Association Between Chronotype and Dysfunctional Beliefs About Sleep in Psychiatric Outpatients With Sleep Disturbance

Narae Lee; Jihye Ahn; Haein Kim; Seog Ju Kim

doi:10.33069/cim.2026.0013

Chronobiol Med > Volume 8(1); 2026 > Article

Lee, Ahn, Kim, and Kim: Association Between Chronotype and Dysfunctional Beliefs About Sleep in Psychiatric Outpatients With Sleep Disturbance

Original Article

Chronobiology in Medicine 2026;8(1):38-43.

Published online: March 31, 2026

DOI: https://doi.org/10.33069/cim.2026.0013

Association Between Chronotype and Dysfunctional Beliefs About Sleep in Psychiatric Outpatients With Sleep Disturbance

Department of Psychiatry, Sungkyunkwan University College of Medicine, Samsung Medical Center, Seoul, Korea

Corresponding author: Seog Ju Kim, MD, PhD, Department of Psychiatry, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Korea.
Tel: 82-2-3410-3583, E-mail: ksj7126@skku.edu

Received March 1, 2026 Revised March 8, 2026 Accepted March 9, 2026

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective

This study investigated the association between chronotype and dysfunctional sleep-related beliefs in psychiatric outpatients presenting with sleep disturbance.

Methods

This study analyzed medical records of 117 psychiatric outpatients with sleep disturbance (mean age 54.24±15.02 years; 65.8% female). Chronotype was assessed using the reduced Morningness-Eveningness Questionnaire (rMEQ). Sleep-related beliefs were evaluated using the Dysfunctional Beliefs and Attitudes about Sleep scale (DBAS-16). The relationship between chronotype and sleep-related beliefs and the interaction effect of age on their relationship were examined.

Results

There was a significant negative association between rMEQ and DBAS-16 (r=-0.267, p<0.01, n=117), indicating that a stronger evening preference was associated with more pronounced dysfunctional sleep beliefs. This association remained significant after adjusting for age and sex (β=-0.299, p<0.01) or after controlling for depression, anxiety, insomnia, daytime sleepiness, and sleep quality (β=-0.277, p<0.01). The rMEQ score was independently associated with DBAS-16, accounting for 7% of the total variance. This negative relationship remained robust even after adjusting for all potential covariates including clinical and demographic factors (β=-0.277, p=0.003). There was a significant interaction of age group on the association between rMEQ and DBAS-16 (F(4, 108)=3.211, p=0.016). The effect of chronotype on dysfunctional beliefs was more evident among middle-aged and older patients.

Conclusion

Chronotype is independently associated with dysfunctional beliefs about sleep in psychiatric outpatients with sleep disturbance.

Keywords: Dysfunctional sleep-related belief; Circadian preference; Chronotype; Insomnia

INTRODUCTION

Insomnia disorder is a common sleep disorder, affecting approximately 10% to 20% of the general population [1]. It is associated with significant negative health outcomes, including functional impairments in daily life, an increased risk of medical and psychological comorbidities, and a diminished quality of life [2,3]. The prevalence of insomnia is even higher among psychiatric populations, with sleep disturbances being a risk factor both for various mental and physical disorders [4-6].

Chronotype represents a fundamental determinant of sleep–wake regulation. It reflects individual preference in sleep-wake behavior based on one’s intrinsic circadian rhythm, typically expressed along a morningness-eveningness continuum [7,8]. In CIMparticular, individuals with an evening type are more vulnerable to sleep disturbance and insomnia across various age groups and clinical populations [9-12].

While the underlying mechanisms by which an evening chronotype contributes to insomnia remain poorly understood [13], one potential explanation is social jetlag. Under conditions of social jetlag, evening type individuals are required to adhere to early-morning social schedules conflicting with their endogenous rhythms [14,15]. This discrepancy leads to recurrent circadian-social misalignment, irregular sleep patterns, and chronic sleep deprivation, which may reinforce negative sleep-related cognitions such as a perceived loss of control over sleep [14-16].

Previous studies have emphasized the importance of dysfunctional sleep beliefs such as excessive worry, unrealistic expectations, and maladaptive coping behaviors in maintenance of insomnia. The Dysfunctional Beliefs and Attitudes about Sleep scale DBAS was developed to measure these sleep-related cognitive factors [17-19]. Circadian preference may affect these sleep-related cognitive patterns [20,21]. The repeated difficulty of falling asleep or waking up at socially mandated times may reinforce negative appraisals of sleep and further strengthen maladaptive beliefs about sleep [22,23]. This finding is consistent with the observation that an evening type is associated with higher neuroticism and negative cognitive patterns [24,25]. In this context, Ong et al. [26] reported that insomnia patients with an evening chronotype show higher levels of DBAS.

