Edited by: Jochen Mutschler, Private Clinic Meiringen, Switzerland
Reviewed by: Masoud Sadeghi, Kermanshah University of Medical Sciences, Iran; Peng Chen, Jilin University, China
*Correspondence: Lu Li,
This article was submitted to Public Mental Health, a section of the journal Frontiers in Psychiatry
†These authors have contributed equally to this work
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This meta-analysis examined the prevalence of poor sleep quality and its associated factors in patients with hypertension in China.
Both English (PubMed, PsycINFO, EMBASE) and Chinese (Wan Fang Database and Chinese National Knowledge Infrastructure) databases were systematically and independently searched. The random-effects model was used to estimate the prevalence of poor sleep quality in Chinese patients with hypertension. The funnel plot and Egger’s tests were used to assess publication bias.
The prevalence of poor sleep quality in 24 studies with 13,920 hypertensive patients was 52.5% (95% confidence interval [CI]: 46.1–58.9%). In contrast, the prevalence of poor sleep quality in six studies with 5,610 healthy control subjects was 32.5% (95% CI: 19.0–49.7%). In these studies, compared to healthy controls, the pooled odds ratio (OR) of poor sleep quality was 2.66 (95% CI: 1.80–3.93) for hypertensive patients. Subgroup and meta-regression analyses revealed that patients in hospitals were more likely to have poor sleep quality than patients in the community. Studies with smaller sample size, studies using convenience or consecutive sampling and those published in Chinese reported higher prevalence of poor sleep quality. Furthermore, poor sleep quality was more common in older and male hypertensive patients, while the proportion of poor sleep quality was negatively associated with survey year.
Appropriate strategies for screening, prevention, and treatment of poor sleep quality in this population should be developed.
Hypertension is a major public health burden and is associated with severe negative health outcomes. The World Health Organization reported that the number of people with raised blood pressure increased from 594 million in 1975 to 1.13 billion in 2015, with the increase mainly occurring in low- and middle-income countries (
Symptoms associated with hypertension, such as headache, chest pain, dizziness, shortness of breath, and nose bleeds (
The findings of numerous epidemiological surveys of poor sleep quality in hypertensive patients vary greatly, with prevalence ranging from 14.9 to 85.7% globally (
To date, no meta-analysis of poor sleep quality in hypertensive patients in China has been reported. Hence, using comparative and epidemiological studies, we conducted a meta-analysis of the pooled prevalence of poor sleep quality in Chinese hypertensive patients and its associated factors. Sleep quality is evaluated either by self-reported or interviewer-rated scales or physiological measures (such as polysomnography and actigraphy) (
The process of the literature search is shown in
PRISMA flowchart.
According to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) recommendation (
Two investigators (LiL and J-XC) independently assessed the methodological quality of the studies using a quality assessment tool consisting of eight items in terms of sampling, measurement, and analysis (
Data were independently extracted by two investigators (LiL and J-XC) and checked by a third investigator (LuL). The following information was extracted and tabulated: study site and time, geographic region, study location, sampling method, mean age, proportion of males, sample size, type of hypertension and cut-off values of instrument on sleep quality, the prevalence of poor sleep quality, and quality assessment. The hospital population refers to the studies that were conducted in hospitals in which participants received treatments, while community population refers to studies of participants with hypertension who lived in the community and received treatments in community clinics or outpatient clinics of general hospitals. In China, maintenance treatments of physical diseases are mainly provided by community clinics or outpatient clinics attached to general hospitals. Hospital- or community-based studies were classified based on the respective study-defined criteria.
The Comprehensive Meta-Analysis Program, Version 2 (Biostat Inc., Englewood, New Jersey, USA) was used to perform the data analysis. Due to different demographic characteristics and sampling methods, data on poor sleep quality were combined using the random-effects model; prevalence and odds ratio (OR) with 95% confidence intervals (CIs) were indicated as effect size. The I2 statistic and Cochran’s Q test were used to evaluate heterogeneity between studies, with I2 values greater than 50% indicating great heterogeneity (
Characteristics of the studies included in the meta-analysis.
