Unraveling the protective effect of weekend catch-up sleep on overactive bladder risk

Abstract

Background

Weekend catch-up sleep (WCS) has emerged as a new research focus in the field of sleep. Earlier research has shown that sleep and overactive bladder (OAB) are closely related; however, the connection between WCS and OAB has yet to be reported.

Methods

Data from the 2017 to 2018 cycle of the National Health and Nutrition Examination Survey (NHANES) database were utilized in this research. The regression analysis was performed by constructing three models with progressively adjusted covariates to assess the association between WCS and the distribution and severity of OAB. A dose-response relationship between WCS duration and OAB risk was further examined by applying restricted cubic splines (RCS) and threshold effect models. Subgroup and interaction analyses were also performed to assess effect stability.

Results

The study involved 4,195 participants, of whom 997 were diagnosed with OAB. Both the distribution and severity of OAB were negatively correlated with WCS, in accordance to the multivariate regression models. RCS model revealed a U-shaped relationship between WCS duration and the distribution of OAB, with an initial decrease followed by an increase. Subsequent threshold effect analysis suggested that the turning point occurred at 1.5 h. Finally, subgroup analyses were conducted, and although interactions were observed, the results remained robust.

Conclusion

WCS may be associated with a reduction in OAB risk and symptom severity. Restricting WCS duration to within 1.5 h may be linked to a lower prevalence of OAB. The simple and feasible approach of moderate WCS holds promise in alleviating the public health burden associated with OAB.

Introduction

OAB is a symptom complex primarily marked by urinary urgency, frequency, and nocturia, which may or may not be accompanied by urgency urinary incontinence (UUI) [1,2]. According to statistical data, the prevalence of OAB in the United States reached 16% [3]. The symptoms of OAB often cause patients embarrassment, but they have a significant impact on their quality of life, leading to decreased work productivity and an increased risk of depression, among other consequences [4]. Given the large patient population and the profound effects on daily living, OAB represents a major public health concern [5].

The American Academy of Sleep Medicine and the Sleep Research Society jointly recommend that adults should aim for at least 7 h of sleep per night [6]. However, with the accelerating pace of society and increasing pressures from work and daily life, sleep deprivation has become increasingly common. According to the Centers for Disease Control and Prevention (CDC), approximately 35% of adults in the United States experience insufficient sleep [7]. The most recent consensus from the National Sleep Foundation (NSF) suggests that WCS can help mitigate the sleep debt accumulated during the workweek [8]. This has made WCS a new area of research focus. As research progresses, findings indicate that WCS may reduce the risk of depression, lower the prevalence of cardiovascular diseases, and decrease systemic inflammation, among other potential health benefits [9–13].

Previous research has demonstrated a strong correlation between sleep and OAB [14]; however, the connection between WCS and OAB has not yet been explored. This study aims to investigate whether WCS, a simple and easily implementable intervention, is associated with a lower prevalence of OAB, which could potentially reduce the burden on public health.

Methods

Study population

This cross-sectional study utilized data from the NHANES database, a research initiative designed to evaluate the health status of the U.S. population. The data collected through this survey are utilized in epidemiological and health sciences research to inform the design and development of health programs and services, as well as to support the establishment of evidence-based public health policies. The research program was validated by the National Center for Health Statistics (NCHS) Ethics Review Committee, and each participant provided written informed permission(https://www.cdc.gov/nchs/nhanes/about/erb.html). This study analyzed data from the NHANES 2017–2018 cycle. Following the removal of samples that lacked relevant covariates, data on weekend and weekday sleep hours, or the measurement of OAB symptoms, 4,195 participants were ultimately included in this study. The specific details regarding participant selection are outlined in the flowchart Figure 1.

Figure 1.

Flowchart in selecting the studying participants.

