Investigating the protective effects of high and moderate intensity cycling on psychological stress and depression in young adults
Bibliographic record
Abstract
With mental illness affecting ~20% of Canadians, there is a need to understand the factors that reduce negative mood. Physical exercise is a powerful stimulus that may regulate mood; however, it is unclear what dose of exercise is needed to induce a positive change in mood. This study examined which psychological factors exercise targets to regulate mood and the optimal intensity of exercise for decreasing negative mood states. It was hypothesized that exercise would target both stress and depression to improve mood, and the high-intensity exercise would improve mood more than moderate-intensity exercise. Twenty-six participants were assigned to one of three groups: 1) High-intensity exercise, 2) Moderate-intensity exercise, and 3) Non-exercise control. The exercise groups underwent exercise training three times per week for six weeks. Aerobic fitness, depression, stress, and anxiety were measured before and after the 6-week intervention. It was found that exercise protected students against depression as indicated by a significant increase in depression for the control group (p< 0.01) that was not observed for either of the exercise groups. Stress and anxiety levels were positively correlated with depression (all correlations: r (55) >.36, p < .001) but were not impacted by the exercise intervention. The results suggest that exercise may help to mitigate depressed mood in young adults as a potential way to reduce their risk of mental illness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".