No. | First author | Study area (Region) | Year of survey | Study location | Sampling method | Sample size | Mean age | Proportion of male (%) | Type of hypertension | Scale score | Cut-off score | Rate of hypertension (%) | Quality assessment |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Fang et al. ( |
Shanghai (S) | 2014 | Community | C | 1,606 | 72 (65–80) | 47.14 | NR | 7.61 ± 3.23 | >7 | 43.2 | 5 |
2 | Zhang et al. ( |
Hubei (S) | 2014 | Hospital | Conv | 70 | 43.4 ± 10.4 | 60 | Essential/Secondary | 9.73 ± 3.47 | >7 | 85.7 | 6 |
3 | Du et al. ( |
Jilin (N) | 2015–2016 | Community | Conv | 208 | 64.6 ± 6.5 | 51.92 | NR | 6.86 ± 2.28 | >7 | 43.75 | 5 |
4 | Hu et al. ( |
Hunan (S) | 2013–2014 | Hospital | R | 610 | 67.5 ± 7.1 | 55.74 | Essential | NR | >10 | 47.4 | 6 |
5 | Mao et al. ( |
Yunnan (S) | 2015 | Community | C | 793 | 68.0 ± 5.6 | 45.4 | NR | 5.7 ± 2.9 | >7 | 14.9 | 5 |
6 | Xiao et al. ( |
Guangdong (S) | 2013–2016 | Hospital | Cons | 176 | 68.0 ± 4.2 | NR | Essential | NR | >7 | 55.68 | 5 |
7 | Huang ( |
Fujian (S) | 2013–2014 | Hospital | Cons | 256 | 58.5 (30–80) | 55.86 | Essential | NR | >6 | 32.8 | 5 |
8 | Liu et al. ( |
Liaoning (N) | 2012–2013 | Community | M, R | 4,800 | 52.1 ± 14.1 | 50.72 | NR | 5.01 ± 2.71 | >5 | 36.02 | 7 |
9 | Ma et al. ( |
Shanxi (N) | 2014 | Hospital | Cons | 135 | 49 ± 6.4 | 100 | Essential | NR | >5 | 71.1 | 5 |
10 | Zheng et al. ( |
Fujian (S) | 2013–2014 | Community | Conv | 729 | 60.3 ± 9.2 | 54.32 | Essential | 5.39 ± 2.77 | >7 | 28.67 | 4 |
11 | Yu et al. ( |
Chongqing (S) | 2013–2014 | Hospital | Cons | 378 | 54.7 ± 11.8 | 51.06 | Essential | 3.65 ± 2.94 | >5 | 56.35 | 6 |
12 | Wei et al. ( |
Guangxi (S) | 2009–2013 | Hospital | Cons | 186 | 70.6 ± 9.7 | 54.84 | Essential | NR | >7 | 48.39 | 5 |
13 | Zhang et al. ( |
Zhejiang (S) | 2010–2012 | Community | Cons | 97 | 62.7 (50–78) | 48.45 | NR | NR | >6 | 80.4 | 4 |
14 | Zhu et al. ( |
Shanghai (S) | 2012 | Community | R | 457 | 64.7 ± 9.60 | 53.61 | Essential | NR | >6 | 65.34 | 5 |
15 | Fang et al. ( |
Hunan (S) | NR | Community | R | 145 | 75.3 ± 12.9 | 55.17 | Essential | 8.98 ± 3.36 | >7 | 45.67 | 6 |
16 | Wang et al. ( |
Guangdong (S) | 2012 | Hospital | Cons | 75 | 50.0 ± 8.7 | 57.33 | NR | 7.80 ± 3.95 | >7 | 44 | 4 |
17 | Wen et al. ( |
Shanxi (N) | 2012–2013 | Hospital | Cons | 268 | 35–75 | 45.15 | Essential | NR | >7 | 50 | 5 |
18 | Luo et al. ( |
Shanghai (S) | NR | Community | C | 629 | NR | NR | NR | NR | >5 | 44.67 | 6 |
19 | Dong et al. ( |
Anhui (S) | 2009 | Community | C, R | 1,110 | 69.1 ± 6.87 | 51.89 | NR | 7.65 ± 3.91 | >7 | 42.7 | 5 |
54 | Cheng et al. ( |
Guangdong (S) | NR | Community | Cons | 122 | 67.9 ± 6.1 | 54.92 | Essential | 8.34 ± 3.81 | >7 | 63.9 | 5 |
21 | Xie et al. ( |
Xinjiang (N) | 2008–2009 | Hospital | Cons | 760 | 56.3 ± 16.6 | 57.5 | NR | 8.42 ± 3.08 | >7 | 62.9 | 5 |
22 | Zhang et al. ( |
Guangdong (S) | 2007–2008 | Hospital | Cons | 100 | 74.0 ± 6.3 | 52 | NR | 9.54 ± 3.00 | >7 | 76 | 5 |
23 | Sun et al. ( |
NR | 2005–2006 | Hospital | Cons | 139 | 54.6 ± 18.7 | 63.31 | Essential | 10.96 ± 2.33 | >7 | 69.8 | 6 |
24 | Zhang et al. ( |
Gansu (N) | NR | Hospital | Cons | 71 | 52.1 ± 12.7 | 64.79 | NR | 10.86 ± 5.10 | >10 | 56.34 | 5 |
Forest plot of prevalence of poor sleep quality in hypertensive patients.
The results of the subgroup analyses are shown in
Subgroup analyses.
