Associations Between Lower Limb Isometric Torque, Isokinetic Torque, and Explosive Force With Phases of Reactive Stepping in Young, Healthy Adults
Bibliographic record
Abstract
This study aimed to determine the relationship between lower limb muscle strength and explosive force with force plate-derived timing measures of reactive stepping. Nineteen young, healthy adults responded to 6 perturbations using an anterior lean-and-release system. Foot-off, swing, and restabilization times were estimated from force plates. Peak isokinetic torque, isometric torque, and explosive force of the knee extensors/flexors and plantar/dorsiflexors were measured using isokinetic dynamometry. Correlations were run based on a priori hypotheses and corrected for the number of comparisons (Bonferroni) for each variable. Knee extensor explosive force was negatively correlated with swing time (r = -.582, P = .009). Knee flexor peak isometric torque also showed a negative association with restabilization time (r = -.459, P = .048); however, this was not statistically significant after correcting for multiple comparisons. There was no significant relationship between foot-off time and knee or plantar flexor explosive force (P > .025). These findings suggest that there may be utility to identifying specific aspects of reactive step timing when studying the relationship between muscle strength and reactive balance control. Exercise training aimed at improving falls risk should consider targeting specific aspects of muscle strength depending on specific deficits in reactive stepping.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".