Functional fitness assessments predict cognitive outcomes
Bibliographic record
Abstract
Abstract Background The rate of Alzheimer’s disease (AD) is anticipated to triple by 2050, affecting 131 million people globally. To combat this dramatic increase, it is imperative to detect early cognitive decline in order to provide timely interventions. As a result, the ability to predict cognitive status through ubiquitous functional fitness assessment would provide a cost‐effective method for identifying cognitively at‐risk older adults. This study sought to determine whether functional fitness measures could accurately predict cognitive outcomes. Method 85 older adults (age: 80.93 ± 5.4) participated in the current study. Each participant completed demographic questionnaires; completed three cognitive tasks: Montreal Cognitive Assessment (MoCA), digit coding symbol test (DCS), and visual paired comparison target foil accuracy (VPCTFA) assessment; and completed six functional fitness assessments : 10‐meter maximal speed walk, dual‐task maximal speed (DTMS), dual‐task habitual speed (DTHS), sit‐to‐stand power, timed up‐and‐go (TUG), and the short physical performance battery (SPPB). Results were analyzed through three multiple linear regressions with MoCA, DCS and VPCTFA test each as the dependent variables, and age, sex, education, DTMS, DTHS, sit‐to‐stand power, TUG, and SPPB as predictor variables. Result The first model explained 55% of the variance of the MoCA (p < .001) with DTMS (45%; p < .001) representing the only significant predictor. The second model explained 13% of the variance of the DCS (p = .13) with age (28%; p = .01) and DTMS (23%; p = .04), representing significant predictors. The last model explained 18% of the variance of the VPCTFA (p = .007) with DTMS (21%; p = .05), TUG (21%; p = .05), 10‐meter maximal walking speed (21%; p = .05) and 4‐meter walk (SPPB variable; 22%; p = .04) representing significant predictors. Conclusion These results suggest functional fitness assessments may predict cognitive outcomes. Functional fitness assessments accounted for more variance of MOCA scores than DCS and VPCTFA outcomes. This suggests functional fitness assessments may be better predictors of global cognition performance than performance on assessments of processing speed or working memory. Future research will investigate whether functional fitness parameters may be cost‐effective evaluation tools for predicting global cognitive performance over time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".