JMIR Diabetes最新文献

{"title":"Insulin Injection Technique Education and Associated Knowledge Factors Among Physicians: Cross-Sectional Survey Study.","authors":"Ida Ayu Made Kshanti, Nadya Magfira, Anak Agung Arie Widyastuti, Jerry Nasarudin, Marina Epriliawati, Md Ikhsan Mokoagow","doi":"10.2196/65359","DOIUrl":"10.2196/65359","url":null,"abstract":"<p><strong>Background: </strong>Insulin therapy is crucial for managing type 2 diabetes mellitus, with its use steadily increasing in Indonesia and its effectiveness well established. However, prescribing insulin poses various challenges that can impact the effectiveness of insulin. Patient education is crucial for the successful implementation of insulin therapy. Proper insulin use remains insufficient in Indonesia.</p><p><strong>Objective: </strong>This study aims to investigate physicians' knowledge and practice in providing education on insulin use to patients with type 2 diabetes mellitus in Indonesia.</p><p><strong>Methods: </strong>This study recruited potential participants (all physicians in Indonesia) through the internet using a convenience sampling method. The participants were asked to fill out a questionnaire. The questionnaire had 32 questions divided into 4 sections: demographics and clinical practice, practice of insulin education, the Indonesian insulin injection technique guideline, and knowledge of insulin injection techniques. The instrument used in this study was developed based on the Pedoman Teknik Menyuntik Insulin Indonesia, which was adapted from the international consensus by the Forum for Injection Technique and Therapy Expert Recommendations. The survey lasted from February 2021 to March 2021. Data were analyzed using the Kruskal-Wallis tests.</p><p><strong>Results: </strong>A total of 823 participants were included in the analysis. Out of 823 participants, 680 (82.6%) had given insulin education to patients at least once during the last 30 days. However, out of 823 participants, only 479 (58.2%) used specific guidelines in their practice, with only 280 (34.0%) aware of the Indonesian guidelines. Out of 823 participants, 815 (99.1%) agreed that insulin injection techniques would affect clinical results. The median score of knowledge about insulin injection techniques was 7 (IQR 2) among the study participants, indicating good knowledge. Profession was the only variable significantly associated with knowledge scores, with consultants in endocrinology, metabolism, and diabetes achieving the highest median scores, and other physicians the lowest (P<.001).</p><p><strong>Conclusions: </strong>Most physicians in this study reported providing education to their patients. However, there was still a gap between the guidelines and the practice of insulin education, as indicated by the lack of awareness and a fair level of knowledge about the Indonesian guidelines.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e65359"},"PeriodicalIF":2.6,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12685283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145710005","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":"An Innovative Insulin Dose Self-Titration Toolkit for Adults Living With Type 2 Diabetes Mellitus.","authors":"Nadin Abbas, Heather Lochnan, Sandhya Goge, Annie Garon-Mailer, Cathy J Sun","doi":"10.2196/75903","DOIUrl":"10.2196/75903","url":null,"abstract":"<p><strong>Unlabelled: </strong>We developed an innovative bilingual toolkit comprising a personalized action plan and educational videos to encourage insulin dose self-titration by adults living with type 2 diabetes.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e75903"},"PeriodicalIF":2.6,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12661589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145642647","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":"Toward a Personalized Basal Tuner for Detecting Basal Rate Inaccuracies in Type 1 Diabetes Mellitus Without Meal Data: Algorithm Development and Retrospective Validation Study.","authors":"Daniel Gasca Garcia, Hood Thabit, Paul W Nutter, Simon Harper","doi":"10.2196/72769","DOIUrl":"10.2196/72769","url":null,"abstract":"<p><strong>Background: </strong>Basal rate (BR) adjustment is crucial for managing type 1 diabetes mellitus, accounting for 30% to 50% of total daily insulin needs. All current closed-loop systems revert to the user's usual pump BR (known as manual mode) in the event of closed loop failure. Furthermore, access to closed-loop systems remains relatively low in low- and middle-income countries and among those without suitable health insurance. Accurately adjusting the BR remains challenging, leading to hypo- or hyperglycemia, and research on optimizing the BR is limited.