TEAMS (Tele-Exercise and Multiple Sclerosis), a Tailored Telerehabilitation mHealth App: Participant-Centered Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: People with multiple sclerosis face varying levels of disability and symptoms, thus requiring highly trained therapists and/or exercise trainers to design personalized exercise programs. However, for people living in geographically isolated communities, access to such trained professionals can be challenging due to a number of barriers associated with cost, access to transportation, and travel distance. Generic mobile health exercise apps often fall short of what people with multiple sclerosis need to become physically active (ie, exercise content that has been adapted to accommodate a wide range of functional limitations). OBJECTIVE: This usability study describes the development process of the TEAMS (Tele-Exercise and Multiple Sclerosis) app, which is being used by people with multiple sclerosis in a large randomized controlled trial to engage in home-based telerehabilitation. METHODS: Twenty-one participants with disabilities (10 people with multiple sclerosis) were involved in the double iterative design, which included the simultaneous development of the app features and exercise content (exercise videos and articles). Framed within a user-centered design approach, the development process included 2 stages: ground-level creation (focus group followed by early stage evaluations and developments), and proof of concept through 2 usability tests. Usability (effectiveness, usefulness, and satisfaction) was evaluated using a mixed-methods approach. RESULTS: During testing of the app's effectiveness, the second usability test resulted in an average of 1 problem per participant, a decrease of 53% compared to the initial usability test. Five themes were constructed from the qualitative data that related to app usefulness and satisfaction, namely: high perceived confidence for app usability, positive perceptions of exercise videos, viable exercise option at home, orientation and familiarity required for successful participation, and app issues. Participants acknowledged that the final app was ready to be delivered to the public after minor revisions. After including these revisions, the project team released the final app that is being used in the randomized controlled trial. CONCLUSIONS: A multi-level user-centered development process resulted in the development of an inclusive exercise program for people with multiple sclerosis operated through an easy-to-use app. The promotion of exercise through self-regulated mHealth programs requires a stakeholder-driven approach to app development. This ensures that app and content match the preferences and functional abilities of the end user (ie, people with varying levels of multiple sclerosis).
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".