the Internet for HCE purposes. Next, we evaluated the association between the use of the Internet for HCE purposes and ARV adherence (excellent versus less than excellent) and HIV viral load (detectable versus undetectable) in two logistic regression models (one per outcome). Lastly, we conducted multivariable logistic regressions to examine these associations while controlling for any demographic variable which had a p-value< 0.25 in its association with ARV adherence or HIV viral load. We implemented backward elimination until all remaining variables had a p-value< 0.05 (Hosmer & Lemeshow, 2000). We used Stata version 13.1 (StatCorp LP, College Station, TX) for our analyses.
Results
Table 1 summarizes demographic information for our survey respondents (N= 1,494), who were mainly male, White, and self-identified as homosexual/gay. Approximately 67% of participants reported excellent ARV adherence in the past 30 days, 85% reported an undetectable HIV viral load, and 43% reported a CD4+ cell count of 500–999 cells/mm3.
Table 1.
Demographics of survey participants
| Demographics | Total N= 1,494 |
Users of Internet for HCE (N=687) |
Non-users of Internet for HCE (N=561) |
|
|---|---|---|---|---|
| Mean age, years (SD) | 45.6 (11.4) | 46.8 (11.0) | 45.7 (11.2) | |
| Sex at birth, N (%) | ||||
| Male | 1,338 (89.6) | 653 (95.1) | 527 (93.9) | |
| Female | 80 (5.4) | 32 (4.7) | 33 (5.9) | |
| Missing | 76 (5.1) | 2 (0.3) | 1 (0.2) | |
| Race, N (%) | ||||
| White | 1,013 (67.8) | 522 (76.0) | 382 (68.1) | |
| Latino | 182 (12.2) | 72 (10.5) | 79 (14.1) | |
| African-American/Black | 126 (8.4) | 50 (7.3) | 58 (10.3) | |
| Other | 103 (6.9) | 43 (6.3) | 42 (7.5) | |
| Missing | 70 (4.7) | 0 | 0 | |
| Sexual identification, N (%) | ||||
| Homosexual/gay | 1,225 (82.0) | 606 (88.2) | 476 (84.9) | |
| Heterosexual | 94 (6.3) | 40 (5.8) | 39 (7.0) | |
| Other | 92 (6.2) | 38 (5.5) | 43 (7.7) | |
| Missing | 83 (5.6) | 3 (0.4) | 3 (0.5) | |
| Perceived financial stability, N (%) | ||||
| Live comfortably | 531 (35.5) | 299 (43.5) | 166 (29.6) | |
| Can barely get by | 642 (43.0) | 293 (42.7) | 281 (50.1) | |
| Cannot get by | 195 (13.0) | 81 (11.8) | 87 (15.5) | |
| Missing | 126 (8.4) | 14 (2.0) | 27 (4.8) | |
| Education, N (%) | ||||
| High school degree or less | 430 (28.8) | 166 (24.2) | 204 (36.4) | |
| Any college experience | 725 (48.5) | 361 (52.6) | 278 (49.6) | |
| Higher than college degree | 253 (16.9) | 158 (23.0) | 73 (13.0) | |
| Missing | 86 (5.8) | 2 (0.3) | 6 (1.1) | |
| School, N (%) | ||||
| Full-time or part-time | 176 (11.8) | 86 (12.5) | 52 (9.3) | |
| Not in school | 1,238 (82.9) | 601 (87.5) | 504 (89.8) | |
| Missing | 80 (5.4) | 0 | 5 (0.9) | |
| Work, N (%) | ||||
| Full-time | 630 (42.2) | 316 (46.0) | 233 (41.5) | |
| Part-time | 194 (13.0) | 93 (13.5) | 69 (12.3) | |
| Other | 549 (36.8) | 268 (39.0) | 233 (41.5) | |
| Missing | 121 (8.1) | 10 (1.5) | 26 (4.6) | |
| Ever been homeless or lived in shelter, N (%) | ||||
| Yes | 278 (18.6) | 130 (18.9) | 116 (20.7) | |
| No | 1,133 (75.8) | 554 (80.6) | 441 (78.6) | |
| Missing | 83 (5.6) | 3 (0.4) | 4 (0.7) | |
| Ever incarcerated, N (%) | ||||
| Yes | 304 (20.4) | 115 (16.7) | 154 (27.5) | |
| No | 1,113 (74.5) | 570 (83.0) | 405 (72.2) | |
| Missing | 77 (5.2) | 2 (0.3) | 2 (0.4) | |
| Self-reported CD4+ cell count, N (%) | ||||
| Don’t know | 283 (18.9) | 118 (17.2) | 139 (24.8) | |
| < 200 cells/mm3 | 62 (4.2) | 29 (4.2) | 29 (5.2) | |
| 200–349 cells/mm3 | 105 (7.0) | 55 (8.0) | 43 (7.7) | |
