Cardiovascular Aging and Exercise in Healthy Older Adults
Bibliographic record
Abstract
OBJECTIVE: Physical inactivity in an aging population is a major contributing factor to the rising numbers of older persons with chronic illnesses and disabilities. The purpose of this article is to review the relationship between physical inactivity and age-associated changes to the cardiovascular system, and provide guidance on prescribing exercise to healthy older persons in order to mitigate the adverse effects of cardiovascular aging. DESIGN: Interpretive review of the literature. RESULTS: A number of structural and functional changes occur in the cardiovascular system with advancing age, many of which are mediated by changes in vascular stiffness. These changes lead not only to cardiovascular events and strokes, but also to frailty, functional decline, and cognitive impairment. A substantial proportion of the decline in aerobic capacity with age may result from physical inactivity. Guidelines for the prescription of aerobic, resistance, and balance training for otherwise healthy older persons are provided. CONCLUSIONS: Lack of physical activity is a major risk factor for the epidemic of chronic disease and disability facing an aging population. Many age-associated changes in cardiovascular function result from physical inactivity. The benefits of regular exercise include prevention of cardiovascular events, disability, and cognitive impairment. Age is not a contraindication to exercise, which can usually be initiated safely in older persons.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".