Joint association of sleep quality and physical activity with hypertension: a cross-sectional population study in agricultural workers

Objective: The study explores the prevalence of hypertension and evaluates the joint association of sleep quality and physical activity (PA) levels in influencing hypertension among the Kazakh agricultural workers in Xinjiang.

Methods: In this chronic disease study conducted in Xinjiang, participants were selected from Habahe County, a predominantly Kazakh region, between October and November 2023. Restricted cubic spline (RCS) analysis and multivariate logistic regression models were used to evaluate the associations between sleep quality, PA levels, and the prevalence of hypertension.

Results: In this study of 2,872 participants, the median age was 49 (42–57) years. Among participants, 1,253 (43.63%) were male. The study resulting in a hypertension prevalence of 54.94%. RCS regression showed a linear association between sleep quality and hypertension prevalence in participants (P overall = 0.007, P nonlinear = 0.214), and the association between PA level and hypertension prevalence was also statistically significant (P overall = 0.022). As PA level increased, hypertension prevalence gradually declined but stabilized at higher level. In a multivariate regression analysis adjusting for potential confounders, poorer sleep quality was associated with an increased prevalence of hypertension (OR = 1.33, 95% CI: 1.07–1.65, P = 0.011; OR = 1.38, 95% CI: 1.12–1.69, P = 0.001), while the association between PA level and hypertension prevalence was not statistically significant (P > 0.05). Further analysis showed that in the moderate PA level group, good sleep quality was significantly associated with a lower prevalence of hypertension (OR = 0.71, 95% CI: 0.52–0.97, P = 0.031). However, in individuals with low (<6,000 METs × min/week) and high (>12,000 METs × min/week) PA levels, the association between sleep quality and hypertension prevalence was not been observed (P > 0.05).

Conclusion: The current study showed that PA and sleep quality are associated with the prevalence of hypertension. Among individuals with moderate PA level, healthy sleep quality may have a protective effect against hypertension.

1 Introduction

Hypertension remains the foremost modifiable risk factor linked to cardiovascular diseases and premature death globally (1). Over the years, its incidence has escalated, imposing a significant burden on public health (2–4). Persistent high blood pressure induces structural and functional alterations in target organs and significantly increases the risk of severe complications, including myocardial infarction, stroke, and kidney failure (5, 6). Furthermore, hypertension adversely affects general health beyond the cardiovascular system, manifesting in symptoms such as headaches (7). These effects markedly compromise patient quality of life, imposing a significant socioeconomic burden on healthcare systems.

Adequate sleep, balanced diet, and regular exercise are pivotal in the holistic approach to managing hypertension (8–10), as delineated in the new ACC/AHA hypertension guidelines (11). The quality of sleep emerges as a prominent influence cardiovascular health, including the risk of hypertension and other cardiovascular diseases (12, 13). Previous studies have emphasized that maintaining healthy sleep pattern and ensuring adequate sleep duration are crucial for reducing hypertension risk (14–16). Moreover, physical activity (PA), particularly during leisure PA, is widely recognized for its beneficial effects on lowering blood pressure and enhancing heart health (17–19). The combined impact of sleep and PA could potentially synergize to lower hypertension risks through diverse mechanisms (20, 21). While much of the existing research has focused on the separate contributions of sleep quality and PA towards hypertension reduction primarily in urban settings (21–23), the association of sleep quality, PA, and their potential interactions in agricultural workers remains unclear. In this study, we leveraged the 2023 baseline data from the population-based cohort study of chronic diseases in Xinjiang (PCCDX) to examine the association between PA level, sleep quality, and the prevalence of hypertension among agricultural workers.

2 Methods

2.1 Participants

This population-based, cross-sectional study was conducted between October and November 2023. A random sampling method was used to select two towns in Habahe County, Altay Region, predominantly inhabited by the Kazakh population. Participants from 27 administrative villages within these towns were recruited using convenience sampling. Documented consent was acquired in writing from each participant prior to the study. The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (Approval No. K202001-06).

2.2 Measures

Before the initiation of the research, 8 researchers were comprehensively trained to harmonize the methodology for data collection. They conducted structured interviews with each participant using a standardized paper questionnaire. This survey was designed to identify and explore potential confounding variables that could affect the relationships among sleep quality, PA levels, and hypertension. Factors such as demographic details, medical history, and related lifestyle factors were meticulously documented to enhance the reliability of the findings.

