Psychosocial Factors and Glycemic Control in Young Adults With Youth-Onset Type 2 Diabetes.

Importance: Youth-onset type 2 diabetes is associated with poor glycemic control and early onset of complications. Identification of psychosocial factors associated with poor glycemic control is needed to inform efficacious interventions. Objective: To identify psychosocial factors associated with glycated hemoglobin (HbA1c) levels in young adults with youth-onset type 2 diabetes. Design, Setting, and Participants: For the iCount cohort study, HbA1c levels were measured twice (at baseline [T1] and at 1 year [T2]) during the last years (2017-2019) of the observational phase of the multicenter Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY2) study. Participants were young adults who had been diagnosed with type 2 diabetes during childhood or adolescence. Data were analyzed from December 2021 to September 2023. Main Outcomes and Measures: Glycemic control was examined categorically (high [≥8.0%] vs low [<8.0%] HbA1c), continuously (HbA1c level), and over time (change in HbA1c: decreased ≥0.5%, remained stable, or increased ≥0.5%). Psychosocial measures included beliefs about medicines, depression and anxiety symptoms, diabetes distress, diabetes self-efficacy, self-management support, and unmet material needs. Multivariable logistic and linear regression models evaluated the association of each psychosocial factor with the probability of T2 HbA1c of 8.0% or greater, T2 HbA1c level, and change in HbA1c. Results: Of the 411 TODAY2 participants approached, 381 enrolled in the iCount study, and 348 with T1 and T2 HbA1c data comprised the analysis group. The 348 participants had a mean (SD) age of 26.1 (2.5) years and a mean (SD) HbA1c of 9.4% (2.8%). Most participants (229 [65.8%]) were women. In adjusted multivariable regressions, greater beliefs that diabetes medicines are necessary (odds ratio [OR], 1.19 [95% CI, 1.03-1.37]; P = .02), concerns about medicines (OR, 1.20 [95% CI, 1.00-1.45]; P = .049), diabetes distress (OR, 1.08 [95% CI, 1.02-1.15]; P = .006), and high distress (OR, 2.18 [95% CI, 1.15-4.13]; P = .02) increased the odds of high HbA1c at T2. Greater support (OR, 0.67 [95% CI, 0.46-0.97]; P = .04) and diabetes self-efficacy (OR, 0.91 [95% CI, 0.84-0.99]; P = .02) decreased the odds of high HbA1c at T2. Diabetes distress was associated with higher HbA1c level at T2 (coefficient, 0.08 [95% CI, 0.02-0.13]; P = .01). Beliefs that diabetes medicines are necessary (OR, 1.20 [95% CI, 1.03-1.39]; P = .02) and concerns about medicines (OR, 1.22 [95% CI, 1.00-1.47]; P = .048) increased the odds of an HbA1c decrease of at least 0.5% over 1 year. Conclusions and Relevance: In this cohort study of young adults with youth-onset type 2 diabetes, beliefs about medicines, high diabetes distress, low diabetes self-efficacy, and self-management support were associated with high HbA1c over time. Future research should assess whether interventions that address these factors result in improved glycemic control in this at-risk group.

Risk factors associated with cognitive performance and cognitive impairment in older adults with type 1 diabetes: Data from the Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study.

BACKGROUND: Adults with type 1 diabetes (T1D) are considered at increased risk for cognitive impairment and accelerated brain aging. However, longitudinal data on cognitive impairment and dementia in this population are scarce. OBJECTIVE: To identify risk factors associated with cognitive performance and cognitive impairment in a longitudinal sample of older adults with T1D. METHODS: We analyzed data collected as part of the Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) Study, in which 22 endocrinology practices participated. Randomized participants with T1D ≥60 years of age who completed at least one cognitive assessment were included in this study (n = 203). Cognitive impairment was classified using published recommendations. RESULTS: Older age, male sex, non-private health insurance, worse daily functioning, diagnosis of neuropathy, and longer duration of diabetes were associated with worse cognitive performance, but not cognitive impairment. 49 % and 39 % of the sample met criteria for cognitive impairment at baseline and 52 weeks respectively. Of the participants that had data at both time points, 10 % were normal at baseline and impaired at 52 weeks and 22 % of participants (44 % of those classified with cognitive impairment at baseline) reverted to normal over 52 weeks. CONCLUSION: This study indicated that several demographic and clinical characteristics are associated with worse cognitive performance in older adults with T1D, but there were no associations between these characteristics and cognitive impairment defined by NIH Toolbox cognitive impairment criteria. Caution is warranted when assessing cognition in older adults with T1D, as a large percentage of those identified as having cognitive impairment at baseline reverted to normal after 52 weeks. There is need for future studies on the interrelationship of cognition and aging to better understand the effects of T1D on cognitive health, to improve clinical monitoring and help mitigate the risk of dementia in this population.

