Investigating the usability of mobile technology for driving rehabilitation post-stroke: a mixed-methods analysis
Bibliographic record
Abstract
Background Stroke can have significant effects on an individual’s ability to drive and remains a leading cause of driving restrictions for medical reasons. Mobile applications show promise in addressing driving-related deficits. However, there is limited research on their use for driving rehabilitation. This study explored how community-dwelling individuals with stroke and their caregivers use and perceive a specific application, DriveFocus®, for driving rehabilitation.Methods: A mixed-methods study with a triangulation design was conducted, tracking the use of DriveFocus® by n = 11 participant dyads (individuals with stroke and their caregivers) over four weeks. Semi-structured interviews were conducted to provide insight into participants’ perceptions of the application. Analysis of the results was guided by the Model of Acceptance of Mobile Technology by Older Adults (MAMTOA).Results Distinct patterns of use corresponding to the phases of the MAMTOA were identified. Two participants (n = 2/11) stopped using DriveFocus® after the second week, while the remaining nine (n = 9/11) continued throughout the study. Initial perceptions of the applications’ usefulness and participants’ experiences with learning barriers influenced acceptance. Having “tech-savvy” family members and engaging with gamification features were crucial for sustained use. Participants highlighted the importance of occupational therapists in connecting their DriveFocus® experience to their driving goal.Discussion Understanding the factors that contribute to the use and acceptance of mobile applications for driving rehabilitation by individuals with stroke plays a critical role in advancing how technology is used to address impairments post-stroke. Future research should explore clinicians’ perspectives on implementing such technology in clinical practice.
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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 | 0.038 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".