A qualitative study of recruitment strategies: Perspectives from older adults living with diabetes.

AIMS: There is a need to increase representation of diverse older adults in health-related qualitative research to better understand and improve chronic disease care over the lifespan. Our aim was to elicit perspectives about research recruitment among a diverse sample of older adults with diabetes participating in a qualitative study. METHODS: Older adults with diabetes and caregivers were recruited through purposive sampling for semi-structured interviews focused on diabetes self care. Six questions were used to explore recruitment strategies and recommendations for engaging older adults in research. We analysed interview transcripts using descriptive analysis to identify themes related to engaging older adults in research studies. RESULTS: Seventeen older adults with diabetes and three caregivers participated (N = 20). Descriptive analysis revealed four themes: (1) Recruitment of older adults requires varied strategies to overcome barriers to engagement and participation; (2) Building and leveraging personal relationships is central to successful recruitment; (3) Transparent communication about the research process and value of the study is needed to inform and motivate older adults to participate; and (4) Research offers a connection to a broader community: sharing, learning and helping others. CONCLUSIONS: We found four main themes related to the complexity of recruiting older adults for research studies. These insights may inform more effective, equitable and inclusive recruitment efforts targeted at older adults in the future.

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.

Use of systems thinking and adapted group model building methods to understand patterns of technology use among older adults with type 1 diabetes: a preliminary process evaluation.

BACKGROUND: A growing number of older adults (ages 65+) live with Type 1 diabetes. Simultaneously, technologies such as continuous glucose monitoring (CGM) have become standard of care. There is thus a need to understand better the complex dynamics that promote use of CGM (and other care innovations) over time in this age group. Our aim was to adapt methods from systems thinking, specifically a participatory approach to system dynamics modeling called group model building (GMB), to model the complex experiences that may underlie different trajectories of CGM use among this population. Herein, we report on the feasibility, strengths, and limitations of this methodology. METHODS: We conducted a series of GMB workshops and validation interviews to collect data in the form of questionnaires, diagrams, and recordings of group discussion. Data were integrated into a conceptual diagram of the "system" of factors associated with uptake and use of CGM over time. We evaluate the feasibility of each aspect of the study, including the teaching of systems thinking to older adult participants. We collected participant feedback on positive aspects of their experiences and areas for improvement. RESULTS: We completed nine GMB workshops with older adults and their caregivers (N = 33). Each three-hour in-person workshop comprised: (1) questionnaires; (2) the GMB session, including both didactic components and structured activities; and (3) a brief focus group discussion. Within the GMB session, individual drawing activities proved to be the most challenging for participants, while group activities and discussion of relevant dynamics over time for illustrative (i.e., realistic but not real) patients yielded rich engagement and sufficient information for system diagramming. Study participants liked the opportunity to share experiences with peers, learning and enhancing their knowledge, peer support, age-specific discussions, the workshop pace and structure, and the systems thinking framework. Participants gave mixed feedback on the workshop duration. CONCLUSIONS: The study demonstrates preliminary feasibility, acceptability, and the value of GMB for engaging older adults about key determinants of complex health behaviors over time. To our knowledge, few studies have extended participatory systems science methods to older adult stakeholders. Future studies may utilize this methodology to inform novel approaches for supporting health across the lifespan.

Machine Learning and Health Science Research: Tutorial.

Machine learning (ML) has seen impressive growth in health science research due to its capacity for handling complex data to perform a range of tasks, including unsupervised learning, supervised learning, and reinforcement learning. To aid health science researchers in understanding the strengths and limitations of ML and to facilitate its integration into their studies, we present here a guideline for integrating ML into an analysis through a structured framework, covering steps from framing a research question to study design and analysis techniques for specialized data types.

More hypoglycemia not associated with increasing estimated adiposity in youth with type 1 diabetes.

