FUNCTIONAL FITNESS LEVELS REFLECT COGNITIVE HEALTH STATUS
Bibliographic record
Abstract
Alzheimer’s disease currently affects 5.8 million people in the US and the number is projected to triple by 2050. As the baby boomer population ages, it is important to identify measures that correlate with cognitive decline. Measures that show a relationship with cognitive decline can serve as early indicators that a person is in need of a cognitive evaluation. PURPOSE: : The purpose of this evaluation was to determine if functional fitness tasks could accurately discriminate between older adults with and without mild cognitive impairment. METHODS: Adults 60+ years participated in the present investigation (n = 107). Each participant completed demographic questionnaires; completed two stationary cognitive tasks: Montreal Cognitive Assessment (MoCA) and visual paired comparison (VPC); and completed four functional cognitive assessments: dual-task maximal speed (DTMS), dual-task habitual speed (DTHS), sit-to-stand power, timed up and go test (TUG). Participants with MoCA scores > 23 were classified as cognitively intact (CIN), whereas participants with MoCA scores < 23 were classified as cognitively impaired (CIM). A one-way ANOVA determined if there were significant differences between groups for each cognitive task. RESULTS: Eighty CIN and twenty-three CIM subjects completed all assessments. The CIN group had higher scores on the VPC task (p = .02), while exhibiting faster times to complete DTMS (p < .001), DTHS (p = .002), and TUG (p = .02) compared to the CIM group. No significant differences were found between the cognitive groups in sit-to-stand power variables: peak power (p = .08), average power (p = .07), and average velocity (p = .08). CONCLUSIONS: Functional fitness assessments distinguished between CIN and CIM groups. As these results indicate, functional fitness may be an indicator of cognitive status. Future investigations should longitudinally track both functional fitness and cognitive function to further elucidate this relationship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".