ReviewDigital health
Use of mobile app to monitoring growth outcome of children: A systematic literature review.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Accuracy of a smartphone-based 3D imaging tool for height measurement and stunting detection among children 24-59 months of age in Nepal: a validation study.The Lancet regional health. Southeast Asia · 2026Article
- Artificial intelligence-driven anthropometric assessment for young children: evaluating the accuracy and practicality of a digital image-based length and weight prediction tool.BMJ health & care informatics · 2025Observational
- Integrating Mobile Health App Data Into Electronic Medical or Health Record Systems and Its Impact on Health Care Delivery and Patient Health Outcomes: Scoping Review.JMIR mHealth and uHealth · 2025Article
- The accuracy of a novel stunting risk detection application based on nutrition and sanitation indicators in children aged under five years.BMC nutrition · 2025Article
- MyKid'sNutrition mobile application: effect on mothers' nutritional knowledge and nutritional status of preschool-aged children with undernutrition - a randomised controlled trial.BMJ nutrition, prevention & health · 2025Article
- Harnessing AI for public health: India's roadmap.Frontiers in public health · 2024Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in knowledge and technology have created opportunities to help monitor child growth. Thus, we conducted a systematic review to determine if the use of mobile apps resulted in improved growth outcomes for children. We include articles published related to children's growth with poor nutritional status. The relevant articles were searched from PubMed, ScienceDirect, Scopus, ProQuest, and Google Scholar. Twelve studies were identified, which is the use of the mobile app to monitor growth in undernutrition and obesity in children. Six studies found that the use of mobile apps improved undernutrition child growth and improved parents' and/or front health workers' knowledge to prevent, treat, and monitor children with undernutrition. Six studies stated that the use of mobile app helps overweight/obese children lose weight and motivate them to achieve ideal body weight. Mobile apps for monitoring the growth of children with various standards are likely a promising means for early detection of growth failure and guiding overweight/obese children in gaining normal weight. Studies with large sample sizes and long-term interventions and follow-ups are needed to help assess the effectiveness of mobile app intervention programs and their impact on multiple growth outcomes more comprehensively and accurately.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.