An Intensive Exercise Program Using a Technology-Enriched Rehabilitation Gym for the Recovery of Function in People With Chronic Stroke: Usability Study
Bibliographic record
Abstract
BACKGROUND: Rehabilitation improves poststroke recovery with greater effect for many when applied intensively within enriched environments. The failure of health care providers to achieve minimum recommendations for rehabilitation motivated the development of a technology-enriched rehabilitation gym (TERG) that enables individuals under supervision to perform high-intensity self-managed exercises safely in an enriched environment. OBJECTIVE: This study aimed to assess the feasibility of the TERG approach and gather preliminary evidence of its effect for future research. METHODS: This feasibility study recruited people well enough to exercise but living with motor impairment following a stroke at least 12 months previously. Following assessment, an 8-week exercise program using a TERG (eg, virtual reality treadmills, power-assisted equipment, balance trainers, and upper limb training systems) was structured in partnership with participants. The feasibility was assessed through recruitment, retention, and adherence rates along with participant interviews. Effect sizes were calculated from the mean change in standard outcome measures. RESULTS: In total, 70 individuals registered interest, the first 50 were invited for assessment, 39 attended, and 31 were eligible and consented. Following a pilot study (n=5), 26 individuals (mean age 60.4, SD 13.3 years; mean 39.0, SD 29.2 months post stroke; n=17 males; n=10 with aphasia) were recruited to a feasibility study, which 25 individuals completed. Participants attended an average of 18.7 (SD 6.2) sessions with an 82% attendance rate. Reasons for nonattendance related to personal life, illness, weather, care, and transport. In total, 19 adverse events were reported: muscle or joint pain, fatigue, dizziness, and viral illness, all resolved within a week. Participants found the TERG program to be a positive experience with the equipment highly usable albeit with some need for individual tailoring to accommodate body shape and impairment. The inclusion of performance feedback and gamification was well received. Mean improvements in outcome measures were recorded across all domains with low to medium effect sizes. CONCLUSIONS: This study assessed the feasibility of a holistic technology-based solution to the gap between stroke rehabilitation recommendations and provision. The results clearly demonstrate a rehabilitation program delivered through a TERG is feasible in terms of recruitment, retention, adherence, and user acceptability and may lead to considerable improvement in function, even in a chronic stroke population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-doi.org/10.3389/fresc.2021.820929.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".