Journal of Diabetes Science and Technology最新文献

{"title":"Impact of Diabetes Camp Attendance on Decision to Use Insulin Pumps Among Youth With Type 1 Diabetes.","authors":"Neha Parimi, Risa M Wolf","doi":"10.1177/19322968251331968","DOIUrl":"https://doi.org/10.1177/19322968251331968","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251331968"},"PeriodicalIF":4.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143810922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring the Mechanical Properties of Insulin: A Potential Solution to Overcoming the Challenges of Real-Time, Point-of-Care Insulin Sensing.","authors":"Emily Young, Stefanie Gutschmidt, J Geoffrey Chase","doi":"10.1177/19322968251331072","DOIUrl":"https://doi.org/10.1177/19322968251331072","url":null,"abstract":"<p><p>It is well established real-time, point-of-care capabilities for insulin sensing would provide valuable insight to enhance diabetes management and care in the human body. However, such suitable technology has not yet been developed or commercialized. While not comprehensive, this commentary provides a concise summary of the motivation and challenges of developing real-time, point-of-care insulin sensing technology and offers some comments on current approaches. This short research analysis presents a new perspective on the problem and introduces a future potential solution via measuring the mechanical properties of insulin and discusses the challenges foreseen in the feasibility of this proposed solution.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251331072"},"PeriodicalIF":4.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143810939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of a Continuous Glucose Monitoring-Based Insulin Titration Protocol for Inpatients With Type 2 Diabetes in Nonintensive Care Unit Settings.","authors":"Mikkel Thor Olsen, Ulrik Pedersen-Bjergaard, Signe Hjejle Jensen, Louise Mathorne Rasmussen, Carina Kirstine Klarskov, Birgitte Lindegaard, Jonas Askø Andersen, Hans Gottlieb, Suzanne Lunding, Katrine Bagge Hansen, Peter Lommer Kristensen","doi":"10.1177/19322968251331628","DOIUrl":"10.1177/19322968251331628","url":null,"abstract":"<p><strong>Background: </strong>No widely adopted continuous glucose monitoring (CGM)-based insulin titration protocol exists, which may limit the effects of inpatient CGM on glycemic and clinical outcomes. We evaluate the acceptability and operability of the protocol proposed by Olsen et al for inpatients with type 2 diabetes in non-intensive care unit (non-ICU) settings.</p><p><strong>Method: </strong>7 inpatient diabetes team members, responsible for daily insulin titration, decided on insulin adjustments for 353 days. The members had the option to follow the CGM-based insulin protocol or override it for basal, prandial, and correctional insulin, separately, in 84 inpatients monitored by CGM. Questionnaires were used to evaluate the protocol's operability by the teams.</p><p><strong>Results: </strong>Of 456 basal insulin titration decisions, 439 (96.3%) adhered to the protocol. For prandial insulin, adherence rates were 83.9% (125/149) for breakfast, 87.2% (130/149) for lunch, and 92.6% (138/149) for dinner (p=0.163). All correctional insulin titrations adhered to the protocol. All team members expressed a preference for having a protocol for CGM-based insulin titration and rated the protocol's usability on a 1 to 10 scale, with mean scores (SD) of 8.7 (0.9) for basal insulin, 8.3 (1.4) for prandial insulin, and 7.4 (1.9) for correctional insulin.</p><p><strong>Conclusions: </strong>The CGM-based insulin titration protocol by Olsen et al has been successfully implemented for titrating basal, prandial, and correctional insulin in inpatients with type 2 diabetes in non-ICU settings. It was highly accepted by inpatient diabetes teams and provides a framework for effective CGM implementation in these settings.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251331628"},"PeriodicalIF":4.1,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accounting for Hypoglycemia Treatments in Continuous Glucose Metrics.","authors":"Elliott C Pryor, Anas El Fathi, Marc D Breton","doi":"10.1177/19322968251329952","DOIUrl":"10.1177/19322968251329952","url":null,"abstract":"<p><strong>Background: </strong>Continuous glucose monitoring (CGM) is increasingly used in the management of diabetes, providing dense data for patients and clinical providers to review and identify patterns and trends in blood glucose. However, behavioral factors like hypoglycemia treatments (HTs) are not captured in CGM data. Hypoglycemia treatments, by definition, reduce the visibility (frequency and duration) of hypoglycemia exposure recorded by CGM, which can lead to errors in treatment management when relying solely on CGM metrics.</p><p><strong>Methods: </strong>We propose a method to incorporate HTs into CGM-based metrics and standardize hypoglycemia exposure quantification for a variety of HT behaviors; specifically (1) treatment proactiveness and (2) potential severity of the avoided hypoglycemia. In addition, we introduce an HT detector to identify instances of HT using in CGM data that otherwise lack HT documentation. We then use the HT-modified hypoglycemia metrics in a previously published run-to-run treatment adaptation system using CGM-based metrics.