ORIGINAL ARTICLE
Jing Huang1†, Xiangshuang Kong2† and Mingzi Li1*
1School of Nursing, Peking University, Beijing, China; 2Department of Endocrinology, Beijing Pinggu District Hospital, Beijing, China
Aim: This study aimed to assess the prevalence and associated factors of excessive daytime sleepiness (EDS) in patients with type 2 diabetes mellitus (T2DM).
Methods: We conducted a cross-sectional study in Beijing, China, from November 2015 to October 2016, and patients with T2DM were invited to participate. Structured questionnaires were used to collect data. EDS was assessed using the Epworth Sleepiness Scale (ESS), and anxiety and depression were assessed using the Hospital Anxiety and Depression Scale (HADS). Logistic regression analysis was used to evaluate factors associated with EDS.
Results: Of the 224 patients with T2DM, 24 (10.7%) had EDS. The proportions of anxiety, depression, and comorbid anxiety and depression were significantly different between the EDS and non-EDS groups. ESS scores, anxiety scores, and depression scores were positively correlated. Logistic regression analysis showed that mild anxiety (OR 11.055; 95% CI 2.272–53.785; P = 0.003), moderate to severe anxiety (OR 33.223; 95% CI 4.896–225.440; P < 0.001), and mild depression (OR 6.227; 95% CI 1.319–29.399; P = 0.021) were associated with EDS in patients with T2DM. These associations remained significant after adjustment for age, sex, body mass index, diabetes duration, and apnoea–hypopnoea index. In addition, the severity of obstructive sleep apnoea–hypopnoea syndrome was not significantly associated with EDS, anxiety, depression, and comorbid anxiety and depression in this study.
Conclusions: The prevalence of EDS was high in patients with T2DM, and anxiety and depression were significantly associated with EDS. When developing interventions to improve EDS in patients with T2DM, healthcare providers may need to consider interventions that target anxiety and depression.
Keywords: type 2 diabetes mellitus; excessive daytime sleepiness; cross-sectional study; anxiety; depression
Citation: International Diabetes Nursing 2024, 17: 332 - http://dx.doi.org/10.57177/idn.v17.332
Copyright: © 2024 Jing Huang et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited and states its license.
Received: 18 November 2023; Revised: 13 June 2024; Accepted: 27 June 2024; Published: 10 October 2024
†Co-authors: Jing Huang and Xiangshuang Kong contributed equally to this study.
Competing interests and funding: The authors declare that they have no conflict of interests. This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.
*Mingzi Li, Peking University Health Science Centre, 38 Huayuan Road, Haidian District, Beijing, China. Email: limingzi@bjmu.edu.cn
Excessive daytime sleepiness (EDS), one of the daily manifestations of obstructive sleep apnoea–hypopnoea syndrome (OSAHS), refers to an individual’s inability to stay awake and alert during the main waking hours of the day, resulting in uncontrolled sleep.1 Patients with OSAHS suffer from recurrent apnoea and hypopnea during nighttime sleep, often accompanied by snoring, frequent arousals, and sleep fragmentation, which can lead to daytime sleepiness.2 People with EDS are prone to experiencing excessive sleepiness, unintentional falling asleep, long periods of sleep, and repeated naps.3 These sleep-related symptoms are associated with cognitive impairment, decreased quality of life, poor work performance, and a high incidence of traffic accidents, posing a serious threat to patients’ work and life.4
By 2021, approximately 537 million people worldwide suffer from diabetes.5 China is a major epicentre of the growing global diabetes epidemic, with approximately 140 million people, more than 90% of whom have type 2 diabetes mellitus (T2DM).5,6 Observational studies have reported that the prevalence of OSAHS in patients with T2DM in China is 60–80%, which is high.7–10 Considering the adverse effects of EDS, the high prevalence of OSAHS in patients with T2DM and the large number of patients with T2DM in China, it is necessary for healthcare providers to focus on daytime sleepiness in patients with T2DM.