</p><p><strong>Objective: </strong>This study proposed an adaptive algorithm that uses continuous glucose monitoring data to identify BR inaccuracies without requiring meal intake information.</p><p><strong>Methods: </strong>The OhioT1DM dataset formed the basis for implementing this methodology. Each composite day was generated by excluding bolus insulin profiles lacking meal intake information and by calculating hourly blood glucose (BG) relative levels along with their corresponding reliability measures, enabling assessment of deviations from the recommended BR (ie, a BG relative change of 0 mg/dL). Both a noninferiority analysis and a classification precision metric were used to assess the practicality of this approach compared to using meal data.</p><p><strong>Results: </strong>Data from 12 participants showed noninferiority of the no-meal method: using a 20% noninferiority margin on absolute BG relative change, 9 of 12 participants met the criterion (1-sided P<.05). Classification precision was 73.9% (139/188) of meals correctly classified on average per participant (SD 11.8%; 95% CI 67.2%-79.7%). The daily cumulative BG average was 200.6 mg/dL (SD 61.7 mg/dL; 11.1 mmol/L, SD 3.4 mmol/L; 95% CI 161.4-239.8 mg/dL), with peak values reaching 270.15 mg/dL (14.99 mmol/L). Furthermore, 99.3% (286/288) of the BG relative values (SD 0.5%; 95% CI 97.5%-99.8%) that were unaffected by external factors were associated with incorrect BR settings, with deviations ranging from -25.5 to 46 mg/dL (-1.58 to 2.59 mmol/L).</p><p><strong>Conclusions: </strong>Current strategies to optimize BR settings are inadequate, and our approach of a personalized basal tuner (PBT) helps better analyze BR without relying on meal intake information. Indeed, without an optimally set BR, in the event of the closed loop reverting to manual mode, patients may be exposed to persistent hypo- or hyperglycemia, leading to safety and efficacy issues. Future work will focus on generating BR recommendations through the application of this algorithm in clinical practice to assist clinicians in setting BR in low- and middle-income countries, where closed-loop systems are not prevalent, to help increase time in range.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e72769"},"PeriodicalIF":2.6,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12661609/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145642680","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":"Coefficient of Variation to Assess the Reproducibility of Meal-Induced Glycemic Responses: Development of a Clustering Algorithm.","authors":"Nicole Lubasinski, Hood Thabit, Paul W Nutter, David Petrescu, Simon Harper","doi":"10.2196/68821","DOIUrl":"10.2196/68821","url":null,"abstract":"<p><strong>Background: </strong>Managing type 1 diabetes (T1D) requires maintaining target blood glucose levels through precise diet and insulin dosing. Predicting postprandial glycemic responses (PPGRs) based solely on carbohydrate content is limited by factors such as meal composition, individual physiology, and lifestyle. Continuous glucose monitors provide insights into these responses, revealing significant individual variability. The statistical clustering method proposed here balances the number of clusters formed and the glycemic variability of the PPGRs within each cluster to offer a clustering technique on which treatment decisions could be based.</p><p><strong>Objective: </strong>This study aims to develop and evaluate a PPGR clustering method that identifies reproducible meal-specific glucose patterns in people with type 1 diabetes.</p><p><strong>Methods: </strong>Blood glucose data from the OhioT1DM dataset were used to assess clustering of PPGR based on the coefficient of variability (CV) of glucose. Clustering was performed using statistical clustering, with each PPGR isolated into 48 data points per event. A CV threshold of <36% was used to define clinically similar clusters. This aimed to cluster PPGRs with minimal glycemic variability. The approach aims to enhance precision in analyzing postprandial glycemic dynamics, assessing cluster cohesion via standard deviation and CV within meal categories.</p><p><strong>Results: </strong>The analysis revealed a reproducible set of PPGR clusters specific to meal types and individuals (mean [SD], 2.4 [1.8] for breakfast, 2.7 [0.9] for lunch, and 3.1 [1.0] for dinner), with the number of clusters varying across participants and meals in the dataset. Carbohydrate intake alone did not affect cluster formation, suggesting a complex relationship between meal composition and PPGR variability. However, certain individuals showed significant associations between carbohydrate intake and cluster formation for specific meals.