Journal of Diabetes Science and Technology最新文献

{"title":"First International Survey on Diabetes Providers' Assessment of Skin Reactions in Youth With Type 1 Diabetes Using Technological Devices.","authors":"Stefano Passanisi, Anna Korsgaard Berg, Agata Chobot, Tiago Jeronimo Dos Santos, Claudia Anita Piona, Laurel Messer, Fortunato Lombardo","doi":"10.1177/19322968231206155","DOIUrl":"10.1177/19322968231206155","url":null,"abstract":"<p><strong>Background: </strong>Advances in diabetes technological devices led to optimization of diabetes care; however, long-lasting skin exposure to devices may be accompanied by an increasing occurrence of cutaneous reactions.</p><p><strong>Methods: </strong>We used an open-link web-based survey to evaluate diabetes-care providers' viewpoint on prevalence, management practices, and knowledge related to skin reactions with the use of diabetes technological devices. A post hoc analysis was applied to investigate differences in the level of awareness on this topic in relation to the experience in diabetes technology.</p><p><strong>Results: </strong>One hundred twenty-five responses from 39 different countries were collected. Most respondents (69%) routinely examine patients' skin at each visit. All the preventive measures are not clear and, mainly, homogenously put into clinical practice. Contact dermatitis was the most frequently reported cutaneous complication due to diabetes devices, and its most common provocative causes are not yet fully known by diabetes-care providers. Almost half of the respondents (42%) had discussed the presence of harmful allergens contained in adhesives with device manufacturers. There is general agreement on the need to strengthen knowledge on dermatological complications.</p><p><strong>Conclusions: </strong>Although diabetes-care providers are quite aware of the chance to develop skin reactions in people with diabetes using technological devices, there are still some unmet needs. Large follow-up studies and further dissemination tools are awaited to address the gaps revealed by our survey.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"666-672"},"PeriodicalIF":4.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035172/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41235750","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":"The Scientific Knowledge of Bard and ChatGPT in Endocrinology, Diabetes, and Diabetes Technology: Multiple-Choice Questions Examination-Based Performance.","authors":"Sultan Ayoub Meo, Thamir Al-Khlaiwi, Abdulelah Adnan AbuKhalaf, Anusha Sultan Meo, David C Klonoff","doi":"10.1177/19322968231203987","DOIUrl":"10.1177/19322968231203987","url":null,"abstract":"<p><strong>Background: </strong>The present study aimed to investigate the knowledge level of Bard and ChatGPT in the areas of endocrinology, diabetes, and diabetes technology through a multiple-choice question (MCQ) examination format.</p><p><strong>Methods: </strong>Initially, a 100-MCQ bank was established based on MCQs in endocrinology, diabetes, and diabetes technology. The MCQs were created from physiology, medical textbooks, and academic examination pools in the areas of endocrinology, diabetes, and diabetes technology and academic examination pools. The study team members analyzed the MCQ contents to ensure that they were related to the endocrinology, diabetes, and diabetes technology. The number of MCQs from endocrinology was 50, and that from diabetes and science technology was also 50. The knowledge level of Google's Bard and ChatGPT was assessed with an MCQ-based examination.</p><p><strong>Results: </strong>In the endocrinology examination section, ChatGPT obtained 29 marks (correct responses) of 50 (58%), and Bard obtained a similar score of 29 of 50 (58%). However, in the diabetes technology examination section, ChatGPT obtained 23 marks of 50 (46%), and Bard obtained 20 marks of 50 (40%). Overall, in the entire three-part examination, ChatGPT obtained 52 marks of 100 (52%), and Bard obtained 49 marks of 100 (49%). ChatGPT obtained slightly more marks than Bard. However, both ChatGPT and Bard did not achieve satisfactory scores in endocrinology or diabetes/technology of at least 60%.</p><p><strong>Conclusions: </strong>The overall MCQ-based performance of ChatGPT was slightly better than that of Google's Bard. However, both ChatGPT and Bard did not achieve appropriate scores in endocrinology and diabetes/diabetes technology. The study indicates that Bard and ChatGPT have the potential to facilitate medical students and faculty in academic medical education settings, but both artificial intelligence tools need more updated information in the fields of endocrinology, diabetes, and diabetes technology.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"705-710"},"PeriodicalIF":4.