Mobile tablet-based therapies following stroke: A systematic scoping review of administrative methods and patient experiences
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Stroke survivors are often left with deficits requiring rehabilitation to recover function and yet, many are unable to access rehabilitative therapies. Mobile tablet-based therapies (MTBTs) may be a resource-efficient means of improving access to timely rehabilitation. It is unclear what MTBTs have been attempted following stroke, how they were administered, and how patients experienced the therapies. The review summarizes studies of MTBTs following stroke in terms of administrative methods and patient experiences to inform treatment feasibility. METHODS: Articles were eligible if they reported the results of an MTBT attempted with stroke participants. Six research databases were searched along with grey literature sources, trial registries, and article references. Intervention administration details and patient experiences were summarized. RESULTS: The search returned 903 articles of which 23 were eligible for inclusion. Most studies were small, observational, and enrolled chronic stroke patients. Interventions commonly targeted communication, cognition, or fine-motor skills. Therapies tended to be personalized based on patient deficits using commercially available applications. The complexity of therapy instructions, fine-motor requirements, and unreliability of internet or cellular connections were identified as common barriers to tablet-based care. CONCLUSIONS: Stroke patients responded positively to MTBTs in both the inpatient and home settings. However, some support from therapists or caregivers may be required for patients to overcome barriers to care. Feasibility studies should continue to identify the administrative methods that minimize barriers to care and maximize patient adherence to prescribed therapy regiments.
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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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".