ArticleMaternal & child nutrition2021
Evaluation of an mHealth tool to improve nutritional assessment among infants under 6 months in paediatric development clinics in rural Rwanda: Quasi-experimental study.
Article in Maternal & child nutrition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 16 citations in OpenAlex.
- Impact of mHealth interventions on maternal, newborn, and child health from conception to 24 months postpartum in low- and middle-income countries: a systematic review.BMC medicine · 2024Pooled it
- The impact of growth monitoring and promotion on health indicators in children under five years of age in low- and middle-income countries.The Cochrane database of systematic reviews · 2023Pooled it
- A novel wide scale well-baby clinic mobile application: an Egyptian pilot study.BMC health services research · 2023Article
- Evaluation of an ARF diagnosis calculator: a survey and content analysis.BMC medical informatics and decision making · 2022Article
- Evaluation of an mHealth tool to improve nutritional assessment among infants under 6 months in paediatric development clinics in rural Rwanda: Quasi-experimental study.Maternal & child nutrition · 2021Article
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Authors and funding
13 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed 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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