Definition of WCS and WCS duration

For this study, participants’ weekdays and weekends sleep duration were acquired from questions in the sleep disorders section of the questionnaire: 1)’Number of hours usually sleep on weekdays or workdays’ and 2)’Number of hours usually sleep on weekends or non-workdays’. In this study, WCS and WCS duration were defined in accordance with Chen et al. [15]. The WCS duration is calculated by deducting the weekdays sleep time from the weekends sleep time. The concept of WCS is characterized by a longer duration of sleep on weekends compared to weekdays, which is determined when the WCS duration exceeds 0 h.

Assessment of OAB

UUI and nocturia are OAB’s essential features. In the NHANES 2017–2018 survey, the following two inquiries were employed to measure UUI symptoms: 1)’During the last 12 months, have you leaked or lost control of even a small amount of urine with an urge or pressure to urinate and you could not get to the toilet fast enough’ and 2)’How frequently does this occur?’ The assessment of nocturia severity was derived from the question’ During the last 30 days, how many times per night did you most typically get up to urinate, from the time you went to bed at night until the time you got up in the morning?’. The Overactive Bladder Symptom Score (OABSS) was subsequently applied to evaluate the degree of overactive bladder symptoms [16]. Patients were regarded to be suffering from the overactive bladder if their OABSS was three or greater [17,18]. More details are provided in Supplementary Figure 1.

Covariates

This paper examined several covariates that may affect the connection between OAB and WCS, drawing on prior research and practical experience. The covariates included gender (Male or Female); age (20–39, 40–59, or 60+); ethnicity (Mexican American, non-Hispanic Black, non-Hispanic White, and other races); educational level (College graduate or above, High school or equivalent, Less than high school, or Some college or AA degree); poverty income ratio (PIR) (<1.3, 1.3–3.5, or >3.5); marital status (Married or Living with partner, Divorced, widowed, or separated, or Never married); smoking status (Never smoker, Former smoker, or Current smoker); drinking status (Non-drinker, Former drinker, or Current drinker); and presence of hypertension, diabetes, and somnipathy. Somnipathy was derived from the NHANES questionnaire item: ‘Have you ever told a doctor or other health professional that you have trouble sleeping?’ Participants who responded ‘Yes’ to this question were classified as having somnipathy, while those who answered ‘No’ were considered not to have somnipathy.

Statistical analyses

This study intends to explore the connection between WCS and OAB. Considering that the NHANES database utilized a complex multistage stratified sampling design, data weighting was applied with the ‘WTMEC2YR’ weight from the 2017–2018 cycle to calculate descriptive statistics. Continuous and categorical variables were analyzed using t tests and chi-square tests, respectively. Three models were subsequently constructed to perform univariate and multivariate regression analyses to explore the correlation between WCS and the risk of OAB: Model 1 (crude model); Model 2 (adjusted for gender, age, ethnicity, educational level, PIR, and marital status); and Model 3 (adjusted for gender, age, ethnicity, educational level, PIR, marital status, smoking, drinking, hypertension, diabetes, and somnipathy). Further, the association between WCS and WCS duration with OAB severity, as measured by OABSS, was also explored. Additionally, a dose-response relationship between WCS duration and OAB risk was examined using restricted cubic splines and threshold effect analysis. Finally, subgroup and interaction analyses were conducted to evaluate the presence of heterogeneity. All statistical analyses in this article were conducted using R version 4.2.2. The threshold for statistical significance was set at p < 0.05.

Results

Baseline characteristics of participants

The study included 4,195 participants in total, with 997 diagnosed with OAB (approximately 23.8% of participants), after applying the specified inclusion criteria. Table 1 divided the participants into two distinct categories according to the existence of OAB and presented descriptive statistics of the baseline characteristics for both groups. OAB patients were more likely to be female (p < 0.001), older adults (p < 0.001), Non-Hispanic Black (p < 0.001), PIR < 1.3 (p < 0.001), be divorced, widowed, or separated (p < 0.001), former drinkers (p < 0.001), have hypertension (p < 0.001), have diabetes (p < 0.001), and suffer from insomnia (p < 0.001). Notably, the WCS duration was significantly shorter in participants with OAB compared to those without OAB (p < 0.001).