In this investigation, sleep quality was evaluated through the application of the Pittsburgh Sleep Quality Index (PSQI), which is widely recognized for its effectiveness in assessing sleep patterns (22–24). Scores from the PSQI were segmented into three categories (25): healthy sleep quality (≤5 scores), intermediate sleep quality (5–8 scores), and poor sleep quality (>8 scores). PA levels were evaluated using the modified short-form International Physical Activity Questionnaire (IPAQ) (26). IPAQ is a widely used for assessing PA levels across diverse populations and various types of PA (27–29). The accumulation of weekly PA was quantified in terms of total metabolic equivalent tasks (METs), determined by the product of MET values assigned to different activities and the duration of these activities each week (30). The categorization of PA levels was established as low PA (≤6,000 METs × min/week), moderate PA (6,000–12,000 METs × min/week), and high PA (>12,000 METs × min/week).

This investigation included multiple covariates such as sex, age, educational attainment (below senior high school or above senior high school), marital status (married or other), household annual total income (<60,000 CNY or ≥60,000 CNY), smoking status (“yes” or “no”), alcohol ingestion (“yes” or “no”), body mass index (BMI), and dietary intakes. Current smokers are defined as individuals who reported having smoked at least one cigarette daily for 6 months, or at least 100 cigarettes in their lives. Current drinkers were specified as those who had drunk alcohol at least once a week during the previous 12 months (18). The assessment of dietary intakes involved querying participants regarding how often they consumed prevalent food types over the previous year (categorized as never/occasionally, ≤3 times per month, 1–2 times per week, 3–4 times per week, 5–6 times per week, or daily). The Dietary diversity score (DDS) (31, 32), which is based on the Chinese Dietary Guidelines, considers nine categories of food: vegetables, fruits, legumes, nuts, meats, eggs, fish, tea, milk, and their derivatives. A DDS between 0 and 9 is used, where higher scores reflect a broader variety of diet.

2.3 Hypertension

Hypertension was defined based on systolic blood pressure (SBP) ≥140 mmHg, or diastolic blood pressure (DBP) ≥90 mmHg, or a previous diagnosis by a professional medical institution, or the use of antihypertensive medication in the past 2 weeks (33, 34).

Blood pressure measurements utilized the professional portable OMRON HEM-7136 BP monitor, manufactured by OMRON in Kyoto, Japan. Participants were advised to avoid smoking and not ingesting alcohol, tea, or coffee for at least 30 min prior to their appointment. Following a resting period of no less than 5 min, the right upper arm served as the site for two sequential blood pressure readings while the individual was seated. These readings were then averaged to ascertain the final blood pressure value.

2.4 Statistical analysis

The software Stata 17.0, R 4.4.0, and GraphPad Prism 9.5 were used for data processing and analysis. Continuous variables at baseline data are reported as mean ± standard deviation or median (interquartile range, IQR), while categorical variables are presented as percentages. Continuous variables with normal distributions were assessed using t-tests or Analysis of Variance (ANOVA), whereas those not normally distributed were examined through rank-sum tests. Categorical data assessment was conducted with chi-square tests. Missing data were imputed using multiple imputation. To assess the potential relationship between sleep quality, PA levels, and hypertension prevalence. A restrictive cubic spline (RCS) model was used to evaluate the dose-response relationship, with adjustments for potential confounding factors such as sex, age, educational attainment, marital status, smoking, alcohol ingestion, DDS, BMI, and total PA or PSQI score. Binary logistic regression analysis was used to explore the associations between various levels of PA, or sleep quality and the prevalence of hypertension. This study further conducted sensitivity analyses to verify the robustness of the results. Model 1 was adjusted for age and sex. Model 2 further adjusted for educational attainment, marital status, smoking, alcohol consumption, DDS, and BMI. The results are presented as odds ratios (OR) with 95% confidence intervals (95% CI), with a two-sided P values <0.05 were considered statistically significant.

3 Results

3.1 Participant characteristics

In our study, a total of 2,966 participants aged 30 years or older were enrolled. After excluding individuals with missing valid blood pressure measurements (n = 94), the final analysis included 2,872 participants (Figure 1). The mean age of the participants was 50 ± 10 years. The median age was 49 (42–57) years, and 1,253 (43.63%) were male. Among the participants, 1,578 were diagnosed with hypertension, yielding a prevalence of 54.94%. Further analysis revealed that individuals with hypertension were older, had a higher BMI, lower educational attainment, and were more likely to have family history of hypertension compared to those non-hypertension. Additionally, hypertensive individuals had lower DDS, longer sedentary time, lower PA, and poorer sleep quality (all P < 0.05). However, no statistically significant differences were observed between the two groups in terms of household annual total income, alcohol ingestion, or tea-drinking habits (P > 0.05). Detailed sociodemographic, behavioral, and health characteristics are presented in Table 1.

Figure 1

Figure 1. Flow chart of the participants of the present study.

Table 1

Table 1. Baseline characteristics of participants.

