Feasibility of a Home-Based Tablet App for Dexterity Training in Multiple Sclerosis: Usability Study
Bibliographic record
Abstract
BACKGROUND: Persons with multiple sclerosis (MS) often experience dexterous difficulties during the performance of activities of daily living, such as fastening buttons, handling coins, or writing, therefore impacting their health-related quality of life. Mobile health (mHealth) solutions, such as tablet apps, may be used to train impaired dexterous skills. The feasibility of a tablet app-based dexterity home-based intervention in MS (TAD-MS) has not been explored yet in persons with MS. OBJECTIVE: The aim of this pilot study was to evaluate the feasibility and usability of home-based dexterity training with a tablet app in both persons with MS and healthy subjects. METHODS: A total of 9 persons with MS, aged 35-71 years, with an Expanded Disability Status Scale score between 2 and 7.5, performed the TAD-MS for 4 weeks, five times a week, with each training session lasting approximately 30 minutes. Participants' impaired dexterity was measured by the Nine-Hole Peg Test. A total of 10 age-matched healthy subjects also tested and rated the usability of the app. Outcome measures were the adherence rate as well as usability measured by the System Usability Scale and a Custom User Engagement Questionnaire (CUEQ). RESULTS: High feasibility of the tablet app-based dexterity training program was shown by a 97% adherence rate to the training protocol (ie, mean 19.4/20 sessions completed, SD 0.8). High system usability scores (ie, mean 85.39%, SD 11.67) and overall high scores given in the CUEQ (ie, mean 8.2/10, SD 1.4) further point to high usability of the app. Neither demographic variables nor dexterity levels affected the use of the app. CONCLUSIONS: This pilot study is the first to demonstrate high feasibility and usability of a new tablet app-based dexterity home-based training program among both persons with MS and healthy individuals. Whether this kind of training improves dexterity will need to be evaluated in a randomized controlled trial.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".