Table 1.

Baseline characteristics of the selected participants.

	Overall (N = 4195)	Non-OAB (N = 3198)	OAB (N = 997)	p value
Gender				<0.001
Male	2,050 (49%)	1,641 (51%)	409 (37%)
Female	2,145 (51%)	1,557 (49%)	588 (63%)
Age				<0.001
20–39	1,269 (36%)	1,155 (40%)	114 (17%)
40–59	1,340 (35%)	1,049 (35%)	291 (33%)
60+	1,586 (29%)	994 (25%)	592 (50%)
Ethnicity				<0.001
Mexican American	534 (8.3%)	427 (8.7%)	107 (6.5%)
Non-Hispanic Black	946 (11%)	652 (9.5%)	294 (16%)
Non-Hispanic White	1,572 (65%)	1,180 (65%)	392 (65%)
Others	1,143 (16%)	939 (17%)	204 (13%)
Education				<0.001
College graduate or above	1,035 (31%)	872 (33%)	163 (23%)
High school or equivalent	1,018 (27%)	747 (27%)	271 (29%)
Less than high school	743 (10%)	496 (8.8%)	247 (16%)
Some college or AA degree	1,399 (31%)	1,083 (31%)	316 (32%)
PIR				<0.001
<1.3	1,156 (19%)	808 (18%)	348 (27%)
1.3–3.5	1,740 (36%)	1,313 (36%)	427 (38%)
>3.5	1,299 (45%)	1,077 (47%)	222 (34%)
Marital				<0.001
Married or Living with partner	2,496 (63%)	1,979 (64%)	517 (60%)
Divorced, widowed, or separated	968 (19%)	618 (16%)	350 (29%)
Never married	731 (18%)	601 (20%)	130 (12%)
Smoking				0.074
Never smoker	2,387 (57%)	1,910 (59%)	477 (51%)
Former smoker	1,045 (25%)	716 (24%)	329 (31%)
Current smoker	763 (17%)	572 (17%)	191 (19%)
Drinking				<0.001
Non-drinker	398 (6.7%)	300 (6.3%)	98 (8.6%)
Former drinker	888 (17%)	579 (15%)	309 (27%)
Current drinker	2,909 (76%)	2,319 (79%)	590 (64%)
Hypertension	1,598 (33%)	1,016 (29%)	582 (51%)	<0.001
Diabetes	660 (11%)	384 (9.0%)	276 (23%)	<0.001
Somnipathy	1,226 (32%)	815 (29%)	411 (43%)	<0.001
WCS duration	0.70 ± (1.47)	0.74 ± (1.46)	0.53 ± (1.49)	<0.001

Association of WCS with distribution of OAB

To explore the correlation between WCS and OAB distribution, three regression models were developed with progressively adjusted covariates. In both Model 1 and Model 2, which examined the link between WCS and OAB distribution, a significant negative correlation was observed, as shown in Table 2. In the completely adjusted Model 3, this association remained statistically significant (OR (95% CI) = 0.79 (0.67, 0.93), p = 0.005), suggesting that WCS is associated with a significant reduction in the distribution of OAB.

Table 2.

Regression analyses for associations between WCS and the distribution of OAB.

Variables	Model 1		Model 2		Model 3
	OR(95%CI)	p value	OR(95%CI)	p value	OR(95%CI)	p value
WCS
No	Ref.		Ref.		Ref.
Yes	0.55(0.47, 0.64)	<0.001	0.76(0.65, 0.90)	0.001	0.79(0.67, 0.93)	0.005
WCS duration	0.88(0.83, 0.92)	<0.001	0.94(0.89, 0.99)	0.020	0.95(0.90, 1.00)	0.073

Model 1: Crude model.

Model 2: Adjusted for gender, age, ethnicity, educational level, PIR, and marital status.

