Identifying and Evaluating Patient-Centered Mobile and Web Apps for Patients With Chronic Spontaneous Urticaria: Systematic Search and Content Analysis
Notice bibliographique
Résumé
Background: Chronic spontaneous urticaria (CSU) is characterized by recurrent wheals or angioedema lasting for more than 6 weeks and substantially affecting the quality of life. Given its fluctuating course, accurate symptom monitoring is essential. Mobile health apps (MHAs) offer promising tools for real-time symptom tracking, patient education, and communication. Systematic evaluation of existing MHAs for CSU is critical to inform the development of effective, patient-centered digital solutions. Objective: This study aimed to identify and evaluate publicly available MHAs for patients with CSU, assessing their quality, usability, and alignment with the needs of both patients and physicians to guide the development of future patient-centered apps. Methods: A systematic search of app stores and the internet was conducted to identify MHAs for CSU. Inclusion required German or English language support and patient-centered content. Apps were excluded if they contained advertisements; lacked patient-centered content, designed to assist patients in the self-management and care of their condition; or were focused on clinical trials or health care professional use. After screening, 1 app, CRUSE Control, met all criteria and was evaluated by 23 physicians and 16 patients with CSU using the German versions of the Mobile Application Rating Scale (MARS and end-user version of MARS) and the mHealth App Usability Questionnaire. Participants' technical affinity was assessed using the affinity for technology interaction scale and the Mobile Device Proficiency Questionnaire. Additionally, they completed a custom questionnaire on their personal needs and expectations for CSU-specific MHAs. Results: Fifteen MHAs were identified, with 12 available on both platforms. Eleven apps were excluded due to lack of specificity to CSU (n=10) or not being patient-centered (n=1). One app, CRUSE Control, met all inclusion criteria and was selected for final evaluation. CRUSE Control received similar mean (SD) quality ratings from physicians (MARS 4.03, SD 0.45) and patients (end-user version of MARS: 4.06, SD 0.40; P=.83). Among the MARS subcategories, functionality was rated significantly higher by patients than by physicians (4.75, SD 0.41 vs 4.47, SD 0.55; P=.04). Usability, measured using the German mHealth App Usability Questionnaire (assessing effectiveness, efficiency, and satisfaction), showed no significant difference between physicians (5.85, SD 0.71) and patients (5.76, SD 0.41; P=.64). Technology affinity was comparable between groups, with physicians scoring 3.50 (SD 0.66) and patients 4.00 (SD 0.88) on the affinity for technology interaction (P=.05). Proficiency with mobile devices, assessed via the Mobile Device Proficiency Questionnaire, also showed similar results (physicians: 4.81, SD 0.26; patients: 4.74, SD 0.45; P=.60). Conclusions: Few high-quality MHAs for CSU are currently available, and only 1 met the inclusion criteria. Patients and physicians rated the app highly, though patients placed greater emphasis on functionality. High technology affinity in both groups supports adoption. Patients prioritized features that facilitate disease management. Although limited to a single app, these findings suggest that MHAs may support CSU care.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».