BACKGROUND: Despite the widespread clinical perception that hypoglycemia may drive weight gain in youth with type 1 diabetes (T1D), there is an absence of published evidence supporting this hypothesis. METHODS: We estimated the body fat percentage (eBFP) of 211 youth (HbA1c 8.0-13.0%, age 13-16) at baseline, 6, and 18 months of the Flexible Lifestyles Empowering Change trial using validated equations. Group-based trajectory modeling assigned adolescents to sex-specific eBFP groups. Using baseline 7-day blinded continuous glucose monitoring data, "more" vs. "less" percent time spent in hypoglycemia was defined by cut-points using sample median split and clinical guidelines. Adjusted logistic regression estimated the odds of membership in an increasing eBFP group comparing youth with more vs. less baseline hypoglycemia. RESULTS: More time spent in clinical hypoglycemia (defined by median split) was associated with 0.29 the odds of increasing eBFP in females (95% CI: 0.12, 0.69; p = 0.005), and 0.33 the odds of stable/increasing eBFP in males (95% CI: 0.14, 0.78; p = 0.01). CONCLUSIONS: Hypoglycemia may not be a major driver of weight gain in US youth with T1D and HbA1c ≥8.0. Further studies in different sub-groups are needed to clarify for whom hypoglycemia may drive weight gain and focus future etiological studies and interventions. IMPACT: We contribute epidemiological evidence that hypoglycemia may not be a major driver of weight gain in US youth with type 1 diabetes and HbA1c ≥8.0% and highlight the need for studies to prospectively test this hypothesis rooted in clinical perception. Future research should examine the relationship between hypoglycemia and adiposity together with psychosocial, behavioral, and other clinical factors among sub-groups of youth with type 1 diabetes (i.e., who meet glycemic targets or experience a frequency/severity of hypoglycemia above a threshold) to further clarify for whom hypoglycemia may drive weight gain and progress etiological understanding of and interventions for healthy weight maintenance.

Extending an Antiracism Lens to the Implementation of Precision Public Health Interventions.

Precision public health holds promise to improve disease prevention and health promotion strategies, allowing the right intervention to be delivered to the right population at the right time. Growing concerns underscore the potential for precision-based approaches to exacerbate health disparities by relying on biased data inputs and recapitulating existing access inequities. To achieve its full potential, precision public health must focus on addressing social and structural drivers of health and prominently incorporate equity-related concerns, particularly with respect to race and ethnicity. In this article, we discuss how an antiracism lens could be applied to reduce health disparities and health inequities through equity-informed research, implementation, and evaluation of precision public health interventions. (Am J Public Health. 2023;113(11):1210-1218. https://doi.org/10.2105/AJPH.2023.307386).

Severe (level 3) hypoglycaemia occurrence in a real-world cohort of adults with type 1 or 2 diabetes mellitus (iNPHORM, United States).

AIMS: Among adults with insulin- and/or secretagogue-treated diabetes in the United States, very little is known about the real-world descriptive epidemiology of iatrogenic severe (level 3) hypoglycaemia. Addressing this gap, we collected primary, longitudinal data to quantify the absolute frequency of events as well as incidence rates and proportions. MATERIALS AND METHODS: iNPHORM is a US-wide, 12-month ambidirectional panel survey (2020-2021). Adults with type 1 diabetes mellitus (T1DM) or insulin- and/or secretagogue-treated type 2 diabetes mellitus (T2DM) were recruited from a probability-based internet panel. Participants completing ≥1 follow-up questionnaire(s) were analysed. RESULTS: Among 978 respondents [T1DM 17%; mean age 51 (SD 14.3) years; male: 49.6%], 63% of level 3 events were treated outside the health care system (e.g. by family/friend/colleague), and <5% required hospitalization. Following the 12-month prospective period, one-third of individuals reported ≥1 event(s) [T1DM 44.2% (95% CI 36.8%-51.8%); T2DM 30.8% (95% CI 28.7%-35.1%), p = .0404, α = 0.0007]; and the incidence rate was 5.01 (95% CI 4.15-6.05) events per person-year (EPPY) [T1DM 3.57 (95% CI 2.49-5.11) EPPY; T2DM 5.29 (95% CI 4.26-6.57) EPPY, p = .1352, α = 0.0007]. Level 3 hypoglycaemia requiring non-transport emergency medical services was more common in T2DM than T1DM (p < .0001, α = 0.0016). In total, >90% of events were experienced by <15% of participants. CONCLUSIONS: iNPHORM is one of the first long-term, prospective US-based investigations on level 3 hypoglycaemia epidemiology. Our results underscore the importance of participant-reported data to ascertain its burden. Events were alarmingly frequent, irrespective of diabetes type, and concentrated in a small subsample.

