Leveraging advances in diabetes technologies in primary care: a narrative review

Efficacy of a Hybrid Closed-Loop Solution in Patients With Excessive Time in Hypoglycaemia: A Post Hoc Analysis of Trials With DBLG1 System

BACKGROUND

Automated insulin delivery is an efficient treatment for patients with type 1 diabetes. Little is known on its impact on patients with excessive time in hypoglycaemia.

METHODS

We performed a post hoc analysis of three randomized control trials that used the DBLG1 (Diabeloop Generation 1) hybrid closed-loop solution. Patients whose time below 70 mg/dL during baseline, open-loop phase exceeded 5% were selected. The outcomes were the differences between the closed-loop and the open-loop phases in time in various ranges and Glycemia Risk Index (GRI).

RESULTS

We identified 45 patients exhibiting ≥5% of time below 70 mg/dL during the open-loop phase. Under closed-loop, the time in hypoglycaemia (54 to <70 mg/dL) dropped from 7.9% (SD 2.4) to 3.2% (SD 1.6) (difference -4.7% [-5.3; -4.1], P < 10-4). The time below 54 mg/dL decreased from 1.9% (SD 1.3) to 0.8% (SD 0.7) (difference -0.9% [-1.4; -0.8], P < 10-4). The time in range (TIR 70-180 mg/dL) improved from 63.3 (SD 9.5) to 68.2% (SD 8.2) (difference 5.1% [2.9; 7.0], P < 10-4). The GRI improved from 51.2 (SD 12.4) to 38.0 (SD 10.9) (difference 13.2 [10.4; 16.0], P < 10-4).

CONCLUSION

DBLG1 decreased time in hypoglycaemia by more than 50% even in patients with excessive time in hypoglycaemia at baseline, while also improving both TIR and GRI, under real-life conditions. The improvement in GRI (13.2%) exceeded that of the improvement in TIR (5.1%) indicating that in this data set, GRI was more sensitive than TIR to the improvement in glycaemia achieved with closed-loop. These results support the safety and efficacy of this treatment.

Effects of Digitization of Self-Monitoring of Blood Glucose Records Using a Mobile App and the Cloud System on Outpatient Management of Diabetes: Single-Armed Prospective Study

BACKGROUND

In recent years, technologies promoting the digitization of self-monitoring of blood glucose (SMBG) records including app-cloud cooperation systems have emerged. Studies combining these technological interventions with support from remote health care professionals have reported improvements in glycemic control.

OBJECTIVE

To assess the use of an app-cloud cooperation system linked with SMBG devices in clinical settings, we evaluated its effects on outpatient management of diabetes without remote health care professional support.

METHODS

In this multicenter, open-label, and single-armed prospective study, 48 patients with diabetes (including type 1 and type 2) at 3 hospitals in Japan treated with insulin or glucagon-like peptide 1 receptor agonists and performing SMBG used the app-cloud cooperation system for 24 weeks. The SMBG data were automatically uploaded to the cloud via the app. The patients could check their data, and their attending physicians reviewed the data through the cloud prior to the patients' regular visits. The primary outcome was changes in glycated hemoglobin (HbA1c) levels.

RESULTS

Although HbA1c levels did not significantly change in all patients, the frequency of daily SMBG following applying the system was significantly increased before induction at 12 (0.60 per day, 95% CI 0.19-1.00; P=.002) and 24 weeks (0.43 per day, 95% CI 0.02-0.84; P=.04). In the subset of 21 patients whose antidiabetic medication had not been adjusted during the intervention period, a decrease in HbA1c level was observed at 12 weeks (P=.02); however, this significant change disappeared at 24 weeks (P=.49). The Diabetes Treatment Satisfaction Questionnaire total score and "Q4: convenience" and "Q5: flexibility" scores significantly improved after using the system (all P<.05), and 72% (33/46) patients and 76% (35/46) physicians reported that the app-cloud cooperation system helped them adjust insulin doses.

CONCLUSIONS

The digitization of SMBG records and sharing of the data by patients and attending physicians during face-to-face visits improved self-management in patients with diabetes.

TRIAL REGISTRATION

Japan Registry of Clinical Trials (jRCT) jRCTs042190057; https://jrct.niph.go.jp/en-latest-detail/jRCTs042190057.

When Positive is Negative: Health Literacy Barriers to Patient Access to Clinical Laboratory Test Results

BACKGROUND

Health literacy is a multidimensional set of skills (e.g., narrative, numeracy, digital, medication) that patients need to access and understand health information timely and accurately to make evidence-based informed decisions.

CONTENT

Multiple barriers prevent patients from effectively interacting with health information. The most salient barriers are poor overall health literacy skills and linguistic proficiency in English. As patients prefer direct access to laboratory test results, especially those of routine tests, contextualization and provider-directed interpretation of results are required to foster shared decision-making to address their healthcare issues and improve health outcomes.

SUMMARY

The use of systematic approaches that account for poor health literacy skills and include culturally and linguistically appropriate planning and availability of resources is warranted at individual and population health levels (e.g., human-centered design of patient portals).

