Desired Time in Bed Independently Predicts Subjective Memory Complaints in Obstructive Sleep Apnea

Jaejong Lee; Young Kyung Moon; Kayoung Song; Seockhoon Chung; Hayun Choi

doi:10.17241/smr.2026.03769

Sleep Med Res > Volume 17(2); 2026 > Article

Lee, Moon, Song, Chung, and Choi: Desired Time in Bed Independently Predicts Subjective Memory Complaints in Obstructive Sleep Apnea

Original Article

Sleep Med Res 2026; 17(2): 115-121.

Published online: Jun 30, 2026

DOI: https://doi.org/10.17241/smr.2026.03769

Desired Time in Bed Independently Predicts Subjective Memory Complaints in Obstructive Sleep Apnea

Jaejong Lee, MD¹

, Young Kyung Moon, MD¹

, Kayoung Song, MD¹

, Seockhoon Chung, MD, PhD²

, Hayun Choi, MD¹

¹Department of Psychiatry, Veteran Health Service Medical Center, Seoul, Korea

²Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

Corresponding Authors: Hayun Choi, MD, Department of Psychiatry, Veteran Health Service Medical Center, 53 Jinhwangdo-ro 61-gil, Gangdong-gu, Seoul 05368, Korea, Tel +82-2-2225-1158, Fax +82-2-2225-3947, E-mail chhy0402@gmail.com

Seockhoon Chung, MD, PhD, Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, 86 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea, Tel +82-2-3010-3411, Fax +82-2-485-8381, E-mail schung@amc.seoul.kr

Received May 19, 2026 Revised Jun 8, 2026 Accepted Jun 20, 2026

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://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

Background and Objective

Although obstructive sleep apnea (OSA) has been linked to cognitive complaints, the severity of objective disease often does not fully account for subjective memory complaints. This study aimed to investigate whether desired time in bed (dTIB) is associated with, and independently predicts subjective memory complaints in patients with OSA.

Methods

This study analyzed data from a prospective case-control study conducted at the Veteran Health Service Medical Center, Seoul, from August 2021 to December 2023. Out of 127 participants who underwent nocturnal polysomnography, self-report questionnaires, and clinical interviews, 92 individuals meeting diagnostic criteria for OSA were included in the final analysis.

Results

Among the 92 participants, 87.0% were male, with a mean age of 66.90±12.76 years and an Apnea-Hypopnea Index (AHI) of 45.78±23.26 events/hr. Participants reported mean Insomnia Severity Index (ISI) scores of 12.18±6.93, Beck Depression Inventory-II (BDI-II) scores of 13.97±9.20, and Subjective Memory Complaints Questionnaire (SMCQ) scores of 4.95±3.82. SMCQ scores showed significant correlations with age, BDI-II, total sleep time, dTIB, and desired total sleep time. After controlling for age, sex, body mass index, AHI, ISI, BDI-II, and wake after sleep onset, a longer dTIB remained an independent predictor of subjective memory complaints (B=1.00, p=0.017, ΔR²=0.05).

Conclusions

Longer dTIB was independently associated with subjective memory complaints, while objective OSA severity was not. These findings suggest that subjective memory complaints in OSA may be more closely linked to maladaptive sleep-related perceptions and behavioral factors than to objective respiratory disturbance itself.

Key words: Obstructive sleep apnea, Desired time in bed, Subjective Memory Complaints Questionnaire

INTRODUCTION

Patients with obstructive sleep apnea (OSA) frequently experience sleep fragmentation, non-restorative sleep, and daytime sleepiness, which are all linked to cognitive complaints [1,2]. OSA affects 9%–38% of the general adult population [3], and is often accompanied by psychiatric comorbidities, such as insomnia (up to 39%–58% [4]) and depression (up to 7%–63% [5]). OSA has been implicated in cognitive decline, affecting functions from attention and vigilance to memory and executive functions. This decline is thought to occur through multiple mechanisms, including intermittent hypoxia and neuroinflammation [6].

