Soohyun Nam

The relationship between sleep and glucose metabolism underscores the importance of evaluating sleep, especially given the high prevalence of sleep disturbances in individuals with Type 2 diabetes (T2D). Objective sleep measurements like actigraphy may not always align with subjective assessments, such as the Pittsburgh Sleep Quality Index (PSQI). Although discrepancies between objectively measured sleep and subjectively measured sleep have been highlighted in healthy populations and other clinical populations, the relationship has not been well studied in individuals with T2D. We aimed to assess the discrepancies and correlations between actigraphy‐measured and self‐reported sleep parameters in individuals with T2D, and to identify demographic, psychological, and clinical factors contributing to these discrepancies. Adults ( n = 134) with T2D wore actigraphy devices for 14 days and completed study surveys assessing subjective sleep (PSQI), demographics, and psychological and clinical factors. Findings indicated significant discrepancies in total sleep time (TST) and sleep efficiency between the two measurement methods, with self‐reported TST being higher. Discrepancy levels varied by sex, depressive symptoms, and perceived stress. After adjusting for covariates, sex and depressive symptoms remained significant factors of greater discrepancies in TST and sleep efficiency. Age, marital status, housing stability, and sleep quality were also associated with discrepancies in sleep efficiency. Employment status and housing stability were factors associated with discrepancies in sleep midpoint. Discrepancies between subjective and objective sleep assessments suggest that integrating both methods could provide a more comprehensive understanding of sleep health. The findings also emphasize the need to consider individual psychological and demographic factors when interpreting sleep measurements.

Sleep is a critical component of cardiometabolic health, yet individuals with Type 2 diabetes (T2D) experience disproportionately poor sleep quality. While extensive research links sleep duration and quality with HbA1c, less is known about the relationship between sleep and continuous glucose monitoring (CGM)–derived metrics, which capture short‐term glycemic variability (GV) and time in range (TIR). Growing evidence suggests that CGM‐derived metrics, particularly GV and TIR, are strongly associated with diabetes‐related complications and all‐cause mortality, underscoring their clinical importance. We conducted a study to examine associations between self‐reported sleep quality and CGM‐derived metrics among 137 adults with T2D. Participants completed the Pittsburgh Sleep Quality Index (PSQI) and wore blinded CGM devices for 14 days. CGM‐derived metrics included intraday‐ and interday‐GV (coefficient of variation, J‐index, high/low blood glucose indices, mean of daily differences [MODD]), TIR, time above range (TAR) and time below range (TBR). Multivariable linear regression adjusted for age, sex, body mass index, diabetes duration, depressive symptoms and race/ethnicity. Overall, 69% of participants reported poor sleep. Poor sleep quality was independently associated with higher TAR (daytime β = 0.18, p = 0.04; nighttime β = 0.13, p = 0.04), lower TIR (daytime β = −0.09, p = 0.04; nighttime β = −0.05, p = 0.04) and greater day‐to‐day GV (β = 0.22, p = 0.03) and higher hyperglycemia risk (β = 0.23, p = 0.04). These findings suggest that poor sleep quality in T2D is linked to increased hyperglycemia exposure, reduced TIR and unstable day‐to‐day GV, independent of clinical factors. Addressing sleep as a modifiable lifestyle factor and integrating sleep assessments with CGM may provide actionable insights to guide personalised diabetes management.

Studies on diabetes-related stigma rarely focus on identifying or examining protective factors (e.g., social support and healthcare environment) that can mitigate the adverse effect of this social phenomenon. In this cross-sectional study, we examined perceived autonomy support, a concept from the self-determination theory, as a moderator of the association between diabetes-related stigma and its adverse behavioral and psychological (depression, diabetes distress, and anxiety) correlates. We recruited 190 adults with type 2 diabetes (T2D) from a tertiary hospital in Ghana. We assessed diabetes-related stigma, depression, anxiety, diabetes distress, diabetes self-management, and perceived autonomy support using psychometric instruments. Hierarchical multivariable linear regressions were used to evaluate moderation effects of perceived autonomy support. Participants had an average age of 59.44 (standard deviation [SD] = 10.7) years, were mostly female (70.5%, n = 134), and had had T2D diagnosis for a median of 14.5 years. Autonomy support was directly associated with lower anxiety and depression and better diabetes self-management behaviors. Greater perceived autonomy support reduced the negative association between diabetes-related stigma and diabetes self-management (β = 0.20, 95% confidence interval [CI]: 0.01 to 0.39; p = 0.041). Perceived autonomy support buffered the negative effects of diabetes stigma on self-management. These findings highlight autonomy-supportive care as a promising strategy to address the adverse effects of diabetes-related stigma in Ghana.

