Abstract
Recent studies support technology-based behavioral interventions for individuals with HIV. This study focused on the use of cell phone and internet technologies among a cohort of 515 HIV-infected individuals. Socio-demographic and clinic data were collected among individuals presenting at an urban Midwestern university HIV clinic in 2007. Regular internet usage occurred more often with males, Caucasians, those who were employed, had higher salaries, and were more educated. Higher levels of education and salary >$10,000 predicted regular usage when controlling for race, employment, and gender. Cell phone ownership was associated with being Caucasian, employed, more educated, and salary > $10,000. Employment was the only predictor of owning a cell phone when controlling for income, race, and education. Individuals who were <40 years of age, employed, and more educated were more likely to know how to text message. Employment and post-high school education predicted knowledge of text messaging, when controlling for age. Disparities among internet, cell phone, and text messaging usage exist among HIV-infected individuals.
Keywords: HIV/AIDS, technology-based interventions, cell phones, internet usage, HIV-infected, socioeconomic barriers
Introduction
For the past decade, HIV incidence in the U.S. has remained stable. The CDC recently revised estimates for HIV incidence in this country and now indicates that ~56,000 individuals were infected in 2007 (CDC, 2008). This ongoing incidence combined with a significant decline in mortality, has led to the current estimated prevalence of 1.2 million persons living with HIV/AIDS in the U.S. There is an urgent need to reduce HIV incidence and to improve the health status of individuals living with HIV/AIDS. Given the advancements in the care and treatment of HIV, secondary prevention efforts and quality of life improvements have been incorporated into standard clinical care (CDC, 2003; Janssen et al., 2001). Well-established primary care guidelines for HIV prioritize key areas for prevention, including behavioral assessments improving adherence to care and treatment efforts; serostatus disclosure; and enhancing patient understanding of safer sex techniques (CDC, 2003; Jemmott, Jemmott, & O'Leary, 2007; Nilsson-Schonnesson, 2002).
Other fields have successfully implemented technology-based behavioral interventions. For instance, internet-based interventions increased levels of physical activity and weight loss (Tate, Wing, & Winett, 2001; Vandelanotte, Spathonis, Eakin, & Owen, 2007); successful smoking cessation intervention messages have been sent via text messages (Rodgers et al., 2005); clinic visit reminders can be delivered via text messages and cell phone calls (Leong et al., 2006); and text messaging has been shown to enhance medication adherence for chronic diseases, including psychiatric disorders (McDonald, Garg, & Haynes, 2002). These advances in intervention design have also been explored in the field of HIV. Some examples include internet-delivered and telephone-based mental health care for HIV-infected individuals in rural areas (Heckman & Carlson, 2007; M. L. Ybarra & Eaton, 2005), telephone-based medication adherence messages (Puccio, 2006; Vidrinea, 2006), partner notification via text messaging among STD clinics (Levine, McCright, Dobkin, Woodruff, & Klausner, 2008), and smoking cessation messages delivered through cell phones (Vidrinea, 2006). In a recent meta-analysis, Ybarra and Bull (2007) found that much effort has been placed on developing technology-based interventions for HIV-infected populations to offer new avenues of message delivery, yet large-scale effectiveness of these programs have yet to be reported (Ybarra & Bull, 2007).
Not only do these efforts have the potential to enhance health-promoting behaviors among HIV-infected populations by reaching individuals where they actively live, but also the interventions may be more effective and cost-efficient than traditional efforts. Many of the studies conducted in the U.S. have focused on populations that are avid internet consumers, specifically young adults, and most often, young men who have sex with men (MSM) (Ybarra & Bull, 2007). Examining the availability and accessibility of these technologies among more diverse HIV-infected populations in the U.S. would be beneficial to the developing field of HIV intervention design. The goal of this study was to increase the understanding of access to and use of currently available technologies among an urban cohort of HIV-infected patients.
Methods
This cross-sectional study was conducted between June and September 2007 as part of standard of care behavioral assessment among HIV-infected patients in an urban, Midwestern university HIV clinic. All patients were asked to participate in this assessment prior to their clinic visit. Interviews were conducted while patients were waiting to be seen by their health care providers. All HIV-infected patients who presented in the clinic during that time frame were eligible. This study was approved by Washington University School of Medicine Human Research Protection Office.