Despite increasing evidence connecting OSA to cognitive decline, the relationship between objective disease severity and subjective memory complaints (SMCs) remains inconsistent [2]. For example, Chen et al. [7] found no significant correlations between objective cognitive tests (for attention, memory, and executive function) and subjective cognitive ratings. Similarly, Vernet et al. [8] observed that even after successful continuous positive airway pressure treatment, OSA patients often reported memory and inattention complaints despite showing no objective cognitive decline on tests of memory, attention, and executive function.

While previous studies [9–11] have explored the association between non-cognitive variables like depressive symptoms and SMCs, subjective perceptions of sleep and behavioral factors have received less attention compared to objective sleep parameters. According to the cognitive model of insomnia [12], dysfunctional beliefs about sleep—such as the belief that an earlier bedtime promotes sleep onset—can paradoxically increase time in bed (TIB) and worsen sleep quality. Furthermore, an inverted U-shaped association has been observed between sleep duration and cognitive decline; extreme sleep durations (less than 4 hours or more than 10 hours per night) were significantly associated with lower baseline cognitive function and faster cognitive decline over the follow-up period [13].

One such behavioral factor that has received limited attention is desired time in bed (dTIB), defined as the amount of time an individual believes they need to spend in bed to achieve adequate rest [14,15]. Longer dTIB may indicate maladaptive sleep beliefs or compensatory behaviors in response to perceived poor sleep quality or daytime dysfunction. As noted, excessive daytime sleepiness and prolonged sleep duration are negatively associated with cognitive function, suggesting that modifying dTIB might be beneficial. However, the role of dTIB in OSA, particularly its relationship to SMCs, has not been thoroughly investigated.

Therefore, we aimed to determine whether dTIB independently predicts SMCs after controlling for demographic variables, objective sleep parameters, and psychological factors, including insomnia severity and depressive symptoms.

METHODS

Participants

This study utilized data from a previous prospective case-control study conducted from August 2021 to December 2023 at the Veteran Health Service Medical Center, Seoul. Participants were recruited from patients visiting the sleep clinic at the VHS Medical Center through direct clinic visits or IRB-approved recruitment posters displayed on bulletin boards within the hospital. Prior to enrollment, the research team provided detailed explanations to prospective participants and obtained written informed consent. This study was approved by the Institutional Review Board of the VHS Medical Center (IRB No. 2021-06-022).

Adult males and females, aged 20 years or older, were included if they expressed willingness to participate and were capable of undergoing overnight polysomnography (PSG) specifically performed for this study at the hospital’s sleep laboratory [16].

Exclusion criteria included individuals with dementia or uncontrolled neuropsychiatric disorders, those unable to comprehend the research explanation and/or consent form, and individuals who failed to obtain any sleep during PSG (total sleep time [TST] of zero), as valid PSG-based sleep data were required for analysis. PSG data were also excluded if they contained excessive artifacts that hindered interpretation.

Demographics and Questionnaires

Prior to undergoing PSG, participants had an outpatient interview with psychiatrists to establish agreements and completed a self-report questionnaire. This questionnaire collected details such as age, sex, weight, height, body mass index (BMI) (kg/m²), sleep-related lifestyle, medical history, current medications, and sleeping habits. Psychological evaluations were conducted using the Insomnia Severity Index (ISI) [17] to measure insomnia severity, the Subjective Memory Complaints Questionnaire (SMCQ) [10] for SMCs, and Beck Depression Inventory-II (BDI-II) [18] for depression.

The ISI is a brief self-report instrument measuring the patient’s perception of their insomnia. It assesses the subjective symptoms and consequences of insomnia, as well as the degree of concern or distress caused by those difficulties. The 7-item ISI measures the severity of insomnia on a 28-point scale. Scores between 0 and 7 indicate minimal insomnia, 8 to 14 indicate mild insomnia, 15 to 21 indicate moderate insomnia, and 22 to 28 indicate severe insomnia, with scores of 15 or above indicating clinically meaningful insomnia [17,19].

