Development and Formative Evaluation of a Virtual Exercise Platform for a Community Fitness Center Serving Individuals With Physical Disabilities: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: People with disabilities experience numerous barriers to being physically active, such as transportation issues, a lack of trained exercise professionals who understand disabilities, and facility access. The use of a virtual exercise platform (VEP) may provide an alternative and limit certain barriers. OBJECTIVE: The aim of this mixed method study was to evaluate user interaction (effectiveness, efficiency, and satisfaction), the strengths and weaknesses of the user interface, and the user experience with a VEP. METHODS: Participants were recruited from a community fitness facility that offers programs for people with disabilities. Inclusion criteria were being older than 18 years, fluent in English, and availability of internet access. Features of the VEP included articles, prerecorded videos, live Zoom classes, web-based class registration, weekly progress tracking, incentives, and surveys. A one-on-one Zoom session was scheduled with each participant, during which they completed certain tasks: (1) create an account or login, (2) register for class, (3) join class, (4) add to calendar, and (5) complete surveys. As participants completed tasks, quantitative observations (time on task, task success, rate of task completion, and number of errors by users, which determined task difficulty), qualitative observations were made and interviews were conducted at the end of the session. The "concurrent think-aloud" method was encouraged by the moderator to gauge participants' thoughts as they worked through testing. Participants also completed the System Usability Scale (SUS) and Questionnaire for User Interface Satisfaction (QUIS). RESULTS: A total of 5 people with disabilities (3 male, 2 female), aged 36-78 (mean 54) years, with education levels from high school to PhD, were recruited. Devices used for testing included a laptop (n=3), a Chromebook (n=1), and a desktop (n=1). All participants completed tasks #1 and #2 without errors but could not complete task #4. One participant completed task #5 with difficulty and another completed task #3 with difficulty. The average time to complete each task was: (1) 82 seconds (55-110), (2) 11 seconds (4-21), (3) 9 seconds (5-27), and (4) 921.5 seconds (840-958). The mean SUS score was 86.5/100, and the mean user QUIS score was 8.08 out of 10. Qualitative observations indicated that the system was simple, user-friendly, and accessible. CONCLUSIONS: People with disabilities reported high usability and user satisfaction with the web-based exercise platform, and the system appears to be an efficient and effective physical activity option.
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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 | 0.052 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".