Effects of ADHD and ADHD treatment on glycemic management in type 1 diabetes: A systematic review and meta-analysis of observational studies.

AIMS: Studies suggested a higher prevalence of Attention-deficit/hyperactivity disorder (ADHD) in individuals with Type 1 Diabetes Mellitus (T1D). However, it is unclear how ADHD impacts glycemia and diabetes-related complications. This systematic review and meta-analysis aimed to investigate the effect of ADHD and ADHD medications on HbA1c and acute complications in T1D. METHODS: A literature search was conducted in PubMed, EMBASE, CINAHL, Scopus, PsycINFO, CENTRAL, and Web of Science collections up to November 22, 2023. Seventeen studies were selected for the systematic review by independent reviewers, with twelve included in the meta-analysis. RESULTS: Mean HbA1c levels were significantly higher in T1D individuals with ADHD compared to those without ADHD (MD = 0.60; 95 % CI: 0.41, 0.79; I2 = 90.1 %; p-value < 0.001). The rates of suboptimal HbA1c levels, hospitalization, diabetic ketoacidosis, and hypoglycemia were all substantially higher in T1D individuals with ADHD than those without ADHD. No difference was found in mean HbA1c between those who received ADHD treatment and those who did not (mean difference = -0.52; 95 % confidence interval: -1.16, 0.13; I2 = 78.6 %; p-value = 0.12). CONCLUSIONS: ADHD is associated with higher HbA1c and increased acute diabetes-related complications. More research is needed to assess the effects of ADHD treatments on T1D management.

Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case-control study.

INTRODUCTION: Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures. RESEARCH DESIGN AND METHODS: Data from a case-control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics. RESULTS: Data from 191 OAs with type 1 diabetes (47.1% female, 92.1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM, % time blood glucose below 70 mg/dL, and trail making test B score. CONCLUSIONS: Machine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics.

Nocturnal hypoglycemia is associated with next day cognitive performance in adults with type 1 diabetes: Pilot data from the GluCog study.

OBJECTIVE:  Individuals with type 1 diabetes (T1D) have increased risk for cognitive dysfunction and high rates of sleep disturbance. Despite associations between glycemia and cognitive performance using cross-sectional and experimental methods few studies have evaluated this relationship in a naturalistic setting, or the impact of nocturnal versus daytime hypoglycemia. Ecological Momentary Assessment (EMA) may provide insight into the dynamic associations between cognition, affective, and physiological states. The current study couples EMA data with continuous glucose monitoring (CGM) to examine the within-person impact of nocturnal glycemia on next day cognitive performance in adults with T1D. Due to high rates of sleep disturbance and emotional distress in people with T1D, the potential impacts of sleep characteristics and negative affect were also evaluated. METHODS:  This pilot study utilized EMA in 18 adults with T1D to examine the impact of glycemic excursions, measured using CGM, on cognitive performance, measured via mobile cognitive assessment using the TestMyBrain platform. Multilevel modeling was used to test the within-person effects of nocturnal hypoglycemia and hyperglycemia on next day cognition. RESULTS:  Results indicated that increases in nocturnal hypoglycemia were associated with slower next day processing speed. This association was not significantly attenuated by negative affect, sleepiness, or sleep quality. CONCLUSIONS:  These results, while preliminary due to small sample size, showcase the power of intensive longitudinal designs using ambulatory cognitive assessment to uncover novel determinants of cognitive fluctuation in real world settings, an approach that may be utilized in other populations. Findings suggest reducing nocturnal hypoglycemia may improve cognition in adults with T1D.

Understanding Providers' Readiness and Attitudes Toward Autoantibody Screening: A Mixed-Methods Study.

