Abstract
Introduction:
Urinary incontinence (UI) is common among women Veterans in the United States and may be influenced by sleep health and psychological stress. Understanding relationships between aspects of sleep health and UI, and variation by stress level, may inform novel interventions.
Objective:
To examine associations between aspects of sleep health (sleep quality, sleep efficiency, daily disturbances) and UI severity, and explore differences by perceived stress level.
Methods:
We conducted an exploratory cross-sectional analysis of baseline data (04/2020) from women Veterans in the United States in a clinical trial of remote UI treatments. UI severity was measured with the International Consultation on Incontinence-UI Short Form. Sleep health was measured with the Pittsburgh Sleep Quality Index 3-factor model to assess sleep quality, sleep efficiency, and daily disturbances. Stress was assessed with the Perceived Stress Scale-10. We used multivariable linear regression to examine associations between each aspect of sleep health and UI severity. We conducted stratified analyses by stress level (low, moderate, high).
Results:
Overall, 246 women Veterans were included (mean age=53, SD=11.3). Most (211/246, 85.8%) were poor sleepers, with moderate UI (131/246, 53.3%) and moderately impaired sleep health. In adjusted models, worse sleep quality (β=0.40, 95% CI [0.04, 0.76], p=.03), reduced sleep efficiency (β=0.57, 95% CI [0.15, 0.98], p=.01), and greater daily disturbances (β=0.98, 95% CI [0.43, 1.54], p<.001) were associated with worse UI. Compared to women with low perceived stress, those with high stress had worse sleep health and UI (all p<.001).
Conclusion:
Sleep health may be a modifiable factor for improving UI-related outcomes, with stress as a potentially relevant contextual factor.
Keywords: perceived stress, sleep health, urinary incontinence, women Veterans
Brief Summary
Worse sleep health was associated with greater urinary incontinence severity. Women with high stress had worse sleep health and urinary incontinence.
Introduction
Approximately 20% of women Veterans in the United States report urinary incontinence (UI), although the actual prevalence is likely higher due to delayed care-seeking or underreporting in clinical settings [1]. Types of UI include stress UI – leakage with physical pressure on the bladder (e.g., with coughing or exercise); urgency UI – leakage with a sudden urge to urinate; or mixed – a combination of both [2]. Regardless of type, UI is associated with reduced quality of life and impaired functioning [3]. Sleep health may be an important determinant of these consequences [3]. Sleep health is defined as a multidimensional pattern of sleep and wakefulness that influences physical and psychosocial well-being [4]. While UI may disrupt sleep health through nocturia and frequent awakenings, sleep health may also influence UI through several physiological pathways. Poor sleep health can contribute to persistent autonomic and neuroendocrine activation, which may increase bladder sensitivity, urgency, and leakage [3]. Additionally, poor sleep health can alter circadian regulation of renal function and urine production, potentially contributing to nocturia and worsening UI [5].
Perceived stress (i.e., the extent to which individuals feel overwhelmed or unable to manage life demands [6]) is a major factor related to sleep health, particularly among those with high sleep reactivity [7]. Sleep reactivity refers to the propensity for sleep to become disrupted in response to perceived stress or environmental changes [7] High sleep reactivity is common among individuals with anxiety, post-traumatic stress disorder (PTSD), and trauma exposure, which are prevalent conditions among women Veterans in the United States [8]. Consistent with this, evidence supports poor sleep health among women Veterans in the United States, with insomnia, short sleep duration, and fragmented sleep frequently occurring [9]. Perceived stress may be heightened in the context of UI due to embarrassment, social isolation, and reduced self-esteem [10]. Additionally, relationships between perceived stress and UI may involve similar autonomic and neuroendocrine activation implicated in the connection between sleep health and UI [11, 12]. Thus, perceived stress may represent an important co-occurring contextual factor related to sleep health and UI.
Prior studies examining associations between sleep health and UI typically rely on composite measures of overall sleep quality or broad cutoffs that classify individuals as poor or good sleepers [3,13]. While this approach examines general linkages between sleep health and UI, it does not identify which specific aspects of sleep health may influence UI, limiting the ability to develop targeted interventions for subgroups who may benefit most. Additionally, the extent to which associations between sleep health and UI differ across levels of perceived stress remains poorly understood, particularly among women Veterans in the United States who experience a high burden of poor sleep health, psychological stress, and UI. Therefore, we aimed to examine associations between aspects of sleep health (i.e., sleep quality, sleep efficiency, and daily disturbances) and UI severity among women Veterans in the United States, and explore differences by perceived stress level.
Materials and Methods
Study Design and Sample
We conducted an exploratory cross-sectional analysis of baseline data from women Veterans in the United States who enrolled and completed baseline assessments for a clinical trial conducted to evaluate the effectiveness of two remote behavioral treatment modalities for UI in three southeastern United States Veterans Health Administration (VHA) systems in April 2020 [14]. Participants were nonpregnant women Veterans aged ≥18 years who had self-reported UI for at least three months and access to email. Women were excluded if they started new UI treatments in the previous three months, had planned UI treatments during the study period, unstable health conditions that could influence UI (e.g., uncontrolled diabetes), unstable housing, neurological conditions that influence UI, genitourinary cancer, or were <12 weeks postpartum. The study was conducted in accordance with all regulatory requirements and ethical principles for human subjects’ research, including the Declaration of Helsinki, and was approved by the Institutional Review Board at each VHA site. All participants signed informed consent documents prior to randomization [14].
Independent Variables: Sleep Health - Sleep Quality, Sleep Efficiency, and Daily Disturbances
Sleep health was assessed using the Pittsburgh Sleep Quality Index (PSQI) [15]. This is a widely used, validated measure that contains 18 items to evaluate sleep health in the past month in seven domains, including subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction [15]. Domain scores range from 0 to 3, with 0 indicating no disturbance and 3 indicating severe disturbance [15]. The total score ranges from 0 to 21, with higher scores indicating worse sleep quality [15]. Total scores >5 have been shown to distinguish individuals as poor or good sleepers (sensitivity=89.6%, specificity=86.5%) [15]. The PSQI demonstrates good reliability (α≈.80) and validity across diverse adult populations [15].