Dual self-regulated delivery of insulin and glucagon by a hybrid patch.

Reduced ß-cell function and insulin deficiency are hallmarks of diabetes mellitus, which is often accompanied by the malfunction of glucagon-secreting α-cells. While insulin therapy has been developed to treat insulin deficiency, the on-demand supplementation of glucagon for acute hypoglycemia treatment remains inadequate. Here, we describe a transdermal patch that mimics the inherent counterregulatory effects of ß-cells and α-cells for blood glucose management by dynamically releasing insulin or glucagon. The two modules share a copolymerized matrix but comprise different ratios of the key monomers to be "dually responsive" to both hyper- and hypoglycemic conditions. In a type 1 diabetic mouse model, the hybrid patch effectively controls hyperglycemia while minimizing the occurrence of hypoglycemia in the setting of insulin therapy with simulated delayed meal or insulin overdose.

Inequalities in Glycemic Control in Youth with Type 1 Diabetes Over Time: Intersectionality Between Socioeconomic Position and Race and Ethnicity.

BACKGROUND: Racial/ethnic health inequities have been well-documented among youth and young adults with type 1 diabetes (T1D), yet little is known about how socioeconomic position (SEP) intersects with the risk marker of race/ethnicity to predict inequities in longitudinal glycemic control. PURPOSE: To identify patterns of SEP, race/ethnicity, and clinical characteristics that differentiate hemoglobin A1c (HbA1c) trajectories among youth and young adults after T1D diagnosis. METHODS: The SEARCH for Diabetes in Youth cohort includes youth with diabetes diagnosed from 2002 to 2006 and 2008 who were followed through 2015. We analyzed data from 1,313 youth and young adults with T1D with ≥3 HbA1c measures. Classification tree analysis identified patterns of baseline demographic, SEP, and clinical characteristic that best predicted HbA1c trajectories over an average of 8.3 years using group-based trajectory modeling. RESULTS: Two HbA1c trajectories were identified: Trajectory 1 (77%) with lower baseline HbA1c and mild increases (from mean 7.4% to 8.4%) and Trajectory 2 (23%) with higher baseline HbA1c and major increases (from 8.5% to 11.2%). Race/ethnicity intersected with different SEP characteristics among non-Hispanic white (NHW) than in non-whites. Public health insurance predicted high-risk Trajectory 2 membership in non-whites, whereas parental education, household structure, diagnosis age and glucose checking frequency predicted membership for NHW youth and young adults. Two characteristics, race/ethnicity and parental education alone identified 80% of the Trajectory 2 members. CONCLUSIONS: Race/ethnicity intersects with multiple SEP and clinical characteristics among youth and young adults with T1D, which is associated with particularly high risk of poor long-term glycemic control.

Mindfulness, disordered eating, and impulsivity in relation to glycemia among adolescents with type 1 diabetes and suboptimal glycemia from the Flexible Lifestyles Empowering Change (FLEX) intervention trial.

OBJECTIVE: To assess the relationship between mindfulness and glycemia among adolescents with type 1 diabetes (T1D) with suboptimal glycemia, and evaluate the potential mediation by ingestive behaviors, including disordered eating, and impulsivity. RESEARCH DESIGN AND METHODS: We used linear mixed models for hemoglobin A1c (HbA1c) and linear regression for continuous glucose monitoring (CGM) to study the relationship of mindfulness [Child and Adolescent Mindfulness Measure (CAMM)] and glycemia in adolescents with T1D from the 18-month Flexible Lifestyles Empowering Change (FLEX) trial. We tested for mediation of the mindfulness-glycemia relationship by ingestive behaviors, including disordered eating (Diabetes Eating Problem Survey-Revised), restrained eating, and emotional eating (Dutch Eating Behavior Questionnaire); and impulsivity (total, attentional, and motor, Barrett Impulsiveness Scale). RESULTS: At baseline, participants (n = 152) had a mean age of 14.9 ± 1.1 years and HbA1c of 9.4 ± 1.2% [79 ± 13 mmol/mol]. The majority of adolescents were non-Hispanic white (83.6%), 50.7% were female, and 73.0% used insulin pumps. From adjusted mixed models, a 5-point increase in mindfulness scores was associated with a -0.19% (95%CI -0.29, -0.08, p = 0.0006) reduction in HbA1c. We did not find statistically significant associations between mindfulness and CGM metrics. Mediation of the relationship between mindfulness and HbA1c by ingestive behaviors and impulsivity was not found to be statistically significant. CONCLUSIONS: Among adolescents with T1D and suboptimal glycemia, increased mindfulness was associated with lower HbA1c levels. Future studies may consider mindfulness-based interventions as a component of treatment for improving glycemia among adolescents with T1D, though more data are needed to assess feasibility and efficacy.