| 350–499 cells/mm3 | 161 (10.8) | 102 (14.9) | 55 (9.8) | |
| 500–999 cells/mm3 | 544 (36.4) | 302 (44.0) | 226 (40.3) | |
| ≥ 1000 cells/mm3 | 119 (8.0) | 64 (9.3) | 53 (9.5) | |
| Missing | 220 (14.7) | 17 (2.5) | 16 (2.9) | |
| Self-reported HIV RNA, N (%) | ||||
| Undetectable | 1,068 (71.5) | 584 (85.0) | 437 (77.9) | |
| Detectable | 191 (12.8) | 86 (12.5) | 98 (17.5) | |
| Missing | 235 (15.7) | 17 (2.5) | 26 (4.6) | |
| Taking ARV medications, N (%) | 1,383 (92.6) | 651 (94.8) | 519 (92.5) | |
| Missing | 0 | 0 | 0 | |
| ARV adherence rating in past 30 days, N (%) | ||||
| Less than excellent | 409 (33.6) | 192 (28.0) | 200 (35.7) | |
| Excellent | 810 (66.5) | 457 (66.5) | 319 (56.9) | |
| Missing | 275 (18.4) | 38 (5.5) | 42 (7.5) |
ARV: antiretroviral; SD: standard deviation
The usage of mobile telephones and the Internet are summarized in Tables 2 and 3. A large majority of participants (94.9%) accessed the Internet on a daily or nearly daily basis and reported a mean 5.2 hours per day spent on the Internet. Approximately 55% of participants used the Internet for HCE purposes, with the majority sending/receiving emails to/from their health care providers. Approximately 72% of participants owned a smartphone and few (9.2%) had any breaks in service in the past six months. Approximately 17% used the mobile telephone as a reminder device and medication management mobile apps were used by 5.4% of participants.
Table 2.
Description of Internet usage among study participants
| Mean number of hours per day on Internet, hours (SD) | 5.2 (4.3)a | |
| Missing (%) | 320 (21.4) | |
| Frequency of Internet access in past month, N (%) | ||
| Daily | 1,084 (72.6) | |
| Nearly every day | 96 (6.4) | |
| 3 or 4 times a week | 29 (1.9) | |
| Once or twice a week | 13 (0.9) | |
| 2 or 3 times a month or less frequently | 9 (0.6) | |
| Never | 12 (0.8) | |
| Missing | 251 (16.8) | |
| Internet use for any health-related purposes, N (%)a | 1,063 (85.2) | |
| Look for health or medical information | 694 (55.6) | |
| Send/receive emails to/from your HCPb | 513 (41.1) | |
| Search for information about HIV or STDs | 511 (41.0) | |
| Read someone else's commentary or experience about health or medical issues | 467 (37.4) | |
| Refill medicationsb | 407 (32.6) | |
| Post comments, questions, or information about health or medical issues on social networking site | 346 (27.7) | |
| Watch a video about health or medical issues | 298 (23.9) | |
| Schedule, cancel, or change appointmentb | 295 (23.6) | |
| Find others who have similar health concerns | 249 (20.0) | |
| Sign up to receive email updates or alerts about health or medical issues | 211 (16.9) | |
| Track weight, diet, or exercise routine online | 146 (11.7) | |
| Track CD4+ cell count or viral load | 127 (10.2) | |
| Internet use for HCE purposesc, N (%) | ||
| Yes | 687 (46.0) | |
| No | 561 (37.6) | |
| Missing | 246 (16.5) | |
| Trust of health information on Internet, N (%) | ||
| Just about always | 80 (5.4) | |
| Most of the time | 465 (31.1) | |
| Some of the time | 558 (37.4) | |
| Never | 32 (2.1) | |
| Missing | 359 (24.0) | |
| Ever use Internet to find sex partner, N (%) | ||
| Yes | 903 (60.4) | |
| No | 327 (21.9) | |
| Missing | 264 (17.7) |
HCE: healthcare engagement; HCP: healthcare provider; SD: standard deviation; STDs: sexually transmitted disease
From total N= 1,248 Internet users
Variables summed to calculate use of Internet for HCE purposes
HCE: including emailing HCP, refilling medications online, or making medical appointments online
Table 3.