In the past decade, several studies have reported the prevalence of EDS in patients with T2DM. Ramtahal et al. reported an EDS rate of 11.3% in 291 patients with T2DM in Trinidad and found that the body mass index (BMI) of patients in the EDS group was significantly higher than that of patients in the non-EDS group (P = 0.04).11 According to Arosemena Coronel et al., the prevalence of EDS was 56.8% in 290 participants with T2DM with severe hypoglycaemic episodes in Guayaquil, Ecuador, and a higher proportion of EDS was found in individuals aged 56–75 years (P < 0.001).12 Raj et al. reported a prevalence of EDS of 17.5% in 102 patients with T2DM in Erode, Tamil Nadu, India.13 However, these previous studies lacked the consideration of exploring factors associated with EDS in patients with T2DM. In addition, to the best of our knowledge, EDS in Chinese patients with T2DM remains to be explored.
Therefore, this study aimed to evaluate the prevalence and associated factors of EDS in patients with T2DM in China and to provide a reference for healthcare providers to develop appropriate interventions to improve EDS in patients with T2DM.
This was a cross-sectional study to investigate the prevalence and associated factors of EDS in patients with T2DM.
This study followed the Helsinki Declaration and was reviewed and approved by the Ethics Committee of Beijing Pinggu Hospital. All participants signed a written informed consent before participating in the study.
This study was conducted at the Department of Endocrinology, Pinggu District Hospital, Beijing, from November 2015 to October 2016, and the participants were hospitalised patients. Inclusion criteria: 1) T2DM according to WHO 1999 criteria; 2) age ≥ 18 years; 3) no history of mental illness and no sedatives were used; 4) able to communicate with the researchers. Patients with serious physical illnesses such as malignant tumours and stroke who were unable to complete the survey were excluded. Information on history of mental illness was obtained by reviewing the electronic medical record (EMR) system and interviewing the patient.
The demographic and clinical information considered in this study consisted of age, gender, height, weight, BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), waist circumference (WC), smoking status, drinking status, duration of diabetes, glycosylated haemoglobin (HbA1c), fasting plasma glucose (FPG), serum creatinine (sCr), cholesterol (CHOL), triglycerides (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), urine microalbumin (uMA), lipid accumulation product (LAP), apnoea–hypopnoea index (AHI), oxygen desaturation index (ODI), lowest pulse oxygen saturation (LSPO2), hypertension, dyslipidaemia, coronary heart disease, cerebrovascular disease, and snoring. Patients enrolled in the study were invited for sleep monitoring and sleep-related data were recorded by the investigator. Demographic and clinical information was obtained from the EMR system.
BMI was calculated by dividing the weight (kg) by the square of height (m). Blood pressure was measured with an Omron electronic blood pressure monitor. FPG, sCr, CHOL, TG, LDL, and HDL were detected by the automatic biochemical analyser AU5400 (Beckman, USA). HbA1c was evaluated by automatic saccharification analyser D10 (Bio-Rad, USA), and uMA was detected by multifunctional quantitative gold standard detector NycoCard Reader II (Axis-shield, Norway). AHI, ODI, and LSPO2 were measured by overnight polysomnography using the Alice PDx portable sleep diagnostic system (Philips, USA). LAP (male) = (WC-65)*TG, and LAP (female) = (WC-58)*TG.
Daytime sleepiness was measured using the 8-item Epworth Sleepiness Scale (ESS).14,15 Each item on the ESS is scored from 0 to 3, and the total score ranges from 0 to 24. The higher the total score, the greater the degree of daytime sleepiness. In this study, an ESS score ≥ 9 was defined as EDS. The Cronbach’s alpha for the ESS in this sample was 0.808.
The Hospital Anxiety and Depression Scale (HADS) has 14 items that can assess anxiety and depression, respectively.16,17 Items 1, 3, 5, 7, 9, 11, and 13 assess anxiety, and items 2, 4, 6, 8, 10, 12, and 14 assess depression. Each item is scored from 0 to 3, and total scores for both the anxiety and depression subscales range from 0 to 21. A score of 0 to 7 indicates normal, 8 to 10 indicates mild anxiety or depression, 11 to 14 indicates moderate anxiety or depression, and 15 to 21 indicates severe anxiety or depression. In this study, the Cronbach’s alpha of the anxiety subscale was 0.791 and the Cronbach’s alpha of the depression subscale was 0.776.