Screening for autoantibodies associated with type 1 diabetes can identify people most at risk for progressing to clinical type 1 diabetes and provide an opportunity for early intervention. Drawbacks and barriers to screening exist, and concerns arise, as methods for disease prevention are limited and no cure exists today. The availability of novel treatment options such as teplizumab to delay progression to clinical type 1 diabetes in high-risk individuals has led to the reassessment of screening programs. This study explored awareness, readiness, and attitudes of endocrinology providers toward type 1 diabetes autoantibody screening.

Two Years with a Tubeless Automated Insulin Delivery System: A Single-Arm Multicenter Trial in Children, Adolescents, and Adults with Type 1 Diabetes.

Background: The Omnipod® 5 Automated Insulin Delivery (AID) System was shown to be safe and effective following 3 months of use in people with type 1 diabetes (T1D); however, data on the durability of these results are limited. This study evaluated the long-term safety and effectiveness of Omnipod 5 use in people with T1D during up to 2 years of use. Materials and Methods: After a 3-month single-arm, multicenter, pivotal trial in children (6-13.9 years) and adolescents/adults (14-70 years), participants could continue system use in an extension phase. HbA1c was measured every 3 months for up to 15 months; continuous glucose monitor metrics were collected for up to 2 years. Results: Participants (N = 224) completed median (interquartile range) 22.3 (21.7, 22.7) months of AID. HbA1c was reduced in the pivotal trial from 7.7% ± 0.9% in children and 7.2% ± 0.9% in adolescents/adults to 7.0% ± 0.6% and 6.8% ± 0.7%, respectively, (P < 0.0001), and was maintained at 7.2% ± 0.7% and 6.9% ± 0.6% after 15 months (P < 0.0001 from baseline). Time in target range (70-180 mg/dL) increased from 52.4% ± 15.6% in children and 63.6% ± 16.5% in adolescents/adults at baseline to 67.9% ± 8.0% and 73.8% ± 10.8%, respectively, during the pivotal trial (P < 0.0001) and was maintained at 65.9% ± 8.9% and 72.9% ± 11.3% during the extension (P < 0.0001 from baseline). One episode of diabetic ketoacidosis and seven episodes of severe hypoglycemia occurred during the extension. Children and adolescents/adults spent median 96.1% and 96.3% of time in Automated Mode, respectively. Conclusion: Our study supports that long-term use of the Omnipod 5 AID System can safely maintain improvements in glycemic outcomes for up to 2 years of use in people with T1D. Clinical Trials Registration Number: NCT04196140.

Supporting the 'lived expertise' of older adults with type 1 diabetes: An applied focus group analysis to characterize barriers, facilitators, and strategies for self-management in a growing and understudied population.

INTRODUCTION: There is a growing number of older adults (≥65 years) who live with type 1 diabetes. We qualitatively explored experiences and perspectives regarding type 1 diabetes self-management and treatment decisions among older adults, focusing on adopting care advances such as continuous glucose monitoring (CGM). METHODS: Among a clinic-based sample of older adults ≥65 years with type 1 diabetes, we conducted a series of literature and expert informed focus groups with structured discussion activities. Groups were transcribed followed by inductive coding, theme identification, and inference verification. Medical records and surveys added clinical information. RESULTS: Twenty nine older adults (age 73.4 ± 4.5 years; 86% CGM users) and four caregivers (age 73.3 ± 2.9 years) participated. Participants were 58% female and 82% non-Hispanic White. Analysis revealed themes related to attitudes, behaviours, and experiences, as well as interpersonal and contextual factors that shape self-management and outcomes. These factors and their interactions drive variability in diabetes outcomes and optimal treatment strategies between individuals as well as within individuals over time (i.e. with ageing). Participants proposed strategies to address these factors: regular, holistic needs assessments to match people with effective self-care approaches and adapt them over the lifespan; longitudinal support (e.g., education, tactical help, sharing and validating experiences); tailored education and skills training; and leveraging of caregivers, family, and peers as resources. CONCLUSIONS: Our study of what influences self-management decisions and technology adoption among older adults with type 1 diabetes underscores the importance of ongoing assessments to address dynamic age-specific needs, as well as individualized multi-faceted support that integrates peers and caregivers.

Antihypertensive and Lipid-Lowering Medication Adherence in Young Adults With Youth-Onset Type 2 Diabetes.