3.2 Associations of PA level or sleep quality with the prevalence of hypertension

As illustrated in Figure 2, the results of dose-response relationship indicate a significant positive association between sleep quality and the prevalence of hypertension (P overall = 0.002), suggesting that poor sleep quality may increase the prevalence of hypertension. However, the nonlinear trend was not statistically significant (P nonlinear = 0.130). Additionally, the association between PA level and hypertension prevalence was statistically significant (P overall = 0.022), while the nonlinear relationship remained nonsignificant (P nonlinear = 0.426). Notably, individuals with lower PA level (<6,000 METs × min/week) exhibited a relatively higher prevalence of hypertension. As PA level increased (>6,000 METs × min/week), the prevalence of hypertension gradually declined. However, at higher PA levels (>12,000 METs × min/week), the curve tended to plateau. This study further analyzed the effects of sleep quality and PA levels on the prevalence of hypertension in Table 2. Poor sleep quality was positively associated with the prevalence of hypertension. In Model 2, after adjusting for potential confounders, compared with individuals with healthy sleep quality, the prevalence of hypertension increased in the intermediate sleep quality group (OR = 1.33, 95% CI: 1.07–1.65, P = 0.011) and the poor group (OR = 1.38, 95% CI: 1.12–1.69, P = 0.001). However, compared with individuals with low PA level, the prevalence reduction in individuals with moderate PA and high PA level was no longer statistically significant (OR = 0.86, 95% CI: 0.72–1.02, P = 0.083; OR = 1.00, 95% CI: 0.74–1.34, P = 0.186). The trend test results showed that sleep quality was significantly positively correlated with hypertension prevalence (P for trend = 0.002), with the prevalence increasing as poorer sleep quality. However, the trend between PA level and hypertension prevalence was non-significant (P for trend = 0.186).

Figure 2

Figure 2. Dose-response relationship between sleep quality (A), PA levels (B) and hypertension. In the RCS plot, sleep quality had three knots set at the 10th, 50th, and 90th percentiles of PSQI scores, while PA had four knots set at the 5th, 35th, 65th, and 95th percentiles of total PA. The solid line represents the point estimates; the shaded area represents the confidence intervals; and the black line is the reference line. Model was adjusted for age, sex, educational attainment, marital status, smoking status, household annual total income, alcohol ingestion, DDS, BMI, and PA or sleep quality.

Table 2

Table 2. Logistic regression analysis for the associations between PA level/sleep quality and the prevalence of hypertension.

Further analysis of the relationship between sleep quality and the prevalence of hypertension at different levels of PA is presented in Figure 3. In the moderate PA group, after adjusting for age and sex, individuals with healthy sleep quality had 32% lower risk of hypertension compared with those with poor sleep quality (OR = 0.68, 95% CI: 0.51–0.92, P = 0.011; P for trend = 0.006). This association remained statistically significant after additional adjustment for confounding factors (OR = 0.71, 95% CI: 0.52–0.97, P = 0.031), and trend analyses indicated that improved sleep quality might provide a protective effect against hypertension (P for trend <0.05). Further analysis was conducted to examine the relationship between sleep quality and hypertension prevalence (Figure 3). In the moderate-intensity physical activity (PA) group, after adjusting for age and sex, individuals with healthy sleep quality had a 32% lower risk of hypertension compared to those with poor sleep quality (OR = 0.68, 95% CI: 0.51–0.92, P = 0.011; P for trend = 0.006). This association remained statistically significant after further adjustment for potential confounders (OR = 0.71, 95% CI: 0.52–0.97, P = 0.031), suggesting that improving sleep quality may have a protective effect against hypertension in this population (P for trend <0.05). In the low physical activity group, where model 1 (adjusted for age and sex) showed that individuals with healthy sleep quality had a lower risk of hypertension (OR = 0.74, 95% CI: 0.55–0.98, P = 0.039), although this difference did not reach statistical significance in model 2 (OR = 0.76, 95% CI: 0.56–1.02, P = 0.076). Interaction analyses suggested that there was no significant interaction between PA level and sleep quality (P for interaction >0.05). Notably, in the high PA group, the association between sleep quality and hypertension prevalence did not also have statistical significance (P > 0.05).

Figure 3

Figure 3. The relationship between sleep quality and the prevalence of hypertension at different PA levels. The figure presents odds ratios (ORs) and 95% confidence intervals (CIs) for hypertension prevalence across different sleep quality categories (poor, intermediate, and healthy) stratified by PA levels (>12,000 METs × min/week, 6,000–12,000 METs × min/week, and 56,000 METs × min/week). Model 1 was adjusted for age and sex. Model 2 was adjusted for age, sex, educational attainment, marital status, smoking status, household annual total income, alcohol ingestion, DDS, BMI. P for trend indicates the trend across sleep quality categories within each PA level group, and P for interaction assesses the interaction between sleep quality and PA level on hypertension prevalence.