However, it remains unclear whether this correlation is a secondary effect of depression, anxiety, or sleep symptoms, or if it represents an independent association. To clarify this relationship, the present study aimed to investigate the association between chronotype and dysfunctional sleep beliefs in a psychiatric outpatient setting, including whether reduced Morningness-Eveningness Questionnaire (rMEQ) independently accounts for the Dysfunctional belief about sleep measured by the abbreviated version of original DBAS (DBAS-16) even after adjusting for demographic factors and clinical symptoms such as depression, anxiety, and sleep-related symptoms.

METHODS

Participants

Medical records from patients who visited the Department of Psychiatry at Samsung Medical Center (Seoul, South Korea) between January 2018 and December 2022 were investigated. Participants with sleep disturbances who completed rMEQ and DBAS-16 were included. Sleep disturbance was defined to be present when one of the following was met: Pittsburgh Sleep Quality Index (PSQI) score ≥5, the Insomnia Severity Index (ISI) score ≥10, or Epworth Sleepiness Scale (ESS) score ≥8. Finally, 117 patients with sleep disturbances were selected, and their records were utilized for the final analysis. Demographic and clinical characteristics are summarized in Table 1.

Diagnosis of psychiatric disorders

The participants were diagnosed by clinical psychologists using structured diagnostic interviews such as the Mini-International Neuropsychiatric Interview-Plus for DSM (Diagnostic and Statistical Manual of Mental Disorders)-IV (MINI-IV) or the Structured Clinical Interview for DSM-5. To effectively bridge the gap between DSM-IV and DSM-5 criteria, diagnoses were reclassified into 14 mutually non-exclusive categories consistent with DSM-5: 1) neurodevelopmental disorders, 2) psychotic disorders, 3) bipolar disorders, 4) depressive disorders, 5) anxiety disorders, 6) obsessive-compulsive disorders, 7) trauma- and stressor-related disorders, 8) somatic symptom disorders, 9) eating disorders, 10) sleep disorders, 11) impulse control disorders, 12) substance use disorders, 13) neurocognitive disorders, and 14) personality disorders. Diagnostic categories with prevalence <5% were merged into “Others.” The study received approval from the Institutional Review Board of Samsung Medical Center (No. 2020-11-107), which granted an exemption from the requirement for written informed consent.

Clinical rating scales

Clinician-administered rating scales and self-report questionnaires were employed to assess participants’ clinical symptoms. Participants’ circadian preferences were measured using the Korean version of the rMEQ, with total scores on this 5-item scale ranging from 4 to 25 [7,27]. Participants were categorized into evening type group (≤11), intermediate group (scores of 12–17), and morning group (≥18) based on rMEQ score. The Korean version of the DBAS-16, a 16-item self-report scale, was utilized for the assessment of sleep-related cognitions [19]. The total score ranges from 0 to 160, with higher scores reflecting more severe dysfunctional beliefs.

Subjective sleep quality over the past month was evaluated using PSQI [28]. The PSQI score ranges from 0 to 21, with scores greater than 5 indicating poor sleep quality. Insomnia symptom severity was measured with the Korean version of ISI [29,30]. The score is rated on a 0–4 scale and the total score ranges from 0 to 28, indicating that the higher score suggests more severe insomnia symptoms. Daytime sleepiness was assessed with the Korean version of ESS [31]. Each item is rated on a 0 to 3 scale, with total scores ranging from 0 to 24. A total score ≥10 indicates excessive daytime sleepiness.

Depressive and anxiety symptoms were assessed using clini-cian-administered scales. The Korean version of the Hamilton Depression Rating Scale (HDRS) is a 17-item instrument designed to evaluate depressive symptoms experienced over the preceding week through a semi-structured interview [32]. The total score ranges from 0 to 52. Similarly, the Korean version of the Hamilton Anxiety Rating Scale (HARS) consists of 14 items, focusing on anxiety severity, with a total score ranging from 0 to 56 [33].

