WhatsApp-Based Intervention for Diabetes Prevention and Care in Argentina: Implementation and Process Evaluation
Notice bibliographique
Résumé
BACKGROUND: In Argentina, diabetes is a growing public health concern, with a prevalence of 14% in 2024 and projections reaching 15.4% by 2050. In this context, a Diabetes Prevention and Care Program was implemented in low-income areas across 3 provinces. A key component of the program was a WhatsApp (WhatsApp LLC)-based intervention aimed at promoting self-care, encouraging healthy behaviors, and supporting follow-up among people with diabetes, those at risk, and pregnant women. OBJECTIVE: This study aimed to describe the implementation and process evaluation of a WhatsApp-based intervention within Argentina's public health system, using the Carroll Implementation Fidelity Framework, focusing on challenges encountered, implementation strategies used, and lessons learned across the 3 target populations. METHODS: The intervention was implemented in 40 primary care centers. The population included adults residing in the catchment areas of the selected primary care centers. Participants included adults with type 2 diabetes, people at moderate or high risk based on the Finnish Diabetes Risk Score, and pregnant women. A set of 192 educational and reminder messages was developed and validated through expert input and community feedback. Messages were tailored to each target population and delivered through WhatsApp via Twilio (Twilio Inc) Business API (application programming interface). We assessed implementation fidelity focusing on adherence to the intervention, participant responsiveness, quality of delivery, and contextual barriers. RESULTS: A total of 11,029 participants were enrolled in this study, of whom 9983 (90.5%) had a valid mobile phone number registered in the system. Among these, 32.8% (3276/9983) had a diagnosis of type 2 diabetes, 53.3% (5320/9983) were identified as being at moderate or high risk based on the Finnish Diabetes Risk Score questionnaire, and 13.9% (1387/9983) were pregnant women. Overall, 67.3% (n=5749) opted in to receive messages, with the highest acceptance among those with diabetes (n=2169, 74.3%) and the lowest among at-risk people (n=2935, 62.1%). Message adherence was high: 88.7% (n=5004) of participants received at least the minimum number of educational messages expected, and the mean proportion of messages read per participant was 82.2% (SD 29.8). The dropout rate was low (6.1%) but higher among pregnant participants (14.6%). Message delivery issues mostly included problems with WhatsApp on the mobile phones of participants. Technical challenges, including server overload, were addressed during implementation. CONCLUSIONS: The WhatsApp-based intervention was feasible and well-received in public primary care settings in Argentina, particularly among people with diabetes. The experience illustrates how a WhatsApp-based intervention can be leveraged to strengthen service delivery in low-resource contexts, while also highlighting the need for further work on integration with electronic health records, tailoring of content to population needs, and strategies to enhance digital inclusion for underserved populations.
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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 | 0,022 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».