Glucose transporter inhibitor-conjugated insulin mitigates hypoglycemia.

Insulin therapy in the setting of type 1 and advanced type 2 diabetes is complicated by increased risk of hypoglycemia. This potentially fatal complication could be mitigated by a glucose-responsive insulin analog. We report an insulin-facilitated glucose transporter (Glut) inhibitor conjugate, in which the insulin molecule is rendered glucose-responsive via conjugation to an inhibitor of Glut. The binding affinity of this insulin analog to endogenous Glut is modulated by plasma and tissue glucose levels. In hyperglycemic conditions (e.g., uncontrolled diabetes or the postprandial state), the in situ-generated insulin analog-Glut complex is driven to dissociate, freeing the insulin analog and glucose-accessible Glut to restore normoglycemia. Upon overdose, enhanced binding of insulin analog to Glut suppresses the glucose transport activity of Glut to attenuate further uptake of glucose. We demonstrate the ability of this insulin conjugate to regulate blood glucose levels within a normal range while mitigating the risk of hypoglycemia in a type 1 diabetic mouse model.

Impact of Hurricane Matthew on Diabetes Self-Management and Outcomes.

BACKGROUND: Individuals with diabetes require extensive self-management. Little is known about how Hurricane Matthew (Matthew) or Hurricane Florence (Florence) impacted diabetes self-management and outcomes in Robeson County, North Carolina. METHODS: Mixed methods were used to assess the impact of hurricanes on diabetes self-management and outcomes. Individuals with diabetes were recruited for focus groups to understand the perceived impact on diabetes self-management. Health care providers were recruited for parallel key informant interviews. Mean hemoglobin A1c (HbA1c) and frequency of diabetic ketoacidosis (DKA) from hospital data six months before and after Matthew were compared using Student t-tests. RESULTS: A demographic breakdown of 34.25% white, 21.70% Black or African American, and 21.38% American Indian or Alaska Native was observed from focus groups. Qualitative results highlight a limited access to a balanced diet and medications. No significant differences were found between mean HbA1c values before and after Matthew (before Matthew: mean HbA1c 8.34 ± 1.87%; after Matthew: mean HbA1c 8.31 ± 1.93 %; P = .366). The period prevalence (PP) of DKA was higher after Matthew than before (before Matthew: 39 cases out of 4,025 visits, PP = .010; after Matthew: 87 cases out of 3,779 visits, PP = .023; P <.0001). LIMITATIONS: Limitations include non-random sampling and limited sample sizes. Also, the cross-sectional panel approach did not follow the same individuals over time. CONCLUSIONS: The period prevalence of DKA was higher in the six-month time period following Matthew compared to before the hurricane. Future interventions may improve outcomes via increased access to foods and medications recommended for those with diabetes.

Synthetic beta cells for fusion-mediated dynamic insulin secretion.

Generating artificial pancreatic beta cells by using synthetic materials to mimic glucose-responsive insulin secretion in a robust manner holds promise for improving clinical outcomes in people with diabetes. Here, we describe the construction of artificial beta cells (AßCs) with a multicompartmental 'vesicles-in-vesicle' superstructure equipped with a glucose-metabolism system and membrane-fusion machinery. Through a sequential cascade of glucose uptake, enzymatic oxidation and proton efflux, the AßCs can effectively distinguish between high and normal glucose levels. Under hyperglycemic conditions, high glucose uptake and oxidation generate a low pH (<5.6), which then induces steric deshielding of peptides tethered to the insulin-loaded inner small liposomal vesicles. The peptides on the small vesicles then form coiled coils with the complementary peptides anchored on the inner surfaces of large vesicles, thus bringing the membranes of the inner and outer vesicles together and triggering their fusion and insulin 'exocytosis'.

