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. Author manuscript; available in PMC: 2026 Jul 28.
Published before final editing as: Behav Sleep Med. 2026 Jul 3:1–10. doi: 10.1080/15402002.2026.2695068

Sleep Mediates the Relationship Between Stress and Blood Pressure in Older African American Women: A Cross-Sectional RiSE Study

Sueyeon Lee a, Linda Janusek a, Cara Joyce b, Jennifer Vasquez Gonzalez b, Lamise Shawahin c, Jacquelyn Y Taylor d, Karen Saban a
PMCID: PMC13404136  NIHMSID: NIHMS2197352  PMID: 42394432

Abstract

Objectives:

To examine whether sleep quality mediates the relationship between perceived stress and blood pressure among older African American women (AAW).

Methods:

A cross-sectional analysis was conducted using baseline data from older AAW (aged 50–75) enrolled in the Resilience, Stress, and Ethnicity (RiSE) study. Perceived stress was assessed using the 10-item Perceived Stress Scale, and sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI). Blood pressure was measured three times and averaged to calculate mean systolic and diastolic blood pressure. Structural equation modeling was used to test mediation, adjusting for age, body mass index, and antihypertensive medication use.

Results:

Among 158 women (mean age 63.0 ± 6.7 years), 57.6% were taking antihypertensive medications, mean systolic blood pressure was 129.5 ± 15.4 mmHg, and mean PSQI score was 7.6 ± 3.5, indicating poor sleep quality. Perceived stress was associated with poorer sleep quality (b = 0.21, p < .001), and poorer sleep quality was related to higher systolic blood pressure (b = 0.81, p = .037). Sleep quality significantly mediated the effect of stress on systolic blood pressure (b = 0.17, p = .049), with no direct effect observed (b = 0.10, p = .594).

Conclusion:

Sleep quality mediated the relationship between perceived stress and systolic blood pressure in older AAW. These findings support the role of sleep as a behavioral mechanism linking stress to cardiometabolic risk and suggest the importance of developing culturally tailored sleep interventions to mitigate the adverse cardiometabolic effects of stress in this high-risk population.

Introduction

Cardiometabolic disease is an interconnected set of cardiovascular and metabolic conditions, encompassing cardiovascular disease and metabolic disorders such as type 2 diabetes and obesity (Eroglu et al., 2024). In the United States, cardiovascular disease remains the leading cause of death among women (Martin et al., 2025), and the prevalence of metabolic conditions continues to rise in women, exacerbating their cardiometabolic burden (Eroglu et al., 2024; Martin et al., 2025). African American women are disproportionately affected by cardiometabolic risk factors, including hypertension, diabetes, and obesity, compared to men and other racial/ethnic groups (Flack et al., 2003; Kalinowski et al., 2021; Martin et al., 2025; Mehta et al., 2023). Specifically, African American women have the highest prevalence of hypertension, with approximately 58.4% of those aged ≥20 years diagnosed with the condition (Martin et al., 2025). Moreover, only 25.6% achieve blood pressure control, and they also experience the highest rate of hypertension-related mortality (Martin et al., 2025). These disparities contribute to elevated cardiometabolic morbidity and mortality in this population (Martin et al., 2025; Mehta et al., 2023; Williams, 2009).

African American women experience multiple psychosocial stressors – including racial and gender discrimination, caregiving demands, financial strain, and neighborhood stressors – that elevate perceived stress levels (Din-Dzietham et al., 2004; Erving, 2024; Kalinowski et al., 2021; Mehta et al., 2023). These psychosocial stress exposures are associated with heightened sympathetic activation and dysregulated cardiovascular responses, ultimately contributing to elevated blood pressure (Erving, 2024). The persistent psychological and contextual stressors may help explain the earlier onset and greater severity of hypertension observed among African American women (Flack et al., 2003; Kalinowski et al., 2021).

In addition, sleep is a key behavioral determinant of cardiometabolic health (Lloyd-Jones et al., 2022). Poor sleep has been associated with increased risks for hypertension, inflammation, and metabolic dysregulation (Rogers et al., 2024). African American women, however, face unique challenges in maintaining sleep health (Chen et al., 2015). Research indicates that they experience poorer sleep quantity and quality, both subjectively and objectively, compared to their White counterparts (Lee et al., 2025). Emerging evidence suggests that sleep may play as a mechanistic role in the pathway linking stress to cardiometabolic risk, including hypertension (Franzen et al., 2011; Gallegos-Gonzalez et al., 2021; Rogers et al., 2024). A recent review proposed a plausible biological mechanism by which psychosocial stress activates the hypothalamic-pituitary-adrenal axis and the sympathetic-adrenal-medullary system (Rogers et al., 2024). This activation increases arousal and impairs sleep regulation. In turn, these physiological responses elevate peripheral resistance, cardiac output, heart rate, blood pressure, and levels of inflammatory cytokines.