The SMCQ is a 14-item yes/no questionnaire assessing SMCs, including metacognition of general and specific memories [10]. It consists of 4 items assessing global memory function and 10 items assessing everyday memory function.

The BDI-II [20] is one of the most widely used self-report measures for assessing depressive symptoms in both clinical and research settings. The BDI-II screens for depression with 21 questions, each rated from 0 to 3, with total scores ranging from 0 to 63. Scores between 14 and 19 indicate mild depression, 20 to 28 indicate moderate depression, and 29 to 63 indicate severe depression [18,21].

dTIB and desired total sleep time (dTST), as well as the discrepancy between desired time in bed and desired total sleep time (DBST index) were collected through clinical interviews. These measures were introduced during the course of the study, following the method described in a previous study [14]. The dTIB was assessed using the question, “From what time to what time do you want to sleep?,” and the dTIB was estimated by calculating the duration of the response. The dTST was assessed using the question, “How many hours do you want to sleep in a day?,” and the DBST index was calculated as dTIB minus dTST, representing the discrepancy between desired time in bed and desired amount of actual sleep.

Nocturnal PSG

This study utilized the Grael PSG device (Compumedics Limited), standardized electrodes, and detectors. Electrodes were positioned according to the international 10–20 system, including F4/A1, F3/A2, C4/A1, C3/A2, O1/A2, and O2/A1. Two electrooculography electrodes were placed on each side of the eyes to monitor horizontal and vertical eye movements. Electromyography electrodes were affixed to the submentalis muscle and both anterior tibialis muscles to track lower limb movements during sleep. Thoracic and abdominal respiratory movements were detected using strain gauges, and nasal airflow was measured with a nasal pressure cannula. Blood oxygen saturation levels were monitored at the tip of the left index finger using a pulse oximeter. Sleep architecture was analyzed following standardized criteria from the American Academy of Sleep Medicine [22]. Nocturnal PSG measurements included TST, wake after sleep onset (WASO), sleep latency, sleep efficiency, duration of N1, N2, and N3 sleep stages, rapid eye movement sleep duration, Apnea-Hypopnea Index (AHI), respiratory-related sleep wakefulness, and periodic limb movement index.

Statistical Analysis

All statistical analyses were performed using R version 4.1.2 (R Foundation for Statistical Computing; https://www.R-project.org) and SPSS version 31.0.2.0 (IBM Corp.). Statistical significance was set at p<0.05 for all analyses.

Descriptive statistics summarized the participants’ demographic and clinical characteristics. Continuous variables are presented as mean±standard deviation, and categorical variables as numbers and percentages. Pearson’s correlation coefficients examined associations among age, self-report measures (ISI, SMCQ, BDI-II), polysomnographic variables (TST, WASO, AHI), and sleep-related variables (dTIB, dTST, DBST index). Variables significantly correlated with SMCQ were considered candidate predictors for subsequent regression analysis. A sequential multiple linear regression approach determined whether dTIB was independently associated with SMCQ scores. Models 1 through 4 were constructed by sequentially adding potential confounders: Model 1 included age, sex, BMI, and AHI as demographic and disease-related covariates. ISI was added in Model 2 to adjust for insomnia severity, BDI-II in Model 3 to control for depressive symptoms, and WASO in Model 4 to account for sleep fragmentation. dTIB was then entered in Model 5 to assess its independent contribution to SMCs beyond the covariates established in the preceding models. Sex, BMI, and AHI were retained in all models as clinically essential variables for studies involving patients with OSA. The change in R² at each step assessed the incremental variance explained by each variable. Multicollinearity was assessed using variance inflation factors (VIFs); all VIF values were below 2.0 across all five models, indicating no evidence of problematic multicollinearity.

