Facilitated WhatsApp Support Groups for Youth Living With HIV in Nairobi, Kenya: Single-Arm Pilot Intervention Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».