Lau Y, Cheng JY, Wong SH, Yen KY, Cheng LJ. Effectiveness of digital psychotherapeutic intervention among perinatal women: A systematic review and meta-analysis of randomized controlled trials. World J Psychiatr 2021; 11(4): 133-152 [PMID: 33889538 DOI: 10.5498/wjp.v11.i4.133]
Corresponding Author of This Article
Ying Lau, BSc, MSN, PhD, RN, Associate Professor, Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Block MD 11, Level 2, 10 Medical Drive, Singapore 117597, Singapore. nurly@nus.edu.sg
Research Domain of This Article
Obstetrics & Gynecology
Article-Type of This Article
Meta-Analysis
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Ying Lau, Jing-Ying Cheng, Sai-Ho Wong, Kai-Yoong Yen, Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
Ling-Jie Cheng, Nursing Research Unit, Department of Nursing, Khoo Teck Puat Hospital, Yishun Health Campus, National Healthcare Group, Singapore 768828, Singapore
Author contributions: Lau Y conceived and guided the study; Cheng JY, Wong SH and Yen KY carried out the literature searches; Cheng JY and Yen KY extracted the data; Cheng JY and Yen KY assessed the study quality; Lau Y, Wong SH, and Cheng LJ performed the statistical analysis; Lau Y wrote the manuscript; Lau Y, Cheng JY, Wong SH, Yen KY and Cheng LJ revised the manuscript.
Supported byMinistry of Education Academic Research Fund (AcRF) Tier 1, No. NUHSRO/2017/054/T1.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ying Lau, BSc, MSN, PhD, RN, Associate Professor, Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Block MD 11, Level 2, 10 Medical Drive, Singapore 117597, Singapore. nurly@nus.edu.sg
Received: December 30, 2020 Peer-review started: December 30, 2020 First decision: January 11, 2021 Revised: January 11, 2021 Accepted: March 10, 2021 Article in press: March 10, 2021 Published online: April 19, 2021 Processing time: 98 Days and 19.5 Hours
ARTICLE HIGHLIGHTS
Research background
Perinatal women are at increased susceptibility of psychological problems, including depression, anxiety, and stress. Perinatal psychological problems are associated with considerable adverse effects on women, offspring, family, and healthcare services.
Research motivation
Previous reviews were limited to few selected trials, a mixture of different research designs, few databases, and only narrative synthesis. None of the previous reviews investigated the preferred features of digital psychotherapeutic intervention and the influence of covariates on study effect size.
Research objectives
This review aimed to synthesize the best evidence to (1) assess the effectiveness of digital psychotherapeutic intervention in reducing depression, anxiety, and stress symptoms; and (2) evaluate the preferred design features of the intervention.
Research methods
A comprehensive three-step search strategy was conducted in congruence with the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions from eight databases. Comprehensive Meta-analysis 3.0 software was used to conduct meta- and meta-regression analyses. The individual and overall quality of the evidence were evaluated using the Cochrane risk-of-bias tool and the Grading of Recommendations, Assessment, Development, and Evaluation criteria.
Research results
A total of 25 randomized controlled trials that included 3239 perinatal women were identified. Meta-analyses revealed that digital psychotherapeutic intervention significantly improved the depression (g = 0.49), anxiety (g = 0.25), and stress (g = 0.47) symptoms of perinatal women compared to the control. Subgroup analyses demonstrated that a website platform with ≥ eight therapist-guided sessions using the cognitive behavioral therapy theoretical principle was more effective than other treatments in postnatal women. Meta-regression analyses observed that the age of perinatal women and the type of psychotherapy had statistically significant effects on depression symptoms. Egger’s regression asymmetry tests found no publication biases, but the overall quality of evidence was very low.
Research conclusions
This systematic review provides evidence for the effectiveness of digital psychotherapeutic intervention in reducing psychological problems during the perinatal period, particularly depression, anxiety, and stress symptoms. Future designs should consider the use of the cognitive behavioral therapy principle, therapist support, and ≥ eight sessions using a website platform for the treatment of postnatal young women.
Research perspectives
Given that the poor quality of the existing evidence reduced certainty in implementing digital psychotherapeutic intervention at this phase, further high-quality randomized controlled trials with large sample sizes are required to evaluate the sustainability of the intervention.
