An Internet-Based Cognitive Behavioral Therapy Program Adapted to Patients With Cardiovascular Disease and Depression: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Depression is a common cause of reduced well-being and prognosis in patients with cardiovascular disease (CVD). However, there is a lack of effective intervention strategies targeting depression. OBJECTIVE: The study aimed to evaluate the effects of a nurse-delivered and adapted internet-based cognitive behavioral therapy (iCBT) program aimed at reducing depression in patients with CVD. METHODS: A randomized controlled trial was conducted. A total of 144 patients with CVD with at least mild depression (Patient Health Questionnaire-9 [PHQ-9] score ≥5) were randomized 1:1 to a 9-week program of iCBT (n=72) or an active control participating in a Web-based discussion forum (online discussion forum [ODF], n=72). The iCBT program, which included 7 modules, was adapted to fit patients with CVD. Nurses with an experience of CVD care provided feedback and a short introduction to cognitive behavioral therapy. The primary outcome, depression, was measured using PHQ-9. Secondary outcomes were depression measured using the Montgomery-Åsberg Depression Rating Scale-self-rating version (MADRS-S), health-related quality of life (HRQoL) measured using Short Form 12 (SF-12) survey and EuroQol Visual Analogue Scale (EQ-VAS), and the level of adherence. An intention-to-treat analysis with multiple imputations was used. Between-group differences in the primary and secondary outcomes were determined by the analysis of covariance, and a sensitivity analysis was performed using mixed models. RESULTS: Compared with ODF, iCBT had a significant and moderate treatment effect on the primary outcome depression (ie, PHQ-9; mean group difference=-2.34 [95% CI -3.58 to -1.10], P<.001, Cohen d=0.62). In the secondary outcomes, compared with ODF, iCBT had a significant and large effect on depression (ie, MADRS-S; P<.001, Cohen d=0.86) and a significant and moderate effect on the mental component scale of the SF-12 (P<.001, Cohen d=0.66) and the EQ-VAS (P<.001, Cohen d=0.62). Overall, 60% (n=43) of the iCBT group completed all 7 modules, whereas 82% (n=59) completed at least half of the modules. No patients were discontinued from the study owing to a high risk of suicide or deterioration in depression. CONCLUSIONS: Nurse-delivered iCBT can reduce depression and improve HRQoL in patients with CVD, enabling treatment for depression in their own homes and at their preferred time. TRIAL REGISTRATION: ClinicalTrials.gov NCT02778074; https://clinicaltrials.gov/ct2/show/NCT02778074.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".