User Retention and Engagement in the Digital-Based Diabetes Education and Self-Management for Ongoing and Newly Diagnosed (myDESMOND) Program: Descriptive Longitudinal Study
Notice bibliographique
Résumé
BACKGROUND: Digital health interventions have the potential to improve the physical and psychosocial health of people living with type 2 diabetes. However, research investigating the long-term (≥1 year) retention and engagement of users within these programs is limited. OBJECTIVE: The aim of this study was to evaluate long-term user retention and engagement in the digital-based Diabetes Education and Self-Management for Ongoing and Newly Diagnosed (myDESMOND) program, using real-world data. METHODS: Anonymized data from all myDESMOND users who registered with the program on or before November 16, 2020, were included in the analyses. User retention was defined as the period between the day a user registered with the myDESMOND program and their last day of access. The primary engagement outcome was defined as the total number of log-ins to the program per user. The associations between retention, engagement, and sociodemographic factors (age, sex, and ethnicity) were tested using Cox regression models and Wilcoxon rank sum tests. RESULTS: A total of 9522 myDESMOND users were included in this analysis. Of the 9522 users, 5360 (56.29%) remained on the program for at least a month, whereas 1676 (17.6%) remained on the program for at least 1 year. Retention was significantly higher among older users; the adjusted hazard ratio (representing the risk of users leaving the program within the first year) among users aged ≥50 years, compared with those aged <50 years, was 0.79 (95% CI 0.75-0.84; P<.001). The median number of myDESMOND log-ins per user was 8 (IQR 4-8); however, this was significantly lower among users aged <50 years (P<.001). Engagement metrics also differed according to sociodemographic characteristics; the estimated time spent per log-in was 5.35 (IQR 2.22-11.80) minutes among all users; however, this was significantly higher among female users (P<.001), users aged ≥50 years (P<.001), and users of White ethnicity (P=.02). CONCLUSIONS: Although retention and engagement of users within myDESMOND were found to be high, these findings highlight the need for age- and culture-specific implementation strategies and content adaptations to improve retention and engagement among all users of self-management programs.
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 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».