Usability Testing of a Digitized Interventional Prehabilitation Tool for Health Care Professionals and Patients Before Major Surgeries: Formative and Summative Evaluation
Notice bibliographique
Résumé
BACKGROUND: The development of a medical device requires strict adherence to regulatory processes. Prehabilitation in this context is a new area in surgery that trains, coaches, and advises patients in mental well-being, nutrition, and physical activity. As staff is permanently drained from clinical care, remote and digital solutions with real-time assessments of data, including patient-related outcome reporting, may simplify preparation before major surgeries. OBJECTIVE: This study aimed to evaluate the usability engineering process for the Prehab App, a newly developed medical device, in order to identify and adapt any design and usability flaws found. METHODS: We hypothesized that formative and summative usability testing would achieve 80% interrater and intrarater reliability and consistency and that the safety-relevant scenarios would uncover undetected risks of the medical device (stand-alone software class IIa). In total, 8 experts and 8 laypersons (patients and potential patients) were asked to evaluate paper-based mockups, followed by an evaluation of the minimal viable product (MVP) of the Prehab App at least more than 8 weeks later after instruction and training. The experts had to face 5 and the laypersons 6 usability scenarios. Their evaluations were measured with the Mobile App Rating Scale (MARS) and trustworthiness checklists (range 0-64, with higher scores indicating trustworthiness), and the usability scenarios were evaluated with the After Scenario Questionnaire (ASQ) and a judgment by an observer. The time taken for the scenarios was also recorded. RESULTS: MARS achieved constant scores of more than 4 out of 5 points for both experts and laypersons. The mean trustworthiness score was 51.3 (SD 2.7) for the experts and 50.8 (SD 2.1) for the laypersons (P=.68) in task I. The interrater correlation, shown by the Fleiss-Kappa value, was 0.87 (range 0.85-0.89) for all raters (N=16), 0.86 (range 0.82-0.91) for the experts (n=8, 50%), and 0.88 (range 0.84-0.93) for the laypersons (n=8, 50%), reflecting almost perfect agreement between the raters. This indicated the high quality of the usability. The usability scenarios were performed with ease, except for the onboarding part, when the wearable was required to be connected; this took a considerable amount of time and was recognized as a challenge to good usability. CONCLUSIONS: The formative and summative evaluation of the Prehab App design resulted in good-to-acceptable results of the design and usability of the critical and safety-relevant areas of the medical device and stand-alone software. Usability testing improves medical devices early in the design and development process, reduces errors, and mitigates risks, and in this study, it delivered a profound ethical and medical justification for a randomized controlled trial (RCT) of the Prehab App in a remote setting as a next step in the development process. TRIAL REGISTRATION: German Registry for Clinical Trials (DRKS00026985); https://drks.de/search/en/trial/DRKS00026985.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,038 | 0,068 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».