Mobile Tablet-Based Stroke Rehabilitation: Using mHealth Technology to Improve Access to Early Stroke Rehabilitation
Bibliographic record
Abstract
Mobile health (mHealth) technology represents a means through which more stroke survivors could access early stroke rehabilitation. Although rehabilitation is most effective when begun early post-stroke, limited resources (facilities, therapists) prevent survivors from initiating therapy. Furthermore, the coupling of an aging population with advances in acute therapy has led to an increase in the absolute number of individuals suffering from and surviving strokes which in turn has put further strain on already scarce rehabilitation resources. There is an urgency to conduct high-quality research exploring cost-effective and creative mHealth devices for early rehabilitation in the acute setting. Mobile technology allows therapists to prescribe apps based on standard cognitive/physical assessments in the acute setting, remotely monitor patient progress across individual carepaths, and update prescribed therapies based on patient feedback and recovery. Recognition of the growing problem of accessing early stroke rehabilitation, and the possibilities offered by mHealth technology led to the development of the RecoverNow platform for stroke rehabilitation in the acute setting. RecoverNow is a custom built, tablet-based stroke rehabilitation platform that houses a variety of previously existing apps with activities analogous or identical to exercises in speech language and/or occupational therapy. While RecoverNow represents how mobile technology can be utilized to address a growing public health issue, the feasibility, acceptability and efficacy of tablet-based stroke rehabilitation are unknown. Studies with the goal of establishing feasibility of early tablet-based stroke rehabilitation are needed and, if appropriate, a randomized controlled trial to establish efficacy.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".