Ethics and consent statement
The survey was administered by researchers assisted by undergraduate students majoring in health management, master of health management students, professional lecturers and associate professors. This research team was trained before distributing the questionnaires. We confirmed that all methods were performed in accordance with the relevant guidelines and regulations. Verbal Informed consent was obtained from all subjects to the questionnaire, and for those under 18 years of age, verbal informed consent was obtained from their parents and/or legal guardians. This approval procedure was approved by the Ethics Committee of Anhui Medical University.
Sample and data collection
Anhui Province is an important part of the Yangtze River Delta, which embraces the Yangtze River channel internally, and is promoted by the economy of coastal areas externally. It is located in the strategic center of national economic development and the docking zone of several domestic economic plates. Since the 18th National Congress of the Communist Party of China, Anhui's economy has been developing rapidly and its comprehensive strength has been steadily improved. With the booming economy, people's pressure is also increasing, and the public's health status has attracted much attention. This study selected physical examinees from the physical examination center of Anhui province's tertiary grade-A hospitals from June to September 2019 as the research subjects, and explored the relationship between public risk perception level and physical sub-health status. The study protocol received the appropriate human subjects review and approval. According to the sample size of 5 ~ 10 times of the number of items, 785 questionnaires were collected in this study. After eliminating invalid questionnaires, 770 valid questionnaires were obtained, and the response rate was 98%. The average age of the subjects was 34 (sd= 11), with 312 males and 458 females. The characteristics of other demographic variables and control variables are shown in Table 1.
Table 1 Characteristics of demographic variables and control variables of subjects
Variable
|
|
Number
|
Percentage(%)
|
Gender
|
Male
|
312
|
40.5
|
|
Female
|
458
|
59.5
|
Age/years
|
<31
|
384
|
49.9
|
|
31-45
|
256
|
33.2
|
|
46-60
|
96
|
12.5
|
|
≥61
|
34
|
4.4
|
Education
|
Primary or below
|
44
|
5.7
|
|
Junior high school
|
103
|
13.4
|
|
Senior high school
|
111
|
14.4
|
|
Junior college
|
146
|
19.0
|
|
Undergraduate
|
304
|
39.5
|
|
Master's degree and above
|
62
|
8.1
|
Years of Working
|
<5
|
306
|
39.7
|
|
5~10
|
186
|
24.2
|
|
11~20
|
161
|
20.9
|
|
21~30
|
74
|
9.6
|
|
>30
|
43
|
5.6
|
Attribute of living place
|
Rural
|
117
|
15.2
|
|
Cities and towns
|
132
|
17.1
|
|
Third-tier city
|
110
|
14.3
|
|
Second-tier city
|
411
|
53.4
|
Marital status
|
Unmarried
|
287
|
37.3
|
|
Married
|
457
|
59.4
|
|
Others
|
26
|
3.4
|
Number of children
|
0
|
315
|
40.9
|
|
1
|
244
|
31.7
|
|
2
|
158
|
20.5
|
|
≥3
|
53
|
6.9
|
Average annual household income
|
<30,000 yuan
|
88
|
11.4
|
|
30,000~60,000 yuan
|
152
|
19.7
|
|
60,000~100,000 yuan
|
227
|
29.5
|
|
100,000~200,000 yuan
|
205
|
26.6
|
|
>200,000 yuan
|
98
|
12.7
|
Self-health evaluation
|
Serious than sub-health
|
67
|
8.7
|
|
Sub-health
|
499
|
64.8
|
|
Unclear
|
61
|
7.9
|
|
Health
|
143
|
18.6
|
Sub-health duration
|
<3 months
|
126
|
16.4
|
|
3~6 months
|
88
|
11.4
|
|
6 months~1 year
|
161
|
20.9
|
|
<2 years
|
162
|
21.0
|
|
≥3 years
|
233
|
30.3
|
Number of employees in the unit
|
Freelancer
|
171
|
22.2
|
|
<50
|
152
|
19.7
|
|
50~150
|
141
|
18.3
|
|
150~500
|
139
|
18.1
|
|
≥500
|
167
|
21.7
|
Sub-health proportion in the unit
|
10%
|
97
|
12.6
|
|
30%
|
174
|
22.6
|
|
50%
|
246
|
31.9
|
|
70%
|
182
|
23.6
|
|
≥90%
|
71
|
9.2
|
The relationship between physical sub-health and risk perception
Risk perception reflects people's cognition and intuitive judgment of risk [7], which belongs to the field of psychology [8]. The term "risk perception" was first introduced by Professor Bauer at Harvard University in 1960 and applied to consumer behavior research [9]. Risk perception is one of the core elements of health behavior theories [10], which suggest that when exposed to risk, risk perception motivates people to stop unhealthy behaviors and adopt healthy behaviors to avoid it. For example, the Health Belief Model [11-13] takes susceptibility to disease and severity as pretest factors of whether an individual adopts a healthy behavior, where the cognition of susceptibility to disease and severity is the risk perception; the Protective Motivation Theory [14-15] explains why health behaviors occur from the perspective of motivation, in which risk perception plays a central role. The level of risk perception affects the behavioral lifestyle of the public [16], while lifestyle is one of the most important factors influencing health status. Positive risk perception attitudes of the public play a key role in driving individual adaptive behavior [17] and are a precondition for the public to make healthier lifestyle choices, participate in health screenings, and adhere to health care [18]. People with higher risk perception level are more likely to resist unhealthy behaviors and adopt healthy lifestyle habits, which helps to increase their level of self-protection against risks and reduce health threats due to various risks. [19-23]. In this study, two variables, physical sub-health risk perception level and clinical manifestations of physical sub-health, were used to collect data from physical examinees through cross-sectional survey. Python-3.8.6 software was used to analyze the data of physical sub-health status and risk perception level. We found that a few physical examinees were in poor risk perception level, most of them were in good risk perception level, and as the risk perception level increased, the rate of physical sub-health status tended to increase and then decrease, and peaked at the good risk perception level (except when the physical sub-health score was 2, which peaked at the general risk perception level). The details are shown in Figure 1.
Note. 5、4、3、2、1、0、-1、-2、-3、-4、-5 represent the physical health scores of the physical examinees. The closer the score is to 5, the lower the risk of sub-health is, the better the health status is; the closer the score is to - 5, the greater the sub-health risk is, the worse the health status is; poor, general, good, excellent represent the risk perception level respectively. “Poor” indicates the score of risk perception scale is between 18 ~ 35; “general” indicates the score of risk perception scale is between 36 ~ 53; “good” indicates the score of risk perception scale is between 54 ~ 71; “excellent” indicates the score of risk perception scale is between 72 ~ 90.
To sum up, it is believed that improving the public's physical sub-health risks perception level may also prompt the public to take measures to protect their own health, thus avoiding or delaying the occurrence of public sub-health symptoms, and it even helps to transform the existing sub-health symptoms into healthy ones. Therefore, hypothesis 1 and hypothesis 2 are proposed.
Hypothesis 1: Risk perception level is significantly positively correlated with physical sub-health.
Hypothesis 2: Physical sub-health has a negative effect on individual risk perception.
Risk perception level is affected by demographic variables. Many studies have pointed out that demographic variables have significant relationship with risk perception and risk behavior reduction. Relevant studies show that risk behavior will decrease accordingly with the increase of age [24], the level of risk perception of women is higher than that of men [25], education level is negatively correlated with risk perception level [26], and higher income will reduce the occurrence of risk behavior [27].
Therefore, on the basis of the existing literature, we put forward the following hypothesis 3 and hypothesis 4.
Hypothesis 3: Demographic variables and related control variables have a significant impact on the level of risk perception.
Hypothesis 4: Demographic variables and related control variables have a significant impact on physical sub-health.
The specific conceptual model is shown in Figure 2.
The rest of the article is organized as follows: The next section introduces the methods of data collection and analysis of physical sub-health and risk perception level as well as the relevant tools used, followed by a section that presents the results of the study and discussion of the results. Finally, conclusions are provided in the last section of the article.
Physical Sub-health Risk Perception Scale
Based on the theory of Protective Action Decision Model, we designed a physical sub-health risk perception questionnaire. On this basis, the public sub-health risk perception survey was carried out to explore the physical sub-health risk perception scale, and the reliability and validity of the physical sub-health risk perception scale were tested. The Cronbach α coefficient of the scale was 0.889, which also passed the validity test. It provides the basis for sub-health research and fills in the blank of this research field in China.
