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Record W4408128157 · doi:10.2196/67386

Internet-Based Cognitive Behavioral Therapy for Preventing Postpartum Depressive Symptoms Among Pregnant Individuals With Depression: Multicenter Randomized Controlled Trial in China

2025· article· en· W4408128157 on OpenAlexaff
Chen-Chi Duan, Chen Zhang, Hua-Lin Xu, Jing Tao, Jia-Le Yu, Dan Zhang, Shan Wu, Zeng Xiu, Wan-Ting Zeng, Zhi-Yin Zhang, Cindy‐Lee Dennis, Han Liu, Jiaying Wu, Ben W. Mol, Hefeng Huang, Yanting Wu

Bibliographic record

VenueJournal of Medical Internet Research · 2025
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRandomized controlled trialDepression (economics)Postpartum depressionCognitive behavioral therapyMedicineDepressive symptomsEdinburgh Postnatal Depression ScaleCognitionClinical psychologyPsychiatryPregnancyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Women are particularly vulnerable to depression during pregnancy, which is one of the strongest risk factors for developing postpartum depression (PPD). Addressing antenatal depressive symptoms in these women is crucial for preventing PPD. However, little is known about the effectiveness of internet-based cognitive behavioral therapy (ICBT) in preventing PPD in this high-risk group. OBJECTIVE: This study aims to evaluate the short- and long-term effects of ICBT in preventing PPD among women with antenatal depressive symptoms. METHODS: Participants were screened for antenatal depressive symptoms using the Edinburgh Postnatal Depression Scale (EPDS) and randomly allocated (1:1) to either the ICBT group (receiving weekly online modules starting antenatally and continuing into early postpartum) or the control group (observed without treatment). Follow-up assessments were conducted up to 12 months postpartum, and data were analyzed using generalized estimating equations. The primary outcome was the prevalence of depressive symptoms at 6 weeks postpartum. A subgroup analysis based on the severity of antenatal depressive symptoms was also performed. The secondary outcomes included the long-term effects of ICBT on maternal depression, as well as its impact on anxiety, sleep quality, social support, parenting stress, co-parenting relationships, and infant development. RESULTS: Between August 2020 and September 2021, 300 pregnant individuals were recruited from 5 centers across China. No significant differences were observed in depressive symptoms at 6 weeks postpartum (P=.18) or at any longer-term follow-up time points (P=.18). However, a post hoc subgroup analysis showed that participants with antenatal EPDS scores of 10-12 in the ICBT group had a lower risk of developing depression during the first year postpartum (odds ratio 0.534, 95% CI 0.313-0.912; P=.02), but this was not observed for participants with more severe depression. Additionally, this subgroup demonstrated higher levels of co-parenting relationships (P=.02). CONCLUSIONS: Among individuals with antenatal depression, ICBT did not prevent the development of PPD. However, ICBT may be a preferable option for those with mild to moderate antenatal depressive symptoms. Future research is needed to explore modifications to ICBT to address more severe depressive symptoms. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2000033433; https://www.chictr.org.cn/showproj.html?proj=54482. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-022-06728-5.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.423
Teacher spread0.383 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2025
Admission routes1
Has abstractyes

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