%0 Journal Article %@ 2371-4379 %I JMIR Publications %V 10 %N %P e70475 %T Digital Decision Support for Perioperative Care of Patients With Type 2 Diabetes: A Call to Action %A Cai,Jianwen %A Li,Peiyi %A Li,Weimin %A Hao,Xuechao %A Li,Sheyu %A Zhu,Tao %K perioperative diabetes %K artificial intelligence %K clinical decision support systems %D 2025 %7 8.4.2025 %9 %J JMIR Diabetes %G English %X Type 2 diabetes mellitus affects over 500 million people globally, with 10%‐20% requiring surgery. Patients with diabetes are at increased risk for perioperative complications, including prolonged hospital stays and higher mortality, primarily due to perioperative hyperglycemia. Managing blood glucose during the perioperative period is challenging, and conventional monitoring is often inadequate to detect rapid fluctuations. Clinical decision support systems (CDSS) are emerging tools to improve perioperative diabetes management by providing real-time glucose data and medication recommendations. This viewpoint examines the role of CDSS in perioperative diabetes care, highlighting their benefits and limitations. CDSS can help manage blood glucose more effectively, preventing both hyperglycemia and hypoglycemia. However, technical and integration challenges, along with clinician acceptance, remain significant barriers. %R 10.2196/70475 %U https://diabetes.jmir.org/2025/1/e70475 %U https://doi.org/10.2196/70475 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 10 %N %P e66831 %T Applications of AI in Predicting Drug Responses for Type 2 Diabetes %A Garg,Shilpa %A Kitchen,Robert %A Gupta,Ramneek %A Pearson,Ewan %K type 2 diabetes %K artificial intelligence %K machine learning %K drug response %K treatment response prediction %K ML %K AI %K deep learning %D 2025 %7 27.3.2025 %9 %J JMIR Diabetes %G English %X Type 2 diabetes mellitus has seen a continuous rise in prevalence in recent years, and a similar trend has been observed in the increased availability of glucose-lowering drugs. There is a need to understand the variation in treatment response to these drugs to be able to predict people who will respond well or poorly to a drug. Electronic health records, clinical trials, and observational studies provide a huge amount of data to explore predictors of drug response. The use of artificial intelligence (AI), which includes machine learning and deep learning techniques, has the capacity to improve the prediction of treatment response in patients. AI can assist in the analysis of vast datasets to identify patterns and may provide valuable information on selecting an effective drug. Predicting an individual’s response to a drug can aid in treatment selection, optimizing therapy, exploring new therapeutic options, and personalized medicine. This viewpoint highlights the growing evidence supporting the potential of AI-based methods to predict drug response with accuracy. Furthermore, the methods highlight a trend toward using ensemble methods as preferred models in drug response prediction studies. %R 10.2196/66831 %U https://diabetes.jmir.org/2025/1/e66831 %U https://doi.org/10.2196/66831 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 10 %N %P e68324 %T Enhancing Health Equity and Patient Engagement in Diabetes Care: Technology-Aided Continuous Glucose Monitoring Pilot Implementation Project %A Thakur,Madhur %A Maurer,Eric W %A Tran,Kim Ngan %A Tholkes,Anthony %A Rajamani,Sripriya %A Dwivedi,Roli %+ Department of Family Medicine and Community Health, Medical School, University of Minnesota, 516 Delaware St. SE, 6-240 Phillips-Wangensteen Building, Minneapolis, MN, 55455, United States, 1 612 638 0700, dwive003@umn.edu %K consumer health informatics %K patient engagement %K diabetes mellitus %K DM %K glucose monitoring %K continuous glucose monitoring %K CGM %K health equity %K health information technology %K patient centered care %K diabetes %K pharmacists %K clinicians %K nurses %K device %K patient monitoring %K technology-aided %K health informatics %D 2025 %7 5.2.2025 %9 Viewpoint %J JMIR Diabetes %G English %X Federally Qualified Health Centers (FQHCs) provide service to medically underserved areas and communities, providing care to over 32 million patients annually. The burden of diabetes is increasing, but often, the vulnerable communities served by FQHCs lag in the management of the disease due to limited resources and related social determinants of health. With the increasing adoption of technologies in health care delivery, digital tools for continuous glucose monitoring (CGM) are being used to improve disease management and increase patient engagement. In this viewpoint, we share insights on the implementation of a CGM program at an FQHC, the Community-University Health Care Center (CUHCC) in Minneapolis, Minnesota. Our intent is to improve diabetes management through better monitoring of glucose and to ensure that the CGM program enables our organization’s overarching digital strategy. Given the resource limitations of our population, we provided Libre Pro devices to uninsured patients through grants to improve health care equity. We used an interdisciplinary approach involving pharmacists, nurses, and