Effects of a 12-week running programme in youth and adults with complex mood disorders
Bibliographic record
Abstract
OBJECTIVE: Although numerous studies suggest a salutary effect of exercise on mood, few studies have explored the effect of exercise in patients with complex mental illness. Accordingly, we evaluated the impact of running on stress, anxiety and depression in youth and adults with complex mood disorders including comorbid diagnoses, cognitive and social impairment and high relapse rates. METHODS: Participants were members of a running group at St Joseph Healthcare Hamilton's Mood Disorders Program, designed for clients with complex mood disorders. On a weekly basis, participants completed Cohen's Perceived Stress Scale, Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI) questionnaires, providing an opportunity to evaluate the effect of running in this population. RESULTS: Data collected for 46 participants from April 2012 to July 2015 indicated a significant decrease in depression (p<0.0001), anxiety (p<0.0001) and stress (p=0.01) scores. Whereas younger participant age, younger age at onset of illness and higher perceived levels of friendship with other running group members (ps≤0.04) were associated with lower end-of-study depression, anxiety and stress scores, higher attendance was associated with decreasing BDI and BAI (ps≤0.01) scores over time. CONCLUSIONS: Aerobic exercise in a supportive group setting may improve mood symptoms in youth and adults with complex mood disorders, and perceived social support may be an important factor in programme's success. Further research is required to identify specifically the mechanisms underlying the therapeutic benefits associated with exercise-based therapy programmes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".