Social jetlag, sleep-related parameters, and glycemic control in adults with type 1 diabetes: Results of a cross-sectional study

Social jet lag impairs exercise volume and attenuates physiological and metabolic adaptations to voluntary exercise training

Social jet lag (SJL) is a misalignment between sleep and wake times on workdays and free days. SJL leads to chronic circadian rhythm disruption and may affect nearly 70% of the general population, leading to increased risk for cardiometabolic diseases. This study investigated the effects of SJL on metabolic health, exercise performance, and exercise-induced skeletal muscle adaptations in mice. Ten-week-old C57BL/6J mice (n = 40) were allocated to four groups: control sedentary (CON-SED), control exercise (CON-EX), social jet lag sedentary (SJL-SED), and social jet lag exercise (SJL-EX). CON mice were housed under a 12:12-h light-dark cycle. SJL was simulated by implementing a 4-h phase delay for 3 days to simulate "weekends," followed by a 4-h phase advance back to "weekdays," for 6 wk. EX mice had free access to a running wheel. Graded exercise tests (GXTs) and glucose tolerance tests (GTTs) were performed at baseline and after intervention to monitor the effects of exercise and social jet lag on cardiorespiratory and metabolic health, respectively. SJL led to alterations in activity and running patterns and clock gene expression in skeletal muscle and decreased average running distance (P < 0.05). SJL-SED mice gained significantly more weight compared with CON-SED and SJL-EX mice (P < 0.01). SJL impaired fasting blood glucose and glucose tolerance compared with CON mice (P < 0.05), which was partially restored by exercise in SJL-EX mice. SJL also blunted improvements in exercise performance and mitochondrial content in the quadriceps. These data suggest that SJL blunted some cardiometabolic adaptations to exercise and that proper circadian hygiene is necessary for maintaining health and performance.NEW & NOTEWORTHY In mice, disrupting circadian rhythms with social jet lag for 6 wk caused significant weight gain, higher fasting blood glucose, and impaired glucose tolerance compared with control. Voluntary exercise in mice experiencing social jet lag prevented weight gain, though the mice still experienced increased fasting blood glucose and impaired exercise performance compared with trained mice not experiencing social jet lag. Social jet lag seems to be a potent circadian rhythm disruptor that impacts exercise-induced training adaptations.

Association between sleep variability and time in range of glucose levels in patients with type 1 diabetes: Cross-sectional study

OBJECTIVE

Sleep and circadian disturbances emerge as novel factors influencing glycemic control in type 1 diabetes (T1D). We aimed to explore the associations among sleep, behavioral circadian parameters, self-care, and glycemic parameters in T1D.

METHODS

Seventy-six non-shift-working adult T1D patients participated. Blinded 7-day continuous glucose monitoring (CGM) and hemoglobin A1C (A1C) were collected. Percentages of time-in-range (glucose levels 70-180 mg/dL) and glycemic variability (measured by the coefficient of variation [%CV]) were calculated from CGM. Sleep (duration and efficiency) was recorded using 7-day actigraphy. Variability (standard deviation) of midsleep time was used to represent sleep variability. Nonparametric behavioral circadian variables were derived from actigraphy activity recordings. Self-care was measured by diabetes self-management questionnaire-revised. Multiple regression analyses were performed to identify independent predictors of glycemic parameters.

RESULTS

Median (interquartile range) age was 34.0 (27.2, 43.1) years, 48 (63.2%) were female, and median (interquartile range) A1C was 6.8% (6.2, 7.4). Sleep duration, efficiency, and nonparametric behavioral circadian variables were not associated with glycemic parameters. After adjusting for age, sex, insulin delivery mode/CGM use, and ethnicity, each hour increase in sleep variability was associated with 9.64% less time-in-range (B = -9.64, 95% confidence interval [-16.29, -2.99], p ≤ .001). A higher diabetes self-management questionnaire score was an independent predictor of lower A1C (B = -0.18, 95% confidence interval [-0.32, -0.04]).

CONCLUSION

Greater sleep timing variability is independently associated with less time spent in the desirable glucose range in this T1D cohort. Reducing sleep timing variability could potentially lead to improved metabolic control and should be explored in future research.

DATA AVAILABILITY STATEMENT

Data are available upon a reasonable request to the corresponding author.

