Figure 1 is A flowchart dissecting the inclusion and exclusion of participants. A total of 16,978 participants were involved in this study representing about 174 million USA adult population (mean [SE] age, 46.4 [0.3] years), of which 3537 participants were considered OAB with UUI representing about 31 million USA population (mean [SE] age, 56.1 [0.4] years). The characteristics of the participants according to OAB (urgency incontinence-based) status are presented in Table 1. There were significant differences between the groups with and without OAB in terms of age, sex, marital status, household income, education, medical comorbidities (such as coronary heart disease, hypertension and diabetes), alcohol use status, BMI, and moderate recreational activities. Compared to participants without OAB, those with OAB had a higher likelihood of being middle-aged or older (50–80), female, married, had a higher BMI, lower income, low education, being inactive in moderate recreational activity, and heavy smoking, and less likely to be alcohol lovers. In particular, sleep factors (sleep pattern grouping, sleep duration grouping, sleep trouble, sleep disorders) were significantly associated with OAB. With the aggravation of OAB, more patients develop unhealthy sleep patterns, sleep trouble and sleep disorders and are prone to abnormal sleep duration. In addition, the prevalence of medical comorbidities such as cardiovascular disease, hypertension and diabetes mellitus was also increasing with higher OABSS (Table 1).

 A flowchart depicting the inclusion and exclusion of participants

The baseline characteristics of the study population with different sleep patterns are listed in Table 2. Compared with participants with healthy sleep patterns, participants with unhealthy sleep patterns appeared to be middle-aged and older (40–69), more frequently female, with higher BMI, lower education and income, heavy smokers and alcohol users, insufficient moderate recreational activities, long sitting time, and higher rates of cardiovascular disease, hypertension and diabetes mellitus. We also found that patients with unhealthy sleep patterns had a higher incidence of OAB, higher OABSS, and nocturia scores than those with healthy sleep patterns.

The association between sleep pattern grouping and OAB is shown in Table 3. Our original model (adjusted for no variable) indicated a significant association between sleep pattern grouping and OAB status. This relationship remained significant in the other two models, in which the incidence of OAB increased with worsening sleep patterns. In model 1, compared with healthy patients, patients with unhealthy sleep patterns were more likely to develop OAB, and the probability was increased by 29% and 78% for intermediate and poor sleep patterns, respectively. Intermediate sleep patterns and bad sleep patterns were significantly increased by 31% and 57%, respectively, after adjustment for sex, age, race, marital status, annual household income, and education (model 2). In model 3 (fully adjusted model), after adjusting for all the variables, the intermediate and poor sleep patterns obviously increased by 26% and 38%, respectively. In summary, the incidence of OAB can be higher with poorer sleep quality.

Table 4 describes the relationship between sleep pattern grouping and the severity of OAB. In model 1, where we did not adjust for any covariates, patients with OAB had sleep problems more frequently than those without OAB, with mild, moderate and high OAB being 1.30, 1.59 and 1.76 times more likely than patients without OAB, respectively. After adjustment for age, sex, race, marital status, annual household income, and education (model 2), patients with all three OAB levels had higher odds of sleep problems than those without OAB, increasing by 26%, 60%, and 82%, respectively. After additional adjustments for cardiovascular disease, hypertension, diabetes, smoking, alcohol consumption, recreational activity, sitting time and BMI (model 3), mild (OR = 1.21, 95% CI [1.03, 1.42]), moderate (OR = 1.45, 95% CI [1.27, 1.66]) and high (OR = 1.57, 95% CI [1.18, 2.09]) OAB were significantly associated with sleep pattern grouping.

Besides, the interaction between sleep pattern group and OAB was not statistically significant (Table S1 and S2). In summary, the association between OAB and sleep pattern grouping is positive significantly.