Facilitated WhatsApp Support Groups for Youth Living With HIV in Nairobi, Kenya: Single-Arm Pilot Intervention Study
Bibliographic record
Abstract
BACKGROUND: Mobile technology can support HIV care, but studies in youth are limited. In 2014, youth receiving HIV care at several health care facilities in Nairobi, Kenya spontaneously formed peer support groups using the social media platform WhatsApp. OBJECTIVE: Inspired by youth-initiated groups, we aimed to evaluate the use of WhatsApp to deliver a social support intervention to improve HIV treatment and psychosocial outcomes in youth. We developed a facilitated WhatsApp group intervention (named Vijana-SMART), which was grounded in social support theory and guided by the design recommendations of youth living with HIV. This paper evaluates the intervention's acceptability and pre-post changes in health outcomes. METHODS: The intervention involved interactive WhatsApp groups facilitated by study staff for 6 months, with each group having approximately 25 members. Study staff sent weekly structured messages, and the message content was based on social support theory and encouraged unstructured peer-to-peer messaging and support. We conducted a single-arm pilot among 55 youth living with HIV aged 14-24 years recruited from a government health care facility serving a mixed-income area of Nairobi. At enrollment and follow-up, self-report questionnaires assessed acceptability; antiretroviral therapy (ART) information, motivation, and behavioral skills (IMB); depression; social support; stigma; resilience; and ART adherence. All participants received the intervention. We used generalized estimating equations (GEEs) clustered by participant to evaluate changes in scores from baseline to follow-up, and correlates of participant WhatsApp messaging. RESULTS: The median participant age was 18 years, and 67% (37/55) were female. Intervention acceptability was high. All participants reported that it was helpful, and 73% (38/52) sent ≥1 WhatsApp message. Messaging levels varied considerably between participants and were higher during school holidays, earlier in the intervention period, and among youth aged ≥18 years. IMB scores increased from enrollment to follow-up (66.9% to 71.3%; P<.001). Stigma scores also increased (8.3% to 16.7%; P=.001), and resilience scores decreased (75.0% to 70.0%; P<.001). We found no significant change in ART adherence, social support, or depression. We detected a positive association between the level of messaging during the study and the resilience score, but no significant association between messaging and other outcomes. Once enrolled, it was common for participants to change their phone numbers or leave the groups and request to be added back, which may present implementation challenges at a larger scale. CONCLUSIONS: Increased IMB scores following WhatsApp group participation may improve HIV outcomes. Increased stigma and decreased resilience were unintended consequences and may reflect transient effects of group sharing of challenging experiences, which should be addressed in larger randomized evaluations. WhatsApp groups present a promising and acceptable modality to deliver supportive interventions to youth living with HIV beyond the clinic, and further evaluation is warranted. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05634265); https://clinicaltrials.gov/study/NCT05634265.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".