</p><p><strong>Results: </strong>Using in-silico data to correct time-below-range with HT, we reconstruct the avoided hypoglycemia exposure with high fidelity (<i>R</i>² = .94). Our HT detector has an F1 score of 0.72 on clinical data with labeled HT. In the example run-to-run application, we reduce the average number of HT per day from 3.3 in the HT-unaware system to 1.6, while maintaining 84% time in 70 to 180 mg/dL.</p><p><strong>Conclusion: </strong>This new metric integrates HT behaviors into CGM-based analysis, offering a behavior-sensitive measure of hypoglycemia exposure for more robust T1D management. Our results show that HT can be seamlessly incorporated into existing CGM methods, enhancing treatment insights by accounting for HT variability.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251329952"},"PeriodicalIF":4.1,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy of a 15-day Factory-Calibrated Continuous Glucose Monitoring System With Improved Sensor Design.","authors":"Shridhara Alva, Anuj Bhargava, Bruce Bode, Ronald Brazg, Kristin Castorino, Mark Kipnes, David R Liljenquist, Hien Tran, Hanqing Liu, Mohamed Nada","doi":"10.1177/19322968251329364","DOIUrl":"10.1177/19322968251329364","url":null,"abstract":"<p><strong>Background: </strong>This study evaluates the performance of a 15-day factory-calibrated continuous glucose monitoring sensor used in FreeStyle Libre 2 Plus (Libre 2 Plus) and FreeStyle Libre 3 Plus (Libre 3 Plus) Systems, featuring an improved sensor design to reduce vitamin C interference.</p><p><strong>Methods: </strong>Participants aged 2 years and above were enrolled for this study at seven sites in the United States. Depending on their age and bodyweight, participants attended up to three in-clinic sessions where venous blood was obtained for comparator measurement. For 2- to 5-year-olds, only capillary comparator data were collected. Participants aged 11 years and older underwent supervised glycemic manipulation to achieve glucose levels across the sensor's measurement range. Performance measures included the proportion of continuous glucose monitoring (CGM) values within ±20%/±20 mg/dL of comparator glucose values and mean absolute relative difference (MARD) between CGM and comparator values.</p><p><strong>Results: </strong>Of the total 332 participants enrolled in the study, 149 adults and 124 pediatric participants (ages 6-17 years) had paired data for analysis against YSI comparator. Percentages within ±20 mg/dL/20% were 94.2% and 94.0%, and MARDs were 8.2% and 8.1% for the adults and pediatric participants, respectively. For 12 pediatric participants of 2 to 5 years, the percentage within ±20 mg/dL/20% was 86.6%, with an MARD of 11.2% against self-monitoring of blood glucose (SMBG) comparator. The sensor performed well in the hypoglycemic range, with 97.1% and 98.0% of results within ±15 mg/dL of the YSI comparator for the adult and pediatric participants, respectively.</p><p><strong>Conclusions: </strong>The Libre 2 Plus and Libre 3 Plus Systems provide accurate glucose results across the dynamic range during the 15-day sensor wear period.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251329364"},"PeriodicalIF":4.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143780129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Compliance With FDA iCGM Special Controls is Dependent on Study Design and Procedures.","authors":"Manuel Eichenlaub, Delia Waldenmaier, Stefan Pleus, Cornelia Haug, Derek Brandt, Guido Freckmann","doi":"10.1177/19322968251329879","DOIUrl":"10.1177/19322968251329879","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251329879"},"PeriodicalIF":4.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11969480/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Control-IQ Technology Provides Similar Glycemic Outcomes Across Two Different CGM Sensors.","authors":"Halis Kaan Akturk, Andy K Johnson, John B Welsh, Laurel H Messer","doi":"10.1177/19322968251330308","DOIUrl":"10.1177/19322968251330308","url":null,"abstract":"<p><p>The t:slim X2 insulin pump with Control-IQ technology (Tandem Diabetes Care) is interoperable with G6 and, more recently, G7 sensors (Dexcom). CGM-derived metrics from customers who transitioned from using Control-IQ technology with G6 and then G7 sensors were compared. Median times in various glucose concentration ranges for the final 30 days of G6 use and the initial 30 days of G7 use changed by <1% and remained within consensus target recommendations for TIR (70-180 mg/dL) >70% and TBR (<70 mg/dL) <4%. Differences in sensor use, time in closed loop, and median glucose levels were clinically insignificant. Control-IQ-based pump users experienced similar outcomes and achieved excellent glycemic control with G6 or G7 sensors.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251330308"},"PeriodicalIF":4.1,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diabetes and Wellness Smartphone Applications for Self-Management among Adults With Diabetes in the United States.","authors":"Sherecce Fields, Kianna Arthur, Samantha R Philip, Rachel Smallman, Vishaka Kalra, Kirsten Yehl, Felix Lee, David Kerr","doi":"10.1177/19322968251322189","DOIUrl":"10.1177/19322968251322189","url":null,"abstract":"<p><strong>Background: </strong>Diabetes self-management plays a vital role in improving clinical outcomes and the quality of life of individuals living with diabetes. Despite considerable research on its impact on clinical outcomes, diabetes self-management continues to be challenging for many individuals living with the condition. As part of the growth in digital health technologies for diabetes care, smartphone applications present potential opportunities to bridge the existing gaps in self-management and improve patient outcomes.