Importance: Youth-onset type 2 diabetes is associated with early development of chronic complications. Treatment of elevated blood pressure (BP), nephropathy, and dyslipidemia are critical to reduce morbidity. Data are needed on adherence to BP- and lipid-lowering medications in young adults with youth-onset diabetes. Objective: To assess adherence and factors associated with adherence to BP- and lipid-lowering medications in young adults with youth-onset type 2 diabetes and diagnoses of hypertension, nephropathy, or dyslipidemia. Design, Setting, and Participants: This cohort study measured medication adherence with 3 monthly unannounced pill counts at 2 time points 1 year apart during iCount, conducted during the last years (2017-2019) of the observational phase of the Treatment Options for Type 2 Diabetes in Adolescents and Youth study. Psychosocial factors associated with medication adherence were examined. Participants included individuals with youth-onset type 2 diabetes with hypertension, nephropathy, or dyslipidemia receiving diabetes care in their communities. Data were analyzed from September 2022 to September 2023. Main Outcomes and Measures: The main outcome was BP- and lipid-lowering medication adherence, with low adherence defined as using less than 80% of pills and high adherence, at least 80% of pills. Psychosocial factors were measured using the Beliefs about Medicines Questionnaire and Material Needs Insecurities Survey. Results: Of 381 participants in iCount, 243 participants (mean [SD] age, 26.12 [2.51] years; 159 [65.43%] women) with hypertension, nephropathy, or dyslipidemia were included in analysis. Among 196 participants with hypertension or nephropathy, 157 (80.1%) had low adherence. Participants with low adherence, compared with those with high adherence, were younger (mean [SD] age, 25.99 [2.41] vs 27.26 [2.41] years; P = .005), had higher glycated hemoglobin A1c (mean [SD], 10.33% [2.66 percentage points] vs 8.85% [2.39 percentage points]; P = .001), shorter diabetes duration (mean [SD], 12.32 [1.49] vs 12.90 [1.46] years; P = .03), and less education (eg, 17 participants [10.83%] vs 0 participants with no high school diploma; P = .004). Of 146 participants with dyslipidemia, 137 (93.8%) had low adherence and only 9 participants (6.2%) had high adherence. Of 103 participants with low adherence to BP-lowering medications and using oral hypoglycemic agents, 83 (80.58%) had low adherence to oral hypoglycemic agents. Beliefs that medications are necessary were higher for participants with high adherence to BP-lowering medications than those with low adherence in unadjusted analyses (mean [SD] necessity score, 16.87 [6.78] vs 13.89 [9.15]; P = .03). In adjusted multivariable analyses of participants with hypertension or nephropathy, having at least 1 unmet social need (odds ratio [OR], 0.20; 95% CI, 0.05-0.65; P = .04) and medication concerns (OR, 0.63; 95% CI, 0.40-0.96; P = .01) were associated with worse medication adherence 1 year follow-up. Diabetes distress, self-efficacy, depressive and anxiety symptoms, and self-management support were not associated with 1-year medication adherence. Conclusions and Relevance: These findings suggest that adherence to BP- and lipid-lowering medications was very poor in this cohort. To improve medication adherence and prevent early vascular events, approaches that identify and address medication concerns and unmet social needs are needed.

Comprehensive Telehealth Model to Support Diabetes Self-Management.