Exploring the relationship between social jetlag with gut microbial composition, diet and cardiometabolic health, in the ZOE PREDICT 1 cohort

PURPOSE

In this study, we explore the relationship between social jetlag (SJL), a parameter of circadian misalignment, and gut microbial composition, diet and cardiometabolic health in the ZOE PREDICT 1 cohort (NCT03479866).

METHODS

We assessed demographic, diet, cardiometabolic, stool metagenomics and postprandial metabolic measures (n = 1002). We used self-reported habitual sleep (n = 934) to calculate SJL (difference in mid-sleep time point of ≥ 1.5 h on week versus weekend days). We tested group differences (SJL vs no-SJL) in cardiometabolic markers and diet (ANCOVA) adjusting for sex, age, BMI, ethnicity, and socio-economic status. We performed comparisons of gut microbial composition using machine learning and association analyses on the species level genome bins present in at least 20% of the samples.

RESULTS

The SJL group (16%, n = 145) had a greater proportion of males (39% vs 25%), shorter sleepers (average sleep < 7 h; 5% vs 3%), and were younger (38.4 ± 11.3y vs 46.8 ± 11.7y) compared to the no-SJL group. SJL was associated with a higher relative abundance of 9 gut bacteria and lower abundance of 8 gut bacteria (q < 0.2 and absolute Cohen's effect size > 0.2), in part mediated by diet. SJL was associated with unfavourable diet quality (less healthful Plant-based Diet Index), higher intakes of potatoes and sugar-sweetened beverages, and lower intakes of fruits, and nuts, and slightly higher markers of inflammation (GlycA and IL-6) compared with no-SJL (P < 0.05 adjusted for covariates); rendered non-significant after multiple testing adjustments.

CONCLUSIONS

Novel associations between SJL and a more disadvantageous gut microbiome in a cohort of predominantly adequate sleepers highlight the potential implications of SJL for health.

Sleep quality and sex-related factors in adult patients with immune-mediated diabetes: a large cross-sectional study

AIM

To analyze sleep quality and its relationships with clinical and biochemical features in a large cohort of adults with autoimmune diabetes.

METHODS

We administered to 553 patients with autoimmune diabetes the questionnaires: Pittsburgh Sleep Quality Index (PSQI), diabetes distress scale, diabetes-related quality of life and diabetes treatment satisfaction questionnaire. We excluded patients with missing HbA1c ± 4 months from PSQI administration or incorrect PSQI compilation (n = 110).

RESULTS

Altered sleep quality was recorded in 142/443 subjects (32%), insufficient total sleep time in 177/443 (40%). The altered sleep quality group had higher HbA1c (median 56 mmol/mol [interquartile range-IQR 49-62] vs 59 [IQR 52-68]; P < 0.001), higher average HbA1c in the previous 36 months (59 mmol/mol [IQR 54-68] vs 56 [IQR 51-62]; P < 0.001), and more individuals with HbA1c > 53 mmol/mol (74.6% vs 62.8%; P = 0.014). Diabetes duration (P = 0.63), type of insulin delivery (P = 0.48) and glucose monitoring (P = 0.35) were uninfluential. Patients with altered sleep quality showed higher prevalence of autoimmune (42 vs 28%; P = 0.005) and mental diseases (12 vs 4%; P = 0.002); there were greater emotional distress, and lower quality of life and treatment satisfaction (P < 0.001 for all), irrespective of sex. Men with altered sleep quality had higher HbA1c and prevalence of autoimmune diseases. Women showed greater prevalence of psychiatric disorders. Average HbA1c of the previous 36 months, autoimmune or psychiatric disorders were independent predictive factors for altered sleep quality.

CONCLUSION

One-third of the patients with autoimmune diabetes showed altered sleep quality, which associates with worse glycemic control, and autoimmune and mental disorders, with sex-specific differences.

Social jet lag and (changes in) glycemic and metabolic control in people with type 2 diabetes

OBJECTIVE

Social jet lag, i.e., the discordance among social and biological rhythms, is associated with poor metabolic control. This study aimed to assess cross-sectional and longitudinal associations among social jet lag and glycemic and metabolic control in people with type 2 diabetes.

METHODS

In a prospective cohort (N = 990) with type 2 diabetes, social jet lag was measured at baseline using daily diaries and was categorized (high, moderate, or low). Metabolic outcomes were assessed at baseline and at 1 and 2 years of follow-up. Associations among social jet lag and glycemic and metabolic control were analyzed using linear regression and linear mixed models adjusted for confounding factors. Analyses were stratified for work status (retired vs. working; p value for interaction = 0.007 for glycated hemoglobin [HbA1c]).