RESULTS

Demographic and Clinical Characteristics

Of the participants in the previous prospective cohort study [23], 127 who submitted self-report questionnaires and underwent nocturnal PSG and additional clinical interviews were included in this study. Among them, one participant did not report dTIB, and 11 participants did not report SMCQ. After excluding participants with an AHI of less than 15 (n=23), 92 participants who met the diagnostic criteria for moderate-to-severe OSA [24] based on PSG findings alone, independent of clinical symptoms, were included in the final analysis (Fig. 1). A total of 92 participants (87.0% male) with a mean age of 66.90±12.76 years and a mean BMI of 25.77±3.81 kg/m² were included in the study (Table 1). Participants had ISI scores of 12.18±6.93, SMCQ scores of 4.95±3.82, and BDI-II scores of 13.97±9.20. Participants demonstrated severe OSA (AHI, 45.78±23.26 events/hr) with sleep efficiency of 73.80%±17.22% on PSG. The mean dTIB was 7.58±0.89 hours, the mean dTST was 7.32±1.05 hours, and the mean DBST index was 0.27±1.03 hours.

Correlation Analysis

Pearson’s correlation coefficients among the variables are presented in Table 2. SMCQ scores were significantly correlated with age, BDI-II, TST, dTIB, and dTST, but not with BMI, ISI, WASO, AHI, or DBST index. Correlations among other variables are also presented in Table 2.

Hierarchical Multiple Regression Analysis

A hierarchical multiple regression analysis was conducted to examine whether dTIB independently predicts SMCQ scores after controlling for potential confounders (Table 3). Participants who did not report BDI (n=4) were excluded from this analysis. In Model 1, age was the only significant predictor of SMCQ scores (B=0.10, p=0.004), with the model explaining 11% of the variance (R²=0.11). ISI was added in Model 2 and was found to be a significant predictor (B=0.12, p=0.045), increasing the explained variance to 15% (R²=0.15). In Model 3, BDI-II was additionally entered and was significantly associated with SMCQ scores (B=0.10, p=0.035), with the model explaining 20% of the variance (R²=0.20), while ISI was no longer significant. In Model 4, WASO was found to be a significant negative predictor of SMCQ scores (B=-0.02, p=0.007), with the model explaining 27% of the variance (R²=0.27). Finally, in Model 5, dTIB remained an independent predictor of SMCs after controlling for age, sex, BMI, AHI, ISI, BDI-II, and WASO (B=1.00, p=0.017). The final model was significant and explained 32% of the variance in SMCQ scores (R²=0.32), with dTIB contributing a significant incremental increase in explanatory power (ΔR²=0.05, p=0.017). Multicollinearity diagnostics confirmed that the possibility of redundant adjustment was minimal across all models.

DISCUSSION

In this study, we aimed to determine whether dTIB is associated with SMCs in patients with OSA and whether dTIB independently predicts SMCs after controlling for demographic factors, OSA severity, insomnia symptoms, depressive symptoms, and sleep fragmentation. Our findings demonstrated that dTIB was significantly and positively correlated with SMCQ scores and remained an independent predictor of SMCs even after adjusting for the potential confounders described above. However, AHI, which reflects the objective severity of OSA, revealed no significant association with SMCQ scores. These results suggest that SMCs in patients with OSA may be more closely related to sleep-related cognitive and psychological factors and behavioral characteristics than to the objective severity of sleep-disordered breathing alone.

The participants in this study had a mean age in the mid-60s, were predominantly male, and had a mean BMI of 25 or above, which is consistent with the known epidemiological characteristics of patients with OSA, such as a higher prevalence in older males with elevated BMI [3]. Despite severe OSA, the mean TIB and TST were relatively short, which may partly reflect age-related decreases in sleep duration in this elderly population. The relatively short TST may also reflect first-night effects and the high prevalence of insomnia symptoms in this cohort. This should be considered when interpreting the association between dTIB and SMCs. Participants exhibited a mean AHI over 30, indicating severe OSA [24], with a mean sleep efficiency below 75% reflecting low sleep efficiency. Participants reported mild insomnia symptoms and mild depressive symptoms on average, and some reported SMCQ scores reflecting SMCs.