3.3 Sensitivity analysis

To assess the robustness of our findings under different hypertension diagnostic criteria, we redefined hypertension by lowering the threshold from 140/90 to 130/80 mmHg and conducted a sensitivity analysis (Supplementary Tables S1, S2). The results demonstrated that the associations between sleep quality, PA levels, and hypertension prevalence remained consistent regardless of the diagnostic criteria applied. Even under the more stringent blood pressure threshold, sleep quality and PA continued to exert a significant influence on hypertension risk. These findings further reinforce the reliability and generalizability of our conclusions.

4 Discussion

To the best of the authors' knowledge, this is the first study to focus on agricultural workers on aged 30 and above in the remote regions of northwest China, aiming to investigate the impact of physical activity and sleep quality on the prevalence of hypertension among participants. The results indicated that the median PA level among participants was approximately 6,659 METs × min/week, which is significantly higher than the 3,213 METs × min/week observed in a previous study on Chinese adults (35). This elevated level of physical activity may be attributed to the participants’ rural backgrounds and their involvement in labor-intensive agricultural work. Such occupational activities are characterized by high intensity, long duration, and repetitive motions, typically utilizing only about 30% of maximal aerobic capacity, which does not lead to a sustained increase in blood pressure (36).

We observed a complex relationship between PA levels and hypertension prevalence. When PA exceeded 6,000 METs × min/week, a significant reduction in hypertension prevalence was observed. Previous research has confirmed that PA levels above the thresholds recommended by The WHO Guidelines on PA and sedentary behavior can significantly reduce the risk of cardiovascular diseases, including hypertension (17, 29). Proposed mechanisms include enhanced cardiovascular function, improved endothelial function, reduced systemic inflammation, better glycemic control, and weight management (19, 37). However, there remains controversy regarding whether excessive or high-intensity PA continues to confer health benefits (18, 35). In our study, the decreasing trend in hypertension prevalence plateaued beyond 12,000 METs × min/week, a finding consistent with prior research (35, 38), suggesting a potential “threshold effect”: while moderate exercise is beneficial for blood pressure control, excessive PA may induce oxidative stress and autonomic nervous system imbalance, partially offsetting its protective effects. Similar findings have been reported in studies examining PA and mortality risk (20, 36, 39). According to the “physical activity paradox”, different types of PA have different effects on health (40, 41). Previous research has predominantly focused on leisure-time physical activity (LTPA) and its significant health benefits (42). However, the PA observed in this study is predominantly occupational, particularly related to agricultural work, and its effects may differ from those associated with LTPA (43). LTPA primarily comprises dynamic activities of higher intensity and shorter durations, while occupational physical activity is characterized by repetitive, high-intensity activities, often involving continuous static muscle load and limited recovery time between high-intensity tasks, leading to increased physiological stress and potentially limiting cardiovascular benefits (41). In the study, less than 10% of the participants reported that PA exceeding 12,000 MET-min/week. Beyond this threshold, the negative impacts such as physiological strain, insufficient recovery, and occupational stressors may potentially overshadow the benefits associated with PA, compounding the relationship between occupational activity and hypertension. In contrast, sleep quality demonstrated a significant inverse association with hypertension prevalence. Compared to individuals with good sleep quality, those with intermediate and poor sleep quality had 33% and 38% higher risks of hypertension, respectively. This aligns with previous research (12, 44, 45), indicating that sleep disorders may elevate hypertension risk via mechanisms such as sympathetic nervous system activation, increased inflammation, and endothelial dysfunction (46, 47). A prospective cohort study based on UK Biobank data showed that maintaining healthy sleep patterns is associated with reduced hypertension incidence and fewer cardiovascular events in individuals already diagnosed with hypertension (2). Similarly, a longitudinal cohort study among younger and middle-aged adults supported the conclusion that poor sleep quality increases the risk of developing hypertension (48). Our findings provide additional empirical evidence for the relationship between sleep quality and hypertension risk.

Further stratified analysis revealed that, among participants with moderate PA, those with good sleep quality had significantly lower hypertension risk than those with poor sleep quality, suggesting that improving sleep may be particularly beneficial in this subgroup. Moreover, our study did not find a significant interaction between sleep quality and PA level in relation to hypertension prevalence. This differs from findings in some previous studies. For example, an Australian study on middle-aged women reported that the coexistence of sleep disturbances and insufficient PA significantly increased hypertension risk (49). Another study exploring the joint effects of PA and sleep duration on mortality risk suggested additive or multiplicative effects on cardiovascular and all-cause mortality (50). The lack of interaction observed in our study may be due to differences in study populations, assessment methods for PA and sleep, or statistical modeling approaches. Additionally, the observed differences in ORs across strata might be due to chance, given the non-significant interaction. Thus, further investigation in studies adequately powered to detect such interactions is required.

