ABSTRACT
Poor sleep quality and psychological stress are interrelated and disproportionately affect adults with multiple cardiovascular disease (CVD) risk factors. Maintaining an optimal home environment and engaging in healthy bedtime behaviors are important components of sleep hygiene practices that influence sleep health. This study sought to examine: (1) the association between sleep hygiene and psychological stress, and (2) the moderating effect of sleep quality in the relationship between sleep hygiene and psychological stress, among adults with multiple CVD risk factors. A cross‐sectional study was conducted with 300 adults diagnosed with hypertension and diabetes. Individuals were recruited from a large academic health center and were asked to complete an online survey. Sleep hygiene was assessed by nine individual factors focusing on negative household environment (safety, physical comfort, temperature, noise, and light) and poor bedtime behaviors (watching TV, playing video games, using small screens, and eating), and by a composite score. Multivariable linear regression was employed to examine the associations. Of the sample, 78% reported poor sleep quality and 44% reported high psychological stress. The composite sleep hygiene score was significantly associated with higher psychological stress after controlling for sleep quality, and the relationship was not modified by sleep quality. Unsafe household, uncomfortable physical environment, uncomfortable temperature, and eating at bedtime were independently related to increased stress levels. The study highlights strong links between sleep hygiene and psychological stress. Current evidence suggests that promoting home environment and bedtime behaviors may alleviate psychological burdens in adults with multiple CVD risk factors.
Keywords: cardiovascular risks, psychological stress, sleep hygiene, sleep quality
1. Introduction
Cardiovascular disease (CVD) remains the leading cause of death in the USA (Tsao et al. 2023). Hypertension and type 2 diabetes are two of the most common risk factors of CVD, affecting 122.4 million and 29.3 million US adults, respectively (Tsao et al. 2023). These chronic conditions frequently coexist. Adults who have hypertension for 5–10 years are four times more likely to develop diabetes compared to those with hypertension for less than 5 years (Wuhib Shumye et al. 2021). Similarly, up to 75% of adults with diabetes have hypertension (Jia and Sowers 2021). Together, people with both conditions are at substantially increased risks of CVD morbidities, such as stroke, coronary heart disease, congestive heart failure, and peripheral vascular disease (Yildiz et al. 2020).
Psychological stress is a critical risk factor for CVD (Covassin and Singh 2016; Levine 2022; Levine et al. 2021). A large population‐based cohort study conducted across 21 countries found that higher levels of stress were associated with increased risks of developing CVD (hazard ratio [HR]: 1.22, 95% confidence interval [CI]: 1.08–1.37) and stroke (HR: 1.30, 95% CI: 1.09–1.56) over a median follow‐up of 10 years (Santosa et al. 2021). Psychological stress prevalently affects individuals with multiple CVD risk factors. One study revealed that nearly half participants who had hypertension and/or diabetes suffered from psychological stress (Borie et al. 2024). While several methods have been explored to mitigate stress, growing evidence suggests that improving sleep may be an effective strategy to alleviate mental distress. Clinical trials indicate that sleep promotion interventions lead to significant small‐to‐medium‐sized effects in reducing psychological stress (Scott et al. 2021). This evidence is particularly relevant to individuals at risk for CVD, as better sleep can not only reduce stress but also improve cardiovascular health.
Room environment and bedtime behaviors are essential components of sleep hygiene practices. While their effects on sleep are well‐established, many factors within these components may also influence psychological stress. For example, living in an unsafe household can serve as a direct source of daily stress (Robinette et al. 2021). Additionally, contextual factors such as uncomfortable temperature, excessive noise, and ambient light exposure can disrupt sleep (Billings et al. 2020). Chronic sleep disturbances may cause dysregulation of emotional responses and activation of the hypothalamic–pituitary–adrenal axis, which in turn exacerbates stress responses (Herman et al. 2016; Palagini et al. 2024). Beyond the living environment, research indicates that nighttime behaviors may contribute to poor sleep. Johnson et al. (2021) found that listening to the radio or music, reading books, and consuming meals or snacks in bed were significantly related to actigraphy‐based sleep measures in a cohort of African–American older adults. Although there is limited research linking bedtime behaviors to psychological stress, it is plausible that these activities may indirectly raise stress levels through poor sleep.