We analyzed PSQI scores based on the empirically derived and psychometrically validated 3-factor scoring model that maps PSQI domains onto distinct, clinically meaningful aspects of sleep health: sleep quality, sleep efficiency, and daily disturbances [16]. Given that individual PSQI domain scores have narrow ranges (i.e., 0–3) limiting variability and statistical power if analyzed independently, the 3-factor model aggregates related domains into broader constructs with wider score ranges, improving the ability to detect meaningful associations while reducing the number of comparisons. Importantly, individual PSQI domains are not excluded from the analysis; rather, they are incorporated within the sleep quality, sleep efficiency, and daily disturbances factors, preserving their contribution within a more robust analytic framework. Sleep quality reflects perceptions of sleep experience including the restorative quality of sleep and medication use, and is derived from the sum of PSQI domain scores for subjective sleep quality, sleep latency, and use of sleep medications (score range=0-9). Sleep efficiency reflects the ability to obtain sufficient continuous sleep relative to time spent in bed, and is derived from the sum of PSQI domain scores for sleep duration and habitual sleep efficiency (score range=0-6). Daily disturbances reflect functional impairments related to sleep, including nighttime sleep disruptions and daytime consequences such as fatigue or difficulty maintaining alertness, and is derived from PSQI domain scores for sleep disturbances and daytime dysfunction (score range=0-6) [16]. More detailed operational definitions and measurement are provided in Supplementary Table S1. Sleep quality, sleep efficiency, and daily disturbances were modeled in their original scaling as defined by the 3-factor scoring model [16] to preserve clinical interpretability, such that effect estimates reflect changes in UI severity associated with one-unit increases within each sleep health scale.
Outcome: UI severity
UI severity was assessed using the International Consultation on Incontinence Questionnaire-UI Short Form (ICIQ-UI SF) [17]. The ICIQ-UI SF is a validated scale commonly used in UI research to assess UI severity based on responses to items that assess UI frequency, amount leaked, and interference with everyday life. Scores range from 0-21 with higher scores indicating more severe UI [17]. The ICIQ-UI SF has excellent reliability (α=.95) and is sensitive to change in clinical trials of behavioral UI treatments [17]. For our primary analyses, UI severity was modeled as a continuous variable. For descriptive analyses, UI severity was categorized as mild (scores 1-5), moderate (scores 6-12), or severe (scores 13-21) [17].
Stratification Variable: Perceived Stress
Perceived stress was conceptualized as a co-occurring contextual factor that may be related to sleep health and UI severity. Thus, perceived stress was used as a stratification variable to explore whether associations between sleep health and UI severity differed across levels of perceived stress. Perceived stress was measured with the 10-item Perceived Stress Scale (PSS-10), which is a validated measure to quantify the degree to which respondents find their lives uncontrollable or overwhelming [6]. This scale has good reliability (α=.84-.86) and validity in predicting health outcomes among community-dwelling adults [6]. Scores range from 0 to 40, with higher scores indicating higher levels of stress [6]. For our primary analyses, perceived stress was categorized as low (scores ≤13), moderate (scores 14–25), and high (scores 26–40).(6) For descriptive analyses, we also calculated a continuous measure of perceived stress.
Covariates
We selected sociodemographic and clinical covariates based on literature [18] and domain expertise in UI and United States women Veterans’ healthcare. We included age, race, ethnicity, education level, body mass index, menopausal status, vaginal parity, hysterectomy status, diabetes, medication for UI, and medication for sleep as covariates in our primary models. In sensitivity analyses, we included anxiety, PTSD, and military sexual trauma as covariates [10]. Anxiety, PTSD, and military sexual trauma were included in sensitivity analyses rather than the primary models because of their potential overlap with perceived stress and sleep health. Including these variables in the primary models may have resulted in overadjustment by accounting for factors that could be closely related to relationships between sleep health and UI severity. Therefore, sensitivity analyses were conducted to evaluate the robustness of findings after accounting for these factors.
Statistical Analysis
Descriptive statistics were used to summarize the overall study sample and by perceived stress level (low, moderate, high). We evaluated normality for all continuous measures and reported median and interquartile ranges for variables with skewed distributions. With an alpha level set at 0.05, one-way analysis of variance (ANOVA) or Kruskal Wallis tests were used to compare continuous variables, and Chi-square or Fisher’s exact tests were used to compare categorical variables, as appropriate. We used multivariable linear regression models to examine associations between each aspect of sleep health (i.e., sleep quality, sleep efficiency, and daily disturbances) and UI severity adjusting for age, race, ethnicity, education level, body mass index, menopausal status, vaginal parity, hysterectomy status, diabetes, medication for UI, and medication for sleep. Next, we conducted stratified analyses by perceived stress level to explore potential differences in associations between each aspect of sleep health and UI severity across stress levels. We performed sensitivity analyses using a complete case approach and additionally adjusting for anxiety, PTSD, and military sexual trauma. Given that this was an exploratory analysis, we did not adjust for multiple comparisons. We did not conduct additional stratified analyses because subgroup sizes for key variables of interest (e.g., sleep quality category, menopausal status, age group) were small or unbalanced, limiting statistical power and the interpretability of subgroup comparisons. All analyses were performed using R version 4.2.2.
Missing Data
We examined the percentage and pattern of missing data for each variable. One participant had missing data on three of the 18 PSQI items, representing item-level missingness among an otherwise complete case. These missing items were imputed using the participant’s mean of completed PSQI items [19]. There were no missing data for perceived stress or UI severity. Overall, 32/246 (13.0%) of women had missing data on vaginal parity. We examined patterns of missingness by modeling the probability of missing parity data as a function of observed participant characteristics and found no significant associations. Thus, women with missing data for this covariate were retained in the final analytic sample by including a missing indicator category. In sensitivity analyses, we conducted a complete case analysis excluding participants with missing parity data.
Results
Sample Characteristics
In total, 246 United States women Veterans (mean age=53 years, SD=11.3) were included (Figure 1, Table 1). Most participants (211/246, 85.8%) were classified as poor sleepers, while 35/246 (14.2%) were classified as good sleepers. Their mean PSQI total score was 11.2 (SD=4.5, range 1–21), and scores for sleep quality (mean=5.0, SD=2.3, range=0-9), sleep efficiency (mean=2.9, SD=1.9, range=0-6), and daily disturbances (mean=3.4, SD=1.4, range=0-6) reflected moderately impaired sleep health. Most participants (129/246, 52.4%) had moderate perceived stress (mean PSS-10 score=18.0, SD=8.6, range= 0-37) and moderate UI (131/246, 53.3%; mean ICIQ-UI SF score=11.3, SD=4.3, range=3-21).
Figure 1. Sample Selection Flowchart.