The scale has 18 items, which are divided into 5 dimensions, including the health knowledge (I'm more knowledgeable about sub-health/unhealthy than the people around me; I regularly browse and read health newsletters/exam related websites/sub-health related brochures. There are two items.), trust selection (Doctors at local community hospitals; doctors in provincial and municipal hospitals; provincial or national public health administrators; experts/scholars at medical research institutions. There are four items.), information channel (I obtain sub-health related information through Internet search (Baidu, Soso, etc.); I obtain sub-health related information through related Hospital Websites; I need to search for more information about sub-health/unhealthy; I will compare this information with other relevant information. There are four items.), risk perception (Total presence of sub-health/unhealthy indicators in an individual's body; sub-healthy/unhealthy physical symptoms that I fear are a threat to my quality of life; sub-health/unhealthy symptoms in my body and I feel anxious and scared; do you think the occurrence of sub-health/unhealthy is related to the individual's behavioral habits? ; do you think the occurrence of sub-health/unhealthy is related to the degree of integrity of an individual's family structure? There are five items.), social groups (Family members; social networks (QQ, WeChat, Weibo, etc.); friends, relatives, neighbors and colleagues. There are three items.). Using Likert's 5-grade scoring standard, each item has five options, namely “totally disagree, basically disagree, neither agree nor disagree, basically agree, fully agree”, which scored 1~5 points respectively, and the score of this scale ranged from 18 to 90. The higher the score, the better the individual's competence of physical sub-health risk perception.
Self-compiled questionnaire on demographic variables
The questionnaire included parts for gathering demographic data about the subjects (gender, age, education, years of working, attribute of living place, marital status, number of children, average annual household income) and control variables data (self-health evaluation, sub-health duration, number of employees in the unit, sub-health proportion in the unit). The data is formatted as follows: male = 1, female = 2. Age is measured in years. Education is a six[1]point variable with primary or below= 1, junior high school= 2, senior high school= 3, junior college= 4, undergraduate= 5, master's degree and above= 6. Years of working has five categories, less than 5 years= 1, 5 to 10 years= 2, 11 to 20 years= 3, 21 to 30 years= 4, more than 30 years= 5. Attribute of living place has four categories, rural= 1, cities and towns= 2, third-tier city= 3, second-tier city= 4. Marital status has three categories,unmarried=1,Married=2, Others (eg.,remarried, divorced, widower/Widow) = 3. Number of children has four categories, zero child= 1, one child= 2, two children= 3, three or more children= 4. Average annual household income has five categories, less than 30,000 yuan= 1, 30,000 to 60,000 yuan= 2, 60,000 to 100,000 yuan= 3, 100,000 to 200,000 yuan= 4, more than 200,000 yuan= 5. Self-health evaluation has four categories, serious than sub-health= -2, sub-health= -1, unclear= 0, health= 1. Sub-health duration has five categories, less than 3 months= 1, 3 to 6 months= 2, 6 months to 1 year= 3, less than 2 years= 4, more than 3 years= 5. Number of employees in the unit has five categories, freelance= 1, less than 50= 2, 50 to 150 = 3, 150 to 500 = 4, more than 500= 5. Sub-health proportion in the unit has five categories, 10%= 1, 30%= 2, 50%= 3, 70%= 4, more than 90%= 5.
Self-compiled questionnaire for clinical manifestations of physical sub-health
We compiled a questionnaire to collect individual sub-health clinical data. Specifically, participants were asked if they had the following clinical symptoms that lasted for three months or longer. Clinical symptoms mainly include: short-term knee pain symptoms; gastrointestinal and liver abnormalities (e.g., nausea in the morning, palpitations, hunger, and similar symptoms); cardiac abnormalities (e.g., shortness of breath, arrhythmia, snoring, sexual dysfunction, and similar symptoms ); stool abnormalities (e.g., alternating diarrhea and constipation and long-term chronic diarrhea); painlessness abnormalities (e.g., painless neck mass, painless hematuria, and similar symptoms); and other abnormalities (e.g., dizziness, dry throat, itching, pain, edema of both eyes). For each item, clinical manifestation options are divided into three categories “No, Yes and No idea”, which scored 1,-1,0 points respectively, the score of this questionnaire ranged from -5 to 5. The closer the score to 5 the individual gets, the healthier the body he has.
Statistical methods
EpiData 3.1 software was used to establish the database and double input data. Then data cleaning was carried out. SPSS 23.0 was used for descriptive statistics, and R software and python software were used for statistical analysis of the measured data. Pearson correlation coefficient was used to analyze the correlation among physical sub-health, risk perception, demographic variables and related control variables. Factor analysis was used to test the multicollinearity, and multiple linear stepwise regression analysis was used to explore the effects of demographic variables and control variables on physical sub-health and risk perception. Two-way interactions in moderated multiple regression were used to test the moderating effect of demographic variables and control variables on physical sub-health and risk perception. Differences are considered to be statistically significant when P<0.05.