Exercise as a transdiagnostic intervention for improving mental health: An umbrella review
Bibliographic record
Abstract
Exercise is beneficial for mental health in general, but no review has systematically assessed its potential transdiagnostic nature, i.e. whether it is beneficial across specific disorders. We performed a systematic umbrella review of meta-analyses of randomized controlled trials (RCTs) of exercise in participants with mental disorders defined according to the International Classification of Diseases (ICD) or the Diagnostic and Statistical Manual of Mental Disorders (DSM), assessing exercise's transdiagnostic nature with TRANSD criteria, including eight meta-analyses (six included in the TRANSD meta-analysis), encompassing 99 RCTs (n = 5,656) across 11 disorders. Moderate/vigorous aerobic exercise was an effective transdiagnostic intervention for disease-specific primary symptoms across 11 disorders (recurrent depressive disorder, social phobia, panic disorder, generalized anxiety disorder, post-traumatic stress disorder, brief psychotic disorder, schizophrenia, schizoaffective disorder, delusional disorder, schizophreniform disorder, attention-deficit/hyperactivity disorder) and four spectra (depressive disorders, anxiety disorders, schizophrenia-spectrum disorders, neurodevelopmental disorders) with a medium effect size (SMD = -0.67, 95%CI = -0.84, -0.50). Moderate/vigorous aerobic exercise also improved cognition across two disorders (schizophrenia, attention-deficit/hyperactivity disorder) and two spectra (schizophrenia-spectrum disorders, neurodevelopmental disorders), with a large effect size (SMD = 0.92, 95%CI = 0.52, 1.33). According to TRANSD criteria, moderate/vigorous aerobic exercise is a transdiagnostic intervention to improve disease-specific primary symptoms of 11 mental disorders, and cognition in two mental disorders.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".