Note. The parent randomized controlled trial was conducted from April 2020 to September 2023 to evaluate the effectiveness of two evidence-based remote urinary incontinence behavioral treatment modalities among women Veterans in three southeastern United States Veterans Health Administration systems.
Inclusion criteria: nonpregnant women Veterans in the United States aged 18 years or older who had self-reported urinary incontinence for at least three months and access to email via computer, tablet, or mobile phone. Exclusion criteria: started new UI treatments in the prior three months, had planned urinary incontinence treatments for the duration of the study (24 weeks), unstable health conditions that could influence UI (e.g., uncontrolled diabetes), unstable housing, neurological conditions influencing UI, genitourinary cancer, or were less than 12 weeks postpartum.
For the current study, we included women who completed baseline assessments and used data collected at the baseline timepoint.
Abbreviations: RCT, randomized controlled trial
Table 1.
Sociodemographic and Clinical Characteristics of the Study Sample
| Characteristic n (%) |
Total sample N=246 |
Low perceived stress (n=78) |
Moderate perceived stress (n=129) |
High perceived stress (n=39) |
p-value* |
|---|---|---|---|---|---|
| Age; mean (SD), range | 53 (11.3), 23-83 | 58 (10.9), 24-83 | 52 (10.7), 28-74 | 48 (10.2), 23-65 | <.001 |
| Age group | .01 | ||||
| <65 years | 207 (84.1) | 58 (74.4) | 113 (87.6) | 36 (92.3) | |
| ≥65 years | 39 (15.9) | 20 (25.6) | 16 (12.4) | 3 (7.7) | |
| Race | .34 | ||||
| Black/African American | 142 (57.7) | 41 (52.6) | 76 (58.9) | 25 (64.1) | |
| White | 88 (35.8) | 34 (43.6) | 44 (34.1) | 10 (25.6) | |
| More than one | 6 (2.4) | 1 (1.3) | 3 (2.3) | 2 (5.1) | |
| Othera | 2 (0.8) | 1 (1.3) | 5 (3.9) | 1 (2.6) | |
| Unknown/not reported | 3 (1.2) | 1 (1.3) | 1 (0.8) | 1 (2.6) | |
| Ethnicity | .47 | ||||
| Hispanic | 16 (6.5) | 4 (5.1) | 7 (5.4) | 5 (12.8) | |
| Non-Hispanic | 227 (92.3) | 73 (93.6) | 120 (93.0) | 34 (87.2) | |
| Unknown/not reported | 3 (1.2) | 1 (1.3) | 2 (1.6) | 0 (0.0) | |
| Marital status | .12 | ||||
| Married | 98 (39.8) | 32 (41.0) | 51 (39.5) | 15 (38.5) | |
| Divorced | 79 (32.1) | 27 (34.6) | 44 (34.1) | 8 (20.5) | |
| Widowed | 14 (5.7) | 6 (7.7) | 6 (4.7) | 2 (5.1) | |
| Separated | 11 (4.5) | 2 (2.6) | 9 (7.0) | 13 (33.3) | |
| Never Married | 44 (17.9) | 11 (14.1) | 19 (14.7) | 1 (2.6) | |
| Education level | .90 | ||||
| High school | 11 (4.5) | 2 (2.6) | 9 (7.0) | 0 (0.0) | |
| Some college | 45 (18.3) | 16 (20.5) | 24 (18.6) | 5 (12.8) | |
| Associate degree | 55 (22.4) | 15 (19.2) | 29 (22.5) | 11 (28.2) | |
| Bachelor’s degree | 77 (31.3) | 23 (29.5) | 40 (31.0) | 14 (35.9) | |
| Graduate degree | 58 (23.6) | 22 (28.2) | 27 (20.9) | 9 (23.1) | |
| Branch of service | .60 | ||||
| Army | 135 (54.9) | 37 (47.4) | 74 (57.4) | 24 (61.5) | |
| National Guard | 7 (2.8) | 1 (1.3) | 5 (3.9) | 1 (2.6) | |
| Navy | 33 (13.4) | 13 (16.7) | 13 (10.1) | 7 (17.9) | |
| Air Force | 40 (16.3) | 18 (23.1) | 18 (14.0) | 4 (10.3) | |
| Marine Corps | 8 (3.3) | 2 (2.6) | 5 (3.9) | 7 (17.9) | |
| Coast Guard | 3 (1.2) | 2 (2.6) | 1 (0.8) | 0 (0.0) | |
| Reserve | 19 (7.7) | 5 (6.4) | 12 (9.3) | 2 (5.1) | |
| NOAA | 1 (0.4) | 0 (0.0) | 1 (0.8) | 0 (0.0) | |
| Years of service; median (IQR) | 6 (9.5) | 8 (16) | 5.5 (8.25) | 4 (5) | .02 |
| Body mass index; mean (SD), range | 31.9 (6.6), 16.6-56.8 | 32.3 (7.2), 19.7-56.8 | 31.8 (6.1), 17.6-51.5 | 31.8 (7.0), 16.6-45.9 | .86 |
| Vaginal parity | .60 | ||||
| 0 | 49 (19.9) | 12 (15.4) | 26 (20.2) | 11 (28.2) | |
| 1 | 59 (24.0) | 13 (16.7) | 37 (28.7) | 9 (23.1) | |
| 2 | 61 (24.8) | 22 (28.2) | 29 (22.5) | 10 (25.6) | |
| 3 | 30 (12.2) | 13 (16.7) | 16 (12.4) | 1 (2.6) | |
| 4 or more | 15 (6.1) | 5 (6.4) | 8 (6.2) | 2 (5.1) | |
| Missing | 32 (13.0) | 13 (16.7) | 13 (10.1) | 6 (15.4) | |
| Hysterectomy | 100 (40.7) | 31 (39.7) | 54 (41.9) | 15 (38.5) | .91 |
| Post-menopause | 104 (42.3) | 45 (57.7) | 49 (38.0) | 10 (25.6) | .002 |
| Medication for UIb | 25 (10.2) | 5 (6.4) | 11 (8.5) | 4 (10.3) | .73 |
| Medication for sleepc | 34 (13.8) | 4 (5.1) | 22 (17.1) | 8 (20.5) | .02 |
| Diabetes | 31 (12.6) | 15 (19.2) | 13 (10.1) | 3 (7.7) | .13 |
| Anxiety | 169 (68.7) | 26 (33.3) | 104 (80.6) | 39 (100.0) | <.001 |
| PTSD | 122 (49.6) | 22 (28.2) | 75 (58.1) | 25 (64.1) | <.001 |
| Military sexual traumad | 143 (58.1) | 35 (44.9) | 78 (60.5) | 30 (76.9) | .003 |
| UI severity scoree; mean, (SD), range | 11.3 (4.3), 3-21 | 9.9 (3.9), 3-18 | 11.4 (4.2), 3-21 | 13.5 (4.3), 6-21 | <.001 |
| UI severity category | .01 | ||||
| Mild | 20 (8.1) | 10 (12.8) | 10 (7.8) | 0 (0.0) | |
| Moderate | 131 (53.3) | 46 (59.0) | 68 (52.7) | 17 (43.6) | |
| Severe | 95 (38.6) | 22 (28.2) | 51 (39.5) | 22 (56.4) | |
