Time-efficient physical training for enhancing cardiovascular function in midlife and older adults: promise and current research gaps
Bibliographic record
Abstract
Cardiovascular diseases (CVD) remain the leading cause of death in developed societies, and "midlife" (50-64 yr) and older (65+) men and women bear the great majority of the burden of CVD. Much of the increased risk of CVD in this population is attributable to CV dysfunction, including adverse changes in the structure and function of the heart, increased systolic blood pressure, and arterial dysfunction. The latter is characterized by increased arterial stiffness and vascular endothelial dysfunction. Conventional aerobic exercise training, as generally recommended in public health guidelines, is an effective strategy to preserve or improve CV function with aging. However, <40% of midlife and older adults meet aerobic exercise guidelines, due in part to time availability-related barriers. As such, there is a need to develop evidence-based time-efficient exercise interventions that promote adherence and optimize CV function in these groups. Two promising interventions that may meet these criteria are interval training and inspiratory muscle strength training (IMST). Limited research suggests these modes of training may improve CV function with time commitments of ≤60 min/wk. This review will summarize the current evidence for interval training and IMST to improve CV function in midlife/older adults and identify key research gaps and future directions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".