Telerehabilitation in Stroke Recovery: A Survey on Access and Willingness to Use Low-Cost Consumer Technologies
Bibliographic record
Abstract
Background/Introduction: Early telerehabilitation trials with stroke survivors have shown promising results, but there remains a lack of knowledge of what areas of rehabilitation people with stroke are interested and willing to receive using technology. The purpose of this study was to describe the access to low-cost consumer technologies and willingness to use them to receive rehabilitation services among stroke survivors. MATERIALS AND METHODS: Participants were included in this survey study if they had a stroke, lived in the community, were 19 years of age or older, and able to understand English. Participants completed a study-specific telerehabilitation survey via phone call, mail, in-person, or online. Descriptive statistics were used to characterize the sample and survey responses. RESULTS: One hundred two survey responses were returned, representing a 79.1% response rate. The mean age of this urban (67.3%) and rural (32.7%) sample was 67.6 years. The technologies most commonly owned were as follows: televisions (91%), landline telephones (88.0%), and computers (79.0%). A large proportion of the sample reported an interest to receive assessments (58.4%), training and exercise programs (64.0%), and education (61.4%) via telerehabilitation, however, many were not interested to receive telerehabilitation (∼39%) and believed that the quality of care would be less than in-person rehabilitation (71.0%). CONCLUSIONS: The use of consumer technologies for the delivery of rehabilitation services is both feasible and desirable by stroke survivors. Telerehabilitation services at present should augment and not replace in-person rehabilitation. However, in cases where in-person rehabilitation is neither accessible nor possible, telerehabilitation could serve as an acceptable alternative and is a key area for future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".