A Randomized Control Trial of a Virtually Delivered Program for Increasing Upper Limb Activity After Stroke
Bibliographic record
Abstract
Background Upper limb activity following stroke is low, which may limit recovery. We investigated whether a virtually-delivered upper limb program, that included a wearable device with reach-to-grasp feedback, would increase upper limb activity after stroke. Methods This was a parallel-group, assessor-blinded, randomized control trial conducted at 6 sites across 5 provinces of the CanStroke Recovery Trials Platform between 2020 to 2022. Participants (n = 73) were community-living, less than 1 year post stroke, and had residual arm movement and upper limb use limitations. Participants were randomized via a central web-based randomization service to receive a virtually delivered program (Virtual Arm Boot Camp [V-ABC], n = 36) or waitlist control (n = 37) receiving usual care. V-ABC consisted of a home exercise program, feedback from a wrist-worn device to monitor reach-to-grasp counts, and 6 virtual sessions with a trained therapist over 3 weeks. The primary outcome was the average daily reach-to-grasp counts over 3 days at 4 weeks post baseline assessment. Secondary outcomes included upper limb function, self-reported use, and quality of life. Within-subject changes between pre, post treatment, and 2 months follow up for all participants were also examined as a tertiary analysis. Results The V-ABC group demonstrated greater average daily reach-to-grasp counts (primary outcome) at 4 weeks compared to control (mean difference = 368, 95% confidence interval = 6-730, P = .046). Conclusions This study provided evidence that a virtually delivered upper limb program that consists of exercise, feedback from a wearable device, and therapist support can increase real-world upper limb activity following stroke. Clinical Trial Registration: NCT04232163.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".