Mobile App-Based Interactive Care Plan for Migraine: Survey Study of Usability and Improvement Opportunities
Notice bibliographique
Résumé
Background: We implemented a novel mobile app-based Migraine Interactive Care Plan (MICP) integrated with our electronic health records (EHRs). The MICP facilitates remote assessment of adult patients with migraine, educational content delivery, and care team communication. Feasibility of the MICP was demonstrated in a pilot implementation study. Objective: We aimed to assess the preferences and satisfaction of patients with migraine users of a mobile app-based care plan integrated with the EHR. Methods: An electronic survey was administered to a single cohort of MICP users between December 6, 2021, and December 30, 2021. The survey assessed patient preferences for which data to track, frequency of tracking, and satisfaction with the MICP. Survey responses were compared between subsets determined by patient-reported headache frequency and treatment with and without botulinum toxin and calcitonin gene-related peptide (CGRP) antagonist therapy. The Wilcoxon rank-sum test was used for continuous variables and the χ2 test or Fisher exact test for categorical variables. Results: The total sample size was 184 and the survey response rate was 30.4% (56/184). No significant differences in age (P=.26) or sex (P=.19) between respondents and nonrespondents were observed. Respondent median age was 42 (range 20-72) years and 94.6% (53/56) were female. Headache frequency was (1) 0 to 8 days (26/56, 46.4%), (2) 9 to 14 days (12/56, 21.4%), and (3) 15 or more days (18/56, 32.1%). No difference was observed in any survey responses based on headache frequency or treatment. The majority of respondents preferred to track headache days weekly (30/56, 53.6%) or daily (15/56, 26.8%) and preferred to change the frequency of headache tracking reminders (42/56, 75%). Respondents were somewhat or very interested in daily tracking personal observations in free text (41/52, 78.8%), medication treatment (43/52, 82.7%) and treatment response (39/56, 69.6%), class of medication treatment (36/52, 69.2%), severity of functional impairment (39/56, 69.6%), type of functional impairment (35/53, 66%), headache day (40/54, 74.1%), and headache pain level on a scale of 1 to 10 (38/53, 71.7%). Respondents agreed or strongly agreed that the education content was useful (31/51, 60.8%) but lacked personalization (25/51, 49%). Most respondents agreed or strongly agreed that they were satisfied with the MICP (38/50, 76%) and that it helped them communicate with their care team (38/53, 71.7%). Conclusions: Most MICP users were motivated to track headache frequency, medication treatment with response, functional impairment, and pain intensity. Opportunities to improve the MICP include (1) allowing patients to change the frequency of assessments and notifications; (2) recording personal observations or comments through free text, which may include headache triggers; (3) assessment of headache severity using a 1 to 10 pain scale; and (4) tailoring headache education based on frequency and severity (episodic vs chronic migraine). These observations may be useful to improve the usability of the MICP and similar EHR-integrated migraine care platforms that others may develop.
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 | 0,007 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».