Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 27, 2026
Open Peer Review Period: Jan 27, 2026 - Mar 24, 2026
Date Accepted: Jul 15, 2026
Date Submitted to PubMed: Jul 29, 2026
(closed for review but you can still tweet)
The Effects of Digital Health Interventions on Breastfeeding Outcomes: A Systematic Review and Meta-Analysis
ABSTRACT
Background:
The current global breastfeeding landscape presents both progress and challenges. The rise of artificial intelligence (AI) has emerged as a promising new strategy to enhance breastfeeding practices.
Objective:
To evaluate the impact of AI-driven tools on breastfeeding practices and outcomes.
Methods:
We searched PubMed, Web of Science, Cochrane Library, Embase, and CINAHL from inception to October 2025 for randomized controlled trials (RCTs) and quasi-experimental studies. The risk of bias in individual studies was assessed using the Cochrane risk of bias tool for randomized controlled trials (RoB 2) and the risk of bias in non-randomized studies of interventions tool (ROBINS-I). Data were extracted independently by two reviewers and combined using Review Manager 5.4 and R-4.5.2 to obtain pooled results via random-effects models, with subgroup analyses based on intervention type, timing of implementation, population characteristics, and country income level.
Results:
This review included 39 studies with 10735 participants from 15 countries. AI-driven tools increased exclusive breastfeeding (EBF) rates (at <3 months: relative risk [RR] 1.21, 95% CI 1.13-1.29; P<.001, I²=56%; at 3–6 months: RR 1.54; 95% CI 1.29-1.85; P<.001, I2=69%; at ≥6 months: RR 1.47, 95% CI 1.22-1.77, P<.001, I2=78%), breastfeeding self-efficacy (BSE) (standardized mean difference [SMD] 0.41, 95% CI: 0.04-0.78; P=.03, I2=93%), and breastfeeding knowledge (SMD 1.69; 95% CI: 0.54-2.84, P=.004, I2=98%).
Conclusions:
AI-driven tools effectively increase exclusive breastfeeding rates, breastfeeding self-efficacy, and breastfeeding knowledge. Future studies are needed to provide stronger evidence about clinical care interventions. Clinical Trial: PROSPERO CRD420251233352; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251233352
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.