Despite the proposed mechanistic pathway linking stress, sleep, and hypertension, few empirical studies have examined sleep as a mediator in the relationship between stress and hypertension, particularly in African American women who face heightened cardiometabolic risk. Given evidence that psychosocial stress can disrupt sleep (Rogers et al., 2024) and that poor sleep is associated with blood pressure (Palagini et al., 2013), stress may influence blood pressure through its effects on sleep. Therefore, this study examined whether sleep quality mediates the relationship between perceived stress and blood pressure in older African American women. We hypothesized that poorer sleep quality would mediate the relationship between higher levels of perceived stress and elevated blood pressure in this population.

Methods

Study design and population

This cross-sectional study utilized baseline data from the Resilience, Stress, and Ethnicity (RiSE) study (R01AG081251), which was designed to examine the effects of a race-based stress reduction program on well-being, inflammation, and DNA methylation in African American women at risk for cardiometabolic disease. The detailed study protocol has been published previously (Saban et al., 2025; Taylor et al., 2024). Since 2023, we have recruited approximately 176 self-identified African American women from Chicago and New York City. Eligible participants were 50–75 years, postmenopausal, and had at least one cardiometabolic risk factor such as type 2 diabetes or hypercholesterolemia. Participants provided written informed consent and were randomized to either an 8-week race-based stress reduction intervention or a health education program, both delivered via Zoom. The current study used data collected at baseline, prior to randomization. At the baseline visit, participants completed self-report questionnaires assessing psychosocial symptoms and health behaviors, including perceived stress and sleep quality. Anthropometric measures such as body weight and blood pressure were also collected. Participants with missing data on key study variables (stress, sleep, or blood pressure) were excluded from the analysis, resulting in a final analytic sample of 158.

Measures

Perceived stress was assessed using the Perceived Stress Scale (PSS-10), which measures the degree to which individuals appraise situations in their life as stressful in the past month (Cohen et al., 1983). The PSS includes 10 items rated on a 5-point Likert scale ranging from 0 (never) to 4 (very often), assessing how often individuals felt or thought a certain way over the past month (Cohen et al., 1983). Higher scores indicate higher levels of perceived stress. The PSS-10 has demonstrated good reliability and validity in adult populations, including African Americans (J. M. Taylor, 2015), and in Black adolescents (Ahmed, 2023).

Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), which assesses sleep quality during the previous month. It consists of 19 items that generate seven component scores – subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. Each item is scored from 0 (no difficulty) to 3 (severe difficulty), with a global score ranging from 0 to 21. Higher scores indicate poorer sleep quality, and a global score greater than 5 indicates poor sleep quality (Buysse et al., 1989). The PSQI has been widely validated in various population including older African American women (Beaudreau et al., 2012; Mollayeva et al., 2016) and is considered a reliable measure of subjective sleep quality.

Blood pressure was measured by a trained research associate using an Omron HEM-907XL professional digital blood pressure monitor with a properly sized cuff, following Joint National Committee (JNC)-7 guidelines (Chobanian et al., 2003). Three assessments were taken from the participant’s left arm while at rest (with exceptions when the left arm could not be used), and the mean of the three readings was used to determine systolic and diastolic blood pressure. Covariates included age, body mass index (BMI), and use of antihypertensive medications a priori based on prior evidence linking these factors to blood pressure (Martin et al., 2025). Age and medication were assessed via self-report questionnaires. BMI was calculated using the formula: 703 ×weight (lbs) / [height (in)]2, based on measured weight and height.

Data analysis

All statistical analyses were conducted using STATA version 16.0 (StataCorp LLC, College Station, Texas, USA). Descriptive statistics were used to summarize sample characteristics and examine stress and sleep quality. Structural equation modeling was employed to test whether sleep quality mediated the relationship between perceived stress and blood pressure. Systolic and diastolic blood pressure were analyzed in separate models. The models specified perceived stress as the independent variable, sleep quality as the mediator, and blood pressure as the dependent variable. Age (years), BMI (kg/m2), and antihypertensive medication use (yes/no) were included as covariates. Direct, indirect, and total effects were estimated with 95% confidence intervals (CI). Model fit was assessed using the likelihood-ratio chi-square test comparing the fitted model to a saturated model, along with additional indices including the comparative fit index (CFI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). A p value ≤ 0.05 was considered statistically significant.

