98%
921
2 minutes
20
Background: Optimal insulin titration is essential in helping people with type 2 diabetes mellitus (T2DM) to achieve adequate glycemic control. Barriers of people with diabetes to implementation of titration include lack of self-efficiency and self-management skills, increased diabetes-related distress, low treatment satisfaction, poor well-being, as well as concerns about hypoglycemia and insulin overdose. My Dose Coach is a digital health tool for optimizing titration of basal insulin that combines a smartphone app for patients with T2DM and a Web portal for healthcare professionals.
Methods/design: This is a prospective, open-label, multicenter, randomized controlled parallel study conducted in approximately 50 centers in Germany that are specialized in the treatment of diabetes. Patients in the intervention group will use the titration app and will be registered on the Web portal by their treating physician. Control group patients will continue their current basal insulin titration without using the app. The primary outcome is the mean change in HbA1c levels at the 12-week follow-up. The secondary outcome measures include patient-reported outcomes such as diabetes-related distress, self-management, empowerment, self-efficacy, treatment satisfaction, and psychological well-being as well as fasting blood glucose values.
Conclusion: This digital health tool has been previously implemented in several independent pilot studies. The findings from this multicenter randomized controlled trial can provide further evidence supporting the effectiveness of this tool in patients with T2DM and serve as a basis for its clinical integration.
Trial Registration: German Register for Clinical Studies-DRKS-ID: DRKS00024861.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11307223 | PMC |
http://dx.doi.org/10.1177/19322968221148756 | DOI Listing |
BMC Med Inform Decis Mak
September 2025
Emergency Department, Helios Spital, Überlingen, Germany.
Background: The increasing amount of data routinely collected on ICUs poses a challenge for clinicians which is aggravated with data-heavy therapies like Continuous Kidney Replacement Therapy (CKRT). We developed the CKRT Supporting Software Prototype (CKRT-SSP), a clinical decision support system for use before, during and after CKRT. The aim of this user experience (UX) study was to prospectively evaluate CKRT-SSP in terms of usability, user experience, and workload in a simulated ICU setting.
View Article and Find Full Text PDFBMC Rheumatol
September 2025
Department of Environment and Biosciences, School of Business, Innovation and Sustainability, Halmstad University, Halmstad, Sweden.
J Orthop Res
September 2025
Interdisciplinary Orthopedics, Department of Orthopedics Surgery, Aalborg University Hospital, Aalborg, Denmark.
Functional recovery after total knee arthroplasty (TKA) varies widely among individuals, and traditional assessments often fail to detect subtle changes in real-world walking ability. Wearable sensors offer continuous and objective tracking of gait outside of clinical settings. In this prospective, longitudinal study, thirty-one patients undergoing unilateral TKA wore thigh-mounted accelerometers continuously from 2 weeks before surgery through 90 days postoperatively.
View Article and Find Full Text PDFNeurol Sci
September 2025
Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.
The rapid evolution of digital tools in recent years after COVID-19 pandemic has transformed diagnostic and therapeutic practice in neurology. This shift has highlighted the urgent need to integrate digital competencies into the training of future specialists. Key innovations such as telemedicine, artificial intelligence, and wearable health technologies have become central to improving healthcare delivery and accessibility.
View Article and Find Full Text PDFJ Imaging Inform Med
September 2025
Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.
Large language models (LLMs) have been successfully used for data extraction from free-text radiology reports. Most current studies were conducted with LLMs accessed via an application programming interface (API). We evaluated the feasibility of using open-source LLMs, deployed on limited local hardware resources for data extraction from free-text mammography reports, using a common data element (CDE)-based structure.
View Article and Find Full Text PDF