Exercise as a Strategy for Brain Aging: Prevention and Treatment Insights
Bibliographic record
Abstract
Objectives: Aging is a comprehensive biological process that leads to a decrease in the physiological function of many body organs. Among them, the central nervous system plays a pivotal role in aging, and in this regard, physical and cognitive activities and exercise are among of the most important interventions known for managing and hindering nervous disorders associated with aging. The purpose of this study was to investigate the effect of various types of activities and exercise on neurological disorders in older adults. Material and methods: To conduct this research, databases including Google Scholar, PubMed, Web of Science, and Scopus were thoroughly searched using the following terms: effect of aging on the brain, Alzheimer's, Parkinson's disease, stroke, and the effect of different types of exercises on the aging brain, focusing on articles published between 2005 and 2024. The selection of articles followed the framework for scoping reviews as outlined by Arksey and O'Malley. A total of 56 relevant articles were carefully examined and included in this review. Findings: The results of various studies showed that different physical exercises have different effects on diseases related to the nervous system and aging. Among them, the best types of physical exercises were moderate and high intensity exercises combined with cognitive exercises. Conclusion: Physical exercises of moderate to high intensity and the combination of physical and cognitive activities have more beneficial effects than exercises with lower intensity. In addition, exercises and dual physical-cognitive exercises group have a preventive and more effective role than other exercise modalities in dealing with brain and nervous system disorders in older adults.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".