Data were collected between November 2015 and October 2016. All investigators received training prior to data collection. Structured questionnaires were used to collect information on demographic and clinical characteristics, anxiety, depression, and daytime sleepiness. The first two patients admitted to the hospital each day who met the inclusion criteria were invited to participate in the study. If only one case met the inclusion criteria that day, only that case would be recruited. Patients would not be recruited on that day if neither of the first two cases met the inclusion criteria. Patients took approximately 25 min to complete the questionnaire, which was collected on site by the investigator upon completion.
SPSS software (version 23.0) was used for statistical analysis. Participants were divided into EDS (ESS ≥ 9) and non-EDS (ESS < 9) groups based on their ESS scores. Continuous variables were expressed as mean and standard deviation (SD). The Kolmogorov–Smirnov test indicated that all continuous variables were not normally distributed, so the Mann–Whitney U test was used to compare group differences. Categorical variables were described using frequencies and percentages. Pearson’s χ2 test, continuity-corrected χ2 test, and Fisher’s exact test were used to compare group differences and post hoc analyses were performed with Bonferroni correction. Spearman correlation analysis was used to evaluate the correlation between variables. Logistic regression analysis was used to examine factors associated with EDS. A two-tailed P < 0.05 was considered statistically significant.
A total of 237 patients with T2DM were enrolled, of whom 13 were excluded due to incomplete data, and 224 were included in the final analysis. The mean age of the patients was 52.88 ± 12.90 years old, and 118 (52.7%) patients were male. The demographic and clinical characteristics of the patients with T2DM are shown in Table 1. There were 24 (10.7%) patients with anxiety, including 11 (4.9%) patients with mild anxiety, and 13 (5.8%) patients with moderate to severe anxiety. There were 29 (12.9%) patients with depression, including 16 (7.1%) patients with mild depression and 13(5.8%) patients with moderate to severe depression. In addition, 11(4.9%) patients had a comorbid anxiety and depression, and 24 (10.7%) patients had EDS.
Table 2 compares the demographic and clinical characteristics of the EDS and non-EDS groups. The prevalence of EDS in patients with different levels of anxiety was significantly different (P < 0.001). Post hoc multiple comparisons showed that the prevalence of EDS was significantly higher in the mild (P = 0.003, Bonferroni correction P = 0.0167) and moderate-to-severe (P < 0.001, Bonferroni correction P = 0.0167) anxiety groups compared to the normal group. The level of depression was also significantly correlated with the prevalence of EDS (P = 0.001). Post hoc multiple comparisons showed that the prevalence of EDS was significantly higher in the mild depression group than in the normal group (P = 0.001, Bonferroni correction P = 0.0167). Patients with both anxiety and depression had significantly higher rates of EDS than those who didn’t have both conditions (P < 0.001). Other participant characteristics were not significantly different between the EDS and non-EDS groups (P > 0.05). The Bonferroni correction is the correction for P-values. For example, if three groups are compared, three pairwise comparisons are needed to see if there is a difference between each group. If one comparison is made, the P-value is 0.05; if three comparisons are made, the P-value is 0.167.
Table 3 shows the results of the correlation analysis of anxiety scores, depression scores, ESS scores, and AHI. ESS scores were positively correlated with anxiety scores (r = 0.185, P < 0.01) and depression scores (r = 0.282, P < 0.001), and anxiety scores were also positively correlated with depression scores (r = 0.602, P < 0.001). There was no significant correlation between AHI and ESS scores, anxiety scores, and depression scores (P > 0.05).
| Mean ± SD | Anxiety scores | Depression scores | ESS scores | AHI | |
| Anxiety scores | 3.16 ± 3.79 | 1 | |||
| Depression scores | 3.13 ± 3.91 | 0.602*** | 1 | ||
| ESS scores | 2.97 ± 3.93 | 0.185** | 0.282*** | 1 | |
| AHI | 14.60 ± 15.57 | -0.091 | -0.082 | 0.114 | 1 |
| Note: **P < 0.01; ***P < 0.001. SD, standard deviation; AHI, apnoea–hypopnoea index; ESS, Epworth Sleepiness Scale. |
|||||
Variables with P < 0.05 in the univariate analysis were included in the logistic regression analysis. The results of the logistic regression analysis of factors associated with EDS are shown in Table 4. Mild anxiety (OR 11.055; 95%CI 2.272–53.785; P = 0.003), moderate to severe anxiety (OR 33.223; 95%CI 4.896–225.440; P < 0.001), and mild depression (OR 6.227; 95% CI 1.319–29.399; P = 0.021) were associated with EDS in patients with T2DM. These associations remained after further adjustment for age, gender, BMI, diabetes duration, and AHI.
