Internet-Based Cognitive Behavioral Therapy and its Association With Self-efficacy, Depressive Symptoms, and Physical Activity: Secondary Analysis of a Randomized Controlled Trial in Patients With Cardiovascular Disease
Bibliographic record
Abstract
BACKGROUND: In patients with cardiovascular disease (CVD), knowledge about the associations among changes in depressive symptoms, self-efficacy, and self-care activities has been requested. This is because such knowledge can be helpful in the design of behavioral interventions aimed to improve self-efficacy, reduce depressive symptoms, and improve performance of self-care activities in CVD patients. OBJECTIVE: We aim to evaluate if internet-based cognitive behavioral therapy (iCBT) improves self-efficacy and explore the relationships among changes in depressive symptoms, self-efficacy, and physical activity, as well as the influence of iCBT on these relationships. METHODS: This study received funding in January 2015. Participant recruitment took place between January 2017 and February 2018, and the main findings were published in 2019. This study is a secondary analysis of data collected in a randomized controlled study evaluating the effects of a 9-week iCBT program compared to an online discussion forum (ODF) on depressive symptoms in patients with CVD (N=144). Data were collected at baseline and at the 9-week follow-up. Analysis of covariance was used to evaluate the differences in self-efficacy between the iCBT and ODF groups. Structural equation modeling explored the relationships among changes in depressive symptoms, self-efficacy, and physical activity, as well as the influence of iCBT on these relationships. RESULTS: At follow-up, a significant difference in the increase in self-efficacy favoring iCBT was found (P=.04, Cohen d=0.27). We found an indirect association between changes in depressive symptoms and physical activity (β=-.24, P<.01), with the change in self-efficacy acting as a mediator. iCBT had a direct effect on the changes in depressive symptoms, which in turn influenced the changes in self-efficacy (β=.23, P<.001) and physical activity (β=.12, P<.001). CONCLUSIONS: Self-efficacy was improved by iCBT. However, the influence of iCBT on self-efficacy and physical activity was mostly mediated by improvements in depressive symptoms. TRIAL REGISTRATION: ClinicalTrials.gov NCT02778074; https://clinicaltrials.gov/ct2/show/NCT02778074.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".