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41156314","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":"Gaming the System: A Fun Continuous Glucose Monitor Interface Improves Glycemic Outcomes for Children.","authors":"Farhaneh Ahmadi, Alonso Lucero","doi":"10.1177/19322968231223759","DOIUrl":"10.1177/19322968231223759","url":null,"abstract":"<p><p>Achieving optimal glycemic control in children with type 1 diabetes (T1D) is challenging even when wearing a continuous glucose monitor (CGM). We measured the impact of eddii, a gamified real-time app connected to a CGM, on glycemic control. An open label, eight-week randomized controlled trial (RCT) compared glycemic control utilizing the gamified CGM app connected to Dexcom G6 with only Dexcom G6 usage. Children with T1D using Dexcom G6 were enrolled (N=92, ages 5-12 years). Time-in-range (TIR) data were collected four weeks prior to and during the study period. The gamified CGM app utilization effect was measured by difference-in-difference (D-I-D) models. The TIR and time-above-range (TAR) improved among users of the gamified CGM app; 5.38% higher and 5.80% lower than controls (<i>P</i> = .001 and <i>P</i> = .019, respectively).</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"836-842"},"PeriodicalIF":4.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11572176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139424905","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":"Results From a Virtual Clinic for the Follow-up of Patients Using the Advanced Hybrid Closed-Loop System.","authors":"Ana María Gómez Medina, Diana Cristina Henao Carrillo, Julio David Silva León, Javier Alberto Gómez González, Oscar Mauricio Muñoz Velandia, Lucia Conde Brahim, Guillermo Andrés Mecón Prada, Martin Rondón Sepúlveda","doi":"10.1177/19322968231204376","DOIUrl":"10.1177/19322968231204376","url":null,"abstract":"<p><strong>Background: </strong>Evidence regarding the implementation of medium-term strategies in advanced hybrid closed-loop (AHCL) system users is limited. Therefore, this study aimed to describe the efficacy and safety of the AHCL system in patients with type 1 diabetes (T1D) on a six-month follow-up in a virtual diabetes clinic (VDC).</p><p><strong>Method: </strong>A prospective cohort of adult patients with T1D treated using the AHCL system (Mini Med 780G; Medtronic, Northridge, California) in a VDC follow-up. Standardized training and follow-up were conducted virtually. Clinical data and metabolic control outcomes were reported at baseline, and at three and six months.</p><p><strong>Results: </strong>Sixty-four patients (mean age = 42 ± 14.6 years, 65% men, 54% with graduate education) were included. Percentage time in range (%TIR) increased significantly regardless of prior therapy with intermittently scanned continuous glucose monitoring + multiple daily injections and sensor-augmented pump therapy with predictive low-glucose management after starting AHCL and persisted during the follow-up period with no hypoglycemic events. The %TIR 70 to 180 mg/dL according to socioeconomic strata was 73.4% ± 5.3%, 78.1% ± 8.1%, and 84.2% ± 7.5% for the lower, middle, and upper strata, respectively. The sensor was used more frequently in the population with a higher education level. Adherence to sensor use and SmartGuard retention were higher in patients who underwent the VDC follow-up.</p><p><strong>Conclusions: </strong>Medium-term follow-up of users of AHCL systems in a VDC contributes to safely achieving %TIR goals. Virtual diabetes clinic follow-up favored adherence to sensor use and continuous SmartGuard use. Socioeconomic strata were associated with a better glycemic profile and education level was associated with better adherence to sensor use.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"778-786"},"PeriodicalIF":4.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71521641","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":"Reliability of Handheld Blood Glucose Monitors in Neonates: Trustworthy Arterial Readings but Capillary Results Warrant Caution for Hypoglycemia.","authors":"David Brooks, James C Slaughter, James H Nichols, Justin M Gregory","doi":"10.1177/19322968231207861","DOIUrl":"10.1177/19322968231207861","url":null,"abstract":"<p><strong>Background: </strong>Accurate glucose monitoring is vitally important in neonatal intensive care units (NICUs) and clinicians use blood glucose monitors (BGM), such as the Inform II, for bedside glucose monitoring. Studies on BGM use in neonates have demonstrated good reliability; however, most studies only included healthy-term neonates. Therefore, the applicability of results to the preterm and/or ill neonate is limited.</p><p><strong>Objectives: </strong>In preterm and ill neonates, quantify differences in glucose concentrations between (1) capillary glucose (measured by BGM) and arterial glucose (measured by YSI 2300 Stat Plus) and (2) between aliquots from the same arterial blood sample, one measured by BGM versus one by YSI.</p><p><strong>Design/methods: </strong>Forty neonates were included in the study. Using Inform II, we measured glucose concentrations on blood samples simultaneously collected from capillary circulation via heel puncture and from arterial circulation via an umbilical catheter. Plasma was then separated from the remainder of the arterial whole blood sample and a YSI 2300 Stat Plus measured plasma glucose concentration.</p><p><strong>Results: </strong>The dominant majority of arterial BGM results met the Clinical and Laboratory Standard Institute (CLSI) and Food and Drug Administration (FDA) tolerance criteria. Greater discrepancy was observed with capillary BGM values with an average of 27.5% of results falling outside tolerance criteria.