Promoting brain health through exercise and diet in older adults: a physiological perspective
Bibliographic record
Abstract
The rise in incidence of age-related cognitive impairment is a global health concern. Ageing is associated with a number of changes in the brain that, collectively, contribute to the declines in cognitive function observed in older adults. Structurally, the ageing brain atrophies as white and grey matter volumes decrease. Oxidative stress and inflammation promote endothelial dysfunction thereby hampering cerebral perfusion and thus delivery of energy substrates and nutrients. Further, the development of amyloid plaques and neurofibrillary tangles contributes to neuronal loss. Of interest, there are substantial inter-individual differences in the degree to which these physical and functional changes impact upon cognitive function as we grow older. This review describes how engaging in physical activity and cognitive activities and adhering to a Mediterranean style diet promote 'brain health'. From a physiological perspective, we discuss the effects of these modifiable lifestyle behaviours on the brain, and how some recent human trials are beginning to show some promise as to the effectiveness of lifestyle behaviours in combating cognitive impairment. Moreover, we propose that these lifestyle behaviours, through numerous mechanisms, serve to increase brain, cerebrovascular and cognitive reserve, thereby preserving and enhancing cognitive function for longer.
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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 | 0.001 |
| 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) | 0.003 | 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".