WhatsApp-Based Intervention for Diabetes Prevention and Care in Argentina: Implementation and Process Evaluation
Bibliographic record
Abstract
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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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".