Evaluating User Experience and Satisfaction in a Concussion Rehabilitation App: Usability Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».