Importance: As the number of patients with diabetes continues to increase in the United States, novel approaches to clinical care access should be considered to meet the care needs for this population, including support for diabetes-related technology. Objective: To evaluate a virtual clinic to facilitate comprehensive diabetes care, support continuous glucose monitoring (CGM) integration into diabetes self-management, and provide behavioral health support for diabetes-related issues. Design, Setting, and Participants: This cohort study was a prospective, single-arm, remote study involving adult participants with type 1 or type 2 diabetes who were referred through community resources. The study was conducted virtually from August 24, 2020, to May 26, 2022; analysis was conducted at the clinical coordinating center. Intervention: Training and education led by a Certified Diabetes Care and Education Specialist for CGM use through a virtual endocrinology clinic structure, which included endocrinologists and behavioral health team members. Main Outcomes and Measures: Main outcomes included CGM-measured mean glucose level, coefficient of variation, and time in range (TIR) of 70 to 180 mg/dL, time with values greater than 180 mg/dL or 250 mg/dL, and time with values less than 70 mg/dL or 54 mg/dL. Hemoglobin A1c was measured at baseline and at 12 and 24 weeks. Results: Among the 234 participants, 160 had type 1 diabetes and 74 had type 2 diabetes. The mean (SD) age was 47 (14) years, 123 (53%) were female, and median diabetes duration was 20 years. Median (IQR) CGM use over 6 months was 96% (91%-98%) for participants with type 1 diabetes and 94% (85%-97%) for those with type 2 diabetes. Mean (SD) hemoglobin A1c (HbA1c) in those with type 1 diabetes decreased from 7.8% (1.6%) at baseline to 7.1% (1.0%) at 3 months and 7.1% (1.0%) at 6 months (mean change from baseline to 6 months, -0.6%, 95% CI, -0.8% to -0.5%; P < .001), with an 11% mean TIR increase over 6 months (95% CI, 9% to 14%; P < .001). Mean HbA1c in participants with type 2 diabetes decreased from 8.1% (1.7%) at baseline to 7.1% (1.0%) at 3 months and 7.1% (0.9%) at 6 months (mean change from baseline to 6 months, -1.0%; 95% CI, -1.4% to -0.7%; P < .001), with an 18% TIR increase over 6 months (95% CI, 13% to 24%; P < .001). In participants with type 1 diabetes, mean percentage of time with values less than 70 mg/dL and less than 54 mg/dL decreased over 6 months by 0.8% (95% CI, -1.2% to -0.4%; P = .001) and by 0.3% (95% CI, -0.5% to -0.2%, P < .001), respectively. In the type 2 diabetes group, hypoglycemia was rare (mean [SD] percentage of time <70 mg/dL, 0.5% [0.6%]; and <54 mg/dL, 0.07% [0.14%], over 6 months). Conclusions and Relevance: Results from this cohort study demonstrated clinical benefits associated with implementation of a comprehensive care model that included diabetes education. This model of care has potential to reach a large portion of patients with diabetes, facilitate diabetes technology adoption, and improve glucose control.

Longitudinal Trends in Glycemic Outcomes and Technology Use for Over 48,000 People with Type 1 Diabetes (2016-2022) from the T1D Exchange Quality Improvement Collaborative.

Objective: Previous studies revealed that hemoglobin A1c (HbA1c) increased overall in the United States in the past decade. In addition, health inequities in type 1 diabetes (T1D) outcomes by race/ethnicity and insurance type persist. This study examines the trends in HbA1c from 2016 to 2022 stratified by race/ethnicity and insurance in a large multicenter national database. Research Design and Methods: We analyzed glycemic outcomes and diabetes device use trends for >48,000 people living with type 1 diabetes (PwT1D) from 3 adult and 12 pediatric centers in the T1D Exchange Quality Improvement Collaborative (T1DX-QI), comparing data from 2016 to 2017 with data from 2021 to 2022. Results: The mean HbA1c in 2021-2022 was lower at 8.4% compared with the mean HbA1c in 2016-2017 of 8.7% (0.3% improvement; P < 0.01). Over the same period, the percentage of PwT1D using a continuous glucose monitor (CGM), insulin pump, or hybrid closed-loop system increased (45%, 12%, and 33%, respectively). However, these improvements were not equitably demonstrated across racial/ethnic groups or insurance types. Racial/ethnic and insurance-based inequities persisted over all 7 years across all outcomes; comparing non-Hispanic White and non-Hispanic Black PwT1D, disparate gaps in HbA1c (1.2%-1.6%), CGM (30%), pump (25%-35%), and hybrid-closed loop system (up to 20%) are illuminated. Conclusion: Population-level data on outcomes, including HbA1c, can provide trends and insights into strategies to improve health for PwT1D. The T1DX-QI cohort showed a significant improvement in HbA1c from 2016 to 2022. Improvements in diabetes device use are also demonstrated. However, these increases were inconsistent across all racial/ethnic groups or insurance types, an important focus for future T1D population health improvement work.

Psychosocial Factors Predicting Healthcare Usage in Young Adults with Youth-Onset Type 2 Diabetes: The TODAY2 iCount Observational Study.

