What this is
- This research investigates mental health outcomes among Chinese adults during the COVID-19 epidemic.
- It examines associations with , depression, anxiety, and symptoms.
- Data were collected through an online survey in February 2020, involving 1456 participants.
Essence
- Mental health issues, including (38.7%), depression (11.3%), anxiety (7.6%), and (33.9%), were prevalent among Chinese adults during COVID-19. Various biopsychosocial factors influenced these outcomes.
Key takeaways
- was significantly linked to being single or divorced, lower education, and higher somatic symptoms. Those with higher self-efficacy reported lower .
- Depressive symptoms were associated with binge drinking, fear of infection, and lower self-efficacy. Individuals with chronic diseases also reported higher depressive symptoms.
- symptoms correlated with fear of infection and perceived negative influence from COVID-19. Higher self-efficacy was protective against .
Caveats
- The study's online survey method may not represent the entire population, as it could exclude those without internet access.
- Using abbreviated scales for mental health screening may limit the depth of understanding regarding participants' mental health status.
Definitions
- Loneliness: A feeling of social isolation or lack of companionship.
- PTSD: A mental health condition triggered by experiencing or witnessing a traumatic event.
AI simplified
Introduction
In early December 2019, a confirmed case of Coronavirus disease 2019 (COVID-19) was reported in Wuhan, the capital city of Hubei province in China [1]. With a large crowd flow during the Spring Festival period in China, the number of COVID-19 cases increased rapidly. On 26th February 2020, there were over 78,000 cases in China, which included more than 65,000 cases in Hubei province [2]. On 11th March 2020, the World Health Organization (WHO) declared COVID-19 a worldwide pandemic [3].
The Chinese government had taken strict and effective public health measures to control the spread of the epidemic at the earliest time. Wuhan, where the epidemic was most serious, has been on lockdown since 23rd January [4]. On 26th January 2020, 30 provinces or cities in China had announced the launch of the first-level public health emergency response [4]. These safety measures included the cancelation of mass gatherings, limitation of transportation capacity, and the postponement of the spring semester [4]. Residents were suggested to stay home, maintain social distancing, wear protective face masks, and wash hands frequently. The government updated infection information to the public daily.
The unexpected COVID-19 epidemic, as well as the strict measures against the epidemic, may threaten peopleβs mental health. Behavioral Immune System (BIS) theory suggests that negative emotions, such as depressive and anxiety symptoms, and avoidance behaviors, including avoidance of human contacts or avoiding relevant information or activities, would appear for self-protection from external dangers [5, 6]. Some studies have been conducted to examine the level of mental health problems during the early epidemic in China. A nationwide survey indicated a mean COVID-19 Peri-traumatic Distress Index (CPDI) score of 23.65, and nearly 35% of respondents had experienced psychological distress [7]. Another study reported the prevalence of depression, anxiety, or the combination of the two were 48.3%, 22.6%, and 19.4%, respectively [8]. A web-based survey found an overall prevalence of anxiety symptoms, depressive symptoms, and poor sleep quality of 34.0%, 18.1%, and 18.1%, respectively [9]. Globally, the prevalence of depression and anxiety was 20% and 35%, respectively, during the COVID-19 outbreak [10].
Apart from the above-mentioned mental health problems, the unexpected epidemic and corresponding safety measures such as social distancing and home quarantine may also bring significant loneliness to the residents. During the epidemic of Severe Acute Respiratory Syndrome (SARS) in 2003, up to 38.5% of the people who experienced quarantine reported feelings of loneliness [11]. However, relatively few studies measured the prevalence of loneliness during COVID-19 in the general population. An online survey revealed that up to 47% of Chinese adults believed they may feel lonely for most of 2020 [12]. Overall, loneliness and other mental health problems may cause a significant burden to both individuals and society. As loneliness is an independent risk factor for many chronic diseases such as cardiovascular diseases, obesity, and mortality [13].
Given the high prevalence and potential health burden of mental health problems, targeted policies and interventions are urgently needed for the prevention and treatment during COVID-19 as well as for future readiness in unexpected pandemics or disasters. Therefore, understanding the risk factors associated with loneliness and other mental health problems is important. As researchers can design more targeted interventions by understanding and ameliorating these possible modifiable risk factors for improvement during this special time. Previous studies have found that many factors are associated with loneliness and other psychological symptoms, including physical health factors (e.g. chronic illness), social-cultural factors (e.g. social support, family, and marriage), and social environmental factors (e.g. rural versus urban environments) [14]. Although previous studies have examined the prevalence of depression and anxiety during COVID-19 [7, 9, 15], few studies have extensively examined the risk factors, especially COVID-19 related factors and self-efficacy, which are very likely to be associated with loneliness, depression, anxiety, and post-traumatic stress disorder (PTSD).
