Predicting interest to use mobile-device telerehabilitation (mRehab) by baby-boomers with stroke
Bibliographic record
Abstract
<em>Context and purpose:</em> Demand for stroke rehabilitation services are reaching unprecedented levels due to an overall population aging, driven by the aging of the baby-boomer generation. Delivery of rehabilitation via mobile-device technologies may provide advantages towards meeting the increasing demands on the rehabilitation system by providing individuals with rehabilitation services in their homes and communities. The aim of this paper is to gain an understanding of the interest of current baby-boomers with stroke to use mobile-device technology to receive rehabilitation services such as education, assessments and exercise programs (mRehab). <em>Methods:</em> People living in the community with stroke born between 1946 and 1964 (i.e., baby-boomer generation) who participated in a larger telerehabilitation survey were included in this study. Regression modeling was used to evaluate personal, health/disability and technological predictors of interest to use mobile-devices for telerehabilitation. <em>Results and significance:</em> Fifty people with stroke, mean age 62.7 (4.4) years, 58% male, 54.2% with moderate or moderately severe disability were included; 86% had access to a mobile phone or tablet. Regression analysis resulted in statistically significant personal (education, β = 0.29 [95% CI = 0.05 to 1.11], population of residence, β = 0.30 [95% CI = 0.07 to 0.69]), health (comorbid conditions, β = 0.30 [95% CI = 0.02 to 0.20]) technology (ownership, β = 0.26 [95% CI = 0.01 to 0.86] and attitude towards telerehabilitation, β = 0.25 [95% CI = 0.01 to 0.79]) predictors of interest to use mobile-devices for telerehabilitation (R<sup>2</sup> = 33.1%).<br />This study identifies personal, health and technological factors which predict interest of baby-boomers with stroke with ongoing and complex health needs to use mRehab. Health professionals can use this information as they integrate mRehab into their practice and inform future development of mRehab solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".