| UI type | .43 | ||||
| Stress UI | 10 (4.1) | 4 (5.1) | 4 (3.1) | 2 (5.1) | |
| Urgency UI | 10 (4.1) | 4 (5.1) | 6 (4.7) | 0 (0.0) | |
| Mixed UI | 193 (78.5) | 56 (71.8) | 106 (82.2) | 31 (79.5) | |
| Other | 33 (13.4) | 14 (17.9) | 13 (10.1) | 6 (15.4) | |
| Perceived stress severity scoref; mean (SD), range | 18.0 (8.6), 0-37 | 7.9 (3.8), 0-13 | 20.2 (3.5), 14-26 | 31.2 (2.8), 27-37 | <.001 |
| Perceived stress severity category | – | ||||
| Low | 78 (31.7) | 78 (100.0) | 0 (0.0) | 0 (0.0) | |
| Moderate | 129 (52.4) | 0 (0.0) | 129 (100.0) | 0 (0.0) | |
| High | 39 (15.9) | 0 (0.0) | 0 (0.0) | 39 (100.0) | |
| Sleep healthg; mean (SD), range | |||||
| Sleep quality | 5.0 (2.3), 0-9 | 3.5 (2.1), 0-8 | 5.3 (2.1), 1-9 | 6.6 (1.7), 3-9 | <.001 |
| Sleep efficiency | 2.9 (1.9), 0-6 | 2.2 (1.7), 0-6 | 3.1 (1.9), 0-6 | 3.6 (2.0), 0-6 | <.001 |
| Daily disturbances | 3.4 (1.4), 0-6 | 2.3 (1.2), 0-5 | 3.6 (1.1), 1-6 | 4.8 (1.0), 3-6 | <.001 |
| Total score | 11.2 (4.5), 1-21 | 8.0 (3.8), 1-17 | 12.0 (3.8), 4-21 | 15.1 (3.5), 8-21 | <.001 |
| Sleep quality category | <.001 | ||||
| Poor sleeper | 211 (85.8) | 51 (65.4) | 121 (93.8) | 39 (100) | |
| Good sleeper | 35 (14.2) | 27 (34.6) | 8 (6.2) | 0 |
Note.
Other race category included American Indian, Alaskan Native, Asian, and Native Hawaiian or other Pacific Islander participants.
Medication for urinary incontinence included Mirabegron or anticholinergics.
Medication for sleep included sedatives, flurazepam, doxepin, or hydroxyzine.
Military sexual trauma was assessed using two validated United States VHA items to ask participants if while they were in the military they ever received unwanted, threatening, or repeated sexual attention; or had sexual contact against their will or when they were unable to say no.
Scores based on responses to the International Consultation on Incontinence Questionnaire-Urinary Incontinence Short Form; score range=0-21, with higher scores indicating greater UI severity. Scores were categorized as mild (1-5), moderate (6-12), or severe (13-21).
Scores based on responses to the Perceived Stress Scale-10; score range=0-40, with higher scores indicating greater stress. Scores were categorized as low (1-13), moderate (14-25), or high (27-40).
Scores based on responses to the Pittsburgh Sleep Quality Index; total score range=0-21, with higher scores indicating worse sleep health. Scores were calculated in the seven scale domains to reflect subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, use of sleep medication, and daytime dysfunction. Domain scores range from 0-3 and were summed to yield scores for sleep quality (sleep quality + sleep latency + use of sleep medications; score range=0-9), sleep efficiency (sleep duration + habitual sleep efficiency; score range=0-6), and daily disturbances (sleep disturbances + daytime dysfunction; score range=0-6) based on the validated 3-factor scoring model. Total Pittsburgh Sleep Quality Index scores were categorized based on established cutoffs to characterize participants as poor sleepers (total score >5) and good sleepers (total score ≤5).
One-way analysis of variance or Kruskal Wallis tests were used to compare continuous variables; Chi-square or Fisher’s exact tests were used to compare categorical variables, as appropriate.
Abbreviations: SD, standard deviation; IQR, interquartile range; UI, urinary incontinence; PTSD, post-traumatic stress disorder
Participants differed significantly across perceived stress categories. Compared to participants with low perceived stress, those with high perceived stress reported worse sleep health (mean PSQI total score=8.0 [SD=3.9], range=1-17 versus mean PSQI total score=15.1 [SD=3.5], range=8-21, p<.001) and more impairments in sleep quality (mean=3.5 [SD=2.1], range=0-8 versus mean=6.6 [SD=1.7] range=3-9, p<.001), sleep efficiency (mean= 2.2 [SD=1.7], range=0-6 versus mean=3.6, [SD=2.0], range=0-6, p<.001), and daily disturbances (mean=2.3 [SD=1.2], range=0-5 versus mean=4.8, SD=1.0, range=3-6, p<.001). Additionally, compared to participants with low perceived stress, those with high perceived stress had more severe UI (mean ICIQ-UI SF score=9.9, SD=3.9, range=3-18 versus mean ICIQ-UI SF score=13.5, SD=4.3, range=6-21, p<.001).
Associations Between Sleep Health (Sleep Quality, Sleep Efficiency, Daily Disturbances) and UI Severity
In the unadjusted models, worse sleep quality, (β=0.38, 95% CI [0.15, 0.61], p=.001), reduced sleep efficiency (β=0.75, 95% CI [0.48, 1.01], p<.001), and greater daily disturbances (β=1.09, 95% CI [0.73, 1.4], p<.001) were independently associated with greater UI severity. All associations remained significant in the adjusted models, with the largest magnitude of association observed for daily disturbances in relation to UI severity (Table 2).
Table 2.
