Tamara K. Oser

Purpose: Of the 88 million American adults with prediabetes, 1.5 million transition to type 2 diabetes annually. Lifestyle changes could delay progression to type 2 diabetes development. Glycemic Excursion Minimization/Glucose Everyday Matters (GEM) is a self-guided intervention that empowers individuals via a guide paired with a continuous glucose monitor and activity monitor to understand how dietary and activity choices impact their blood glucose excursions. This qualitative study aimed to explore participant perspectives and experiences of GEM. Design: Semi-structured qualitative interviews. Setting: The study was conducted at an urban medical center. Interviews took place via HIPAA compliant Zoom. Participants: Participants from a larger clinical trial who completed the GEM intervention for prediabetes (n = 16). Method: Interviews were audio-recorded and transcribed. Using two coders, iterative cycles of inductive thematic analysis was performed to yield overarching themes related to the GEM guide and intervention. Results: Analysis of 16 interviews revealed five major themes which highlighted the positive outcomes in health status, disease insight and support experienced during the GEM trial. Conclusion: Key findings included increased knowledge on the relationship between lifestyle choices and glucose excursions, and positive insight on the GEM intervention and its components. Feedback from this study will be incorporated into further adoption of the GEM intervention for a prediabetes population.

Introduction: The Virtual Diabetes Specialty Clinic (VDiSC) study demonstrated the feasibility of providing comprehensive diabetes care entirely virtually by combining virtual visits with continuous glucose monitoring support and remote patient monitoring (RPM). However, the financial sustainability of this model remains uncertain. Methods: We developed a financial model to estimate the variable costs and revenues of virtual diabetes care, using visit data from the 234 VDiSC participants with type 1 or type 2 diabetes. Data included virtual visits with certified diabetes care and education specialists (CDCES), endocrinologists, and behavioral health services (BHS). The model estimated care utilization, variable costs, reimbursement revenue, gross profit, and gross profit margin per member, per month (PMPM) for privately insured, publicly insured, and overall clinic populations (75% privately insured). We performed two-way sensitivity analyses on key parameters. Results: Gross profit and gross profit margin PMPM (95% confidence interval) were estimated at $−4 ($−14.00 to $5.68) and −4% (−3% to −6%) for publicly insured patients; $267.26 ($256.59-$277.93) and 73% (58%-88%) for privately insured patients; and $199.41 ($58.43-$340.39) and 67% (32%-102%) for the overall clinic. Profits were primarily driven by CDCES visits and RPM. Results were sensitive to insurance mix, cost-to-charge ratio, and commercial-to-Medicare price ratio. Conclusions: Virtual diabetes care can be financially viable, although profitability relies on privately insured patients. The analysis excluded fixed costs of clinic infrastructure, and securing reimbursement may be challenging in practice. The financial model is adaptable to various care settings and can serve as a planning tool for virtual diabetes clinics.

Few primary care practices have access to clinical pharmacists and certified diabetes care and education specialists (CDCESs), despite the critical role they play in diabetes management. Diabetes technologies, including Continuous Glucose Monitors (CGMs), are associated with improved patient outcomes; however, disparities exist in obtaining CGMs for primary care patients compared with endocrinology patients. To address this barrier, a virtual CGM Initiation Service (virCIS) was created and implemented in primary care practices in Colorado. Clinical pharmacists served key roles in service implementation, including leading virtual visits for CGM initiation and data interpretation, providing patient education about CGM results and potential therapy implications, creating education and actionable recommendations for primary care providers and assistance with the CGM prescription coverage process. Implementation of this model supported patient access to CGMs, while providing comprehensive patient and provider education about CGM use. Overcoming the multitude of CGM coverage barriers identified led to the development of recommended practices for CGM coverage.

[This corrects the article DOI: 10.2196/62916.].

