Examining Patient Characteristics Associated With Digital Outpatient Care for Type 1 Diabetes (DigiDiaS): Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: People with type 1 diabetes require ongoing self-management and frequent follow-up care. Digital care models might offer flexible solutions and increased sustainability, and while the clinical opportunities of these care models have been explored, the characteristics of patients inclined to opt for digital care remain unclear. OBJECTIVE: This study aimed to investigate which selected sociodemographic and disease-related patient characteristics are associated with opting for digital outpatient care among patients with type 1 diabetes. METHODS: This cross-sectional study was conducted at the endocrinology department of Akershus University Hospital in Norway, as part of a larger longitudinal study. Adult patients with type 1 diabetes were eligible to participate and were recruited consecutively. Patients could choose either a novel, mobile health-based, digital, tailored, outpatient care model (DigiDiaS care) or continuation of usual care. DigiDiaS care is delivered via an app comprising a message service; preconsultation questionnaires; options for physical, video, or telephone consultations; an information page; and an e-learning course. Sociodemographic and clinical data were collected from the participants' medical records and the national diabetes registry. Self-reported data comprised self-management measured using the Patient Activation Measure 13 (PAM-13), diabetes distress (20-item Problem Areas in Diabetes; PAID-20), well-being (World Health Organization-Five Well-Being Index; WHO-5), and health literacy (12-item short version of the European Health Literacy Survey Questionnaire; HLS-19 Q12) questionnaires. We explored group differences and conducted logistic regression to identify factors associated with opting for DigiDiaS care versus usual care. RESULTS: A total of 237 patients consented to participate in the study, with 185 (78.1%) opting for DigiDiaS care and 52 (21.9%) opting for usual care. The DigiDiaS care group had a statistically significantly shorter duration of diabetes (median 19, range 0-51 years) compared with the usual care group (median 29, range 3-58 years; P<.001); higher proportions of users of insulin pumps than insulin pens for insulin delivery (DigiDiaS care: 74/185, 40%; usual care: 12/185, 23.1%; P=.007), and a lower median well-being care score (DigiDiaS care: median score 60, range 4-96; usual care: median score 68, range 16-100; P=.04). The DigiDiaS care and usual care groups did not differ in sociodemographic variables, presence of late complications from diabetes or comorbidities, self-management, diabetes distress, or health literacy. CONCLUSIONS: This study reveals that most patients with type 1 diabetes choose digital outpatient care when it is offered. Our study suggests that those opting for DigiDiaS care are already familiar with using diabetes-related technology, have a shorter diabetes duration, and have lower well-being. It is essential to understand the characteristics of patients who opt for usual care to ensure high-quality health care services. Further studies should also focus on how the implementation of digital solutions in outpatient care can affect how and if patients use them. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/52766.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».