Currently, few studies have examined sleep hygiene practices, specifically room environment and bedtime behaviors, in adults with multiple CVD risk factors. The purpose of this study was to examine the associations between sleep hygiene, sleep quality, and psychological stress. We assessed the relationship between sleep hygiene (including household environment and bedtime behaviors) and psychological stress, and the moderating effect of sleep quality in this relationship. We hypothesized that sleep hygiene independently associates with psychological stress and that sleep quality plays a moderating role in the relationship between sleep hygiene and psychological stress.
2. Methods
2.1. Study Design and Setting
We conducted a cross‐sectional study among adults who received primary care at Johns Hopkins Medicine, a large academic healthcare system affiliated with Johns Hopkins University, which provides a range of specialized and general medical services in the Maryland and Washington, DC metropolitan areas.
2.2. Sample Size Estimation
We calculated the sample size using G*Power 3.1 (Faul et al. 2007). The statistical test used was multivariate linear regression, with an assumed moderate effect size ( f 2 = 0.15), α = 0.05, power = 0.80, and 11 predictors included in the most adjusted model (Model 4). The minimum sample size required was estimated to be 123 participants. To ensure sufficient statistical power, we targeted a larger sample size for recruitment.
2.3. Participants
Participants were identified through a retrospective review of electronic health records. Prior to conducting the chart review, our research team obtained a waiver of the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule from the University's Institutional Review Board. The inclusion criteria were: (1) aged 18 years or older; (2) had a diagnosis of primary hypertension (ICD‐10 code I‐10) either from a recent encounter or listed as an active problem within the last 12 months; (3) had a diagnosis of type 2 diabetes (ICD‐10 code E‐11) either from a recent encounter or listed as an active problem within the last 12 months; and (4) had the most recent systolic blood pressure ≥ 130 mmHg and HbA1c ≥ 6.5%. The exclusion criteria were: (1) treated as inpatients; (2) pregnant or lactating; or (3) had conditions including type 1 diabetes, gestational diabetes, end‐stage renal disease, acute cardiovascular event, acute cerebrovascular event, or metastatic cancer. After identifying potential participants, the data acquisition team securely shared their contact information with the study team.
2.4. Data Collection
We obtained informed consent electronically through an online form before participants completed an anonymous online survey, which included questions on sociodemographic backgrounds, clinical characteristics, room environment, bedtime behaviors, sleep quality, and psychological stress. Confidentiality was maintained by removing personally sensitive information and storing the survey data on password‐protected devices accessible only to the study team. This study was approved by the University's Institutional Review Board.
2.5. Measures
2.5.1. Outcome Variable
Psychological Stress was measured by the Perceived Stress Scale 4 (PSS‐4) (Cohen et al. 1983). The 4‐item instrument examines the level at which situations are appraised as stressful in an individual's life during the past month. The PSS‐4 estimates how unpredictable, uncontrollable, and overloaded individuals perceive their lives to be. Respondents rate the occurrence of their feelings or thoughts in the past month on a scale of 0 (never) to 4 (very often). The total score is calculated by summing all ratings across the items, with an average score ≥ 6 indicating high levels of stress (Warttig et al. 2013). The PSS‐4 has been considered a valid tool, with adequate reliability (Cronbach's α 0.75), for assessing psychological stress (Ruisoto et al. 2020; Sanabria‐Mazo et al. 2024).
2.5.2. Independent Variables
Sleep Quality was measured by the Pittsburgh Sleep Quality Index (PSQI) (Buysse et al. 1989). The 19‐item instrument evaluates sleep quality and disturbances over the past month. The items cover seven components, including subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. These components are summed to generate a global score ranging from 0 to 21, where higher scores reflect poorer sleep quality. A global PSQI score > 5 indicates poor sleep quality. The PSQI has demonstrated satisfactory internal consistency reliability (Cronbach's α ranges from 0.70 to 0.83) and construct validity across diverse populations (Carpenter and Andrykowski 1998; Jerković et al. 2022; Zhong et al. 2015).