There are several limitations to this study. First, as a cross-sectional study, it is unable to establish causal relationships between sleep quality, PA levels, and hypertension. Future prospective cohort studies will be essential to validate our findings and provide clearer insights into the causal relationships. Second, the sample size of the high PA group was small, which may potentially bias the results and limit the generalizability of the findings for this specific subgroup. Third, questionnaire survey was used in the study, and there was a possibility of meeting bias when participants recalled related sleep and PA. Finally, although we accounted for numerous hypertension-related risk factors and adjusted for them in the analysis, the potential confounding from unmeasured variables cannot be completely ruled out. Despite these limitations, our study is well-designed, employs reliable methodology, and considers a comprehensive range of factors, providing valuable empirical support for the prevention and management of hypertension among agricultural workers.

PA and sleep quality are associated with the prevalence of hypertension. Among individuals with moderate levels of PA, poor sleep quality is significantly linked to an increased risk of hypertension. Our findings underscore the importance of implementing health interventions that target both PA and sleep quality. Future research should incorporate objective devices to assess the health impacts of both sleep and PA, and conduct prospective studies focused on these behaviors.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

Y-FC: Data curation, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review & editing. QZ: Supervision, Writing – review & editing, Conceptualization, Formal analysis, Software, Validation. XH: Software, Validation, Writing – review & editing. LC: Software, Writing – review & editing, Investigation. XQ: Investigation, Visualization, Writing – review & editing. JM: Investigation, Data curation, Software, Writing – review & editing. FL: Funding acquisition, Methodology, Supervision, Validation, Writing – review & editing. X-ML: Supervision, Conceptualization, Project administration, Resources, Writing – review & editing. Y-NY: Supervision, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the Key R & D Program of the Xinjiang Uygur Autonomous Region (2022B03022-1); Youth Science and Technology Elite Talent Program, Xinjiang Medical University (XYD2024Q06); Youth Science and Technology Elite Talent Program of the Tianshan Talent Project (2022TSYCCX0033).

Acknowledgments

The authors thank the other investigators, staff, and study participants for their contribution.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2025.1618094/full#supplementary-material

References

1. GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet. (2020) 396(10258):1223–49. doi: 10.1016/S0140-6736(20)30752-2

PubMed Abstract | Crossref Full Text | Google Scholar

2. Charchar FJ, Prestes PR, Mills C, Ching SM, Neupane D, Marques FZ, et al. Lifestyle management of hypertension: international society of hypertension position paper endorsed by the world hypertension league and European society of hypertension. J Hypertens. (2024) 42(1):23–49. doi: 10.1097/HJH.0000000000003563

PubMed Abstract | Crossref Full Text | Google Scholar

3. Belay GJ, Fentanew M, Belay M, Gobezie M, Bekele G, Getie K, et al. Physical activity and its associated factors among patients with hypertension at Amhara region comprehensive specialised hospitals, northwest Ethiopia: an institutional based cross-sectional study. BMJ Open. (2023) 13(9):e073018. doi: 10.1136/bmjopen-2023-073018

PubMed Abstract | Crossref Full Text | Google Scholar

4. World Health Organization. Global Report on Hypertension: The Race Against a Silent Killer. Geneva: World Health Organization (2023). Available online at: https://www.who.int/publications/i/item/9789240081062 (Accessed October 28, 2010).

Google Scholar

5. Alansare AB, Paley JL, Quinn TD, Gibbs BB. Paradoxical associations of occupational and nonoccupational sedentary behavior with cardiovascular disease risk measures in desk workers. J Occup Environ Med. (2023) 65(7):e506–13. doi: 10.1097/JOM.0000000000002873

PubMed Abstract | Crossref Full Text | Google Scholar

6. Valenzuela PL, Carrera-Bastos P, Gálvez BG, Ruiz-Hurtado G, Ordovas JM, Ruilope LM, et al. Lifestyle interventions for the prevention and treatment of hypertension. Nat Rev Cardiol. (2021) 18(4):251–75. doi: 10.1038/s41569-020-00437-9

PubMed Abstract | Crossref Full Text | Google Scholar

7. Mohammadi M, Ayoobi F, Khalili P, Soltani N, La Vecchia C, Vakilian A. Relation of hypertension with episodic primary headaches and chronic primary headaches in population of Rafsanjan cohort study. Sci Rep. (2021) 11(1):24071. doi: 10.1038/s41598-021-03377-7

PubMed Abstract | Crossref Full Text | Google Scholar

8. Jaspan VN, Greenberg GS, Parihar S, Park CM, Somers VK, Shapiro MD, et al. The role of sleep in cardiovascular disease. Curr Atheroscler Rep. (2024) 26(7):249–62. doi: 10.1007/s11883-024-01207-5