Description of mobile telephone usage among study participants
| Have access to mobile telephone, N (%) | 1,221 (96.2) | |
| Missing | 225 (15.1) | |
| Own mobile telephone, N (%)a | 1,160 (91.4) | |
| Own smartphone, N (%)a | 907 (71.5) | |
| Type of mobile telephone data/minute plans, N (%)a | ||
| Limited minutes and data | 313 (24.7) | |
| Unlimited minutes and data | 508 (40.0) | |
| Unlimited minutes and limited data | 196 (15.5) | |
| Limited minutes and unlimited data | 133 (10.5) | |
| Missing | 119 (9.4) | |
| Any breaks in mobile telephone service in past 6 months, N (%)a | 115 (9.1) | |
| Missing | 24 (1.9) | |
| Use mobile telephone for any health-related purposes, N (%)a | ||
| Reminder to keep medical appointments | 506 (39.9) | |
| Send/receive text messages to/from your HCP | 225 (17.7) | |
| Reminder to take medications | 218 (17.2) | |
| Keep a list of questions to ask HCP | 169 (13.3) | |
| Types of mobile health apps, N (%)b | ||
| Exercise, fitness | 182 (20.1) | |
| Diet, calorie counter, weight management | 117 (12.9) | |
| Medication management | 67 (7.4) | |
| HIV/STD prevention, care or treatment | 63 (7.0) | |
| Wellness | 55 (6.1) |
HCP: healthcare provider; STDs: sexually transmitted disease
From total N= 1,269 individuals with access to mobile telephone
From total N= 907 individuals who owned a smartphone
Despite lack of difference among racial/ethnic groups in their access to the Internet or frequency in use (all p-values> 0.17), in bivariate analyses, African-Americans and Latinos had a statistically significant lower odds of using the Internet for HCE purposes compared to White participants (African-American odds ratio [OR]= 0.63, 95% confidence interval [CI]= 0.42–0.94, p= 0.02; Latino OR= 0.67, 95% CI= 0.47–0.94, p= 0.02; other race OR= 0.75, 95% CI= 0.48–1.17, p= 0.20; overall p= 0.02). Additionally, perceived financial stability (moderate stability OR= 0.58, 95% CI= 0.45–0.74, p< 0.001; low stability OR= 0.52, 95% CI= 0.36–0.74, p< 0.001; overall p< 0.0001, compared to high stability), education (“any college experience” OR= 1.60, 95% CI=1.23–2.07, p< 0.001; “higher than college degree” OR= 2.66, 95% CI=1.88–3.76, p< 0.001; overall p< 0.0001, compared to “high school degree or less”), and history of incarceration (OR= 0.53, 95% CI= 0.40–0.70, p< 0.001) were associated with a lower odds of using the Internet for HCE purposes.
