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Record W7156310763 · doi:10.2196/76028

Efficacy of a CBT-Based Online Self-Help Group for Depression and Suicide Ideation: Randomized Controlled Trial (Preprint)

2025· article· en· W7156310763 on OpenAlexvenueno aff
Minkyung Yim, Haeun Kim, Soo‐Eun Lee, Eunjin Jo, Ji‐Won Hur

Bibliographic record

VenueJMIR mhealth and uhealth · 2025
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialDepression (economics)Suicide preventionPoison controlOccupational safety and healthSuicide attemptInjury preventionHuman factors and ergonomics

Abstract

fetched live from OpenAlex

Background: Despite the high prevalence of depressive disorders, access to effective treatment remains limited due to financial, geographic, and social barriers. Online self-help groups offer a promising and scalable form of peer-based support beyond traditional clinical settings. Integrating cognitive behavioral therapy (CBT) techniques such as cognitive restructuring and behavioral activation into self-help groups may enhance their effectiveness. Objective: This study evaluated the efficacy of a cognitive behavioral therapy-based online self-help group (COS) that integrates structured CBT techniques with peer-led group support as a low-intensity intervention for individuals with depressive symptoms. A randomized controlled trial (RCT) comparing COS with a CBT-based mobile application was conducted. Additionally, a separately recruited waitlist control group was included as a supplementary comparison condition. Methods: Participants were recruited online. After eligibility screening via a structured clinical interview, participants were randomly assigned to a COS group (n=79) or a CBT-based mobile application group (n=39). An additional waitlist control group (n=48) was recruited separately during the second phase of the study. The COS intervention involved 7 videoconferencing sessions that incorporated peer-led group discussions, sharing lived experiences, and core CBT techniques such as cognitive restructuring. The primary outcome measure was depressive symptoms, assessed using the Beck Depression Inventory-II, and the secondary outcome was suicidal ideation, estimated using the Beck Scale for Suicide Ideation, measured at baseline, postintervention, and 3-month follow-up. Linear mixed models were used to evaluate group × time interaction effects. Reliable change indices were also calculated to assess clinical significance. All statistical tests were 2-tailed. Results: Among participants assigned to the COS group, 61% (48/79) completed all 7 sessions, and 84% (66/79) attended 5 or more sessions. A significant time × group interaction was observed for depressive symptoms (F4,288.47=7.23, P<.001). The COS group exhibited a substantial reduction in depressive symptoms from baseline to postintervention (t285.76=10.77, two-tailed; P<.001), with a large within-group effect size (d=1.38); this improvement was maintained at the 3-month follow-up. Suicidal ideation also significantly decreased in the COS group (t277.11=4.55, two-tailed; P<.001), with sustained effects at follow-up. Clinically meaningful improvement in depressive symptoms, as defined by the reliable change index, was observed in 75% (56/75) of COS participants. While both the COS and app-based CBT groups achieved comparable reductions in depressive symptoms, only the COS group demonstrated a significant reduction in suicidal ideation. Conclusions: This RCT provides evidence that a structured, CBT-informed online self-help group can reduce depressive symptoms and suicidal ideation. The COS program offers a scalable, accessible alternative to traditional therapy, particularly in settings with limited access to mental health professionals.

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)0.003
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 score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0210.002

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 head0.042
GPT teacher head0.443
Teacher spread0.401 · 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

Citations0
Published2025
Admission routes1
Has abstractno

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