Digital Health Interventions to Improve Parental Literacy and Nutritional Status of Preschool-Aged Children: A Systematic Review

Authors

  • Ropitasari Ropitasari Diploma III of Midwifery, Vocational School, Universitas Sebelas Maret Doctoral Program in Public Health, Universitas Negeri Semarang, Central Java Indonesia
  • Fanny Kartika Fajriyani Faculty of Psychology and Health, Universitas Islam Negeri Walisongo Semarang
  • Oktia Woro Kasmini Handayani Doctoral Program in Public Health, Universitas Negeri Semarang, Central Java, Indonesia
  • Nahdiyah Karimah Diploma III of Midwifery, Vocational School, Universitas Sebelas Maret
  • Herliana Riska Diploma III of Midwifery, Vocational School, Universitas Sebelas Maret

DOI:

https://doi.org/10.26911/thejhpb.2025.10.04.06

Abstract

Background: Nutrition of preschool children, especially toddlers, greatly determines the quality of life of children in the future. In line with the rapid development of information technology in the digital era, many health promotion media have been developed to improve the digital literacy of parents. Assessment of children's nutritional status by utilizing technology is considered more accurate and easily accessible to parents. This study aims to conduct a systematic review related to digital health interventions as an effort to improve parental literacy and nutritional status of children under five.

Subjects and Method: This study is a systematic review using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach. Articles were obtained from Scopus, PubMed, ScienceDirect, Web of Science, and Google Scholar databases for the period 2020-2025. Inclusion criteria include English-language studies, quantitative, qualitative, and mixed-methods research, full text, evaluating digital tools, AI-based models, or behavioral-based approaches to improve the nutritional status and eating behavior of toddlers. The assessment of the quality of the study uses Critical Appraisal Skills Programs (CASP) for quantitative and qualitative research.

Results: 7 articles were found consisting of 2 articles from China and Sweden, 1 article each from Indonesia, Thailand, Nigeria. The use of digital technology has developed in various countries with a focus on improving parents' digital literacy, especially related to children's health, including anthropometric measurements, assessment of children's nutritional status, and dietary recommendations based on the results of nutritional status analysis.

Conclusion: Digital health intervention innovations are considered effective in improving parental literacy and nutritional status of children under five

 

Keywords:

toddler nutrition, nutrition monitoring, digital health, mHealth, artificial intelligence, diet, parental literacy

Published

2025-10-16

Downloads

Issue

Section

Articles

How to Cite

Digital Health Interventions to Improve Parental Literacy and Nutritional Status of Preschool-Aged Children: A Systematic Review. (2025). Journal of Health Promotion and Behavior, 10(4), 450-459. https://doi.org/10.26911/thejhpb.2025.10.04.06

How to Cite

Digital Health Interventions to Improve Parental Literacy and Nutritional Status of Preschool-Aged Children: A Systematic Review. (2025). Journal of Health Promotion and Behavior, 10(4), 450-459. https://doi.org/10.26911/thejhpb.2025.10.04.06

References

Alexandrou C, Henriksson H, Henström M, Henriksson P, Delisle Nyström C, Bendtsen M, Löf M. (2023). Effec-tiveness of a smartphone app (MINI-STOP 2.0) integrated in primary child health care to promote healthy diet and physical activity behaviors and prevent obesity in preschool-aged children: randomized controlled trial. Int J Behav Nutr Phys Act. 20: 22. doi: 10.1186/s12966-023-01405-5.

Areemit R, Lumbiganon P, Suphakunpinyo C, Jetsrisuparb A, Sutra S, Sripa-nidkulchai K. (2023). Effectiveness of a mobile app (KhunLook) versus the maternal and child health handbook on Thai parents’ health literacy, accuracy of health assessments, and convenience of use: randomized con-trolled trial. J Med Internet Res. 25: e43196. doi: 10.2196/43196.

Azriani D, Agustian DA, Zuhairini Y, Yulita IN, Dhamayanti M (2025). Prediction models for stunting using machine learning: stunting risk prediction from early-life indicators. BMC Pediatr. 25:718. https://doi.org/10.1186/s128-87-025-06096-4.

Duan Y, Liang W, Guo L, Zhan H, Xia C, Ma H, et al. (2025). Effectiveness of a WeChat mini-program–based inter-vention on child and parent health behaviors. J Med Internet Res. 27(e66249): 1-16. https://doi.org/10.-2196/66249.

Dusabe C, Abimpaye M, Kabarungi N, Uwamahoro MD. (2023). Monitoring, evaluation and accountability evidence use for design, adaptation, and scale-up of an early childhood development program in Rwanda. Front Public Health. 11:1165353. https://doi.org/-10.3389/fpubh.2023.1165353.

Gizaw AT, Sopory P, Sudhakar M. (2023). Effectiveness of a positive deviance approach to improve mother’s nutri-tional knowledge, attitude, self-effi-cacy, and child’s nutritional status in Maji district, Ethiopia: a cluster randomized control trial. Front Public Health. 11:1277471. https://doi.org/-10.3389/fpubh.2023.1277471.

Hojati A, Farhangi MA (2025). Mykid's-nutrition mobile app: effect on mater-nal nutrition literacy and child out-comes. BMJ Nutr Prev Health. 8(1): e001007. https://doi.org/10.1136/-bmjnph-2024-001007.

McKechnie AC, Swanson NM, Jantraporn R, Elgersma KM, Wagner TI, et al. (2025). Systematic review evaluating publicly available mhealth apps for parents. Mhealth. 11:38. https://doi.-org/10.21037/mhealth-24-84.

Rao B, Rashid M, Hasan G, Thunga G (2025). Machine learning in predicting child malnutrition: meta-analysis of machine learning applications on DHS and other datasets. Int J Environ Res Public Health. Int J Environ Res Public Health. 22(3): 449; https://doi.org/-10.3390/ijerph22030449.

Roe LS, Keller KL, Rolls BJ. (2023). Food properties and individual charac-teristics influence children's intake accross multiple days of weighed assessments in childcare programs. Am J Clin Nutr. 117(3): 544–552. doi: 10.1016/j.ajcnut.2022.12.015.

Santoso HA, Dewi NS. (2025). Enhancing nutritional status prediction through attention-based deep learning and explainable AI. Intelligence-Based Med. https://doi.org/10.1016/j.ibme-d.2025.100255.

Singh B, Walker K, Fildes J, Morley J, Dankiw K, Ferguson T, Maher C. (2025). Evaluation of the early years South Australia mobile application. BMC Public Health. 25(1):2299. https://doi.org/10.1186/s12889-025-23302-1.

Sosanya ME, Samuel FO. (2024). A mobile gaming app to train teenage mothers on appropriate child feeding practices: development and validation study. J Med Internet Res. 26: e53560. https://doi.org/10.2196/53560.

Thomas K, Löf M, Lundgren M, Fagerström M, Hesketh KD, Brown V, Delisle Nyström C. (2024). MINISTOP 3.0: protocol for a cluster randomized implementation trial to prevent obesity in preschool children in Swedish child healthcare. BMC Public Health. 24(1):2594. https://doi.org/10.1186/-s12889-024-20137-0.

Zhou P, Song H, Lau PW, Shi L, Wang J. (2024). Effectiveness of a parent-based ehealth intervention for physical activity, dietary behavior, and sleep among preschoolers: protocol for a randomized controlled trial. JMIR Res Protoc. 13:e58344. https://doi.org/-10.2196/58344.

Most read articles by the same author(s)