The Science of Diabetes Self-Management and Care2024, Vol. 50(5) 383–393© The Author(s) 2024Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/26350106241268377journals.sagepub.com/home/tde
AbstractPurpose: The purpose of this study was to examine the relationships between symptom burden and sleep problems and the role of depressive symptoms on these relationships in middle-aged and older adults with type 2 diabetes.
Methods: The study employed a cross-sectional, correlational design with secondary analysis. De-identified data sets from three original studies were combined. A total of 189 men and women with type 2 diabetes were recruited using convenience sampling in midwestern United States. Symptom burden, sleep impairment and sleep disturbance, depressive symptoms, demographics, and diabetes-related variables were assessed. The participants were grouped into glucose-controlled and -uncontrolled groups to examine a multigroup effect. Multigroup path analyses were conducted.
Results: The results indicated that symptom burden had direct and indirect effects through depressive symptoms on sleep disturbance in the controlled group, whereas only a direct effect was found in the uncontrolled group. For sleep-related impairment, significant direct and indirect effects of symptom burden were found via depressive symptoms in both groups, and the strength of the effects on each path differed between the groups.
Conclusions: Diabetes symptom burden was associated with sleep disturbance and sleep-related impairment in middle-aged and older adults with diabetes. A different approach should be considered for sleep management according to their A1C levels, and depressive symptoms can be a therapeutic target to treat sleep problems in the population.
The global prevalence of diabetes is rapidly increasing. The number of adults with diabetes worldwide was 537 million in 2021.1 The world prevalence is expected to increase to 783 million by 2045, the majority of whom are middle-aged (55–64 years) and older adults (≥65 years).1 Although the aging population with diabetes keep rising, no consensus exists on the effective lifestyle interventions for older adults with diabetes.2 To facilitate development of the most appropriate care for the growing population with diabetes, it is important to understand their diabetes symptoms and the resulting problems.
People with diabetes should monitor and manage various symptoms every day, and these symptoms can play as a facilitator or barrier to diabetes self-care according to the theory.3 Those who have diabetes usually experience more than 1 symptom in clusters, and living with multiple concurrent symptoms can be burdensome and decrease one’s quality of life.4 Because older adults tend to experience more physical symptoms than younger adults,5 the older adults would feel more burden of symptom management.
Diabetes symptoms are multidimensional and include not only hypo- or hyperglycemia but also psychological, neurological, cardiologic, and ophthalmologic symptoms.6 In the middle-aged and older adults with diabetes, more than half of the participants (58%) reported high burden with physiological (eg, pain, tiredness, nausea, drowsiness) and psychological (eg, depression, anxiety) symptoms.7 Similarly, regardless of the disease severity, adults with type 2 diabetes (T2DM) prevalently reported burden with acute and chronic pain, neuropathy, fatigue, depression, and sleep disturbance in a large national survey study.5 Given the high prevalence of symptom burden and its detrimental effects, including decreased diabetes self-care and quality of life,4,8 symptom dynamics of people with T2DM should be better understood than the current state.
Patients with diabetes are vulnerable to sleep disorders and diabetes-specific symptoms (eg, fatigue or neuropathy) that may worsen sleep quality.9 In addition, increased disease burden is associated with abnormal sleep duration, and this phenomenon is more prominent among middle-aged and older adults than among young adults.10 Greater neurological pain symptoms related to diabetes was associated with higher day-to-day variability in sleep duration among adults with diabetes.11 Higher symptom burden was also associated with poorer sleep quality in patients with other conditions, such as heart failure12 or cancer.13 However, the associations between diabetes symptom burden and sleep and the mechanisms by which symptom burden is associated with sleep problems in adults with T2DM remains unclear.
Depression is common in patients with diabetes.12 Increased diabetes symptoms, such as retinopathy, neuropathy, and cerebrovascular or cardiovascular complications, were found to increase the risk of depression among adults with T2DM in a national cohort study.13 Because depression is also highly linked to sleep deprivation,14 it is possible that depression could be a potential mediator of the relationship between diabetes symptom burden and sleep problems in people with T2DM.