BACKGROUND: Established diabetes care ("diabetes home") and regular healthcare visits are important to achieve optimal health. Nothing is known about psychosocial factors that predict healthcare usage (HCU) in young adults with youth-onset type 2 diabetes, at risk for early complications. OBJECTIVE: To identify psychosocial predictors of HCU in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY2) cohort. DESIGN: Longitudinal, measured at T1 (baseline) and T2 (1 year later). Logistic and linear regressions, adjusted for potential confounders, identified predictors of sub-optimal HCU (defined as no diabetes home, 0 visits for routine care, or ≥ 1 urgent care visit in prior 6 months). PARTICIPANTS: N = 366 TODAY2 participants with T1 and T2 data (381 consented). Mean age = 26.0 years, 67.8% female, 37.7% non-Hispanic Black, 35.8% Hispanic, 20.2% non-Hispanic white, 6.3% "other," mean HbA1c = 9.4%. MAIN MEASURES: HCU survey; reliable and valid measures of diabetes self-efficacy, depressive symptoms, anxiety symptoms, diabetes distress, beliefs about medicines, diabetes attitudes, material need insecurities, self-management support. KEY RESULTS: 25.4% had no diabetes home, 23.7% had 0 routine care visits, 46% had ≥ 1 urgent care visit (prior 6 months). Beliefs in the necessity of (adjusted odds ratio [OR] = 1.28; 95% confidence interval [CI] = 1.12, 1.46, p < 0.001), and concerns about (OR = 1.29;CI = 1.08,1.54, p = 0.004), diabetes medicines, and its negative psychosocial impacts (OR = 1.57;CI = 1.04, 2.38, p = 0.03), predicted higher odds of having a diabetes home at T2. Beliefs that medicines are harmful predicted lower odds of a diabetes home (OR = 0.56;CI = 0.37,0.85, p = 0.006). Necessity beliefs (OR = 1.2;CI = 1.06,1.36, p = 0.004), and self-management support (OR = 1.5;CI = 1.08,2.07, p = 0.01) predicted higher odds of having ≥ 1 diabetes care visit, harm beliefs predicted lower odds (OR = 0.6;CI = 0.41,0.88, p = 0.01). CONCLUSIONS: Sub-optimal healthcare usage, common in young adults with youth-onset type 2 diabetes, is predicted by beliefs about medicines, diabetes impact, and self-management support. We must address these factors to help this vulnerable group establish stable diabetes care.

The Growing Role of Technology in the Care of Older Adults With Diabetes.

The integration of technologies such as continuous glucose monitors, insulin pumps, and smart pens into diabetes management has the potential to support the transformation of health care services that provide a higher quality of diabetes care, lower costs and administrative burdens, and greater empowerment for people with diabetes and their caregivers. Among people with diabetes, older adults are a distinct subpopulation in terms of their clinical heterogeneity, care priorities, and technology integration. The scientific evidence and clinical experience with these technologies among older adults are growing but are still modest. In this review, we describe the current knowledge regarding the impact of technology in older adults with diabetes, identify major barriers to the use of existing and emerging technologies, describe areas of care that could be optimized by technology, and identify areas for future research to fulfill the potential promise of evidence-based technology integrated into care for this important population.

Ecological Momentary Assessment of Cognition in Clinical and Community Samples: Reliability and Validity Study.

BACKGROUND: The current methods of evaluating cognitive functioning typically rely on a single time point to assess and characterize an individual's performance. However, cognitive functioning fluctuates within individuals over time in relation to environmental, psychological, and physiological contexts. This limits the generalizability and diagnostic utility of single time point assessments, particularly among individuals who may exhibit large variations in cognition depending on physiological or psychological context (eg, those with type 1 diabetes [T1D], who may have fluctuating glucose concentrations throughout the day). OBJECTIVE: We aimed to report the reliability and validity of cognitive ecological momentary assessment (EMA) as a method for understanding between-person differences and capturing within-person variation in cognition over time in a community sample and sample of adults with T1D. METHODS: Cognitive performance was measured 3 times a day for 15 days in the sample of adults with T1D (n=198, recruited through endocrinology clinics) and for 10 days in the community sample (n=128, recruited from TestMyBrain, a web-based citizen science platform) using ultrabrief cognitive tests developed for cognitive EMA. Our cognitive EMA platform allowed for remote, automated assessment in participants' natural environments, enabling the measurement of within-person cognitive variation without the burden of repeated laboratory or clinic visits. This allowed us to evaluate reliability and validity in samples that differed in their expected degree of cognitive variability as well as the method of recruitment. RESULTS: The results demonstrate excellent between-person reliability (ranging from 0.95 to 0.99) and construct validity of cognitive EMA in both the sample of adults with T1D and community sample. Within-person reliability in both samples (ranging from 0.20 to 0.80) was comparable with that observed in previous studies in healthy older adults. As expected, the full-length baseline and EMA versions of TestMyBrain tests correlated highly with one another and loaded together on the expected cognitive domains when using exploratory factor analysis. Interruptions had higher negative impacts on accuracy-based outcomes (ß=-.34 to -.26; all P values <.001) than on reaction time-based outcomes (ß=-.07 to -.02; P<.001 to P=.40). CONCLUSIONS: We demonstrated that ultrabrief mobile assessments are both reliable and valid across 2 very different clinic versus community samples, despite the conditions in which cognitive EMAs are administered, which are often associated with more noise and variability. The psychometric characteristics described here should be leveraged appropriately depending on the goals of the cognitive assessment (eg, diagnostic vs everyday functioning) and the population being studied.

