Improvement of cognitive and fall risk associated with exercise in persons with Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background The study aim was to elucidate the relationship between change in cognition and fall risk in persons with Alzheimer’s disease (AD) stratified for severity of illness and following a 10‐week exercise program. The authors hypothesize that a linear relationship exists between the severity of AD and decrease fall risk and severity of AD and improvement in cognition. Method Participants underwent a 10‐week exercise program designed to improve cognition and decrease fall risk. Cognition (Montreal Cognitive Assessment (MoCA)) and fall risk (Timed Up and Go (TUG)) were assessed prior to and after the exercise program. Normality was established for change in MoCA and change in TUG and for age of participants. RM‐MANOVA statistics were used to evaluate changes in the outcome measures prior to and after the exercise program. A post hoc Bonferroni evaluated differences between groups. The severity of AD was compared with change in cognition and fall risk outcome measures to determine if disease severity influenced cognition and fall risk with a 10‐week exercise program. Results The TUG analysis yielded two significant mean differences (p<0.05) between groups ‐ the mild/severe (p<0.001) and moderate/severe (p = 0.03) comparisons. However, there was no significant difference between the mild/moderate severity groups (p = 0.085). MoCA group differences comparison produced all statistically significant (p<0.001) mean differences, the largest occurring between the mild and severe groups. These data indicate a statistically significant relationship between severity of AD and change in TUG and MoCA scores following a ten‐week exercise program. Conclusion Persons with AD demonstrate impairments in cognition and fall risk leading to increased stress and costs. Exercise is an effective intervention for improving cognition and reducing fall risk in persons with AD. The relationship found between improvement of cognition and fall risk with exercise appears to be linear. The lesser severity of AD, the greater the cognitive and fall risk improvement. These data suggest that a 10‐week exercise program may improve cognition and decrease fall risk in persons with AD. The results from this study suggest that healthcare practitioners should consider early referral of persons with AD to group exercise programs to reduce the likelihood of falls and improve cognition.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".