One recent study conducted in patients with heart failure examined the mediating effect of depression on symptom burden and sleep quality.15 In this study, a higher symptom burden was associated with higher psychological distress and poor sleep quality, and depression had a significant indirect effect on the relationship between symptom burden and sleep. Among patients with T2DM, the relationship between symptom burden and sleep is not well understood, nor is the role of depression in this relationship. Understanding the mechanisms underlying poor sleep in people with T2DM is crucial for future development of effective sleep interventions for the growing population.
Glycated hemoglobin (A1C) is a measure of glucose control from the prior 3 months. A1C is a widely used clinical indicator of the past diabetes management quality and disease severity. Therefore, the experiences of diabetesrelated and depressive symptoms differ according to the A1C level in people with diabetes. In fact, 1% reduction in A1C (10.9 mmol/mol) was previously associated with a lower symptom count and symptom distress in people with diabetes.16 A1C level was also significantly associated with depressive symptoms and diabetes symptoms when A1C was poorly controlled.17 Because poor sleep quality can also increase A1C,18 it is necessary to separately examine the mediating effects of depressive symptoms on the relationship between diabetes symptom burden and sleep problems according to A1C levels. Based on the results of previous studies, a conceptual model was developed to guide this study, as shown in Figure 1.
This study aimed to examine the associations between diabetes symptom burden and sleep problems (sleep impairment and sleep disturbance) and to evaluate whether depressive symptoms mediate these associations according to A1C levels (controlled vs uncontrolled) in middle-aged and older adults with T2DM. Based on the current guidelines for glucose control targets in nonpregnant adults,19 the study participants were divided into the controlled (A1C ≤ 7%) and uncontrolled (A1C > 7%) groups based on their A1C values.
This study employed a cross-sectional, correlational design using secondary analysis. Three data sets were used that included the variables for this study. The variables were collected using the same measures. Secondary analysis is an excellent method for answering new questions with existing data. In this study, the use of the combined 3 data sets increased the sample size and minimized the risk of Type II errors. The secondary data analysis for this study fostered new insights about diabetes symptoms and their relationships with depression and sleep in adults with T2DM. The research questions here were not employed in any of the original studies.
The specific hypotheses for this study included:
Hypothesis 1: Diabetes symptom burden is positively associated with sleep problem, either sleep disturbance or sleep-related impairment (direct effect).
Hypothesis 2: The degree of depressive symptoms mediates the relationship between the diabetes symptom burden and sleep problem, either sleep disturbance or sleep-related impairment (indirect effect).
Hypothesis 3: The strength of the mediating effects of depressive symptoms on the relationship between diabetes symptom burden and sleep problems, either sleep disturbance or sleep-related impairment, differs according to A1C levels (controlled vs uncontrolled; multigroup effect).
Demographics and other information. Demographic information included age, sex, and race/ethnicity. The level of A1C was measured using the Bayer A1CNow+ (Bayer Healthcare, Indianapolis, IN), a bloodspot point-of-care analyzer. The A1CNow+ has been certified by the National Glycohemoglobin Standardization Program, and results obtained from the A1CNow+ have been found to be comparable to laboratory methods using highperformance liquid chromatography.20
Diabetes symptom burden. The Diabetes Symptom Checklist-Revised (DSC-R) is a subjective measure of the diabetes-specific symptom burden. The DSC-R has 34 items that collectively address the burden of a wide range of diabetes symptoms, including hypoglycemia, hyperglycemia, and psychological (fatigue, cognitive), neurological (pain, sensory), cardiologic, and ophthalmologic symptoms.6 The DSC-R assesses whether diabetes-specific symptoms occurred over the past month and if so, how troublesome each symptom was to the responder over the past month. The answer is coded as 0 if the symptom did not occur; if the symptom occurred, the responder chooses the degree of trouble from a Likert scale ranging from 1 (not at all) to 5 (extremely). The item scores are summed and divided by the total number of items to obtain the total DSC-R score. A higher score reflects a greater diabetes symptom burden.6 The reliability of the DSC-R total score was supported in this study, with a Cronbach’s α value of .95.
