Journal of Diabetes Science and Technology最新文献

An Office of the Inspector General (OIG) report on September 19, 2024, highlighted the need for additional oversight of remote patient monitoring (RPM), which is covered by Medicare. OIG noted that Medicare claims frequently lack crucial information that would facilitate proper oversight. While Medicare has published guidelines for reimbursement according to RPM billing codes, greater clarity is needed to avoid inadvertent improper billing practices. In this article, we discuss potential revisions in Medicare policies for documenting RPM services. Such revisions will result in enhanced accountability and traceability of RPM services and reduce the potential for improper billing. Adherence to clear billing guidelines will ensure appropriate allocation of funds and safeguards the future of RPM services for Medicare beneficiaries who genuinely need them.

Introduction: mHealth technology has the potential to deliver personalized health care; however, data on cardiometabolic risk factors are limited. This study aims to assess the effectiveness of mobile health applications (apps) on cardiometabolic risk factor reduction in adults aged 25 to 60 years in urban and rural India.

Methods: The study design was a pilot randomized controlled trial conducted in Tamil Nadu, India. Smartphone users (25-60 years) with basic literacy and at high risk of developing diabetes (Indian Diabetes Risk Score ≥30 and/or fasting blood sugar [FBS] 100-125 mg/dL) were recruited. Four mobile apps (two commercially available, two novel) for cardiometabolic risk reduction were evaluated. Primary outcome (weight loss) was analyzed using intention-to-treat analysis with post hoc analysis and logistic regression models adjusted for confounders.

Results: A total of 5264 participants were screened, and 610 were recruited into the study. Participants (7%) dropped out largely due to the COVID-19 pandemic. Data from 567 participants were used for the final analysis. In the intention-to-treat analysis, a significant reduction in body weight was observed in the intervention group as compared with control, more so in the urban (-2.40 kg, 95% confidence interval [CI] = [-3.10, -1.69], P < .001) compared with rural population (-1.19 kg, 95% CI = [-1.55, -0.82], P < .001). Intervention group participants showed significant reductions in body mass index, waist circumference, blood pressure, FBS, total serum cholesterol, and a positive effect on dietary and physical activity behaviors compared with controls.

Conclusions: mHealth interventions can reduce diabetes risk, improve cardiometabolic health, and improve lifestyle behaviors in South Asian populations.

Trial registration: The trial is registered with the Central Trials Registry, India (CTRI/2020/03/024327).

Background: Diet interventions often have poor adherence due to burdensome food logging. Approaches using photographs assessed by artificial intelligence (AI) may make food logging easier, if they are adequately accurate.

Method: We used OpenAI's GPT-4o model with one-shot prompts and no fine-tuning to assess energy, fat, protein, carbohydrate, fiber, and salt through photographs of 22 meals, comparing assessments to weighed food records for each meal and to assessments of dieticians.

Results: The model had poor performance overall. For fiber, though, the model achieved an intraclass correlation coefficient of 0.71 (0.67-0.74 95% CI), well above the dietician performance of 0.57.

Conclusions: The simplest use of current AI via one-shot prompting and no fine-tuning accurately assesses fiber content in meals but is inaccurate for other nutritional parameters.

Monogenic diabetes mellitus (MDM) is a group of relatively rare disorders caused by pathogenic variants in key genes that result in hyperglycemia. Lack of identified cases, along with absent data standards, and limited collaboration across institutions have hindered research progress. To address this, the UChicago Monogenic Diabetes Registry (UCMDMR) and UChicago Data for the Common Good (D4CG) created a national consortium of MDM research institutions called the PREcision DIabetes ConsorTium (PREDICT). Following the D4CG model, PREDICT has successfully established a multicenter MDM data commons. PREDICT has created a consensus data dictionary that will be utilized to address critical gaps in understanding of these rare types of diabetes. This approach may be useful for other rare conditions that would benefit from access to harmonized pooled data.

Background: Interoperability is a critical enabler for integrated Personalized Diabetes Management (iPDM), automated insulin delivery (AID), and the digital transformation of healthcare in general. However, manufacturers still create closed ecosystems (ie, solutions designed to work end-to-end minimizing collaboration with other organizations) with proprietary interfaces because of various interoperability challenges. Therefore, the aim of this article is to provide an overview of how to achieve organizational interoperability in an open ecosystem (ie, solutions designed to integrate different organizations via interoperability standards) for diabetes management.

