“Connecting patients and therapists remotely using technology is feasible and facilitates exercise adherence after stroke”
Bibliographic record
Abstract
Purpose: Repetitive task practice after stroke is important to improve function, yet adherence to exercise is low. The aim of this study was to determine whether using the internet, a tablet application, and a chair sensor that connected to a therapist was feasible in monitoring adherence and progressing a functional exercise at home.Methods: Ten participants with stroke completed a 4-week sit-to-stand exercise using the technology at home (ACTRN12616000051448). A therapist remotely monitored exercise adherence, progressed goals, and provided feedback via the app. Measures of feasibility (design, recruitment/withdrawals, adherence, safety, participant satisfaction and estimates of effect on function) were collected.Results: Participants' mean age was 73.6 years [SD 9.9 years]. The system was feasible to deliver and monitor exercise remotely. All participants completed the study performing a mean 125% of prescribed sessions and 104% of prescribed repetitions. Participants rated the system usability (78%), enjoyment (70%) and system benefit (80%) as high. No adverse events were reported. The mean pre- and post-intervention difference in the total short performance physical battery score was 1.4 (95% CI 0.79, 2.00).Conclusions: It was feasible and safe to prescribe and monitor exercises using an app and sensor-based system. A definitive trial will determine whether such technology could facilitate greater exercise participation after 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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".