Association between sleep quality and living environment among Chinese older persons: a cross-sectional study

Abstract

Sleep quality significantly affects the quality of life of older persons. Therefore, this study explored the relationship between sleep quality and living environment of older persons in China to provide a theoretical basis for therapies to alleviate sleep disorders in older persons. A total of 6211 subjects > 60 years of age in Anhui Province, China, were evaluated using the Pittsburgh Sleep Quality Index and a self-reported questionnaire. Multivariate logistic regression analysis revealed that living alone (OR = 1.26, 95% CI 1.09–1.46) and living in a rural area (OR = 1.19, 95% CI 1.06–1.34) were significantly associated with a high incidence of sleep disorders in older persons. Living near a park or foot paths suitable for exercise or walking was significantly associated with a lower incidence of sleep disorders in older persons (OR = 0.87, 95% CI 0.77–0.96). Individual factors such as female sex (OR = 1.30, 95% CI 1.14–1.48) and depression (OR = 2.80, 95% CI 2.47–3.19) were also associated with sleep quality in older persons. These data indicate a correlation exists between living environment and sleep quality.

Introduction

Aging is a major public health challenge worldwide. In older persons, sleep quality is a crucial factor that affects overall quality of life. Poor sleep is a commonly reported clinical health complaint [1], and it can significantly increase morbidity and mortality, particularly among older persons [2, 3]. Older persons with sleep problems are twice as likely to die from heart disease, stroke, or cancer as older persons who sleep well [4].

Epidemiological evidence indicates that over half of all seniors currently suffer from sleep disorders, and the proportion of complaints about sleep quality among seniors is much higher than that among younger adults (50% vs 15.9%) [5]. A survey of the prevalence of sleep disorders among older Mexican adults found that 58% of men and 76% of women exhibited at least one sleep disorder symptom [6]. A meta-analysis indicated that the overall prevalence of sleep disorders among Chinese seniors is 41.2% (95% confidence interval [CI] 36.2–46.2%) [7].

Adequate sleep quality depends on whether an individual gets enough sleep during appropriate circadian phases and generally adheres to basic sleep hygiene recommendations, including implementation of sleep-friendly environments [8]. Social determinants of health have become topics of study in recent years, and the data clearly indicate that in addition to individual factors, the external environment also has an impact on health [9]. In 2011, the World Health Organization (WHO) established the WHO Global Network of Age-friendly Cities and Communities, with a focus on social determinants of health [9]. An inappropriate sleep environment, which includes factors such as exposure to uncomfortable noises, suboptimal ambient temperature, too much light, and poor air quality can disrupt sleep and reduce sleep quality even if an individual has enough time available for sleep at an optimal circadian phase [10]. Epidemiologic studies conducted in Western societies show that older age, living alone, the physical and social conditions of the home, as well as characteristics of the neighborhood, are associated with poor sleep quality [11, 12]. Chu et al. reported that compared to older persons who live with others, older persons living alone exhibit longer sleep latency, shorter sleep duration, reduced sleep efficiency, greater use of sleeping pills, and increased daytime dysfunction [13]. Relevant studies have also shown that getting an appropriate amount of physical activity is the most effective behavioral modification for improving sleep [14, 15].

Lifestyles and living environment have undergone significant changes in China in recent years. As a result of China’s demographic shift and the downsizing of families, an increasing proportion of older persons are living alone. Moreover, the rapid urbanization and infrastructure development in China have greatly impacted the living environments and lifestyles of older persons. Infrastructure developments have increased access to activities for older persons. However, changes in living environments of older persons have also had a negative effect on sleep quality due to factors such as reduced green spaces, increased artificial light at night, traffic noise, and air pollution [16]. Chinese researchers have primarily utilized epidemiological approaches to examine senior citizens’ sleep quality and associated impacts, including depression, cognitive disorders, and other mental health conditions, but knowledge regarding the effects of living environment on sleep in elderly individuals remains rather limited [17, 18]. The aims of the present study were to increase understanding of the status quo of sleep quality among the elderly population in Anhui Province, China, and to determine whether there is correlation between sleep quality and living environment among this population.

