Exercise-Induced Improvements in Cognitive Functioning and Brain Structure in Older Adults
Bibliographic record
Abstract
Physical activity is often associated with benefits such as reduced risk of diabetes and cardiovascular disease; however, the benefits of physical activity are not only limited to physical health, but also extend to cognition (Warburton, Nicol & Bredin, 2006). Exercise in older populations results in improved cognitive functioning and decreased risk of cognitive decline (Muscari et al., 2010). This current literature review examines the association between physical activity and cognitive functioning in older adults. Studies suggest that a variety of types of exercise have cognitive benefits, although it is not clear which type of activity has the largest effect. Additionally, exercise increases total brain volume and the connectivity of neural networks in areas such as the hippocampus, and decreases the presence of white matter hyperintensities in areas involved in motor control and coordination (Erickson et al., 2010; Tseng et al., 2013). Taken together, the research indicates that physical activity improves cognitive functioning and causes exercise-induced changes in the brain. Limitations of the reviewed research include a lack of generalizable results due to a lack of diversity of samples, as well as the presence of cross-sectional designs that are unable to define the direction of the relationship between exercise and cognition. Future research should investigate the inconsistencies reported in the literature with the goal of developing programs to improve cognitive function in older populations and to reduce the burden of cognitive impairment on the health care system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".