Sleep Hygiene (home environment and bedtime behaviors) was measured using the questions developed by Johnson et al. (2021). We created an adapted version by including the questions that adversely impact sleep. The adapted instrument consisted of nine items, with five assessing household environment and four assessing bedtime behaviors. The household environment questions assessed the following aspects: unsafe household, uncomfortable physical environment, uncomfortable temperature, noise disturbances, and light disturbances. The bedtime behavior items included watching TV, using small screens (e.g., computer, tablet, or phone), playing video games, and eating meals or snacks. Following the scoring approach suggested by the authors, all responses were coded on a scale of 0–1 (see Supporting Information S1: Table 1). A composite score was then created based on the average of nine items described above (range 0–1), where higher scores represented poorer room environment and bedtime behaviors. To facilitate interpretation, the composite score and three components with more than two responses (unsafe household, uncomfortable physical environment, uncomfortable temperature) were standardized to a normal distribution with a mean of 0 and a standard deviation (SD) of 1.
2.6. Covariates
Age and body mass index (BMI) were assessed as continuous variables. Other sociodemographic variables were categorized as biological sex (male or female), race (White or non‐White), education (high school/general educational development [GED] or less, associate degree/some college, and bachelor's degree or above), employment status (employed or unemployed), current smoker (yes or no), current drinker (yes or no), and history of obstructive sleep apnea (OSA) (yes or no).
2.7. Statistical Methods
Descriptive statistics were reported as mean ± SD for continuous variables and frequency (%) for categorical variables. Group differences in psychological stress and sleep quality across clinical profiles of patients (smoking status, drinking status, BMI category, and history of OSA) were assessed with independent‐samples t‐tests, Mann–Whitney U tests, or one‐way ANOVA as appropriate. Multivariable linear regression was used to examine the association between sleep hygiene and psychological stress. Four models were established: Model 1 was unadjusted; Model 2 was adjusted for age, sex, race, BMI, employment status, smoking status, drinking status, and history of OSA; Model 3 was further adjusted for sleep quality (poor sleep vs. good sleeper); Model 4 extended Model 3 by adding an interaction term between sleep quality and standardized composite sleep hygiene score to assess whether sleep quality modified the association between sleep hygiene and psychological stress. Following the composite sleep hygiene score analysis, we examined the independent associations between each sleep hygiene component (e.g., feeling unsafe at night) and psychological stress using multivariable linear regression, adjusting for the same covariates as in Model 3. Multicollinearity in the regression analysis was evaluated by the variation inflation factor, with values < 5 generally indicating low multicollinearity (Hair et al. 2010).
We additionally conducted sensitivity analyses using a standardized, weighted composite sleep hygiene score. To construct the weighted score, each item was individually regressed on psychological stress, adjusting for age, sex, race, and sleep quality. The resulting β coefficients (including both positive and negative values) were retained as item weights. These weights were then normalized to sum to 1, preserving their relative magnitudes and directions (see Supporting Information S2: Table 2). Each participant's score was calculated as the weighted average of the rescaled items and subsequently standardized to a mean of 0 and a SD of 1. The standardized composite score (weighted) was then analyzed using the same four multivariable linear regression models specified in the primary composite sleep hygiene score (unweighted) analysis. All analyses were performed using R version 4.3.1 (R Core Team 2024). The statistical significance was set at p < 0.05.
3. Results
3.1. Characteristics of Study Participants
A total of 300 adults diagnosed with hypertension and type 2 diabetes participated in the study. The mean age was 62 ± 12 years. Over half were female, 58% were White adults, 41% were current smokers, 21% were current drinkers, and 67% had a history of OSA. Of the sample, about 78% experienced poor quality of sleep in the past month, and 44% of the sample reported a high level of psychological stress. The practices of unhealthy sleep hygiene ranged from 8% for uncomfortable bedroom temperatures to 66% for using small screens at bedtime (Table 1).
Table 1.
