Test-retest reliability of force plate-derived measures of reactive stepping
Bibliographic record
Abstract
Abstract Background Characterizing reactive stepping is important to describe the response’s effectiveness. Measures of reactive stepping related to step initiation, execution, and termination phases have been frequently reported to characterize reactive balance control. However, the test-retest reliability of these measures are unknown. Research questions What is the between- and within-session test-retest reliability of various force plate-derived measures of reactive stepping? Methods Nineteen young, healthy adults responded to 6 small (~8-10% of body weight) and 6 large perturbations (~13-15% of body weight) using an anterior lean-and-release system. Tests were conducted on two visits separated by at least two days. Participants were instructed to recover balance in as few steps as possible. Step onset, foot-off, swing, and restabilization times were extracted from force plates. Relative test-retest reliability was determined through intraclass correlation coefficients (ICCs) and 95% confidence intervals (CIs). Absolute test-retest reliability was assessed using the standard error of the measurement (SEM). Results Foot-off and swing times had the highest between- and within-session test-retest reliabilities regardless of perturbation size (between-session ICC=0.898–0.942; within-session ICC=0.455–0.753). Conversely, step onset and restabilization time had lower ICCs and wider CIs (between-session ICC=0.495–0.825; within-session ICC=−0.040–0.174). Between-session test-retest reliability was higher (ICC=0.495-0.942) for all measures than within-session test-retest reliability (ICC=−0.040–0.753). SEMs were low (3–10% of mean) for all measures, except time to restabilization (SEM=15-20% of mean), indicating good absolute reliability. Significance These findings suggest multiple baseline sessions are needed for measuring restabilization and step onset times. The SEMs provide an index for measuring meaningful change due to an intervention.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".