Materials and methods

Participants

This cross-sectional study utilized data from the Anhui Health and Longevity Survey (AHLS), which employed a multilevel sampling strategy [19]. The study began by selecting four administrative regions of Anhui Province located in the eastern (Chuzhou), western (Lu’an), southern (Xuan Cheng), and northern areas (Fuyang). Local coordinators who were leaders of the local Health Commission or the Centers for Disease Control and Prevention in each of the four areas were consulted to discuss research objectives. Then, 3–5 urban communities (streets) and rural communities (villages) were selected in each area by the local coordinator. All elderly individuals in the selected communities (villages) who met the requirements (≥ 60 years old, conscious, and able to complete the survey without communication barriers) were included in the survey, and those who could not return on the day they went out (e.g., due to living in the homes of children from other locations) or who were unwilling to participate (very few) were not included in the survey. Surveys were conducted at the community level in each selected region until at least 1500 volunteers aged 60 or older had completed the questionnaire, with equal numbers of respondents from urban and rural communities. Data were collected between July and August 2019, with daily review of all questionnaires. Participants who returned incomplete questionnaires were revisited or contacted on the following day.

Measurements

Sleep quality

The Pittsburgh Sleep Quality Index (PSQI) was utilized as one of the study’s tools to assess sleep quality. The PSQI includes seven dimensions, namely subjective sleep quality (positive: score 2–3, negative: score 0–1), sleep onset latency (positive: score 2–3, negative: score 0–1), sleep duration (positive: score 2–3, negative: score 0–1), sleep efficiency (positive: score 2–3, negative: score 0–1), sleep disturbance (positive: score 1–3, negative: score 0), use of hypnotic medications (positive: score 1–3, negative: score 0), and daytime dysfunction (positive: score 2–3, negative: score 0–1) [20]. Our study analyzed the impact of the living environment on each dimension to implement targeted measures to improve the sleep quality of older persons. Each dimension has four levels, ranging from 0 to 3, with higher scores indicating more-severe sleep dysfunction. The scores of the seven dimensions are added to the total score of the PSQI. The total PSQI score, which can be considered an indicator of comprehensive sleep quality, is categorized into four levels, as follows: “very good” (0–5), “fairly good” (6–10), “fairly bad” (11–15), and “very bad” (16–21). In this study, comprehensive sleep quality was divided into two categories according to the total PSQI score: “normal sleep” (total PSQI score in the first two levels) and “sleep dysfunction” (total PSQI score in the last two levels).

Living environment variables

The human environment encompasses a vast array of factors that can influence an individual’s life, including people, events, and circumstances. In the present study, the living environment of elderly individuals was examined using relevant variables from the AHLS, including (1) residential location, living in urban community or rural community(village); (2) living alone or not; and (3) living in a neighborhood environment (presence of distracting noise and dust, parks and foot paths suitable for exercise or walking). No hierarchies were utilized; respondents were only asked if there were unbearable noises, dust from the surrounding living environment or construction sites (the latter defined as unbearable dust), and parks or foot paths suitable for exercise or walking 500 m from their home. The responses were “yes” or “no.”

Sociodemographic information

Respondents were categorized into three groups by age (60–70/71–80/ > 80 years). According to data from the Sixth Population Census in China, the education level of the elderly was low; the majority of participants were only educated in primary school, and a number of them were illiterate [21]. Therefore, education level was categorized into three groups: “illiterate,” “primary school,” “secondary school and above” (illiterate/primary/secondary and above). Marital status was categorized into two groups (married/other). Individual income was categorized into two groups (< 6500 RMB and ≥ 6500 RMB [¥6.88 = $1]) [19].

Health behaviors

Smoking and alcohol consumption were recorded as binary variables (yes or no). Subjects who had smoked continuously or cumulatively for 6 months or more throughout their lives or who had smoked within 30 days prior to the survey were categorized as “smoking” [22, 23]. Subjects were categorized as “not smoking” if they had never smoked or had not smoked at the time of the survey [22, 23]. Within the previous 1 year, regardless of the type of alcohol, if the average alcohol consumption was ≥ 1 time per month, subjects were categorized as “drinking.” Not drinking alcohol at the time of the survey and having no previous history of alcohol consumption was categorized as “not drinking” [24].