RESULTS

In working people, a cross-sectional association between high social jet lag and HbA1c (1.87 mmol/mol [95% CI: 0.75 to 2.99]) and blood pressure (5.81 mm Hg [95% CI: 4.04 to 7.59]) was observed. For retired people, high social jet lag was negatively associated with HbA1c (-1.58 mmol/mol [95% CI: -2.54 to -0.62]), glucose (-0.19 mmoL/L [95% CI:-0.36 to -0.01]), and blood pressure (-3.70 mm Hg [95% CI: -5.36 to -2.04]), and the association with BMI was positive (1.12 kg/m² [95% CI: 0.74 to 1.51]). Prospective associations had the same direction as cross-sectional findings but were nonsignificant for working or retired people.

CONCLUSIONS

Social jet lag was cross-sectionally, but not prospectively, associated with glycemic and metabolic markers. Interaction with work status was present, and directions of the associations were generally detrimental in the working population, whereas higher social jet lag was associated with improved glycemic and metabolic control for retired people.

Sleep-Opt-In: A Randomized Controlled Pilot Study to Improve Sleep and Glycemic Variability in Adults With Type 1 Diabetes

PURPOSE

The purpose of this study was to evaluate the feasibility and acceptability of a technology-assisted behavioral sleep intervention (Sleep-Opt-In) and to examine the effects of Sleep-Opt-In on sleep duration and regularity, glucose indices, and patient-reported outcomes. Short sleep duration and irregular sleep schedules are associated with reduced glycemic control and greater glycemic variability.

METHODS

A randomized controlled parallel-arm pilot study was employed. Adults with type 1 diabetes (n = 14) were recruited from the Midwest and randomized 3:2 to the sleep-optimization (Sleep-Opt-In) or Healthy Living attention control group. Sleep-Opt-In was an 8-week, remotely delivered intervention consisting of digital lessons, sleep tracker, and weekly coaching phone calls by a trained sleep coach. Assessments of sleep (actigraphy), glucose (A1C, continuous glucose monitoring), and patient-reported outcomes (questionnaires for daytime sleepiness, fatigue, diabetes distress, and depressive mood) were completed at baseline and at completion of the intervention.

RESULTS

Sleep-Opt-In was feasible and acceptable. Those in Sleep-Opt-In with objectively confirmed short or irregular sleep demonstrated an improvement in sleep regularity (25 minutes), reduced glycemic variability (3.2%), and improved time in range (6.9%) compared to the Healthy Living attention control group. Patient-reported outcomes improved only for the Sleep-Opt-In group. Fatigue and depressive mood improved compared to the control.

CONCLUSIONS

Sleep-Opt-In is feasible, acceptable, and promising for further evaluation as a means to improve sleep duration or regularity in the population of people with type 1 diabetes.

Irregular sleep and cardiometabolic risk: Clinical evidence and mechanisms

Sleep regularity is an essential part of the multidimensional sleep health framework. The phenomenon of irregular sleep patterns is widespread in contemporary lifestyles. This review synthesizes clinical evidence to summarize the measures of sleep regularity and discusses the role of different sleep regularity indicators in developing cardiometabolic diseases (coronary heart disease, hypertension, obesity, and diabetes). Existing literature has proposed several measurements to assess sleep regularity, mainly including the standard deviation (SD) of sleep duration and timing, sleep regularity index (SRI), interdaily stability (IS), and social jetlag (SJL). Evidence on associations between sleep variability and cardiometabolic diseases varies depending on the measure used to characterize variability in sleep. Current studies have identified a robust association between SRI and cardiometabolic diseases. In comparison, the association between other metrics of sleep regularity and cardiometabolic diseases was mixed. Meanwhile, the associations of sleep variability with cardiometabolic diseases differ across the population. SD of sleep characteristics or IS may be more consistently associated with HbA1c in patients with diabetes compared with the general population. The association between SJL and hypertension for patients with diabetes was more accordant than in the general population. Interestingly, the age-stratified association between SJL and metabolic factors was observed in the present studies. Furthermore, the relevant literature was reviewed to generalize the potential mechanisms through which irregular sleep increases cardiometabolic risk, including circadian dysfunction, inflammation, autonomic dysfunction, hypothalamic-pituitary-adrenal (HPA) axis disorder, and gut dysbiosis. Health-related practitioners should give more attention to the role of sleep regularity on human cardiometabolic in the future.

