Differential associations between simple physical performance tests with global and specific cognitive functions in cognitively normal and mild cognitive impairment: a cross-sectional cohort study of Asian community-dwelling older adults
Bibliographic record
Abstract
BACKGROUND: Physical performance declines and executive dysfunctions are predictors of dementia. However, their associations are not well understood in Asian older adults without dementia (cognitively normal [CN] and mild cognitive impairment [MCI]), especially in a single study. OBJECTIVE: Examine the associations between physical performance measures with executive function (EF)-based and non-EF-based neurocognitive tests and whether preclinical dementia cognitive status i.e., CN and MCI, moderated these associations. METHODS: We examined cross-sectional cohort of 716 community-dwelling older adults without dementia (CN = 562 and MCI = 154) using multivariable linear regression models. We associated three simple physical performance measures, namely timed-up-and-go (TUG), fast gait speed (FGS), and 30-s chair stand test (30 s-CST), with a comprehensive neurocognitive test battery measuring EF and non-EF cognitive functions. Moderating effects of cognitive status on the associations were examined. In all models, we controlled for pertinent covariates, including age, education, medical and psychiatric status. RESULTS: Upon controlling for covariates, TUG was most strongly and positively associated with multiple EF-based neurocognitive tests, followed by FGS, with 30 s-CST having the weakest associations. For all physical performance measures, no significant associations with non-EF-based neurocognitive tests were detected. Cognitive status significantly moderated the associations between all physical measures and several neurocognitive tests, with stronger associations in the MCI than CN. CONCLUSION: Compared to FGS and 30 s-CST, TUG had the most robust associations with multiple EF-based cognitive functions. Given their differential associations with global and detailed neurocognitive tests and significant moderating effects of cognitive status, findings highlight a need to carefully consider the choices of simple physical performance tests when using these tests with a heterogenous group of community-dwelling older adults without dementia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".