Patient Perspectives on the Benefits and Challenges of Diabetes and Digital Technology

Diabetes technology continues to evolve, advancing with our understanding of human biology and improving our ability to treat people with diabetes. Diabetes devices are broadly classified into the following categories: glucose sensors, insulin delivery devices, and digital health care technology (i.e., software and mobile applications). When supported by education and individually tailored, technology can play a key role in optimizing outcomes. Digital devices assist in diabetes management by tracking meals, exercise, sleep, and glycemic measurements in real time, all of which can guide physicians and other clinicians in their decision-making. Here, as people with diabetes and patient advocates, as well as diabetes specialists, primary care providers, and diabetes care and education specialists, we present our perspectives on the advances, benefits, and challenges of diabetes technology in primary care practices.

Using Continuous Glucose Monitoring Values for Bolus Size Calculation in Smart Multiple Daily Injection Systems: No Negative Impact on Post-bolus Glycemic Outcomes Found in Real-World Data

BACKGROUND

Recent evidence shows that it may be safe to estimate bolus sizes based on continuous glucose monitoring (CGM) rather than blood glucose (BG) values using glycemic trend-adjusted bolus calculators. Users may already be doing this in the real world, though it is unclear whether this is safe or effective for calculators not employing trend adjustment.

METHODS

We assessed real-world data from a smart multiple daily injections (MDIs) device users with a CGM system, hypothesizing that four-hour post-bolus outcomes using CGM values are not inferior to those using BG values. Our data set included 184 users and spanned 18 months with 79 000 bolus observations. We tested differences using logistic regression predicting CGM or BG value usage based on outcomes and confirmed initial results using a mixed model regression accounting for within-subject correlations.

RESULTS

Comparing four-hour outcomes for bolus events using CGM and BG values revealed no differences using our initial approach (P > .183). This finding was confirmed by our mixed model regression approach in all cases (P > .199), except for times below range outcomes. Higher times below range were predictive of lower odds of CGM-based bolus calculations (OR = 0.987, P < .0001 and OR = 0.987, P = .0276, for time below 70 and 54 mg/dL, respectively).

CONCLUSIONS

We found no differences in four-hour post-bolus glycemic outcomes when using CGM or BG except for time below range, which showed evidence of being lower for CGM. Though preliminary, our results confirm prior findings showing non-inferiority of using CGM values for bolus calculation compared with BG usage in the real world.

Demographic Variables Associated With Diabetes Technology Awareness or Use in Adults With Type 2 Diabetes

Background

Studies in populations with type 1 diabetes highlight racial/ethnic disparities in the use of diabetes technology; however, little is known about disparities among those with type 2 diabetes. This project investigates the racial/ethnic and socioeconomic disparities in diabetes technology awareness and use in adults with type 2 diabetes in the ambulatory setting.

Methods

Adults ≥40 years of age with type 2 diabetes in ambulatory care were invited to participate via an e-mail link to a de-identified REDCap (Research Electronic Data Capture) questionnaire. Variables, including awareness and use of continuous glucose monitoring (CGM) and insulin pumps, were summarized descriptively using frequencies and percentages and were compared across racial/ethnic groups, education level, and income using Pearson χ2 or Fisher exact tests.

Results

The study included 116 participants, most of whom (62%) were White, elderly Medicare recipients. Compared with White participants, those of racially/ethnically minoritized groups were less likely to be aware of CGM (P = 0.013) or insulin pumps (P = 0.001). Participants with a high school education or less were also less likely to be aware of insulin pumps (P = 0.041). Interestingly, neither awareness nor use of CGM or insulin pumps was found to be associated with income.

Conclusion

This cross-sectional analysis suggests that racially/ethnically minoritized groups and individuals with lower education have less awareness of CGM or insulin pumps.

Telementoring With Project ECHO: A New Era in Diabetes-Related Continuing Education for Primary Care to Address Health Disparities

Project ECHO® is a telementoring workforce development model that targets under-resourced communities lacking access to specialty care. The model builds virtual communities of practice, including specialists and community primary care professionals (PCPs) to combat clinical inertia and health disparities. While the ECHO model has gained global recognition, implementation of the model related to diabetes is lagging compared to other specialty conditions. This review highlights diabetes-endocrine (ENDO)-focused ECHOs using data reported in the ECHO Institute's centralized data repository (iECHO) and the learning collaborative for diabetes ECHOs. It also describes the implementation of diabetes ECHOs and their evaluation. Learner and patient-centered outcomes related to diabetes ECHOs are reviewed. Program implementation and evaluations have demonstrated utility of the ECHO model for diabetes programs to (1) address unmet needs of diabetes care in the primary care setting, (2) improve knowledge and confidence in managing complex diabetes and change provider prescribing habits, (3) improve patient outcomes, and (4) address diabetes quality improvement practices in primary care. More studies with broader collaboration among sites are needed to evaluate the model related to diabetes, especially applied to addressing therapeutic inertia, adoption of diabetes technology, and reducing health disparities.