PubMed Abstract | Crossref Full Text | Google Scholar

9. Hill EB, Cubellis LT, Wexler RK, Taylor CA, Spees CK. Differences in adherence to American heart association’s life’s essential 8, diet quality, and weight loss strategies between those with and without recent clinically significant weight loss in a nationally representative sample of US adults. J Am Heart Assoc. (2023) 12(8):e026777. doi: 10.1161/JAHA.122.026777

PubMed Abstract | Crossref Full Text | Google Scholar

10. Lopes S, Mesquita-Bastos J, Garcia C, Bertoquini S, Ribau V, Teixeira M, et al. Effect of exercise training on ambulatory blood pressure among patients with resistant hypertension. JAMA Cardiol. (2021) 6(11):1317. doi: 10.1001/jamacardio.2021.2735

PubMed Abstract | Crossref Full Text | Google Scholar

11. Flack JM, Adekola B. Blood pressure and the new ACC/AHA hypertension guidelines. Trends Cardiovasc Med. (2020) 30(3):160–4. doi: 10.1016/j.tcm.2019.05.003

PubMed Abstract | Crossref Full Text | Google Scholar

12. Martynowicz H, Wichniak A, Więckiewicz M. Sleep disorders and cardiovascular risk: focusing on sleep fragmentation. Dent Med Probl. (2024) 61(4):475–7. doi: 10.17219/dmp/185395

PubMed Abstract | Crossref Full Text | Google Scholar

13. Seweryn P, Orzeszek SM, Waliszewska-Prosół M, Jenča A, Osiewicz M, Paradowska-Stolarz A, et al. Relationship between pain severity, satisfaction with life and the quality of sleep in Polish adults with temporomandibular disorders. Dent Med Probl. (2023) 60(4):609–17. doi: 10.17219/dmp/171894

PubMed Abstract | Crossref Full Text | Google Scholar

14. Pu L, Zhang R, Wang H, Zhao T, Zeng J, Yang H, et al. Association between sleep pattern and incidence of hypertension: a prospective cohort study of older adult participants in the Chinese longitudinal healthy longevity survey. Arch Gerontol Geriatr. (2024) 119:105314. doi: 10.1016/j.archger.2023.105314

PubMed Abstract | Crossref Full Text | Google Scholar

15. Bock JM, Vungarala S, Covassin N, Somers VK. Sleep duration and hypertension: epidemiological evidence and underlying mechanisms. Am J Hypertens. (2022) 35(1):3–11. doi: 10.1093/ajh/hpab146

PubMed Abstract | Crossref Full Text | Google Scholar

16. Scott H, Lechat B, Guyett A, Reynolds AC, Lovato N, Naik G, et al. Sleep irregularity is associated with hypertension: findings from over 2 million nights with a large global population sample. Hypertension. (2023) 80(5):1117–26. doi: 10.1161/HYPERTENSIONAHA.122.20513

PubMed Abstract | Crossref Full Text | Google Scholar

17. Xu J-P, Zeng R-X, Lu H-N, Zhang Y-Z, Mai X-Y, Mao S, et al. Analysis of the dose-response effects of physical activity on cardiocerebrovascular and all-cause mortality in hypertension. Front Cardiovasc Med. (2022) 9:844680. doi: 10.3389/fcvm.2022.844680

PubMed Abstract | Crossref Full Text | Google Scholar

18. Zhou T, Su J, Tao R, Qin Y, Zhou J, Lu Y, et al. The association between daily total physical activity and risk of cardiovascular disease among hypertensive patients: a 10-year prospective cohort study in China. BMC Public Health. (2021) 21(1):517. doi: 10.1186/s12889-021-10551-z

PubMed Abstract | Crossref Full Text | Google Scholar

19. MacDonald CJ, Madika AL, Lajous M, Laouali N, Artaud F, Bonnet F, et al. Associations between physical activity and incident hypertension across strata of body mass index: a prospective investigation in a large cohort of French women. J Am Heart Assoc. (2020) 9(23):e015121. doi: 10.1161/JAHA.119.015121

PubMed Abstract | Crossref Full Text | Google Scholar

20. Cheng Z, Zeng Q, Zhu C, Yang G, Zhong L. Association between joint physical activity and sleep duration and hypertension in US adults: cross-sectional NHANES study. Sleep Health. (2024) 10(6):628–34. doi: 10.1016/j.sleh.2024.08.005

PubMed Abstract | Crossref Full Text | Google Scholar

21. Fei Z, Zhu X, Shan Q, Wan F, Tu Y, Lv X. Association between sleep disorders and physical activity in middle-aged Americans: a cross-sectional study from NHANES. BMC Public Health. (2024) 24(1):1248. doi: 10.1186/s12889-024-18665-w

PubMed Abstract | Crossref Full Text | Google Scholar

22. Zhang H, Li Y, Zhao X, Mao Z, Abdulai T, Liu X, et al. The association between PSQI score and hypertension in a Chinese rural population: the Henan rural cohort study. Sleep Med. (2019) 58:27–34. doi: 10.1016/j.sleep.2019.03.001