Estimating individualized treatment regimes from crossover designs.

The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way. To this end, estimating an optimal individualized treatment regime (ITR) that recommends treatment decisions based on patient characteristics to maximize the mean of a prespecified outcome is of particular interest. Several methods have been proposed for estimating an optimal ITR from clinical trial data in the parallel group setting where each subject is randomized to a single intervention. However, little work has been done in the area of estimating the optimal ITR from crossover study designs. Such designs naturally lend themselves to precision medicine since they allow for observing the response to multiple treatments for each patient. In this paper, we introduce a method for estimating the optimal ITR using data from a 2 × 2 crossover study with or without carryover effects. The proposed method is similar to policy search methods such as outcome weighted learning; however, we take advantage of the crossover design by using the difference in responses under each treatment as the observed reward. We establish Fisher and global consistency, present numerical experiments, and analyze data from a feeding trial to demonstrate the improved performance of the proposed method compared to standard methods for a parallel study design.

Validation of distinct type 2 diabetes clusters and their association with diabetes complications in the DEVOTE, LEADER and SUSTAIN-6 cardiovascular outcomes trials.

AIMS: To validate the clusters of Swedish individuals with recent-onset diabetes at differential risk of complications, which were identified in a previous study, in three global populations with long-standing type 2 diabetes (T2D) who were at high cardiovascular risk, and to test for differences in the risk of major diabetes complications and survival endpoints. MATERIALS AND METHODS: We assigned participants from recent global outcomes trials (DEVOTE [n = 7637], LEADER [n = 9340] and SUSTAIN-6 [n = 3297]) to the previously defined clusters according to age at diabetes diagnosis, baseline glycated haemoglobin (HbA1c) and body mass index (BMI). Outcomes were assessed using Kaplan-Meier analysis and log-rank tests. RESULTS: The T2D clusters were consistently replicated across the three trial cohorts. The risk of major adverse cardiovascular events and cardiovascular death differed significantly, in all trials, across clusters over a median follow-up duration of 2.0, 3.8 and 2.1 years, respectively, and was highest for the cluster of participants with high HbA1c and low BMI (P < 0.05 in DEVOTE and LEADER). In LEADER and SUSTAIN-6, the risk of nephropathy differed across clusters (P < 0.0001 and P = 0.003, respectively). The risk of severe hypoglycaemia differed in DEVOTE (P = 0.006). CONCLUSIONS: Previously identified clusters can be replicated in three geographically diverse cohorts of long-standing T2D and are associated with cluster-specific risk profiles for additional clinical and survival outcomes, providing further validation of the clustering methodology. The external validity and stability of clusters across cohorts provides a premise for future work to optimize the clustering approach to yield T2D subgroups with maximum predictive validity who may benefit from subtype-specific treatment paradigms.

Dietary intake on days with and without hypoglycemia in youth with type 1 diabetes: The Flexible Lifestyle Empowering Change trial.

OBJECTIVE: To address a common perception that hypoglycemia is associated with increased dietary intake, we examined calorie and carbohydrate consumption on days with and without hypoglycemia among adolescents with type 1 diabetes (T1D). METHODS: Days (N = 274) with 24-hour dietary recalls and continuous glucose monitoring were available for 122 adolescents with T1D in the Flexible Lifestyle Empowering Change trial (age 13-16 years, diabetes duration >1 year, hemoglobin A1c 8%-13%). Days with no hypoglycemia, clinical hypoglycemia (54-69 mg/dL) or clinically serious hypoglycemia (<54 mg/dL) were further split into night (12-5:59 am) and day (6 am-11:59 pm). Mixed models tested whether intake of calories or carbohydrates was greater on days with than without hypoglycemia. RESULTS: Fifty-nine percent, 23% and 18% of days had no hypoglycemia, clinical hypoglycemia and clinically serious hypoglycemia, respectively. Intake of calories and carbohydrates was not statistically significantly different on days with clinical hypoglycemia (57.2 kcal [95% CI -126.7, 241.5]; 12.6 g carbohydrate [95% CI -12.7, 38.0]) or clinically serious hypoglycemia (-74.0 kcal [95% CI -285.9, 137.9]; (-7.8 g carbohydrate [95% CI -36.8, 21.1]), compared to days without hypoglycemia. Differences by day and night were not statistically significant. CONCLUSIONS: Among adolescents with T1D, daily intake of calories and carbohydrates did not differ on days with and without hypoglycemia. It is possible that hypoglycemic episodes caused by undereating relative to insulin dosing, followed by overeating, leading to a net neutral difference. Given the post-hoc nature of these analyses, larger studies should be designed to prospectively test the hypoglycemia-diet relationship.