Table 5 shows the relationship between OSAHS severity and EDS, anxiety, depression, and comorbid anxiety and depression. EDS, anxiety, depression, and comorbid anxiety and depression were not significantly associated with OSAHS severity (P > 0.05).
This study investigated the prevalence and associated factors of EDS in patients with T2DM. The results showed that approximately one tenth (10.7%) of patients with T2DM had EDS, which is consistent with the results reported by Ramtahal et al. and Raj et al. (11.3 and 17.5%, respectively).11,13 Given the high prevalence of EDS, healthcare providers should promptly assess daytime sleepiness in patients with T2DM to further develop strategies for improvement.
The significant relationship between anxiety and EDS has been reported in many groups, such as patients with OSAHS, medical students, and adults of different ages.18–21 This study found that anxiety was associated with EDS in patients with T2DM, which identified a significant relationship between anxiety and EDS in the field of T2DM. Previous studies have shown that patients with anxiety are prone to sleep disturbances due to excessive anxiety and worry, such as insomnia, difficulty staying asleep, and poor sleep quality.22–24 These sleep problems interfere with the normal nocturnal sleep process and may lead to increased daytime sleepiness.25,26 Therefore, the co-occurrence of anxiety and sleep problems may explain the significant association between anxiety and EDS in T2DM patients. Furthermore, studies have shown that sleep problems may trigger or further exacerbate anxiety, which means that the relationship between anxiety and sleep problems may be bidirectional.22,27,28 As reported by Luo et al., EDS at baseline could predict new-onset anxiety at follow-up in rural Chinese adolescents.29 Future longitudinal studies are needed to further investigate the relationship between daytime sleepiness and anxiety in patients with T2DM. In conclusion, it is necessary for healthcare providers to assess the anxiety levels in patients with T2DM and interventions targeting anxiety may be helpful for EDS.
We also found that depression was associated with EDS in patients with T2DM. Previous studies have focused on the association between EDS and depression in different populations, such as schoolchildren, adolescents, college students, patients with myotonic dystrophy, and older adults with DM.25,30–33 This study confirmed the significant association between depression and EDS in the context of T2DM. Previous studies have reported that more than half of people with depression have sleep disturbances, such as insomnia, leading to impaired concentration and increased daytime sleepiness.34–36 According to the Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5), insomnia is also one of the diagnostic criteria for the major depressive disorder.37 Thus, depression is most often associated with sleep problems, which may explain the significant association between depression and EDS in patients with T2DM. Furthermore, insomnia increases the risk of depression,25,38 suggesting that there may also be a bidirectional relationship between sleep problems and depression. Future longitudinal studies are also needed to further investigate the causal relationship between daytime sleepiness and depression in patients with T2DM. Healthcare providers should promptly assess depression levels in patients with T2DM, and treatment of depression may help to reduce EDS.