In the correlation analysis, SMCQ scores were significantly associated with age, depressive symptoms (BDI-II), TST, dTIB, and dTST. This finding is consistent with previous studies demonstrating that older age and depressive symptoms are associated with subjective cognitive decline [25,26]. However, no significant association was observed between AHI and SMCQ scores, suggesting that the objective severity of OSA may not necessarily correspond to patients’ subjective cognitive symptoms. Indeed, prior studies have reported limited associations between objective neuropsychological test results and SMCs [6,7]. Furthermore, no significant correlation was found between ISI and SMCQ scores in this study. This suggests that cognitive attitudes about sleep or compensatory behaviors, rather than insomnia symptoms, may be more closely associated with subjective cognitive decline. The negative association between WASO and SMCQ was unexpected. Given the substantial correlations among sleep-related variables, this finding may reflect a suppressor effect or a residual statistical artifact arising from multivariable adjustment, rather than a true protective association. Therefore, this result should be interpreted cautiously and requires replication in future studies.

Various biological mechanisms have been proposed to explain the relationship between OSA and cognitive decline. Intermittent hypoxia and recurrent sleep fragmentation can induce oxidative stress and neuroinflammation, leading to cerebrovascular endothelial dysfunction and cerebral hypoperfusion [6,27]. Additionally, impairment of the glymphatic system during sleep may reduce the clearance of amyloid-β and tau proteins. Disruption of hippocampal neurogenesis and long-term potentiation may also contribute to long-term cognitive decline and neurodegenerative changes [6]. These mechanisms are considered important biological evidence explaining the increased risk of memory impairment and dementia in patients with OSA.

However, in this study, subjective and behavioral factors such as dTIB showed stronger associations with SMCs than objective sleep parameters like AHI and WASO. This suggests that subjective cognitive symptoms in patients with OSA cannot be fully explained by biological damage alone; psychological distress and maladaptive sleep perceptions may also play important roles. SMCs are particularly clinically important as they may precede objective cognitive decline. Previous studies have suggested that subjective cognitive decline may serve as an early indicator of Alzheimer’s disease and other dementias [28], and that persistent subjective cognitive decline over time is associated with an increased risk of progression to mild cognitive impairment and dementia [29].

Notably, dTIB independently predicted SMCs even after adjusting for various confounders. dTIB is defined as the amount of time an individual believes they need to spend in bed to achieve adequate rest. It can be viewed as a behavioral indicator reflecting subjective beliefs and expectations about sleep rather than actual sleep duration [14,15]. According to the cognitive model of insomnia [12], patients may tend to go to bed excessively early or extend their TIB due to excessive worry about sleep deprivation and dysfunctional beliefs. Such behaviors may paradoxically reduce sleep efficiency and worsen sleep quality. The present findings suggest that these maladaptive sleep behaviors may also be associated with SMCs in patients with OSA.

Furthermore, individuals with SMCs have been shown to exhibit increased depression, anxiety, and neuroticism, as well as a poorer quality of life, irrespective of objective cognitive decline [26]. These characteristics are particularly relevant in patients with OSA, who often experience comorbid depressive symptoms and insomnia. In this study, BDI-II scores significantly correlated with SMCQ scores, suggesting that psychological distress may contribute to subjective cognitive symptoms.

dTIB is clinically important because, unlike age or AHI, it represents a potentially modifiable behavioral factor. However, the cross-sectional design of this study prevents us from determining the direction of this association. Further research is needed to investigate whether modifying dTIB through interventions like CBT-I can alter subjective cognitive symptoms.