</p><p><strong>Conclusions: </strong>The meal-based glycemic clustering algorithm provides a promising framework for predicting PPGRs in people with type 1 diabetes, independent of carbohydrate intake. It emphasizes the need for personalized prediction models to optimize time in range and enhance diabetes management. Efforts to refine treatment strategies are crucial in reducing T1D-related complications.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e68821"},"PeriodicalIF":2.6,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12633838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145566105","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":"Natural Language AI Models and Pediatric Type 1 Diabetes: Can Chatbots Help With Diabetes Self-Management and Patient Education?","authors":"Flavia Voiculescu, Paul Darvasi, Esli Osmanlliu, Preetha Krishnamoorthy, Angeliki Makri","doi":"10.2196/76986","DOIUrl":"10.2196/76986","url":null,"abstract":"<p><strong>Unlabelled: </strong>Diabetes self-management plays a major role in controlling blood sugar levels and avoiding chronic complications. In this report, we investigate the strengths and limitations of artificial intelligence chatbots in supporting patients with type 1 diabetes and their families. With the growing accessibility of these constantly evolving tools, front-line providers must advocate for their responsible use.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e76986"},"PeriodicalIF":2.6,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12594437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145471974","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":"Prevalence and Risk Factors of Metabolic Dysfunction-Associated Steatotic Liver Disease in Patients With Type 2 Diabetes Mellitus at a Tertiary Center in Saudi Arabia: Cross-Sectional Questionnaire Study.","authors":"Abdulrahman M Elnasieh, Mohammed Almesned, Akram N Al Hazmi, Atheer Alturki, Faisal I Alhawaidi, Razan K Alhadlq, Maryam Alramadhan, Nasser Alobilan, Yasser Sheikh Qroosh","doi":"10.2196/77772","DOIUrl":"10.2196/77772","url":null,"abstract":"<p><strong>Background: </strong>The escalating rates of obesity and type 2 diabetes mellitus (T2DM) in Saudi Arabia highlight the impending burden of metabolic dysfunction-associated steatotic liver disease (MASLD) and nonalcoholic steatohepatitis.</p><p><strong>Objective: </strong>This study aimed to identify MASLD among patients with T2DM at King Saud Medical City family medicine clinics, Riyadh, and explore associated factors to facilitate early intervention and prevention strategies.</p><p><strong>Methods: </strong>This cross-sectional study identified patients with T2DM who attended King Saud Medical City, Riyadh, underwent an abdominal ultrasound, and were diagnosed with MASLD. The study data were collected by a peer-reviewed validated data extraction sheet and analyzed by SPSS (version 26.0; IBM Corp).</p><p><strong>Results: </strong>Our study included 292 participants, with 47.3% (n=138) males and 52.7% (n=154) females. Notably, the prevalence of MASLD was 54.5% (n=159). Prevalent comorbidities included dyslipidemia (218/292, 74.7%) and hypertension (209/292, 71.6%). Most participants were nonsmokers (218/292, 74.7%). Higher waist circumference was significantly associated with MASLD (P=.02), with >80 cm among females (85/141, 60.3%) and >94 cm among males (60/141, 54.5%) affected across different stages of MASLD. Obesity (BMI>30 kg/m2) also significantly correlated with MASLD (P<.001). Individuals taking aspirin had half the odds of MASLD development (odds ratio [OR] 0.523, 95% CI 0.331-0.844; P=.007). Biochemical analysis revealed significant associations between MASLD and elevated alanine aminotransferase (P=.009), aspartate aminotransferase (P=.01), and homeostatic model assessment of insulin resistance (P=.001). Total cholesterol (P=.01), triglycerides (P=.03), and low-density lipoprotein (P=.04) were significantly elevated in patients with MASLD. Insulin exhibited a significant positive correlation with MASLD (r=0.24; P=.001). Glucose levels showed no significant association (r=0.03; P=.63).</p><p><strong>Conclusions: </strong>Our study highlights significant associations between MASLD and various factors, including waist circumference, obesity, and certain biochemical markers. Furthermore, the protective effect of aspirin against MASLD warrants further investigation. These findings underscore the importance of early intervention and targeted preventive strategies.