Chronic illness status

Chronic illnesses included eight diseases (type 1 or type 2 diabetes, hypertension, hyperlipidemia, chronic hepatitis, heart disease, cancer, chronic lung disease, and psychiatric disorders). Those who reported that they had been diagnosed with any one or more of the above chronic diseases by a medical institution at or above the county level were categorized as having a “chronic illness” [19].

Cognitive function

Cognitive impairment was identified using education-specific cutoff [25] points for the Mini-Mental State Examination (MMSE) score, with the illiterate group at ≤ 17 points, the primary school group at ≤ 20 points, and the secondary school or above group at ≤ 24 points classified as “cognitive impairment” [26].

Depression

The Patient Health Questionnaire-9 (PHQ-9) was utilized as one of the study’s tools to assess depression. A PHQ-9 score of < 10 was considered non-depressive, and a PHQ-9 score of ≥ 10 was considered depressive [27].

Data analysis

To evaluate the relationship between sleep quality and living environment, odds ratios (ORs) and 95%CIs were calculated using logistic regression analysis. Separate associations were first examined using univariate logistic regression analysis. Multiple logistic regression analysis was then performed to adjust for the confounding effects of other factors. The statistical significance level was set at p < 0.05 for the two-tailed test. Finally, a multiple logistic regression was conducted to assess the relationship between living environment and each of the seven dimensions in the PSQI. Stata version 15.1 software (StataCorp, College Station, TX) was used for all statistical analyses. All tests were two-sided, and the significance level was set at p < 0.05.

Ethics statement

This cross-sectional study received ethical approval from the Institutional Review Committee of Anhui Medical University (IRB identification code: 2020H011).

Results

Characteristics of participants

Participants’ main sociodemographic variables and chronic illness, depression, and sleep quality data are summarized in Table 1. A total of 6312 older persons participated in the survey, with 6211 valid questionnaires returned, for a 98.4% effective response rate. The participants included 2830 males (45.56%) and 3381 females (54.44%), among which 4736 (76.25%) had normal sleep and 1475 (23.75%) had a sleep disorder. Most participants were 60–80 years old (88.50%). Overall, 49.07% of respondents were illiterate. More than half of respondents (71.87%) were married. A total of 3683 (59.30%) respondents had a personal income of less than ¥6500 (¥6.88 = $1). Older persons with chronic diseases accounted for 72.08% of participants. Older persons with depression accounted for 30.59% of participants.

Table 1.

Participants’ main sociodemographic variables chronic illness, depression, and sleep quality data

Variables	Number (n = 6211)	Percentage
Gender
Male	2830	45.56
Female	3381	54.44
Age (years)
60–70	3302	53.16
70–80	2195	35.34
80 and above	714	11.50
Education
Illiterate	3048	49.07
Primary school	1731	27.87
Secondary school and above	1432	23.06
Marital status
Married	4464	71.87
Other	1747	28.13
Individual income
< ¥6500	3683	59.30
¥6500–	2528	40.70
Chronic illness
Yes	4198	67.59
No	1626	26.18
Depression
Yes	1900	30.59
No	4201	67.64
Sleep quality
Normal sleep	4736	76.25
Sleep disorder	1475	23.75

