One in three new cases of HIV in South Africa is among adolescents. Given that adolescents are particularly affected, scalable and cost-effective prevention programs are urgently needed. This study aims to identify opportunities to integrate technology into youth HIV prevention efforts. In 2012, 1,107 8th – 11th graders completed a paper-and-pencil survey. Respondents were enrolled in one of three public high schools in Langa. Because it is the closest black township to Cape Town, Langa has the highest density of people in the region. Eighty-nine percent of respondents have used text messaging (SMS) and 86% have gone online. If an HIV prevention program was offered online, 66% of youth would be somewhat or extremely likely to access it; slightly fewer (55%) felt the same about SMS-based programming. In comparison, 85% said they would be somewhat or extremely likely to access a school-based HIV prevention program. Interest in Internet-(60%) and SMS-based (54%) HIV prevention programming was similar for youth who had a self-appraised risk for HIV compared to youth who appraised their risk to be lower, as it was for youth who were tired of hearing messages about HIV prevention.
Technology use is common – even among high school students who live in lower income communities. At the same time, these data reveal that it is not uncommon for youth to be tired of hearing messages about HIV prevention, and many of the typical topics key to HIV prevention have low interest levels among youth. HIV prevention researchers need to be mindful of the extent of existing programming that youth are exposed to. Technology-based programming may be especially amenable to meeting these requirements because of its novelty especially in developing countries, and because interactive functionality can be easily integrated into the program design. Given the preference for school- and Internet-based programming, it seems that a hybrid approach is likely feasible and acceptable.
Despite intensive HIV prevention research (
To promote behavior change, HIV prevention programs need to be efficacious
but also easily implemented. Data suggest explosive technology growth in South
Africa (
The research protocol was reviewed and approved by the University of the Western Cape and the Chesapeake Institutional Review Board. Data were collected between April-August 2012. All participants provided written informed consent.
Respondents attended one of three partner schools in Langa, a low income community with the highest density of people in the region because it is the closest black township to Cape Town. All students in Grade 8 to Grade 11 who were 16 years of age (the legal age of consent in South Africa) or older were invited to voluntarily participate in the study.
Surveys were completed via paper and pencil in the absence of the teachers and school administrators. To assure anonymity, names were not collected on the survey instrument.
Respondents took an average of one hour to complete the survey. The
survey was written in English, which is the official language of South Africa
and the language of instruction in the high schools. There are multiple living
languages in South Africa, however (
All youth were asked: “If there was a health education program about HIV/AIDS prevention for teenagers, how likely would you go to it if it was …. a) at school, b) at a religious organization, c) over e-mail, d) over text (SMS) messages, and e) on the Internet”.
Topics were chosen to reflect key components of HIV prevention
programs, as well as those that were posited to be salient to youth based
upon our previous work with sub-Saharan adolescents (
Youth were asked to appraise their personal risk of getting HIV; and
how strongly they agreed or disagreed with the following statement:
“I am
Vaginal sex was queried: “Have you
All surveys were double entered by project staff to ensure accuracy.
Missing data were imputed using the “impute” command in Stata,
which estimates missing values using best set regression (
Of the total 1,460 students who were enrolled in the three partner schools,
1,279 students (88%) were present on the day of the survey. 1,107 of the
1,191 eligible students completed the survey (Response rate = 93%).
Demographic and technology use characteristics are shown in
As shown in
Of the five access points queried, youth were most likely to indicate
that they would be somewhat or very likely to access an HIV prevention program
if it were delivered at school (85%,
Sixty percent of youth who appraised their likelihood of getting HIV as
above average chance said they were somewhat or very likely to access an HIV
prevention program if it were online and 54% if it were via SMS. Similar
rates of interest in Internet (60%) and SMS (53%) programming
were noted among youth who agreed they were tired of HIV prevention messaging,
and for youth who reported ever having vaginal or anal sex (67% and
55%, respectively). As shown in
Technology use is common among adolescents attending our three partner schools in the lower income community, Langa, South Africa. Yet, these data reveal that it is not uncommon for youth to be tired of hearing messages about HIV prevention, and many of the typical key HIV prevention topics key have low interest levels among youth. HIV prevention researchers need to be mindful of the extent of existing programming that youth are exposed to. Technology-based programming may be especially amenable to meeting these requirements because of its novelty especially in developing countries, and because interactive functionality can be easily integrated into the program design.
According to youth in this study, the likelihood of accessing an HIV prevention program was greatest if the program were offered at school. Perhaps this is because currently available programming is predominantly delivered in schools, so this is the most familiar option for youth. This does not mean that youth were opposed to technology-based programs, however: Two in three youth said they were at least somewhat likely to access an Internet-based program, and slightly more than one in two youth would access an SMS-based program. Given the preference for school- and Internet-based programming (among the technology-related access points), a hybrid approach may be feasible and acceptable. For example, perhaps an Internet program could be offered as an after-school activity on school grounds. By utilizing the Internet as the program delivery mechanism, one safeguards program fidelity by ensuring all youth have access to the same, accurate sexual health information, while simultaneously allowing youth greater privacy to complete the program when and where they are comfortable. It also takes the onus off the teacher to deliver what can be uncomfortable or embarrassing information to their students, as well as the burden of finding classroom time to teach the material.
The generalizability of these data to greater South Africa, and youth who do not speak English fluently or are not enrolled in high school is unknown. Furthermore, it is possible that some youth did not answer honestly. Additionally, technology use was lower among the 92 respondents excluded from the analyses. Differences in findings may have been noted if these youth or those who were absent on the day of the survey had been included in the analytical sample.
In South Africa, like many countries, people who live in low income households are at
higher risk for HIV (
This research was funded by a grant from the National Institute of Mental Health at the National Institutes of Health (R03MH094238; PI: Ybarra). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Youth characteristics (n=1,015)
| Youth characteristics | % (n) | |
|---|---|---|
| Demographic characteristics | ||
| Female | 63.7% (647) | |
| Age (Range: 16–24; M:SD) | 17.5 (1.2) | |
| Grade | ||
| Grade 8 | 0.2% (2) | |
| Grade 9 | 0.1% (1) | |
| Grade 10 | 52.0% (528) | |
| Grade 11 | 47.7% (484) | |
| Race | ||
| African | 97.7% (992) | |
| Colored | 0.6% (6) | |
| Indian | 0.1% (1) | |
| White | 0.1% (1) | |
| Other | 1.5% (15) | |
| Father's education | ||
| No formal education | 2.4% (24) | |
| Primary school | 6.5% (66) | |
| Secondary school | 36.6% (371) | |
| Tertiary institution / University graduate | 25.8% (262) | |
| I am not sure | 28.8% (292) | |
| Income | ||
| Lower than the average family | 29.0% (294) | |
| Similar to the average family | 60.8% (617) | |
| Higher than the average family | 10.3% (104) | |
| Mother's education | ||
| No formal education | 2.6% (26) | |
| Primary school | 5.2% (53) | |
| Secondary school | 39.8% (404) | |
| Tertiary institution / University graduate | 31.1% (316) | |
| I am not sure | 21.3% (216) | |
| Importance of religion on respondent's life | ||
| Not at all important | 3.0% (30) | |
| Somewhat unimportant | 1.4% (14) | |
| Somewhat important | 11.8% (120) | |
| Very important | 83.8% (851) | |
| Technology use | ||
| Ever used the Internet | 85.8% (871) | |
| Ever used SMS (text messaging) | ||
| Do not have a phone | 9.9% (100) | |
| Have a phone, do not SMS | 1.5% (15) | |
| SMS | 88.7% (900) | |
| Self-appraised chance of getting HIV is above average / very strong | 12.7 (129) | |
Colored refers to youth who are mixed race (White and African)
Interest in specific HIV prevention programming-related topics (n=1,015)
| Appraisal of HIV prevention programming | All | Males | Females | p- |
|---|---|---|---|---|
| Somewhat / very tired of hearing about how to prevent HIV | 37.5% (381) | 42.1% (155) | 34.9% (226) | 0.02 |
| Interested in learning about… | ||||
| How to end a relationship | 43.8% (445) | 36.7% (135) | 47.9% (310) | 0.001 |
| How to avoid sex if you don't want to have sex | 43.4% (440) | 28.8% (106) | 51.6% (334) | <0.001 |
| How to use a condom | 42.2% (428) | 43.5% (160) | 41.4% (268) | 0.52 |
| How drugs and alcohol affect your decision making | 39.1% (397) | 50.3% (185) | 32.8% (212) | <0.001 |
| Birth control / family planning | 31.5% (320) | 27.5% (101) | 33.9% (219) | 0.04 |
| How to refuse sex from a sugar daddy / mommy | 22.7% (230) | 22.3% (82) | 22.9% (148) | 0.83 |
| Where to get an HIV test | 21.9% (222) | 23.9% (88) | 20.7% (134) | 0.24 |
| How to develop and maintain a romantic relationship | 16.7% (169) | 22.0% (81) | 13.6% (88) | 0.001 |
p-value based upon a chi-square testing the relative difference in endorsement for males versus females
Likelihood of accessing an HIV prevention program based upon different access points (n=1,015)
| Likelihood of | School | Religious | SMS (text | Internet | |
|---|---|---|---|---|---|
| Not at all likely | 10.1% (102) | 17.8% (181) | 33.1% (336) | 27.5% (279) | 20.9% (212) |
| Somewhat unlikely | 4.7% (48) | 12.3% (125) | 24.6% (250) | 17.4% (177) | 13.0% (132) |
| Somewhat likely | 38.3% (389) | 46.1% (468) | 22.2% (225) | 30.6% (311) | 31.4% (319) |
| Extremely likely | 46.9% (476) | 23.7% (241) | 20.1% (204) | 24.4% (248) | 34.7% (352) |
Relative odds of being somewhat or very likely to access an HIV
prevention program
| HIV risk attitudes and behaviors | School (n=1015) | Religious | Email (n=1015) | SMS
(text | Internet |
|---|---|---|---|---|---|
| aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | aOR (95% CI) | |
| Above average chance of getting HIV | 0.97 (0.54, 1.73) | 1.00 (0.60, 1.66) | 0.82 (0.55, 1.23) | 0.86 (0.57, 1.32) | 1.26 (0.78, 2.04) |
| Tired of hearing about HIV prevention | 0.79 (0.56, 1.11) | 0.79 (0.59, 1.06) | |||
| Ever had vaginal or anal sex | 0.82 (0.51, 1.33) | 0.82 (0.56, 1.20) | 0.89 (0.66, 1.22) | 0.95 (0.69, 1.32) | |
Five different multivariate logistic regression models were estimated, one for each access point (e.g., school). Each model estimates the relative odds of being somewhat or very likely to access an HIV prevention program through the access point in question, given the indicator of HIV risk behavior or attitudes in question. Odds ratios are adjusted for the other two HIV risk attitudes and behaviors, as well as demographic characteristics (i.e., biological sex, age, grade, race, paternal and maternal education, income, i.e., religiosity), technology use (i.e., Internet, SMS), self-reported honesty, and the number of variables imputed. Bold text denotes statistical significance (p<0.05). Italicized text denotes borderline statistical significance (p<0.10).