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AUTHOR 
 
Zhihui Jiang, Gaohui Cao, Reima Suomi, Daolin Zha 
 
 
TITLE 
 
Health anxiety and related factors among the rural 
population: A cross-sectional study in China 
 
YEAR 2025 
 
 
DOI https://doi.org/10.1177/13591053241301201 
 
 
VERSION 
 
Author’s accepted manuscript 
 
 
CITATION Zhihui Jiang, Gaohui Cao, Reima Suomi and Daolin Zha (2024), 
Health anxiety and related factors among the rural 
population: A cross-sectional study in China. Journal of Health 
Psychology, volume 30, issue 11, September 2025, pages 
3017-3031. https://doi.org/10.1177/13591053241301201  
 
 
 
Health Anxiety and Related Factors Among the Rural Population: A
Cross-Sectional Study in China
Zhihui Jiang 1, Gaohui Cao 1*, Reima Suomi 2, Daolin Zha 1
1 School of Information Management, Central China Normal University, Wuhan, China;
2 Turku School of Economics, University of Turku, Turku, Finland
*Correspondence: Gaohui Cao, School of Information Management, Central China Normal University,
Wuhan, Hubei, China. Email: ghcao@mail.ccnu.edu.cn
Abstract
This study investigated the prevalence and correlates of health anxiety among rural populations in
China, due to the unique socio-economic challenges in these areas, such as limited healthcare access. A
sample of rural residents (N=909) was analyzed to identify key predictors of health anxiety, such as age,
education level, living type, anxiety sensitivity, and perceived information overload. Results indicated
that older individuals and those with lower educational levels exhibited significantly higher levels of
health anxiety. Furthermore, living alone was associated with increased health anxiety, while anxiety
sensitivity and perceived information overload were found to exacerbate anxiety levels. These findings
highlight the critical need for tailored interventions aimed at mitigating health anxiety in rural areas,
thereby contributing to improved mental health outcomes and overall well-being.
Keywords
health anxiety; rural population; related factor; perceived information overload
Introduction
Anxiety represents a multifaceted adaptive psychological response, serving to focus attention and
stimulate necessary actions in response to perceived threats (Fu, 2018). When an individual’s excessive
attention to the body manifests as severe neurotic anxiety, it will evolve into health anxiety (Marcus et
al., 2008). People have a certain level of concern about their health, but this sometimes turns into a
constant, excessive fear of serious illness (Al-Amad and Hussein, 2021). Health anxiety manifests as
excessive attention to specific bodily symptoms or excessive worry about the possibility of suffering
from a severe disease (Bajcar and Babiak, 2019). When health anxiety levels are excessive, health anxiety
can become a problem (Lee et al., 2015a). People with health anxiety often have misconceptions about
their feelings or symptoms and worry about their current and future physical condition, even in the
absence of physiological or pathological symptoms (Abramowitz et al., 2007b). The effects of mild health
anxiety are minor and not socially detrimental, but severe health anxiety may affect the patient’s normal
life (Ferguson, 2009). Individuals with severe health anxiety may suffer a diminished quality of life, and
their continuous medical treatment behavior will also increase the burden on the medical system (Lee et
al., 2015b).
Health anxiety is caused by a variety of complex factors, including cognitive beliefs (Rachor and
Penney, 2020), living habits (Coulthard et al., 2021), and information behavior (Dattilo et al., 2021). For
example, immunocompromised people and those with chronic diseases are prone to fear and anxiety
during infectious disease epidemics such as COVID-19 (Carmassi et al., 2022). Low self-esteem (Bajcar
and Babiak, 2019), anxiety temperament (Oniszczenko, 2021), and fear of self-infection (Yalcin et al.,
2022) are important predictors of health anxiety. Based on the health belief model, cognitive load theory,
and protection motivation theory, Laato & al (2020) verified that perceived trust in online information,
perceived susceptibility, and perceived severity led to health anxiety.
Numerous studies have demonstrated a link between socioeconomic status and health. In general,
rural populations experience poorer health and higher morbidity and mortality rates compared to urban
populations(Mackenbach, 2019). Rural residents also face higher rates of disease and greater health
burdens(Moreno-Peral et al., 2014). Some studies have found that socially deprived individuals exhibit
increased health anxiety(Huang and Shi, 2016; Creed and Barsky, 2004). Previous studies have found
that health anxiety is more prevalent and severe in rural areas compared to urban areas (Wang and Pan,
2021). However, existing research has yet to thoroughly explore the causes of health anxiety in rural
populations.