UNSTRUCTURED Diabetes distress (DD) is a common emotional response to living with diabetes. If not addressed, DD can have negative impacts on diabetes management, including the progression to mental health conditions such as depression and anxiety. Routine screening and treatment for DD is recommended, with primary care being an ideal setting given that the majority of people with diabetes receive their diabetes care from primary care providers. However, consistent screening of DD does not routinely occur in primary care settings. Research is needed to understand how to effectively and feasibly integrate DD screening and treatment into routine diabetes care. This study aims to (1) design and implement individualized technology-supported DD workflows, (2) evaluate the primary outcome of determining the acceptability and feasibility of integrating technology-based workflows to provide treatment for DD, and (3) evaluate the secondary outcomes of changes in DD, depression, and anxiety (baseline, 3 months, and 6 months) in patients receiving screening and personalized treatment. In total, 30 English and Spanish-speaking primary care patients with either type 1 or type 2 diabetes will receive screening for DD during clinical visits and subsequent support from an artificial intelligence (AI)–based health care chatbot with interactive tailored messaging. In addition, the use of electronic consultation with a specialist or referral to a behavioral health provider could occur depending on the severity and source of DD. The use of electronic consultations allows providers convenient and timely asynchronous access to a range of specialty care providers. Health outcomes will be measured through changes in validated screening measures for DD, depression, and anxiety. Digital outcomes will be measured through surveys assessing user experience with technology and system usability, and by system performance data. Qualitative data on acceptability and satisfaction with the clinical workflows and technological interventions will be collected through interviews with patients and clinical providers. Descriptive statistics will summarize quantitative outcome measures and responses to closed-ended survey items, and rapid thematic and content analysis will be conducted on open-ended survey and interview data. Workflows for screening and treating DD have been approved and clinical staff have received training on the process. Electronic surveys for screening measure collection have been created. Data from visit screeners will be entered into the electronic medical record during the medical appointment. Recruitment will begin late June-July 2024. This study is expected to demonstrate the feasibility and acceptability of integrating individualized workflows for DD into primary care. Improving clinical and digital interventions for addressing DD in primary care can provide alternative care options for busy clinical providers. This study is intended to deliver whole-person diabetes care to people with diabetes within a primary care setting.

Background: Diabetes distress (DD) is a common emotional response to living with diabetes. If not addressed, DD can have negative impacts on diabetes management, including the progression to mental health conditions such as depression and anxiety. Routine screening and treatment for DD is recommended, with primary care being an ideal setting given that the majority of people with diabetes receive their diabetes care from primary care providers. However, consistent screening of DD does not routinely occur in primary care settings. Research is needed to understand how to effectively and feasibly integrate DD screening and treatment into routine diabetes care. Objective: This study aims to (1) design and implement individualized technology-supported DD workflows, (2) evaluate the primary outcome of determining the acceptability and feasibility of integrating technology-based workflows to provide treatment for DD, and (3) evaluate the secondary outcomes of changes in DD, depression, and anxiety (baseline, 3 months, and 6 months) in patients receiving screening and personalized treatment. Methods: In total, 30 English and Spanish-speaking primary care patients with either type 1 or type 2 diabetes will receive screening for DD during clinical visits and subsequent support from an artificial intelligence (AI)–based health care chatbot with interactive tailored messaging. In addition, the use of electronic consultation with a specialist or referral to a behavioral health provider could occur depending on the severity and source of DD. The use of electronic consultations allows providers convenient and timely asynchronous access to a range of specialty care providers. Health outcomes will be measured through changes in validated screening measures for DD, depression, and anxiety. Digital outcomes will be measured through surveys assessing user experience with technology and system usability, and by system performance data. Qualitative data on acceptability and satisfaction with the clinical workflows and technological interventions will be collected through interviews with patients and clinical providers. Descriptive statistics will summarize quantitative outcome measures and responses to closed-ended survey items, and rapid thematic and content analysis will be conducted on open-ended survey and interview data. Results: Workflows for screening and treating DD have been approved and clinical staff have received training on the process. Electronic surveys for screening measure collection have been created. Data from visit screeners will be entered into the electronic medical record during the medical appointment. Recruitment will begin late June-July 2024. Conclusions: This study is expected to demonstrate the feasibility and acceptability of integrating individualized workflows for DD into primary care. Improving clinical and digital interventions for addressing DD in primary care can provide alternative care options for busy clinical providers. This study is intended to deliver whole-person diabetes care to people with diabetes within a primary care setting. International Registered Report Identifier (IRRID): PRR1-10.2196/62916