Assessment of sleep disorders and associated factors

Table 2 shows the univariate (model 1) and multivariate (model 2) analyses of factors and sleep disorders when the dependent variable was set to binary (with or without a sleep disorder). ORs and 95% CIs were calculated using logistic regression analysis. Separate associations were first examined using univariate logistic regression analysis. Multiple logistic regression analysis was then performed to adjust for potential confounding variables. After adjusting for potential confounding variables, a significant correlation was still observed between comprehensive sleep quality and whether participants lived alone or whether there were parks and foot paths nearby suitable for exercise or walking. Older persons living alone (OR = 1. 26, 95% CI 1.09–1.46) were 1.26 times more likely to have a sleep disorder than those who did not live alone. Older persons living in rural areas (OR = 1.19, 95% CI 1.06–1.34) were 1.19 times more likely to have a sleep disorder than those who living in an urban areas. Older persons living near a park or foot path suitable for exercise or walking (OR = 0.87, 95% CI 0.77–0.96) were 0.83 times more likely to have a sleep disorder than those living without a park or foot path nearby suitable for exercise or walking. Furthermore, older persons with depression (OR = 2.80, 95% CI 2.47–3.19) were 2.80 times more likely to have a sleep disorder than those without depression. Females (OR = 1.30, 95% CI 1.14–1.48) were 1.30 times more likely to have a sleep disorder than males.

Table 2.

Logistic regression analysis of risk factors associated with sleep disorders

	Model 1a		Model 2a
	OR (95%CI)	P	OR (95%CI)	P
Living alone
No	Reference
Yes	1.28 (1.12–1.47)	< 0.001	1.26 (1.09–1.46)	0.002
Location
Urban	Reference
Rural	1.00 (0.86–1.16)	0.001	1.19 (1.06–1.34)	0.003
Parks and foot paths suitable for exercise or walking
No	Reference
Yes	0.80 (0.71–0.90)	< 0.001	0.87 (0.77–0.96)	< 0.001
Unbearable noise
No	Reference
Yes	0.92 (0.78–1.09)	0.334	0.95 (0.79–1.15)	0.620
Unbearable dust
No
Yes	1.19 (1.10–1.44)	0.059	1.18 (0.96–1.46)	0.114
Gender
Male	Reference
Female	1.38 (1.23–1.56)	< 0.001	1.30 (1.14–1.48)	< 0.001
Age (years)
60–70	Reference
70–80	1.17 (1.03–1.33)	0.014	1.13 (0.99–1.31)	0.073
80 and above	1.29 (0.62–2.68)	0.496	1.05 (0.85–1.30)	0.651
Education
Illiterate	Reference
Primary school	0.88 (0.77–1.01)	0.067	1.01 (0.86–1.18)	0.900
Secondary school and above	0.67 (0.57–0.78)	< 0.001	0.92 (0.76–1.11)	0.398
Marital status
Married	Reference
Other	0.87 (0.60–1.26)	0.453	0.78 (0.51–1.18)	0.248
Individual income
< ¥6500	Reference
¥6501–	0.78 (0.65–0.91)	0.203	0.92 (0.79–1.08)	0.335
Depression
Yes
No	2.19 (1.78–2.99)	< 0.001	2.80 (2.47–3.19)	< 0.001
Cognitive impairment
Yes	Reference
No	0.96 (0.81–0.96)	0.555	0.95 (0.82–1.09)	0.472

^aModel 1: univariate logistic analysis; model 2: multivariate logistic analysis

Multivariate analysis of the relationship between living environment and seven subdimensions of the PSQI

Multivariate logistic analysis showed the relationship between living environment and the seven sub-dimensions of the PSQI. The seven subdimensions of the PSQI were separately analyzed as the dependent variable (0 = positive, 1 = negative), and living environment of older persons was analyzed as the independent variable. After adjusting for potential confounding variables (gender, age, education, marital status, individual income, depression, and cognitive impairment), living alone was significantly related to subjective sleep quality, sleep onset latency, sleep efficiency, and sleep duration. In terms of living environment, respondents’ residence type was significantly related to subjective sleep quality, daytime dysfunction, sleep disturbance, sleep efficiency, and sleep duration. Daytime dysfunction and sleep duration among older persons were significantly related to whether participants lived in an unbearable dust environment, and six of the seven sub-dimensions of the PSQI (subjective sleep quality, sleep onset latency, daytime dysfunction, sleep disturbance, sleep efficiency, and sleep duration) were significantly correlated with the availability of parks and foot paths suitable for exercise or walking (Table 3).

Table 3.