Statistical analysis

A hierarchical multiple regression analysis was conducted to determine the independent association of rMEQ on DBAS-16 with 104 participants who provided complete data for all variables. A listwise deletion was used to handle missing data. Out of the initial 117 participants, 13 were excluded due to incomplete responses on the ISI (n=2) or PSQI (n=12) scales. One participant was missing both scales, resulting in a final sample of 104 participants. No significant differences were observed in demographic or clinical characteristics between the final sample and those with missing data through independent t-test (all p>0.05 ). All fundamental assumptions for hierarchical regression were satisfied. The Durbin-Watson statistics of 1.817 confirmed no autocorrelation, and variance inflation factor (VIF) values (1.110–4.321) indicated no multicollinearity, while residual plot inspection confirmed linearity, homoscedasticity, and normality.

The rMEQ total score was entered as the primary independent variable to assess its baseline explanatory power in Model 1. In Model 2, demographic factors (age and sex) were added to account for their contribution to the variance in DBAS-16. In Model 3, clinical symptom factors including anxiety (HARS), depression (HDRS), daytime sleepiness (ESS), subjective sleep quality (PSQI), and insomnia severity (ISI) were introduced in the final model to identify whether rMEQ remained a significant association even after adjusting for a wide range of comorbid symptoms.

A two-way analysis of variance (ANOVA) was performed to examine the main effects of chronotype (rMEQ) and age group on dysfunctional sleep beliefs (DBAS-16), as well as their interaction effect on total sample (n=117). Simple main effect analyses were subsequently conducted to evaluate the differences in sleep beliefs according to chronotype within each age group. Fisher’s least significant difference test was used for post-hoc comparisons to determine specific group differences. Homogeneity of variance was confirmed via Levene’s test (p>0.05). All statistical analyses were conducted using SPSS (version 22.0; IBM Corp.) and R (version 4.5.3; R Foundation for Statistical Computing).

RESULTS

Participant characteristics

Table 1 summarizes the demographic and clinical characteristics in samples (n=117, 40 males and 77 females, mean age 54.24± 15.02). The mean age of males was 51.33±17.58 years, while the mean age of females was 55.73±13.48 years. The majority of participants were in the ≥60 years group (n=49, 41.9%), followed by the 40–59 years group (n=43, 36.8%) and the ≤39 years group (n=25, 21.4%). Mean ISI score was 19.65±5.23 (n=115), and mean PSQI score was 15.21±3.51 (n=105).

The mean rMEQ score was 14.18±4.05. The majority were classified as intermediate type (n=61, 52.1%), followed by evening (n=31, 26.5%) and morning (n=25, 21.3%) types. The rMEQ score was correlated with age (r=0.377, p<0.001), sex (r=-0.196, p<0.05), and DBAS-16 (r=-0.267, p<0.01, n=117) but was not with HDRS, HARS, PSQI, ISI, or ESS. The value of DBAS-16 (mean 101.64± 26.85) was positively correlated with HDRS (r=0.196, p=0.035), HARS (r=0.226, p=0.014), PSQI (r=0.271, p=0.005), and ISI (r=0.477, p<0.001), but was not with age (r=-0.087, p=0.349) or ESS (r=0.065, p=0.360).

The most frequent diagnosis was sleep-wake disorders (59.5%), followed by depressive disorders (40.5%) and anxiety disorders (30.2%). Diagnoses were non-mutually exclusive. Independentsamples t-tests revealed that there were no significant differences in rMEQ or DBAS-16 scores based on these diagnostic categories.

Association between rMEQ and DBAS-16

Males showed significantly higher rMEQ score than females (t=2.146, p=0.034). The hierarchical regression revealed that rMEQ significantly accounted for 7% of the variance in DBAS-16 (F(1, 102)=7.700, p=0.007) in Model 1 (Table 2). The addition of age and sex in Model 2 increased the total variance explained to 9.8% (R²=0.098). rMEQ total remained a significant association of DBAS-16 (β=-0.299, p<0.01). However, this change in explanatory power was not statistically significant (F(3, 100)=3.611, p=0.222 [ΔR²=0.028, ΔF=1.527, p>0.05]). The addition of clinical symptoms in Model 3 significantly increased the explanatory power of the model (F(8, 95)=9.284, p<0.001 [ΔR²=0.244, ΔF=7.028, p<0.001]). Interestingly, rMEQ remained a consistently significant factor even in Model 3 (β=-0.277, p=0.003), suggesting its robust and independent contribution to sleep related dysfunctional beliefs with mood and demographic characteristics.

The most powerful factor was ISI score (β=0.491, p<0.001) followed by Sex (β=-0.227, p<0.05), both of which were statistically significant. HDRS, HARS, ESS, PSQI, and age did not show significant independent effects on DBAS-16 in Model 3. All VIF values were below 5, indicating no multi-collinearity issues.