Use of the Internet for HCE purposes was associated with higher odds of reporting excellent ARV adherence (OR= 1.49, 95% CI= 1.17–1.91, p= 0.001) and undetectable HIV viral load (OR= 1.52, 95% CI= 1.11–2.09, p= 0.009). In the multivariable logistic regression for the self-reported adherence outcome, reporting excellent adherence was correlated with higher odds of Internet use for HCE purposes (OR= 1.31, p= 0.04), while controlling for age (OR= 1.02, p= 0.01), sex at birth (female OR= 0.39, p= 0.001), education (“any college experience” OR= 1.17, p= 0.29; “higher than college degree” OR= 2.02, p= 0.001; overall p= 0.003, compared to “higher school education or less”), history of homelessness or living in a shelter (OR= 0.66, p= 0.01), and history of incarceration (OR= 0.72, p= 0.04). In this model, race/ethnicity, sexual identity, perceived financial stability, and school and work status were not statistically significant and were eliminated from the model. In the multivariable regression analysis for the self-reported virologic outcome, reporting an undetectable HIV viral load was significantly associated with use of the Internet for HCE purposes (OR= 1.49, p= 0.02), while controlling for age (OR= 1.04, p< 0.001) and race/ethnicity (African-American OR= 0.63, p= 0.08; Latino OR= 1.88, p= 0.03; other race OR= 0.92, p= 0.78; overall p= 0.03, compared to White). In this model, all demographic variables except age and race/ethnicity were not statistically significant and were eliminated from the model.
Discussion
Through our survey of HIV-positive users of online social media, we showed that using the Internet for HCE purposes (i.e., to email health care providers, refill medications online, and make medical appointments online) was associated with better self-reported virologic and ARV adherence outcomes, even after controlling for other potential confounders. Despite lack of difference in access or frequency in use, there are racial/ethnic, educational, and economic disparities in the use of the Internet for HCE purposes, such that African Americans and Latinos, those with lower perceived financial stability, lower education, and any history of incarceration were less likely to report using the Internet for these purposes. Given the favorable association between the use of the Internet for HCE purposes and self-reported HIV clinical outcomes, it is important to further examine these disparities and study methods of diminishing the gap in the use of technology to improve treatment outcomes and engagement in care.
A large proportion of participants owned a smartphone, subscribed to mobile telephone plans with unlimited data and minutes, and spent a substantial amount of time on the Internet. Therefore, interventions that use the participant’s own smartphones or require the use of online programs are likely to be feasible in terms of capability for regular use. Despite the ubiquity of text messaging (Lenhart, 2010), participants did not commonly use this method to contact their health care providers. This mode of communication can potentially be an effective way of improving HIV clinical outcomes and engaging patients in their own care (Finitsis, Pellowski, & Johnson, 2014; Horvath, Azman, Kennedy, & Rutherford, 2012; Mbuagbaw et al., 2013). Therefore, future studies should examine successful clinic protocols and cost-effective methods of implementing text messaging in clinical care. Additionally, future research should explore opportunities to leverage text messaging technology while being mindful of potentials risks associated with loss of privacy and confidentiality inherent in those approaches. Lastly, given that few individuals in our study reported the use of medication management apps, it is important to examine such currently available apps in order to improve their use in research and clinical practice.
Other noteworthy results included the high level of trust in information found on the Internet and the large percentage of participants who had used the Internet to find sex partners in the past. To minimize propagation of inaccurate or incomplete information, we believe that it may be helpful for clinicians to provide patients with reputable websites where they can find up to date information about HIV care. Additionally, there is extensive research on the use of the Internet to meet sex partners and increased risk behavior, especially among men who have sex with other men (Chiasson et al., 2007; Grov, 2006; Liau, Millett, & Marks, 2006). Therefore, clinicians may consider asking patients about finding sex partners on the Internet, informing patients of the risks in trusting information provided by potential partners about their HIV/STD status, and offering further counseling on safer sex practices in this regard.
