Formulation of evidence-based messages to promote the use of physical activity to prevent and manage Alzheimer’s disease
Bibliographic record
Abstract
BACKGROUND: The impending public health impact of Alzheimer's disease is tremendous. Physical activity is a promising intervention for preventing and managing Alzheimer's disease. However, there is a lack of evidence-based public health messaging to support this position. This paper describes the application of the Appraisal of Guidelines Research and Evaluation II (AGREE-II) principles to formulate an evidence-based message to promote physical activity for the purposes of preventing and managing Alzheimer's disease. METHODS: A messaging statement was developed using the AGREE-II instrument as guidance. Methods included (a) conducting a systematic review of reviews summarizing research on physical activity to prevent and manage Alzheimer's disease, and (b) engaging stakeholders to deliberate the evidence and formulate the messaging statement. RESULTS: The evidence base consisted of seven systematic reviews focused on Alzheimer's disease prevention and 20 reviews focused on symptom management. Virtually all of the reviews of symptom management conflated patients with Alzheimer's disease and patients with other dementias, and this limitation was reflected in the second part of the messaging statement. After deliberating the evidence base, an expert panel achieved consensus on the following statement: "Regular participation in physical activity is associated with a reduced risk of developing Alzheimer's disease. Among older adults with Alzheimer's disease and other dementias, regular physical activity can improve performance of activities of daily living and mobility, and may improve general cognition and balance." The statement was rated favourably by a sample of older adults and physicians who treat Alzheimer's disease patients in terms of its appropriateness, utility, and clarity. CONCLUSION: Public health and other organizations that promote physical activity, health and well-being to older adults are encouraged to use the evidence-based statement in their programs and resources. Researchers, clinicians, people with Alzheimer's disease and caregivers are encouraged to adopt the messaging statement and the recommendations in the companion informational resource.
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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 | 0.364 | 0.438 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".