Therefore, we conducted this study to investigate these possible risk factors associated with loneliness, depressive symptoms, anxiety symptoms, and PTSD symptoms among Chinese adults one month after the closure of Wuhan city during COVID-19.
Materials and methods
Study design and study population
This is a cross-sectional study during the COVID-19 epidemic in China. The data were collected from 21-26th February 2020. The target population comprised of Chinese adults aged 18 or above and were reached using convenience and snowball sampling methods. The questionnaire was developed on the platform of Wenjuanxing (www.wjx.cnβ). The investigators distributed the online survey link via Wechat, one of the most popular mobile applications for instant messages in China. The survey required about 10β15 minutes to complete and was completely voluntary and anonymous. The informed consent was provided at the beginning of the questionnaire (S1 File). After finishing the survey, all the participants would have received a report regarding their physical and mental health with information on help-seeking hotlines in case of need, and a lucky draw of 1β10 RMB. The study was approved by the Survey and Behavioural Research Ethics Committee of The Chinese University of Hong Kong and had been registered in a World Health Organization recognized registry (Registration No.: ChiCTR2000030223) before commencement.
Measurements
The detailed measurements and data can be found inandFiles. S1 S2
COVID-19 related factors
COVID-19 related factors included taking part in anti-epidemic related work by oneself or family members, having confirmed or suspected cases among oneself, family members, acquaintances, and nearby residents in the city, perceived risk of being infected, fear of being infected, the perceived time needed for infection control, and overall self-rated influence due to COVID-19 epidemic.
Mental health outcomes
The primary outcome was loneliness, measured by a three-item UCLA Loneliness Scale (UCLA-3) [16]. A cut-off score of β₯4 represented a high level of loneliness [17]. Secondary outcomes included depressive symptoms, anxiety symptoms, and PTSD. Depressive symptoms were measured using a two-item Patient Health Questionnaire (PHQ-2), with a cut-off score of β₯3 considered positive [18]. Anxiety symptoms were assessed using a two-item Generalized Anxiety Disorder Questionnaire (GAD-2) [19]. A cut-off point of β₯3 was considered positive. PTSD symptoms were assessed by two questions about recurrent dreams and their avoidance, which were extracted from the Clinician-Administered post-traumatic stress disorder (PTSD) Scale [20]. A summation score of β₯3 signifies the existence of PTSD symptoms.
Self-efficacy
Self-efficacy, measuring the confidence to deal with unexpected events, was measured by one item from the General Self-Efficacy Scale; possible scores ranged from 1β4 with a higher score signifying a higher level of self-efficacy [21].
Physical health
The number of chronic diseases and the number of regular medications were self-reported. Somatic symptoms were measured by the validated 15-item Patient Health Questionnaire (PHQ-15) [22]. Self-reported overall physical health was rated from 1 = poor to 5 = excellent. Higher scores denote severer somatic symptoms.
Lifestyle
Smoking was recorded as current smoker, former smoker, and never smoker. Drinking was measured using one item in AUDIT-3, asking the frequency of binge drinking in the past year [23]. The exercise was the total hours doing physical exercise in the past week. Sedentary time was the average total hours of sitting or lying per day when awake in the past week. Screen time was the average daily use time of the mobile phone, Internet, TV, and video games in the past two weeks. The frequency of going outdoor and the distance walked in the past two weeks were also recorded.
Statistical analysis
The demographic characteristics were described as frequency and percentage, or mean and standard deviation (SD). Logistic regressions were conducted to explore the potential risk factors influencing depressive and anxiety symptoms, loneliness, and PTSD. The associations between factors and loneliness, depressive symptoms, anxiety symptoms, and PTSD were demonstrated by the odds ratios (ORs), adjusted odds ratios (AORs), and their corresponding 95% confidence intervals (95%CIs). A two-tailed p-value less than 0.05 was considered statistically significant. All statistical analysis was performed using Stata version 13.1 (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP.).
Results
Participant characteristics
A total of 1456 adults completed the online survey. The demographic data of participants is presented in Table 1. Most of our participants were female (59.1%), married (59.6%), employed (69.7%), living in the urban area (78.7%), and had at least a bachelorβs degree (73.3%). The mean age of the participants was 33.8Β±10.5 years old.