Subgroups | Categories (Number of studies) | Proportion (%) | 95% CI(%) | Events | Sample size | Q (P) | |
---|---|---|---|---|---|---|---|
Yes (11) | 45.1 | 37.7–52.8 | 4,119 | 10,696 | 97.8 | ||
No (13) | 58.2 | 51.4–64.6 | 1,788 | 3,224 | 92.2 | ||
North (6) | 53.3 | 40.0–66.2 | 2,567 | 6,242 | 98.0 | 0.09 (0.76) | |
South (17) | 51.0 | 43.6–58.3 | 3,243 | 7,539 | 97.1 | ||
Chinese (22) | 53.6 | 46.7–60.4 | 3,899 | 8,491 | 97.0 | ||
English (2) | 40.0 | 32.0–53.8 | 2,009 | 5,429 | 94.2 | ||
≥63.7 (11) | 57.1 | 46.5–67.0 | 3,117 | 7,510 | 97.8 | 1.34 (0.24) | |
<63.7 (11) | 48.9 | 39.9–57.9 | 2,373 | 5,513 | 97.3 | ||
2013–2017 (10) | 46.9 | 36.6–57.4 | 1,952 | 4,961 | 97.7 | 2.28 (0.13) | |
2007–2012 (10) | 57.9 | 48.2–67.0 | 3,488 | 7,992 | 97.8 | ||
≥232 (12) | 42.9 | 36.0–50.2 | 5,002 | 12,396 | 98.1 | ||
<232 (12) | 62.3 | 54.1–69.9 | 903 | 1,524 | 89.7 | ||
Probability (8) | 41.5 | 33.9–49.5 | 3,950 | 10,150 | 98.0 | ||
Non-probability (16) | 58.2 | 49.8–66.1 | 1,955 | 3,770 | 95.6 | ||
>5 (4) | 51.7 | 39.2–63.8 | 2,319 | 5,942 | 50.4 | 0.52 (0.91) | |
>6 (3) | 60.5 | 33.8–82.2 | 461 | 810 | 97.5 | ||
>7 (15) | 51.1 | 42.6–59.5 | 2,796 | 6,487 | 97.7 | ||
>10 (2) | 50.1 | 42.1–58.1 | 329 | 681 | 97.3 |
Bolded values: P < 0.05; Q: Cochran’s Q;
a: Continuous variables, such as age, survey year and sample size, were dichotomized using median splitting methods in the subgroup analyses. CPSQI: Chinese version of the Pittsburgh Sleep Quality Index; Probability sampling method: cluster sampling; multistage sampling; random sampling; stratified sampling; Non-probability sampling method; convenience and consecutive sampling.
Visual funnel plot and the Egger’s tests (t = 6.18, P < 0.001) both indicated significant publication bias (
Funnel plot of publication bias for 24 studies with available data on prevalence of poor sleep quality.
When studies were excluded one by one, the recalculated results did not change significantly. Therefore, no individual study significantly influenced the primary results.
This was the first meta-analysis of the prevalence of poor sleep quality in hypertensive patients in China. We found that more than half of the patients with hypertension had poor sleep quality, which is around two times higher than in healthy controls (OR: 2.66). There were approximately 325 million hypertensive patients in China, which translates to around 170.6 million hypertensive patients with poor sleep quality based on a prevalence of 52.5% (
The subgroup analyses found that hypertensive patients in hospitals had a higher risk of poor sleep quality than those in the community (58.2
Unlike previous findings (
Studies with small sample size reported a higher rate of poor sleep quality (β = −0.00012, p < 0.001), while those using convenience or consecutive sampling also reported a higher rate of poor sleep quality. We assumed that studies with small sample size and those using convenience or consecutive sampling may have relatively unstable results (
The strength of this meta-analysis includes the moderate-high quality of the included studies that were conducted across broad regions of China. However, several limitations should be considered. First, certain factors related to sleep quality in hypertensive patients, such as education level, physical exercise, anti-hypertensive treatments, and duration of hypertension, were not examined due to inadequate data. In addition, poor sleep quality had a bidirectional association with psychiatric disorders (
In conclusion, more than half of the Chinese patients with hypertension in this meta-analysis suffered from poor sleep quality which was significantly associated with male gender and older age. Considering the negative impact of sleep quality, appropriate strategies for the screening, prevention, and treatment of poor sleep quality in hypertensive patients should be developed.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.
Study design: LiL, Y-TX. Data collection, analysis, and interpretation: LiL, LuL, J-XC, LX. Drafting of the manuscript: LiL, Y-TX. Critical revision of the manuscript: CN, GU.
The study was supported by the National Science and Technology Major Project for investigational new drug (2018ZX09201-014), the Beijing Municipal Science & Technology Commission (No. Z181100001518005), the University of Macau (MYRG2019-00066-FHS) and Science and Technology Plan Project of Guangdong Province (No.2019B030316001).
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.
The Supplementary Material for this article can be found online at: