Effect of reactive balance training on physical fitness poststroke: study protocol for a randomised non-inferiority trial
Bibliographic record
Abstract
INTRODUCTION: Regular exercise is essential in the chronic phase of stroke recovery for improving or maintaining function, and reducing the risk of a second stroke. To achieve these goals, multiple components of fitness should be targeted with poststroke exercise, including aerobic capacity, strength and balance. However, following the recommended frequency and duration of each component separately can take a long time and lead to fatigue in people with stroke. Therefore, finding types of exercise that target multiple components of fitness all together is valuable.Reactive balance training (RBT) is a novel type of exercise where individuals repeatedly lose their balance in order to practise balance reactions. When people do RBT, they increase their heart rate and exert forces with their leg muscles which could improve aerobic fitness and muscle strength, respectively. This means that RBT could have the potential to improve multiple components of fitness, simultaneously. METHODS AND ANALYSIS: This is a randomised controlled non-inferiority trial with internal pilot study. Participants with chronic stroke will be randomly assigned to one of two groups: (1) RBT or (2) aerobic and strength training (AST). Participants in both groups will complete 1 hour of exercise, three times/week for 12 weeks. The primary objective is to determine the effect of RBT on aerobic capacity and knee muscles' strength. The secondary objective is to determine the effects of RBT and AST on balance control and balance confidence. We expect to find that RBT is superior to AST in terms of improving balance control and balance confidence, yet not inferior to AST in terms of its effects on aerobic capacity and strength. ETHICS AND DISSEMINATION: Research ethics approval has been received. Results will be disseminated directly to study participants at the end of the trial, and to other stakeholders via publication in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: NCT04042961.
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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 | 0.037 | 0.035 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.007 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.053 | 0.011 |
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".