Associations Between Aspects of Sleep Health and Urinary Incontinence Severity Among Women Veterans in the United States (N=246)
| Unadjusted models | Adjusted models | |||
|---|---|---|---|---|
| PSQI domaina | β (95% CI) | p-value | β (95% CI) | p-value |
| Worse sleep quality | 0.38 (0.15, 0.61) | .001 | 0.40 (0.04, 0.76) | .03 |
| Reduced sleep efficiency | 0.75 (0.48, 1.01) | <.001 | 0.57 (0.15, 0.98) | .01 |
| Greater daily disturbances | 1.09 (0.73, 1.44) | <.001 | 0.98 (0.43, 1.54) | <.001 |
Note.
Scores based on responses to the validated 3-factor Pittsburgh Sleep Quality Index scoring model to reflect sleep quality (score range=0-9), sleep efficiency (score range=0-6), and daily disturbances (score range=0-6), with higher scores reflecting greater sleep health impairments.
Abbreviations: PSQI, Pittsburgh Sleep Quality Index; CI, confidence interval
Adjusted models included age, race, ethnicity, education level, body mass index, menopausal status, vaginal parity, hysterectomy status, diabetes, medication for UI, and medication for sleep as covariates.
Multicollinearity was evaluated with variance inflation factors; for all variables in all models, variance inflation factors were <5 indicating that multicollinearity was not present.
p-values <.05 were considered statistically significant. Significant p-values are displayed in bold.
Stratified Analyses by Perceived Stress Level
In the adjusted models, among women with low perceived stress, greater daily disturbances were associated with worse UI severity (β=1.25, 95% CI [0.25, 2.24], p=.02). No significant associations were observed between daily disturbances and UI severity among women with moderate or high perceived stress. Similarly, sleep quality and sleep efficiency were not significantly associated with UI severity across strata (Table 3).
Table 3.
Associations Between Aspects of Sleep Health and Urinary Incontinence Severity Stratified by Perceived Stress Level Among Women Veterans in the United States (N=246)
| Unadjusted models | Adjusted models | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Stress levela | Low n=78 |
Moderate n=129 |
High n=39 |
Low n=78 |
Moderate n=129 |
High n=39 |
||||||
| PSQI domainb | β (95% CI) | p | β (95% CI) | p | β (95% CI) | p | β (95% CI) | p | β (95% CI) | p | β (95% CI) | p |
| Worse sleep quality | 0.37 (−0.04, 0.78) | .08 | 0.04 (−0.34, 0.43) | .83 | −0.07 (−0.90, 0.76) | .87 | 0.40 (−0.22, 1.03) | .20 | −0.05 (−0.50, 0.61) | .86 | 1.19 (−4.75, 7.15) | .57 |
| Reduced sleep efficiency | 0.59 (0.08, 1.10) | .02 | 0.49 (0.08, 0.90) | .02 | 0.81 (0.14, 1.47) | .02 | 0.55 (−0.19, 1.30) | .15 | 0.17 (−0.46, 0.80) | .60 | 0.79 (−2.07, 3.67) | .44 |
| Greater daily disturbances | 1.09 (0.38, 1.81) | .003 | 0.29(−0.40, 0.98) | .41 | 1.78 (0.42, 3.14) | .01 | 1.25 (0.25, 2.24) | .02 | −0.17 (−1.11, 0.75) | .70 | 3.78 (−1.59, 9.15) | .12 |
Note.
Scores based on responses to the Perceived Stress Scale-10; score range=0-40, with higher scores indicating greater stress. Scores were categorized as low (1-13), moderate (14-25), or high (27-40).
Scores based on responses to the validated 3-factor Pittsburgh Sleep Quality Index scoring model to reflect sleep quality (score range=0-9), sleep efficiency (score range=0-6), and daily disturbances (score range=0-6), with higher scores reflecting greater sleep health impairments.
Abbreviations: PSQI, Pittsburgh Sleep Quality Index; CI, confidence interval
Adjusted models included age, race, ethnicity, education level, body mass index, menopausal status, vaginal parity, hysterectomy status, diabetes, medication for UI, and medication for sleep as covariates.
Multicollinearity was evaluated with variance inflation factors; for all variables in all models, variance inflation factors were <5 indicating that multicollinearity was not present.
p-values <.05 were considered statistically significant. Significant p-values are displayed in bold
Sensitivity Analyses
Sensitivity analyses yielded similar results in the complete case subset (n=214) in which participants with missing vaginal parity data were removed (Supplementary Tables S2 and S3). However, when anxiety, PTSD, and military sexual trauma were included as covariates, associations between sleep health and UI severity were attenuated. Specifically, sleep quality (p=.58) and daily disturbances (p=.08), which were significantly associated with UI severity in the primary adjusted analyses, were no longer statistically significant after adjustment for these factors (Supplementary Table S4). Additionally, in stratified analyses, among women with low perceived stress, the association between daily disturbances and UI severity was attenuated (p=.06), and a significant association between sleep efficiency and UI severity emerged (p=.04, Supplementary Table S5).
Discussion
In this study of nearly 250 United States women Veterans, worse sleep quality, reduced sleep efficiency, and greater daily disturbances were independently associated with greater UI severity. In the adjusted models, daily disturbances demonstrated a significant association with UI severity among women with low perceived stress, whereas associations between other aspects of sleep health and UI severity did not differ significantly across stress levels. Findings suggest that multiple aspects of sleep health are associated with UI severity, although evidence of differences by perceived stress level in the adjusted models was limited. However, women with high perceived stress exhibited the poorest sleep health and the greatest UI severity, suggesting that perceived stress may be a relevant contextual factor in sleep health and UI.
