Standing Versus Stepping—Exploring the Relationships Between Postural Steadiness and Dynamic Reactive Balance Control
Bibliographic record
Abstract
While the literature has characterized balance control during quasi-static and/or dynamic tasks, comparatively few studies have examined relationships across paradigms. This study investigated whether quiet-stance postural steadiness metrics were associated with reactive control parameters (during both stepping and restabilization phases) following a lean-and-release perturbation. A total of 40 older adults participated. Postural steadiness (center of the pressure range, root mean square, velocity, and frequency) was evaluated in "feet together" and "tandem stance" positions. During the reactive control trials, the step length, step width, movement time, and reaction time were measured, in addition to the postural steadiness variables measured during the restabilization phase following the stepping response. Out of 64 comparisons, only 10 moderate correlations were observed between postural steadiness and reactive spatio-temporal stepping parameters (P ≤ .05, r = -.312 to -.534). However, postural steadiness metrics were associated with the center of pressure velocity and frequency during the restabilization phase of the reactive control trials (P ≤ .02, r = .383 to .775 for velocity and P ≤ .01, r = .386 to .550 for frequency). Although some elements of quasi-static center of pressure control demonstrated moderate associations with dynamic stepping responses, relationships were stronger for restabilization phase dynamics after foot-contact. Future work should examine the potential association between restabilization phase control and older adult fall-risk.
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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 | 0.000 | 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".