Our study provides valuable information concerning the use of mobile telephones and the Internet in HIV-positive users of online social media and the proportional relationship between using the Internet for HCE purposes and self-reported HIV clinical outcomes. However, this study has several limitations. We used a cross-sectional study design with which causality cannot be established and results may be confounded by other unmeasured variables such as participants’ personality traits and level of proactivity and vigilance in health care. Therefore, future research should examine the impact of an intervention to promote Internet use for HCE purposes on objectively assessed HIV clinical outcomes. Given that we sampled users of online social media, our results may not be generalizable to the general HIV-positive population living in the US. However, the advantage of Internet-based surveys is the ability to access wide geographic regions and potentially gather data on hidden populations who may be concerned about revealing their identities due to perceived HIV-related stigma (Marcus, Schmidt, Hamouda, & Bochow, 2009). Other limitations include the possibility of duplicate or false responses; however, given that we provided non-monetary incentives and the survey automatically disqualified duplicate IP addresses, we believe that the chance of these occurrences are low. Lastly, a disadvantage of self-reported data is that respondents tend to over-estimate adherence due to social desirability bias and/or self-denial. However, we used a previously validated approach to ask participants about viral suppression (Kalichman et al., 2000).
In conclusion, the findings generated by this study offer insight into the potential beneficial association between the uses of technology-based approaches to improving HIV clinical outcomes among PLWH. Of particular note is that although Internet access and use was comparable across racial/ethnic, educational, and socioeconomic groups, African Americans, Latinos, and those with lower education and socioeconomic status may not use the technology in ways that may optimize health, i.e., for health care engagement purposes. Future research is needed to better understand how technology can be further harnessed to optimize HIV clinical outcomes and how to foster motivation and self-efficacy for increased engagement in HIV care through the use of technology.
Acknowledgments
Sources of support: This project was supported by the National Institute of Health under award numbers K23MH097649, K24MH087220, and the University of California, San Francisco-Gladstone Institute of Virology & Immunology Center for AIDS Research, P30AI027763. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Contributor Information
Parya Saberi, University of California, San Francisco.
Mallory O. Johnson, University of California, San Francisco.
References
- Amico KR, Harman JJ, Johnson BT. Efficacy of antiretroviral therapy adherence interventions: a research synthesis of trials. 1996 to 2004. J Acquir Immune Defic Syndr. 2006;41(3):285–297. doi: 10.1097/01.qai.0000197870.99196.ea. [DOI] [PubMed] [Google Scholar]
- Bangsberg DR, Perry S, Charlebois ED, Clark RA, Roberston M, Zolopa AR, Moss A. Non-adherence to highly active antiretroviral therapy predicts progression to AIDS. AIDS. 2001;15(9):1181–1183. doi: 10.1097/00002030-200106150-00015. [DOI] [PubMed] [Google Scholar]
- CDC. Estimated HIV incidence in the United States, 2007–2010. [Retrieved 1/29/2014];HIV Surveillance Supplemental Report. 2012a 17(No. 4) from http://www.cdc.gov/hiv/library/reports/surveillance/ [Google Scholar]
- CDC. HIV in the United States: The Stages of Care. [Retrieved 1/29/2014];2012b from http://www.cdc.gov/nchhstp/newsroom/docs/2012/Stages-of-CareFactSheet-508.pdf.
- CDC. Monitoring selected national HIV prevention and care objectives by using HIV surveillance data—United States and 6 U.S. dependent areas—2011. [Retrieved 1/29/2014];HIV Surveillance Supplemental Report. 2013 18(No. 5) from http://www.cdc.gov/hiv/library/reports/surveillance/ [Google Scholar]