Sleep and Circadian Rhythm Disturbances in Diabetes: A Narrative Review

Sleep and circadian rhythm disturbances are less-known risk factors for the development and suboptimal outcomes of diabetes. The goal of this narrative review is to highlight the importance of sleep and circadian rhythm disturbances in the development and outcomes of type 1 diabetes (T1D) and type 2 diabetes (T2D), assess current treatment options and the possible mediating mechanisms. We performed a literature search using PubMed and selected relevant English and Dutch papers. Disturbances of sleep and circadian rhythm are common in people with diabetes. They are associated with an increased risk of developing T2D as well as with suboptimal diabetes outcomes (including higher HbA1c levels and reduced quality of life) for T1D and T2D. Preliminary data suggest that treatment of sleep and circadian rhythm disturbances could improve diabetes outcomes in people with T1D and T2D. Finally, the association with medical parameters appears to be mediated by disturbance in hormones, and by suboptimal self-care including forgetting or postponing glucose monitoring or medication use as well as higher consumption of high fat/high sugary foods. Diabetes may also disturb sleep, for example through nocturnal hypoglycemia and nocturia. We concluded that sleep and circadian rhythm disturbances are closely linked with diabetes. More attention to sleep in regular diabetes care is warranted, while further research is needed on treatment of sleep and circadian rhythm disturbances in the prevention of diabetes and its suboptimal outcomes.

The association between social jetlag and parameters of metabolic syndrome and type 2 diabetes: a systematic review and meta‐analysis

This study aims to determine the association between social jetlag and parameters of metabolic syndrome and type 2 diabetes (T2D) in a systematic review and meta-analysis. A systematic literature search was conducted in PubMed/Embase/Scopus until May 2022. Included studies described an association between social jetlag and parameters of the metabolic syndrome and/or T2D, were available full text and written in English or Dutch. Data extraction and quality assessment were performed on pre-piloted forms independently by two reviewers. Results were meta-analysed using random-effects analysis. A total of 6,290 titles/abstracts were screened, 176 papers were read full-text, 68 studies were included. Three studies were rated as low quality, 27 were moderate, and 38 were high quality. High quality studies showed that having social jetlag compared to no social jetlag was significantly associated with higher body mass index in 20 studies (0.49 kg/m² , 95% confidence interval [CI] 0.21-0.77; I²  = 100%), higher waist circumference in seven studies (1.11 cm, 95% CI 0.42-1.80; I²  = 25%), higher systolic blood pressure in 10 studies (0.37 mmHg, 95% CI 0.00-0.74; I²  = 94%) and higher glycated haemoglobin in 12 studies (0.42%, 95% CI 0.12- 0.72; I²  = 100%). No statistically significant associations were found for obesity, abdominal obesity, high- and low-density lipoprotein levels, cholesterol, triglycerides, diastolic blood pressure, hypertension, fasting glucose, homeostatic model assessment for insulin resistance, metabolic syndrome or T2D. Sensitivity analyses did not reduce heterogeneity. Despite substantial heterogeneity, social jetlag is associated with certain parameters of the metabolic syndrome and T2D, but not with prevalent metabolic syndrome or T2D. These findings should be interpreted with caution as the level of evidence is low and mostly based on cross-sectional data. Longitudinal studies are needed to further assess the direction of causality.

Influence of the COVID-19 Pandemic on Quality of Life, Mental Health, and Level of Physical Activity in Colombian University Workers: A Longitudinal Study

The COVID-19 pandemic has had a strong influence on people’s quality of life, and the different restrictive measures during the phases of the pandemic have had consequences for physical and mental health. This study determined the changes in health-related quality of life (HRQoL), sleep quality, depression symptoms (DS), and physical activity (PA) level of middle-aged Colombian university personnel during the COVID-19 pandemic. The analysis was performed following a longitudinal design on a sample of 354 people at four points before and during the pandemic. The International Physical Activity Questionnaire (IPAQ), the SF-12v2, the Pittsburgh Sleep Quality Index (PSQI), and the Zung Self-Rating Depression Scale (ZSDS) were included in an online survey. The measurements showed a decrease in quality of life with respect to PCS from 91.66 ± 10.78 to 83.64 ± 17.22 (p = 0.000) and MCS from 87.57 ± 12.23 to 80.90 ± 17.31, while poor sleep quality increased from 25.99% to 47.46% (p = 0.000), with DS reaching the peak during mandatory confinement (14.69%). Regarding the level of physical activity, the period of mandatory confinement inverted the proportion, highlighting active people as a minority (32.2%). In the multivariate analysis, we adjusted for age, sex, BMI, and socioeconomic level, discarding confounding effects and their interactions with the results obtained. In conclusion, this study showed that the pandemic has had a negative impact on sleep quality, HRQoL, DS, and PA level.