This study has several limitations. First, its cross-sectional design precludes establishing causal relationships between variables. The direction of the association remains unclear; for instance, individuals with more SMCs may simply desire more TIB as a compensatory response. Second, this study relied on self-reported measures, such as the SMCQ, rather than objective cognitive assessments like the MMSE. This reliance may have led to overestimation or underestimation of actual cognitive decline. Specifically, individuals with greater objective sleep fragmentation might have underreported their memory complaints, underscoring the need for objective cognitive assessments in future studies. Third, the SMCQ used in this study has undergone only limited validation as a measure of subjective cognitive decline, so the results should be interpreted cautiously. Fourth, the study population consisted predominantly of elderly male veterans, which may limit the generalizability of the findings to broader populations. Additionally, age-related decreases in sleep duration within this population may have contributed to the relatively short TIB and TST observed, potentially restricting the variance in dTIB and underestimating the strength of its association with SMCs. Fifth, TST was excluded from the regression models due to its strong collinearity with WASO. Its potential role as an underlying variable connecting dTIB, dTST, and WASO warrants further investigation. Finally, the potential influence of unmeasured confounders cannot be fully excluded.

In conclusion, this study demonstrated that dTIB independently predicts SMCs in patients with OSA. In contrast, AHI showed no significant association with these complaints, suggesting that subjective cognitive symptoms in patients with OSA may be more closely related to psychological and behavioral factors than to the objective severity of sleep-disordered breathing. dTIB is clinically important because it is a potentially modifiable behavioral factor. Further longitudinal or interventional studies are needed to clarify the direction of this relationship and determine whether modifying dTIB can lead to improvements in cognitive symptoms.

NOTES

Availability of Data and Material

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

Author Contributions

Conceptualization: Seockhoon Chung, Hayun Choi. Data curation: Hayun Choi. Formal analysis: Jaejong Lee, Hayun Choi. Funding acquisition: Hayun Choi. Methodology: Seockhoon Chung, Hayun Choi. Project administration: Hayun Choi. Supervision: Young Kyung Moon, Kayoung Song, Hayun Choi, Seockhoon Chung. Visualization: Jaejong Lee. Writing— original draft: Jaejong Lee. Writing—review & editing: all authors.

Conflicts of Interest

Seockhoon Chung, a contributing editor of the Sleep Medicine Research, was not involved in the editorial evaluation or decision to publish this article. All remaining authors have declared no conflicts of interest.

Funding Statement

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), which is funded by the Ministry of Health & Welfare, Republic of Korea (Grant No. HI21C0852).

Acknowledgements

We would like to express our deepest gratitude to Researcher Young Lee (Veterans Medical Research Institute), who made valuable contributions to statistical analyses.

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Fig. 1

Flowchart of participants. PSG, polysomnography; dTIB, desired time in bed; SMCQ, Subjective Memory Complaints Questionnaire; dTST, desired total sleep time; DBST index, discrepancy between desired time in bed and desired total sleep time; OSA, obstructive sleep apnea; AHI, Apnea-Hypopnea Index.

Table 1

Clinical characteristics of the study participants

Variables	Value
Male	80 (87.0)
Age (yr)	66.90±12.76
BMI (kg/m²)	25.77±3.81
Self-report measures
ISI	12.18±6.93
SMCQ	4.95±3.82
BDI-II	13.97±9.20
dTIB (hr)	7.58±0.89
dTST (hr)	7.32±1.05
DBST index (hr)	0.27±1.03
Polysomnographic variables
AHI (events/hr)	45.78±23.26
TIB (min)	367.51±24.81
TST (min)	270.21±64.73
WASO (min)	78.86±56.65
SE (%)	73.80±17.22

Values are presented as n (%) or mean±standard deviation.

BMI, body mass index; ISI, Insomnia Severity Index; SMCQ, Subjective Memory Complaints Questionnaire; BDI-II, Beck Depression Inventory-II; dTIB, desired time in bed; dTST, desired total sleep time; DBST index, discrepancy between desired time in bed and desired total sleep time; AHI, Apnea-Hypopnea Index; TIB, time in bed; TST, total sleep time; WASO, wake after sleep onset; SE, sleep efficiency.

