The Association of the 24-Hour Activity Cycle Profiles With Cognition in Older Adults With Mild Cognitive Impairment: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The relationship of cognition and the 24-h activity cycle (24-HAC), encompassing physical activity, sedentary behavior, and sleep, in older adults with mild cognitive impairment (MCI) remains uncertain. Distinct combinations of 24-HAC behaviors can characterize unique activity profiles and influence cognition. We aimed to characterize 24-HAC activity profiles in older adults with MCI and assess whether differences in cognition exist across profiles. METHODS: We conducted a cross-sectional analysis utilizing baseline data from 3 randomized controlled trials involving 253 community-dwelling older adults (55 + years) with MCI (no functional impairment, dementia diagnosis, and Montreal Cognitive Assessment score <26/30). Using MotionWatch8© wrist-worn actigraphy (+5 days), we captured the 24-HAC. Cognition was indexed by the Alzheimer's Disease Assessment Scale Cognitive Plus (ADAS-Cog-Plus). Compositional data and latent profile analyses identified distinct 24-HAC activity profiles. Analysis of covariance examined whether 24-HAC activity profiles differed in cognition. RESULTS: Four distinct activity profiles were identified. Profile 1 ("Average 24-HAC," n = 103) engaged in all 24-HAC behaviors around the sample average. Profile 2 ("Active Chillers," n = 70) depicted lower-than-average engagement in physical activity and higher-than-average sedentary behavior. Profile 3 ("Physical Activity Masters," n = 54) were the most active and the least sedentary. Profile 4 ("Sedentary Savants," n = 26) were the least active and the most sedentary. Sleep was similar across profiles. There were no significant differences in ADAS-Cog-Plus scores between 24-HAC activity profiles (p > .05). CONCLUSIONS: Older adults with MCI exhibited four 24-HAC activity profiles conforming to recommended physical activity and sleep guidelines. Nonetheless, cognition was similar across these profiles.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".