Model 3: Adjusted for gender, age, ethnicity, educational level, PIR, marital status, smoking, drinking, hypertension, diabetes, and somnipathy.

In the analysis of WCS duration and OAB distribution, a significant negative correlation was found in both Model 1 and Model 2. However, in the fully adjusted Model 3, while a negative trend persisted, it was no longer statistically significant (OR (95% CI) = 0.95 (0.90, 1.00), p = 0.073). This suggests that WCS duration does not exhibit a significant linear relationship with the distribution of OAB.

Association between WCS and the severity of OAB

Next, we utilized the three models mentioned above to further explore the correlation between WCS and the severity of OAB. The details are presented in Table 3. In the completely adjusted Model 3, WCS was significantly negatively correlated with OAB severity (β (95% CI) = −0.097 (−0.178, −0.015), p = 0.02).

Table 3.

Regression analyses for associations between WCS and the severity of OAB.

Variables	Model 1		Model 2		Model 3
	β(95%CI)	p value	β(95%CI)	p value	β(95%CI)	p value
WCS
No	Ref.		Ref.		Ref.
Yes	−0.376(–0.462, −0.290)	<0.001	−0.117(–0.200,−0.035)	0.005	−0.097(–0.178,−0.015)	0.020
WCS duration	−0.079(–0.106,−0.052)	<0.001	−0.026 (−0.052,−0.001)	0.045	−0.019 (−0.044,0.006)	0.135

Model 1: Crude model.

Model 2: Adjusted for gender, age, ethnicity, educational level, PIR, and marital status.

Model 3: Adjusted for gender, age, ethnicity, educational level, PIR, marital status, smoking, drinking, hypertension, diabetes, and somnipathy.

In Models 1 and 2, which were based on WCS duration and OAB severity, the analyses showed a statistically significant negative correlation between WCS duration and OAB severity. However, in the fully adjusted Model 3, the negative correlation between WCS duration and OAB severity was no longer statistically significant (β (95% CI) = −0.019 (−0.044,0.006), p = 0.135). Despite the lack of statistical significance in Model 3, the effect direction remained consistent with that observed in Models 1 and 2, suggesting that the association may still exist.

Dose–response relationship and threshold effect analysis between WCS duration and OAB distribution

This study employed RCS to assess the dose–response relationship between WCS duration and the distribution of OAB (Figure 2). After adjusting for gender, age, ethnicity, educational level, PIR, marital status, smoking, drinking, hypertension, diabetes, and somnipathy, we found an association between WCS duration and OAB distribution. Specifically, as WCS duration increased, the distribution of OAB initially decreased before rising again.

Figure 2.

Restr icted cubic spl ine pl ot of the associ ation bet ween WCS durat ion and distribut ion of OAB. RCS regression was adjusted for gender, age, ethnicity, educational level, PIR, marital status, smoking, drinking, hypertension, diabetes, and somnipathy. Solid lines indicate ORs, and shadow shape indicate 95% CIs. OR: odds ratio; CI: confidence interval.

Although the non-linear test did not achieve statistical significance (p for non-linear = 0.053), a clear U-shaped trend was observed in the graphical representation. Consequently, we performed a further threshold effect analysis. As shown in Table 4, the threshold effect analysis indicates that the turning point occurs at 1.5 h. The distribution of OAB is seen to decrease by 11.3% with each extra unit in WCS duration when it is less than 1.5 h (OR (95% CI)=0.887(0.812, 0.969), p = 0.007). However, when the WCS duration exceeds 1.5 h, the correlation between the two variables becomes statistically insignificant.

Table 4.

Threshold effect analysis of the association between WCS duration and the distribution of OAB.

OAB	OR(95%)	p value
Fitting by standard linear regression	0.953(0.903,1.005)	0.073
Fitting by two-piecewise linear regression
Inflection point	1.5
<1.5	0.887(0.812,0.969)	0.007
>1.5	1.049(0.942,1.164)	0.370
p for likelihood ratio test	0.046

Gender, age, ethnicity, educational level, PIR, marital status, smoking, drinking, hypertension, diabetes, and somnipathy were adjusted.

