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DOI 10.2196/92184

Медицинская библиотека · Journal of medical Internet research · 2026

Effects of Digital Health Interventions on Breastfeeding Rates, Self-Efficacy, and Knowledge: Systematic Review and Meta-Analysis.

Sun Jiahe, Wang Yu, Hu Shuang, Ding Yajie, Pu Congshan, Song Danni, Xia Jiaai, Shan Chunjian

Систематический обзорСтатьяМетаанализПеревод в очереди

Аннотация

BACKGROUND

The current global status of breastfeeding is marked by both progress and challenges. Digital health interventions (DHIs) have emerged as a promising strategy for improving breastfeeding practices, yet evidence regarding their impact on breastfeeding outcomes remains limited.

OBJECTIVE

This study aimed to evaluate the impact of DHIs on breastfeeding practices and outcomes.

METHODS

Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched PubMed, Web of Science, the Cochrane Library, Embase, CINAHL, Scopus, IEEE Xplore, and gray literature databases, from inception to April 2026. We included randomized controlled trials (RCTs) and quasi‑experimental studies that enrolled pregnant women or lactating mothers using DHIs vs standard care, waiting lists, or placebo. Outcomes were exclusive breastfeeding (EBF) rates, self‑efficacy, and knowledge. Studies recruiting mothers with infectious diseases or severe substance use disorders, non‑English publications, unavailable full texts or data, animal studies, letters, conference proceedings, and studies rated as "high risk" were excluded. Study quality was evaluated using the Cochrane Risk of Bias Tool version 2 and the Risk of Bias in Nonrandomized Studies of Interventions tool. Evidence certainty was assessed using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). We performed the analysis using Review Manager (version 5.4) and R software (version 4.6.0), estimated using the Hartung-Knapp-Sidik-Jonkman random-effects model.

RESULTS

A total of 52 studies involving 11,704 participants were included. DHIs improved EBF rates (at <3 months: relative risk [RR] 1.33, 95% CI 1.18-1.50, 95% prediction interval [PI] 0.85-2.08; P<.001; I2=84%; at 3-6 months: RR 1.46, 95% CI 1.22-1.75, 95% PI 0.83-2.55; P<.001; I2=75%; at ≥6 months: RR 1.64, 95% CI 1.27-2.11, 95% PI 0.74-3.62; P<.001; I2=86%) and breastfeeding self-efficacy (standardized mean difference 0.67, 95% CI 0.31-1.03, 95% PI -1.17 to 2.51; P<.001; I2=95%). No statistically significant effects were observed for breastfeeding knowledge. The subgroup analysis suggested that antenatal intervention delivery; intervention duration ≥3 months, developing-country setting; and participants who were preterm parturients, adult women, or mothers undergoing cesarean delivery could improve the EBF rate. In contrast, larger effect sizes for breastfeeding self-efficacy were observed among women with intervention duration <3 months, full-term delivery, and singleton pregnancy. Among RCTs, 12 were low risk, and 30 had some issues; among the quasi-experimental studies, 3 were low risk, and 7 were moderate risk. Evidence certainty ranged from low to moderate.

CONCLUSIONS

DHIs show promise for improving EBF rates and breastfeeding self-efficacy. Compared with previous reviews, this review provides a more comprehensive evaluation of DHIs and identifies potential effect modifiers through subgroup analyses, offering updated evidence for digital breastfeeding support. However, moderate risk of bias, substantial heterogeneity, wide 95% PI, and low to moderate GRADE certainty warrant cautious interpretation. Further high-quality studies are needed to confirm effectiveness and optimize implementation of DHIs in clinical practice.

TRIAL REGISTRATION: PROSPERO CRD420251233352; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251233352.

Для цитирования: Sun Jiahe, Wang Yu, Hu Shuang, Ding Yajie, Pu Congshan, Song Danni, Xia Jiaai, Shan Chunjian. Effects of Digital Health Interventions on Breastfeeding Rates, Self-Efficacy, and Knowledge: Systematic Review and Meta-Analysis.. Journal of medical Internet research. 2026. DOI: 10.2196/92184.

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