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Record W4389783911 · doi:10.2196/49685

Development and Formative Evaluation of a Virtual Exercise Platform for a Community Fitness Center Serving Individuals With Physical Disabilities: Mixed Methods Study

2023· article· en· W4389783911 on OpenAlexvenueno aff
Sangeetha Mohanraj, Laurie A. Malone, Christen J. Mendonca, Mohanraj Thirumalai

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersAdministration for Community LivingNational Institute on Disability, Independent Living, and Rehabilitation ResearchU.S. Department of Health and Human Services
KeywordsSession (web analytics)Task (project management)UsabilityPsychologyApplied psychologyThink aloud protocolClass (philosophy)LoginMedical educationComputer scienceHuman–computer interactionMedicineWorld Wide WebEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.052
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.050
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.313
GPT teacher head0.551
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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