- Chiasson MA, Hirshfield S, Remien RH, Humberstone M, Wong T, Wolitski RJ. A comparison of on-line and off-line sexual risk in men who have sex with men - An event-based on-line survey. Jaids-Journal of Acquired Immune Deficiency Syndromes. 2007;44(2):235–243. doi: 10.1097/QAI.0b013e31802e298c. doi: [DOI] [PubMed] [Google Scholar]
- Courtenay-Quirk C, Horvath KJ, Ding H, Fisher H, McFarlane M, Kachur R, Harwood E. Perceptions of HIV-Related Websites Among Persons Recently Diagnosed with HIV. Aids Patient Care and Stds. 2010;24(2):105–115. doi: 10.1089/apc.2009.0228. doi: [DOI] [PubMed] [Google Scholar]
- Finitsis DJ, Pellowski JA, Johnson BT. Text Message Intervention Designs to Promote Adherence to Antiretroviral Therapy (ART): A Meta-Analysis of Randomized Controlled Trials. Plos One. 2014;9(2) doi: 10.1371/journal.pone.0088166. doi: ARTN e88166 DOI 10.1371/journal.pone.0088166. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Garcia de Olalla P, Knobel H, Carmona A, Guelar A, Lopez-Colomes JL, Cayla JA. Impact of adherence and highly active antiretroviral therapy on survival in HIV-infected patients. J Acquir Immune Defic Syndr. 2002;30(1):105–110. doi: 10.1097/00042560-200205010-00014. [DOI] [PubMed] [Google Scholar]
- Gardner EM, McLees MP, Steiner JF, Del Rio C, Burman WJ. The spectrum of engagement in HIV care and its relevance to test-and-treat strategies for prevention of HIV infection. Clin Infect Dis. 2011;52(6):793–800. doi: 10.1093/cid/ciq243. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Greenfield S, Kaplan SH, Ware JE, Yano EM, Frank HJL. Patients Participation in Medical-Care - Effects on Blood-Sugar Control and Quality of Life in Diabetes. Journal of General Internal Medicine. 1988;3(5):448–457. doi: 10.1007/BF02595921. doi: [DOI] [PubMed] [Google Scholar]
- Grov C. Barebacking websites: electronic environments for reducing or inducing HIV risk. Aids Care-Psychological and Socio-Medical Aspects of Aids/Hiv. 2006;18(8):990–997. doi: 10.1080/09540120500521137. doi: [DOI] [PubMed] [Google Scholar]
- Hogg RS, Heath K, Bangsberg D, Yip B, Press N, O'Shaughnessy MV, Montaner JS. Intermittent use of triple-combination therapy is predictive of mortality at baseline and after 1 year of follow-up. AIDS. 2002;16(7):1051–1058. doi: 10.1097/00002030-200205030-00012. [DOI] [PubMed] [Google Scholar]
- Horstmann E, Brown J, Islam F, Buck J, Agins BD. Retaining HIV-infected patients in care: Where are we? Where do we go from here? Clin Infect Dis. 2010;50(5):752–761. doi: 10.1086/649933. [DOI] [PubMed] [Google Scholar]
- Horvath T, Azman H, Kennedy GE, Rutherford GW. Mobile phone text messaging for promoting adherence to antiretroviral therapy in patients with HIV infection. Cochrane Database of Systematic Reviews. 2012;(3) doi: 10.1002/14651858.CD009756. doi: Artn Cd009756 Doi 10.1002/14651858.Cd009756. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hosmer DW, Lemeshow S. Applied Logistic Regression. New York, NY: John Wiley & Sons; 2000. [Google Scholar]
- Joosten EAG, DeFuentes-Merillas L, de Weert GH, Sensky T, van der Staak CPF, de Jong CAJ. Systematic review of the effects of shared decision-making on patient satisfaction, treatment adherence and health status. Psychotherapy and Psychosomatics. 2008;77(4):219–226. doi: 10.1159/000126073. doi: [DOI] [PubMed] [Google Scholar]
- Kalichman SC, Benotsch EG, Weinhardt LS, Austin J, Luke W. Internet use among people living with HIV/AIDS: Association of health information, health behaviors, and health status. Aids Education and Prevention. 2002;14(1):51–61. doi: 10.1521/aeap.14.1.51.24335. doi: [DOI] [PubMed] [Google Scholar]
- Kalichman SC, Cain D, Cherry C, Pope H, Eaton L, Kalichman MO. Internet use among people living with HIV/AIDS: Coping and health-related correlates. Aids Patient Care and Stds. 2005;19(7):439–448. doi: 10.1089/apc.2005.19.439. doi: [DOI] [PubMed] [Google Scholar]