Approximately 85% of the sample was classified as poor sleepers. This is comparable with recent meta-analytic estimates of poor sleep health in United States Veteran populations of about 83% [9]. Notably, poor sleep health is prevalent among military populations globally, with a pooled prevalence of about 70% across military personnel and Veterans worldwide [20]. Poor sleep health is consistently associated with older age and PTSD [9], with higher rates observed among women compared to men [21]. One potential explanation for this disparity is the high prevalence of military sexual trauma among women Veterans in the United States [22]. Prior research has consistently demonstrated strong associations between military sexual trauma and poor sleep health among women [23]. Nearly 60% of the study sample reported military sexual trauma, which is higher than previously reported estimates of 26–44% among women Veterans in the United States [24]. International data suggest a similar burden with approximately 45% of the Canadian Armed Forces reporting military sexual trauma [25]. The higher prevalence observed in our sample may reflect differences in study populations. Specifically, participants in our sample were women Veterans in the United States who enrolled in a clinical trial focused on UI, a condition that may co-occur with trauma exposure [10]. Additionally, prior research has demonstrated associations between military sexual trauma worse physical and mental health outcomes among women Veterans in the United States [24]. Future research should further examine the role of military sexual trauma in shaping sleep health and UI outcomes. The high burden of poor sleep health and trauma exposure may be associated with elevated psychological stress and related physiological responses, which have been implicated in UI [26]. Physiological responses may include heightened autonomic activation, dysregulation of the hypothalamic–pituitary–adrenal axis, and increased inflammatory signaling, all of which may influence bladder sensitivity and urinary control [26]. These processes may be underlying even when stress is not perceived or reported. Prior research suggests that women Veterans in the United States may underreport certain health concerns due to stigma or barriers to care, including UI [27]. Similar factors may influence reporting of perceived stress, particularly among women accustomed to managing chronically high stress levels. Thus, physiological stress responses may still influence sleep health and UI even when perceived stress scores do not capture these experiences. Future studies incorporating objective biomarkers of physiological stress (e.g., cortisol or inflammatory markers) may help clarify underlying mechanisms linking sleep health and UI.
Overall, worse sleep quality, reduced sleep efficiency, and greater daytime disturbances were independently associated with worse UI severity. Our findings are consistent with prior research including participants with broader overactive bladder symptoms, including UI, which showed associations between poor sleep health and UI severity, even after adjusting for nocturia [13]. Additionally, a large prospective cohort study found that poor sleep health was significantly associated with incident UI [28]. Additional evidence suggests a bidirectional relationship between sleep health and lower urinary tract symptoms, including UI [29]. Nocturia, urgency, and leakage may disrupt sleep continuity and contribute to worsening sleep health over time, which in turn, may contribute to worsening UI [29]. Although we were unable to investigate directionality in this study, our findings provide a foundation for future investigations and highlight the potential value of addressing aspects of sleep health as potentially modifiable factors to improve UI among women.
In stratified adjusted models, among women with low perceived stress, greater daily disturbances were associated with worse UI severity. No significant associations were observed between sleep quality or sleep efficiency and UI severity and across perceived stress levels; and daily disturbances were not consistently associated with UI severity in moderate or high stress groups. This may reflect restricted variability in perceived stress within strata and limitations of global self-report measures in capturing momentary or physiological stress reactivity. Given that perceived stress was examined as a stratification variable rather than a mediator or moderator, findings should be interpreted as exploratory and do not establish a causal role for stress in the relationship between sleep health and UI. Notably, in bivariate analyses, compared to women with low perceived stress, women with high perceived stress had worse sleep health and greater UI severity. These findings further support perceived stress as a relevant contextual factor in sleep health and UI. Daily disturbances captured daytime sequelae of sleep disruptions and short sleep duration, such as persistent fatigue or trouble staying alert [16]. Such disturbances are linked to both nocturia and daytime lower urinary tract symptoms, suggesting that the relationship extends beyond nighttime voiding patterns [30]. Fatigue and reduced daily functioning could result from both poor sleep health and the burden of managing UI in everyday life. Additionally, compared to moderate sleep duration (6–8 hours), short sleep duration (<6 hours) is associated with the urgency UI type [31]. We did not differentiate by UI type in the current study, as 79% of the sample had mixed UI. Given that mixed UI is more prevalent as women age, and behavioral interventions are effective across UI types [32], differentiating by UI type may not have provided additional insight in this study. However, future studies with larger samples should investigate mechanisms of physiological dysregulation linking sleep health and UI types to inform more precise interventions targeting potentially modifiable factors.
When we added anxiety, PTSD, and military sexual trauma as covariates, associations between certain aspects of sleep health and UI severity were attenuated, with sleep quality and daily disturbances no longer statistically significant. This attenuation suggests that the associations between sleep health and UI severity may overlap with broader psychological and trauma-related factors. Given that these factors are closely related to perceived stress and sleep health, they were not included in the primary models to avoid potential overadjustment. However, attenuation of findings in sensitivity analyses highlight the importance of trauma-informed approaches in UI care. Concurrent evaluation and treatment of anxiety, PTSD, and military sexual trauma in the context of UI may help address interrelated processes that contribute to UI and treatment outcomes. Although PTSD prevalence among United States Veterans exceeds estimates from studies including Veterans in other countries such as the United Kingdom (approximately 14% versus 8%) [33], trauma-informed UI care may have relevance beyond the United States Veteran population. Additionally, although this study focused on women Veterans in the United States, poor sleep health, psychological stress, and trauma exposure are not unique to military populations. However, the high prevalence of psychological stress and trauma exposure among women Veterans may amplify these relationships. The observed relationships between sleep health and UI may reflect broader biopsychosocial processes relevant across populations. Thus, non-Veteran women with UI who experience stress or trauma may also benefit from such approaches to complement established behavioral UI treatments. However, investigations in non-Veteran and international populations are needed to determine the extent to which these findings generalize beyond United States Veteran populations.
Clinical Implications
Identifying high-risk subgroups based on individual risk factors may help determine who is most likely to benefit from UI treatment, including non-surgical (e.g., behavioral interventions, medications), and surgical interventions. Adherence to non-surgical UI treatments may likely improve when high-risk subgroups are identified. Despite the high rates of poor sleep health and mental health conditions, women Veterans in the parent clinical trial of behavioral UI treatments reported clinically relevant improvements in UI severity. Future studies should further evaluate and appropriately address relevant high-risk subgroups for treatment outcomes. Targeting potentially modifiable factors such as sleep health or psychological stress may help improve the effectiveness of UI-focused behavioral interventions. However, leaving women in a chronically dysregulated state may perpetuate UI and other adverse health outcomes, given the likely bidirectional and complex relationships between sleep health and UI. Thus, approaches that address upstream contributors to UI may enhance the effectiveness and sustainability of both non-surgical and surgical UI treatments.
Limitations
Given that we conceptualized perceived stress as a co-occurring contextual factor, we conducted stratified analyses to explore potential differences in associations between sleep health and UI severity. Additionally, the cross-sectional design of this study precluded evaluation of causality. Most participants (85%) were classified as poor sleepers, and the majority had mixed UI (79%), limiting the ability to conduct additional stratified analyses. Although we adjusted for several sociodemographic and clinical covariates, and the association between aspects of sleep health and UI severity remained remarkably stable, residual confounding is possible. Given the exploratory nature of the analyses and the number of statistical tests performed, the possibility of Type I error cannot be ruled out. As such, findings should be considered preliminary and hypothesis-generating and warrant confirmation in future studies. Notably, we did not have data on obstructive sleep apnea, which may influence both sleep health and UI outcomes. While future studies should prioritize including this factor, we adjusted for key clinical factors, and the absence of these data is unlikely to fully account for the observed associations. Additionally, we did not have data regarding markers of physiological stress or other objective sleep parameters. Although we used validated scales, measures were based on self-report, which may be subject to recall or social desirability bias.
