Examining How Adults With Diabetes Use Technologies to Support Diabetes Self-Management: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Technologies such as mobile apps, continuous glucose monitors (CGMs), and activity trackers are available to support adults with diabetes, but it is not clear how they are used together for diabetes self-management. OBJECTIVE: This study aims to understand how adults with diabetes with differing clinical profiles and digital health literacy levels integrate data from multiple behavior tracking technologies for diabetes self-management. METHODS: Adults with type 1 or 2 diabetes who used ≥1 diabetes medications responded to a web-based survey about health app and activity tracker use in 6 categories: blood glucose level, diet, exercise and activity, weight, sleep, and stress. Digital health literacy was assessed using the Digital Health Care Literacy Scale, and general health literacy was assessed using the Brief Health Literacy Screen. We analyzed descriptive statistics among respondents and compared health technology use using independent 2-tailed t tests for continuous variables, chi-square for categorical variables, and Fisher exact tests for digital health literacy levels. Semistructured interviews examined how these technologies were and could be used to support daily diabetes self-management. We summarized interview themes using content analysis. RESULTS: Of the 61 survey respondents, 21 (34%) were Black, 23 (38%) were female, and 29 (48%) were aged ≥45 years; moreover, 44 (72%) had type 2 diabetes, 36 (59%) used insulin, and 34 (56%) currently or previously used a CGM. Respondents had high levels of digital and general health literacy: 87% (46/53) used at least 1 health app, 59% (36/61) had used an activity tracker, and 62% (33/53) used apps to track ≥1 health behaviors. CGM users and nonusers used non-CGM health apps at similar rates (16/28, 57% vs 12/20, 60%; P=.84). Activity tracker use was also similar between CGM users and nonusers (20/33, 61% vs 14/22, 64%; P=.82). Respondents reported sharing self-monitor data with health care providers at similar rates across age groups (17/32, 53% for those aged 18-44 y vs 16/29, 55% for those aged 45-70 y; P=.87). Combined activity tracker and health app use was higher among those with higher Digital Health Care Literacy Scale scores, but this difference was not statistically significant (P=.09). Interviewees (18/61, 30%) described using blood glucose level tracking apps to personalize dietary choices but less frequently used data from apps or activity trackers to meet other self-management goals. Interviewees desired data that were passively collected, easily integrated across data sources, visually presented, and tailorable to self-management priorities. CONCLUSIONS: Adults with diabetes commonly used apps and activity trackers, often alongside CGMs, to track multiple behaviors that impact diabetes self-management but found it challenging to link tracked behaviors to glycemic and diabetes self-management goals. The findings indicate that there are untapped opportunities to integrate data from apps and activity trackers to support patient-centered diabetes self-management.
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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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».