Socioeconomic position is associated with glycemic control in youth and young adults with type 1 diabetes.

OBJECTIVE: Health inequities persist in youth and young adults (YYA) with type 1diabetes in achieving optimal glycemic control. The purpose of this study was to assess the contribution of multiple indicators of social need to these inequities. RESEARCH DESIGN AND METHODS: Two hundred and twenty two YYA withtype 1 diabetes enrolled in the SEARCH Food Insecurity Study in South Carolina and Washington between the years 2013 and 2015 were included. Latent class analysis was used to identify socioeconomic profiles based on household income, parental education, health insurance, household food insecurity, and food assistance. Profiles were evaluated in relation to glycemic control using multivariable linear and logistic regression, with HbA1c > 9%(75 mmol/mol) defined as high-risk glycemic control. RESULTS: Two profiles were identified: a lower socioeconomic profile included YYA whose parents had lower income and/or education, and were more likely to be uninsured, receive food assistance, and be food insecure. A higher socioeconomic profile included YYA whose circumstances were opposite to those in the lower socioeconomic profile. Those with a lower socioeconomic profile were more likely to have high-risk glycemic control relative to those with a higher socioeconomic profile (OR = 2.24, 95%CI = 1.16-4.33). CONCLUSIONS: Lower socioeconomic profiles are associated with high-risk glycemic control among YYA with type 1 diabetes. This supports recommendations that care providers of YYA with type 1 diabetes assess individual social needs in tailoring diabetes management plans to the social context of the patient.

Assessment of third-year medical students' comfort and preparedness for navigating challenging clinical scenarios with patients, peers, and supervisors.

BACKGROUND: Medical training focuses heavily on clinical skills but lacks in training for navigating challenging clinical scenarios especially with regard to diversity issues. Our objective was to assess third-year medical students' preparedness to navigate such scenarios. METHODS: A 24-item survey was administered electronically to third-year medical students describing a range of specific interactions with patients, peers, and "upper-levels" or superiors including residents and attendings, spanning subjects including gender, race/ethnicity, politics, age, sexual orientation/identity, disability, and religion. Students rated their level of comfort via a 5-point Likert scale ranging from 1 ("Very Uncomfortable") to 5 ("Very Comfortable"). Basic demographics were collected and data were summarized for trends. RESULTS: Data were analyzed from 120 students (67% response rate, 54.2% female, 60.8% non-Hispanic white). Students reported lower comfort with peer and superiors compared to patient interactions (p < 0.0001). Students reported the highest comfort with sexual orientation/identity- and religion-related interactions (median (IQR): 3.3 (1.3) and 3.4 (10.0), respectively) and the lowest comfort with gender-, race/ethnicity-, and disability- related interactions (median (IQR): 2.3 (1.3), 2.0 (1.0), 2.5 (1.5), respectively). Males reported significantly higher median comfort levels for scenarios with upper-level, gender, and religion related interactions. Males were more likely to be completely comfortable versus females across the 24 scenarios, although multiple male response patterns showed evidence of a bimodal distribution. CONCLUSIONS: Third-year medical students report generally inadequate comfort with navigating complex clinical scenarios, particularly with peers and supervisors and relating to gender-, race/ethnicity-, and disability-specific conflicts. There are differences across gender with regards to median comfort and distribution of scores suggesting that there is a subgroup of males report high/very high comfort with challenging clinical scenarios. Students may benefit from enhanced training modules and personalized toolkits for navigating these scenarios.

