Association of Membership at a Medical Fitness Facility With Adverse Health Outcomes
Bibliographic record
Abstract
INTRODUCTION: Interventions that increase physical activity behavior can reduce morbidity and prolong life, but long-term effects in large populations are unproven. This study investigates the association of medical fitness facility membership and frequency of attendance with all-cause mortality and rate of hospitalization. METHODS: A propensity weighted retrospective cohort study was conducted by linking individuals who attended medical fitness facilities in Winnipeg, Canada to provincial health administrative databases. Members aged ≥18 years who had ≥1 year of provincial health coverage from their index date between January 1, 2005 and December 31, 2015 were included. Controls were assigned a pseudo-index date at random on the basis of the frequency distribution of index dates in the intervention group. Members were stratified into low-frequency attenders (<1 weekly visit), moderate-frequency attenders (1-3 weekly visits), and high-frequency attenders (>3 weekly visits). The primary outcomes were time to all-cause mortality and rate of hospitalizations. Statistical analyses were performed between 2018 and 2020. RESULTS: Among 19,300 adult members and 515,810 controls, members had a 60% lower risk of all-cause mortality during the first 651 days and 48% after 651 days. Membership was associated with a 13% lower risk of hospitalizations. A dose-response effect was apparent because higher weekly attendance was associated with a lower risk of hospitalizations (low frequency: 9%, moderate frequency: 20%, high frequency: 39%). CONCLUSIONS: Membership at a medical fitness facility was associated with a reduced risk of all-cause mortality and hospitalizations. Healthcare systems should consider the medical fitness model as a preventative public health strategy to encourage physical activity participation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".