Interaction between age and chronotype on DBAS-16

A two-way ANOVA was conducted to examine the effects of chronotype (evening, intermediate, and morning) and age group (young, middle-aged, and older) on DBAS-16 scores (Table 3). A significant interaction effect between rMEQ group and age group was found (F (4, 108)=3.211, p=0.016, η²=0.106). The results indicate that the impact of circadian preference on DBAS-16 depends on age. The main effects of age group (F(2, 108)=1.465, p=0.236, η²=0.026) and chronotype (F(2, 108)=2.051, p=0.134, η²=0.037) were not statistically significant.

To further clarify the significant interaction between chronotype and age group, simple main effect analyses were conducted. Simple main effect analyses revealed a significant effect of chronotype on DBAS-16 specifically within the middle-aged group (F(2,108)=3.609, p=0.030) and older group (F(2,108)=4.008, p=0.021), but not in the young adult group (F(2,108)=1.319, p=0.272). Post-hoc pairwise comparisons revealed that in the middle-aged group, evening types have significantly higher DBAS-16 score than intermediate types (mean difference=22.187, p=0.019). For older aged group, morning types showed significantly lower DBAS-16 score than Intermediate types (mean difference=23.00, p=0.007).

DISCUSSION

Our findings revealed that circadian preference independently influences dysfunctional beliefs about sleep, even after adjusting for depression, anxiety, and insomnia. This suggests that the maladaptive sleep-related cognition associated with circadian preference is not merely a byproduct of mood or poor sleep quality, but is inherently tied to circadian preference [34]. Previous studies suggested several plausible mechanisms for this relationship. The chronic circadian misalignment experienced by evening types results from a mismatch between their endogenous circadian rhythm and social schedules. This may result in accumulated sleep debt and irregular sleep timing, which strengthens the perceived loss of control over sleep [26,35]. One prior study also showed that hyperarousal, which is inherently associated with an evening chronotype, plays a critical role in the development of insomnia [36]. Both cognitive and physiological hyperarousal influence dysfunctional sleep beliefs, exacerbating anxiety regarding the unpredictability of sleep. This may maintain maladaptive sleep cognitions and insomnia.

The significant interaction effect revealed distinct patterns across age groups. During middle age, the evening chronotype was more associated with dysfunctional beliefs about sleep than intermediate type. Similarly, in older adults, the intermediate type reported significantly elevated dysfunctional beliefs than morning type. These findings suggest that the dysfunctional sleep-related belief associated with chronotype becomes progressively more prominent after middle age. This may reflect effects of chronic circadian misalignment over years of working life, combined with age-related changes in circadian rhythms and sleep architecture [11]. Evening-type individuals in the middle-aged group face peak occupational and caregiving demands that conflict with their delayed biological clock, generating chronic social jetlag, which may lead to repeated experiences of curtailed sleep and reinforces maladaptive beliefs such as perceived loss of control over sleep and catastrophic appraisals of sleep loss consequences in this group [37].

In addition, elderly morning-type people showed significantly lower dysfunctional belief, which suggests that an advanced circadian preference may act as a protective factor in late adulthood. Chronotype shifts forward toward morning type in the elderly [8]. The current result may suggest that the congruence between innate chronotype and age-related sleep phase advancement acts as a protective factor against sleep-related cognitive distortions in later life.

On the contrary, there is no significant chronotype effect in the young adults. A relatively smaller sample size or a large portion of evening group in young people may have reduced the discriminative power of chronotype. In addition, young adults may still be in academic or early professional contexts, where sufficient experiences with persistent sleep failure have not been accumulated to consolidate chronic maladaptive sleep beliefs.

As expected, insomnia severity remained the most significant associated factor of dysfunctional sleep-related belief, which aligns with previous studies [6,38]. Given that restructuring these maladaptive cognitions is a core component in Cognitive Behavioral Therapy for Insomnia, which is a treatment of choice for chronic insomnia [2,39]. The current study suggests that clinical outcomes for sleep problems in psychiatric patients may be optimized by accounting for a patient’s innate circadian preference, especially after middle age.