Zhang et al. and Shao et al. reported that among patients with OSAHS, the AHI of patients with daytime sleepiness was significantly higher than that of patients without daytime sleepiness.39,40 Patients with OSAHS have sleep disturbances due to recurrent apnoea and hypopnoea during nocturnal sleep, and daytime sleepiness is a common clinical symptom.2 AHI is an important diagnostic indicator of OSAHS and the basis for assessing the severity of OSAHS.41 The higher the AHI, the more severe the patient’s daytime sleepiness. However, in patients with T2DM, we did not observe a significant difference in AHI between the EDS and non-EDS groups. We also grouped patients with T2DM according to AHI and compared the group differences in EDS. The results showed no significant difference in EDS between the non-OSAHS group, the mild OSAHS group, and the moderate to severe OSAHS group in patients with T2DM. Interestingly, the relationship between daytime sleepiness and AHI was also insignificant in chronic kidney disease, coronary heart disease, and heart failure.42–44 The body’s response to these conditions, such as the impact and damage to the nervous system, may affect an individual’s subjective perception of sleepiness caused by apnoea and hypopnoea.42 Therefore, the mechanism of daytime sleepiness in patients with T2DM needs further investigation. In addition, we found that anxiety, depression, and comorbid anxiety and depression were not significantly associated with different levels of AHI in patients with T2DM, similar to previous studies.45,46 However, other studies reported that anxiety and depression were significantly associated with OSAHS.47,48 Considering the possible influence between anxiety, depression and AHI, we controlled for AHI in the logistic regression model. Therefore, the effects of anxiety and depression on EDS in our sample were independent of AHI.
This study has several strengths. Firstly, the study reported a high prevalence of EDS in patients with T2DM, which helps to promote the attention of healthcare providers to EDS in patients with T2DM. Secondly, the finding that anxiety and depression are factors associated with EDS in patients with T2DM provides concrete guidance for healthcare providers to develop strategies to improve EDS in patients with T2DM. For example, interventions for anxiety and depression may benefit EDS in patients with T2DM. Thirdly, this study found that the relationship between EDS and AHI in patients with T2DM was not significant, which provides a basis for further research into the potential mechanism of EDS in patients with T2DM.
The study also has several limitations. Firstly, the cross-sectional study design limited the causal inference ability of the results of this study and the results of logistic regression could only show a significant association between anxiety and depression and EDS. Secondly, only hospitalised patients with T2DM were included in this study, which may limit the applicability of these results to non-hospitalised patients with T2DM. Compared to non-hospitalised patients with T2DM, hospitalised patients with T2DM may be less healthy and the neurological impact of the disease may be more severe, which may be associated with EDS. Thirdly, although there are many objective indicators in this study, anxiety, depression, and EDS were all assessed by patient self-report questionnaires, which may lead to inevitable recall and reporting bias.
Patients with T2DM have a high prevalence of EDS, and anxiety and depression are factors associated with EDS. Healthcare providers should pay more attention to anxiety, depression, and daytime sleepiness in patients with T2DM and implement timely interventions to promote mental and physical health and reduce daytime sleepiness. Interventions for anxiety and depression may be beneficial in improving daytime sleepiness levels in patients with T2DM. Future studies should use more rigorous designs to investigate the causal relationship between anxiety, depression and EDS.