This study focuses on health anxiety within a Chinese rural population, a demographic that has been
historically underrepresented in health research. Rural areas in China encompass various settings such as
villages, small towns, and agricultural regions, each with its own socio-economic dynamics. This
definition highlights that rurality is not merely the absence of urban characteristics but encompasses
unique lifestyles, cultural beliefs, and community structures that influence health-related behaviors and
attitudes. However, the status and related factors of health anxiety in rural areas are still unclear.
Exploring the related factors of health anxiety in rural areas is beneficial for understanding the formation
mechanism of health anxiety. Research findings could also offer recommendations for alleviating health
anxiety levels.
The perceived information overload is one of the important factors affecting health anxiety (Mathes
et al., 2018). Individuals tend to experience heightened concern when health information sources are
ambiguous (Starcevic and Berle, 2013). The abundance of information can act as a trigger for health
anxiety. The uncertainty inherent in health information can exacerbate health anxiety (Taylor et al., 2007).
Individuals experiencing worry tend to seek out multiple sources of information in an attempt to alleviate
symptoms of health anxiety (Lagoe and Atkin, 2015). Eastin (2006) also found that ambiguity in health
information can exacerbate degree of health anxiety. Baumgartner & al (2022) study elucidated that
discrepancies within health information can evoke heightened anxiety among individuals already
predisposed to health anxiety. Therefore, this study hypothesized that perceived information overload
impacted health anxiety among rural population.
Catastrophic misconceptions refer to individuals’ misconceptions and assumptions regarding their
physical symptoms, often involving an exaggerated perception of their severity or potential consequences
(Baerg and Bruchmann, 2022). An individual’s personality and psychology can significantly influence
their cognitive processes, which in turn play a key role in the development of health anxiety (Carleton et
al., 2007). Catastrophic misconceptions represent one of the potential influences of health anxiety, often
arising unconsciously when individuals lack knowledge about health-related matters. This deficiency in
understanding can subsequently contribute to the development of health anxiety. A positive correlation
between catastrophic misinterpretation and health anxiety was revealed in (Bailey and Wells, 2015;
Bailey and Wells, 2016). In their study investigating the relationship between paranoid uncertainty
moderating health anxiety and positive thinking, Kraemer et al. (2026) proposed that health anxiety is
characterized by catastrophic misinterpretations of somatic symptoms. These misinterpretations
contribute to health-related concerns and an increased utilization of healthcare resources. Researchers
studying health anxiety have described its causes as stemming from catastrophic misinterpretation
(Thorgaard et al., 2017).
Sensitivity comes from the cognitive process through which individuals acquire knowledge about
the objective world (Fergus and Russell, 2016). Anxiety sensitivity arises from an individual’s
apprehension regarding the adverse outcomes of anxiety, which is believed to be a contributing factor to
the onset of panic (Tanis et al., 2016). Anxiety sensitivity is a cognitive and interpersonal psychological
factor that predisposes individuals to develop various mental illnesses. In terms of the impact of anxiety
sensitivity on health anxiety (Berrocal et al., 2007), it is considered a relatively stable indicator of anxiety,
as individuals perceive anxiety as detrimental to their psychological, physical, and social well-being,
leading to heightened worry and panic (Taylor et al., 1998). In a study examining anxiety sensitivity and
health anxiety, Abramowitz et al. (2007a) concluded that anxiety sensitivity exerted a significant positive
influence on health anxiety. In a study on unwarranted phobias using the catastrophic misunderstanding
model, Cox and Therapy (1996) found that patients with high anxiety sensitivity experienced more
catastrophic misunderstandings than those with low anxiety. Therefore, we hypothesize that anxiety
sensitivity has a significant impact on health anxiety.
