Low Cognitive Function Is Associated With Reduced Functional Fitness Among Community-Dwelling Older Adults
Bibliographic record
Abstract
Abstract Among older adults over 70, 22-30% report difficulty performing at least one activity of daily living (ADL). While the precipitants of ADL decline are multifactorial, over 50% of cognitively impaired adults require assistance with ADLs. The exact relationship between cognitive and functional decline remains unknown, but it is important to understand their relationship. Eighty-three older adults (80.9 + 5.4 years) enrolled in this study and completed functional fitness and cognitive assessments. Functional fitness assessments included: Short Physical Performance Battery (SPPB), 10-meter walk, dual-task, and power chair stand (average and peak). Cognition was assessed using the Montreal Cognitive Assessment (MoCA) and Visual Paired Comparison task (VPC). Categories of low cognitive function (LCF) and high cognitive function (HCF) were determined by VPC scores. SPPB was 10.2% greater among the HCF group. The HCF group walked 12.6% (0.16 m/s) faster than the LCF group. Dual-task (fast) performance was 13.2% faster among the HCF group. Additionally, when rising from a seated position during the average and peak power chair stand task, the HCF group moved 16.7% and 16.1% faster than the LCF group, respectively. MoCA scores were 2.8 points greater among the HCF group. Based on the current results, significant differences exist between cognitive groups suggesting a relationship between functional fitness and cognition. What remains unknown is the ability to influence functional fitness by changing cognition or vice versa. Future research is warranted to determine the relationship of change in either domain 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".