부산대학교 도서관

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.