Dysglycemia among youth with type 1 diabetes and suboptimal glycemic control in the Flexible Lifestyle Empowering Change trial.

OBJECTIVE: To examine the prevalence and correlates of non-severe hypoglycemia among adolescents with type 1 diabetes and suboptimal glycemic control, an understudied topic in this group. METHODS: Seven days of blinded continuous glucose monitor data were analyzed in 233 adolescents at baseline of the Flexible Lifestyle Empowering Change trial (13-16 years, type 1 diabetes duration >1 year, and hemoglobin A1c [HbA1c] 8-13% [64-119 mmol]). Incidence of clinical hypoglycemia (54-69 mg/dL) and clinically serious hypoglycemia (<54 mg/dL) was defined as number of episodes ≥15 minutes. Logistic regression modeling was used to determine the correlates of long duration of hypoglycemia, categorized by median split among those who experienced hypoglycemia. RESULTS: The sample was 76.1% non-Hispanic white, 49.8% female, age = 14.9 ± 1.1 years, diabetes duration = 6.4 ± 3.7 years, and HbA1c = 9.6 ± 1.2% (81 ± 13 mmol/mol). Over 7 days, 79.4% of youth experienced ≥1 hypoglycemic episodes of <70 mg/dL, and 55.4% of youth experienced ≥1 hypoglycemic episodes of <54 mg/dL. Among all adolescents, the median duration of clinical hypoglycemia and clinically serious hypoglycemia was 21.9 (range 0-250.2) and 4.3 (range 0-209.7) minutes/day, respectively. Long duration of clinical hypoglycemia (range 1.8-17.4% time overall) and clinically serious hypoglycemia (range 1.2-14.6% time overall) was associated with older age and decreasing HbA1c. Long duration of clinically serious hypoglycemia also was associated with insulin pump use. CONCLUSIONS: Almost 80% of adolescents with elevated HbA1c had an episode of clinical hypoglycemia, and >50% had clinically serious hypoglycemia in a week. Increased education alongside access to emerging diabetes technologies may help to prevent hypoglycemia while improving glycemic control.

Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c.

BACKGROUND/OBJECTIVE: To identify and characterize subgroups of adolescents with type 1 diabetes (T1D) and elevated hemoglobin A1c (HbA1c) who share patterns in their continuous glucose monitoring (CGM) data as "dysglycemia phenotypes." METHODS: Data were analyzed from the Flexible Lifestyles Empowering Change randomized trial. Adolescents with T1D (13-16 years, duration >1 year) and HbA1c 8% to 13% (64-119 mmol/mol) wore blinded CGM at baseline for 7 days. Participants were clustered based on eight CGM metrics measuring hypoglycemia, hyperglycemia, and glycemic variability. Clusters were characterized by their baseline features and 18 months changes in HbA1c using adjusted mixed effects models. For comparison, participants were stratified by baseline HbA1c (≤/>9.0% [75 mmol/mol]). RESULTS: The study sample included 234 adolescents (49.8% female, baseline age 14.8 ± 1.1 years, baseline T1D duration 6.4 ± 3.7 years, baseline HbA1c 9.6% ± 1.2%, [81 ± 13 mmol/mol]). Three Dysglycemia Clusters were identified with significant differences across all CGM metrics (P < .001). Dysglycemia Cluster 3 (n = 40, 17.1%) showed severe hypoglycemia and glycemic variability with moderate hyperglycemia and had a lower baseline HbA1c than Clusters 1 and 2 (P < .001). This cluster showed increases in HbA1c over 18 months (p-for-interaction = 0.006). No other baseline characteristics were associated with Dysglycemia Clusters. High HbA1c was associated with lower pump use, greater insulin doses, more frequent blood glucose monitoring, lower motivation, and lower adherence to diabetes self-management (all P < .05). CONCLUSIONS: There are subgroups of adolescents with T1D for which glycemic control is challenged by different aspects of dysglycemia. Enhanced understanding of demographic, behavioral, and clinical characteristics that contribute to CGM-derived dysglycemia phenotypes may reveal strategies to improve treatment.