Characteristics of participants (N = 300).
| Study variables | N (%) |
|---|---|
| Sociodemographic characteristics | |
| Age in years, mean ± SD | 62 ± 12 |
| Sex | |
| Male | 146 (49) |
| Female | 152 (51) |
| Race | |
| White | 174 (58) |
| Non‐White | 126 (42) |
| Education | |
| High school/general education development or less | 43 (14) |
| Associate degree/some college | 100 (34) |
| Bachelor's degree or above | 154 (52) |
| Employed status | 153 (51) |
| Clinical characteristics | |
| Current smokers | 123 (41) |
| Current drinkers | 63 (21) |
| BMI, mean ± SD | 33.4 ± 7.9 |
| Normal/underweight (≤ 24.9) | 26 (9) |
| Overweight (25.0–29.9) | 86 (29) |
| Obesity (≥ 30.0) | 187 (62) |
| Sleep quality (PSQI score), mean ± SD | 7.9 ± 3.9 |
| Good sleeper | 65 (22) |
| Poor sleeper | 235 (78) |
| Psychological stress (PSS‐4 score), mean ± SD | 5.0 ± 3.3 |
| Low stress | 169 (56) |
| High stress | 131 (44) |
| History of obstructive sleep apnea | 201 (67) |
| Sleep hygiene practices | |
| Household environment | |
| Unsafe at night | 26 (9) |
| Uncomfortable physical environment | 39 (13) |
| Uncomfortable temperature | 24 (8) |
| Noise disturbances | 81 (27) |
| Light disturbances | 27 (9) |
| Bedtime behaviors | |
| Watch TV | 181 (60) |
| Use computer/tablet/phone | 198 (66) |
| Play video games | 43 (14) |
| Eat meals or snacks | 88 (29) |
| Sleep Hygiene Composite Score (0–1), nonstandardized, mean ± SD | 0.27 (0.16) |
Note: The non‐White racial group includes individuals who identified as Black, Asian, Asian Indian or Alaska Native, Native Hawaiian or Other Pacific Islander. The composite sleep hygiene score was calculated as the average of 9 items assessing household environment and bedtime behaviors, with higher scores indicating poorer sleep hygiene practices. Column percentages are shown; counts may not sum to 300 due to missing data.
Abbreviations: BMI, body mass index; PSQI, pittsburgh sleep quality index; PSS‐4, perceived stress scale; SD, standard deviation.
3.2. Group Differences in Psychological Stress and Sleep Quality Across Clinical Profiles
As shown in Table 2, psychological stress and sleep quality did not differ statistically significantly by smoking status, drinking status, BMI category, or history of OSA (all p > 0.05). However, the pattern suggests that current drinkers, individuals with obesity, and those with a history of OSA tended to have higher scores in these categories.
Table 2.
Group differences in sleep quality and psychological stress across clinical profiles of participants.
| Sleep quality | Psychological stress | |||
|---|---|---|---|---|
| Clinical profiles | Mean ± SD | p | Mean ± SD | p |
| Smoking status | ||||
| Not currently smoking | 7.8 ± 4.0 | 0.715 | 5.0 ± 3.4 | 0.618 |
| Current smokers | 7.9 ± 3.7 | 5.0 ± 3.1 | ||
| Drinking status | ||||
| Not currently drinking | 7.7 ± 3.8 | 0.150 | 4.9 ± 3.3 | 0.154 |
| Current drinkers | 8.7 ± 4.1 | 5.4 ± 3.2 | ||
| BMI | ||||
| Normal/underweight (≤ 24.9) | 7.5 ± 3.8 | 0.383 | 4.7 ± 3.4 | 0.428 |
| Overweight (25.0–29.9) | 7.4 ± 4.1 | 4.6 ± 3.1 | ||
| Obesity (≥ 30.0) | 8.1 ± 3.8 | 5.2 ± 3.4 | ||
| History of obstructive sleep apnea | ||||
| No | 7.6 ± 3.7 | 0.162 | 4.9 ± 3.2 | 0.551 |
| Yes | 8.4 ± 4.2 | 5.2 ± 3.5 | ||
Note: p values were derived from independent‐sample t‐tests or Mann–Whitney U tests for two‐group comparisons and from one‐way ANOVA for BMI categories.
