Optimal reactive balance training characteristics post-stroke: secondary analysis of a randomized controlled trial
Bibliographic record
Abstract
ABSTRACT Background and purpose Reactive balance training (RBT) has shown promise for enhancing reactive balance control and reducing falls post-stroke. However, the optimal training parameters (e.g., intensity, duration) are unknown. This study aimed to investigate the relationship between different reactive balance training characteristics and improvements in reactive balance control and fall rates. Methods People with chronic stroke completed up to 12 one-hour reactive balance training sessions, twice per week. Training included experiencing losses of balance due to internal or external perturbations while performing voluntary tasks. The tasks were of four types: stable, quasi-mobile, mobile, and unpredictable, each with choice of three difficulty levels (normal, increased, or reduced). We analyzed the relationships between training characteristics (total number of perturbations, difficulty levels, perceived level challenge, and success rate) and fall rates post-training and changes in the reactive balance control sub-score of the mini-Balance Evaluation Systems Test (mini-BESTest). Results A higher number of perturbations was significantly associated with better post-intervention reactive balance scores on the mini-BEST (p=0.010). There were no significant associations with any other training characteristics and post-intervention mini-BEST Scores. For falls in daily life there was no significant association between any training characteristic. Discussion Greater exposure to RBT was associated with improvements in reactive balance control among individuals with chronic stroke. Participants who completed more sessions, and consequently experienced more perturbations, achieved better outcomes. These findings highlight the importance of sufficient training volume, suggesting that a higher number of perturbations may be optimizing the effects of RBT in stroke rehabilitation. Trial registration ISRCTN05434601
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".