Purpose of Review Addressing diabetes distress (DD), the emotional demands of living with diabetes, is a crucial component of diabetes care. Most individuals with type 2 diabetes and approximately half of adults with type 1 diabetes receive their care in the primary care setting. This review will provide guidance on addressing DD and implementing targeted techniques that can be tailored to primary care patients. Recent Findings Structured educational, behavioral, and emotion-focused techniques have promise for treating DD. These interventions are unlikely to require advanced training and can be feasibly integrated into primary care settings without creating additional burdens on time or resources. Summary Interventional studies examining treatment for DD are limited, leaving a gap for clear direction and consensus on how to target and treat DD in primary care patients. This review consolidates recommendations and approaches from recent findings on how to treat DD within the context of primary care.

Background: Older adults may be less comfortable with continuous glucose monitoring (CGM) technology or require additional education to support use. The Virtual Diabetes Specialty Clinic study provided the opportunity to understand glycemic outcomes and support needed for older versus younger adults living with diabetes and using CGM. Methods: Prospective, virtual study of adults with type 1 diabetes (T1D, N = 160) or type 2 diabetes (T2D, N = 74) using basal-bolus insulin injections or insulin pump therapy. Remote CGM diabetes education (3 scheduled visits over 1 month) was provided by Certified Diabetes Care and Education Specialists with additional visits as needed. CGM-measured glycemic metrics, HbA1c and visit duration were evaluated by age (<40, 40-64 and ≥65 years). Results: Median CGM use was ≥95% in all age groups. From baseline to 6 months, time 70 to 180 mg/dL improved from 45% ± 22 to 57% ± 16%; 50 ± 25 to 65 ± 18%; and 60 ± 28 to 69% ± 18% in the <40, 40-64, and ≥65-year groups, respectively ( 0.05). Visit duration was 41 min longer for ages ≥65 versus 65 years is longer compared with younger adults. Addressing individual training-related needs, including needs that may vary by age, should be considered.

Background: Continuous glucose monitoring (CGM) can improve health for people with diabetes but is limited in primary care (PC). Nurse Practitioners (NPs) in PC can improve diabetes management through CGM, but NPs' interest in CGM and support needed are unclear. Purpose: We describe behaviors and attitudes related to CGM for diabetes management among NPs in PC. Methodology: This cross-sectional web-based survey of NPs practicing in PC settings used descriptive statistics to describe CGM experience and identify resources to support prescribing. We used multivariable regression to explore characteristics predicting prescribing and confidence using CGM for diabetes. Results: Nurse practitioners in hospital-owned settings were twice as likely to have prescribed CGM (odds ratio [OR] = 2.320, 95% CI [1.097, 4.903]; p = .002) than private practice; those in academic medical centers were less likely (OR = 0.098, 95% CI [0.012, 0.799]; p = .002). Past prescribing was associated with favorability toward future prescribing (coef. = 0.7284, SE = 0.1255, p < .001) and confidence using CGM to manage diabetes (type 1: coef. = 3.57, SE = 0.51, p < .001; type 2: coef. = 3.49, SE = 0.51, p < .001). Resources to prescribe CGM included consultation with an endocrinologist (62%), educational website (61%), and endocrinological e-consultations (59%). Conclusions: Nurse practitioners are open to prescribing CGM and can improve diabetes management and health outcomes for PC patients. Implications: Research should explore mechanisms behind associations with CGM experience and attitudes. Efforts to advance CGM should include educational websites and endocrinology consultations for NPs in PC.