Abbreviation: BMI, body mass index.
3.3. Association Between Sleep Hygiene and Psychological Stress
Table 3 presents the results of sequential multivariable linear regression models assessing the association between the standardized composite sleep hygiene score and psychological stress. Models 1–3 each demonstrated a statistically significant positive association: Model 1 (β = 0.96, 95% CI 0.60–1.32), Model 2 (β = 0.66, 95% CI 0.27–1.05), and Model 3 (β = 0.61, 95% CI 0.22–1.01). Additionally, Model 3 also showed that poor sleepers reported significantly higher levels of stress than good sleepers (β = −1.09, 95% CI −1.97 to −0.21). In Model 4, the interaction term between sleep quality and standardized composite sleep hygiene score was nonsignificant (β = −0.25, 95% CI −1.14 to 0.65), suggesting no modification effect by sleep quality. The main effects in Model 4 remained consistent with Model 3 (sleep hygiene: β = 0.67, 95% CI 0.23–1.12; sleep quality: β = −1.15, 95% CI −2.06 to −0.25).
Table 3.
Association between sleep hygiene and psychological stress.
| Variables | Psychological stress | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | Model 4 | |||||||||
| β | 95% CI | p | β | 95% CI | p | β | 95% CI | p | β | 95% CI | p | |
| Sleep hygiene | 0.96 | (0.60, 1.32) | < 0.001 | 0.66 | (0.27, 1.05) | 0.001 | 0.61 | (0.22, 1.01) | 0.002 | 0.67 | (0.23, 1.12) | 0.003 |
| Sleep quality | ||||||||||||
| Poor sleeper | Ref | Ref | ||||||||||
| Good sleeper | — | — | — | — | — | — | −1.09 | (−1.97, −0.21) | 0.015 | −1.15 | (−2.06, −0.25) | 0.013 |
| Sleep hygiene × good sleeper | — | — | — | — | — | — | — | — | — | −0.25 | (−1.14, 0.65) | 0.590 |
Note: Model 1 was unadjusted. Models 2, 3, and 4 were adjusted for the covariates. The sleep hygiene score was calculated as the average of 9 items assessing household environment and bedtime behaviors. This score was standardized to a normal distribution (mean = 0, standard deviation = 1), with higher values indicating poorer sleep hygiene practices.
Abbreviations: CI, confidence interval; Ref, reference group.
3.4. Association Between Individual Sleep Hygiene Components and Psychological Stress
Table 4 shows the associations of individual sleep hygiene components with psychological stress, fully adjusting for sleep quality and the covariates. Unsafe at night (β = 0.90, 95% CI 0.55–1.25), uncomfortable physical environment (β = 0.72, 95% CI 0.35–1.08), uncomfortable temperature (β = 0.77, 95% CI 0.41–1.13), and eating at bedtime (β = 1.29, 95% CI 0.48–2.10) were all positively related to psychological stress. Comparison across the standardized coefficients showed that feeling unsafe was the factor most strongly linked to psychological stress.
Table 4.
Association between individual sleep hygiene components and psychological stress.
| Sleep hygiene practices | Psychological stress | ||
|---|---|---|---|
| Standardized β | 95% CI | p | |
| Household environment | |||
| Unsafe at night | 0.90 | (0.55, 1.25) | < 0.001 |
| Uncomfortable physical environment | 0.72 | (0.35, 1.08) | < 0.001 |
| Uncomfortable temperature | 0.77 | (0.41, 1.13) | < 0.001 |
| Noise disturbances | |||
| Always | Ref | ||
| Sometimes | 0.80 | (−0.06, 1.65) | 0.068 |
| Never | 0.11 | (−1.69, 1.91) | 0.903 |
| Light disturbances | |||
| No | Ref | ||
| Yes | 0.45 | (−0.83, 1.73) | 0.489 |
| Bedtime behaviors | |||
| Watch TV | |||
| No | Ref | ||
| Yes | −0.02 | (−0.76, 0.73) | 0.959 |
| Use computer/tablet/phone | |||
| No | Ref | ||
| Yes | −0.29 | (−1.08, 0.49) | 0.463 |
| Play video games | |||
| No | Ref | ||
| Yes | −0.09 | (‐1.16, 0.98) | 0.872 |
| Eat meals or snacks | |||
| No | Ref | ||
| Yes | 1.29 | (0.48, 2.10) | 0.002 |
Note: Unsafe at night, uncomfortable physical environment, and uncomfortable temperature were standardized to a normal distribution with a mean of 0 and a standard deviation of 1. All models were adjusted for age, sex, race, BMI, employment status, smoking status, drinking status, and history of obstructive sleep apnea.