Methods: The proposed interoperability design approach called Secure Plug and Play Interoperability (SPPI) supports building and using interoperable system elements in an open ecosystem. Secure Plug and Play Interoperability enables interoperability over the entire system life cycle with its reference architecture, secure interoperability standards, and organizational capabilities. These standards were developed with participation from healthcare providers, regulatory authorities, payers, academia, and manufacturers. Publicly available information provides examples of implementation support and practical usage.

Results: Organizational interoperability in an open ecosystem can be achieved through organizational capabilities and a selection of secure interoperability standards. ISO/IEEE 11073, Bluetooth profiles, and HL7 FHIR with test specifications, test tools, software development kits, and quality assurance programs represent a coordinated selection suitable for building an open ecosystem. Practical usage is demonstrated with real-world solutions that build on these standards.

Conclusions: Secure Plug and Play Interoperability facilitates the end-to-end integration of devices, digital products, and services from partners in an open ecosystem. Moreover, even a single manufacturer, who provides all system elements of a solution, can use and benefit from SPPI.

Background: The glycemia risk index (GRI) is a novel composite continuous glucose monitoring (CGM) metric composed of hypoglycemia and hyperglycemia components and is weighted toward extremes. This study aimed to investigate the association between GRI and the risk of albuminuria in type 1 diabetes.

Methods: The 90-day CGM tracings of 330 individuals with type 1 diabetes were included in the analysis. Glycemia risk index was divided into five risk zones (A-E), and hypoglycemia and hyperglycemia components were divided into quintiles. Albuminuria was defined as a spot urine albumin-to-creatinine ratio ≥30 mg/g. Associations of albuminuria with GRI and its hypoglycemia and hyperglycemia components were estimated.

Results: Mean GRI and glycated hemoglobin (HbA1c) were 40.9 ± 21.3 and 7.3 ± 1.0%, respectively, and the overall prevalence of albuminuria was 17.6%. Prevalence of albuminuria differed significantly by GRI zone (P = .023). In logistic regression analysis, the adjusted odds ratio (OR) of albuminuria per increase in the GRI zone was 1.70 (95% confidence interval [CI]: 1.19-2.41) after adjusting for various factors affecting albuminuria. The association remained significant after adjusting for achievement of the recommended target of time in range (70-180 mg/dL; >70%) or HbA1c (<7%). The hyperglycemia component of GRI was also associated with albuminuria, and the association remained significant even after adjusting for HbA1c level itself (adjusted OR 1.44, 95% CI: 1.05-1.98).

Conclusions: Glycemia risk index is significantly associated with albuminuria in individuals with type 1 diabetes.

Aims: According to the 2023 International Consensus, glucose metrics derived from two-week-long continuous glucose monitoring (CGM) can be extrapolated up to 90 days before. However, no studies have focused on adults with type 1 diabetes (T1D) on multiple daily injections (MDIs) and with second-generation intermittently scanned CGM (isCGM) sensors in a real-world setting.

Methods: This real-world, retrospective study included 539 90-day isCGM data from 367 adults with T1D on MDI therapy. For each sensor metric, the coefficients of determination (R²) were computed for sampling periods from 2 to 12 weeks versus the whole 90-day interval. Correlations were considered strong for R² ≥0.88.

Results: The two-week sampling period displayed strong correlations for time in range (TIR, 70-180 mg/dl; R² = 0.89) and above range (TAR, >180 mg/dl; R² = 0.88). The four-week sampling period showed additional strong correlations for time in tight range (TITR, 70-140 mg/dl; R² = 0.92), for the coefficient of variation (CV; R² = 0.88), and for the glycemia risk index (GRI; R² = 0.92). The six-week sampling period displayed an additional strong correlation for time below range (TBR, <70 mg/dl; R² = 0.90). After stratification by clinical variables, lower R² values were found for older age quartiles (>40 years), higher CV (>36%), lower sensor use (≤94%), and higher HbA1c (>7.5%).

Conclusion: In patients with T1D on MDI, two- to six-week intervals of isCGM use can provide clinically useful estimates of TIR, TAR, TITR, TBR, CV, and GRI, which can be extrapolated to longer (up to 90 days) time intervals. Longer intervals might be needed in case of older age, higher glucose variability, lower sensor use, and higher HbA1c.