Multivariate analysis of the relationship between living environment and seven subdimensions of PSQI

	Subjective sleep quality		Sleep onset letency		Use of hypnotic medication		Daytime dysfunction		Sleep disturbance		Sleep efficiency		Sleep duration
	OR (95%CI)	P	OR (95%CI)	P	OR (95%CI)	P	OR (95%CI)	P	OR (95%CI)	P	OR (95%CI)	P	OR (95%CI)	P
Living alone
No	Reference
Yes	1.28 (1.01–1.50)	0.002	1.18 (1.04–1.35)	0.013	1.00 (0.72–1.39)	0.990	0.94 (0.83–1.07)	0.288	1.10 (0.80–1.53)	0.558	1.33 (1.18–1.51)	< 0.001	1.24 (1.09–1.41)	0.001
Location
Urban	Reference
Rural	1.16 (1.02–1.31)	0.020	1.04 (0.93–1.15)	0.504	0.79 (0.60–1.03)	0.078	1,59 (1.43–1.77)	< 0.001	1.67 (1.27–2.17)	< 0.001	1.16 (1.05–1.28)	0.005	0.81 (0.73–0.90)	< 0.001
Unbearable noise
No	Reference
Yes	0.87 (0.71–1.08)	0.211	0.92 (0.77–1.10)	0.346	0.71 (0.48–1.07)	0.107	0.96 (0.81–1.15)	0.697	0.64 (0.39–1.05)	0.080	0.97 (0.82–1.15)	0.735	1.00 (0.83–1.18)	0.928
Unbearable dust
No	Reference
Yes	1.04 (0.83–1.32)	0.698	0.90 (0.74–1.09)	0.266	0.89 (0.55–1.45)	0.651	1.36 (1.11–1.67)	0.003	1.26 (0.72–2.20)	0.409	1.08 (0.90–1.30)	0.410	1.19 (1.07–1.99)	0.033
Parks and foot paths suitable for exercise or walking
No	Reference
Yes	0.89 (0.82–0.95)	0.001	0.85 (0.73–0.98)	0.030	1.24 (0.94–1.64)	0.133	0.64 (0.56–0.73)	< 0.001	0.70 (0.63–0.94)	0.016	0.80 (0.69–0.93)	0.004	0.67 (0.56–0.84)	< 0.001

Discussion

The present study may be one of the few that have focused on the relationship between aged people’s sleep quality and living environment in China. This is probably also the first large-scale study examining the relationship between living style, living environment, and sleep quality of older persons in Anhui Province. The results indicated that living alone, living in a rural area, and lack of parks or foot paths suitable for exercise or walking were significantly associated with decreased sleep quality among senior citizens in communities in Anhui Province. Furthermore, there was no significant correlation observed between the presence of unbearable dust, unbearable noise, and sleep quality among older Chinese persons in our study.

A previous systematic review found that sleep disorders in older persons have not received adequate attention in many developing countries, despite being a significant public health concern [28]. Sleep quality is a complex problem that is challenging to quantify with objective data. Among older persons, sleep quality is not determined by a single factor but rather a combination of factors, such as lifestyle, physical activity, and physical fitness [29]. Our study also showed that respondents’ living environment, as well as their gender, age, and education level, was significantly related to sleep quality.

The present study found that living alone was significantly associated with poor comprehensive sleep quality. Respondents living alone appear to take longer to fall asleep and experience less-efficient sleep and shorter sleep duration than those living with family members. This finding was consistent with previous research indicating that elderly people who had a negative marital experience or lived alone could experience psychological tension due to social pressure, ultimately leading to psychological disorders that could adversely affect sleep quality [30]. Our study also found a significant association between depression and sleep quality. Older persons living alone were more likely to experience depressive symptoms than those living with others [31]. Relevant studies have indicated that elderly persons living alone or as “empty-nesters” with depressive symptoms are significantly more likely to have poor sleep quality. A cross-sectional study showed that individuals who reported greater loneliness also had poorer sleep quality [32]. Therefore, older persons living alone should be considered a priority group for social support. These data suggest that communities should implement various activities that would enhance the connection between older persons and society, thereby reducing their loneliness and improving their sleep quality. Park and Choi reported that many aged people believe their living environment affects sleep quality and that living in a healthy and friendly community for a period of time can significantly improve sleep [33].

