Background: 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.
Objective: We aimed to explore the experience of using the eButton and CGM for dietary management among Chinese Americans with T2D.
Methods: 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.
Results: 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.
Conclusions: 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.
Background: Clinicians currently lack an effective means for identifying youth with type 1 diabetes (T1D) who are at risk for experiencing glycemic deterioration between diabetes clinic visits. As a result, their ability to identify youth who may optimally benefit from targeted interventions designed to address rising glycemic levels is limited. Although electronic health records (EHR)-based risk predictions have been used to forecast health outcomes in T1D, no study has investigated the potential for using EHR data to identify youth with T1D who will experience a clinically significant rise in glycated hemoglobin (HbA1c) ≥0.3% (approximately 3 mmol/mol) between diabetes clinic visits.
Objective: We aimed to evaluate the feasibility of using routinely collected EHR data to develop a machine learning model to predict 90-day unit-change in HbA1c (in % units) in youth (aged 9-18 y) with T1D. We assessed our model's ability to augment clinical decision-making by identifying a percent change cut point that optimized identification of youth who would experience a clinically significant rise in HbA1c.
Methods: From a cohort of 2757 youth with T1D who received care from a network of pediatric diabetes clinics in the Midwestern United States (January 2012-August 2017), we identified 1743 youth with 9643 HbA1c observation windows (ie, 2 HbA1c measurements separated by 70-110 d, approximating the 90-day time interval between routine diabetes clinic visits). We used up to 5 years of youths' longitudinal EHR data to transform 17,466 features (demographics, laboratory results, vital signs, anthropometric measures, medications, diagnosis codes, procedure codes, and free-text data) for model training. We performed 3-fold cross-validation to train random forest regression models to predict 90-day unit-change in HbA1c(%).
Results: Across all 3 folds of our cross-validation model, the average root-mean-square error was 0.88 (95% CI 0.85-0.90). Predicted HbA1c(%) strongly correlated with true HbA1c(%) (r=0.79; 95% CI 0.78-0.80). The top 10 features impacting model predictions included postal code, various metrics related to HbA1c, and the frequency of a diagnosis code indicating difficulty with treatment engagement. At a clinically significant percent rise threshold of ≥0.3% (approximately 3 mmol/mol), our model's positive predictive value was 60.3%, indicating a 1.5-fold enrichment (relative to the observed frequency that youth experienced this outcome [3928/9643, 40.7%]). Model sensitivity and positive predictive value improved when thresholds for clinical significance included smaller changes in HbA1c, whereas specificity and negative predictive value improved when thresholds required larger changes in HbA1c.
Conclusions: Routinely collected EHR data can be used to create an ML model for predicting unit-change in HbA1c between diabetes clinic visits amo
Background: The COVID-19 pandemic catalyzed the adoption of digital technologies in health care. This study assesses a digital-first integrated care model for type 2 diabetes management in Western Sydney, using continuous glucose monitoring (CGM) and virtual Diabetes Case Conferences (DCC) involving the patient, general practitioner (GP), diabetes specialist, and diabetes educator at the same time.
Objective: This study aims to assess the effectiveness of the innovative diabetes clinics in Western Sydney.
Methods: In 2020, a total of 833 new patients with type 2 diabetes were seen at Western Sydney Diabetes (WSD) clinics. An early cohort of 103 patients was evaluated before and after participation in virtual DCC, incorporating CGM data analysis, digital educational resources, and remote consultations with a diabetes multidisciplinary team. Assessments were conducted at baseline and 3-4 months post DCC.
Results: The integration of CGM and virtual consultations significantly improved glycemic control. Hemoglobin A1c (HbA1c) levels decreased notably from 9.6% to 8.2% (average reduction of 1.4%; 95% CI 1.03-1.82; P<.001). Time in range (TIR) as measured by CGM increased substantially from 46% to 73% (95% CI 20-32; P<.001), and the glucose management indicator (GMI) improved from 7.9% to 7% (average reduction of 0.9%; 95% CI 0.55-1.2; P<.001). Despite no significant change in the total daily insulin dose, the proportion of patients on insulin therapy rose from 27% to 39% (P<.001), indicating more targeted and effective diabetes management.
Conclusions: Our findings demonstrate the effectiveness of a digitally enabled integrated care model in managing type 2 diabetes. The use of CGM technology, complemented by virtual DCCs and digital educational tools, not only facilitated better disease management and patient engagement but also empowered primary care providers with advanced management capabilities. This digital approach addresses traditional barriers in diabetes care, highlighting the potential for scalable, technology-driven solutions in chronic disease management.
Unstructured: To encourage insulin dose self-titration by adults living with type 2 diabetes, we developed an innovative bilingual toolkit comprised of a personalized action plan and educational videos.
Background: Inequity in diabetes technology use persists among Black and Hispanic youth with type 1 diabetes (T1D). Community health workers (CHWs) can address social and clinical barriers to diabetes device use. However, more information is needed on clinicians' perceptions to inform the development of a CHW model for youth with T1D.
Objective: This study aimed to identify barriers to diabetes technology use and cocreate solutions in collaboration with diabetes and school-based clinicians serving Black and Hispanic youth with T1D.