Results

Participant characteristics

Participant demographic and clinical characteristics are presented in Table 1. The mean age was 63.0 ± 6.7 years. The largest proportion of participants were single (38.0%), working full-time (36.1%) or retired (32.9%), and had completed some college or an associate degree (38.6%). Most participants had been diagnosed with hypertension (62.7%) and were taking antihypertensive medications (57.6%). In addition, 56.3% reported at least one comorbid condition, most commonly high cholesterol (29.1%), diabetes (19.6%), and back pain (18.4%). The average BMI was 34.2 ± 7.5, indicating an overall obese status. Participants also reported poor sleep quality, with a mean global PSQI score of 7.6 ± 3.5. The mean systolic and diastolic blood pressures were 129.5 ± 15.4 mmHg and 82.1 ± 11.1 mmHg, respectively, indicating elevated blood pressure (Jones et al., 2025). Data on participants’ stress, sleep, and blood pressure are presented in Table 2.

Table 1.

Participants’ demographic and clinical characteristics (N = 158).

Characteristic n (%) M ± SD Range
Age (years) 63.0 ± 6.7 50–75
Marital status
 Married 36 (22.8%)
 Single 60 (38.0%)
 Divorced/Separated 45 (28.5%)
 Widowed 15 (9.5%)
 Domestic partnership 2 (1.3%)
Education level
 Less than high school 1 (0.6%)
 High school graduate or GED 12 (7.6%)
 Some college/Associate 61 (38.6%)
 College graduate 40 (25.3%)
 Graduate school 44 (27.8%)
Employment status
 Working full-time 57 (36.1%)
 Working part-time 21 (13.3%)
 Retired 52 (32.9%)
 Not working 28 (17.7%)
Household income (annual)
 Less than $16,000 21 (13.3%)
 $16,000 to $24,999 15 (9.5%)
 $25,000 to $49,999 42 (26.6%)
 $50,000 to $74,999 27 (17.1%)
 $75,000 to $99,999 20 (12.7%)
 $100,000 or higher 21 (13.3%)
 Refused/Unsure 12 (7.6%)
Body mass index (kg/m2) 34.2 ± 7.5 17.1–57.6
Diagnosis of hypertension
 Yes 99 (62.7%)
 No 59 (37.3%)
Medication for blood pressure
 Yes 91 (57.6%)
 No 66 (41.8%)
Not applicable 1 (0.6%)
Presence of other illnesses
 Yes 89 (56.3%)
 No 68 (43.0%)
 Not applicable 1 (0.6%)
  Most common comorbidities
  High cholesterol 46 (29.1%)
  Diabetes 31 (19.6%)
  Back pain 29 (18.4%)
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GED = General Educational Development.

Table 2.

Participants’ stress, sleep, and blood pressure (N = 158).

Characteristic M ± SD Min Max
Perceived Stress Scale 15.9 ± 6.9 0 34
Pittsburgh Sleep Quality Index
 Sleep duration 1.0 ± 1.0 0 3
 Sleep disturbance 1.5 ± 0.6 0 3
 Sleep latency 1.3 ± 1.0 0 3
 Daytime dysfunction due to sleepiness 0.8 ± 0.8 0 3
 Sleep efficiency 1.0 ± 1.2 0 3
 Overall sleep quality 1.5 ± 0.9 0 3
 Need medication to sleep 0.6 ± 1.1 0 3
 Total score 7.6 ± 3.5 1 17
Blood pressure
 Systolic blood pressure 129.5 ± 15.4 94.7 178
 Diastolic blood pressure 82.1 ± 11.1 52.7 119
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Mediating effect of sleep quality between stress and blood pressure

As shown in Figure 1, mediation analysis showed that higher perceived stress was associated with poorer sleep quality (b = 0.21, p < .001; 95% CI: 0.143–0.285), and poorer sleep quality was related to higher systolic blood pressure (b = 0.81, p = .037; 95% CI: 0.048–1.562), but not diastolic blood pressure (b = 0.40, p = .145; 95% CI: −0.137–0.929), adjusting for age, BMI, and antihypertensive medication use. That is, a 1-point increase in perceived stress was associated with a 0.21-point increase in the PSQI global score (worse sleep quality), which in turn predicted a 0.81-mmHg increase in systolic blood pressure. The indirect effect of perceived stress on systolic blood pressure through sleep quality was statistically significant (b = 0.17, p = .049; 95% CI: 0.001–0.344), while the direct effect was not significant (b = 0.10, p = .594; 95% CI: −0.276–0.483). The model demonstrated good fit to the data. The likelihood-ratio chi-square test comparing the fitted model to the saturated model was non-significant, indicating an adequate model fit (χ2 (3) = 2.99, p = .394). Fit indices also supported good model fit (CFI = 1.000, RMSEA = 0.000, and SRMR = 0.029).