Background: To identify diurnal glycemic patterns in adults with type 2 diabetes (T2D) using continuous glucose monitoring (CGM)–based machine learning and examine their association with diabetes distress, a key psychosocial outcome. Methods: In this observational study, 137 adults with T2D wore blinded CGM (FreeStyle Libre Pro), yielding 1657 days of data. Glycemic patterns were identified using unsupervised machine learning via Gaussian mixture modeling, validated with Bayesian information criterion and silhouette scores. Diabetes distress was assessed with the 17-item Diabetes Distress Scale and analyzed through analysis of covariance (ANCOVA), adjusting for age, sex, body mass index, diabetes duration, and glucose management indicator. Results: Clustering identified four distinct glycemic profiles: Cluster 1 (suboptimal control, nocturnal hypoglycemia; 15.8%), Cluster 2 (suboptimal control, nocturnal hyperglycemia; 27.1%), Cluster 3 (poorly controlled, prolonged hyperglycemia; 21.1%), and Cluster 4 (well controlled; 36.1%). Diabetes distress scores varied significantly: participants in Cluster 3 reported the highest distress (mean = 2.37, 95% CI = 1.99-2.76), while Cluster 4 reported the lowest (mean = 1.67, 95% CI = 1.48-1.86; P = .03). Effect sizes indicated differences corresponded to clinically meaningful categories of “little or no distress” vs “moderate distress.” Conclusions: CGM-based machine learning identified physiologically distinct glycemic phenotypes that were also associated with psychosocial burden. This work demonstrates the added value of integrating CGM-derived profiles with patient-reported outcomes. These findings highlight the potential of CGM phenotyping to support precision diabetes care by enabling early identification of high-risk subgroups, guiding tailored behavioral and psychosocial interventions, and informing technology-enabled decision tools that connect physiological monitoring with emotional well-being in T2D management.

Background: Numerous barriers to moderate to vigorous physical activity exist for youths with type 1 diabetes (T1D). The virtual exercise games for youth with T1D (ExerT1D) intervention implement synchronous support of moderate to vigorous physical activity including T1D peers and role models. Objective: This study aims to understand the acceptability of this intervention to participants. Methods: We conducted postprogram, semistructured, televideo interviews with participating youths to elicit perspectives on the acceptability of the intervention and experience with the program. Two coders independently reviewed and analyzed each transcript using a coding scheme developed inductively by senior researchers. Discrepancies were resolved by team discussion, and multiple codes were grouped together to produce 4 main thematic areas. Results: All 15 participants provided interviews (aged 14-19 years; 2 nonbinary, 6 females; median hemoglobin A1c level of 7.8% (IQR 7.4%-11.2%), 5 with a hemoglobin A1c level of ≥10%). Qualitative data revealed four themes: (1) motivation to engage in physical activity (PA)—improving their physical capabilities and stabilizing glucose levels were cited as motivation for PA and challenges of living with T1D were cited as PA barriers; (2) experience with and motivation to manage diabetes while engaging in PA—participants provided details of accommodating the inherent uncertainty or limitations of PA with diabetes and sometimes preparing for PA involved psychological and motivational adjustments while some relayed feelings of avoidance; (3) peer support encouraged engagement with the intervention—participants appreciated the peer aspects of components of ExerT1D and participants’ reflections of the facilitated group experience highlight many benefits of a small-group virtual program; and (4) improvements in PA and diabetes self-management efficacy—all participants credited the program with improving or at least raising awareness of T1D management skills. Conclusions: Our virtual PA intervention using an active video game and discussion component provided adolescents with T1D the confidence and peer support to engage in PA, improved awareness of diabetes-specific tasks to prepare for exercise, and improved understanding of the effect of PA on glucose levels. Engaging youths with a virtual video game intervention is a viable approach to overcome barriers to PA for adolescents with T1D. Trial Registration: ClinicalTrials.gov NCT05163912; https://clinicaltrials.gov/ct2/show/NCT05163912