Association of ADHD symptoms with type 2 diabetes and cardiovascular comorbidities in adults receiving outpatient diabetes care.

Background: The relationship between attention-deficit/hyperactivity disorder (ADHD) symptoms and type 2 diabetes mellitus (T2D) and its cardiovascular outcomes have not been sufficiently studied. Methods: 2,986 adults with T2D from the Joslin Diabetes Center at Upstate Medical University were assessed for ADHD-like symptoms, executive dysfunction, and emotional control using the Adult Self-Report Scale V1.1 (ASRS) expanded version. Surveys were sent electronically, and clinical data were obtained from the electronic medical record. Pearson chi-square test was used for categorical variables association. When ASRS scores were the dependent variable, negative binomial regression correcting for demographic variables that were associated with the ASRS scores was used. Results: 155 (49.2%) of respondents met DSM-5 criteria for ADHD using the ASRS scores; Only ten (3.6%) of respondents had an ICD10 diagnosis of ADHD in their medical record; Forty-three (13.7%) had either a diagnosis of ADHD in the medical history or were taking medications used by people with ADHD. Higher levels of ADHD-like symptoms were found in patients with T2D compared with population norms. There was a modest association of the ASRS executive dysfunction subscale with overall cardiovascular comorbidities (p = 0.03). However, the p-value did not survive the multiple testing correction. Both ADHD-like symptoms and symptoms associated with emotional control, however, were not associated with specific cardiovascular diseases, hypertension, or with HbA1c, LDL-cholesterol, triglycerides, ALT, creatinine, or eGFR. Conclusion: Our results suggest that adults with T2D attending a tertiary care diabetes clinic are at risk for having ADHD-like symptoms, highlighting the importance of screening for ADHD symptoms in this specialty setting and referring undiagnosed adult patients for further assessment and treatment of ADHD. Larger studies are needed to clarify the relationship between ADHD-like symptoms, executive dysfunction, and emotional control with diabetic control and comorbidities.

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.

Psychosocial factors predict medication adherence in young adults with youth-onset type 2 diabetes: Longitudinal results from the TODAY2 iCount study.

AIM: To identify psychosocial predictors of medication adherence in young adults with youth-onset type 2 diabetes in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY2) cohort. METHODS: Participants (mean age: 26 years) completed validated psychosocial measures. Unannounced telephone pill counts were completed at T1 (baseline) and T2 (follow-up, approximately 1 year later) to assess adherence to oral hypoglycaemia agents (OHAs). Adherence to insulin was assessed by self-report. Logistic and linear regressions identified factors that predicted 'low adherence' (<80% of pills/insulin) and per cent adherence, adjusted for potential confounders. RESULTS: Of 171 participants with OHA adherence scores at T1 and T2 (65% women, 43% Hispanic and 35% non-Hispanic Black), 65.4% were low adherent. After adjustment (including T1 adherence), concerns about diabetes medicines (adverse effects, dependence) at T1 predicted higher odds of being low adherent (categorical) at T2 (p = 0.019). Housing insecurity (p = 0.045) and reporting ≥2 need insecurities (p = 0.027) at T1 predicted lower per cent adherence (continuous) at T2. Of 157 participants with insulin adherence scores at T1 and T2 (69% women, 38% Hispanic and 38% non-Hispanic Black), 36.3% were low adherent. After adjustment (including T1 adherence), beliefs that medicines are overused predicted higher odds of insulin low adherence at T2 (p = 0.013), and beliefs that medicines are harmful (p = 0.004) and overused (p = 0.010) predicted lower per cent insulin adherence at T2. CONCLUSIONS: Suboptimal medication adherence, common in young adults with youth-onset type 2 diabetes, is predicted by interfering beliefs about medicines and social factors. We must address these beliefs and unmet needs to develop tailored interventions for this vulnerable group.

Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study.

BACKGROUND: The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitoring (BGM). We explored heterogeneous treatment effects of CGM on hypoglycemia by formulating a data-driven decision rule that selects an intervention (ie, CGM vs BGM) to minimize percentage of time <70 mg/dL for each individual WISDM participant. METHOD: The precision medicine analyses used data from participants with complete data (n = 194 older adults, including those who received CGM [n = 100] and BGM [n = 94] in the trial). Policy tree and decision list algorithms were fit with 14 baseline demographic, clinical, and laboratory measures. The primary outcome was CGM-measured percentage of time spent in hypoglycemic range (<70 mg/dL), and the decision rule assigned participants to a subgroup reflecting the treatment estimated to minimize this outcome across all follow-up visits. RESULTS: The optimal decision rule was found to be a decision list with 3 steps. The first step moved WISDM participants with baseline time-below range >1.35% and no detectable C-peptide levels to the CGM subgroup (n = 139), and the second step moved WISDM participants with a baseline time-below range of >6.45% to the CGM subgroup (n = 18). The remaining participants (n = 37) were left in the BGM subgroup. Compared with the BGM subgroup (n = 37; 19%), the group for whom CGM minimized hypoglycemia (n = 157; 81%) had more baseline hypoglycemia, a lower proportion of detectable C-peptide, higher glycemic variability, longer disease duration, and higher proportion of insulin pump use. CONCLUSIONS: The decision rule underscores the benefits of CGM for older adults to reduce hypoglycemia. Diagnostic CGM and laboratory markers may inform decision-making surrounding therapeutic CGM and identify older adults for whom CGM may be a critical intervention to reduce hypoglycemia.

Glycemic Variability and Fluctuations in Cognitive Status in Adults With Type 1 Diabetes (GluCog): Observational Study Using Ecological Momentary Assessment of Cognition.

BACKGROUND: Individuals with type 1 diabetes represent a population with important vulnerabilities to dynamic physiological, behavioral, and psychological interactions, as well as cognitive processes. Ecological momentary assessment (EMA), a methodological approach used to study intraindividual variation over time, has only recently been used to deliver cognitive assessments in daily life, and many methodological questions remain. The Glycemic Variability and Fluctuations in Cognitive Status in Adults with Type 1 Diabetes (GluCog) study uses EMA to deliver cognitive and self-report measures while simultaneously collecting passive interstitial glucose in adults with type 1 diabetes. OBJECTIVE: We aimed to report the results of an EMA optimization pilot and how these data were used to refine the study design of the GluCog study. An optimization pilot was designed to determine whether low-frequency EMA (3 EMAs per day) over more days or high-frequency EMA (6 EMAs per day) for fewer days would result in a better EMA completion rate and capture more hypoglycemia episodes. The secondary aim was to reduce the number of cognitive EMA tasks from 6 to 3. METHODS: Baseline cognitive tasks and psychological questionnaires were completed by all the participants (N=20), followed by EMA delivery of brief cognitive and self-report measures for 15 days while wearing a blinded continuous glucose monitor. These data were coded for the presence of hypoglycemia (<70 mg/dL) within 60 minutes of each EMA. The participants were randomized into group A (n=10 for group A and B; starting with 3 EMAs per day for 10 days and then switching to 6 EMAs per day for an additional 5 days) or group B (N=10; starting with 6 EMAs per day for 5 days and then switching to 3 EMAs per day for an additional 10 days). RESULTS: A paired samples 2-tailed t test found no significant difference in the completion rate between the 2 schedules (t17=1.16; P=.26; Cohen dz=0.27), with both schedules producing >80% EMA completion. However, more hypoglycemia episodes were captured during the schedule with the 3 EMAs per day than during the schedule with 6 EMAs per day. CONCLUSIONS: The results from this EMA optimization pilot guided key design decisions regarding the EMA frequency and study duration for the main GluCog study. The present report responds to the urgent need for systematic and detailed information on EMA study designs, particularly those using cognitive assessments coupled with physiological measures. Given the complexity of EMA studies, choosing the right instruments and assessment schedules is an important aspect of study design and subsequent data interpretation.