Demographic characteristics (race, age, employment, education, income, and gender), self-reported medication adherence (4 day recall and visual analogue scale), internet usage (daily, 2–3 times per week, weekly, 2–3 times per month, and never), owning a cellular phone (yes/no), and knowledge of text messaging (yes/no) were collected in the assessment. The validated 4-day AIDS Clinical Trial Group (ACTG) measure of self-reported medication adherence was used (Chesney et al., 2000). The Patient Health Questionnaire (PHQ-9) was also completed during these interviews, which screens depressive symptomatology and calculates symptom counts that signify major depressive disorder and other depressive disorders (Kroenke, Spitzer, & Williams, 2001).
Medical measures including current CD4 cell count, plasma HIV RNA level, and use of antiretroviral therapies were collected at time of the visit. Virologic suppression was defined as having an HIV RNA level of < 400 copies/ml.
Statistical Analyses
Descriptive and bivariate analyses were conducted to illustrate and assess differences among the sample. Logistic regression analyses were conducted to determine factors that serve as predictors to technology use (internet, cell phones, and text messages). Internet use was dichotomized to (1) daily to weekly use or (2) two to three times per month to never. Education levels were categorically defined (less than high school degree; high school degree; some college, vocational, or associate’s degree; and college or graduate degree). Race was categorized into African American, Caucasian, or other based on self reporting. Employment status was categorized into unemployed, employed (part- or full-time), or receiving disability benefits. Depression severity was dichotomized to those who expressed major depressive disorder symptoms and those who did not. Age was categorized for regression analyses (18–25, 26–39, 40–55, >55 years). Medication adherence was dichotomized above and below 95% adherent. HIV RNA level was transformed into log10 for normality. All tests were 2-tailed and p< 0.05 was considered significant. Data analyses were performed using SPSS software (version 15.0).
Results
A total of 515 individuals completed the assessments between June and September 2007. The majority of the sample was male (n = 349; 68%) and African American (n = 305; 60%). Half of the sample reported having a high school education or less (n = 258) and 42% (n = 212) of the sample completed a college or graduate degree. A large proportion of the sample reported an annual salary of < $10,000 (n = 239; 46.6%), while 16% (n = 76) reported a 12 month salary of > $30,000. One-fifth of the patients reported being unemployed (n = 111), 40% (n = 209) were employed either part- (n = 52; 9.9%) or full-time (n = 158; 30.7%), and 32% (n = 164) reported receiving disability benefits. About 12% of the sample reported being currently married (n = 62), while 62% of the sample reported never being married (n = 320). About 40% (n = 201) of the sample reported currently having a sexual partner and 90% (n = 165) of those, reported one sexual partner in the past 30 days.
The median CD4 cell count of the sample was 440.0 cells/mm3 (IQR 268.75–635.25). Almost three-quarters (n = 373) of the sample was receiving antiretroviral medication (ARV) and 65% of the sample had a HIV RNA viral load of < 400 copies/ml, signifying viral suppression. Almost half of the sample (n = 248) had ≥ 95% self-reported medication adherence, when measuring the 4-day recall. About 40% of the sample endorsed criteria for major depressive disorder, as measured by the PHQ-9 and about 15% (n = 145) endorsed having suicidal thoughts at least once within the past 2 weeks.
Daily internet usage was reported among 31% (n = 160) of the sample, while 44% (n = 228) reported never using the internet. The majority of the sample reported having a cellular telephone (60%; n = 310), with 68% (n = 210) of cell phone owners reporting knowing how to text message. Combined, 38% (n = 194) of the sample reported owning a cell phone and using the internet at least weekly, and 9% (n = 46) reported neither owning a cell phone nor any internet use.
Regular internet usage occurred more often with male (74% vs. 21%; p = 0.003), Caucasian (63% vs. 34%; p < 0.001), more educated (67% vs. 29%, p < 0.001), higher salaried (63% vs. 26%; p < 0.001), and employed (62% vs.38%; p < 0.001) individuals. Logistic regression analyses found that highest levels of education attainment (p < 0.001) and an annual income over $10,000 (p < 0.001) predicted regular internet usage when controlling for race, employment, and gender.
Cell phone usage was assessed; individuals who were Caucasian (76% vs. 51%; p< 0.001), making > $10,000 annually (72% vs. 46%; p < 0.001), employed (77% vs. 47%; p < 0.001), and had completed post-high school education (70% vs. 53%, p < 0.001) were more likely to report having cell phones. Additionally, of patients on antiretroviral medications, those reporting to be > 95% adherent were more likely to own a cell phone (65% vs. 53%; p < 0.001). Logistic regression analyses found being employed as the only predictor of owning a cellular phone (p < 0.001) when controlling for income, education, and self-reported medication adherence.