Friend or Foe: a Narrative Review of the Impact of Diabetes Technology on Sleep

PURPOSE OF REVIEW

The purpose of this review is to present a review of sleep science, the relationship between sleep and type 1 diabetes, and highlight the current literature on sleep outcomes in adult and pediatric diabetes technology research.

RECENT FINDINGS

Sleep quality is associated with glycemic outcomes, diabetes self-management, and mental health in people with type 1 diabetes. Diabetes technologies, including insulin pumps, continuous glucose monitors, and hybrid closed-loop systems improve glycemic outcomes. However, many people find this technology challenging for a variety of reasons, including increased burden and frequent alarms, especially during the night. The impact of different devices on sleep quality and quantity has been mixed. The newest technology, the hybrid closed-loop systems, offers the best opportunity for nocturnal glycemic regulation and has improved patient and family perspectives on sleep quality. However, objective sleep assessment has not shown significant improvement on sleep duration. Sleep quality and quantity in people with type 1 diabetes are widely recognized as an important component of health care, and the literature regarding the impact of diabetes devices on sleep is increasing. However, sleep disruptions are common and a barrier to device use. Despite finding minimal changes to sleep duration with device use, subjective accounts of sleep quality are overall positive, especially in those using hybrid closed-loop systems. Sleep quantity and quality are important outcomes to consider as diabetes technology continues to evolve.

Social Jetlag and Related Risks for Human Health: A Timely Review

The term social jetlag is used to describe the discrepancy between biological time, determined by our internal body clock, and social times, mainly dictated by social obligations such as school or work. In industrialized countries, two-thirds of the studying/working population experiences social jetlag, often for several years. Described for the first time in 2006, a considerable effort has been put into understanding the effects of social jetlag on human physiopathology, yet our understanding of this phenomenon is still very limited. Due to its high prevalence, social jetlag is becoming a primary concern for public health. This review summarizes current knowledge regarding social jetlag, social jetlag associated behavior (e.g., unhealthy eating patterns) and related risks for human health.

Type 1 Diabetes, Sleep, and Hypoglycemia

PURPOSE OF REVIEW

To review the relationship between sleep and hypoglycemia, sleep characteristics, and their associations with glycemic control in persons with type 1 diabetes (T1D). The effects of sleep interventions and diabetes technology on sleep are summarized.

RECENT FINDINGS

Nocturnal hypoglycemia affects objective and subjective sleep quality and is related to behavioral, psychological, and physiological factors. Sleep disturbances are common, including inadequate sleep, impaired sleep efficiency, poor subjective satisfaction, irregular timing, increased daytime sleepiness, and sleep apnea. Some have a bidirectional relationship with glycemic control. Preliminary evidence supports sleep interventions (e.g., sleep extension and sleep coach) in improving sleep and glycemic control, while diabetes technology use could potentially improve sleep. Hypoglycemia and sleep disturbances are common among persons with T1D. There is a need to develop sleep promotion programs and test their effects on sleep, glucose, and related outcomes (e.g., self-care, psychological health).

The Association of Cardiometabolic Disease with Psychological Factors in Colombian People during the COVID-19 Pandemic: A Cross-Sectional Study

During the COVID-19 pandemic, psychological disorders have been documented in the population, and their exacerbation in vulnerable populations such as those with Cardiometabolic Diseases (CD) might challenge health systems. This study determined psychological factors associated with CD in Colombian adults during the COVID-19 pandemic. For this purpose, 284 persons were evaluated, 142 without CD and 142 with CD. Sociodemographic data were collected, and the International Physical Activity Questionnaire (IPAQ), the SF-12v2, the Pittsburgh Sleep Quality Index (PSQI) and the Zung Self-Rating Depression Scale (ZSDS), which were integrated into an online form, were used. Through a simple and multiple logistic regression model, it was shown that CD was associated with low sleeping quality (LSQ) (OR = 3.51) and with depressive symptoms (DS) (OR = 1.98). In addition, in the group with CD, the presence of DS was related to BMI (OR = 2.45), and LSQ was related to living with persons at risk for COVID-19 (OR = 3.64) and BMI (OR = 5.88). In conclusion, this study showed that people with CD have a higher chance of presenting DS and LSQ. Furthermore, living with people at risk for COVID-19 was related to the presence of LSQ.