</p><p><strong>Conclusions: </strong>Blood glucose monitor testing provided reliable results from arterial blood. However, users should interpret hypoglycemic results obtained from capillary blood with caution.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"729-738"},"PeriodicalIF":4.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49677979","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":"Personalized Prediction of Change in Fasting Blood Glucose Following Basal Insulin Adjustment in People With Type 2 Diabetes: A Proof-of-Concept Study.","authors":"Camilla Heisel Nyholm Thomsen, Thomas Kronborg, Stine Hangaard, Peter Vestergaard, Ole Hejlesen, Morten Hasselstrøm Jensen","doi":"10.1177/19322968231201400","DOIUrl":"10.1177/19322968231201400","url":null,"abstract":"<p><strong>Aims: </strong>For people with type 2 diabetes treated with basal insulin, suboptimal glycemic control due to clinical inertia is a common issue. Determining the optimal basal insulin dose can be difficult, as it varies between individuals. Thus, insulin titration can be slow and cautious which may lead to treatment fatigue and non-adherence. A model that predicts changes in fasting blood glucose (FBG) after adjusting basal insulin dose may lead to more optimal titration, reducing some of these challenges.</p><p><strong>Objective: </strong>To predict the change in FBG following adjustment of basal insulin in people with type 2 diabetes using a machine learning framework.</p><p><strong>Methods: </strong>A multiple linear regression model was developed based on 786 adults with type 2 diabetes. Data were divided into training (80%) and testing (20%) sets using a ranking approach. Forward feature selection and fivefold cross-validation were used to select features.</p><p><strong>Results: </strong>Participants had a mean age of approximately 59 years, a mean duration of diabetes of 12 years, and a mean HbA_1c at screening of 65 mmol/mol (8.1%). Chosen features were FBG at week 2, basal insulin dose adjustment from week 2 to 7, trial site, hemoglobin level, and alkaline phosphatase level. The model achieved a relative absolute error of 0.67, a Pearson correlation coefficient of 0.74, and a coefficient of determination of 0.55.</p><p><strong>Conclusions: </strong>A model using FBG, insulin doses, and blood samples can predict a five-week change in FBG after adjusting the basal insulin dose in people with type 2 diabetes. Implementation of such a model can potentially help optimize titration and improve glycemic control.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"769-777"},"PeriodicalIF":4.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41140570","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":"Methodology for Safe and Secure AI in Diabetes Management.","authors":"Remco Jan Geukes Foppen, Vincenzo Gioia, Shreya Gupta, Curtis L Johnson, John Giantsidis, Maria Papademetris","doi":"10.1177/19322968241304434","DOIUrl":"10.1177/19322968241304434","url":null,"abstract":"<p><p>The use of artificial intelligence (AI) in diabetes management is emerging as a promising solution to improve the monitoring and personalization of therapies. However, the integration of such technologies in the clinical setting poses significant challenges related to safety, security, and compliance with sensitive patient data, as well as the potential direct consequences on patient health. This article provides guidance for developers and researchers on identifying and addressing these safety, security, and compliance challenges in AI systems for diabetes management. We emphasize the role of explainable AI (xAI) systems as the foundational strategy for ensuring security and compliance, fostering user trust, and informed clinical decision-making which is paramount in diabetes care solutions. The article examines both the technical and regulatory dimensions essential for developing explainable applications in this field. Technically, we demonstrate how understanding the lifecycle phases of AI systems aids in constructing xAI frameworks while addressing security concerns and implementing risk mitigation strategies at each stage. In addition, from a regulatory perspective, we analyze key Governance, Risk, and Compliance (GRC) standards established by entities, such as the Food and Drug Administration (FDA), providing specific guidelines to ensure safety, efficacy, and ethical integrity in AI-enabled diabetes care applications. By addressing these interconnected aspects, this article aims to deliver actionable insights and methodologies for developing trustworthy AI-enabled diabetes care solutions while ensuring safety, efficacy, and compliance with ethical standards to enhance patient engagement and improve clinical outcomes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"620-627"},"PeriodicalIF":4.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11672366/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142894881","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":"The Development and Potential Applications of an Automated Method for Detecting and Classifying Continuous Glucose Monitoring Patterns.","authors":"Mansur Shomali, Shiping Liu, Abhimanyu Kumbara, Anand Iyer, Guodong Gordon Gao","doi":"10.1177/19322968241232378","DOIUrl":"10.1177/19322968241232378","url":null,"abstract":"<p><strong>Introduction: </strong>Continuous glucose monitoring (CGM) is emerging as a transformative tool for helping people with diabetes self-manage their glucose and supporting clinicians in effective treatment. Unfortunately, many CGM users, and clinicians, find interpreting the large volume of CGM data to be overwhelming and complex. To address this challenge, an efficient, intelligent method for detecting and classifying discernable patterns in CGM data was desired.