PubMed Abstract | Crossref Full Text | Google Scholar

23. Jeong M. Associations of sleep quality and physical activity with diabetes quality of life in Korean Americans with type 2 diabetes: a cross-sectional study. Healthcare. (2024) 12(7):756. doi: 10.3390/healthcare12070756

PubMed Abstract | Crossref Full Text | Google Scholar

24. Chen S, Song X, Shi H, Li J, Ma S, Chen L, et al. Association between sleep quality and hypertension in Chinese adults: a cross-sectional analysis in the Tianning cohort. Nat Sci Sleep. (2022) 14:2097–105. doi: 10.2147/NSS.S388535

PubMed Abstract | Crossref Full Text | Google Scholar

25. Dzierzewski JM, Mitchell M, Rodriguez JC, Fung CH, Jouldjian S, Alessi CA, et al. Patterns and predictors of sleep quality before, during, and after hospitalization in older adults. J Clin Sleep Med. (2015) 11(1):45–51. doi: 10.5664/jcsm.4362

PubMed Abstract | Crossref Full Text | Google Scholar

26. Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World health organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. (2020) 54(24):1451–62. doi: 10.1136/bjsports-2020-102955

PubMed Abstract | Crossref Full Text | Google Scholar

27. Cleven L, Krell-Roesch J, Nigg CR, Woll A. The association between physical activity with incident obesity, coronary heart disease, diabetes and hypertension in adults: a systematic review of longitudinal studies published after 2012. BMC Public Health. (2020) 20(1):726. doi: 10.1186/s12889-020-08715-4

PubMed Abstract | Crossref Full Text | Google Scholar

28. Wang J, Wang Y, Korivi M, Chen X, Zhu R. Status of sedentary time and physical activity of rural residents: a cross-sectional population-based study in eastern China. Front Public Health. (2022) 10:838226. doi: 10.3389/fpubh.2022.838226

PubMed Abstract | Crossref Full Text | Google Scholar

29. Bennett DA, Du H, Clarke R, Guo Y, Yang L, Bian Z, et al. Association of physical activity with risk of major cardiovascular diseases in Chinese men and women. JAMA Cardiol. (2017) 2(12):1349. doi: 10.1001/jamacardio.2017.4069

PubMed Abstract | Crossref Full Text | Google Scholar

30. Mendes MA, da Silva I, Ramires V, Reichert F, Martins R, Ferreira R, et al. Metabolic equivalent of task (METs) thresholds as an indicator of physical activity intensity. PLoS One. (2018) 13(7):e0200701. doi: 10.1371/journal.pone.0200701

PubMed Abstract | Crossref Full Text | Google Scholar

31. Qorbani M, Mahdavi-Gorabi A, Khatibi N, Ejtahed HS, Khazdouz M, Djalalinia S, et al. Dietary diversity score and cardio-metabolic risk factors: an updated systematic review and meta-analysis. Eat Weight Disord. (2022) 27(1):85–100. doi: 10.1007/s40519-020-01090-4

PubMed Abstract | Crossref Full Text | Google Scholar

32. Zhang M, Zhang N, Zhou M, Ma G. Association between neighborhood food environment and dietary diversity score among older people in Beijing, China: a cross-sectional study. Front Nutr. (2022) 9:903214. doi: 10.3389/fnut.2022.903214

PubMed Abstract | Crossref Full Text | Google Scholar

33. National Institute for Health and Care Excellence (NICE). Hypertension in Adults: Diagnosis and Management. London: National Institute for Health and Care Excellence (NICE) (2023).

Google Scholar

34. Wang JG. Chinese Guidelines for the prevention and treatment of hypertension (2024 revision). Chin J Hypertens. (2024) 32(7):603–700. doi: 10.16439/j.issn.1673-7245.2024.07.002

Crossref Full Text | Google Scholar

35. Guan T, Cao M, Zheng C, Zhou H, Wang X, Chen Z, et al. Dose–response association between physical activity and blood pressure among Chinese adults: a nationwide cross-sectional study. J Hypertens. (2024) 42(2):360–70. doi: 10.1097/HJH.0000000000003587

PubMed Abstract | Crossref Full Text | Google Scholar

36. Li J, Zhang X, Zhang M, Wang L, Yin P, Li C, et al. Urban-rural differences in the association between occupational physical activity and mortality in Chinese working population: evidence from a nationwide cohort study. Lancet Reg Health West Pac. (2024) 46:101083. doi: 10.1016/j.lanwpc.2024.101083

PubMed Abstract | Crossref Full Text | Google Scholar

37. Keiser T, Katz S, Robson SM, Greaney JL, Healy S, Malone SK, et al. Association between time-of-day for eating, exercise, and sleep with blood pressure in adults with elevated blood pressure or hypertension: a systematic review. J Hypertens. (2024) 42(6):951–60. doi: 10.1097/HJH.0000000000003732