The authors would like thank the nurses in the endocrinology department of Beijing Pinggu District Hospital for their help in data collection.
The datasets generated during and/or analysed in this study are available from the corresponding author upon reasonable request.
Mingzi Li designed the study and revised the manuscript, Xiangshuang Kong collected the data, and Jing Huang performed the statistical analysis and wrote the manuscript.
| 1. | Zhu K, van Hilten JJ, Marinus J. Course and risk factors for excessive daytime sleepiness in Parkinson’s disease. Parkinsonism Relat Disord 2016; 24: 34–40. https://doi.org/10.1016/j.parkreldis.2016.01.020 |
| 2. | Patel SR. Obstructive sleep apnea. Ann Intern Med 2019; 171(11): Itc81–96. https://doi.org/10.7326/AITC201912030 |
| 3. | Gandhi KD, Mansukhani MP, Silber MH, Kolla BP. Excessive daytime sleepiness: a clinical review. Mayo Clin Proc 2021; 96(5): 1288–301. https://doi.org/10.1016/j.mayocp.2020.08.033 |
| 4. | Roth T, Rosenberg RP. Managing excessive daytime sleepiness. J Clin Psychiatry 2015; 76(11): 1518–21; quiz 21. https://doi.org/10.4088/JCP.14019co5c |
| 5. | Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, 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. https://doi.org/10.1016/j.diabres.2021.109119 |
| 6. | Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol 2018; 14(2): 88–98. https://doi.org/10.1038/nrendo.2017.151 |
| 7. | Zhang P, Zhang R, Zhao F, Heeley E, Chai-Coetzer CL, Liu J, et al. The prevalence and characteristics of obstructive sleep apnea in hospitalized patients with type 2 diabetes in China. J Sleep Res 2016; 25(1): 39–46. https://doi.org/10.1111/jsr.12334 |
| 8. | Ding S, Zhang P, Wang L, Wang D, Sun K, Ma Y, et al. Prevalence of obstructive sleep apnea syndrome in hospitalized patients with type 2 diabetes in Beijing, China. J Diabetes Investig 2022; 13(11): 1889–96. https://doi.org/10.1111/jdi.13868 |
| 9. | Zhang R, Guo X, Guo L, Lu J, Zhou X, Ji L. Prevalence and associated factors of obstructive sleep apnea in hospitalized patients with type 2 diabetes in Beijing, China J Diabetes 2014; 7(1): 16–23. |
| 10. | Zheng C, Zheng X, Lin X, Ye J, Xu Z, Hu H, et al. Visceral adipose tissue indices independently correlated with obstructive sleep apnea in patients with type 2 diabetes. J Diabetes Res 2022; 2022: 4950528. https://doi.org/10.1155/2022/4950528 |
| 11. | Ramtahal R, Khan C, Maharaj-Khan K, Nallamothu S, Hinds A, Dhanoo A, et al. Prevalence of self-reported sleep duration and sleep habits in type 2 diabetes patients in South Trinidad. J Epidemiol Global Health 2015; 5(4 Suppl 1): S35–43. https://doi.org/10.1016/j.jegh.2015.05.003 |
| 12. | Arosemena Coronel M, Sánchez Armijos J, Tettamanti Miranda D, Vásquez Cedeño D, Mariani Carrera R, Navarro Chávez M, et al. Excessive daytime somnolence is associated with hypoglycemia in adult Latinos with type 2 diabetes mellitus. Sleep Med 2017; 36: 6–9. https://doi.org/10.1016/j.sleep.2017.04.012 |
| 13. | Raj JP, Hansdak SG, Naik D, Mahendri NV, Thomas N. SLEep among diabetic patients and their GlycaEmic control (SLEDGE): a pilot observational study. J Diabetes 2019; 11(2): 122–8. https://doi.org/10.1111/1753-0407.12825 |
| 14. | Johns MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep 1991; 14(6): 540–5. https://doi.org/10.1093/sleep/14.6.540 |
| 15. | Wang Q, Zhang C, Jia P, Zhang J, Feng L, Wei S, et al. The association between the phenotype of excessive daytime sleepiness and blood pressure in patients with obstructive sleep apnea-hypopnea syndrome. Int J Med Sci 2014; 11(7): 713–20. https://doi.org/10.7150/ijms.7487 |
| 16. | Chan YF, Leung DY, Fong DY, Leung CM, Lee AM. Psychometric evaluation of the Hospital Anxiety and Depression Scale in a large community sample of adolescents in Hong Kong. Qual Life Res 2010; 19(6): 865–73. https://doi.org/10.1007/s11136-010-9645-1 |
