The relationship between clinical measures of cognitive function and grip strength in healthy older adults
Bibliographic record
Abstract
BACKGROUND: Handgrip strength is considered a surrogate for musculoskeletal strength, however there is emerging evidence of an association with cognition. The specific neurocognitive attribute which best associates with grip strength is unknown. METHODS: We performed a secondary analysis on baseline data in 49 healthy older adults. Grip strength was corrected for body mass index. Control independent variables included age, Montreal Cognitive Assessment, and Trails B. Experimental variables included a clinical measure of simple reaction time, and clinical and computerized go/no-go tasks. The clinical Go/No-Go measure was determined with ReacStick, a rod-shaped device which - when released by the examiner - requires the participant to decide within 390 ms whether to catch the device or let it fall to the ground. RESULTS: Bivariate analysis demonstrated that age and all cognitive measures other than the computer go/no-go response accuracy related to grip strength. Multivariate analyses showed that following inclusion of the control variables, only ReacStick measures (reaction accuracy/simple reaction time) significantly predicted grip strength, explaining an additional 15.90% variance (p = 0.026). In contrast, computerized Go/No-Go accuracy (p = 0.391), response time variability (p = 0.463), and the control variables (p value range = 0.566-0.942) did not predict grip strength. CONCLUSION: A short latency (< 390 ms) visuomotor Go/No-Go task independently predicted over 15% of grip strength variance, whereas a slower screen-based Go/No-Go task did not. These findings support the notion that declining grip strength likely reflects sub-clinical brain changes as well as musculoskeletal dysfunction, possibly explaining the potent relationships between grip strength, disability, chronic disease, and mortality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".