Conversational mHealth Platform Designed to Support Tuberculosis Treatment Adherence in Low-Income South African Patients: Pilot Cohort Study Examining Coverage, Engagement, and Treatment Outcomes (Preprint)
Notice bibliographique
Résumé
Background: Tuberculosis is a leading cause of death in South Africa, with poor adherence undermining treatment success. Findings from recent research on the impact of mHealth (mobile health) interventions on tuberculosis treatment outcomes show promise, yet many interventions remain untested in African contexts. Rising smartphone ownership in South Africa enables more complex mHealth interventions, offering an opportunity to deploy behavioral tools within high-burden, resource-constrained settings. Objective: This pilot study evaluates the feasibility and effectiveness, among low-income patients at a South African clinic, of a WhatsApp (Meta)-based conversational mHealth platform designed to tackle specific behavioral barriers to adherence. Aims include the following: (1) evaluating coverage by studying the proportion of patients within the target group who own smartphones, (2) describing patterns of engagement with the platform and the role of mobile data scarcity as a barrier to use, and (3) producing evidence on the impact that a behavioral mHealth intervention can have on tuberculosis treatment success. Methods: Patients newly diagnosed with drug-susceptible pulmonary tuberculosis between August 2022 and October 2023 completed a screening survey. Those owning compatible mobile phones were invited to enroll. The platform provided reminders alongside behavioral support features. Coverage was studied by estimating smartphone ownership among screened patients and comparing characteristics between enrolled patients (n=42) and those receiving standard care (n=102) using standardized differences. Engagement was analyzed using local polynomial regressions for usage trends and logistic regressions to estimate the impact of mobile data top-ups. The marginal effect of enrollment on the probability of successfully completing tuberculosis treatment was studied using a t test and logistic regressions with and without covariates. Results: A total of 34% (49/146) of screened participants owned a phone that could use WhatsApp. There were differences in characteristics by enrollment status. Further, 50% of patients engaged with the platform each day until the end of treatment. Overcoming an initial inability to send unprompted messages to inactive patients was associated with an immediate 13-percentage-point increase in aggregate engagement the following month. Mobile data scarcity hindered use-receiving mobile data top-ups within the previous week was associated with a 3.37-percentage-point increase (95% CI 0.0007 to 0.0666) in platform engagement. The estimated marginal effect of enrollment was a 17.6-percentage-point (95% CI 0.003 to 0.348) increase in treatment completion, becoming attenuated after adjusting for patient characteristics (12.8 percentage points, 95% CI -0.048 to 0.304). Conclusions: While phone ownership and mobile data constraints represent barriers to feasibility, findings suggest that smartphone-based mHealth interventions may aid successful treatment completion-alleviating health system burdens by automating care for less vulnerable patients. Engagement with the platform throughout tuberculosis treatment was high and stable, and enrolled users experienced a higher success rate. A randomized controlled trial is required for impact evaluation.
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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 | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 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 ».