Table 2

Pearson’s correlation coefficients (r) of each variable

Variables	Age	1	2	3	4	5	6	7	8
1. ISI	−0.093
2. SMCQ	0.290**	0.166
3. BDI-II	0.251*	0.428***	0.314**
4. TST	−0.211*	−0.037	0.246*	−0.186
5. WASO	0.193	0.160	−0.199	0.248*	−0.912***
6. AHI	−0.150	0.076	−0.130	−0.004	−0.371***	0.322**
7. dTIB	0.091	−0.002	0.306**	0.009	0.154	−0.130	−0.137
8. dTST	0.040	−0.096	0.242*	−0.113	0.352***	−0.371***	−0.158	0.453***
9. DBST index	0.038	0.097	0.018	0.136	−0.227*	0.269**	0.043	0.403***	−0.633***

^* p<0.05;

^** p<0.01;

^*** p<0.001.

ISI, Insomnia Severity Index; SMCQ, Subjective Memory Complaints Questionnaire; BDI-II, Beck Depression Inventory-II; TST, total sleep time; WASO, wake after sleep onset; AHI, Apnea-Hypopnea Index; dTIB, desired time in bed; dTST, desired total sleep time; DBST index, discrepancy between desired time in bed and desired total sleep time.

Table 3

Hierarchical regression analysis of predictors of subjective memory complaints (SMCQ)

Dependent variable: SMCQ

Variables	Model 1			Model 2			Model 3			Model 4			Model 5

	B (95% CI)	p-value	R²	B (95% CI)	p-value	R²	B (95% CI)	p-value	R²	B (95% CI)	p-value	R²	B (95% CI)	p-value	R²
Age	0.10 (0.03, 0.17)	0.004**	0.11	0.12 (0.05, 0.19)	0.002**	0.15	0.10 (0.03, 0.17)	0.008**	0.20	0.11 (0.04, 0.17)	0.003**	0.27	0.09 (0.02, 0.16)	0.008**	0.32
Sex	1.53 (−0.83, 3.90)	0.201		2.08 (−0.30, 4.47)	0.086		2.15 (−0.19, 4.48)	0.070		1.99 (−0.25, 4.24)	0.081		1.91 (−0.27, 4.10)	0.084
BMI	0.10 (−0.16, 0.35)	0.456		0.12 (−0.13, 0.37)	0.340		0.15 (−0.10, 0.39)	0.226		0.06 (−0.19, 0.30)	0.645		0.03 (−0.20, 0.27)	0.779
AHI	−0.02 (−0.05, 0.02)	0.415		−0.02 (−0.06, 0.02)	0.346		−0.02 (−0.06, 0.02)	0.308		0.01 (−0.03, 0.04)	0.809		0.01 (−0.03, 0.05)	0.554
ISI				0.12 (0.00, 0.23)	0.045*		0.06 (−0.07, 0.18)	0.379		0.08 (−0.05, 0.20)	0.227		0.07 (−0.05, 0.19)	0.246
BDI-II							0.10 (0.01, 0.20)	0.035*		0.12 (0.03, 0.21)	0.013*		0.12 (0.03, 0.21)	0.010**
WASO										−0.02 (−0.04, −0.01)	0.007**		−0.02 (−0.03, −0.01)	0.008**
dTIB													1.00 (0.19, 1.82)	0.017*

^* p<0.05;

^** p<0.01.

SMCQ, Subjective Memory Complaints Questionnaire; CI, confidence interval; BMI, body mass index; AHI, Apnea-Hypopnea Index; ISI, Insomnia Severity Index; BDI-II, Beck Depression Inventory-II; WASO, wake after sleep onset; dTIB, desired time in bed.