The Importance of Exercise in Alzheimer’s Disease and the Minds in Motion® Program: An Editorial
Bibliographic record
Abstract
Alzheimer’s Disease (AD) and other dementias are prevalent neurodegenerative diseases characterized by decreased cognition, physical function, and quality of life. Currently, millions of people are living with AD and other dementias. With no cure, research has examined the use of non-pharmacological treatment options including exercise. Many high-quality studies demonstrate that physical activity slows the progression of AD’s many outcomes, and is beneficial to overall quality of life in those living with AD. However, creating exercise interventions at the community level that individuals will adhere to is often a challenge. The Alzheimer’s Society of Canada developed a unique program that combines physical activity with cognitive stimulation in a social atmosphere: The Minds in Motion® program. Minds in Motion® addresses many of the barriers often linked to poor physical activity participation in chronic diseases (such as inclusion of the care partner), to ensure the best program uptake. The Minds in Motion® program has anecdotally been successful in helping to increase physical function and social skills in those living with dementia. However, it is important to connect community-driven programs with the academic research community, to create an opportunity for high quality evaluation metrics that can be disseminated at multiple levels: to research audiences, clinical audiences, and to those in the community. With ongoing collaborations between research and community programs, there is a greater opportunity to understand the positive impact of a program, which ultimately increases the chance of funding for the program. In this editorial, we highlight that community-integrated research is an important priority for future collaborations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".