RUNNING AND WALKING IN DEMENTIA AND MILD COGNITIVE IMPAIRMENT: A SCOPING REVIEW
Bibliographic record
Abstract
Abstract Growing evidence suggests physical exercise may reduce dementia risk, but there has been little investigation of specific forms of exercise for people living with dementia and cognitive impairment. Prior reviews show running and walking have mental health benefits in the general adult population, but no reviews of walking and running among people living with dementia and mild cognitive impairment have been published. A scoping review was conducted to identify and evaluate evidence concerning whether running and walking affect dementia risk, improve/maintain cognitive functioning, and improve quality of life and psychological well-being. PsycInfo, Medline, CINAHL, and SPORTDiscus were searched to identify studies with the target population (people living with dementia or mild cognitive impairment) and target activities (running, jogging, walking). 1153 records were uploaded into Covidence and 821 studies were excluded based on abstract reviews. Full text review yielded 49 papers. Forty-seven studies examined walking and a broad range of outcomes: cognition, ADL functioning, quality of life, behavioral expressions of distress, social participation, and identity. The remaining two studies examined (1) a combination of running and walking among people with mild cognitive impairment and (2) the link between cognitive decline and a history of running, jogging, and walking (past ten years). This review shows growing interest in walking as an intervention with many possible benefits for people living with dementia or mild cognitive impairment. Studies of jogging and running are still relatively rare in this population. Additional contributing authors: Hannah Gardner, Clarissa Geibel, Katherine King, Jessica Strong, Travis Saunders, Christine Wise.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".