clinicians and used hemoglobin A1c (HbA1c) levels as a measure of diabetes management. We assessed the CGM program and noted key aspects to guide future implementation and scalability. We recruited 148 participants with a mean age of 54 years; 39.8% (59/148) self-identified their race as non-White, 9.5% (14/148) self-identified their ethnicity as Hispanic or Latino, and one-third (53/148, 35.8%) were uninsured. Participants had diverse language preferences, with Spanish (54/148, 36.5%), English (52/148, 35.1%), Somali (21/148, 14.2%), and other languages (21/148, 14.2%). Their clinical characteristics included an average BMI of 29.91 kg/m2 and a mean baseline HbA1c level of 9.73%. Results indicate that the CGM program reduced HbA1c levels significantly from baseline to first follow-up (P<.001) and second follow-up (P<.001), but no significant difference between the first and second follow-up (P=.94). We share key lessons learned on cultural and language barriers, the digital divide, technical issues, and interoperability needs. These key lessons are generalizable for improving implementation at FQHCs and refining digital strategies for future scalability. %R 10.2196/68324 %U https://diabetes.jmir.org/2025/1/e68324 %U https://doi.org/10.2196/68324 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 9 %N %P e58680 %T Lightening the Load: Generative AI to Mitigate the Burden of the New Era of Obesity Medical Therapy %A Stevens,Elizabeth R %A Elmaleh-Sachs,Arielle %A Lofton,Holly %A Mann,Devin M %K obesity %K artificial intelligence %K AI %K clinical management %K GLP-1 %K glucagon-like peptide 1 %K medical therapy %K antiobesity %K diabetes %K medication %K agonists %K glucose-dependent insulinotropic polypeptide %K treatment %K clinician %K health care delivery system %K incretin mimetic %D 2024 %7 14.11.2024 %9 %J JMIR Diabetes %G English %X Highly effective antiobesity and diabetes medications such as glucagon-like peptide 1 (GLP-1) agonists and glucose-dependent insulinotropic polypeptide/GLP-1 (dual) receptor agonists (RAs) have ushered in a new era of treatment of these highly prevalent, morbid conditions that have increased across the globe. However, the rapidly escalating use of GLP-1/dual RA medications is poised to overwhelm an already overburdened health care provider workforce and health care delivery system, stifling its potentially dramatic benefits. Relying on existing systems and resources to address the oncoming rise in GLP-1/dual RA use will be insufficient. Generative artificial intelligence (GenAI) has the potential to offset the clinical and administrative demands associated with the management of patients on these medication types. Early adoption of GenAI to facilitate the management of these GLP-1/dual RAs has the potential to improve health outcomes while decreasing its concomitant workload. Research and development efforts are urgently needed to develop GenAI obesity medication management tools, as well as to ensure their accessibility and use by encouraging their integration into health care delivery systems. %R 10.2196/58680 %U https://diabetes.jmir.org/2024/1/e58680 %U https://doi.org/10.2196/58680 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 7 %N 2 %P e20774 %T Accuracy of the Standard GlucoNavii Mentor in Blood Glucose Monitoring According to International Organization for Standardization 15197:2013 Criteria %A Hwang,Heeyoung %A Leonardi,Luca %A Nicolucci,Antonio %+ SD Biosenser, C-4&5 Floor, 16, Deogyeong-daero 1556beon-gil, Yeongtong-gu, Suwon-si, 16690, Republic of Korea, 82 43 909 3009, hwhy@sdbiosensor.com %K blood glucose self-monitoring %K diabetes mellitus %K reference standards %K quality control %K biosensing techniques %D 2022 %7 20.5.2022 %9 Viewpoint %J JMIR Diabetes %G English %X This study was performed to assess the system accuracy of the blood glucose monitoring system SD GlucoNavii Mentor (SD Biosensor Inc, Korea). The study procedures were based on International Organization for Standardization 15197:2013, in that capillary blood samples from 100 participants’ fingertips were measured with three reagent system lots of the self-monitoring blood glucose system. Samples were collected for comparison measurements on a hexokinase-based glucose analyzer (Cobas Integra400 Plus, Roche Instrument Center, Switzerland). Glucose concentrations were distributed as required by International Organization for Standardization 15197. For each of the 100 evaluable samples, duplicate measurements were taken from three different reagent lots, for a total of 600 measurements. Overall, 98.3% (590/600) of individual measurement results (185/186, 99.5% for glucose values <100 mg/dl and 405/414, 97.8% for glucose values ≥100 mg/dl) were within ±15 mg/dl or ±15% of the corresponding comparison method results. All results (100%) fell into the consensus error grid zones A and B, indicating only clinically acceptable results. In conclusion, the blood glucose monitoring system SD GlucoNavii Mentor device fulfilled the system accuracy criteria of the International Organization for Standardization 15197, indicating measurement accuracy sufficient for diabetes therapy. %M 35594134 %R 