Cognitive Behavioral Therapy combined with physical exercise for depression, anxiety, fatigue and pain in adults with chronic diseases: systematic review and meta-analysis
Bibliographic record
Abstract
Objective. The present meta-analysis aimed to determine the overall effect of cognitive behavior therapy combined with physical exercise (CBTEx) interventions on depression, anxiety, fatigue, and pain in adults with chronic illness; to identify the potential moderators of efficacy; and to compare the efficacy of CBTEx versus each condition alone (CBT and physical exercise). Methods. Relevant randomized clinical trials, published before July 2017, were identified through database searches in Pubmed, PsycArticles, CINAHL, SportDiscus and the Cochrane Central Register for Controlled Trials.Results. A total of 30 studies were identified. CBTEx interventions yielded small-to-large effect sizes for depression (SMC = -0.34, 95% CI [-0.53; -0.14]), anxiety (SMC = -0.18, 95% CI [-0.34; -0.03]) and fatigue (SMC = -0.96, 95% CI [-1.43; -0.49]). Moderation analyses revealed that longer intervention was associated with greater effect sizes for depression and anxiety outcomes. Low methodological quality was also associated with increased CBTEx efficacy for depression. When compared directly, CBTEx interventions did not show greater efficacy than CBT alone or physical exercise alone for any of the outcomes. Conclusion. The current literature suggests that CBTEx interventions are effective for decreasing depression, anxiety, and fatigue symptoms, but not pain. However, the findings do not support an additive effect of CBT and exercise on any of the four outcomes compared to each condition alone.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".