Figure 1.

Figure 1.

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Mediating effect of sleep quality between stress and blood pressure (N = 158). Adjusted for age, body mass index, and antihypertensive medication use. Dashed line indicates non-significant result.

Discussion

This study examined the mediating role of sleep in the relationship between perceived stress and blood pressure among older African American women. The findings demonstrated that sleep quality significantly mediated the relationship between perceived stress and systolic blood pressure. However, perceived stress did not directly predict systolic blood pressure. This suggest that poor sleep may be a critical mechanism through which stress influences cardiometabolic health in this population.

Participants in this study exhibited poor subjective sleep quality, as reflected by a mean PSQI score greater than 5. This finding is consistent with previous studies reporting that African American women experience poorer sleep compared to other demographic groups (Hall et al., 2009; Jones et al., 2018; Matthews et al., 2019). Several factors of the participants may contribute to this pattern of disturbed sleep, particularly elevated BMI. On average, participants were classified as obese, which may exacerbate sleep disturbances. Numerous studies have demonstrated a bidirectional relationship between adiposity and sleep disturbances (Bidulescu et al., 2010; Garfield, 2019; Hayes et al., 2023). Greater adiposity, such as higher BMI, waist circumference, and hip circumference, is associated with poorer sleep quality and worse sleep disturbances, including more daytime sleepiness and shorter sleep duration (Bidulescu et al., 2010; Garfield, 2019; Hayes et al., 2023). Conversely, sleep disturbances, daytime dysfunction, and insomnia symptoms have been shown to increase adiposity (Bidulescu et al., 2010; Hayes et al., 2023), potentially mediated by impaired metabolic regulation and dysregulated hunger and satiety, thereby contributing to weight gain and adiposity (Rogers et al., 2024). Given the high prevalence of obesity among African American women and its increasing prevalence with age (Agyemang & Powell-Wiley, 2013; Martin et al., 2025), interventions to improve sleep quality in this population should concurrently incorporate lifestyle modification strategies, including weight control, diet management, and physical activity, as modifiable factors contributing to sleep disturbances.

The mediating role of sleep in the stress-blood pressure pathway aligned with prior research highlighting the impact of stress and psychological factors on cardiometabolic health through sleep. For instance, among university students (Gallegos-Gonzalez et al., 2021), perceived stress was associated with elevated systolic blood pressure via shorter sleep duration. In that study, stress also influenced various cardiometabolic indices – glucose, triglycerides, and systolic blood pressure – through sleep quality. Other studies have shown that insomnia symptoms partially mediated the relationship between anxiety and brachial artery diameter in White postmenopausal women (Fonkoue et al., 2024), and that sleep disturbances mediated the relationship between depression and the diagnosis of cardiovascular diseases, such as coronary heart disease, heart attack, and stroke (Chen et al., 2024). Collectively, these findings suggest that sleep serves as a critical behavioral pathway linking psychological stress to cardiometabolic health, emphasizing the importance of addressing sleep quality in interventions for improving older African American women’s cardiometabolic health.

Our study showed that stress did not directly influence blood pressure in older African American women. However, previous evidence has reported that stress can increase blood pressure through mechanisms such as overactivation of the sympathetic nervous system and the hypothalamic-pituitary-adrenocortical axis, impaired endothelial function, increased oxidative stress and inflammation (Marwaha, 2022), and elevated allostatic load (Logan & Barksdale, 2008). Several possible explanations may account for the non-significant direct effect observed in this study. First, the stress measure used in this study (PSS) asks respondents to indicate the extent to which events in their life were uncontrollable over the past month. As such, the PSS does not assess chronic stress exposure, which may exert a more sustained effect on blood pressure. Second, both overall poor sleep quality and generally elevated blood pressure levels in the sample may have reduced variability, limiting the ability to detect significant associations. Third, the relatively modest sample size may have limited the statistical power to detect small or moderate direct effects. Finally, because age and BMI were included as covariates in the model, it is possible that adjustment for these factors attenuated the association between perceived stress and blood pressure, contributing to the non-significant direct effect observed. Therefore, future research is warranted to explore this relationship in larger samples, using comprehensive assessments of both acute and chronic stress.

