Efficacy of guided self-help behavioural activation and physical activity for depression: a randomized controlled trial
Bibliographic record
Abstract
Behavioural activation and physical activity have received empirical support that highlight their efficacy in reducing depression. Even though both behavioural activation and physical activity share the common goal of reactivating the individual, limited research has directly compared these interventions, and more research is required to evaluate their efficacy when offered in low-intensity formats. The present study involves a randomized controlled clinical trial comparing the efficacy of two guided self-help interventions for the treatment of depression: behavioural activation and physical activity. Fifty-nine participants presenting mild-to-moderate symptoms of depression were randomized either to a behavioural activation intervention (n = 20), a physical activity intervention (n = 19) or a wait-list control group (n = 20). All participants completed symptom measure pre-, mid- and post-intervention, as well as at a two-month follow-up. Mixed-model analyses of variance revealed that both interventions were significantly more efficacious in reducing depressive symptoms in comparison with the control group. Physical activity involved significantly less time-investment compared to the behavioural activation condition (less than half the amount of time). These results indicate that physical activity and behavioural activation both effectively reduce depressive symptoms and are favourably applicable in low-intensity formats. Implications of these results and avenues for future research are discussed.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".