Depressive symptoms. The Patient-Reported Outcomes Measurement Information System (PROMIS) was used to assess participants’ self-reported depressive symptoms. The PROMIS measures use a Likert scale ranging from “not at all” to “very” much and provide a standardized t score with a mean score of 50 and a standard deviation of 10 compared to the scores of a reference population.21 A higher score indicates more symptoms of the concept being measured. A computerized adaptive testing method was used to administer the PROMIS. In this study, the 28-item PROMIS Depressive Symptoms item bank version 1.0 was used to assess the degree of negative mood, self-image, social, cognition, and reduced positive affect and engagement. The PROMIS Depressive Symptoms scale has also shown good validity and reliability (α > .92) for most t-score distributions.22
Sleep problems. Sleep problems were defined as sleep disturbance and sleep-related impairment in the study. PROMIS measures were also used to measure these 2 sleep problems. The PROMIS measures use standardized t scores where a score of 50 represents the average score of the US population. A score increase of 10 indicates 1 SD above the mean of the representative sample.22 A higher score on the PROMIS sleep measures indicates greater sleep disturbance and sleep-related impairment. In this study, both PROMIS Sleep Disturbance and Sleep-Related Impairment were implemented as part of the online survey using a computerized adaptive testing format. The PROMIS Sleep Disturbance item bank version 1.0 has 27 items and assesses the responder’s overall sleep quality or difficulties in the past 7 days. The PROMIS Sleep-Related Impairment Item Bank version 1.0 has 16 items and measures the degree of interference of sleepiness and tiredness with daytime activities in the past 7 days. PROMIS sleep disturbance (α = .88) and sleep-related impairment (α = .84) have both shown good validity and reliability across various t-score distributions.22
De-identified data sets from the 3 original studies were combined in this study. Data from the first study were collected between June 2020 and December 2020. Data were collected for the second study between September 2011 and March 2014. Data from the third study were collected between September 2016 and September 2017. In the last 2 studies, data were collected at baseline and follow-up; only baseline data were used in this study. Data were collected electronically in all 3 studies. Each study included the same variables as in this study. All 3 original studies were conducted at a single midwestern university in the United States.
A total of 189 men and women with T2DM were included from the 3 original studies. Participants were recruited using convenience sampling through online announcements, posting flyers, and word-of-mouth in all 3 original studies.24-26 In one original study,24 however, Research Match, a national research volunteer registry, was additionally used to recruit a large population of potentially eligible participants who agreed to be contacted by the researchers for future study participation opportunities.23
Each of the 3 original studies had different aims and inclusion criteria, as listed in the following. The first study examined the relationship between cognitive function and self-management behaviors in adults ages 60 years or older with T2DM.24 Eighty-four older adults were included in the study. People with any diagnosis characterized by impaired cognitive function, such as dementia, Alzheimer’s disease, cerebrovascular disease, or serious mental illnesses, were excluded. The second study ecologically assessed the temporal relationships among fatigue, physical activity, and blood glucose levels.25 Ninety-one men and women ages 45 years or older with T2DM were included. Only patients without fibromyalgia or kidney disease and who were not undergoing chemotherapy were included in the study. The third study tested the feasibility of a walking exercise intervention for inflammation, fatigue, and sleep among women ages 45 years or older with T2DM.26 Fourteen women participated in the third study. This study employed the same inclusion criteria as the second original study, but patients with any condition that impaired walking were excluded.
Overall, all 3 original studies targeted people with T2DM but had different inclusion criteria for age; 1 included older adults only,24 whereas the other 2 included both middle-aged and older adults.25,26 All 3 studies investigated diabetes symptom burden, depressive symptoms, and sleep problems as secondary outcomes, but these variables were not included in their objectives and thus not included in the published findings. By combining these three existing data sets, this secondary analysis could provide new insight not addressed in the original studies.
