Using WhatsApp for Nutrition Surveillance Among Children Under 5 Years in West Java, Indonesia: Cross-Sectional Survey and Feasibility Study
Bibliographic record
Abstract
Background: Large-scale programs involving nutrition-specific interventions have been carried out in Indonesia as a community-based approach at the primary care level across cities and districts, throughout the age-specific target population (ie, children under 5 years). Objective: The aim of this paper is to describe the potential use of WhatsApp as a tool for recording and monitoring the growth of children under 5 years by Posyandu (Pos Pelayanan Terpadu or community-based health service post), investigating its potential in enhancing health programs and services. Methods: Data were collected from Posyandu cadres in Bogor District, West Java, from March to June 2022. The anthropometric measurement data were reported in real time through a WhatsApp chatbot, automatically analyzed by the system, and presented in a structured dashboard. A qualitative assessment was carried out using a cross-sectional survey conducted from March to July 2022. Results: The study involved 42 Posyandu in 3 villages, engaging 282 staff, and the WhatsApp chatbot recorded anthropometric data for 4571 children under 5 years. The qualitative assessment indicated widespread system utilization, with 50% (45/90) affirming comprehensive data input. Additionally, 66.4% (83/129) found the system easy to use, and 66.7% (82/123) expressed clarity in comprehending variables. Moreover, 75.6% (93/123) found the data input flow easily understood, and 74% (91/123) suggested that the system contributed to enhancing Posyandu activities and the quality of data reporting. Regarding staff proficiency, 63.5% (80/126) affirmed their adeptness in using the system, and 71% (88/124) asserted their high capability in providing training to colleagues. Conclusions: The potential use of WhatsApp as a surveillance tool for recording children's nutritional status is promising, suggesting broader applications within health programs. Nonetheless, this expansion requires additional improvements, including human resource preparation, Posyandu infrastructure development, and strong regulatory support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".