The Science of Diabetes Self-Management and Care2023, Vol. 49(5) 384–391© The Author(s) 2023Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/26350106231192352journals.sagepub.com/home/tde
Purpose: The purpose of this study was to determine the associations between perceived sleep quality and individual cardiovascular health (CVH) factors (A1C and body mass index [BMI]) and CVH behaviors (physical activity and dietary diabetes self-management) in young adults ages 18 to 25 years with type 1 diabetes (T1D).
Methods: Associations among perceived sleep quality and CVH factors and behaviors were examined using multivariable linear regression in 69 participants ages 18 to 25 years (mean age 21.4 ± 2.0, mean T1D duration 9.7 ± 5.6 years).
Results: Lower perceived sleep quality was associated with multiple lower CVH factors and behaviors (higher A1C and BMI, lower physical activity, poorer diet) even after adjusting for covariates (age, T1D duration, sex assigned at birth).
Conclusion: Experimental studies are needed to better understand the impact of modifying sleep habits on both short- and long-term CVH in adults with T1D.
Despite recent advancements in diabetes management and technology, including continuous glucose monitors (CGMs) and continuous insulin pumps, only 1 in 5 young adults with type 1 diabetes (T1D) achieve A1C targets of <7.0%.1 Achieving this glycemic target is important to reduce and prevent diabetes-related complications.2 Insufficient sleep duration and irregular sleep timing are increasingly reported as modifiable contributors to achieving glycemic targets in T1D.3,4 A majority of individuals with T1D do not meet sleep duration recommendations (duration >6.5 hours) and report poor sleep quality,3,4 which are known to increase insulin resistance and A1C.5,6 Compared to the general population, young adults with T1D are more likely to perceive low sleep quality.7 Short sleep and not achieving glycemic targets are associated with increased risk for developing cardiovascular complications compared to peers without diabetes.8
Hyperglycemia is an established contributor to microvascular and macrovascular damage and subsequent atherosclerotic cardiovascular disease (CVD).9-11 The risk of CVD is more profound and unique in the T1D population compared to the type 2 diabetes population.11 Individuals living with T1D also have a higher risk of CVD than the general population. Furthermore, CVD events occur more frequently, with a higher chance of mortality, and occur about 10 to 15 years earlier on average than individuals without T1D in the general population.12 Therefore, it is important to mitigate cardiovascular risk among individuals living with T1D, and previous studies have shown that effectively managing cardiovascular health (CVH) factors (eg, cholesterol, blood pressure, BMI, and glycemic targets) and CVH behaviors (diet, physical activity, smoking cessation) has positive effects on microvascular and macrovascular complications in this population.13 Mitigating cardiovascular risk is especially important among young adults with T1D because clinical and behavioral risk factors of CVD present earlier in life and are associated with future cardiovascular events.14
CVH factors and behaviors are commonly examined as individualized phenomena when studied in young adults with T1D. However, CVH is increasingly conceptualized as a construct consisting of both CVH factors and CVH behaviors that interact synergistically. In 2022, the American Heart Association (AHA) updated and enhanced its conceptualization of CVH to include 4 health factors (i.e., BMI, blood lipids, blood glucose, and blood pressure) and 4 health behaviors (i.e., diet, physical activity, nicotine exposure, and sleep health) that interact with elements of psychological well-being and social determinants of health across the life span, including at a critical transition period in early adulthood.15 Life’s Essential 8 expands on AHA’s previous Life’s Simple 7 model by incorporating sleep as an important component of cardiovascular health.16,17 AHA’s Life’s Essential 8 model of CVH guided the design of this study.
The purpose of this study was to examine associations between perceived sleep quality and individual CVH factors and behaviors in young adults with T1D while adjusting for covariates (eg, age, T1D duration, and sex at birth). The hypothesis was that lower perceived sleep quality would be associated with lower CVH (defined by a higher A1C and body mass index [BMI] and lower physical activity and diet) in young adults with T1D.