Data were analyzed using StataIC 15.0 (StataCorp LP, College Station, TX). Variable distributions (eg, skewness and kurtosis), outliers, and missing values were examined prior to the data analysis. Only a few cases were identified as having missing values (<1%). Pairwise deletion was used to handle the missing data. Continuous variables are expressed as mean and standard deviation, and categorical variables are expressed as frequency and percentage. An independent 2-sample t test, analysis of variance, and chi-square tests were used to examine the significant differences between the participants from the 3 original studies. These analysis methods were also used to compare the demographic and main study variables between the controlled and uncontrolled A1C groups. Pearson’s correlation coefficients were used to identify the associations between diabetes symptom burden, sleep problems, depressive symptoms, and A1C values.
In this study, a multigroup structural path analysis with maximum likelihood estimation was used to simultaneously test the hypothesized paths in the model and to compare the model coefficients between the controlled and uncontrolled A1C groups. An initial path analysis was conducted to test the overall direct and indirect effects of the diabetes symptom burden and sleep problems in the total sample. The degree of depressive symptoms was considered a potential mediator in the model (Figure 1). Model fit was evaluated with the goodness-of-model-fit assessment for χ2 (P value ≥ 0.05 is acceptable fit), root mean square error of approximation (a value of 0 indicates the best result), comparative fit index (a value of 1 indicates the best result), standardized root mean squared residual (a value of 0 indicates perfect fit), and coefficient of determination (a value of 1 indicates good fit).27
A multigroup structural path analysis was then performed to examine whether the strength of the mediating effects of depressive symptoms on the relationship between diabetes symptom burden and sleep problems differed by A1C levels (controlled vs uncontrolled). First, the model was simultaneously tested in both groups with equality constraints imposed (constrained model). The model was then allowed to have varied coefficients across models (unconstrained model). Finally, the constrained and unconstrained models’ fit was compared using a nested χ2 statistic. If the constrained and unconstrained models are statistically significant, this suggests that the structural coefficients differ between the controlled and uncontrolled groups, indicating a potential moderating effect of A1C levels. A separate path analysis was conducted for sleep disturbance and sleep-related impairment, with each sleep problem as a dependent variable. All analyses were controlled for age, sex, and race/ethnicity. A P value below 0.05 was considered statistically significant.
The university’s Institutional Review Board approved the current study (IRB No. 4-2021-1041).
The mean age of the participants was 62.71 (SD, 8.61) years, ranging from 45 to 86 years. Most of the participants were female (56.61%). Majority were non-Hispanic (89.95%) and White (48.15%). The average diabetes duration was 11.06 (SD, 7.74) years. The mean A1C level was 7.24% (SD, 1.78). Based on the DSC-R Total scores in Table 1, participants were found to have a low diabetes-related symptom burden (mean = .99 ± .80). The participants were also found to have lower depressive symptoms (mean t score = 49.93 ± 7.82) and slightly more sleep disturbance (mean t score = 50.62 ± 8.58) and sleep-related impairment (mean t score = 50.36 ± 9.67) compared to the reference population.
Table 2 illustrates the differences in demographics and other characteristics between the controlled (≤7%) and uncontrolled (>7%) A1C groups. The uncontrolled group had a significantly higher A1C level (t = −16.51, P < 0.001) and higher symptom burden scores for total (t = −2.77, P = 0.006), neurologic (pain, t = −3.20, P = 0.002; sensory, t = −2.80, P = 0.006), ophthalmologic (t = −3.88, P < 0.001), and hyperglycemic symptoms (t = −3.05, P = 0.003) than the controlled group.
In the bivariate correlation analyses (see Table 3), diabetes symptom burden, sleep problems (sleep disturbance and sleep-related impairment), and depressive symptoms were all moderately correlated (r = .42–.68). Participants’ A1C levels were weakly associated with total diabetes symptom burden (r = .34), sleep-related impairment (r = .15), and depressive symptoms (r = .13).