Abbreviations: CI, confidence interval; Ref, reference group.
3.5. Sensitivity Analysis
A sensitivity analysis using the standardized, weighted composite sleep hygiene score produced results consistent with those in Table 3. Across Models 1–3, sleep hygiene remained positively associated with higher levels of psychological stress, with slightly larger coefficients. Poor sleepers continued to report greater stress than good sleepers, and the interaction between sleep quality and the weighted composite score was not significant (β = −0.40, 95% CI −1.29 to 0.48; see Supporting Information S3: Table 3).
4. Discussion
Our study investigated the relationship between sleep hygiene, sleep quality, and psychological stress among 300 adults diagnosed with hypertension and diabetes. Results highlighted prevalent issues with poor sleep environments and unhealthy bedtime behaviors in individuals with multiple CVD risk factors. Poor sleep hygiene practices were found to be independently associated with psychological stress. Contrary to our hypothesis, sleep quality did not moderate the relationship between sleep hygiene and psychological stress. These findings emphasize the importance of promoting effective sleep hygiene practices, which may substantially reduce psychological stress in adults at high risk for CVD.
We found a high prevalence of psychological stress (44%) in this study sample. The stress could be explained by several factors. First, the disease burdens of hypertension and type 2 diabetes can contribute significantly to psychological stress. A study by Adzrago et al. (2025) identified a dose–response relationship between the number of chronic diseases and psychological distress. Additionally, CVD risk factors can disrupt sleep physiology, which consequently leads to sleep disturbances. This corresponds with our finding that 78% of adults with multiple CVD risk factors reported poor sleep quality, a known contributor to psychological stress (Lo Martire et al. 2020; Nollet et al. 2020). Chronic sleep disturbances can stimulate the release of stress hormones and thereby further exacerbate sleep issues in a vicious cycle. Third, the coexistence of hypertension and diabetes increases the risk of OSA, which was observed in 67% of this study cohort, and is another key determinant of psychological stress (Bangash et al. n.d.; Reutrakul and Mokhlesi 2017). OSA leads to intermittent hypoxia caused by repeated airway blockages. The frequent awakenings during sleep activate the sympathetic nervous system, triggering stress responses through sympathetic overactivity (Maniaci et al. 2024).
We observed a positive relationship between sleep hygiene and psychological stress in adults with multiple CVD risk factors. The relationship remained consistent across four statistical models, even after controlling for sleep quality and covariates, suggesting that sleep hygiene independently contributes to psychological stress. The nonsignificant interaction term indicated that the link between sleep hygiene and stress is similar for both good and poor sleepers. That is, even individuals without apparent sleep quality issues might experience elevated psychological stress if they engage in poor sleep hygiene practices. Further analysis identified specific sleep hygiene components associated with psychological stress. Among the nine factors studied, unsafe household presented the strongest relationship. This finding aligns with previous research by Hernández et al. (2016), which found that adults residing in low‐income homes in New York City reported increased stress levels due to concerns regarding home security. In addition to household safety, other independent contributors included uncomfortable physical environments and suboptimal room temperature, highlighting the influence of immediate environmental conditions on mental health. We also found that bedtime eating was significantly associated with psychological stress. This behavior may serve as a coping mechanism for managing emotional distress, as suggested by Nolan and Geliebter (2012). Although our findings revealed a direct relationship between sleep hygiene and psychological stress, it is possible that poor sleep quality could play a role in this relationship. The complex interplay among sleep hygiene, sleep quality, and psychological stress warrants further investigation through longitudinal studies to better understand their causal pathways.
