Association between physical performance and cognitive function in older adults across multiple studies: A pooled analysis study
Bibliographic record
Abstract
Abstract Background and Objectives While several studies have examined the association between cognitive and physical function, the consistency of these associations across functional contexts is unclear. The consistency of association between cognitive and physical function performance was examined at baseline across 17 clinical studies with diverse and heterogeneous conditions such as overweight/obese, sedentary, at risk for a mobility disability, osteoarthritis, low vitamin D, or had signs of cognitive impairment. Research Design and Methods Data are from 1,388 adults 50 years and older who completed a cognitive and physical function assessment as part of a research study at the Wake Forest Alzheimer’s Disease Research Center or the Wake Forest Older Americans Independence Center. Linear regression models were used to relate cognitive measures [Mini Mental Status Exam (MMSE), Montreal Cognitive Assessment (MoCA), and the Digit Symbol Substitution Task (DSST)], and physical measures [the Short Physical Performance Battery (SPPB) and hand grip strength] for the whole sample and treat each study as a fixed effect. All models controlled for age, sex, race, and body mass index (BMI). Results Overall, there was a significant association between higher scores on the MMSE (per standard deviation) and better physical function performance (SPPB score b= 0.24, p <0.001) and its components (gait speed, chair rise, and standing balance; p’s <0.05). Higher scores on the MoCA produced similar results (SPPB score b= 0.31, p= <0.001) and higher scores on the DSST were also significantly associated with a better SPPB score (b= 0.75, p <0.001). The relationship between DSST and physical function performance demonstrated a stronger magnitude of association compared to the MMSE or MoCA. Discussion and Implications Older adults with heterogenous health conditions showed a consistent pattern between better cognitive function and better physical function performance with the strongest association among DSST scores.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.024 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".