Partners In Health/Inshuti Mu Buzima, Kigali, Rwanda.
Harvard Medical School, Boston, Massachusetts, USA.
Matern Child Nutr. 2021 Oct;17(4):e13201. doi: 10.1111/mcn.13201. Epub 2021 May 7.
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.
早产儿、低出生体重儿或有其他围产期并发症的婴儿需要频繁且准确的生长监测，以实现最佳营养和生长。我们开发了一种移动医疗工具来改善高危婴儿的生长监测和营养状况评估。我们在卢旺达两个农村地区的儿科发育诊所进行了一项预-后准实验研究，同时设立了对照组。在干预前阶段（2017 年 8 月至 2018 年 1 月），所有诊所均使用标准的纸质世界卫生组织（WHO）生长图表。在干预阶段（2018 年 8 月至 2019 年 1 月），基里希区采用了移动医疗工具来进行儿童生长监测和营养状况评估。每次就诊时都会记录身长/身高、体重、身长/身高年龄（L/HFA）、身长/身高体重（WFL/H）和体重年龄（WFA）Z 评分以及间隔生长数据。我们进行了“差异中的差异”分析，以评估移动医疗工具是否与更完善的营养评估和 2 个月和 6 个月时的营养状况相关。我们观察了 3529 次就诊。移动医疗干预诊所的完善程度明显提高，特别是在矫正年龄（终线：65% vs. 55%；p=0.036）、L/HFA（终线：82% vs. 57%；p≤0.001）、WFA（终线：93% vs. 67%；p≤0.001）和 WFL/H（终线：90% vs. 59%；p≤0.001）Z 评分方面，而对照组则没有。生长监测的准确性没有提高。6 个月时，干预诊所的发育迟缓、体重不足和间隔生长不足的患病率明显低于对照组。结果表明，整合移动医疗营养干预是可行的，可以改善儿童营养状况。改进工具设计可能会更好地提高准确性。
