Associations of Living Environment and Lifestyle Factors with Sub-Health Status: A Population-Based Cross-Sectional Study in Urban China

Background: Sub-health status (SHS) is an increasingly concerned issue with a high prevalence worldwide. Although some factors of lifestyle and environment has been study, few studies have determined comprehensively the associations of lifestyle and living environmental factors with general, physical, mental and social SHS. Methods: A cross-sectional face-to-face survey (Sep 2017 to Sep 2018) was conducted via questionnaire among 6,750 urban residents aged 14 years and above randomly selected from ve Chinese cities. There were 5,881 valid questionnaires with a response rate of 87%. General linear model and structure equation model were developed to quantifying the effects of lifestyle behaviors and living environmental factors on SHS. Results: Detection rates of general SHS, physical SHS, mental SHS and social SHS were 66.7%, 67.0%, 65.5% and 70.0%, respectively. Signicant association of general SHS, physical SHS, mental SHS and social SHS had been found in smoking, bad dietary habits, breakfast consumption, physical exercise, sleep duration, air, neighbor harmony and living convenience, instead of noiseless. However, associations of general SHS, physical SHS, mental SHS and social SHS were different for second-hand smoking inuence, alcohol consumption, sunshine, Bedtime before 11pm, surng the internet, greenery, air, pleasant housing, spacious rooms and tness facility. Good lifestyle behaviors and favorable living environment factors positively affected SHS (P<0.001). Lifestyle behaviors had the largest effect on physical SHS (β=-0.418), but the least on social SHS (β=-0.274). Living environmental factors had the largest effect on mental SHS (β=0.286), but the least on physical SHS (β=0.225). Conclusions: Lifestyle behaviors and living environmental factors were important inuencing factors of SHS. Physical SHS was more associated with lifestyle. Lifestyle and living environment were similarly associated with mental and social SHS.


Background
Health is de ned by the World Health Organization (WHO) in 1946 as "a state of complete physical, mental, and social well-being and not merely the absence of disease or in rmity" [1,2]. Noncommunicable Chronic diseases (NCDs) are the opposite side of health, and are great challenges to health. It is reported that NCDs account for an estimated 80% of total deaths and 70% of the total number of disability-adjusted life-years (DALYs) in early twentieth century [3], and increase steadily with the urbanization and aging [4], with more than 88% of total deaths due to NCDs in 2019 in China [5]. A study pointed that NCDs accounted for 18 of the leading 20 causes of age-standardized years lived with disability on a global scale [6]. Preclinical status of CNDs and its early detection have become major issues in the promotion of the basic health service in the reform of health care [7].
Some studies have shown that SHS or suboptimal health status (SHS) may contribute to the progression or development of chronic disease [8,9]. SHS is an intermediate status between chronic disease and health, which is characterized by chronic fatigue, non-speci c pain (e.g., back and chest pain), headaches, dizziness, anxiety, depression, and functional system disorders [8]. SHS has a prevalence of higher than 65% in China [10][11][12][13]. and become an increasingly concerned problem in many other countries [14,15]. Moreover, the prevalence may be severely underestimated since many individuals may not know that they suffer from SHS. For instance, in an investigation of 6,000 Chinese self-reported "healthy people", 72.8% were in "suboptimal health status" [16]. Identifying the in uencing factors of SHS is important for preventing SHS, and would provide important information for rst-level prevention of CND.
In the recent decades, China underwent a rapid urbanization development, with a proportion of urban population increased from 17.9% in 1978 to 58.5% in 2017 [17]. The rapid environmental changes accompanied with urbanization lead to the increasing prevalence of the major risk factors for SHS, including poor dietary habits, work stress, physical inactivity, breakfast eating habits, smoking, tobacco use, air pollution and noise [2,18,19]. Those risk factors can be concluded into two aspects, lifestyle behaviors and living environmental factors. Although, lifestyle behaviors and living environmental factors associated with SHS has been studied before, the association strengths of the two aspects with SHS have not been well elucidated.
This study aimed to examine the associations of lifestyle behaviors and the living environmental factors with general, physical, mental and social SHS in a large urban population.

Study Design and Population
We conducted a cross-sectional multi-city survey using a four-stage strati ed sampling method from Sep 2017 to Sep 2018. In stage one, we selected Guangdong Province (south east China), Haerbin City in Heilongjiang Province (north east), Sichuan Province (north west), Tianjin City (east), and Lanzhou City in Gansu Province (south west) as representative of different Chinese regions from the aspects of geographic distribution, economic characteristics, and populational demographics. Figure 1 shows the sampling map in China. In stage two, three to ve cities in each province were selected as respective representatives of geographic distribution, economic characteristics, and populational demographics. In stage three, two cities were randomly selected from each of these areas. In the fourth and nal stage, one to three streets were randomly selected from each of these cities. To ensure representativeness, participants on each street were strati ed according to sex (i.e., male or female) and age (i.e., brackets of 14-24, 25-34, 35-44, 45-54, and 55+). At the selected streets, street sampling method was used to select participants. As such, survey participants were representative of the level of SHS in their respective urban areas.
Informed oral consent was obtained from each participant prior to the data collection process. This form of consent was deemed su cient as participants had previously volunteered for inclusion and were told they could still refuse to participate. All data were kept strictly con dential. Further, the Nanfang hospital ethics committee approved this consent procedure(Approval number: NFEC-2019-196) .

