Effects of social media and mobile health apps on pregnancy care: Meta-analysis

Ko Ling Chan, Mengtong Chen

Research output: Contribution to journalReview articlepeer-review

107 Citations (Scopus)


Background: The use of social media and mobile health (mHealth) apps has been increasing in pregnancy care. However, the effectiveness of these interventions is still unclear. Objectives: We conducted a meta-analysis to examine the effectiveness of these interventions with regard to different health outcomes in pregnant and postpartum women and investigate the characteristics and components of interventions that may affect program effectiveness. Method: We performed a comprehensive literature search of major electronic databases and reference sections of related reviews and eligible studies. A random effects model was used to calculate the effect size. Results: Fifteen randomized controlled trial studies published in and before June 2018 that met the inclusion criteria were included in the meta-analysis. The interventions were effective in promoting maternal physical health including weight management, gestational diabetes mellitus control, and asthma control with a moderate to large effect size (d=0.72). Large effect sizes were also found for improving maternal mental health (d=0.84) and knowledge about pregnancy (d=0.80). Weight control interventions using wearable devices were more effective. Conclusion: Social media and mHealth apps have the potential to be widely used in improving maternal well-being. More large-scale clinical trials focusing on different health outcomes are suggested for future studies.

Original languageEnglish
Article numbere11836
JournalJMIR mHealth and uHealth
Issue number1
Publication statusPublished - 2019 Jan

Bibliographical note

Publisher Copyright:
©Ko Ling Chan, Mengtong Chen.

All Science Journal Classification (ASJC) codes

  • Health Informatics


Dive into the research topics of 'Effects of social media and mobile health apps on pregnancy care: Meta-analysis'. Together they form a unique fingerprint.

Cite this