Integrating sleep hygiene assessments, environmental health evaluations, and behavioral counseling could have an impact on stress among adults with multiple CVD risk factors. Clinical education aimed at improving sleep hygiene could offer a practical, low‐cost, and scalable intervention. Targeted strategies might include enhancing household safety by raising personal awareness and fostering partnerships with community organizations to promote neighborhood security, as well as optimizing sleep environment such as using supportive mattresses and pillows, minimizing noise, and maintaining an ideal room temperature (~65°F–68°F) (Pacheco 2024). Importantly, several bedtime habits are frequently normalized and overlooked despite their adverse impact on mental health. Educating individuals on healthy bedtime routines (e.g., avoiding eating or screen exposure before sleep) may substantially reduce stress and improve overall cardiovascular health.
There are several limitations of our study that should be noted. As aforementioned, the cross‐sectional nature of this study prevents us from inferring causal relationships between the variables studied. Longitudinal studies are needed to confirm these findings. Second, our participants were comprised of adults receiving primary care within a single healthcare system in Maryland, which may limit the generalization of the results to the broader US population. Third, while our study assessed important aspects of sleep hygiene practices, it did not capture all relevant factors. Other important practices, such as maintaining consistent sleep schedules, limiting the intake of stimulating foods (e.g., alcohol, caffeine), exercising regularly, and avoiding naps in the late afternoon or evening, were not included in our evaluation (Baranwal et al. 2023). Fourth, sleep was self‐reported in this study. Although the PSQI provides a reliable measure of perceived sleep quality, self‐reporting is often subject to recall bias and may not fully align with objective measures. Fifth, the sleep hygiene measure used in this study lacks detailed psychometric validation, including reliability and validity testing, and this measure does not specify its recall period for respondents. Sixth, the potential comorbidity of CVD risk factors, OSA, and/or psychological disorders may serve as confounders that impact the study results. Finally, future work should consider including other internal factors (e.g., personality traits) and external factors (e.g., family support) to gain a better understanding of how the immediate environment and bedtime rituals influence CVD risk factors.
In conclusion, this study demonstrated a strong association between sleep hygiene practices and psychological stress in adults with multiple CVD risk factors. Factors such as feeling unsafe at night, uncomfortable physical environment, uncomfortable temperature, and eating meals or snacks at bedtime were independently related to elevated stress levels. Clinical education and increased awareness regarding these modifiable sleep hygiene factors could have the potential to improve mental health outcomes and overall cardiovascular well‐being among high‐risk populations.
Author Contributions
Xiaoyue Liu conceived and designed the study, collected data, interpreted results, drafted the initial manuscript, and critically revised the manuscript for important intellectual content. Junxin Li interpreted results, critically revised the manuscript for important intellectual content. Jinyu Hu performed statistical analysis, interpreted results, drafted initial manuscript, critically revised manuscript for important intellectual content. Jason Fletcher interpreted results, critically revised the manuscript for important intellectual content. Yvonne Commodore‐Mensah critically revised the manuscript for important intellectual content. Cheryl R. Himmelfarb critically revised the manuscript for important intellectual content. All authors approved the final manuscript.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Supplementary Table 1: Scaling of Sleep Hygiene Components.
Supplementary Table 2: Weighted Sleep Hygiene Composite Score Construction.
Supplementary Table 3: Association Between Weighted Sleep Hygiene and Psychological Stress.
Acknowledgments
This work was supported by the National Institute on Minority Health and Health Disparities Mid‐Atlantic Center for Cardiometabolic Health Equity (MACCHE) Pilot Grant (5P50MD017348).
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Table 1: Scaling of Sleep Hygiene Components.
Supplementary Table 2: Weighted Sleep Hygiene Composite Score Construction.
Supplementary Table 3: Association Between Weighted Sleep Hygiene and Psychological Stress.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.