Subgroup analysis for the association between WCS and OAB

Finally, this research conducted subgroup analyses and interaction tests to evaluate the stability of the relationship between WCS and both the distribution and severity of OAB across different demographic characteristics and health conditions. The results demonstrated significant interaction influence between WCS and OAB distribution with respect to ethnicity, PIR and drinking status (p for interaction < 0.05) (Figure 3). Additionally, ethnicity also demonstrated a notable interaction effect in the association between WCS and the severity of OAB (p for interaction = 0.018) (Figure 4). The other variables showed insignificant interactions.

Figure 3.

Effect of weekend catch-up sleep on the distribution of OAB in different subgroups. Except the stratification variables themselves, each stratification factor was adjusted for other variables.

Figure 4.

Effect of weekend catch-up sleep on the severity of OAB in different subgroups. Except the stratification variables themselves, each stratification factor was adjusted for other variables.

Discussion

This study employed the NHANES database to explore the association between WCS and the prevalence and severity of OAB in the U.S. The findings demonstrate that WCS might be linked to a decrease in the risk and severity of OAB. This effect remained consistent in subsequent subgroup analyses. Additionally, through RCS and threshold effect analyses, we identified that limiting the duration of WCS to no more than 1.5 h could be associated with a reduced prevalence of OAB.

The relationship between sleep and OAB is closely interconnected. An earlier cross-sectional study indicated that OAB is more common in individuals who had poor sleep habits. After adjusting for all covariates, poor sleep patterns were associated with a 38% increased prevalence of OAB compared to healthy sleep patterns. Moreover, unhealthy sleep patterns may be associated with more severe symptoms of OAB [14]. Patients with Obstructive Sleep Apnea Syndrome (OSAS) frequently have comorbid sleep disorders [19]. A single-center prospective study found that the overall prevalence of OAB in male OSAS patients was 39%, a figure significantly higher than the OAB prevalence in European males. Moderate to severe OSAS is strongly associated with male OAB, and these patients also exhibit notably higher OABSS [20].

Although earlier research has illuminated the link between sleep and OAB, the connection between WCS and OAB has not been investigated. This study suggests that WCS may be associated with a reduction in the risk and severity of OAB symptoms. We hypothesize that the relationship between the two may involve several mechanisms. A prior prospective study suggested that depression could be a risk element for OAB [21]. Serotonin depletion is considered one of the pathophysiological mechanisms of depression. Previous animal studies have shown that a reduction in serotonin levels in the central nervous system can lead to overactivity of the detrusor muscle [22]. Furthermore, research has indicated that moderate WCS can reduce both the prevalence and severity of depression symptoms. Additionally, systemic inflammation is closely associated with an increased risk of OAB [23]. Research has demonstrated that individuals with OAB have elevated levels of nerve growth factor (NGF) in their urine [24]. NGF may exacerbate neurogenic inflammation by promoting the activation and degranulation of mast cells [25]. Dorsal root ganglia neurons may enlarge as a result of repeated inflammatory stimulation, lowering the activation threshold of bladder afferent nerves [26]. Previous research also suggests that moderate WCS may be associated with lower levels of inflammation [11]. A prospective interventional clinical study has shown that catch-up sleep can improve insulin sensitivity [27]. Enhanced insulin sensitivity, in turn, activates smooth muscle insulin receptors, which can reverse urinary dysfunction, improve nerve control, and enhance bladder contraction, ultimately leading to improvements in OAB [28].