</p><p><strong>Method: </strong>Participants (<i>N</i> = 3241 people with diabetes) were recruited to answer questions about diabetes self-management, including their use of digital tools, their preferences for smartphone applications for diabetes, and the preferred functions of these applications they found useful. Frequency distributions and chi-square analyses were performed to examine the demographic differences among users of diabetes and general wellness applications.</p><p><strong>Results: </strong>Among participants, 30.2% reported using health applications specifically made for diabetes management, while 33.9% reported using health applications that were not diabetes-specific. Considerable differences in demographic characteristics were found between users and nonusers of both diabetes-specific and general health applications groups. The most preferred applications provided the opportunity to engage with continuous glucose monitoring data (i.e., continuous measurement; 47.4%) followed by glucose monitoring (i.e., single reading measurement; 20.9%), food intake trackers (23.6%), and fitness goal trackers (22.8%).</p><p><strong>Conclusion: </strong>These findings suggest that the use of digital health technologies is popular for people living with diabetes, but more needs to be done to ensure wider adoption and sustained use.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251322189"},"PeriodicalIF":4.1,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking Real-Time Data Access in Diabetes Management: Toward an Interoperability Model.","authors":"Pietro Randine, Miriam Kopperstad Wolff, Matthias Pocs, Ian R O Connell, Joseph A Cafazzo, Eirik Årsand","doi":"10.1177/19322968251327602","DOIUrl":"10.1177/19322968251327602","url":null,"abstract":"<p><strong>Background: </strong>In today's data-driven era, openness promotes transparency and accessibility, particularly in health initiatives like the European Health Data Space. Diabetes management relies on real-time data from medical devices, such as continuous glucose monitors (CGMs), insulin pumps, and hybrid closed-loop systems. These devices provide critical insights for treatment adjustments, making real-time data access essential.</p><p><strong>Methods: </strong>This article explores real-time data access for third-party applications, focusing on primary (treatment) and secondary (research) use. We examine how application programming interfaces (APIs) enable secure data retrieval and assess the impact of terms of service and copyright law on patient-driven innovation in open-source communities. Our research evaluates diabetes medical devices and software solutions in Norway, assessing their real-time data access and API functionalities. In addition, we analyze legal frameworks governing these technologies, focusing on challenges faced by open-source solutions. Based on our findings, we propose an interoperability model to improve data accessibility while ensuring security and transparency.</p><p><strong>Results: </strong>Findings reveal seven diabetes devices and nine regulated software solutions, with only one offering a publicly accessible API. This emphasizes a significant gap in real-time data access. Comparisons between vendor-specific and open-source software expose interoperability and accessibility challenges. While Do-It-Yourself (DIY) solutions foster innovation, they face technical and legal barriers.</p><p><strong>Conclusion: </strong>Real-time diabetes management presents security, transparency, and access challenges. Regulatory decisions are needed to implement an interoperability model. The lack of real-time data access highlights the necessity of publicly accessible APIs that prioritize transparency, accessibility, and patient-driven innovation-marking a shift from today's constrained diabetes management landscape.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251327602"},"PeriodicalIF":4.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11954377/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143730238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Envisioning Tools to Help Classify Type 1 Diabetes and Type 2 Diabetes in New-Onset Adult Diabetes.","authors":"Alessandra T Ayers, Cindy N Ho, Liana K Billings, Shivani Misra, David C Klonoff","doi":"10.1177/19322968251329055","DOIUrl":"10.1177/19322968251329055","url":null,"abstract":"<p><p>A tool is needed to distinguish type 1 diabetes (T1D) and type 2 diabetes (T2D) in adults with new-onset diabetes because correct classification is needed for correct diagnoses and treatments. Current classification methods are usually applied to biomarkers using binary or quantitative classification with a cut point and may not be adequately nuanced. Combinations of clinical features are not necessarily specific for classifying and may not always indicate a single diagnosis. A probabilistic decision tree classification tool with multiple branches per decision node is needed for adults with new-onset diabetes to avoid misdiagnosis of actual T1D as T2D, misdiagnosis of actual T2D or monogenic diabetes as T1D, and misclassified patients in future population health studies which will lead to incorrect conclusions and suboptimal patient outcomes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251329055"},"PeriodicalIF":4.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11954134/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143730229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}