Evaluation of an mHealth tool to improve nutritional assessment among infants under 6 months in paediatric development clinics in rural Rwanda: Quasi‐experimental study
Bibliographic record
Abstract
Infants born preterm, low birthweight or with other perinatal complications require frequent and accurate growth monitoring for optimal nutrition and growth. We implemented an mHealth tool to improve growth monitoring and nutritional status assessment of high risk infants. We conducted a pre-post quasi-experimental study with a concurrent control group among infants enrolled in paediatric development clinics in two rural Rwandan districts. During the pre-intervention period (August 2017-January 2018), all clinics used standard paper-based World Health Organization (WHO) growth charts. During the intervention period (August 2018-January 2019), Kirehe district adopted an mHealth tool for child growth monitoring and nutritional status assessment. Data on length/height; weight; length/height-for-age (L/HFA), weight-for-length/height (WFL/H) and weight-for-age (WFA) z-scores; and interval growth were tracked at each visit. We conducted a 'difference-in-difference' analysis to assess whether the mHealth tool was associated with greater improvements in completion and accuracy of nutritional assessments and nutritional status at 2 and 6 months of age. We observed 3529 visits. mHealth intervention clinics showed significantly greater improvements on completeness for corrected age (endline: 65% vs. 55%; p = 0.036), L/HFA (endline: 82% vs. 57%; p ≤ 0.001), WFA (endline: 93% vs. 67%; p ≤ 0.001) and WFL/H (endline: 90% vs. 59%; p ≤ 0.001) z-scores compared with control sites. Accuracy of growth monitoring did not improve. Prevalence of stunting, underweight and inadequate interval growth at 6-months corrected age decreased significantly more in the intervention clinics than in control clinics. Results suggest that integrating mHealth nutrition interventions is feasible and can improve child nutrition outcomes. Improved tool design may better promote accuracy.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".