</p><p><strong>Methods: </strong>We developed an automated artificial intelligence (AI)-driven method to detect and classify different discernable CGM patterns which called \"CGM events.\" We trained different models using 60 days of CGM data from 27 individuals with diabetes from a publicly available data set and then evaluated model performance using separate test data from the same group. Each event is classified according to clinical significance based on three parameters: (1) glucose category at or near the beginning of the CGM event; (2) a calculated severity score that encompasses both signal shape and temporal characteristics (e.g., how high the CGM curve goes (measured in mg/dL) and how long it stays above target (as established by published consensus guidelines); and (3) the glucose category at or near the end of the event.</p><p><strong>Results: </strong>The system accurately detected and classified events from actual CGM data. This was also validated with expert diabetes clinicians.</p><p><strong>Conclusions: </strong>Advanced pattern recognition methods can be used to detect and classify CGM events of interest and may be used to provide actionable insights and self-management support to CGM users and decision support to the clinicians caring for them.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"658-665"},"PeriodicalIF":4.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139900000","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":"Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review.","authors":"Mikkel Thor Olsen, Carina Kirstine Klarskov, Arnold Matovu Dungu, Katrine Bagge Hansen, Ulrik Pedersen-Bjergaard, Peter Lommer Kristensen","doi":"10.1177/19322968231221803","DOIUrl":"10.1177/19322968231221803","url":null,"abstract":"<p><strong>Background: </strong>Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical <i>packages</i> for retrospective CGM data analysis and (2) statistical <i>algorithms</i> for retrospective CGM analysis not available in these statistical packages.</p><p><strong>Methods: </strong>A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).</p><p><strong>Results: </strong>A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.</p><p><strong>Conclusion: </strong>This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"787-809"},"PeriodicalIF":4.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139097973","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":"Design and Usability Assessment of a User-Centered, Modular Platform for Real-World Data Acquisition in Clinical Trials Involving Post-Bariatric Surgery Patients.","authors":"Luca Cossu, Giacomo Cappon, Olivia Streicher, David Herzig, Lia Bally, Andrea Facchinetti","doi":"10.1177/19322968231220061","DOIUrl":"10.1177/19322968231220061","url":null,"abstract":"<p><strong>Background: </strong>Clinical trials often face challenges in efficient data collection and participant monitoring. To address these issues, we developed the IMPACT platform, comprising a real-time mobile application for data collection and a web-based dashboard for remote monitoring and management.</p><p><strong>Methods: </strong>This article presents the design, development, and usability assessment of the IMPACT platform customized for patients with post-bariatric surgery hypoglycemia (PBH). We focus on adapting key IMPACT components, including continuous glucose monitoring (CGM), symptom tracking, and meal logging, as crucial elements for user-friendly and efficient PBH monitoring.</p><p><strong>Results: </strong>The adapted IMPACT platform demonstrated effectiveness in data collection and remote participant monitoring. The mobile application allowed patients to easily track their data, while the clinician dashboard provided a comprehensive overview of enrolled patients, featuring filtering options and alert mechanisms for identifying data collection issues. The platform incorporated various visual representations, including time plots and category-based visualizations, which greatly facilitated data interpretation and analysis. The System Usability Scale questionnaire results indicated a high level of usability for the web dashboard, with an average score of 86.3 out of 100. The active involvement of clinicians throughout the development process ensured that the platform allowed for the collection and visualization of clinically meaningful data.</p><p><strong>Conclusions: </strong>By leveraging IMPACT's existing features and infrastructure, the adapted version streamlined data collection, analysis, and trial customization for PBH research. The platform's high usability underscores its alignment with the requirements for conducting research using continuous real-world data in PBH patients and other populations of interest.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"673-682"},"PeriodicalIF":4.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139032376","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}