| 17. | Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand 1983; 67(6): 361–70. https://doi.org/10.1111/j.1600-0447.1983.tb09716.x |
| 18. | Lee SA, Han SH, Ryu HU. Anxiety and its relationship to quality of life independent of depression in patients with obstructive sleep apnea. J Psychosom Res 2015; 79(1): 32–6. https://doi.org/10.1016/j.jpsychores.2015.01.012 |
| 19. | Perotta B, Arantes-Costa FM, Enns SC, Figueiro-Filho EA, Paro H, Santos IS, et al. Sleepiness, sleep deprivation, quality of life, mental symptoms and perception of academic environment in medical students. BMC Med Educ 2021; 21(1): 111. https://doi.org/10.1186/s12909-021-02544-8 |
| 20. | Gould CE, Spira AP, Liou-Johnson V, Cassidy-Eagle E, Kawai M, Mashal N, et al. Association of anxiety symptom clusters with sleep quality and daytime sleepiness. J Gerontol B 2018; 73(3): 413–20. https://doi.org/10.1093/geronb/gbx020 |
| 21. | Guerrini Usubini A, Terrone G, Varallo G, Cattivelli R, Plazzi G, Castelnuovo G, et al. The mediating role of emotion dysregulation and problematic internet use in the relationship between negative affect and excessive daytime sleepiness: a structural equation model. Nat Sci Sleep 2022; 14: 291–302. https://doi.org/10.2147/NSS.S346485 |
| 22. | Chellappa SL, Aeschbach D. Sleep and anxiety: from mechanisms to interventions. Sleep Med Rev 2022; 61: 101583. https://doi.org/10.1016/j.smrv.2021.101583 |
| 23. | Johnson EO, Roth T, Breslau N. The association of insomnia with anxiety disorders and depression: exploration of the direction of risk. J Psychiatr Res 2006; 40(8): 700–8. https://doi.org/10.1016/j.jpsychires.2006.07.008 |
| 24. | Walters EM, Phillips AJ, Hamill K, Norton PJ, Drummond SP. Anxiety predicts dyadic sleep characteristics in couples experiencing insomnia but not in couples without sleep disorders. J Affect Disord 2020; 273: 122–30. https://doi.org/10.1016/j.jad.2020.04.031 |
| 25. | Liu X, Yang Y, Liu Z, Jia C. Associations between insomnia, daytime sleepiness, and depressive symptoms in adolescents: a three-wave longitudinal study. J Clin Med 2022; 11(23): 6912. https://doi.org/10.3390/jcm11236912 |
| 26. | Hoffmann HM, Malo-Juvera V, Statland JM. Self-reported reduced sleep quality and excessive daytime sleepiness in facioscapulohumeral muscular dystrophy. Muscle Nerve 2022; 66(4): 487–94. https://doi.org/10.1002/mus.27688 |
| 27. | Nefs G, Donga E, van Someren E, Bot M, Speight J, Pouwer F. Subjective sleep impairment in adults with type 1 or type 2 diabetes: Results from Diabetes MILES--The Netherlands. Diabetes Res Clin Pract 2015; 109(3): 466–75. https://doi.org/10.1016/j.diabres.2015.07.008 |
| 28. | Narmandakh A, Roest AM, Jonge P, Oldehinkel AJ. The bidirectional association between sleep problems and anxiety symptoms in adolescents: a TRAILS report. Sleep Med 2020; 67: 39–46. https://doi.org/10.1016/j.sleep.2019.10.018 |
| 29. | Luo C, Zhang J, Chen W, Lu W, Pan J. Course, risk factors, and mental health outcomes of excessive daytime sleepiness in rural Chinese adolescents: a one-year prospective study. J Affect Disord 2018; 231: 15–20. https://doi.org/10.1016/j.jad.2018.01.016 |
| 30. | Boz S, Lanquart JP, Mungo A, Delhaye M, Loas G, Hein M. Risk of excessive daytime sleepiness associated to major depression in adolescents. Psychiatr Q 2021; 92(4): 1473–88. https://doi.org/10.1007/s11126-021-09922-x |
| 31. | Chasens ER, Sereika SM, Burke LE. Daytime sleepiness and functional outcomes in older adults with diabetes. Diabetes Educ 2009; 35(3): 455–64. https://doi.org/10.1177/0145721709333857 |
| 32. | Gonsalvez I, Li JJ, Stevens C, Chen JA, Liu CH. Preexisting depression and daytime sleepiness in women and men. Behav Sleep Med 2022; 20(4): 380–92. https://doi.org/10.1080/15402002.2021.1924720 |
