Altered Anticipatory Postural Adjustments During Whole-Body Reaching in Subjects With Stroke
Bibliographic record
Abstract
Background Coordination between arm movements and postural adjustments is crucial for reaching-while-stepping tasks involving both anticipatory postural adjustments (APAs) and compensatory movements to effectively propel the whole-body forward so that the hand can reach the target. Stroke impairs the ability to coordinate the action of multiple body segments but the underlying mechanisms are unclear. Objective. To determine the effects of stroke on reaching performance and APAs during whole-body reaching. Methods We tested arm reaching in standing (stand-reach) and reaching-while-stepping (step-reach; 15 trials/condition) in individuals with chronic stroke (n = 18) and age-matched healthy subjects (n = 13). Whole-body kinematics and kinetic data were collected during the tasks. The primary outcome measure for step-reach was “gain” ( g), defined as the extent to which the hip displacement contributing to hand motion was neutralized by appropriate changes in upper limb movements ( g = 1 indicates complete compensation) and APAs measured as spatio-temporal profiles of the center-of-pressure shifts preceding stepping. Results Individuals with stroke had lower gains and altered APAs compared to healthy controls. In addition, step onset was delayed, and the timing of endpoint, trunk, and foot movement offset was prolonged during step-reach compared to healthy controls. Those with milder sensorimotor impairment and better balance function had higher gains. Altered APAs were also related to reduced balance function. Conclusions Altered APAs and prolonged movement offset in stroke may lead to a greater reliance on compensatory arm movements. Altered APAs in individuals with stroke may be associated with a reduced shift of referent body configuration during the movement
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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".