Sleep Regularity and Cardiometabolic Heath: Is Variability in Sleep Patterns a Risk Factor for Excess Adiposity and Glycemic Dysregulation?

PURPOSE OF REVIEW

Night-to-night variability in sleep patterns leads to circadian disruption and, consequently, could increase cardiometabolic risk. The purpose of this review is to summarize findings from studies published between 2015 and 2020 examining various measures of night-to-night variability in sleep in relation to metabolic syndrome (MetS), type 2 diabetes (T2D), and their risk factors. We illustrate a potential causal pathway between irregular sleep patterns and T2D, highlighting knowledge gaps along the way.

RECENT FINDINGS

Across different measures of sleep variability, irregular sleep patterns were associated with poorer cardiometabolic outcomes. Higher standard deviations (SD) across nights of sleep duration and onset or midpoint of sleep were associated with increased odds of having MetS and clusters of metabolic abnormalities as well as greater adiposity and poorer glycemic control. Conversely, greater regularity of rest-activity patterns related to lower risk for T2D. Social jetlag was associated with glycemic dysregulation, adiposity, T2D, and MetS. These associations are often observed in both metabolically healthy and unhealthy individuals; both higher SD of sleep duration and social jetlag relate to poorer glucose regulation in individuals with diabetes. There is consistent evidence of associations of sleep variability with increased risk for adiposity, glucose dysregulation, T2D, and MetS. Although experimental evidence is needed to determine causation, there is support to recommend stabilizing sleep patterns for cardiometabolic risk prevention.

Sleep and Environmental Factors Affecting Glycemic Control in People with Type 2 Diabetes Mellitus

PURPOSE OF REVIEW

Sleep and environmental factors both impact glycemic control in persons with type 2 diabetes mellitus (T2DM). This narrative article aims to review research within the past 5 years, focusing on chronotype, light, noise, and neighborhood disparities in relation to sleep in people with T2DM.

RECENT FINDINGS

Sleep quality and duration have been shown to impact glycemic control in patients with T2DM. Later chronotype can lead to poorer glycemic control due to disruption of circadian rhythms. Light exposure also has similar effects, likely due to its inherent influence on sleep quality. Environmental determinants, were associated with lower T2DM incidence, and noise and air pollution were associated with increased risks for T2DM. Findings were mixed; while most studies found that later chronotype, light/noise exposure, and neighborhood disadvantages were associated with poorer glycemic control in patients with T2DM, other environmental factors, such as green space, were not significantly associated with diabetes outcomes.

Does sleep duration, napping, and social jetlag predict hemoglobin A1c among college students with type 1 diabetes mellitus?

AIMS

The first aim examined the relationship between sleep behaviors (duration, napping, and social jetlag) and hemoglobin A1c (HbA1c) among emerging young adults (EYAs) with T1DM between 18 and 25 years old, who are living on a college campus. The second aim characterized the gender differences in glucose management, sleep behaviors, caffeine intake, and nighttime technology.

METHODS

A cross-sectional study of eligible participants used a convenience sample of eligible participants. Using Research Electronic Data Capture (REDCap), participants completed surveys about diabetes management, caffeine intake, nighttime technology use, and sleep-related behaviors. Data were analyzed using correlation and multiple linear regression to predict HbA1c from sleep behaviors, adjusting for covariates.

RESULTS

Participants (N = 76) average years with T1DM was 10.25 ± 5.70. Compared to females, males had a longer sleep duration lower HbA1c levels. HbA1c levels were negatively correlated with weekday sleep (r = -0.24, p = 0.03) and positively correlated with napping (r = 0.34, p = 0.003). After adjusting for covariates, participants who napped had a higher HbA1c level (β = 0.74, p = 0.03) compared with non-nappers.

CONCLUSIONS

Higher HbA1c levels were found among EYAs with T1DM in college who were nappers and had a longer sleep duration. Modifying sleep behaviors may be an appropriate target to improve glycemic control.