Effect of mobile application types on stroke rehabilitation: a systematic review
Bibliographic record
Abstract
Abstract Background Stroke is a significant contributor of worldwide disability and morbidity with substantial economic consequences. Rehabilitation is a vital component of stroke recovery, but inpatient stroke rehabilitation programs can struggle to meet the recommended hours of therapy per day outlined by the Canadian Stroke Best Practices and American Heart Association. Mobile applications (apps) are an emerging technology which may help bridge this deficit, however this area is understudied. The purpose of this study is to review the effect of mobile apps for stroke rehabilitation on stroke impairments and functional outcomes. Specifically, this paper will delve into the impact of varying mobile app types on stroke rehabilitation. Methods This systematic review included 29 studies: 11 randomized control trials and 18 quasi-experimental studies. Data extrapolation mapped 5 mobile app types (therapy apps, education apps, rehab videos, reminders, and a combination of rehab videos with reminders) to stroke deficits (motor paresis, aphasia, neglect), adherence to exercise, activities of daily living (ADLs), quality of life, secondary stroke prevention, and depression and anxiety. Results There were multiple studies supporting the use of therapy apps for motor paresis or aphasia, rehab videos for exercise adherence, and reminders for exercise adherence. For permutations involving other app types with stroke deficits or functional outcomes (adherence to exercise, ADLs, quality of life, secondary stroke prevention, depression and anxiety), the results were either non-significant or limited by a paucity of studies. Conclusion Mobile apps demonstrate potential to assist with stroke recovery and augment face to face rehabilitation, however, development of a mobile app should be carefully planned when targeting specific stroke deficits or functional outcomes. This study found that mobile app types which mimicked principles of effective face-to-face therapy (massed practice, task-specific practice, goal-oriented practice, multisensory stimulation, rhythmic cueing, feedback, social interaction, and constraint-induced therapy) and education (interactivity, feedback, repetition, practice exercises, social learning) had the greatest benefits. Protocol registration PROPSERO (ID CRD42021186534). Registered 21 February 2021
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".