Survey Instrument
This study used a SHS questionnaire to investigate urban Chinese residents. It contained two sections (i.e., both a selfdesigned and standardized questionnaire) [10]. The self-designed questionnaire asked for general demographic characteristics as well as information on lifestyle and environment. Here, general demographic characteristic included age, gender, and marital status, while there were 10 lifestyle variables (i.e., smoking, second-hand smoking, alcohol consumption, bad dietary habits, breakfast consumption, sun exposure, physical exercise, early bedtime (before 11 pm), sleep duration, and sur ng the internet), and eight environmental variables (i.e., air quality, noise, housing conditions, living conditions, neighborhood harmony, tness facilities, and supporting facilities). The standardized components were based on the SHS Measurement Scale V1.0 (SHMS V1.0); these were designed to assess participant health status.

SHS Assessment
Health-status assessments were performed in accordance with the SHMS V1.0, which was previously developed by our research group. Testing procedures revealed that it was of high reliability and validity (Cronbach's α and split-half reliability coe cients of 0.917 and 0.831, respectively) [20]. The SHMS V1.0 consists of 39 total items. Respondents were asked to answer each of these items according to a 5-point scale (1 to 5, from very bad to very good). The SHMS V1.0 can be used to detect general SHS (GS) based on three symptom dimensions, including physical SHS (PS), mental SHS (MS), and social SHS (SS). Of the 39 total items, Nos. 4-12, 15, 20-25, 28, and 38-39 were reverse scored (six plus the original score). The original subscale scores were total item sums; higher original scores indicated better health. We calculated and analyzed transformed scores to further understand and compare the data. Transformed scores were determined according to the following formula: (original score -theoretically lowest score) / (theoretically highest scoretheoretically lowest score) × 100. Following our previous study, SHS prevalence was calculated based on transformed scores [21].

Statistical Analysis
A general linear model was used to analyze the association of lifestyles (environmental factors) and SHS as adjusted by demographic characteristics and living environmental factors (lifestyles). Further, a path model of latent variables was constructed based on a hypothesized relationship between items. Structural equation modeling (SEM) was then used to analyze the complexity of associations between lifestyle, health consciousness, and SHS to estimate model tness and analyze the direct and indirect effects of the multiple factors used in the hypothesized model [22]. Model tness was assessed using the ve indices commonly applied in SEM analyses (i.e., the relative (CMIN/DF), root mean-square error of approximation (RMSEA), comparative t index (CFI), goodness-of-t index (GFI), and adjusted goodness-of-t index (AGFI)) [23]. The Bootstrapping method [22] of repeat sampling (i.e., 2,000 times) was applied to verify statistical signi cance and calculate con dence intervals for the direct, indirect, and total effects (P < 0.05). All statistical analyses were conducted using SPSS and AMOS (SPSS Statistics version 20.0, SPSS Inc., Chicago, IL). Two-sided p-values < 0.05 were considered statistically signi cant.

Participant Demographic Characteristics
This study surveyed a total of 6,750 urban Chinese residents who had lived in their locations for more than six months and were at least 14 years of age. A total of 807 participants who con rmed they had incurred diseases within one month of the study were excluded. As such, 5,943 respondents were either healthy or SHS for at least a period of one month prior to study. However, 62 of these surveys were missing values for items of lifestyle, environment, and/or SHS, and were thus excluded. This left a nal total of 5,881 urban residents who were selected for analysis (a valid response rate of 87.13%). Table 1 presents overall baseline, lifestyle, and environmental characteristics. Of the 5,881 analyzed, 2,817 were men and 3,064 were women; mean age was 40.27 ± 15.69 years. Most participants were married (64.50%). Further, 66.7% were in GS, 67.0% were in PS, 65.5% were in MS, and 70.0% were in SS.

Comparison of SHS for Different Lifestyle and living environmental factors
The mean (sd) transformed scores for general, physical, mental, and social sub-health status were 67.23 (12.03), 71.08  (Table 2). Participants who never smoked, had good dietary habits, consumed breakfast daily, performed daily physical exercise, and slept 7-9 hours each night had the highest general, physical, mental, and SS transformed scores. Participants who had not received second-hand smoke and never used alcohol had the highest general, physical, and MS transformed scores. Participants who maintained bedtimes before 11 pm each day had the highest general and PS transformed scores. Sun exposure was only associated with PS; those who received 14 hours or more each week had the highest scores. Sur ng the internet was only associated with MS; those who surfed less than three hours a day had the highest scores. Table 3 shows the effects of environmental factor after adjusting other living environmental factors, demographic characteristics, and lifestyle behaviors. Pleasant housing, harmonious neighborhoods, and convenient living conditions were positively associated with general, physical, mental, and SS. Substantial greenery was positively associated with general and SS. Fresh air was positively associated with general, physical, and MS. The presence of many tness facilities was positively associated with general and MS. There were no signi cant associations between noise and SHS.
However, people with spacious rooms in their homes had lower general and MS scores.

