Age-Related Changes in Postural Control in Physically Inactive Older Women
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The maintenance of postural control is influenced by the complexity of a given task. Tasks that require greater attention and cognitive involvement increase the risk of falls among older adults. The aim of the present study was to evaluate the adaptation of the postural control system to different levels of task complexity in physically inactive young and older women. METHODS: A cross-sectional study was conducted with adult women classified as physically inactive based on the results of the International Physical Activity Questionnaire. The participants were 27 young (20-30 years of age) and 27 older (60-80 years of age) women. Sway velocity of the center of pressure in the anterior-posterior and medial-lateral directions was calculated using a force plate under 6 conditions: standing directly on the force plate or on a foam placed over the force plate, eyes open or closed, and dual-task complexity with and without the foam. RESULTS AND DISCUSSION: A 2-way analysis of variance revealed that sway velocity increased in both groups when the task conditions were altered. The older women exhibited significantly greater sway velocity compared with the young women on all tasks. However, the patterns of postural control adaptation to the different levels of complexity were similar among all participants. CONCLUSIONS: In this study, the adaption of the postural control system to different levels of task complexity did not differ between physically inactive young and physically inactive older women. However, the physically inactive older women exhibited greater sway velocity compared with the young women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".