When assessing knowledge of text messaging, univariate analyses were conducted only among patients who reported having cellular phones (n = 260). Patients < 40 years of age (85% vs. 54%; p < 0.001), who were more educated (74% vs. 64%; p < 0.05), and employed (78% vs. 50%; p < 0.001) knew how to utilize text messaging technology on their cellular phones. Logistic regression analyses found employment (p < 0.001) and more than a high school education (p < 0.02) predicted knowledge of text messaging, when controlling for age.
Discussion
During a three-month period, behavioral assessments were conducted among individuals with HIV presenting at an outpatient clinic. The demographics of the cohort reflect the national HIV epidemic, in that the majority of the sample was low-income, male, and African American. The majority of the sample was on antiretroviral therapy with excellent viral suppression. The study was conducted to assess the usage of newer and prevalent technologies in this urban, Midwestern U.S., university HIV clinic sample. Use of technology (internet, cell phones, and text messaging) was most often associated with employment, higher education, male gender, higher income, and Caucasian race.
Delivering behavioral interventions through new, prevalent technologies creates an opportunity to reach more individuals and collect data that enhance understanding of behavioral determinants in a cost-effective and -efficient manner. Previous research has found that among HIV-infected populations the internet is a useful health resource among those who have been living with HIV for extended period of time (Kalichman, Benotsch, Weinhardt, Austin, & Luke, 2002), for research purposes (Pequegnat et al., 2007; Rhodes, Bowie, & Hergenrather, 2003), and among populations who are already heavy consumers of these common technologies (MSM, young adults) (McFarlane, Kachur, Klausner, Roland, & Cohen, 2005; Mustanski, 2001). These technologies have also shown promise in some developing countries (Curioso, Blas, Nodell, Alva, & Kurth, 2007; Curioso & Kurth, 2007; Kaplan, 2006).
Overall, our clinic cohort reported low rates of daily internet usage. These findings mimic those of a study conducted among newly HIV-infected low-income individuals, which found that education and income impacted internet access, and only one-third of their sample had internet access at home and 3% at work (Mayben & Giordano, 2007). While the internet has become readily available throughout most of the U.S. with an estimated 78% of the general population using frequently (USC Annenberg School, 2007), populations that seldom, if ever, access it. Internet-delivered interventions require a commitment to improve access related resources for those persons who report little or no access to the internet. Similar to the Diffusion of Innovation model, “late adopters” or minimal consumers of the internet (i.e. those who access the internet less than 2–3 times a week), may be more apt to increase internet usage with their participation in an internet-based intervention. However in the current study, participants reported not using the internet, even with access at public libraries and similar locations.
The USC Annenberg report (2007) estimated that 50% of the U.S. population owns cellular phones. Over 60% of our cohort does not have them. Furthermore, consistent cell phone ownership may be an additional barrier, which our study did not assess. Providing cell phones during a study period offers the opportunity to overcome this barrier, yet becomes increasingly difficult to routinely adopt these interventions due to associated costs. Interestingly, of those who owned cell phones, almost 70% reported knowledge of text messaging, which is an important finding for developing interventions. Text messaging is a viable message delivery option for health care providers and intervention developers (Leong et al., 2006; Levine et al., 2008; McDonald et al., 2002; Rodgers et al., 2005). These promising interventions offer little for ongoing standard of care, if resources are not available to provide ongoing technology access.
The role of these technologies and their effect on HIV-related outcomes, including medication adherence is one that deserves further attention. The high rates of depressive symptoms and suicidal ideation was not significantly associated with technology use. In our study, race, gender, education attainment, and employment status were related to consumption of these technologies in this urban, Midwestern HIV clinic. This study also directly highlights individuals who reported to be ≥ 95% adherent more often owned cell phones, which suggests that there are multiple socioeconomic factors that influence medication adherence.