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e77772"},"PeriodicalIF":2.6,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12582413/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145439959","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":"Revolutionizing Hypoglycemia Management in Long-Term Care: Lessons Learned From a Pilot Quality Improvement Initiative Using Continuous Glucose Monitoring.","authors":"Denis O'Donnell, Sue Burns, Shirley Drever, Lisa Quesnelle, Benjamin Yuen","doi":"10.2196/73485","DOIUrl":"10.2196/73485","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Unlabelled: &lt;/strong&gt;Despite efforts to raise glycemic targets and reduce modifiable risk factors, hypoglycemia continues to impact a large number of long-term care (LTC) residents living with diabetes mellitus and remains one of the leading causes of hospitalization in this cohort. Effective, sustainable practice strategies to monitor and maintain glycemic control in LTC are lacking. We describe the stepwise approach used by 2 LTC homes that switched from traditional fingerstick testing to a continuous glucose monitoring (CGM) system as part of a quality improvement initiative to reduce nursing workload and address hypoglycemia. This was an exploratory pilot project. A working group was established at each of the 2 participating LTC homes, including representatives from management and direct care staff. Kickoff meetings were held with key direct care staff to discuss the limitations of current monitoring practices and potential solutions. The following interventions were agreed upon and implemented by the working groups: (1) the initiation of structured glucose monitoring for residents using CGM (FreeStyle Libre 2), requiring scanning of sensors 4 times per day; (2) provision of staff education and training on CGM by a diabetes expert; and (3) scheduling of interdisciplinary rounds as needed to optimize diabetes management. System changes were gradually introduced in a stepwise manner over a 3-month period (intervention phase), during which the LTC homes progressed from traditional fingerstick testing to point-of-care sensor readings and then to full use of the CGM software platform. Hypoglycemia was defined as a glucose reading of ≤4 mmol/L. Glucose readings were collected from 38 residents living with diabetes mellitus and receiving insulin in the 6 months before the start of the intervention phase (baseline evaluation) and in the 6 months after the end of the intervention phase (post-launch evaluation). All hypoglycemic readings detected by a sensor at a point-of-care test were validated using a fingerstick test. Nursing workload associated with glucose testing was assessed in an anonymous survey of nursing staff at baseline and after the launch. The approach resulted in a 40% reduction in nursing time required to complete a glucose reading (from 5.1 min per test at baseline to 3.1 min per test at the post-launch evaluation). The frequency of glucose monitoring increased from a total of 19,438 glucose readings in the baseline evaluation to 35,971 point-of-care sensor scans in the post-launch evaluation. The number of detected hypoglycemic events increased 12-fold, from 88 in the baseline evaluation to 1049 in the post-launch evaluation. Hypoglycemic events continue to impact a large number of LTC residents living with diabetes mellitus. CGM can improve the detection of hypoglycemic events while decreasing nursing workload. A gradual transition to CGM can help overcome underlying barriers and concerns and ensure a sustainable approa","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e73485"},"PeriodicalIF":2.6,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12536920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145337905","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":"Managing Exercise-Related Glycemic Events in Type 1 Diabetes: Development and Validation of Predictive Models for a Practical Decision Support Tool.","authors":"Sisi Ma, Ryan Coopergard, Mark Clements, Lisa Chow","doi":"10.2196/68948","DOIUrl":"10.2196/68948","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Exercise is an important aspect of diabetes self-management. Patients with type 1 diabetes frequently struggle with exercise-induced hyperglycemia and hypoglycemia, decreasing their willingness to exercise.