The Postural Stability Measures Most Related to Aging, Physical Performance, and Cognitive Function in Healthy Adults
Bibliographic record
Abstract
Background . Different measures have been used to quantify body balance; some of which use technology to measure postural sway, others are physical performance or self‐reported. However, there is little information on the best postural sway measures associated with aging, physical performance, and cognitive function measures. Objective . To evaluate the relationship between postural sway measures and aging, physical performance, and cognitive function measures. Methods . A total of 51 subjects (53% female, mean age 53.2 ± 21 years) participated in this cross‐sectional study. The participants completed the Activities‐specific Balance Confidence (ABC) Scale questionnaire, the Functional Gait Assessment (FGA), the Montreal Cognitive Assessment (MoCA) test, and gait speed. Afterward, the participants performed 8 balance exercises, and their postural sway was measured using a force plate. Spearman’s rank correlation coefficient was used to examine the relationship between the study variables. Results . Age was negatively associated with cognitive function, gait speed, ABC scores, and FGA scores. In addition, cognitive ability was associated positively with ABC scores ( r = 0.38, p ≤ 0.01). Age, FGA scores, and gait speed were significantly associated with the postural sway of the AP direction in some exercises and in all exercises in the ML directions ( p < 0.05). The cognitive function and ABC scores were significantly associated with only postural sway measures in the ML direction ( p < 0.05). Conclusion . The postural sway measures in the lateral direction had more and stronger associations with age, physical performance, and cognitive function measures compared to those in the AP direction.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".