Study limitations and strengths
This study had the opportunity to screen all HIV-infected patients who presented for care during a three-month period. Screening patients to assess their behavioral risk factors has been encouraged in HIV primary care to help reveal patient intervention needs. The cross-sectional nature of this study does not allow for assessment of the temporal relationships of the associations. Further, due to the self-reported nature of the study, there is potential for an inherent reporting bias. The rates of technology usage in this sample was lower than national averages; this may be due to the lower socioeconomic status of our sample than the national average. Alternatively, the nature of this Midwestern U.S. sample offers limited insight to the broader HIV-infected populations. Furthermore, the technology use items were current usage measures and did not measure cell phone ownership over a specific time, and these measures have potential to fluctuate over time. Finally, we did not collect sexual orientation data. Given that previous research has illustrated higher use of technology among MSM than other populations, future research should include information related to sexual orientation.
Conclusions
This study revealed low rates of internet, cell phone, and text messaging technology usage among patients with HIV. While technology-based behavioral interventions are successful among heavy technology consumers and in many developing countries, challenges remain among resource-poor populations.
Table 1.
Demographic Characteristics of the Cohort by Gender
| Male (n = 349) | Female (n = 166) | ||||
|---|---|---|---|---|---|
| Race | n | % | n | % | p |
| African American | 190 | 54.4 | 115 | 59.2 | 0.001 |
| Caucasian | 134 | 38.4 | 29 | 17.5 | |
| Other | 25 | 7.2 | 22 | 13.3 | |
| Education level (n = 503) | |||||
| Less than high school degree | 64 | 18.9 | 53 | 32.3 | 0.001 |
| High school degree/GED | 89 | 26.3 | 52 | 31.7 | |
| Some college/vocational schooling | 22 | 6.5 | 11 | 6.7 | |
| Bachelor or graduate degree | 164 | 48.4 | 48 | 29.3 | |
| Employment status (n = 514) | |||||
| Unemployed | 68 | 19.5 | 43 | 26.1 | 0.163 |
| Full-time | 115 | 33.0 | 43 | 26.1 | |
| Part-time | 30 | 8.6 | 21 | 12.7 | |
| Disability benefits | 20 | 5.7 | 10 | 6.1 | |
| Other (retired, student, work rehabilitation) | 116 | 33.2 | 48 | 29.1 | |
| Income (n = 471) | |||||
| < 10k | 147 | 45.2 | 92 | 59.0 | 0.008 |
| 10–20k | 70 | 21.5 | 40 | 25.6 | |
| 20–30k | 32 | 9.8 | 11 | 7.1 | |
| 30–40k | 20 | 6.2 | 8 | 5.1 | |
| 40–50k | 19 | 5.8 | 1 | 0.6 | |
| 50–60k | 12 | 3.7 | 1 | 0.6 | |
| 60–70k | 7 | 2.2 | 1 | 0.6 | |
| 70–80k | 2 | 0.6 | 1 | 0.6 | |
| 80–90k | 2 | 0.6 | 0 | 0.0 | |
| 100k+ | 4 | 1.2 | 1 | 0.6 | |
| Refused | 10 | 3.1 | 0 | ||
| Housing | |||||
| Own house or apartment | 242 | 69.5 | 131 | 78.9 | 0.112 |
| Someone else’s house or apartment | 85 | 24.4 | 30 | 18.1 | |
| Shelter/rooming | 21 | 6.0 | 5 | 3.0 | |
| Consider self homeless | 29 | 29.6 | 10 | 27.8 | 0.509 |
| One current sexual partner (n = 504) | 92 | 83.6 | 73 | 98.6 | 0.05 |
| Marital status (n = 511) | |||||
| Married | 32 | 9.2 | 30 | 18.2 | 0.001 |
| Divorced | 52 | 15.0 | 27 | 16.4 | |
| Separated | 12 | 3.5 | 15 | 9.1 | |
| Never married | 241 | 69.7 | 79 | 47.9 | |
| Widow/Widower | 9 | 2.6 | 14 | 8.5 | |
| Major depressive symptoms | 135 | 39.6 | 68 | 42.0 | 0.339 |
Table 2.
Technology usage among sample
| n | % | |
|---|---|---|
| Cell phone ownership | 310 | 60.2 |
| Text messaging knowledge | 210 | 40.8 |
| Internet usage | ||
| Daily | 160 | 31.1 |
| 2–3×/week | 48 | 9.3 |
| Weekly | 18 | 3.5 |
| 2–3×/month | 26 | 5.0 |
| Monthly | 35 | 6.8 |
| Never | 228 | 44.3 |
Table 3.
HIV-Related medical characteristics
| Median CD4 cell count | 440 | IQR: 268.75–635.25 |
| Currently on HAART | 373 | 73.0 |
| < 400 copies/ml (of those on HAART) | 326 | 87.4 |
| Log10 VL | 2.66 | 1.22 |
Table 4.
