Evaluating the Efficacy of Web-Based Cognitive Behavioral Therapy for the Treatment of Patients With Bipolar II Disorder and Residual Depressive Symptoms: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Bipolar disorder (BD) is a highly prevalent psychiatric condition that can significantly impact every aspect of a person's life if left untreated. A subtype of BD, bipolar disorder type II (BD-II), is characterized by long depressive episodes and residual depression symptoms, with short-lived hypomanic episodes. Medication and psychotherapy, such as cognitive behavioral therapy (CBT), are the main treatment options for BD-II. CBT specific for BD-II involves the recognition of warning signs, potentially triggering stimuli, and the development of coping skills to increase euthymic periods and improve global functioning. However, access to in-person CBT may be limited by several barriers, including low availability, high costs, and geographical limitations. Thus, web-based adaptations of CBT (e-CBT) have become a promising solution to address these treatment barriers. Nevertheless, e-CBT for the treatment of BD-II remains understudied. OBJECTIVE: The proposed study aims to establish the first e-CBT program specific for the treatment of BD-II with residual depressive symptoms. The primary objective of this study will be to determine the effect of e-CBT in managing BD symptomatology. The secondary objective will be to assess the effects of this e-CBT program on quality of life and resilience. The tertiary objective will involve gathering user feedback using a posttreatment survey to support the continuous improvement and optimization of the proposed program. METHODS: Adult participants (N=170) with a confirmed diagnosis of BD-II experiencing residual depressive symptoms will be randomly assigned to either the e-CBT and treatment as usual (TAU; n=85) group or the TAU (n=85) control group. Participants in the control group will be able to participate in the web-based program after the first 13 weeks. The e-CBT program will consist of 13 weekly web-based modules designed following a validated CBT framework. Participants will complete module-related homework and receive asynchronous personalized feedback from a therapist. TAU will consist of standard treatment services conducted outside of this research study. Depression and manic symptoms, quality of life, and resiliency will be assessed using clinically validated symptomatology questionnaires at baseline, week 6, and week 13. RESULTS: The study received ethics approval in March 2020, and participant recruitment is expected to begin in February 2023 through targeted advertisements and physician referrals. Data collection and analysis are expected to conclude by December 2024. Linear and binomial regression (continuous and categorical outcomes, respectively) will be conducted along with qualitative interpretive methods. CONCLUSIONS: The findings will be the first on the effectiveness of delivering e-CBT for patients with BD-II with residual depressive symptoms. This approach can provide an innovative method to address barriers to in-person psychotherapy by increasing accessibility and decreasing costs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04664257; https://clinicaltrials.gov/ct2/show/NCT04664257. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/46157.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.062 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".