The primary aim of this quantitative descriptive study was to examine the association between perceived sleep quality and CVH factors and behaviors in young adults ages 18 to 25 years with T1D in the United States. This study was reviewed and approved by The Case Western Reserve University Institutional Review Board (STUDY20201829) approved. From February 2021 to April 2022, young adults ages 18 to 25 years (1) diagnosed with T1D for at least 6 months, (2) with no other major medical or psychiatric disorders, and (3) who read and communicate in English were invited to participate. Participants were recruited through partnerships with College Diabetes Network and TuDiabetes, ResearchMatch, and social media (Facebook and Twitter). Those with an obstructive sleep apnea diagnosis, pregnancy at the time of recruitment, or worked the night shift (12 AM to 6 AM) were not eligible to participate.
Participants who met the criteria were consented and asked to complete questionnaires, including the 19-item Pittsburgh Sleep Quality Index (PSQI)18 and the 4-item dietary control and 3-item physical activity subscales of the Revised Diabetes Self-Management Questionnaire (DSMQ-R).19 Additionally, demographic information (age, sex assigned at birth, gender, education, etc) was collected for each participant.
Perceived sleep quality. Perceived sleep quality was measured with the 19-item PSQI (PSQI Cronbach’s α = 0.83).18 PSQI component scores are summed and range from 0 to 21, with higher scores indicating poorer sleep quality. Scores ≥5 meet the threshold for poor sleep quality.18 The Cronbach’s alpha for the PSQI global score in the current study was 0.774.
CVH factors. Two CVH factors were assessed using biomarkers indicating risk for cardiovascular disease, including A1C and BMI. The BMI was computed from the participant’s self-reported height and weight (kg/m2 ).
CVH behaviors. Two CVH behaviors, physical activity and diet, were measured with 3-item and 4-item subscales, respectively, derived from DSMQ-R. Physical activity (3 items; Cronbach’s α = 0.76) and dietary control (4 items; Cronbach’s α = .77) components of the DSMQ-R were examined.19,20 Each subscale is standardized by summing the total items, dividing by the potential maximum score (maximum score for physical activity = 9, maximum score for dietary control = 12), and then multiplying by 10. Standardized subscale scores ranged from 0 to 10, with higher scores indicating more optimal diabetes self-management.19,20 The Cronbach’s alphas for the dietary and physical activity subscales were 0.760 and 0.593, respectively, in the current study.
Demographic and clinical characteristics. Clinical and demographic data were obtained from a baseline survey, including age, height and weight, diabetes duration, most recent A1C, medical history, race, ethnicity, education, employment status, full-time student status, cigarette smoking, alcohol or other substance use, insulin therapy regimen, CGM device brand (if applicable).
Data were managed using the REDCap site and exported into the Statistical Package for the Social Sciences version 28.21 Descriptive statistics were used to summarize each of the variables, including scores for multiitem scales. We excluded 6 cases due to missing data in the key variables of interest. A quantitative descriptive approach was used to characterize self-report sleep quality and CVH factors and behaviors among the individuals in the study. Bivariable correlations were used to examine the relationships among perceived sleep quality and CVH factors and behaviors. To evaluate the explanatory contributions of perceived sleep quality to CVH factors and behaviors, we performed a series of multivariable linear regression models. Separate models were examined for each of the CVH factors and behaviors as outcomes. Perceived sleep quality and CVH factors were examined with the covariates age, T1D duration, and sex at birth. Statistical significance was set at P < .05.