Scholars have also studied the development and improvement of health anxiety-related
measurement tools. The Whiteley Index (WI) is the first widely used health anxiety measurement tool
for research and clinical use (Pilowsky, 1967). This scale consists of 14 items to measure the three
components of health anxiety: illness belief, body concern, and disease-related fear (Asmundson et al.,
2008). Warwick developed a scale for measuring the full range of health anxiety (HAI) based on cognitive
theories of health anxiety and hypochondria, which can distinguish between hypochondriacs and non-
clinical users (Warwick and Salkovskis, 1989). Lucock developed the Health Anxiety Questionnaire
(HAQ) (Lucock and Morley, 1996). The design of the HAQ is based on the cognitive-behavioral model
of health anxiety. The primary purpose of the HAQ is to identify individuals highly concerned about their
health. Based on HAI, Salkovskis developed the Short Health Anxiety Inventory Scale (SHAI) to assess
health anxiety (Salkovskis et al., 2002). Since its establishment, the scale has been widely used in various
research scenarios and has proven to be the most effective measurement tool for health anxiety (Alberts
et al., 2011).
Zhang (Zhang et al., 2015) translated the Chinese version of the SHAI scale for measuring health
anxiety in the Chinese population, which was the Chinese-version Short Health Anxiety Inventory
(CSHAI). It has been widely utilized to assess health anxiety in various populations (Ke et al., 2023;
Chen et al., 2019; Li et al., 2020; Zhang et al., 2018). It is considered an effective tool for studying health
anxiety in the Chinese population (Liu et al., 2020), and was used to measure the degree of health anxiety
among participants in our study. The evaluation of the scale takes a short time and has high accuracy.
The scale includes two core factors: the possibility of diseases and the negative consequences of diseases.
The validation results show that the Chinese version of the scale has good validity and reliability (Zhang
et al., 2015). The Chinese version of the scale is easy to understand and more suitable for the rural people
in this study. A health anxiety scale score exceeding 15 points was classified as indicative of the health
anxiety group, while a score surpassing 30 points denoted the serious health anxiety group(Zhang et al.,
2015).
Methods
Data collection
Data collection approach. The data utilized in this study were collected using a structured
questionnaire in January and February 2023. The questionnaire included sections for informed consent,
demographic characteristics, the CSHAI, and related factors. The questionnaires were collected from 30
provinces, municipalities, and autonomous regions in China. Informed consent was obtained from all
participants.
The inclusion criteria for the sample were: (1) living in a rural area for more than ten years; (2)
currently living in a rural area; (3) being willing to provide informed consent and participate in this study;
(4) able to express themselves clearly. We used non-probability sampling methods, including
convenience sampling and snowball sampling.
. Students conducted face-to-face interviews of residents around them when they returned home to
rural areas during the holidays. Before sending the students to conduct interviews, we introduced the
purpose, process, and questionnaire-filling requirements of the survey to the students from rural areas in
the class we taught.
Sample size. A total of 930 questionnaires were collected, of which 909 were valid, with an effective
rate of 97.74%, meeting the standard of valid return rate for questionnaire surveys. The exclusion criteria
for the sample were: (i) all questions were answered with the same option; (ii) the response time was less
than 180 seconds; (iii) participants were under 18 years old.
Sample characteristics. Table 1 displays the demographic information of participants. Clearly over
half of the participants were female (69.31%). The majority of participants were between 50-65 years
old (66.56%). The educational level of the participants was evenly distributed. Most participants reported
an annual income of less than 17,000 yuan (41.8%). 66.34% of participants reported being in good health
recently, while 50.39% reported not being in good health recently. In terms of living type, 39.38% of
participants lived alone. Additionally, 41.69% of participants stated that they usually sought health-
related information through online searches or consultations, while 58.31% preferred to consult relatives,
friends, or medical doctors. This suggests that in rural areas, seeking opinions from opinion leaders or
consulting with professional doctors remains the most common method of obtaining health information.
Table 1. Demographic information of the participants (N = 909).
