Association between Accelerometer-Measured Light-Intensity Physical Activity and Cognitive Function in Older Adults
Bibliographic record
Abstract
OBJECTIVES: This study aimed to investigate whether accelerometer-measured light physical activity (LPA) is associated with cognitive function and whether engaging in ≥3 h/day of LPA can reduce the chance of cognitive impairment among a sample of older adults in Taiwan. DESIGN: Cross-sectional study. SETTING: An outpatient department in a medical center. PARTICIPANTS: Participants were community-dwelling older adults aged 65 years and older who were able to walk independently from September 2020 to March 2021. MEASUREMENTS: A tri-axial accelerometer was used to measure LPA for 7 consecutive days, and the Mini-Mental State Examination (MMSE) scale was used to assess the chance of cognitive impairment. Multiple linear regression model and binary logistic regression model were performed to examine the association between LPA and MMSE scores. RESULTS: 145 older Taiwanese adults (51.7% men; 81.2±6.8 years; 6.9% at chance of cognitive impairment) were included. After adjusting for moderate-to-vigorous physical activity (MVPA) and wear time, we found that there was a significant association between LPA and cognitive function (95% confidence interval [CI]: 0.64-1.65; P<0.001), and further found that those who engaged in LPA ≥3 h/day were at reduced chance of cognitive impairment compared with people who engaged in LPA <3 h/day (odds ratio [OR]: 0.16; 95% CI: 0.03-0.80; P=0.025). CONCLUSION: This study demonstrated that engaging in LPA ≥3 h/day could be viewed as a protective factor for maintaining cognitive function in older adults. We recommend further longitudinal research to elucidate the association between intensity-specific LPA and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".