Exploration of Users’ Perspectives and Needs and Design of a Type 1 Diabetes Management Mobile App: Mixed-Methods Study
Notice bibliographique
Résumé
BACKGROUND: With the popularity of mobile phones, mobile apps have great potential for the management of diabetes, but the effectiveness of current diabetes apps for type 1 diabetes mellitus (T1DM) is poor. No study has explored the reasons for this deficiency from the users' perspective. OBJECTIVE: The aims of this study were to explore the perspectives and needs of T1DM patients and diabetes experts concerning a diabetes app and to design a new T1DM management mobile app. METHODS: A mixed-methods design combining quantitative surveys and qualitative interviews was used to explore users' needs and perspectives. Experts were surveyed at 2 diabetes conferences using paper questionnaires. T1DM patients were surveyed using Sojump (Changsha ran Xing InfoTech Ltd) on a network. We conducted semistructured, in-depth interviews with adult T1DM patients or parents of child patients who had ever used diabetes apps. The interviews were audio-recorded, transcribed, and coded for theme identification. RESULTS: The expert response rate was 63.5% (127/200). The respondents thought that the reasons for app invalidity were that patients did not continue using the app (76.4%, 97/127), little guidance was received from health care professionals (HCPs; 73.2%, 93/127), diabetes education knowledge was unsystematic (52.8%, 67/127), and the app functions were incomplete (44.1%, 56/127). A total of 245 T1DM patient questionnaires were collected, of which 21.2% (52/245) of the respondents had used diabetes apps. The reasons for their reluctance to use an app were limited time (39%, 20/52), complicated operations (25%, 13/52), uselessness (25%, 13/52), and cost (25%, 13/52). Both the experts and patients thought that the most important functions of the app were patient-doctor communication and the availability of a diabetes diary. Two themes that were useful for app design were identified from the interviews: (1) problems with patients' diabetes self-management and (2) problems with current apps. In addition, needs and suggestions for a diabetes app were obtained. Patient-doctor communication, diabetes diary, diabetes education, and peer support were all considered important by the patients, which informed the development of a prototype multifunctional app. CONCLUSIONS: Patient-doctor communication is the most important function of a diabetes app. Apps should be integrated with HCPs rather than stand-alone. We advocate that doctors follow up with their patients using a diabetes app. Our user-centered method explored comprehensively and deeply why the effectiveness of current diabetes apps for T1DM was poor and what T1DM patients needed for a diabetes app and provided meaningful guidance for app design.
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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 | 0,020 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».