Associations between 24‐hour movement behavior and cognition in older adults at risk for dementia: A compositional data analysis
Bibliographic record
Abstract
BACKGROUND: This study aims to explore the associations between the time reallocation of 24-hour movement behaviors (24HMB) and cognition in older adults at risk for dementia. METHOD: This study adopted a cross-sectional design. Community-dwelling older adults at risk for dementia, including subjective cognitive decline and mild cognitive impairment, were recruited from community health centers and memory clinics. Global cognition was assessed by the Montreal Cognitive Assessment scale. 24HMB was measured by wrist-worn ActiGraph for consecutive 7 days. Compositional regression model and compositional isotemporal substitution model were used to analyze the relations between 24HMB time-use data and global cognition. RESULT: A total of 136 participants were included in the analysis. Compositional regression analysis suggested that the time allocation on moderate-to-high physical activities were positively associated with global cognition after adjusting for age, education, and gender. The compositional isotemporal substitution model revealed that replacing 10-minute, 20-minute, 30-minute sedentary or sleep with moderate-to-high physical activities was associated with better cognition performance. No other significant results were found. CONCLUSION: The time-use composition of 24HMB, particularly the time allocated on moderate-to-high physical activities, is associated with cognitive function in older adults at risk for dementia. Replacing sedentary, and sleep with moderate-to-high physical activities may induce positive effects on cognitive function.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".