N %
gender male 279 30.69%female 630 69.31%
age
18-33 105 11.55%
34-49 199 21.89%
>49 605 66.56%
education
primary school 175 19.25%
secondary school 264 29.04%
technical secondary school 161 17.71%
high school 145 15.95%
junior college 95 10.45%
university 49 5.39%
master’s degree and above 20 2.20%
annual income
<17,000 380 41.80%
17,000-33,600 240 26.40%
33,600-50,400 148 16.28%
50,400-67,200 96 10.56%
>67,200 45 4.95%
self-rated health healthy 603 66.34%unhealthy 306 33.66%
family self-rated health healthy 458 50.39%family member is unhealthy 451 49.61%
living type alone 358 39.38%with others 551 60.62%
information seeking
tendency (most frequently)
access to health information through the Internet 379 41.69%
get health information from your friends, relatives, or
doctors 530 58.31%
Measures
Healthy anxiety. Eighteen items were used to measure health anxiety. The first 14 items primarily
assess participants’ psychological state concerning health matters, covering their perspectives on health,
illness, physical changes, psychological adjustment, and attitudes. The final four items assess potential
adverse outcomes and whether participants perceive their health status as declining, encompassing their
attitudes toward life, beliefs, and self-esteem. Through these 18 items, individuals with health anxiety
can be effectively distinguished from those without. The index utilized a 4-point Likert scale, with scores
ranging from 0 to 3, representing levels from mild to severe, respectively. Response options included
“never/completely disagree”, “occasionally/disagree”, “general/agree”, and “often/ very much agree”,
scored as 0, 1, 2, and 3, respectively.
Perceived information overload. Four items were used to measure the perceived information
overload of rural population (Misra et al., 2012). For example: “I receive numerous health messages and
am uncertain about how to handle them”, “I don’t know where to go for health-related information when
I or a family member or friend is unwell”, and “The health information I receive, the health information
my family and friends provide, and the doctor’s advice may not be consistent”. A 5-point Likert scale
was employed, with responses ranging from 1 (never) to 5 (always). A greater score signifies a more
severe level of information overload. In this study, the Cronbach’s alpha coefficient for the scale was
measured at 0.873.
Catastrophic misinterpretation. We primarily utilized Rief et al.’s (1998) questionnaire to
measure catastrophic misinterpretation. The questionnaire demonstrated good overall internal
consistency (α = 0.90) and high reliability specifically for the section on catastrophic misconceptions (α
= 0.88). This scale contains four items: “When I am not feeling well, I often assume that I must be
suffering from some disease”., “I feel sad, anxious, or fearful because I don’t know exactly what disease
I have”., “When I or a family member or friend has a medical condition, I imagine it to be serious and
feel anxious.”, and “When the doctor recommended further tests, it must have been because they
suspected a serious illness.” The options were rated on a five-point Likert scale, ranging from “never” to
“always.” In this study, the Cronbach’s alpha coefficient for the scale was 0.826.
Anxiety sensitivity. Following Taylor et al. (2007), in this study we measured anxiety sensitivity in
three components: physical, cognitive, and social concerns. The scale has good reliability and has been
widely validated, so this study follows the scale and measures anxiety sensitivity by subdividing it into
concerns about the body, cognitive concerns, and social concerns. Three items were used to measure
anxiety sensitivity: “When I am not feeling well, I am afraid that I may suffer from a serious disease.”,
“I feel afraid when I can’t concentrate on my work.”, and “I worry that other people will notice my
anxiety.” The anxiety sensitivity subscale also uses a five-point Likert scale. In this study, the Cronbach’s
alpha coefficient for the scale was 0.85.
Demographic information. The survey included eight items, asking participants about their (1) age,
(2) gender, (3) education level, (4) annual income, (5) health status, (6) family health status, (7) lifestyle,
and (8) information-seeking tendencies. All participants were required to answer these questions.
Ethical approval
The study protocols were approved by the Ethics Committee of the School of Information
Management, Central China Normal University. Written informed consent was obtained from all
participants included in the study.
Results
Table 2 presents the health anxiety scores of the participants. Statistical analysis revealed that out
of the 909 participants studied, 699 (76.9%) exhibited health anxiety, with 12.21% scoring 30 points or
higher.
Table 2. Health anxiety scores of participants.
Score Anxiety level N %
15 marginal anxiety 210 23.10%
15≤CSHAI30 low anxiety 588 64.69%
≥30 high anxiety 111 12.21%
Statistical data analysis was conducted to analyze the factors affecting health anxiety in rural areas
using binary logistic regression. Table 3 reveals that the older people in remote areas are, the more likely
they are to reporting health anxiety (95% CI: 0.531-0.99). The age of the people in rural areas was found
to be significantly associated with their anxiety level (p=0.043). People in rural areas with lower
educational level are more likely to reporting health anxiety than those with higher educational level (p=
0.000). People who lived alone suffered 0.6 times more from anxiety (95% CI: 0.406-0.888) than those
who lived with others. People in rural areas who prefer to seek information offline were 3.079 times (95%
CI: 1.998-4.747) more likely to have a high anxiety disorder than people who prefer to seek information
online. Anxiety sensitivity and the perceived information overload also contribute to the emergence of
health anxiety in people in rural areas. However, the data analysis revealed that the p-values for gender,
annual income, health status, family health status, and catastrophic misconceptions were >0.05,
indicating that these factors did not correlate with health anxiety. In this study, these factors were also
not statistically associated with health anxiety among people in rural areas.