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We aim to build accurate and easy-to-deploy models to forecast exercise-induced glycemic events in real-world settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We analyzed free-living data from the Type 1 Diabetes Exercise Initiative study, where adults with type 1 diabetes wore a continuous glucose monitor (CGM) while performing video-guided exercises (30-minute exercises at least 6 times over 4 weeks), along with concurrent detailed phenotyping of their insulin program and diet. We built models to predict glycemic events (blood glucose ≤54 mg/dL, ≤70 mg/dL, ≥200 mg/dL, and ≥250 mg/dL) during and 1 hour post exercise with variables from 4 data modalities, such as demographic and clinical (eg, glycated hemoglobin; CGM (blood glucose value and their summary statistics); carbohydrate intake and insulin administration; and exercise type, duration, and intensity. We used repeated stratified nested cross-validation for model selection and performance estimation. We evaluated the relative contribution of the 4 input data modalities for predicting glycemic events, which informs the cost and benefit for including them in the decision support tool for risk prediction. We also evaluated other important aspects related to model translation into decision support tools, including model calibration and sensitivity to noisy inputs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our models were built based on 1901 exercise episodes for 329 participants. The median age for the participants was 34 (IQR 26-48) years. Of the participants, 74.8% (246/329) are female and 94.5% (311/329) are White. A total of 182/329 (55.3%) participants used a closed-loop insulin delivery system, while the rest used a pump without a closed-loop system. Models incorporating information from all 4 data modalities showed excellent predictive performance with cross-validated area under the receiver operating curves (AUROCs) ranging from mean 0.880 (SD 0.057) to mean 0.992 (SD 0.001) for different glycemic events. Models built with CGM data alone have statistically indistinguishable performance compared to models using all data modalities, indicating the other 3 data modalities do not add additional information with respect to predicting exercise-related glycemic events. The models based solely on CGM data also showed outstanding calibration (Brier score ≤0.08) and resilience to noisy input.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;We successfully constructed models to forecast exercise-induced glycemic events using only CGM data as input with excellent predictive performance, calibration, and robustness. In addition, these models are based on automatically captured CGM data, thus easy to deploy and maintain and incurring minimal user burden, enabli","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e68948"},"PeriodicalIF":2.6,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12513686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145276544","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":"Experience of Using Wearable Devices for Dietary Management for Chinese Americans With Type 2 Diabetes: One-Group Prospective Cohort Study.","authors":"Michaela Greenlee, Bei Wu, Mingui Sun, Keer Chen, Wenyan Jia, Susan Zweig, Gail Melkus, Niyati Parekh, Yaguang Zheng","doi":"10.2196/73381","DOIUrl":"10.2196/73381","url":null,"abstract":"<p><strong>Background: </strong>Chinese Americans with type 2 diabetes (T2D) face significant challenges in dietary management, which is crucial for glycemic control. Wearable sensors, such as the electronic button (eButton) and continuous glucose monitor (CGM), offer a promising solution.</p><p><strong>Objective: </strong>We aimed to explore the experience of using the eButton and CGM for dietary management among Chinese Americans with T2D.</p><p><strong>Methods: </strong>Chinese Americans with T2D (N=11) participated in a one-group prospective cohort study, recruited via convenience sampling from the electronic medical records of NYU Langone Health. Participants wore an eButton on their chest to record their 10-day meals and a CGM for the 2 weeks and kept a diary to track food intake, medication, and physical activity. Individual interviews were conducted after 2 weeks to discuss their experience, barriers, and facilitators of use. Interview transcripts were thematically analyzed using ATLAS.ti (Scientific Software Development GmbH) software.</p><p><strong>Results: </strong>Facilitators of using an eButton included the device's ease of use, ability to make participants more mindful, and influence on increased sense of control. Greater awareness of food intake enabled participants to eat smaller portions. Reported barriers included privacy concerns, difficulty positioning the camera for pictures, and the lack of a meal photo record to track glucose trends. For the CGM, facilitators included its comfort and ease of use, its ability to increase mindfulness of meal choices, and its motivating changes in eating behaviors. The most common barriers included the sensor falling off, getting trapped in clothes, and causing skin sensitivity.