There are several methodological limitations in this study. As this study was conducted in a single hospital setting in Korea, the generalizability of this to other clinical settings should be interpreted with caution. In addition, the sample size of this study was relatively small. This may have limited statistical power and the ability to evaluate interactions for diagnostic subgroups. Self-report measures, such as the DBAS-16 and rMEQ, are inherently subjective and susceptible to bias. Therefore, future research should integrate objective assessments, including actigraphy, polysomnography, or biomarkers to validate the relationship between biological sleep-activity patterns and sleep-related cognition.

Notwithstanding these limitations, the present findings indicate that circadian preference independently explains dysfunctional beliefs and attitudes about sleep, regardless of various clinical comorbidities, particularly in middle-aged or older psychiatric patients. These findings suggest that the circadian characteristics and age of patients should be integrated with future cognitive interventions for sleep disturbances in people with psychiatric disorders.

NOTES

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Availability of Data and Material

The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.

Author Contributions

Conceptualization: Narae Lee, Jihye Ahn, Seog Ju Kim. Data curation: Jihye Ahn, Haein Kim. Funding acquisition: Seog Ju Kim. Investigation: all authors. Methodology: Narae Lee, Jihye Ahn, Seog Ju Kim. Software: Narae Lee, Seog Ju Kim. Supervision: Seog Ju Kim. Visualization: Narae Lee. Writing—original draft: Narae Lee. Writing—review & editing: Narae Lee, Jihye Ahn, Seog Ju Kim.

Funding Statement

This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) [2022R1A2C2008417].

Acknowledgments

None

Table 1.

Demographic and clinical characteristics

Variable	Value (n=117)
Age (yr)	54.24±15.02 (22–91)
Young group (≤39 yrs)	25 (21.4)
Middle group (40–59 yrs)	43 (36.8)
Older group (≥60 yrs)	49 (41.9)
Male	40 (34.2)
Female	77 (65.8)
rMEQ score	14.18±4.05 (6–25)
Evening type	31 (26.5)
Intermediate type	61 (52.1)
Morning type	25 (21.3)
DBAS-16	101.64±26.85 (30–160)
ISI (n=115)	19.65±5.23 (3–28)
PSQI (n=105)	15.21±3.51 (5–21)
HDRS	15.93±6.20 (5–33)
HARS	17.73±7.22 (5–38)
ESS	7.21±4.83 (0–24)

Values are presented as mean±standard deviation (range) or n (%). rMEQ, reduced Morningness–Eveningness Questionnaire; DBAS-16, Dysfunctional Beliefs and Attitudes about Sleep-16; ISI, Insomnia Severity Index; PSQI, Pittsburgh Sleep Quality Index; HARS, Hamilton Anxiety Rating Scale; HDRS, Hamilton Depression Rating Scale; ESS, Epworth Sleepiness Scale.

Table 2.

Hierarchical regression analysis for factors associated with DBAS-16 (n=104)

Variables	Model 1 (β)	Model 2 (β)	Model 3 (β)	VIF
rMEQ total	-0.265^**	-0.299^**	-0.277^**	1.201
Sex		-0.171	-0.227^*	1.344
Age		-0.026	0.035	1.110
HARS			0.274	4.321
HDRS			-0.268	4.021
ESS			0.141	1.036
PSQI			-0.010	1.549
ISI			0.491^***	1.741
R²	0.07	0.098	0.341
Adjusted R²	0.061	0.071	0.286
ΔR²	0.070^**	0.028	0.244^***
F for ΔR² (sig)	7.700 (0.007)	3.611 (0.222)	9.284 (<0.001)
Durbin-Watson			1.817

Model 1: unadjusted; Model 2: adjusted for demographic variables (age, sex); Model 3: adjusted model for age, sex, HDRS, HARS, ISI, PSQI, and ESS (n=104).

^* p<0.05;

^** p<0.01;

^*** p<0.001.

DBAS-16, Dysfunctional Beliefs and Attitudes about Sleep-16; rMEQ, reduced Morningness-Eveningness Questionnaire; HARS, Hamilton Anxiety Rating Scale; HDRS, Hamilton Depression Rating Scale; ESS, Epworth Sleepiness Scale; PSQI, Pittsburgh Sleep Quality Index; ISI, Insomnia Severity Index; VIF, variance inflation factor.

Table 3.

Interaction effects of age group and chronotype on DBAS-16 (n=117)

Source	df	F	p	Partial η²
Age group	2	1.465	0.236	0.026
Chronotype	2	2.051	0.134	0.037
Age×Chronotype	4	3.211	0.016^*	0.106

^* p<0.05.

DBAS-16, Dysfunctional Beliefs and Attitudes about Sleep-16; df, degrees of freedom.

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