- Kalichman SC, Rompa D, Cage M. Reliability and validity of self-reported CD4 lymphocyte count and viral load test results in people living with HIV/AIDS. Int J STD AIDS. 2000;11(9):579–585. doi: 10.1258/0956462001916551. [DOI] [PubMed] [Google Scholar]
- Lenhart Amanda. Part One: Adults and cell phones: Ownership and use Cell phones and American adults . [Retrieved 1/29/2014];2010 from http://www.pewinternet.org/Reports/2010/Cell-Phones-and-American-Adults/Part-1-Adults-and-cell-phones-Ownership-and-use/Uses-of-the-phone.aspx. [Google Scholar]
- Liau A, Millett G, Marks G. Meta-analytic examination of online sex-seeking and sexual risk behavior among men who have sex with men. Sexually Transmitted Diseases. 2006;33(9):576–584. doi: 10.1097/01.olq.0000204710.35332.c5. doi: [DOI] [PubMed] [Google Scholar]
- Lu M, Safren SA, Skolnik PR, Rogers WH, Coady W, Hardy H, Wilson IB. Optimal recall period and response task for self-reported HIV medication adherence. AIDS Behav. 2008;12(1):86–94. doi: 10.1007/s10461-007-9261-4. [DOI] [PubMed] [Google Scholar]
- Maly RC, Bourque LB, Engelhardt RF. A randomized controlled trial of facilitating information giving to patients with chronic medical conditions - Effects on outcomes of care. Journal of Family Practice. 1999;48(5):356–363. [PubMed] [Google Scholar]
- Marcus U, Schmidt AJ, Hamouda O, Bochow M. Estimating the regional distribution of men who have sex with men (MSM) based on Internet surveys. BMC Public Health. 2009;9:180. doi: 10.1186/1471-2458-9-180. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mbuagbaw L, van der Kop ML, Lester RT, Thirumurthy H, Pop-Eleches C, Ye C, Thabane L. Mobile phone text messages for improving adherence to antiretroviral therapy (ART): an individual patient data meta-analysis of randomised trials. BMJ Open. 2013;3(12):e003950. doi: 10.1136/bmjopen-2013-003950. [DOI] [PMC free article] [PubMed] [Google Scholar]
- McInnes DK, Shimada SL, Rao SR, Quill A, Duggal M, Gifford AL, Justice AC. Personal Health Record Use and Its Association with Antiretroviral Adherence: Survey and Medical Record Data from 1871 US Veterans Infected with HIV. Aids and Behavior. 2013;17(9):3091–3100. doi: 10.1007/s10461-012-0399-3. doi: [DOI] [PubMed] [Google Scholar]
- Mugavero MJ, Amico KR, Horn T, Thompson MA. The state of engagement in HIV care in the United States: from cascade to continuum to control. Clin Infect Dis. 2013;57(8):1164–1171. doi: 10.1093/cid/cit420. [DOI] [PubMed] [Google Scholar]
- Perestelo-Perez L, Gonzalez-Lorenzo M, Perez-Ramos J, Rivero-Santana A, Serrano-Aguilar P. Patient involvement and shared decision-making in mental health care. Curr Clin Pharmacol. 2011;6(2):83–90. doi: 10.2174/157488411796151192. [DOI] [PubMed] [Google Scholar]
- Saberi P, Johnson MO. Technology-based self-care methods of improving antiretroviral adherence: a systematic review. PLoS One. 2011;6(11):e27533. doi: 10.1371/journal.pone.0027533. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Silverberg MJ, Leyden WA, Stewart C, Ralston JD, Horberg MA, Grothaus L, Catz SL. Antiretroviral therapy adherence and use of an electronic shared medical record among HIV-positive individuals; Paper presented at the The Society of Epidemiologic Research; Boston, MA. 2013. [Google Scholar]
- Simoni JM, Pearson CR, Pantalone DW, Marks G, Crepaz N. Efficacy of interventions in improving highly active antiretroviral therapy adherence and HIV-1 RNA viral load. A meta-analytic review of randomized controlled trials. J Acquir Immune Defic Syndr. 2006;43(Suppl 1):S23–S35. doi: 10.1097/01.qai.0000248342.05438.52. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yuan P, Bare MG, Johnson MO, Saberi P. Using online social media for recruitment of human immunodeficiency virus-positive participants: a cross-sectional survey. J Med Internet Res. 2014;16(5):e117. doi: 10.2196/jmir.3229. [DOI] [PMC free article] [PubMed] [Google Scholar]