The model fit indices for the multigroup path analyses of sleep disturbance or sleep-related impairment are summarized in Table 4. When sleep disturbance was the dependent variable in the model, no significant difference was found between the constrained and unconstrained models (Dχ2 = 11.06, Ddf = 7, P = 0.136). However, the fit between the constrained and unconstrained models was significantly different when sleep-related impairment was a dependent variable (Dχ2 = 19.59, Ddf = 7, P = 0.007). In other words, A1C levels (controlled vs uncontrolled) had a significant moderating effect on the relationship between diabetes symptom burden and sleep-related impairment. The results of the multigroup path analysis are illustrated in Figure 2.
As shown in Table 5, diabetes symptom burden had significant direct and indirect effects (through depressive symptoms) on sleep disturbance in the controlled group, whereas only a direct effect was found in the uncontrolled group. For sleep-related impairment, significant direct and indirect effects of symptom burden were found in both controlled and uncontrolled groups, and the strength of the effects on each path differed according to A1C levels (Figure 2).
This study identified an association among diabetes symptom burden and sleep disturbance and sleep-related impairment in middle-aged and older adults with T2DM. Specifically, both direct and indirect effects of diabetes symptom burden on sleep disturbance were significant in the controlled group (A1C ≤ 7%), whereas only the direct effect was significant in the uncontrolled group (A1C > 7%). In contrast, regarding sleep-related impairment, direct and indirect effects were significant in both controlled and uncontrolled groups, and a significant between-group difference was found in all the path coefficients in the model.
Diabetes symptom burden had significant total effects on both sleep disturbance and sleep-related impairment; this supports the first hypothesis of this study. Previous studies have reported similar results. Increased disease burden and diabetes symptoms have been associated with poor sleep quality and abnormal sleep duration.10 Disruption of sleep and the circadian rhythm can result in metabolic abnormalities, including impaired glucose tolerance and insulin insensitivity, through the direct effects of sleep deprivation and its related circadian misalignment.28 Impairment of glucose control by sleep problems can increase the symptoms of diabetes, which can further impair sleep quality. Therefore, adequate sleep is necessary for people with T2DM, especially in the aging population, who are vulnerable to poor sleep and increased symptom burden.5
Symptom burden had an indirect effect on sleep-related impairment through depressive symptoms; this supports the second hypothesis of this study. Although the indirect effect of depressive symptoms on sleep disturbance was significant only in the controlled group, the study results collectively indicate that physical symptoms related to diabetes would increase psychological distress, which can in turn impair sleep quality and next-daytime functioning. A similar result was found in a study conducted among patients with heart failure wherein there was a significant indirect effect of symptom burden on sleep quality through anxiety and depressive symptoms.15 In another study, psychotherapy targeting depression was effective in improving sleep quality in patients with painful diabetic neuropathy.29 Based on the current and previous study results, it can be suggested that increased depressive symptoms can be a clinical target for treating sleep problems in people with T2DM experiencing 1 or more debilitating diabetes symptoms.
In this study, diabetes symptom burden was only directly associated with sleep disturbance in the uncontrolled group, with no indirect effect through depressive symptoms. This result suggests that in people with poorly controlled T2DM, the burden caused by irritating diabetes symptoms (eg, neurologic pain or senses) directly affects sleep disturbance, not through increased emotional distress. Sleep disturbance is defined as reduced sleep quality (eg, frequent awakenings or difficulties in sleep initiation and maintenance) and/or abnormal sleep duration,30 which is likely affected by diabetes symptoms. Specifically, nocturnal hypoglycemic events and calf cramps have been found to reduce sleep quality at night.31,32 Because people with elevated A1C levels experience severer symptoms, their sleep quality is more likely to be affected by nocturnal symptoms.
