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Aim: The objective of this study was to assess the impact of health care-initiated visits versus patient-controlled flexible visits on clinical and patient-reported outcomes in people with type 1 diabetes.
Methods: The DiabetesFlex trial was a randomized controlled, pragmatic non-inferiority 15-month follow-up study comparing standard care (face-to-face visits every 4 months) with DiabetesFlex (patient-controlled flexible visits using patient-reported, outcome-based telehealth follow-up). Of 343 enrolled participants, 160 in each group completed the study. The primary outcome was mean change in HbA from baseline to 15-month follow-up. Secondary outcomes were blood pressure, lipid levels, frequency of visits, the World Health Organization score-five well-being-index (WHO-5), the Problem Areas In Diabetes (PAID) scale and experience of participation in own care (participation score).
Results: The adjusted mean difference in HbA between standard care and DiabetesFlex was similar and below the predefined non-inferiority margin of 0.4% (-0.03% [95%CI: 0.15, 0.11]/-0.27 mmol/mol [-1.71, 1.16]). No intergroup mean changes in lipid or blood pressure were observed. Conversely, DiabetesFlex participants presented an increased mean WHO-5 index of 4.5 (1.3, 7.3), participation score of 1.1 (0.5, 2.0), and decreased PAID score of -4.8 (-7.1, -2.6) compared with standard care. During follow-up, DiabetesFlex participants actively changed 23% of face-to-face visits to telephone consultations, cancelled more visits (17% vs. 9%), and stayed away without cancellation less often (2% vs. 8%).
Conclusion: Compared with standard care, flexible patient-controlled visits combined with patient-reported outcomes in participants with metabolic controlled type 1 diabetes and good psychological well-being further improved diabetes-related well-being and decreased face-to-face visits while maintaining safe diabetes management.
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http://dx.doi.org/10.1111/dme.14791 | DOI Listing |
JMIR Res Protoc
September 2025
Department of Health Services Research & Administration, College of Public Health, University of Nebraska Medical Center, Omaha, NE, United States.
Background: With the availability of more advanced and effective treatments, life expectancy has improved among patients with metastatic breast cancer (MBC), but this makes communication with their medical oncologist more complex. Some patients struggle to learn about their therapeutic options and to understand and articulate their preferences. Mobile health (mHealth) apps can enhance patient-provider communication, playing a crucial role in the diagnosis, treatment, quality of life, and outcomes for patients living with MBC.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
Institute of Hospital Management, Peking University Third Hospital, Beijing, China.
Background: Telemedicine is developing rapidly, presenting new opportunities and challenges for physicians and patients. Limited research has examined physicians' behavior during the process of adopting telemedicine and related factors.
Objective: This study aimed to identify perceived barriers and enablers of physicians' adoption of telemedicine and to develop intervention strategies.
JMIR Res Protoc
September 2025
Institute of Higher Education and Research in Healthcare, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
Background: In pediatric intensive care units, pain, sedation, delirium, and iatrogenic withdrawal syndrome (IWS) must be managed as interrelated conditions. Although clinical practice guidelines (CPGs) exist, new evidence needs to be incorporated, gaps in recommendations addressed, and recommendations adapted to the European context.
Objective: This protocol describes the development of the first patient- and family-informed European guideline for managing pain, sedation, delirium, and IWS by the European Society of Paediatric and Neonatal Intensive Care.
Nutr Health
September 2025
Independent researcher, Rome, Italy.
Artificial intelligence (AI) is increasingly applied in nutrition science to support clinical decision-making, prevent diet-related diseases such as obesity and type 2 diabetes, and improve nutrition care in both preventive and therapeutic settings. By analyzing diverse datasets, AI systems can support highly individualized nutritional guidance. We focus on machine learning applications and image recognition tools for dietary assessment and meal planning, highlighting their potential to enhance patient engagement and adherence through mobile apps and real-time feedback.
View Article and Find Full Text PDF