Internet-Based and Wearable-Device-Assisted Tele-Rehabilitation for Stroke Patients after Discharge: A Randomized Trial
Bibliographic record
Abstract
ABSTRACT Background: Stroke remains a major public health issue globally. Tele-rehabilitation, incorporating internet-based interventions and wearable devices, offers an accessible strategy for post-discharge rehabilitation. This study evaluates their effectiveness in stroke patients. Methods: A total of 160 subacute stroke patients hospitalized between November 2022 and September 2023 were enrolled and randomly allocated to four groups at discharge ( n = 40 per group): a control group receiving conventional rehabilitation, an internet-based tele-rehabilitation (ITR) group, a wearable-device-assisted (WDA) group and a combined intervention (IWT) group, which received both ITR and WDA training. The primary outcome was assessed by the Modified Barthel Index (MBI) at discharge, 4 weeks and 12 weeks post-discharge, with the 12-week score prespecified as the primary endpoint. Secondary outcomes included Berg Balance Scale (BBS), simplified Fugl-Meyer Assessment (sFMA), Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Mini-Mental State Examination (MMSE) and Zarit Burden Interview (ZBI), all assessed at discharge, 4 weeks and 12 weeks post-discharge. Results: At baseline, no significant differences were observed among groups ( P > 0.05). Over 12 weeks, all intervention groups demonstrated significant improvements in MBI, BBS and sFMA compared to the control group ( P < 0.05), with the IWT group achieving the greatest gains ( P < 0.01). Anxiety, depression and caregiver burden significantly decreased across intervention groups, with the IWT group showing the most pronounced reductions ( P < 0.01). Cognitive function also improved significantly, particularly in the IWT group ( P < 0.01). Conclusion: ITR and WDA training enhances functional and psychological recovery in stroke patients, highlighting its potential clinical significance in managing stroke recovery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.010 | 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".