A1C levels moderated the strength of the mediating effects of depressive symptoms on sleep-related impairment; this supports the third hypothesis of this study. The average A1C level of the sample was 7.24% in this study. Although this value is slightly above the glucose control target for nonpregnant adults (7%),19 a less strict glucose target can be applied to older adults (7.0-7.5%).33 The mean age of the study sample was 62.71 years; therefore, the sample seemed to have adequate glucose control based on the flexible glucose target. Nonetheless, the results of the multigroup path analyses in the study indicate that a different approach should be considered for symptom and sleep management in people with T2DM according to their A1C levels.
Sleep disturbance and daytime sleepiness can negatively affect diabetes self-care.34 When self-care is affected by sleep, it can result in a vicious cycle between poor glucose control and increased sleep problems. An aging population with T2DM is more likely to experience sleep problems and increased physical symptoms than a younger population.5,10 Nonetheless, the development of effective sleep interventions for this population is underway. The current study identified the mechanisms between diabetes-specific symptom burden and sleep quality and provided meaningful insights into the development of sleep interventions for older adults. Future studies are necessary to develop and test effective sleep interventions for older individuals with T2DM.
This study had several limitations. First, the study examined the relationships in a cross-sectional manner; thus, establishing causal inference between symptoms and sleep was not feasible. Future longitudinal and interventional studies will provide richer information on these relationships. Second, the participants’ sleep information was selfreported. Objective measurements of sleep have been widely used by researchers and could provide more information on these relationships. A combination of subjective and objective measures of sleep is recommended for future studies. Third, there may have been an association between other blood biomarkers, not only A1C, and increased symptoms and sleep in people with T2DM. A more comprehensive assessment of biomarkers would provide more clinical information regarding the population.
The current national standards for diabetes self-management education and support have identified glucose and symptom monitoring as core elements, with sleep as a measured outcome.35 Incorporating a comprehensive symptom assessment and screening for depression and sleep problems into clinical practices would enable the provision of person-centered diabetes care to people with T2DM. When seeing patients with diabetes, clinicians should explain the effects of glucose control on diabetes symptom burden, depressive symptoms, and sleep problems in addition to what diabetes symptoms can occur in middle-aged and older adults with T2DM. The current study results indicate that different symptom assessment and management strategies would be beneficial according to whether the patient’s blood glucose level is well controlled or not.
The world is rapidly aging, with an increasing prevalence of chronic diseases, such as diabetes. Therefore, the development of effective interventions for symptom management and sleep improvement in older adults with T2DM is necessary. This study evaluated dynamic symptom mechanisms and their potential impact on sleep in middle-aged and older adults with T2DM. The current study findings would provide useful information for identifying a target population and developing effective sleep interventions for the population.
The authors appreciate the participants in the parent studies who provided the data for the current study.
MJK and CF made substantial contributions to conception and design as well as acquisition of the data for the work. MJK analyzed the data, and MJK, EY, and CF interpreted the data. EY and CP substantially contributed to data analysis and interpretation. MJK drafted the work, and EY, CP, EC, and CF critically contributed to revision of the work for important intellectual content and approved the final version of the work to be published.
The authors declare that they have no competing interests.
This study was supported in part by the National Institutes of Health/National Institute for Nursing Research (K99 R00 NR012219, CF), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2023-00250911, MJK), and Basic Science Research Program through the NRF funded by the Ministry of Education (2020R1A6A1A0304198912, EC). The funding body had no role in the design of the study; collection, analysis, and interpretation of data; or writing the article.
