Family-Based WhatsApp Intervention to Promote Healthy Eating Behaviors Among Amazonian School Children: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Stunting and micronutrient deficiencies have persistently affected children in the Brazilian Amazon for decades. However, in recent years, a notable increase in childhood overweight prevalence has been observed, particularly in the context of heightened food insecurity exacerbated by the COVID-19 pandemic. Despite the limited number of effective solutions proposed to tackle this problem, digital interventions have shown great promise worldwide in preventing obesity and promoting healthy diets. OBJECTIVE: This study aims to describe the protocol of a family-based WhatsApp intervention, specifically designed to investigate the efficacy of multimedia messaging in preventing excessive weight gain and improving healthy eating practices among school-aged children in the Amazon region. METHODS: This study protocol outlines a theory-driven randomized controlled trial based on the cognitive theory of multimedia learning and the social cognitive theory. A total of 240 parents or caregivers of children enrolled in the Maternal and Child Health and Nutrition Cohort Study in Acre (MINA-Brazil) will be recruited by phone and social media. The intervention group will receive persuasive multimedia messages through WhatsApp over 19 weeks, while the waitlist control group will remain in the usual care. The primary outcome is a change in children's BMI in z score. Secondary outcomes are changes in dietary intake and biochemical indicators of the children. Outcome measures will be assessed at baseline and 5 months after randomization in comparison to usual care. The analysis will use an intent-to-treat approach and will be conducted using the statistical package Stata (version 18.0), with a significance level set at P<.05. Paired and unpaired 2-tailed t tests will be applied to compare mean changes in the outcomes. RESULTS: Data collection started in June 2023, and final measurements are scheduled to be completed in December 2023. The results of the main analysis are expected to be available in 2024. CONCLUSIONS: This innovative multimedia message intervention holds significant potential for fostering behavioral changes among Amazonian children. TRIAL REGISTRATION: Brazilian Clinical Trials Registry RBR-5zdnw6t; https://ensaiosclinicos.gov.br/rg/RBR-5zdnw6t. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54446.
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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 | 0.030 | 0.029 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.074 | 0.010 |
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".