The Cost-Effectiveness of Exercise Training for the Primary and Secondary Prevention of Cardiovascular Disease
Bibliographic record
Abstract
BACKGROUND: Although exercise training improves cardiovascular disease (CVD) risk factors, few studies have evaluated its potential long-term cost-effectiveness. METHODS: Using the Cardiovascular Disease Life Expectancy Model, a validated disease simulation model, we calculated the life expectancy of average 35- to 74-year-old Canadians found in the 1992 Canadian Heart Health Survey. The impacts of exercise training on cardiovascular risk factors were estimated as a 4% decrease in low-density lipoprotein (LDL) cholesterol, a 5% increase in high-density lipoprotein (HDL) cholesterol, and a 6 mm Hg decrease in both systolic and diastolic blood pressure. Exercise adherence was estimated at 50% for the first year and 30% for all additional years. Costs for a supervised exercise program determined from Canadian sources and converted to US dollars were estimated at $605 for the first year (medical evaluation, stress test, exercise prescription, and program costs) and $367 for all additional years (program costs). For an unsupervised program, the costs were estimated at $311 for the first year and $73 for all additional years. RESULTS: The cost-effectiveness (CE) of an unsupervised exercise program (1996 U.S. dollars) was less than $12,000 per year of life saved (YOLS) for all individuals. The CE of a supervised exercise program was less than $15,000/YOLS for men with CVD, and between $12,000 and $43,000 for women with CVD and men without CVD. CONCLUSIONS: Given the relatively few risks, substantial long-term benefits, and modest costs, an unsupervised exercise training program represents good value for all. A more expensive supervised exercise program is also cost-effective for most individuals with CVD.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".