</p><p><strong>Conclusions: </strong>Our findings suggest that it is feasible for Chinese Americans with T2D to use eButton and CGM for dietary management. When paired, these tools offer a promising method to help patients visualize the relationship between food intake and glycemic response. For clinical implementation, structured support from health care providers-such as dietitians or diabetes educators-is essential to help patients interpret the data meaningfully. Clinicians should also consider cultural factors, privacy concerns, and individual preferences when introducing wearable technologies, ensuring a personalized and patient-centered approach to diabetes care. Future studies should apply these devices to a larger sample over a longer duration to better inform effective diabetes management strategies.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e73381"},"PeriodicalIF":2.6,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12490776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214388","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":"Physicians' Experiences Using Secure Messaging for Diabetes Management: A Qualitative Study.","authors":"Ben Kragen, Maryum Zaidi, Stephanie L Shimada, Ben S Gerber, Cecilia Lozier, Jon A Chilingerian","doi":"10.2196/70816","DOIUrl":"10.2196/70816","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The COVID-19 pandemic led to increased demand for remote management of type 2 diabetes using secure messaging, or patient-provider text-based communication. Prior research on secure messaging has described the content of messages sent for type 2 diabetes management and demonstrated its impact on clinical outcomes. However, there is a gap in knowledge about how secure messaging performs as a communication medium for specific tasks in clinical care (eg, prescription management and discussing medical questions). Additional research is needed to understand physicians' experiences using secure messaging to communicate with patients about clinical tasks that support diabetes management.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to investigate physicians' experience using secure messaging to communicate with patients about specific clinical tasks for type 2 diabetes management.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We interviewed a sample of endocrinologists and internists from 2 different medical facilities who have used secure messaging to communicate with adult patients about type 2 diabetes management. Semistructured interviews were used to solicit physicians' experience using secure messaging for 6 specific tasks that support diabetes management: refilling prescriptions, answering nonurgent medical questions, scheduling appointments, discussing test results, making referral requests, and discussing visit follow-up. Interviews were conducted until we achieved saturation of themes for these tasks. Interview data were collected between 2021 and 2023. Qualitative data were analyzed using the framework method for thematic analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We interviewed 6 internists and 4 endocrinologists (n=10). Physicians reported spending between 2 and 5 hours per day messaging with patients. They observed that secure messaging increased the frequency and timeliness of communication, which improved care coordination and facilitated care delivery between visits. This served as a time-efficient way to iterate specific components of treatment plans, including discussing test results, visit follow-up, scheduling, and prescription refill. Physicians were frustrated with the unstructured nature of secure messages. Patients wrote messages that were often disorganized, confusing, or did not have enough information for the provider to take action. This often made answering nonurgent medical questions difficult. In many cases, poorly structured secure messages resulted in lengthy back-and-forth communications between patients and physicians, which sometimes required a phone call or an office visit to resolve.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Physicians reported that secure messaging supports a longitudinal model of care, where patients can iterate their treatment plan between visits. For tasks with well-defined information boundaries, such as scheduling and prescription refill, physicians reported that secure messag","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"10 ","pages":"e70816"},"PeriodicalIF":2.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468167/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145180146","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}