Our study revealed that sleep disorders were more common in older persons residing in rural areas (25.33%) than urban areas (22.12%), consistent with earlier findings [34]. Li reported that poor sleep quality is correlated with poor quality of life [35]. For example, older adults who live in the countryside in a country with an underdeveloped economy generally have poor quality of life. The livelihoods of most of these individuals involves heavy farm work, which can cause physical pain and therefore adversely affect sleep quality. In addition, inadequate access to healthcare services and limited awareness of sleep hygiene in rural regions can increase the risk of developing sleep problems [36]. Moreover, older persons living in rural areas are often left along in their countryside homes and receive inadequate social support, because most of their offspring have moved to cities for work due to the increasing urbanization of China [37, 38]. As discussed above, loneliness is regarded as an important factor affecting the development of sleep disorders.

The current study revealed a significant association between respondents’ dust exposure and the likelihood of daytime dysfunction and reduced sleep duration. Airborne dust particles and other pollutants can cause respiratory obstruction and hypopnea, ultimately leading to a decrease in sleep efficiency [39]. Older individuals residing in areas near factories or polluted air are at elevated risk of developing adverse health and sleep quality issues [40]. Long-term exposure to such pollutants can lead to sleep disorders, including sleep apnea syndrome [41]. With the rapid growth of urbanization in China, issues related to dust pollution caused by construction, manufacturing, and transportation are becoming increasingly prominent. Thus, it is imperative that the government implement targeted measures to reinforce environmental governance and minimize these issues.

The current study identified a significant association between the presence of nearby parks and foot paths suitable for exercise or walking and better sleep quality among older adults. Community recreation venues are thought to have a positive impact on sleep quality among older persons, as evidenced by their bedtime, daytime function, sleep disorders, and sleep duration. These impacts could occur in three possible ways. First, the availability of fitness or recreational facilities in the community may encourage older adults to extend or increase their physical activity [42]. Sufficient physical activity can improve sleep quality [43]. Second, by spending more time outdoors, older persons can increase their exposure to natural light during the day, which is beneficial for maintaining healthy sleep and reducing the incidence of emotional problems [44, 45]. Third, participating in physical exercise in parks and walking on foot paths near home can provide opportunities for social interaction among older residents in a community, which can help alleviate sleep disorders caused by social isolation [46]. According to Wang’s study, older persons tend to engage in physical activities in leisure places within a 500-m radius of their home [47]. The available data suggest that communities in both urban and rural areas should increase the number and scope of parks and foot paths suitable for exercise or walking to provide suitable leisure environments for older persons, which may have a positive effect on sleep quality as well.

There are several limitations in the present study. First, the respondents were only recruited from four cities in Anhui Province; thus, it may be not appropriate to extrapolate our research findings to other cities in China without more in-depth analysis. Second, it was a cross-sectional study, which cannot determine causal relationships between respondents’ living environment and sleep quality. Third, the survey collected respondents’ subjective perceptions about their sleep quality. As such, the assessment of sleep quality in the present study may have some degree of inaccuracy. Finally, various environmental factors, such as the type of house in which older persons live, were rarely considered.

Conclusion

In summary, the study found a significant correlation between respondents’ living environment and their comprehensive sleep quality, as well as some sub-dimensions of the PSQI. Individual factors, including gender, age, marital status, education, personal income, cognitive impairment, and depression were also associated with sleep quality among older adults. The results of the present study suggest that local governments should take action to improve living environments in rural and urban areas. Further attention should also be focused on sleep problems among certain high-risk groups, such as aged people who live in rural areas, live alone, have no access to parks or foot paths suitable for exercise or walking.

Funding

This research was funded by Province 2020 Outstanding Top Talent Cultivation Project for Universities, Grant no [gxyq2020153], National Natural Science Foundation of China, Grant nos [72004003, 71874002], Science Fund for Distinguished Young Scholars of Hubei Province, Grant no [WGRC201901], cientific Research Projects for Higher Education of Anhui Province under Grant, Grant no [2023AH010036].

Declarations

Conflict of interest

The authors declare no competing interests.