| 33. | Laberge L, Gallais B, Auclair J, Dauvilliers Y, Mathieu J, Gagnon C. Predicting daytime sleepiness and fatigue: a 9-year prospective study in myotonic dystrophy type 1. J Neurol 2020; 267(2): 461–8. https://doi.org/10.1007/s00415-019-09592-7 |
| 34. | Fava M. Daytime sleepiness and insomnia as correlates of depression. J Clin Psychiatry 2004; 65 Suppl 16: 27–32. |
| 35. | Riemann D, Krone LB, Wulff K, Nissen C. Sleep, insomnia, and depression. Neuropsychopharmacology 2020; 45(1): 74–89. https://doi.org/10.1038/s41386-019-0411-y |
| 36. | Chellappa SL, Schröder C, Cajochen C. Chronobiology, excessive daytime sleepiness and depression: is there a link? Sleep Med 2009; 10(5): 505–14. https://doi.org/10.1016/j.sleep.2008.05.010 |
| 37. | Sweetman A, Lack L, Van Ryswyk E, Vakulin A, Reed RL, Battersby MW, et al. Co-occurring depression and insomnia in Australian primary care: recent scientific evidence. Med J Aust 2021; 215(5): 230–6. https://doi.org/10.5694/mja2.51200 |
| 38. | Blanken TF, Borsboom D, Penninx BW, Van Someren EJ. Network outcome analysis identifies difficulty initiating sleep as a primary target for prevention of depression: a 6-year prospective study. Sleep 2020; 43(5): zsz288. https://doi.org/10.1093/sleep/zsz288 |
| 39. | Shao C, Qi H, Lang R, Yu B, Tang Y, Zhang L, et al. Clinical features and contributing factors of excessive daytime sleepiness in Chinese obstructive sleep apnea patients: the role of comorbid symptoms and polysomnographic variables. Can Respir J 2019; 2019: 5476372. https://doi.org/10.1155/2019/5476372 |
| 40. | Zhang F, Wu X, Duan W, Wang F, Huang T, Xiang M. Influencing factors of daytime sleepiness in patients with obstructive sleep apnea hypopnea syndrome and its correlation with pulse oxygen decline rate. Evid Based Complement Altern Med 2021; 2021: 6345734. https://doi.org/10.1155/2021/6345734 |
| 41. | Yeghiazarians Y, Jneid H, Tietjens JR, Redline S, Brown DL, El-Sherif N, et al. Obstructive sleep apnea and cardiovascular disease: a scientific statement from the American Heart Association. Circulation 2021; 144(3): e56–67. https://doi.org/10.1161/CIR.0000000000000988 |
| 42. | Canales MT, Bozorgmehri S, Ishani A, Weiner ID, Berry R, Beyth R. Prevalence and correlates of sleep apnea among US Veterans with chronic kidney disease. J Sleep Res 2020; 29(4): e12981. https://doi.org/10.1111/jsr.12981 |
| 43. | Arzt M, Young T, Finn L, Skatrud JB, Ryan CM, Newton GE, et al. Sleepiness and sleep in patients with both systolic heart failure and obstructive sleep apnea. Arch Intern Med 2006; 166(16): 1716–22. https://doi.org/10.1001/archinte.166.16.1716 |
| 44. | Capodanno D, Cumbo M, Marchese A, Salemi A, Quartarone L, Benvenuto E, et al. Daytime sleepiness does not predict sleep apnoea in patients with coronary artery disease. Int J Cardiol 2011; 151(2): 248–50. https://doi.org/10.1016/j.ijcard.2011.06.076 |
| 45. | Macey PM, Woo MA, Kumar R, Cross RL, Harper RM. Relationship between obstructive sleep apnea severity and sleep, depression and anxiety symptoms in newly-diagnosed patients. PLoS One 2010; 5(4): e10211. https://doi.org/10.1371/journal.pone.0010211 |
| 46. | Asghari A, Mohammadi F, Kamrava SK, Tavakoli S, Farhadi M. Severity of depression and anxiety in obstructive sleep apnea syndrome. Eur Arch Oto-Rhino-Laryngol 2012; 269(12): 2549–53. https://doi.org/10.1007/s00405-012-1942-6 |
| 47. | Duan X, Zheng M, Zhao W, Huang J, Lao L, Li H, et al. Associations of depression, anxiety, and life events with the risk of obstructive sleep apnea evaluated by Berlin Questionnaire. Front Med 2022; 9: 799792. https://doi.org/10.3389/fmed.2022.799792 |
| 48. | Rubio E, Levey EJ, Rondon MB, Friedman L, Sanchez SE, Williams MA, et al. Poor sleep quality and obstructive sleep apnea are associated with maternal mood, and anxiety disorders in pregnancy. Matern Child Health J 2022; 26(7): 1540–8. https://doi.org/10.1007/s10995-022-03449-8 |