Min Jung Kim https://orcid.org/0000-0002-8452-8341
Eunjin Yang https://orcid.org/0000-0002-8669-954X
Cynthia Fritschi https://orcid.org/0000-0001-5447-8315
Sun H, Saeedi P, Karuranga S, et al. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. doi:10.1016/j.diabres.2021.109119
Halter JB, Musi N, McFarland Horne F, et al. Diabetes and cardiovascular disease in older adults: current status and future directions. Diabetes. 2014;63(8):2578-2589. doi:10.2337/db14-0020
Riegel B, Jaarsma T, Lee CS, Strömberg A. Integrating symptoms into the middle-range theory of self-care of chronic illness. ANS Adv Nurs Sci. 2019;42(3):206. doi:10.1097/ANS.0000000000000237
García AA, Bose E, Zuniga JA, Zhang W. Mexican Americans’ diabetes symptom prevalence, burden, and clusters. Appl Nurs Res. 2019;46:37-42. doi:10.1016/j.apnr.2019.02.002
Sudore RL, Karter AJ, Huang ES, et al. Symptom burden of adults with type 2 diabetes across the disease course: diabetes & aging study. J Gen Intern Med. 2012;27(12):1674-1681. doi:10.1007/s11606-012-2132-3
Arbuckle RA, Humphrey L, Vardeva K, et al. Psychometric evaluation of the Diabetes Symptom Checklist-Revised (DSC-R)—a measure of symptom distress. Value Health. 2009;12(8):1168-1175. doi:10.1111/j.1524-4733.2009.00571.x
Wajnberg A, Ornstein K, Zhang M, Smith KL, Soriano T. Symptom burden in chronically ill homebound individuals. J Am Geriatr Soc. 2013;61(1):126-131. doi:10.1111/jgs.12038
Lin LY, Lee BO, Wang RH. Effects of a symptom management program for patients with type 2 diabetes: implications for evidence-based practice. Worldviews Evid Based Nurs. 2019;16(6):433-443. doi:10.1111/wvn.12400
Zhu B, Quinn L, Fritschi C. Relationship and variation of diabetes related symptoms, sleep disturbance and sleep-related impairment in adults with type 2 diabetes. J Adv Nurs. 2018;74(3):689-697. doi:10.1111/jan.13482
Jean-Louis G, Shochat T, Youngstedt SD, et al. Age-associated differences in sleep duration in the US population: potential effects of disease burden. Sleep Med. 2021;87:168-173. doi:10.1016/j.sleep.2021.09.004
Griggs S, Grey M, Ash GI, Li C-SR, Crawford SL, Hickman RL Jr. Objective sleep-wake characteristics are associated with diabetes symptoms in young adults with type 1 diabetes. Sci Diabetes Self Manag Care. 2022;48(3):149-156. doi:10.1177/26350106221094521
Sartorius N. Depression and diabetes. Dialogues Clin Neurosci. 2018;20:47-51. doi:10.31887/DCNS.2018.20.1/nsartorius
Kao K-L, Sung F-C, Tzang R-F, et al. Associations of diabetes severity and risk of depression: a population-based cohort study. J Affect Disord. 2020;273:476-481. doi:10.1016/j.jad.2020.04.066
Nutt D, Wilson S, Paterson L. Sleep disorders as core symptoms of depression. Dialogues Clin Neurosci. 2008;10:329-336. doi:10.31887/DCNS.2008.10.3/dnutt
He D, Pan M. Serial multiple mediators in the relationship between symptom burden and sleep quality among patients with heart failure. Jpn J Nurs Sci. 2022;19(4):e12489. doi:10.1111/jjns.12489
Ali M, Feeney P, Hire D, et al. Glycaemia and correlates of patient-reported outcomes in ACCORD trial participants. Diabet Med. 2012;29(7):e67-e74. doi:10.1111/j.1464-5491.2011.03532.x
Park H, Park C, Quinn L, Fritschi C. Glucose control and fatigue in type 2 diabetes: the mediating roles of diabetes symptoms and distress. J Adv Nurs. 2015;71(7):1650-1660. doi:10.1111/jan.12632
Lee SWH, Ng KY, Chin WK. The impact of sleep amount and sleep quality on glycemic control in type 2 diabetes: a systematic review and meta-analysis. Sleep Med Rev. 2017;31:91-101. doi:10.1016/j.smrv.2016.02.001
American Diabetes Association. 6. Glycemic targets: Standards of Medical Care in Diabetes—2022. Diabetes Care. 2022;45:S83. doi:10.2337/dc22-S006
Bode BW, Irvin BR, Pierce JA, Allen M, Clark AL. Advances in hemoglobin A1c point of care technology. J Diabetes Sci Technol. 2007;1(3):405-411. doi:10.1177/193229680700100314
PROMIS. PROMIS® reference populations. n.d. Accessed July 30, 2024. https://www.healthmeasures.net/score-andinterpret/interpret-scores/promis/reference-populations
Cella D, Riley W, Stone A, et al. The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. J Clin Epidemiol. 2010;63(11):1179-1194. doi:10.1016/j.jclinepi.2010.04.011
ResearchMatch. How do I cite research match? Published 2024. Accessed July 30, 2024. https://www.researchmatch.org/researchers/faq
Kim MJ, Quinn L, Sharp LK, et al. Cognitive function and self-management behaviors of older adults with type 2 diabetes. Nurs Res. 2023;72(1):38-48. doi:10.1097/NNR.0000000000000624
Fritschi C, Martyn-Nemeth P, Zhu B, Jung Kim M. Active learning: Lessons from women with type 2 diabetes in a walking program. Diabetes Educ. 2019;45(4):370-379.
