Quality, Functionality, and Features of Chinese Mobile Apps for Diabetes Self-Management: Systematic Search and Evaluation of Mobile Apps
Notice bibliographique
Résumé
BACKGROUND: The emergence and advancement of mobile technologies offer a promising opportunity for people with diabetes to improve their self-management. Despite the proliferation of mobile apps, few studies have evaluated the apps that are available to the millions of people with diabetes in China. OBJECTIVE: This study aimed to conduct a systematic search of Chinese mobile apps for diabetes self-management and to evaluate their quality, functionality, and features by using validated rating scales. METHODS: A systematic search was conducted to identify Chinese apps for diabetes self-management in the four most popular Chinese language mobile app stores. Apps were included if they were designed for diabetes self-management and contained at least one of the following components: blood glucose management, dietary and physical activity management, medication taking, and prevention of diabetes-related comorbidities. Apps were excluded if they were unrelated to health, not in Chinese, or the targeted users are health care professionals. Apps meeting the identified inclusion criteria were downloaded and evaluated by a team of 5 raters. The quality, functionalities, and features of these apps were assessed by using the Mobile App Rating Scale (MARS), the IMS Institute for Healthcare Informatics Functionality score, and a checklist of self-management activities developed based on the Chinese diabetes self-management guideline, respectively. RESULTS: Among 2072 apps searched, 199 were eligible based on the inclusion criteria, and 67 apps were successfully downloaded for rating. These 67 apps had an average MARS score of 3.42 out of 5, and 76% (51/67) of the apps achieved an acceptable quality (MARS score >3.0). The scores for the four subdomains of MARS were 3.97 for functionality, 3.45 for aesthetics, 3.21 for information, and 3.07 for engagement. On average, reviewed apps applied five out of the 19 examined behavior change techniques, whereas the average score on the subjective quality for the potential impact on behavior change is 3 out of 5. In addition, the average score on IMS functionality was 6 out of 11. Functionalities in collecting, recording, and displaying data were mostly presented in the reviewed apps. Most of the apps were multifeatured with monitoring blood glucose and tracking lifestyle behaviors as common features, but some key self-management activities recommended by clinical guidelines, such as stress and emotional management, were rarely presented in these apps. CONCLUSIONS: The general quality of the reviewed apps for diabetes self-management is suboptimal, although the potential for improvement is significant. More attention needs to be paid to the engagement and information quality of these apps through co-design with researchers, public health practitioners, and consumers. There is also a need to promote the awareness of the public on the benefit and potential risks of utilizing health apps for self-management.
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 | 0,014 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,015 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 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 | 0,000 |
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 ».