Conclusion
In this study of women Veterans in the United States, worse sleep quality, reduced sleep efficiency, and greater daily disturbances were associated with worse UI severity. Although limited statistically significant differences were observed in stratified adjusted models by perceived stress level, descriptive findings suggest that psychological stress may be an relevant contextual factor in sleep health and UI. Findings highlight the importance of addressing sleep health as a complementary target alongside established UI treatments. Future research should employ longitudinal designs, incorporate objective sleep and physiological stress measures, and engage key stakeholders to inform tailored interventions aimed at improving UI and related health outcomes among women.
Supplementary Material
Acknowledgements
This study is a secondary analysis of data from a randomized controlled trial (RCT) that evaluated the effectiveness of two evidence-based remote urinary incontinence behavioral treatment modalities: Markland AD, Goldstein KM, Beasley TM, et al. Remote access to urinary incontinence treatments for women Veterans: The PRACTICAL randomized clinical trial. JAMA Netw Open. 2025;8(9):e2532111. doi:10.1001/jamanetworkopen.2025.32111.
Funding Statement:
This work was supported by the National Institute on Aging (grant numbers T32AG066598 [DS], K24AG073586 [ADM]) and the Department of Veterans Affairs with Health Services Research and Development funding HX002827 (PI: ADM).
Role of the Funder/Sponsor:
The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. This material is based upon work supported by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development" (Health Services Research and Development). The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government. The Department of Veteran Affairs did not have a role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Footnotes
Financial Disclaimers/Conflict of Interest Statement: NONE
Ethics Statement: The IRB at each participating VA site approved the parent RCT and secondary analyses (IRB Reference #: 1570834-35). This study was conducted in accordance with the guidelines outlined in the Declaration of Helsinki and reported in accordance with the The STrengthening the Reporting of OBservational studies in Epidemiology guidelines.
Data Availability Statement:
Data are available upon reasonable request. De-identified data used in this study will be made available upon reasonable request to the senior author (ADM), following completion of an institutional data sharing agreement.
References
- [1].Bradley SE, Cowan LJ, Orozco T, Hale-Gallardo J, Klanchar A, Devendorf A, et al. Understanding incontinence in veterans: perspectives of veterans and their caregivers. J Nurse Pract. 2023;19(10). doi: 10.1016/j.nurpra.2023.104815. [DOI] [Google Scholar]
- [2].Haylen BT, de Ridder D, Freeman RM, Swift SE, Berghmans B, Lee J, et al. An International Urogynecological Association (IUGA)/International Continence Society (ICS) joint report on the terminology for female pelvic floor dysfunction. Neurourol Urodyn. 2010;29(1):4–20. doi: 10.1002/nau.20798. [DOI] [PubMed] [Google Scholar]
- [3].Siddiqui NY, Wiseman JB, Cella D, Bradley CS, Lai HH, Helmuth ME, et al. Mental health, sleep and physical function in treatment-seeking women with urinary incontinence. J Urol. 2018;200(4):848–55. doi: 10.1016/j.juro.2018.04.076. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [4].St-Onge MP, Aggarwal B, Fernandez-Mendoza J, Johnson D, Kline CE, Knutson KL, et al. Multidimensional sleep health: definitions and implications for cardiometabolic health: a scientific statement from the American Heart Association. Circ Cardiovasc Qual Outcomes. 2025;18(5):e000139. doi: 10.1161/HCQ.0000000000000139. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [5].Tyagi S, Resnick NM, Clarkson BD, Zhang G, Krafty RT, Perera S, et al. Impact of sleep on chronobiology of micturition among healthy older adults. Am J Physiol Renal Physiol. 2023;325(4):F407–17. doi: 10.1152/ajprenal.00025.2023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [6].Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24(4):385–96. doi: 10.2307/2136404. [DOI] [PubMed] [Google Scholar]
- [7].Walker JL, Vargas I, Drake CL, Ellis JG, Muench A, Perlis ML. The natural history of insomnia: high sleep reactivity interacts with greater life stress to predict the onset of acute insomnia. Sleep. 2022;45(9):zsac149. doi: 10.1093/sleep/zsac149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Reffi AN, Kalmbach DA, Cheng P, Jennings M, Moore DA, Seymour GM, et al. Sleep reactivity as a vulnerability to posttraumatic stress disorder: identifying patients at risk of acute insomnia within the immediate aftermath of trauma. Psychol Trauma. 2025. doi: 10.1037/tra0002073. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].Bai W, Gui Z, Chen MY, Zhang Q, Lam MI, Si TL, et al. Global prevalence of poor sleep quality in military personnel and veterans: a systematic review and meta-analysis of epidemiological studies. Sleep Med Rev. 2023;71:101840. doi: 10.1016/j.smrv.2023.101840. [DOI] [PubMed] [Google Scholar]
- [10].Scharp D, Goldstein KM, Kelly UA, Burgio KL, Vaughan CP, Intrator O, et al. Associations between psychological and trauma-related factors and urinary incontinence severity and treatment response among women veterans. Am J Obstet Gynecol. 2026. doi: 10.1016/j.ajog.2026.04.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Joinson C, Drake MJ, Fraser A, Tilling K, Heron J. Bidirectional relationships between depression, anxiety and urinary symptoms in women: a prospective cohort study. J Affect Disord. 2025;369:516–22. doi: 10.1016/j.jad.2024.10.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Smith AL, Berry A, Brubaker L, Cunningham SD, Gahagan S, Kane Low L, et al. The brain, gut, and bladder health nexus: a conceptual model linking stress and mental health disorders to overactive bladder in women. Neurourol Urodyn. 2024;43(2):424–36. doi: 10.1002/nau.25356. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].Ge TJ, Vetter J, Lai HH. Sleep disturbance and fatigue are associated with more severe urinary incontinence and overactive bladder symptoms. Urology. 2017;109:67–73. doi: 10.1016/j.urology.2017.07.039. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Markland AD, Goldstein KM, Beasley TM, Boyd EM, Zubkoff L, Kelly UA, et al. Remote access to urinary incontinence treatments for women veterans: the PRACTICAL randomized clinical trial. JAMA Netw Open. 2025;8(9):e2532111. doi: 10.1001/jamanetworkopen.2025.32111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15].Mollayeva T, Thurairajah P, Burton K, Mollayeva S, Shapiro CM, Colantonio A. The Pittsburgh sleep quality index as a screening tool for sleep dysfunction in clinical and non-clinical samples: a systematic review and meta-analysis. Sleep Med Rev. 2016;25:52–73. doi: 10.1016/j.smrv.2015.01.009. [DOI] [PubMed] [Google Scholar]