Sixty-nine young adults in the study had a mean age of 21.4 (±2.0) years, a mean BMI of 24.5 (±4.5) kg/m2, and a mean GPA of 3.7 (±0.3). Of the sample, 79.7% were female, 88.4% were non-Hispanic White, and 90.7% were able to meet their monthly expenses. The mean diabetes duration was 9.7 (±5.6) years, the mean A1C was 6.9 (±1.0%, 51.1 mmol/mol), and most used an insulin pump (67.6%, n = 46) and CGM (92.8%, n = 64) for treatment and monitoring. A total of 1.4% reported an active smoking history, 36.8% were overweight or obese (n = 25), and 40.6% did not meet glycemic targets (A1C > 7.0%, n = 28). Scores for the dietary control subscale ranged from 0 to 9.17, mean of 4.9 (±2.3), and scores for the physical activity subscale ranged from 0 to 6.7, mean of 4.3 (±1.5; Tables 1 and 2).
Sleep characteristics. The mean PSQI global score was 5.6 (±3.4), with 40.6% (n = 28) meeting the cutoff for clinically significant poor sleep quality (PSQI > 5). The mean sleep duration was 7.3 (±1.3) hours, with 24.6% (n = 17) reporting insufficient sleep <7 hours.
Associations between self-report sleep quality and CVH factors and behaviors. Lower perceived sleep quality was associated with lower CVH factors and behaviors. The unadjusted associations between perceived sleep quality and CVH factors were examined in the first set of linear regression models. The unadjusted associations between perceived sleep quality and CVH factors (A1C, physical activity, and diet) were statistically significant (P < .01), however, the association with BMI was not significant (P = .151).
In the next set of linear regression models, the associations were examined after adjusting for covariates (age, sex at birth, and T1D duration). The associations between perceived sleep quality and CVH factors and behaviors (A1C β = 0.389, P = .002; physical activity β = −0.267, P = .038; diet β = −0.358, P = .004) remained statistically significant after controlling for age, sex at birth, and T1D duration accounting for 20.2%, 11.5%, and 20.9% of the variance, respectively (Table 3).
Higher perceived sleep quality was associated with higher CVH factors and behaviors even after considering age, T1D duration, and sex assigned at birth among the young adults with T1D in the current study. The associations persisted after considering multiple covariates. About half reported low sleep quality (42%), a third reported insufficient sleep (<7 hours; 26%), and a little over a third reported overweight or obesity (36.1%). These findings highlight the importance of perceived sleep quality as a contributor to CVH in this population. Also, there is a compelling need to address sleep behavior as a modifiable component of CVH care in young adults with T1D.
The associations between perceived sleep quality and several CVH factors add to previous studies of sleep characteristics and CVH among adults with T1D.22-25 Young adults with T1D in the current study reported poorer sleep quality compared to young adults in the general population in another observational study (mean PSQI 5.7 ± 3.5 vs mean PSQI 4.1 ± 1.5).7 However, sleep duration on average was slightly longer when compared to the general population (mean sleep duration 7.3 hours ± 1.3 hours vs 7.0 hours ± 0.7 hours).7 Higher perceived sleep quality was associated with lower A1C or lower time in range in 3 other studies, which is consistent with our findings.26-28 In previous similar studies, objectively measured sleep quality and sleep duration were associated with lower A1C,5,29,30 suggesting perceived sleep quality is an acceptable indicator.