Table 3. Related factors for health anxiety.
B SE Wald χ2 p-value OR (95% CI)
gender 0.318 0.228 1.955 0.162 1.375（0.88-2.148）
age 0.322 0.159 4.106 0.043* 0.725（0.531-0.99）
education -0.291 0.061 22.621 <0.001* 1.338（1.187-1.508）
annual income 0.037 0.076 0.231 0.631 1.037（0.894-1.204）
self-rated health 0.036 0.226 0.026 0.873 1.037（0.665-1.615）
family self-rated health −0.350 0.210 2.771 0.096 0.705（0.467-1.064）
living type 0.510 0.200 6.520 0.011* 0.6（0.406-0.888）
information seeking tendency −1.125 0.221 25.942 <0.001* 3.079（1.998-4.747）
catastrophic misinterpretation 0.202 0.125 2.590 0.108 1.224（0.957-1.565）
anxiety sensitivity 1.217 0.205 35.403 <0.001* 3.377（2.262-5.042）
perceived information overload 0.426 0.126 11.455 0.001* 1.531（1.196-1.959）
Note: N = 909. * p < 0.05.
According to the scores on the health anxiety scale, individuals with health anxiety are categorized
into two groups: those with severe health anxiety and those with mild health anxiety. Another binary
logistic regression was conducted based on the related factors of health anxiety to further identify the
factors affecting the level of health anxiety and to systematically analyze the related factors at each stage
of health anxiety. As observed from Table 4, among the health anxiety population, factors such as age,
education, living type, anxiety sensitivity, and perceived information overload influence the level of
health anxiety among people in rural areas. As individuals in rural areas age, they often experience
challenges in adapting to changes in life. Their capacity to embrace new knowledge and experiences
tends to decline, and they may have reduced contact with relatives, contributing to feelings of loneliness
and depression. Additionally, individuals residing alone in remote areas exhibited a 2.769 times higher
incidence of severe health anxiety compared to those living with others. Anxiety sensitivity and the
perceived information overload were also closely related to the health anxiety of rural residents.
Table 4. Related factors for the level of health anxiety.
B SE Wald χ2 p-value OR (95% CI)
age 1.778 0.322 30.466 <0.001* 5.915(3.147-11.12)
education -0.161 0.074 4.752 0.029* 0.852(0.737-0.984)
living type 1.018 0.291 12.279 <0.001* 2.769(1.566-4.895)
information seeking tendency -0.294 0.282 1.09 0.296 0.745(0.429-1.295)
anxiety sensitivity 0.86 0.188 20.816 <0.001* 2.363(1.633-3.419)
perceived information overload 0.763 0.185 16.989 <0.001* 2.145(1.492-3.084)
Note: N = 699. * p < 0.05.
Discussion
Health anxiety is influenced by various factors during its development. This study utilized a dataset
collected from rural areas to investigate the impact of age, gender, perceived information overload,
anxiety sensitivity, and other factors on health anxiety among individuals among rural population.
Specifically, using the Chinese-version Short Health Anxiety Inventory (CSHAI) proposed by Zhang et
al. (2015), we examined the related factors at different stages of health anxiety.
The analysis of data revealed that demographic characteristics such as age, education, and living
type significantly influenced the level of health anxiety. Older individuals are more likely to experience
health anxiety, which aligns with the findings of Bourgault-Fagnou et al (Bourgault-Fagnou et al., 2009).
Previous studies have indicated that two demographic factors, gender and age, influence the intensity of
health anxiety groups’ search for health information and their cognitive bias towards health-related
terminology(Reiser et al., 2019). Compared to males, females experiencing health anxiety demonstrate
a greater propensity for seeking health information and initiating searches. However, in our study, no
significant difference in health anxiety levels was found between men and women. Consistent with
previous studies, individuals with lower education levels were more likely to experience health anxiety
(Bleichhardt and Hiller, 2007). When intervening with health anxiety in remote areas, the focus should
be on older adults and those with low levels of education.
