Evaluating User Experience and Satisfaction in a Concussion Rehabilitation App: Usability Study
Bibliographic record
Abstract
Background: Evidence-based guidelines support the use of structured exercise to facilitate concussion recovery. Despite the growing number of mobile health (mHealth) apps aimed at managing concussions, few focus on delivering exercise rehabilitation protocols. Therefore, a mobile app was developed to provide personalized rehabilitation programs based on evidence-based exercise principles designed to cater to individuals recovering from concussions. Objective: This study aimed to evaluate the usability and user experience of a mobile app designed to deliver an evidence-based rehabilitation program to individuals recovering from concussions. Methods: A two-week prospective single-arm pilot study was conducted among adults with a physician-diagnosed concussion. Participants engaged in home-based rehabilitation exercises through a mobile app. Usability was assessed using a combination of the mHealth App Usability Questionnaire (MAUQ) and five custom questions evaluating confidence in recommendations, exercise flow, clarity of voice commands, and usability of the exercise report feature. Following the two-week period, participants rated each question on a 7-point Likert scale ranging from "strongly agree" to "strongly disagree". Results: Twenty-six participants consented and were enrolled in the study, with 23 participants (82%) completing all study components at the end of two weeks. The majority of participants were women, aged 26-38 years, and on average, approximately at three months postconcussion. Responses to both the MAUQ and custom questions were overwhelmingly positive. Overall, seven MAUQ questions received 100% positive responses, with no single question scoring below 83% positive responses. In the "ease of use and satisfaction" category, 100% of users responded positively to questions on ease of learning, usability, interface likeness, and comfort in social settings, while 83%-96% (19-22) of users responded positively to the remaining four questions. In the "system information arrangement" category, 100% (N=23) of users rated screen navigation, function usability, and health care service acceptability positively, with 87%-96% of users approving action acknowledgment, error recovery, and expected functions. Under the "usefulness" category, 96% (n=22) of users found the app beneficial for health and well-being, and 91% (n=21) users felt it effectively managed their health. For the five custom questions, 100% (N=23) users responded positively to voice command clarity, exercise awareness, ease of following exercises, and report understandability, with a single unfavorable response noted for confidence in app recommendations. Conclusions: The findings of this study indicate that the mobile app is a user-friendly platform for delivering evidence-based exercise rehabilitation to individuals recovering from concussions. Positive user feedback, particularly in the areas of recommendation confidence, ease of exercise flow, and clarity of voice commands, highlights the app's potential to support concussion recovery. Future iterations of the app will aim to improve time efficiency and streamline error recovery processes to further enhance the user experience.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".