In our sample, the mediated association of perceived stress with blood pressure was found for systolic but not diastolic blood pressure. Stress triggers the release of catecholamines (epinephrine, norepinephrine) that act on adrenergic receptors (Motiejunaite et al., 2021). Activation of β-receptors increases heart rate and cardiac contractility, leading primarily to higher systolic pressure, whereas α-receptor activation modulates vasoconstriction and peripheral resistance (Motiejunaite et al., 2021), which are more related to diastolic pressure. Both receptor systems are activated during stress (Motiejunaite et al., 2021), but their effects on systolic and diastolic pressure may differ. In addition, vascular aging and arterial stiffening amplify systolic and pulse pressure (Mitchell et al., 2004), while diastolic pressure tends to remain stable or decline with age (Burt et al., 1995). As a result, systolic hypertension becomes the most common form of hypertension in adults over 50 years of age (Franklin et al., 2001), which may explain why only systolic pressure showed a significant association in this study.

This study has several limitations. First, its cross-sectional design limits the ability to draw causal inferences regarding the observed associations among perceived stress, sleep quality, and blood pressure. Second, measurement limitations warrant consideration: stress and sleep were assessed via self-report, which are subject to recall bias and may not capture objective physiological markers; and blood pressure was obtained at a single visit, which may not capture sustained elevations. Moreover, we assessed perceived stress only and did not characterize specific stressors (e.g., socioeconomic, caregiving-related, discrimination-related) that may differentially affect sleep and blood pressure. It is also possible that certain PSQI items (e.g., trouble breathing, sleep fragmentation) may overlap with cardiometabolic symptoms, which could introduce potential bias in the observed associations. Further, obstructive sleep apnea and the use of continuous positive airway pressure were not assessed, which could influence both sleep quality and blood pressure. Given the relatively high mean BMI in this sample, it is possible that participants may have undiagnosed or untreated sleep apnea, which may have influenced the study results. In addition, potential medication effects were not examined; differences across antihypertensive classes may differentially affect systolic versus diastolic pressure, and psychotropic medications may alter perceived stress. Finally, participants generally demonstrated poor sleep quality and elevated blood pressure, reflecting a higher-risk phenotype, which may limit the generalizability of findings to broader populations.

Despite these limitations, the findings provide important implications for reducing cardiometabolic risk among older African American women. Identifying sleep quality as a significant mediator between perceived stress and systolic blood pressure suggests the value of addressing sleep as a potential modifiable behavioral target in interventions aimed at improving cardiometabolic health. These results suggest the need for culturally sensitive, multimodal strategies that integrate stress management and sleep health promotion, particularly in populations disproportionately affected by both psychosocial stress and hypertension. For example, cognitive behavioral therapy for insomnia, which is a well-established first-line treatment for insomnia (Morin et al., 2023), along with mindfulness practices and stress management training, could be adapted for this group (Johnson et al., 2019; Lee et al., 2025). Such approaches can be further tailored by addressing racism-related stress, incorporating cultural concepts and values such as religious beliefs and spirituality, and involving family members and community facilitators (Lee et al., 2025). From a research perspective, future longitudinal and intervention studies are needed to support these pathways across multiple time points and to determine whether improving sleep quality can mitigate the adverse cardiometabolic effects of stress. Although exploratory mediation tests with individual PSQI components were not significant in this study, future research should examine specific domains (e.g., duration, latency, efficiency) in the stress-blood pressure relationship in larger samples. Moreover, incorporating objective and comprehensive assessments of stress and sleep (e.g., actigraphy, biomarkers), as well as assessing and accounting for sleep disorders such as sleep apnea, would further strengthen the precision of the findings.

Conclusions

This study demonstrated that sleep quality mediated the relationship between perceived stress and systolic blood pressure among older African American women, suggesting that sleep may serve as a key behavioral mechanism in the stress-blood pressure relationship. The results suggest the importance of developing culturally sensitive, multimodal interventions that integrate stress management and sleep health promotion to improve blood pressure control and reduce cardiometabolic risk in this high-risk population. Future longitudinal and intervention studies incorporating objective and comprehensive assessments of stress and sleep are warranted to further examine this pathway and to determine whether improving sleep quality can mitigate the adverse cardiometabolic effects of perceived stress.

Acknowledgments

We would like to thank all participants for their valuable contributions to this study.

Funding

The RiSE Study was supported by the National Institute on Aging grant number [R01AG081251].

Footnotes

Disclosure statement

No potential conflict of interest was reported by the author(s).

Consent for participate

All participants provided written informed consent prior to participation and were informed that their involvement was voluntary and that they could withdraw from the study at any time.

Ethical considerations

This study was approved by the Loyola University Chicago Institutional Review Board (IRB# 214,133,013,123).

Data availability statement

The dataset generated and analyzed during the current study will be made available after study completion upon request to the RiSE Research team (rise@luc.edu).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The dataset generated and analyzed during the current study will be made available after study completion upon request to the RiSE Research team (rise@luc.edu).

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