Exercise as an add-on strategy for the treatment of major depressive disorder: a systematic review
Bibliographic record
Abstract
Antidepressants are currently the treatment of choice for major depressive disorder (MDD). Nevertheless, a high percentage of patients do not respond to a first-line antidepressant drug, and combination treatments and augmentation strategies increase the risk of side effects. Moreover, a significant proportion of patients are treatment-resistant. In the last 30 years, a number of studies have sought to establish whether exercise could be regarded as an alternative to antidepressants, but so far no specific analysis has examined the efficacy of exercise as an adjunctive treatment in combination with antidepressants. We carried out a systematic review to evaluate the effectiveness of exercise as an adjunctive treatment with antidepressants on depression. A search of relevant papers was carried out in PubMed/Medline, Google Scholar, and Scopus with the following keywords: "exercise," "physical activity," "physical fitness," "depressive disorder," "depression," "depressive symptoms," "add-on," "augmentation," "adjunction," and "combined therapy." Twenty-two full-text articles were retrieved by the search. Among the 13 papers that fulfilled our inclusion criteria, we found methodological weaknesses in the majority. However, the included studies showed a strong effectiveness of exercise combined with antidepressants. Further analyses and higher quality studies are needed; nevertheless, as we have focused on a particular intervention (exercise in adjunction to antidepressants) that better reflects clinical practice, we can hypothesize that this strategy could be appropriately and safely translated into real-world practice.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".