Differences in internet usage by gender, education, employment and race
| Daily | 2–3×/week | Weekly | 2–3×/month | Monthly | Never | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | n | % | n | % | n | % | p | |
| Male | 126 | 36.1 | 34 | 9.7 | 8 | 2.3 | 18 | 5.2 | 21 | 6 | 142 | 40.7 | 0.003 |
| Employment status | |||||||||||||
| Unemployed | 44 | 20.9 | 5 | 2.3 | 5 | 2.3 | 0 | 0 | 5 | 2.3 | 150 | 72.1 | 0.001** |
| Employed | 99 | 47.4 | 22 | 10.5 | 9 | 4.3 | 15 | 7.2 | 16 | 7.7 | 48 | 23 | |
| Disability | 35 | 21.3 | 13 | 7.9 | 3 | 1.8 | 7 | 4.3 | 8 | 4.9 | 98 | 59.8 | |
| Education level | |||||||||||||
| <High school diploma | 6 | 5.1 | 5 | 4.3 | 3 | 2.6 | 4 | 3.4 | 11 | 9.4 | 88 | 75.2 | 0.001** |
| High school diploma | 26 | 18.4 | 11 | 7.8 | 4 | 2.8 | 5 | 3.5 | 9 | 6.4 | 86 | 61 | |
| Some college | 11 | 33.3 | 7 | 21.2 | 3 | 9.1 | 3 | 9.1 | 1 | 3 | 8 | 24.2 | |
| Bachelor or graduate degree | 109 | 51.4 | 24 | 11.3 | 8 | 3.8 | 14 | 6.6 | 12 | 5.7 | 45 | 21.2 | |
| Race | |||||||||||||
| African American | 60 | 19.7 | 31 | 10.5 | 11 | 3.6 | 13 | 4.3 | 28 | 8.9 | 162 | 53.1 | 0.001 |
| Caucasian | 81 | 50.3 | 13 | 8.1 | 7 | 4.3 | 12 | 7.5 | 7 | 4.3 | 41 | 25.5 | |
| Other | 19 | 40.4 | 2 | 4.3 | 0 | 0 | 1 | 2.1 | 1 | 2.1 | 24 | 51.1 | |
Logistic regression analyses revealed significance at p < 0.001
Table 5.
Differences in cell phone and text message usage by education, employment and race
| Cell phone ownership | n | % | p | Text Message | n | % | p |
|---|---|---|---|---|---|---|---|
| Education level | Education level | ||||||
| <High school diploma | 53 | 45.3 | 0.001 | Less than high school | 28 | 52.8 | 0.019** |
| High school diploma | 77 | 54.6 | High school diploma | 49 | 63.6 | ||
| Some college/vocational school | 25 | 75.8 | Some college/vocational school | 19 | 76 | ||
| Bachelor or graduate degree | 146 | 68.9 | Bachelor or graduate degree | 109 | 74.7 | ||
| Employment level | Employment level | ||||||
| Unemployed | 88 | 41.9 | 0.001** | Unemployed | 10 | 52.6 | 0.001** |
| Employed | 161 | 77 | Employed | 126 | 78.3 | ||
| Disability benefits | 80 | 48.8 | Disability benefits | 39 | 48.8 | ||
| Race | Race | ||||||
| African American | 82 | 51.1 | 0.001 | African American | 90 | 73.1 | 0.054 |
| Caucasian | 123 | 76.4 | Caucasian | 79 | 64.2 | ||
| Other | 31 | 66 | Other | 17 | 53.1 | ||
| >95% medication adherence | 161 | 65 | 0.03 | >95% medication adherence | 105 | 74 | 0.562 |
Logistic regression analyses revealed significance at p < 0.001
Acknowledgements
This publication was partially supported by Grant Number UL1 RR024992 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH) and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH. ETO receives grants and research support from Abbott, GlaxoSmithKline, Merck, Tibotec, Gilead and Bavarian Nordic. He also serves as a consultant for Abbott, GlaxoSmithKline, Tibotec, Bristol-Myers Squibb and Gilead.
Contributor Information
Enbal Shacham, Saint Louis University College for Public Health & Social Justice.
Kate E. Stamm, Kirksville College of Osteopathic Medicine
Edgar T. Overton, Washington University School of Medicine.
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