Fritschi C, Park C, Quinn L, et al. Glucose, physical activity, and fatigue: Temporal relationships in T2DM. Diabetes. 2014;63(Suppl 1).
Kline RB. Principles and Practice of Structural Equation Modeling. Guilford Publications; 2015.
Parameswaran G, Ray DW. Sleep, circadian rhythms, and type 2 diabetes mellitus. Clin Endocrinol. 2022;96(1):12-20. doi:10.1111/cen.14607
Davoudi M, Taheri AA, Foroughi AA, Ahmadi SM, Heshmati K. Effectiveness of acceptance and commitment therapy (ACT) on depression and sleep quality in painful diabetic neuropathy: a randomized clinical trial. J Diabetes Metab Disord. 2020;19(2):1081-1088. doi:10.1007/s40200-020-00609-x
Zhu B, Vincent C, Kapella MC, et al. Sleep disturbance in people with diabetes: a concept analysis. J Clin Nurs. 2018;27(1- 2):e50-e60. doi:10.1111/jocn.14010
Brod M, Christensen T, Bushnell DM. Impact of nocturnal hypoglycemic events on diabetes management, sleep quality, and next-day function: results from a four-country survey. J Med Econ. 2012;15(1):77-86. doi:10.3111/13696998.2011.624144
Hawke F, Chuter V, Burns J. Impact of nocturnal calf cramping on quality of sleep and health-related quality of life. Qual Life Res. 2013;22(6):1281-1286. doi:10.1007/s11136-012-0274-8
Draznin B, Aroda VR, Bakris G, et al. 13. Older adults: Standards of Medical Care in Diabetes-2022. Diabetes Care. 2022;45(suppl 1):S195-S207. doi:10.2337/dc22-S013
Zhu B, Quinn L, Kapella MC, et al. Relationship between sleep disturbance and self-care in adults with type 2 diabetes. Acta Diabetol. 2018;55(9):963-970. doi:10.1007/s00592-018-1181-4
Davis J, Fischl AH, Beck J, et al. 2022 National Standards for Diabetes Self-Management Education and Support. Sci Diabetes Self Manag Care. 2022;48(1):44-59. doi:10.2337/dc21-2396
From Research Institute of AI and Nursing Science, College of Nursing, Gachon University, Incheon, South Korea (Prof Kim, Prof Yang); Department of Population Health Nursing Science, University of Illinois at Chicago College of Nursing, Chicago, Illinois (Prof Park); Mo-Im Kim Nursing Research Institute, College of Nursing, Yonsei University, Seoul, South Korea (Prof Cho); and Department of Biobehavioral Nursing Science, University of Illinois at Chicago College of Nursing, Chicago, Illinois (Prof Fritschi).
Corresponding Author:Cynthia Fritschi, Department of Biobehavioral Nursing Science, University of Illinois at Chicago, College of Nursing (MC 802), 845 South Damen Avenue, Chicago, IL 60612, USA.Email: fritschi@uic.edu