10.2196/20774 %U https://diabetes.jmir.org/2022/2/e20774 %U https://doi.org/10.2196/20774 %U http://www.ncbi.nlm.nih.gov/pubmed/35594134 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 6 %N 4 %P e23646 %T Telemedicine via Continuous Remote Care: A Proactive, Patient-Centered Approach to Improve Clinical Outcomes %A Hallberg,Sarah %A Harrison,David %+ Virta Health, 733 Warrick St, West Lafayette, IN, 47906, United States, 1 765 775 6550, sarah@virtahealth.com %K telemedicine %K continuous remote care %K diabetes %K COVID-19 %K pandemic %D 2021 %7 2.11.2021 %9 Viewpoint %J JMIR Diabetes %G English %X The COVID-19 pandemic has revolutionized health care for patients and providers alike. Telemedicine has moved from the periphery of our health care system to center stage more rapidly than anyone could have envisioned. Currently, virtual care has quite effectively replicated the traditional health system’s care delivery model and reimbursement structure—a patient makes an appointment, then sees a physician (except with video or phone replacing in-office visits) who makes a care plan, and the patient and physician meet again at a later timepoint to assess progress. Replicating this episodic care paradigm virtually has been invaluable for delivering care swiftly during the COVID-19 pandemic; however, we can and should do more with the connectedness and convenience that telemedicine technology enables. Continuous remote care, with a data-driven, proactive outreach to patients, represents a decisive step forward in contrast to the currently available episodic, reactive, patient-initiated care. In the context of continuous remote care, patient biometric and symptom data (patient entered and connected data) are assimilated in real time by artificial intelligence–enabled clinical platforms to bring physicians' and other health care team members’ attention to those patients who need intervention, whether this is via medication adjustments, acute care management, or lifestyle coaching. In this paper, we discuss how an innovative continuous remote care approach has improved outcomes in another deadly pandemic—type 2 diabetes mellitus. %M 34505578 %R 10.2196/23646 %U https://diabetes.jmir.org/2021/4/e23646 %U https://doi.org/10.2196/23646 %U http://www.ncbi.nlm.nih.gov/pubmed/34505578 %0 Journal Article %@ 2561-6722 %I JMIR Publications %V 4 %N 2 %P e25106 %T Challenges and Considerations for Reducing Diabetes Distress and Fear of Hypoglycemia in Parents of Youth With Type 1 Diabetes During the COVID-19 Pandemic %A Monzon,Alexandra %A Kahhan,Nicole %A Marker,Arwen %A Patton,Susana %+ Center for Healthcare Delivery Science, Nemours Children’s Health System, 807 Children’s Way, Jacksonville, FL, 32207, United States, 1 904 697 3595, susana.patton@nemours.org %K type 1 diabetes %K parents %K children %K diabetes distress %K fear of hypoglycemia %K COVID-19 %K telehealth %K diabetes %K challenge %K youth %K young adults %D 2021 %7 23.4.2021 %9 Viewpoint %J JMIR Pediatr Parent %G English %X Type 1 diabetes management can be challenging for children and their families. To address psychosocial concerns for parents of youth with type 1 diabetes, we developed two parent-focused interventions to reduce their diabetes distress and fear of hypoglycemia. Our team conducted several of these interventions during the early stages of the COVID-19 pandemic and recognized a need to make timely adjustments to our interventions. In this viewpoint article, we describe our experience conducting these manualized treatment groups during the pandemic, the range of challenges and concerns specific to COVID-19 that parents expressed, and how we adjusted our approach to better address parents’ treatment needs. %M 33848256 %R 10.2196/25106 %U https://pediatrics.jmir.org/2021/2/e25106 %U https://doi.org/10.2196/25106 %U http://www.ncbi.nlm.nih.gov/pubmed/33848256 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 5 %N 2 %P e19581 %T The Challenges of COVID-19 for People Living With Diabetes: Considerations for Digital Health %A Gamble,Anissa %A Pham,Quynh %A Goyal,Shivani %A Cafazzo,Joseph A %+ Centre for Global eHealth Innovation, Techna Institute, University Health Network, 190 Elizabeth Street, 4th Floor R Fraser Elliot Building, Toronto, ON, M5G2C4, Canada, 1 (416) 340 4800 ext 4765, anissa.gamble@uhn.ca %K diabetes %K digital health %K COVID-19 %K pandemic %D 2020 %7 15.5.2020 %9 Viewpoint %J JMIR Diabetes %G English %X The coronavirus disease (COVID-19) is a global pandemic that significantly impacts people living with diabetes. Diabetes-related factors of glycemic control, medication pharmacodynamics, and insulin access can impact the severity of a COVID-19 infection. In this commentary, we explore how digital health can support the diabetes community through the pandemic. For those living with diabetes, digital health presents the opportunity to access care with greater convenience while not having to expose themselves to infection in an in-person clinic. Digital diabetes apps can increase agency in self-care and produce clinically significant improvement in glycemic control through facilitating the capture of diabetes device data. However, the