Methods: Using human-centered design, the study team conducted 2-hour web-based workshops with clinicians from a diabetes clinic or school-based clinics at a safety net hospital in the Bronx, New York. The workshops promoted active ideation of barriers and co-design of a CHW intervention prototype to address self-reported challenges. Workshops were analyzed using a qualitative inductive approach.
Results: A total of 17 participants completed the human-centered design workshops and surveys. Of these, 11 (65%) were clinicians from the diabetes clinic and 6 (35%) were school-based clinicians from elementary, middle, and high schools in the Bronx. A total of 4 workshops were conducted. The perceived diabetes device barriers for youth with T1D and their families by participants were general health-related social needs (HRSNs) and diabetes technology-specific HRSNs that interfered with technology uptake, such as housing and financial insecurity, as well as digital social needs; and difficulty navigating health care systems, insurance, and pharmacy benefits due to the high level of care coordination required by caregivers. In addition, the participants identified barriers that interfered with their ability to support youth with T1D with diabetes technology, such as limited support for using diabetes technology in school and lack of time and technology support to troubleshoot problems in diabetes clinics. Ways in which a CHW could help mitigate these barriers include (1) identifying and addressing HRSNs by directing patients to appropriate resources; (2) providing peer support for caregivers to navigate diabetes device logistics; (3) acting as a school liaison to improve communication and coordination between caregivers, schools, and diabetes clinicians; and (4) offering administrative support to offload the logistical burden of clinicians.
Conclusions: Important needs related to specialized technology support, enhanced care coordination, family-clinician communication, and administrative task shifting were identified by clinicians to inform a CHW model for youth with T1D. Continued co-design and pilot testing are needed to refine the model.
Background: A novel mobile health (mHealth) app "acT1ve," developed using a co-design model, provides real-time support during exercise for young people with type 1 diabetes (T1D).
Objective: This study aimed to demonstrate the noninferiority of acT1ve compared with "treatment as usual" with regard to hypoglycemic events.
Methods: Thirty-nine participants living with T1D (age: 17.2, SD 3.3 years; HbA1c: 64, SD 6.0 mmol/mol) completed a 12-week single-arm, pre-post noninferiority study with a follow-up qualitative component. During the intervention, continuous glucose monitoring (CGM) and physical activity were monitored while participants used acT1ve to manage exercise. CGM data were used to assess the number of hypoglycemic events (<3.9 mmol/L for ≥15 minutes) in each phase. Using a mixed effects negative binomial regression, the difference in the rates of hypoglycemia between the preapp and app-use phases was analyzed. Participants completed both a semistructured interview and the user Mobile Application Rating Scale (uMARS) questionnaire postintervention. All interviews were audio-recorded for transcription, and a deductive content analysis approach was used to analyze the participant interviews. The uMARS Likert scores for each subscale (engagement, functionality, esthetics, and information) were calculated and reported as medians with IQRs.
Results: The rates of hypoglycemia were similar for both the preapp and app-use phases (0.79 and 0.83 hypoglycemia events per day, respectively). The upper bound of the CI of the hypoglycemia rate ratio met the prespecified criteria for noninferiority (rate ratio=1.06; 95% CI 0.91-1.22). The uMARS analysis showed a high rating (≥4 out of 5) of acT1ve by 80% of participants for both functionality and information, 72% for esthetics, and 63% for overall uMARS rating. Content analysis of the interview transcripts identified 3 main themes: "Provision of information," "Exercising with the App," and "Targeted Population."
Conclusions: The mHealth app "acT1ve," which was developed in collaboration with young people with T1D, is functional, acceptable, and safe for diabetes management around exercise. The study supports the noninferiority of acT1ve compared with "treatment as usual" with regards to hypoglycemic events.
Background: Insulin therapy is crucial for type 2 diabetes mellitus management, with increasing usage in Indonesia, and its effectiveness is 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.
Objective: This study seeks to investigate physicians' knowledge and practice in providing education on insulin use to type 2 diabetes mellitus patients in Indonesia.
Methods: 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, comprising demographics and clinical practice, practice of insulin education, Indonesian insulin injection technique guideline, and knowledge of insulin injection technique. The instrument used in this study was developed based on the Pedoman Teknik Menyuntik Insulin Indonesia (PTMII), which was adapted from the international consensus by the Forum for Injection Technique and Therapy Expert Recommendations (FITTER). The survey lasted from February to March 2021. Data was analysed using Kruskal-Wallis tests.
Results: A total of 823 participants were included in the analysis. A total of 680 out of 823 participants (82.6%) had given insulin education to patients at least once during the last 30 days. However, only 479 out of 823 participants (58.2%) used specific guidelines in their practice, with only 280 out of 823 participants (34.0%) aware of the Indonesian guidelines. Eight hundred and fifteen out of 823 participants (99.0%) agreed that insulin injection techniques would affect clinical results. The median score of knowledge about insulin injection techniques was 7 (interquartile range 2) among the study participants, indicating good knowledge. The profession was the only statistically significant variable associated with knowledge scores, with the highest median score held by consultants in endocrinology, metabolism & diabetes, and the lowest by other doctors (P <.001).
Conclusions: Most physicians in this study had given education to their patients. However, there was still a gap between the guidelines and the practice of insulin education, as shown by the lack of awareness and a fair level of knowledge about the Indonesian guidelines.
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