Assessing the Usability of a Prescription-Based Mobile App for Patients With Panic Disorder and Its Management Console for Clinicians: Controlled User Study
Notice bibliographique
Résumé
Background: Panic disorder is characterized by sudden panic attacks and persistent anticipatory anxiety. While pharmacotherapy remains effective, patients with panic disorder often experience residual symptoms and functional impairments. Lifestyle factors influence symptom severity but are often unaddressed in routine psychiatric care. Most current digital therapeutics for panic disorder have a limited scope, lack integration with clinicians, and fail to consider behavioral patterns. To address these limitations, our research team developed a prescription-based app that supports structured cognitive behavioral therapy practice, real-life symptom management, and lifestyle modifications for patients with panic disorder, and a management console-a web-based platform that allows clinicians to monitor the patients' engagement and progress as well as determine therapeutic options if necessary. Objective: This study aimed to test the usability of the app and management console by evaluating their interface, functionality, and user experience. The primary goal was to identify the strengths and areas for further improvement of these software devices and to develop a list of modifications to improve the user experience and clinical applicability in updates to refine the devices for a future clinical trial. Methods: Usability data were collected by investigators at a medical device usability research center without the involvement of the development research team, and the participants were 15 patients with panic disorder and 15 psychiatrists. Each group completed experimental use of the app or management console and scored the convenience and safety of its modules, questionnaire evaluations for the acceptability, and presentation of verbal subjective feedback on areas for improvement. Based on the participants' suggestions, a list of items that need to be modified to improve functionality and ease of use for each device was created. Results: Patients completed 155 assigned tasks for the app with more than 98% success, and psychiatrists completed 34 tasks for the management console with more than 86% success. The convenience and safety scores for the app and management console exceeded the neutral threshold (mean >4.5). For all statements about the acceptability, both patients and psychiatrists responded at the level of agreeing with a score exceeding 3 (mean: 3.6~4.3 and 4~4.7, respectively). There were 38 suggestions for app improvements and 66 suggestions for management console improvements, most of which were incorporated in the modification list. Conclusions: Patients reported that the app might be easy to use and help manage anxiety, and psychiatrists found the management console practical and well-suited for outpatients. By combining patient-facing therapeutic tools with clinician-driven prescription and monitoring, the devices offer a solution aligned with clinically integrated, real-world psychiatric care. Modified devices based on the improvement suggestions presented in this study will be evaluated in future clinical trials for their impact on engagement and treatment outcomes.
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 | 0,008 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».