Journal of Diabetes Science and Technology最新文献

Artificial intelligence (AI) is increasingly being used to diagnose complications of diabetes. Artificial intelligence is technology that enables computers and machines to simulate human intelligence and solve complicated problems. In this article, we address current and likely future applications for AI to be applied to diabetes and its complications, including pharmacoadherence to therapy, diagnosis of hypoglycemia, diabetic eye disease, diabetic kidney diseases, diabetic neuropathy, diabetic foot ulcers, and heart failure in diabetes.Artificial intelligence is advantageous because it can handle large and complex datasets from a variety of sources. With each additional type of data incorporated into a clinical picture of a patient, the calculation becomes increasingly complex and specific. Artificial intelligence is the foundation of emerging medical technologies; it will power the future of diagnosing diabetes complications.

Background: Diabetes self-management education and support (DSMES) improves diabetes outcomes yet remains consistently underutilized. Chatbot technology offers the potential to increase access to and engagement in DSMES. Evidence supporting the case for chatbot uptake and efficacy in people with diabetes (PWD) is needed.

Method: A diabetes education and support chatbot was deployed in a regional health care system. Adults with type 2 diabetes with an A1C of 8.0% to 8.9% and/or having recently completed a 12-week diabetes care management program were enrolled in a pilot program. Weekly chats included three elements: knowledge assessment, limited self-reporting of blood glucose data and medication taking behaviors, and education content (short videos and printable materials). A clinician facing dashboard identified need for escalation via flags based on participant responses. Data were collected to assess satisfaction, engagement, and preliminary glycemic outcomes.

Results: Over 16 months, 150 PWD (majority above 50 years of age, female, and African American) were enrolled. The unenrollment rate was 5%. Most escalation flags (N = 128) were for hypoglycemia (41%), hyperglycemia (32%), and medication issues (11%). Overall satisfaction was high for chat content, length, and frequency, and 87% reported increased self-care confidence. Enrollees completing more than one chat had a mean drop in A1C of -1.04%, whereas those completing one chat or less had a mean increase in A1C of +0.09% (P = .008).

Conclusion: This diabetes education chatbot pilot demonstrated PWD acceptability, satisfaction, and engagement plus preliminary evidence of self-care confidence and A1C improvement. Further efforts are needed to validate these promising early findings.

Advances in diabetes technologies have enabled automated insulin delivery (AID) systems, which have demonstrated benefits to glycemia, psychosocial outcomes, and quality of life for people with type 1 diabetes (T1D). Despite the many demonstrated benefits, AID systems come with their own unique challenges: continued user attention and effort, barriers to equitable access, personal costs vs benefits, and integration of the system into daily life. The purpose of this narrative review is to identify challenges and opportunities for supporting uptake and onboarding of AID systems to ultimately support sustained AID use. Setting realistic expectations, providing comprehensive training, developing willingness to adopt new treatments and workflows, upskilling of diabetes team members, and increasing flexibility of care to tailor care to individual needs, preferences, lifestyle, and personal goals will be most effective in facilitating effective, widespread, person-centered implementation of AID systems.

Background: Simulated data are a powerful tool for research, enabling benchmarking of blood glucose (BG) forecasting and control algorithms. However, expert created models provide an unrealistic view of real-world performance, as they lack the features that make real data challenging, while black-box approaches such as generative adversarial networks do not enable systematic tests to diagnose model performance.

Methods: To address this, we propose a method that learns missingness and error properties of continuous glucose monitor (CGM) data collected from people with type 1 diabetes (OpenAPS, OhioT1DM, RCT, and Racial-Disparity), and then augments simulated BG data with these properties. On the task of BG forecasting, we test how well our method brings performance closer to that of real CGM data compared with current simulation practices for missing data (random dropout) and error (Gaussian noise, CGM error model).

Results: Our methods had the smallest performance difference versus real data compared with random dropout and Gaussian noise when individually testing the effects of missing data and error on simulated BG in most cases. When combined, our approach was significantly better than Gaussian noise and random dropout for all data sets except OhioT1DM. Our error model significantly improved results on diverse data sets.

