Patient and Physician Perspectives on the Use of a Connected Ecosystem for Diabetes Management: International Cross-Sectional Observational Study
Notice bibliographique
Résumé
BACKGROUND: Collaboration between people with type 2 diabetes (T2DM) and their health care teams is important for optimal control of the disease and outcomes. Digital technologies could potentially tie together several health care-related devices and platforms into connected ecosystems (CES), but attitudes about CES are unknown. OBJECTIVE: We surveyed convenience samples of patients and physicians to better understand which patient characteristics are associated with higher likelihoods of (1) participating in a potential CES program, as self-reported by patients with T2DM and (2) clinical benefit from participation in a potential CES program, as reported by physicians. METHODS: Adults self-reporting a diagnosis of T2DM and current insulin use (n=197), and 33 physicians whose practices included ≥20% of such patients, were enrolled in the United States, France, and Germany. We surveyed both groups about the likelihood of patient participation in a CES. We then examined the associations between patients' clinical and sociodemographic characteristics and this likelihood. We also described characteristics of patients likely to clinically benefit from CES use, according to physicians. RESULTS: Compared with patients in Germany and France, US patients were younger (mean age 45.3 [SD 11.9] years vs 61.9 [SD 9.2] and 65.8 [SD 9.4] years, respectively), more often female, more highly educated, and more often working full-time. In all, 51 (44.7%) US patients, 16 (36.4%) German patients, and 18 (46.3%) French patients indicated strong interest in a CES program, and 115 (78.7%) reported currently using ≥1 connected device or app. However, physicians believed that only 11.3%-19.2% of their patients were using connected devices or apps to manage their disease. Physicians also reported infrequently recommending or prescribing connected devices to their patients, although ≥80% (n=28) of them thought that a CES could help support their patients in managing their disease. The factors most predictive of patient likelihood of participating in a CES program were cost, inclusion of medication reminders, and linking blood glucose levels to behaviors such as eating and exercise. In all countries, the most common patient expectations for a CES program were that it could help them eat more healthfully, increase their physical activity, increase their understanding of how blood glucose relates to behavior such as exercise and eating, and reduce stress. Physicians thought that newly diagnosed patients, sicker patients-those who had been hospitalized for diabetes, were currently using insulin, or who had any comorbid condition-and patients who were nonadherent to treatment were most likely to benefit from CES use. CONCLUSIONS: In this study, there was a high degree of interest in the future use of CES, although additional education is needed among both patients with T2DM and their physicians to achieve the full potential of such systems to improve self-management and clinical care for the disease.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| 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 ».