Recreational Physical Activity Ameliorates Some of the Negative Impact of Major Depression on Health-Related Quality of Life
Bibliographic record
Abstract
BACKGROUND: Major depressive episodes have a negative effect on health-related quality of life (HRQoL). The objective of this study was to determine whether recreational physical activity can ameliorate some of this negative impact. METHODS: The data source for the study was the Canadian National Population Health Survey (NPHS). The NPHS is a longitudinal study that has collected data from a representative cohort of 15,254 community residents. Sixteen years of follow-up data are available. The NPHS included: an instrument to assess MDE (the Composite International Diagnostic Interview Short Form for Major Depression), an inventory of recreational activities (each associated with hours of participation and estimated metabolic expenditures), and a HRQoL instrument (the Health Utility Index, Mark 3, or HUI3). Proportional hazard and linear regression models were used in this study to determine whether MDE-related declines in HRQoL were lessened by participation in an active recreational lifestyle. RESULTS: Consistent with expectation, major depression was associated with a significant decline in HRQoL over time. While no statistical interactions were observed, the risk of diminished HRQoL in association with MDE was reduced by physical activity. In a proportional hazards model, the hazard ratio for transition to poor HRQoL was 0.7 (95% CI: 0.6-0.8, p < 0.0001). In linear regression models, physical activity was significantly associated with more positive HRQoL (β = 0.019, 95% CI 0.004 to -0.034, p = 0.02). CONCLUSION: Recreational physical activity appears to ameliorate some of the decline in HRQoL seen in association with MDE. Physical activity may be an effective tertiary preventive strategy for this condition.
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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 | 0.000 | 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".