Consistent with our results, previous studies have also examined the relationship between living
alone and anxiety. Yu et al. found that individuals living alone face a significantly higher risk of health
anxiety compared to those living with family members (Yu et al., 2020).Research has found that people
who live alone are more likely to reporting health anxiety (Chen et al., 2022), and therefore people who
live alone are also a priority group to focus on. Stahl et al. found that people who live alone have higher
levels of depressive symptoms than those who live with family members (Stahl et al., 2017).
The perceived information overload is a factor that affects health anxiety in rural areas. Information
overload is the psychological feeling of information-poor groups who perceive that they are exposed to
more information than they can process. More than 70% of participants said they get distracted by too
much health information found when searching on the Internet. Individuals experiencing health anxiety
often seek information through the Internet (McManus et al., 2014). However, the abundance of health-
related information can exacerbate their level of health anxiety, with health anxiety stemming from
information overload becoming increasingly pronounced. Due to their limited knowledge, rural
individuals are more prone to experiencing heightened health anxiety when confronted with overload
information (Gibson et al., 2016).
From the survey results, it is possible that due to the differences in the levels of information literacy
and health literacy of users, the professional information provided by health information websites
exceeds the range of users’ understanding and cannot guide users. While both rural and non-rural
populations utilize the internet, the unique factors in rural areas, such as limited access to accurate health
information and greater reliance on non-verified sources, could exacerbate anxiety levels.
In rural China, individuals may have less access to formal healthcare resources, potentially
increasing their dependency on the internet for health-related information. This could amplify health
anxiety, as online sources often present conflicting or alarmist content. While both rural and non-rural
populations utilize the internet, unique factors in rural areas—such as limited access to accurate health
information and a greater reliance on unverified sources—could exacerbate anxiety levels. In rural areas,
individuals may have reduced access to formal healthcare resources, potentially increasing their
dependence on the internet for health-related information.
Anxiety sensitivity predisposes both older and younger adults to health anxiety and predicts the
likelihood of its occurrence in these age groups (Gerolimatos and Edelstein, 2012).Individuals with high
anxiety sensitivity tend to gravitate towards negative interpretations of health-related issues, and these
traits can exacerbate the development of health anxiety. Participants reported that health anxiety arises
when they, a family member, or a friend are not feeling well, or when uncertainty surrounds the nature
of the illness. Our statistical analysis revealed that individuals with high anxiety sensitivity were more
inclined to suspect that they were suffering from a particular disease and were more proactive in seeking
information. In the process of seeking information, individuals with high anxiety sensitivity tend to over-
assess the severity of their own or their family members’ illnesses, thereby exacerbating their health
anxiety.
Our survey found that rural individuals often seek information about disease symptoms from
traditional media sources or online advertisements. Due to their relatively low level of information
literacy and the influence of the perceived information overload, they might be unable to objectively
assess their actual health situation and suspect that they are suffering from one or even more diseases.
Due to the influence of education level and social environment, people in rural areas have might have
substandard cognition and be unable to make objective enough judgments about diseases, so they are
more prone to anxiety (Hong et al., 2022). The dissemination of health knowledge to rural people should
be further strengthened to decrease anxiety sensitivity.
There are still some limitations to this study: 1. There are many factors affecting health anxiety, the
factors selected in this study only encompass some of them, and more factors should be selected for
research in the future; 2. The sample selected in this study is relatively small and should be further
expanded in the future; 3. The present study is a cross-sectional study, and there is no deeper tracking
study on health anxiety in rural areas, so it is challenging to explore many issues in depth, and
longitudinal studies should be conducted in the future.
Conclusion
This study examined the level of health anxiety and explored the factors influencing it in rural areas.
The findings highlight the impact of various factors on health anxiety, providing a foundation for the
development of effective intervention and improvement strategies.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or
publication of this article.
ORCID iD
Zhihui Jiang https://orcid.org/0009-0000-9792-9091
Reima Suomi https://orcid.org/0000-0003-2169-7997
Data Availability Statement
The data presented in this study are available from the corresponding author. Ethics approval,
participant permissions, and all other relevant approvals were granted for this data sharing.
Ethical approval and patient consent
The study protocols were approved by the Ethics Committee of the School of Information
Management, Central China Normal University. Written informed consent was obtained from all
participants included in the study.
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