Mobile Health Solutions for Childhood Stunting: An Integrative Review of Prevention, Monitoring, and Reduction Applications – Review
Bibliographic record
Abstract
Background: This study seeks to provide a comprehensive review of existing literature on the application of mobile health (mHealth) technologies in efforts to prevent, monitor, and reduce the prevalence of childhood stunting. Additionally, it aims to explore the key challenges associated with implementing these digital health interventions. Methods: The literature review was carried out by systematically searching six major databases—PubMed, ScienceDirect, Wiley, SAGE, ProQuest, and Google Scholar—for relevant articles published between February 2015 and February 2025. The inclusion criteria encompassed original, English-language studies that employed either quantitative or qualitative methodologies, focusing on digital applications for the prevention, monitoring, or management of childhood stunting. Only freely accessible full-text articles were considered. Studies that relied solely on traditional, non-digital approaches were excluded. The article selection process adhered to the PRISMA flow diagram, and the methodological quality of the included studies was assessed using the Joanna Briggs Institute (JBI) critical appraisal tool. From an initial pool of 5,797 articles, a structured screening process was conducted using the Rayyan AI platform, culminating in the selection of seven studies for in-depth analysis. Results: Thematic analysis identified five central themes: (1) improvements in knowledge, attitudes, and practices (KAP); (2) enhanced growth monitoring and early detection of stunting; (3) the vital role of community support and health cadres; (4) the overall effectiveness of mHealth interventions; and (5) implementation challenges within vulnerable populations. The findings highlight the substantial potential of mHealth applications in preventing childhood stunting, particularly in resource-constrained settings. Notably, applications such as SCATION and Nutritional Rangers have demonstrated effectiveness in enhancing KAP and improving children’s nutritional outcomes. Conclusion: Integrating mHealth into maternal and child health programs at the community level is essential. Furthermore, supportive policy frameworks and additional research are necessary to overcome existing barriers to implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".