PubMed Abstract | Crossref Full Text | Google Scholar

38. Zhou L, Feng W, Xiang N, Cheng Y, Ya X, Wang M, et al. Association between physical activity dimensions and the risk of hypertension among middle and older adults: a cross-sectional study in China. Front Public Health. (2022) 10:995755. doi: 10.3389/fpubh.2022.995755

PubMed Abstract | Crossref Full Text | Google Scholar

39. Huang B-H, Duncan MJ, Cistulli PA, Nassar N, Hamer M, Stamatakis E. Sleep and physical activity in relation to all-cause, cardiovascular disease and cancer mortality risk. Br J Sports Med. (2022) 56(13):718–24. doi: 10.1136/bjsports-2021-104046

PubMed Abstract | Crossref Full Text | Google Scholar

40. Pronk N. Physical activity paradox: providing evidence-based guidance while closing research gaps. Br J Sports Med. (2024) 58(16):932–3. doi: 10.1136/bjsports-2024-108294

PubMed Abstract | Crossref Full Text | Google Scholar

41. Holtermann A, Krause N, van der Beek AJ, Straker L. The physical activity paradox: six reasons why occupational physical activity (OPA) does not confer the cardiovascular health benefits that leisure time physical activity does. Br J Sports Med. (2018) 52(3):149–50. doi: 10.1136/bjsports-2017-097965

PubMed Abstract | Crossref Full Text | Google Scholar

42. Pescatello LS, Buchner DM, Jakicic JM, Powell KE, Kraus WE, Bloodgood B, et al. Physical activity to prevent and treat hypertension: a systematic review. Med Sci Sports Exerc. (2019) 51(6):1314–23. doi: 10.1249/MSS.0000000000001943

PubMed Abstract | Crossref Full Text | Google Scholar

43. Holtermann A, Schnohr P, Nordestgaard BG, Marott JL. The physical activity paradox in cardiovascular disease and all-cause mortality: the contemporary Copenhagen general population study with 104,046 adults. Eur Heart J. (2021) 42(15):1499–511. doi: 10.1093/eurheartj/ehab087

PubMed Abstract | Crossref Full Text | Google Scholar

44. Lv Y, Jiang G, Tan X, Bao W, Chen L, Liu L. Association of sleep patterns and lifestyles with incident hypertension: evidence from a large population-based cohort study. Front Cardiovasc Med. (2022) 9:847452. doi: 10.3389/fcvm.2022.847452

PubMed Abstract | Crossref Full Text | Google Scholar

45. Li C, Shang S. Relationship between sleep and hypertension: findings from the NHANES (2007-2014). Int J Environ Res Public Health. (2021) 18(15):7867. doi: 10.3390/ijerph18157867

PubMed Abstract | Crossref Full Text | Google Scholar

46. Niu J, Xu D, Huang Y, You J, Zhang J, Li J, et al. Sex-related association of modifiable risk factors with hypertension: a national cross-sectional study of NHANES 2007-2018. Clin Cardiol. (2024) 47(1):e24165. doi: 10.1002/clc.24165

PubMed Abstract | Crossref Full Text | Google Scholar

47. Velasquez-Melendez G, Andrade FCD, Moreira AD, Hernandez R, Vieira MAS, Felisbino-Mendes MS. Association of self-reported sleep disturbances with ideal cardiovascular health in Brazilian adults: a cross-sectional population-based study. Sleep Health. (2021) 7(2):183–90. doi: 10.1016/j.sleh.2020.08.005

PubMed Abstract | Crossref Full Text | Google Scholar

48. Yuan Y, Heizhati M, Wang L, Li M, Lin M, Gan L, et al. Poor sleep quality is associated with new-onset hypertension in a diverse young and middle-aged population. Sleep Med. (2021) 88:189–96. doi: 10.1016/j.sleep.2021.10.021

PubMed Abstract | Crossref Full Text | Google Scholar

49. Duncan MJ, Holliday EG, Oftedal S, Buman M, Brown WJ. Joint association of physical activity and sleep difficulties with the incidence of hypertension in mid-age Australian women. Maturitas. (2021) 149:1–7. doi: 10.1016/j.maturitas.2021.04.006

PubMed Abstract | Crossref Full Text | Google Scholar

50. Liang YY, Feng H, Chen Y, Jin X, Xue H, Zhou M, et al. Joint association of physical activity and sleep duration with risk of all-cause and cause-specific mortality: a population-based cohort study using accelerometry. Eur J Prev Cardiol. (2023) 30(9):832–43. doi: 10.1093/eurjpc/zwad060

PubMed Abstract | Crossref Full Text | Google Scholar