Resisting decline: the neuroprotective role of resistance exercise in supporting cerebrovascular function and brain health in aging
Bibliographic record
Abstract
Reduced cerebral blood flow (CBF) and cerebrovascular function are critical early-stage biomarkers preceding changes in brain function and structure observed in normal aging and during the onset and progression of Alzheimer's Disease and related dementias (ADRD). Though several interventions attempt to curb the effects of aging and brain neurodegeneration, exercise and lifestyle habits remain one of the most impactful and easily modifiable factors for preserving brain health. Although the effects of aerobic exercise on cerebrovascular function and brain health are well established, resistance training (RT) is rapidly increasing in popularity across all age demographics due to its numerous health benefits. Despite the clear physiological benefits of resistance exercise, its potential efficacy for preserving or improving cerebrovascular and overall brain health remains understudied to date. The aim of this review is to examine the literature pertaining to ways in which resistance exercise may reduce the risk of ADRD and slow age-related decline of brain structures and functions. Additionally, this review seeks to highlight key considerations and challenges regarding the feasibility, adoption, and adherence to resistance exercise in the context of normal aging, mild cognitive impairment, and ADRD.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".