In the subsequent analysis of nonlinear regression and threshold effects in this study, it was found that the prevalence of OAB decreases with each additional unit increase in WCS duration when the duration is below 1.5 h. However, when the WCS duration exceeds 1.5 h, the relationship between the two variables becomes statistically insignificant. This is an intriguing phenomenon: excessive catch-up sleep appears to have an adverse effect. This result is consistent with previous studies, which concluded that moderate weekend catch-up sleep may be associated with improved glucose regulation and a reduced prevalence of depression, while excessive WCS duration do not provide the same benefit [9,29]. This paradox may be related to several factors. Both cohort studies from Cleveland and Taiwan had demonstrated that extended sleep durations actually lead to an increase in inflammatory markers, and elevated levels of inflammation are associated with a higher prevalence of OAB [30,31]. Excessive WCS duration might also disrupt circadian rhythms, which can severely impact health, increasing the risks of cardiovascular diseases, diabetes, cancer, and more [32,33]. A prior retrospective study from NHANES also demonstrated a favorable connection between circadian rhythm disorders and the prevalence of OAB [34].

This study also conducted further subgroup analyses and explored interaction effects. The results indicated a significant interaction between WCS and the incidence of OAB with respect to ethnicity, PIR, and drinking status. Additionally, ethnicity was found to have a significant interaction effect on the relationship between WCS and OAB severity. Although the p values for interaction were less than 0.05, these findings may have limited clinical significance, as the observed effects were consistent with the expected directions.

Although the NHANES database selected for this study is authoritative and of high data quality, some limitations are inevitably present. First, as this study is cross-sectional, causal inferences cannot be drawn. Second, although this study controlled for many common confounding factors, it is still impossible to account for all potential variables. Due to the inherent limitations of the NHANES database, some unmeasured or residual confounders, such as metabolic syndrome and ischemic heart disease, may still remain. Third, due to the limitations of the NHANES database, it was not possible to derive the indicator of urinary urgency. However, the method used in this study to define OAB has been widely accepted in previous research. Finally, sleep duration and frequency of nocturia were self-reported by participants, which may introduce recall and reporting biases. However, the NHANES data collection team is well-trained and utilizes computer-assisted personal interviews to ensure participant confidentiality, thereby maximizing accuracy.

The prevalence of OAB in the United States has been climbing annually, and it is anticipated that this trend will continue as the population ages [35,36]. Professor Penny Reeves and colleagues developed a health economic model to predict the prevalence of OAB in the European region and the economic burden it places on healthcare systems. Even when confined solely to direct medical costs, the economic burden of OAB on healthcare systems remains substantial. Reported annual per-patient expenditures range between €269 and €706, reflecting only the financial impact of diagnosing, treating, and managing the long-term consequences of OAB. The study further predicted that the burden of OAB would lead to an increased strain on government healthcare budgets in the future [37]. A systematic literature review of U.S. studies found that few studies have examined the indirect costs associated with OAB, while the annual cost for OAB patients is 1.4 to 2 times higher compared to age- and sex-matched individuals without OAB [38]. Given the significant economic and public health burden OAB imposes on society, it is imperative to take preventive and therapeutic measures. This study identifies a strong correlation between WCS and OAB, and it is hoped that further prospective studies will explore this relationship in greater depth to help alleviate the societal burden caused by OAB.

Conclusion

This study provides evidence suggesting that moderate WCS duration is associated with a lower prevalence of OAB. Given the growing burden of OAB on public health, characterized by its impact on quality of life and healthcare systems, incorporating strategies that adequate weekend catch-up sleep may offer a cost-effective approach to reduce its prevalence. Future research should concentrate on the inherent physiological mechanisms connecting WCS and OAB, aiming to refine sleep-based interventions and assess their long-term efficacy in diverse demographic groups.

Ethical statement

This study was performed in accordance with the 1964 declaration of HELSINKI and later amendments. The data used in this study is from the NHANES database, in which all raw data has been ethically reviewed by the National Center for Health Statistics Ethics Review Board. The data is anonymized and does not contain any personal identifying information, so the Ethics Committee of the First Hospital of Jilin University has exempted this study from ethical review.

Disclosure statement

The authors report no conflict of interest.

Data availability statement

Publicly available datasets were analyzed in this study. These data can be found at: www.cdc.gov/nchs/nhanes/.