- [16].Cole JC, Motivala SJ, Buysse DJ, Oxman MN, Levin MJ, Irwin MR. Validation of a 3-factor scoring model for the Pittsburgh sleep quality index in older adults. Sleep. 2006;29(1):112–6. doi: 10.1093/sleep/29.1.112. [DOI] [PubMed] [Google Scholar]
- [17].Avery K, Donovan J, Peters TJ, Shaw C, Gotoh M, Abrams P. ICIQ: a brief and robust measure for evaluating the symptoms and impact of urinary incontinence. Neurourol Urodyn. 2004;23(4):322–30. doi: 10.1002/nau.20041. [DOI] [PubMed] [Google Scholar]
- [18].Batmani S, Jalali R, Mohammadi M, Bokaee S. Prevalence and factors related to urinary incontinence in older adult women worldwide: a comprehensive systematic review and meta-analysis of observational studies. BMC Geriatr. 2021;21(1):212. doi: 10.1186/s12877-021-02135-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Guide A, Garbett S, Feng X, Mapes BM, Cook J, Sulieman L, et al. Balancing efficacy and computational burden: weighted mean, multiple imputation, and inverse probability weighting methods for item non-response in reliable scales. J Am Med Inform Assoc. 2024;31(12):2869–79. doi: 10.1093/jamia/ocae217. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [20].Bai W, Gui Z, Chen MY, et al. Global prevalence of poor sleep quality in military personnel and veterans: A systematic review and meta-analysis of epidemiological studies. Sleep Med Rev. 2023;71:101840. doi: 10.1016/j.smrv.2023.101840 [DOI] [PubMed] [Google Scholar]
- [21].Gonzalez TM, Froelicher ES, Katzovitz FN, Kostas-Polston EA. Prevalence of insomnia by sex in U.S. service members: a systematic review and meta-analysis. Mil Med. 2025. doi: 10.1093/milmed/usaf516. [DOI] [PubMed] [Google Scholar]
- [22].Sumner JA, Lynch KE, Viernes B, Beckham JC, Coronado G, Dennis PA, et al. Military sexual trauma and adverse mental and physical health and clinical comorbidity in women veterans. Womens Health Issues. 2021;31(6):586–95. doi: 10.1016/j.whi.2021.07.004. [DOI] [PubMed] [Google Scholar]
- [23].Jenkins MM, Colvonen PJ, Norman SB, Afari N, Allard CB, Drummond SPA. Prevalence and mental health correlates of insomnia in first-encounter veterans with and without military sexual trauma. Sleep. 2015;38(10):1547–54. doi: 10.5665/sleep.5044. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [24].Nichter B, Holliday R, Monteith LL, Na PJ, Hill ML, Kline AC, et al. Military sexual trauma in the United States: results from a population-based study. J Affect Disord. 2022;306:19–27. doi: 10.1016/j.jad.2022.03.016. [DOI] [PubMed] [Google Scholar]
- [25].Mota N, Sommer JL, Bolton SL, et al. Prevalence and Correlates of Military Sexual Trauma in Service Members and Veterans: Results From the 2018 Canadian Armed Forces Members and Veterans Mental Health Follow-up Survey. Can J Psychiatry. 2023;68(9):682–690. doi: 10.1177/07067437221125292 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Chess-Williams R, McDermott C, Sellers DJ, West EG, Mills KA. Chronic psychological stress and lower urinary tract symptoms. Low Urin Tract Symptoms. 2021;13(4):414–24. doi: 10.1111/luts.12395. [DOI] [PubMed] [Google Scholar]
- [27].Williston SK, Bramande EA, Vogt DS, Iverson KM, Fox AB. An examination of the roles of mental health literacy, treatment-seeking stigma, and perceived need for care in female veterans’ service use. Psychiatr Serv. 2020;71(2):144–50. doi: 10.1176/appi.ps.201800405. [DOI] [PubMed] [Google Scholar]
- [28].Shen Y, Zhang W, Gao J, Shao S. Sleep disturbance and risk of incident incontinence in middle-aged and older Chinese adults: a 4-year national cohort study. BMJ Open. 2026;16(2):e108424. doi: 10.1136/bmjopen-2025-108424. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [29].Araujo AB, Yaggi HK, Yang M, McVary KT, Fang SC, Bliwise DL. Sleep-related problems and urological symptoms: testing the hypothesis of bidirectionality in a longitudinal, population-based study. J Urol. 2014;191(1):100–6. doi: 10.1016/j.juro.2013.07.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [30].Fantus RJ, Packiam VT, Wang CH, Erickson BA, Helfand BT. The relationship between sleep disorders and lower urinary tract symptoms: results from the NHANES. J Urol. 2018;200(1):161–6. doi: 10.1016/j.juro.2018.01.083. [DOI] [PubMed] [Google Scholar]
- [31].Di X, Chen J, Wang M, Liao B. Association between sleep duration and urinary incontinence in female adults: a cross-sectional study in National Health and Nutrition Examination Survey 2007–2018. Urology. 2023;181:48–54. doi: 10.1016/j.urology.2023.08.019. [DOI] [PubMed] [Google Scholar]
- [32].Balk EM, Rofeberg VN, Adam GP, Kimmel HJ, Trikalinos TA, Jeppson PC. Pharmacologic and nonpharmacologic treatments for urinary incontinence in women: a systematic review and network meta-analysis of clinical outcomes. Ann Intern Med. 2019;170(7):465–79. doi: 10.7326/M18-3227. [DOI] [PubMed] [Google Scholar]
- [33].Rhead R, MacManus D, Jones M, Greenberg N, Fear NT, Goodwin L. Mental health disorders and alcohol misuse among UK military veterans and the general population: a comparison study. Psychol Med. 2022;52(2):292–302. doi: 10.1017/S0033291720001944 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data are available upon reasonable request. De-identified data used in this study will be made available upon reasonable request to the senior author (ADM), following completion of an institutional data sharing agreement.