ability to share these data back to the clinic to inform virtual care and enhance diabetes coaching and guidance remains a challenge. In the end, it requires an unnecessarily high level of technical sophistication on the clinic’s part and on those living with diabetes to routinely use their diabetes device data in clinic visits, virtual or otherwise. As the world comes together to fight the COVID-19 pandemic, close collaboration among the global diabetes community is critical to understand and manage the sustained impact of the pandemic on people living with diabetes. %M 32392473 %R 10.2196/19581 %U http://diabetes.jmir.org/2020/2/e19581/ %U https://doi.org/10.2196/19581 %U http://www.ncbi.nlm.nih.gov/pubmed/32392473 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 2 %N 2 %P e16 %T Addressing Disparities in Diabetes Management Through Novel Approaches to Encourage Technology Adoption and Use %A Sheon,Amy R %A Bolen,Shari D %A Callahan,Bill %A Shick,Sarah %A Perzynski,Adam T %+ Urban Health Initiative, School of Medicine, Case Western Reserve University, 10900 Euclid Avenue, TA-208, Box 4976, Cleveland, OH,, United States, 1 216 368 0915, ars174@case.edu %K diabetes %K chronic illness %K vulnerable populations %K digital divide %K community health workers %K healthcare disparities %K patient portals %K patient engagement %K meaningful use %K health literacy %D 2017 %7 13.07.2017 %9 Viewpoint %J JMIR Diabetes %G English %X Type 2 diabetes (T2D) is one of the nation’s leading drivers of disability and health care utilization, with elevated prevalence among individuals with lower education, income, and racial/ethnic minorities. Health information technology (HIT) holds vast potential for helping patients, providers, and payers to address T2D and the skyrocketing rates of chronic illness and associated health care costs. Patient portals to electronic health records (EHRs) serve as a gateway to consumer use of HIT. We found that disparities in portal use portend growing T2D disparities. Little progress has been made in addressing identified barriers to technology adoption, especially among populations with elevated risk of T2D. Patients often lack digital literacy skills and continuous connectivity and fear loss of the relationship with providers. Providers may experience structural disincentives to promoting patient use of HIT and apply hidden biases that inhibit portal use. Health care systems often provide inadequate training to patients and providers in use of HIT, and lack resources devoted to obtaining and optimizing use of data generated by HIT. Lastly, technology-related barriers include inadequate consideration of user perspectives, lack of evidence for patient-focused apps, and lack of features to enable providers and health care systems to readily obtain aggregate data to improve care and facilitate research. After discussing these barriers in detail, we propose possible solutions and areas where further research is needed to ensure that individuals and health care systems obtain the full benefit of the nation’s planned $38 billion HIT investment. A digital inclusion framework sheds new light on barriers posed for patients with social health inequalities. We have determined that partnerships with community organizations focused on digital inclusion could help health systems explore and study new approaches, such as universal screening and referral of patients for digital skills, health literacy, and Internet connectivity. %M 30291090 %R 10.2196/diabetes.6751 %U http://diabetes.jmir.org/2017/2/e16/ %U https://doi.org/10.2196/diabetes.6751 %U http://www.ncbi.nlm.nih.gov/pubmed/30291090 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 2 %N 1 %P e1 %T The Case for Jointly Targeting Diabetes and Depression Among Vulnerable Patients Using Digital Technology %A Aguilera,Adrian %A Lyles,Courtney Rees %+ School of Social Welfare, University of California, Berkeley, 120 Haviland Hall, MC 7400, Berkeley, CA,, United States, 1 510 642 8564, aguila@berkeley.edu %K diabetes %K depression %K chronic illness %K digital health %K vulnerable populations %D 2017 %7 17.01.2017 %9 Viewpoint %J JMIR Diabetes %G English %X It is well publicized that mobile and digital technologies hold great promise to improve health outcomes among patients with chronic illnesses such as diabetes. However, there is growing concern that digital health investments (both from federal research dollars and private venture investments) have not yet resulted in tangible health improvements. We see three major reasons for this limited real-world impact on health outcomes: (1) lack of solutions relevant for patients with multiple comorbidities or conditions, (2) lack of diverse patient populations involved in the design and early testing of products, and (3) inability to leverage existing clinical workflows to improve both patient enrollment and engagement in technology use. We discuss each of these in depth, followed by new research directions to increase effectiveness in this field. %M 30291080 %R 10.2196/diabetes.6916 %U http://diabetes.jmir.org/2017/1/e1/ %U https://doi.org/10.2196/diabetes.6916 %U http://www.ncbi.nlm.nih.gov/pubmed/30291080