SEM Analysis of Lifestyle, Environment, and SHS
The total associations of lifestyle behaviors and environment with SHS were analyzed through SEM (Fig. 2). Although noiseless areas were not independently associated with SHS, the model was not deemed t without an environmental noise component. The SEM thus included a "noiseless" variable. Except for the CMIN/DF, CFI, and AGFI of Model 1 and the CMIN/DF of Model 2, Table 4 presents information about the tness measurements for all four models. The associations of lifestyle and living environmental factors with SHS are listed in Table 5

Discussion
This cross-sectional study of nationally representative urban Chinese residents found that lifestyle behaviors and living environmental factors were signi cantly associated with SHS. The association strengths between lifestyle behaviors and GS, lifestyle behaviors and PS were bigger than those of living environmental factors. The association of lifestyle behaviors and MS, lifestyle behaviors and SS were almost the same with those of living environmental factors.
To the best of our knowledge, this was the rst study on the association strengths of lifestyle behaviors and living environmental factors with SHS. The ndings concluded in this study were generally in line with previous studies on the relationship between lifestyle and PS [24,25]. Innovative ndings in this study were the signi cant associations between living environmental factors and MS, living environmental factors and SS. Although, the association between living environmental factors and mental health has been elucidated before.
This study used the 39-item SHMS V.1.0 questionnaire to analyze SHS. It included physical, mental, and social dimensions. Our previous research indicated that SHMS V.1.0 had good internal consistency among both a population of southern Chinese medical staff members [21] and urban residents in three Chinese districts [26]; Detective rate of GS in Chinese urban residents was 66.7%, slightly higher than southern China (65.1%) [13] and Tianjin (66.37%) [14].
This study found that bad lifestyle habits such as smoking, alcohol consumption, second-hand smoking in uences, poor dietary habits, and sur ng the internet were associated with low SHS scores. On the other hand, good lifestyle habits such as taking daily breakfast, su cient sun exposure, daily physical exercise, consistently going to bed before 11 pm, and daily sleep durations between 7-9 hours were associated with non-SHS. A study among Chinese university students similarly indicated that sleep durations of less than six hours per day were independently and signi cantly associated with poor self-reported health [27]. Skipping breakfast has also been found in association with a signi cantly increased risk of mortality from cardiovascular disease [28]. Further, an English study pointed out that poor health outcomes were more common among ex-smokers and current smokers than those who had never smoked [29]. Finally, alcohol use and short sleep durations are likely related to poor health among black men living in the United States [30].
Many studies have indicated that exercise, physical activity, and physical-activity interventions are bene cial for several physical and mental-health outcomes. Su cient fresh air and sun exposure are also good for promoting public health [31]. Taken together, these studies and our ndings underscore the importance of maintaining good lifestyle habits while abandoning bad choices as a simple way to prevent SHS and increase overall health.
This study further found that living environmental factors such as su cient greenery, fresh air, pleasant housing, less spacious rooms, harmonious neighborhoods, the presence of many tness facilities, and convenient living conditions were associated with high SHS scores. It is well-known that positive environments (especially natural outdoor areas) are good for human health [32]. Contrary to our general expectations, however, we found that people who lived in spacious rooms had a greater probability of developing both mental and GS. This may be due to feelings of emptiness in one's surroundings. For example, a systematic review indicated that living alone may be associated with low levels of positive mental health [33].

Study Strengths
This population-based study examined a sample of urban residents (5,881 respondents), thus facilitating overall generalizability to the entire urban population in China. Further, we illustrated the relative strengths of lifestyle behaviors and living environmental factors on the associations with SHS. We rstly illustrated the important association of living environmental factors and MS, living environmental factors and SS, which almost the same with lifestyle behaviors.
What's more, association factors were comprehensive, including 10 lifestyle behaviors and eight living environmental factors, which can be intervened and was helpful for preventing of SHS and chronic noncommunicable diseases.

Limitations
First, because of the cross-sectional design, it was not possible to con rm causal relationships of SHS with lifestyle behaviors and living environmental factors. Second, lifestyle factors were self-reported in this study, which reduced the precision of measurement of lifestyle behaviors and living environmental factors. Third, although we have considered as many factors as possible, bias may happen inevitably due to some factors not being included.

Conclusions
This large-scale cross-sectional study of Chinese urban residents aged 14 and above found that good lifestyle behaviors and living environmental factors were both signi cantly associated with low rates of SHS (i.e., high SHS scores). Lifestyle behaviors were more signi cantly associated with physical and GS. However, living environmental factors and lifestyle behaviors were nearly identically associated with mental and SS.

Consent for publication
Not applicable.

Availability of data and materials
Data used in this study were obtained under the support of Nanfang Hospital, Southern Medical University. The ownership of the data belongs to Nanfang Hospital, Southern Medical University. Researchers who meet the criteria for access to con dential data can contact Jun Xu (drugstat@163.com) at the Nanfang Hospital, Southern Medical University to request the data.      Figure 1 Sampling points in the survey on the map of China. Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.