Conclusions: We find a significant gap between BG forecasting performance on simulated and real data, and our method can be used to close this gap. This will enable researchers to rigorously test algorithms and provide realistic estimates of real-world performance without overfitting to real data or at the expense of data collection.

Background: There is plenty of evidence supporting the clinical benefits of mHealth interventions for type 2 diabetes, but despite often being promoted as cost-effective or cost-saving, there is still limited research to support such claims. The objective of this review was to summarize and critically analyze the current body of economic evaluation (EE) studies for mHealth interventions for type 2 diabetes.

Methods: Using a comprehensive search strategy, five databases were searched for full and partial EE studies for mHealth interventions for type 2 diabetes from January 2007 to March 2022. "mHealth" was defined as any intervention that used a mobile device with cellular technology to collect and/or provide data or information for the management of type 2 diabetes. The CHEERS 2022 checklist was used to appraise the reporting of the full EEs.

Results: Twelve studies were included in the review; nine full and three partial evaluations. Text messages smartphone applications were the most common mHealth features. The majority of interventions also included a Bluetooth-connected medical device, eg, glucose or blood pressure monitors. All studies reported their intervention to be cost-effective or cost-saving, however, most studies' reporting were of moderate quality with a median CHEERS score of 59%.

Conclusion: The current literature indicates that mHealth interventions for type 2 diabetes can be cost-saving or cost-effective, however, the quality of the reporting can be substantially improved. Heterogeneity makes it difficult to compare study outcomes, and the failure to report on key items leaves insufficient information for decision-makers to consider.

Background: The magnitude and importance of higher HbA1c levels not due to mean blood glucose (MBG) in non-Hispanic black (B) versus non-Hispanic white (W) individuals is controversial. We sought to clarify the relationship of HbA1c with glucose data from continuous glucose monitoring (CGM) in a young biracial population.

Methods: Glycemic data of 33 B and 85 W, healthy youth with type 1 diabetes (age 14.7 ± 4.8 years, M/F = 51/67, duration of diabetes 5.4 ± 4.7 years) from a factory-calibrated CGM was compared with HbA1c. Hemoglobin glycation index (HGI) = assayed HbA1c - glucose management index (GMI).

Results: B patients had higher unadjusted levels of HbA1c, MBG, MBGSD, GMI, and HGI than W patients. Percent glucose time in range (TIR) and percent sensor use (PSU) were lower for B patients. Average HbA1c in B patients 8.3% was higher than 7.7% for W (P < .0001) after statistical adjustment for MBG, age, gender, insulin delivery method, and accounting for a race by PSU interaction effect. Higher HbA1c persisted in B patients when TIR was substituted for MBG. Predicted MBG was higher in B patients at any level of PSU. The 95th percentile for HGI was 0.47 in W patients, and 52% of B patients had HGI ≥ 0.5. Time below range was similar for both.

Conclusions: Young B patients have clinically relevant higher average HbA1c at any given level of MBG or TIR than W patients, which may pose an additional risk for diabetes complications development. HGI ≥ 0.5 may be an easy way to identify high-risk patients.

Globally, health care workers (HCWs) are at a high risk of occupational exposure to needlestick injuries (NSIs). Needlestick injuries not only are associated with an increased risk of infections caused by bloodborne pathogens but are also a primary source of emotional distress and job burnout for HCWs and patients. Insulin injection-related NSIs are common among HCWs working in hospitals in the Asia-Pacific (APAC) region and impose a significant burden. Insulin pen needles have a high risk of transmitting infections (at both the patient-end and cartridge end of the sharp) after use. Recapping a needle after administering an insulin injection poses a major risk to HCWs. Currently, several safety-engineered needle devices (SENDs) are available with active or passive safety mechanisms. Passive insulin safety pen needles with dual-ended protection and automatic recapping capabilities have resulted in a significant drop in accidental punctures to HCWs while administering insulin to patients with diabetes. In this article, we have reviewed the burden and common causes of NSIs with insulin injections among HCWs in the APAC region. We have discussed current approaches to address the issues associated with NSIs and the benefits of introducing SENDs in health care settings, including long-term care facilities, nursing homes, and home care settings where patients may require assisted insulin injections. This review also summarizes key strategies/recommendations to prevent NSIs in HCWs and patients with diabetes in the APAC region.