Timing of reactive stepping among individuals with sub-acute stroke: effects of ‘single-task’ and ‘dual-task’ conditions
Bibliographic record
Abstract
Performance decrements in balance tasks are often observed when a secondary cognitive task is performed simultaneously. This study aimed to determine whether increased cognitive load resulted in altered reactive stepping in individuals with sub-acute stroke, compared to a reactive stepping trial with no secondary task. The secondary purpose was to determine whether differences existed between the first usual-response trial, subsequent usual-response trials, and the dual-task condition. Individuals with sub-acute stroke were exposed to external perturbations to elicit reactive steps. Perturbations were performed under a usual-response (single-task) and dual-task condition. Measures of step timing and number of steps were based on force plate and video data, respectively; these measures were compared between the usual-response and dual-task trials, and between the first usual-response trial, later usual-response trials (trials 2-5) and a dual-task trial. A longer time of unloading onset and greater number of steps were identified for the first usual-response trial compared to later usual-response trials. No significant differences were identified between usual-response and dual-task trials. Although improvements were observed from the first to subsequent usual-response lean-and-release trials, performance then tended to decrease with the introduction of the dual-task condition. These findings suggest that when introduced after usual-response trials, the dual-task trial may represent the first trial of a new condition, which may be beneficial in reducing the potential for adaptation that may occur after multiple repetitions of a reactive stepping task. Therefore, these findings may lend support to the introduction of a new condition (i.e. a dual-task trial) in addition to usual-response trials when assessing reactive balance in individuals with stroke.
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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".