Impact of the medical fitness model on long term health outcomes in older adults
Bibliographic record
Abstract
BACKGROUND: Physical inactivity is common among older adults and is associated with poor health outcomes. Medical fitness facilities provide a medically focused approach to physical fitness and can improve physical activity in their communities. This study aimed to assess the relationship between membership in the medical fitness model and all-cause mortality, health care utilization, and major adverse cardiac events in older adults. METHODS: A propensity weighted retrospective cohort study linked individuals that attended medical fitness facilities to provincial health administrative databases. Older adults who had at least 1 year of health coverage from their index date between January 1st, 2005 to December 31st 2015 were included. Controls were assigned a pseudo-index date at random based on the frequency distribution of index dates in members. Members were stratified into low frequency attenders (< 1 Weekly Visits) and regular frequency attenders (> 1 Weekly Visits). Time to event models estimated the hazard ratios (HRs) for risk of all-cause mortality and major adverse cardiac event. Negative binomial models estimated the risk ratios (RRs) for risk of hospitalizations, outpatient primary care visits and emergency department visits. RESULTS: Among 3,029 older adult members and 91,734 controls, members had a 45% lower risk of all-cause mortality (HR: 0.55, 95% CI: 0.50 - 0.61), 20% lower risk of hospitalizations (RR: 0.80, 95% CI: 0.75 - 0.84), and a 27% (HR: 0.72, 95% CI: 0.66 - 0.77), lower risk of a major adverse cardiovascular event. A dose-response effect with larger risk reductions was associated with more frequent attendance as regular frequency attenders were 4% more likely to visit a general practitioner for a routine healthcare visit (RR: 1.04, 95% CI: 1.01 - 1.07), but 23% less likely to visit the emergency department (RR: 0.87, 95% CI: 0.82 - 0.92). CONCLUSIONS: Membership at a medical fitness facility was associated with a decreased risk of mortality, health care utilization and cardiovascular events. The medical fitness model may be an alternative approach for public health strategies to promote positive health behaviors in older adult populations.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".