The association between perceived sleep quality and BMI was not significant in the current study. Poor perceived sleep quality may result in an increase in the hunger hormone ghrelin and a decrease in the satiety hormone leptin, contributing to poor dietary choices31,37 and an elevated BMI.31,32 However, BMI as a single indicator of health outcomes is not ideal because it does not differentiate between visceral fat, subcutaneous fat, and skeletal muscle mass.33 The AHA recommends that in addition to BMI, waist circumference should be used to determine abdominal obesity, which is a CVD risk marker independent of BMI that is predictive of mortality.34 BMI along with waist circumference adds critical information about body composition and would be beneficial to measure in future studies.34 In addition to CVH factors, sleep characteristics have been associated with some CVH behaviors. Perceived sleep quality was associated with physical activity and diet before and after adjusting for age, sex assigned at birth, and T1D duration. Adults experiencing lower sleep quality may feel fatigued and less willing to engage in physical activity as a result even though increased physical activity may improve sleep quality.35,36
The findings should be interpreted within the context of the strengths and limitations of the current study. First, the present study sample was primarily non-Hispanic White (86.7%) and college-educated (57.3%); therefore, demographic differences in these variables could not be determined. Some other traits were represented disproportionately relative to the national T1D population, including the female sex (74.7% vs 50%) and optimal glycemia (60.6% vs 30%).1 Thus, some of the participant demographic characteristics (sex, ethnicity, and education), perceived sleep duration (mean of 7.3 hours), and T1D clinical characteristics (insulin pump and CGM used by majority) may affect the generalizability of the results. The relatively small sample size raises the possibility of type II statistical error in the associations between perceived sleep quality and CVH factors and behaviors. Not all CVH factors were measured (eg, blood lipids or blood pressure), and the CVH behaviors, physical activity and diet, were limited to how they apply to diabetes self-management. Furthermore, obtaining waist circumference in addition to BMI would add further insight into the findings presented here. Due to the nature of the questionnaires, the interpretation of the questions may yield results for how participants perceive their diet and quality of sleep rather than the objective diet and sleep quality. Additionally, nicotine use was excluded from the analysis because only 1 participant reported active smoking; however, researchers in the future may consider a focus on perceived use of cigarettes or inhaled nicotine delivery systems or exposure to secondhand smoke. Future researchers may also explore the direction of associations between hypoglycemic excursions related to T1D management and achieving adequate physical activity and dietary intake. The current study also had several strengths. Young adults were the focus of the study, and this stage of development is a high-risk understudied population with unique chronobiological and environmental threats to sleep and CVH. Also, multiple variable relationships beyond correlation were investigated to strengthen and verify the correlational findings.
There are several implications for clinical practice and future research on young adults with T1D. Clinicians and diabetes educators can use the AHA Life’s Essentials 8 model to guide diabetes care, particularly regarding the multiple associations between CVH factors (glycemia, BMI, cholesterol) and CVH behaviors (sleep duration, diet, and physical activity). Several of the CVH factors discussed involve modifiable behaviors, such as engaging in physical activity and choosing healthy foods, to promote health especially in this population.
While the focus of the current study was on young adults with T1D, this further provides an opportunity to look at potentially beneficial interventions through early intervention. Earlier intervention through healthy behavior promotion may help to decrease the reliance on medications to treat comorbidities such as hypertension or hyperlipidemia that can be attributed to CVH factors and behaviors. Achieving the recommended sleep duration (7-9 hours)38 and achieving glycemic targets have been demonstrated to influence multiple aspects of health, thus there should be a larger focus on the clinical significance of diabetes self-management education and incorporating healthy sleep habits in young adults with T1D.
According to the author guidelines, each of the authors named on the manuscript fit the criteria for authorship. Stephanie Griggs, PI on the grant (R00NR018886), secured the funding; designed the study; collected, analyzed, interpreted the data; and wrote the manuscript. Johnathan Huynh, Jorden Rieke, and Quiana Howard interpreted the findings and cowrote the manuscript.
The authors have no conflicts of interest to disclose.
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The first author Stephanie Griggs is funded by the National Institute of Nursing Research (NINR) R00NR018886. Jonathan Huynh was funded by the Provost Summer Undergraduate Research Grant through Case Western Reserve University. Jorden Rieke and Quiana Howard are funded by the Dean’s Legacy Fellowship through Frances Payne Bolton School of Nursing, Case Western Reserve University.
Stephanie Griggs https://orcid.org/0000-0001-9901-3853
Johnathan Huynh https://orcid.org/0000-0002-0125-9618
From Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, Ohio (Dr Griggs, Mr Huynh, Ms Rieke, Ms Howard).
Corresponding Author: Stephanie Griggs, Frances Payne Bolton School of Nursing, Case Western Reserve University, 2120 Cornell Road, Cleveland, OH 44106, USA.Email: Stephanie.griggs@case.edu