| Characteristics | Number (n) | Percentage (%) |
|---|---|---|
| Gender | ||
| Male | 596 | 40.9 |
| Female | 860 | 59.1 |
| Age (meanΒ±SD) | 33.8Β±10.5 | |
| Marriage | ||
| Married | 867 | 59.6 |
| Single | 552 | 37.9 |
| Separated/divorce/widowed | 37 | 2.5 |
| Education | ||
| Primary school and below | 7 | 0.5 |
| Middle school | 64 | 4.4 |
| High school | 114 | 7.8 |
| College degree | 219 | 15 |
| Bachelor degree | 659 | 45.3 |
| Postgraduate or above | 393 | 27 |
| Job | ||
| Employed | 1015 | 69.7 |
| Unemployed | 112 | 7.7 |
| Student | 305 | 20.9 |
| Unknown | 24 | 1.6 |
| Income level | ||
| Highest | 17 | 1.2 |
| Quite high | 81 | 5.6 |
| High | 160 | 11 |
| Average | 874 | 60 |
| Low | 248 | 17 |
| Quite low | 47 | 3.2 |
| Lowest | 29 | 2 |
| Current residence | ||
| Rural | 310 | 21.3 |
| Urban | 1146 | 78.7 |
| Past-year residence | ||
| Rural | 172 | 11.8 |
| Urban | 1284 | 88.2 |
| Current location | ||
| Other provinces | 1277 | 87.7 |
| Hubei | 114 | 7.8 |
| Overseas | 65 | 4.5 |
Factors associated with mental health outcomes
Loneliness (UCLA-3)
About 38.7% (n = 563) of the participants were screened positive for loneliness. In univariable logistic regression, the UCLA-3 score was significantly lower in people with older age, higher education, better self-rated health, and higher self-efficacy (Table 2, p<0.05). The UCLA-3 score was significantly higher in unmarried people, students, binge drinkers, people with more chronic diseases, more medications, higher PHQ-15, having infected or suspected COVID-19 cases around, perceiving higher infection risk, longer sedentary and screen time, perceived longer time needed for infection control, fear of being infected, and reported being negatively influenced by the epidemic (Table 2, p<0.05).
In the multivariable regression (Table 3), higher UCLA-3 score was still associated with being single (OR = 1.891, 95%CI: 1.316β2.717) or separated/divorced/widowed (OR = 2.675, 95%CI: 1.284β5.569), more medications (OR = 1.372, 95%CI: 1.087β1.731), higher PHQ-15 score (OR = 1.176, 95%CI: 1.134β1.220), higher going out frequency (OR = 1.110, 95%CI: 1.016β1.214). Lower UCLA-3 was still associated with higher education (OR = 0.787, 95%CI: 0.687β0.901), current location in Hubei province (OR = 0.483, 95%CI: 0.288β0.809), and higher self-efficacy (OR = 0.568, 95%CI: 0.469β0.688).
| Variables | Crude OR (95%CI) | |||
|---|---|---|---|---|
| Depression (PHQ-2) | Anxiety (GAD-2) | Loneliness (UCLA-3: 4β9) | PTSD (3β10) | |
| Age | *** | *** | *** | ** |
| Gender (Female) | 1.075 (0.772, 1.497) | 0.852 (0.576, 1.261) | 1.018 (0.821, 1.261) | 0.906 (0.727, 1.129) |
| Marriage | ||||
| Married | ref | ref | ref | ref |
| Single | *** | *** | *** | ** |
| Separated/divorced/widowed | 0.974 (0.292, 3.250) | 1.020 (0.238, 4.372) | ** | 0.338 (0.130, 0.877)* |
| Education | 1.082 (0.926, 1.264) | 1.060 (0.881, 1.275) | * | 1.013 (0.916, 1.120) |
| Job | ||||
| Employed | ref | ref | ref | ref |
| Unemployed | 1.463 (0.817, 1.621) | 1.106 (0.517, 2.368) | 1.189 (0.799, 1.770) | 1.006 (0.664, 1.523) |
| Student | ** | ** | * | 1.269 (0.973, 1.654) |
| Unknown | 0.860 (0.199, 3.714) | 0.625 (0.083, 4.710) | 0.703 (0.289, 1.710) | 0.680 (0.267, 1.728) |
| Income | 1.010 (0.853, 1.197) | 1.140 (0.931, 1.396) | 0.932 (0.834, 1.041) | 1.013 (0.905, 1.135) |
| Current residence (Urban) | 1.005 (0.676, 1.494) | 0.913 (0.574, 1.454) | 0.870 (0.674, 1.123) | 0.950 (0.730, 1.237) |
| Past-year residence (Urban) | 1.106 (0.659, 1.856) | * | 0.837 (0.606, 1.156) | 1.011 (0.722, 1.415) |
| Current location | ||||
| Other provinces | ref | ref | ref | ref |
| Hubei | * | * | 0.973 (0.655, 1.444) | 1.361 (0.920, 2.014) |
| Overseas | 1.752 (0.894, 3.434) | 1.631 (0.723, 3.679) | 1.377 (0.835, 2.271) | 1.178 (0.702, 1.975) |
| Smoking # | 0.646 (0.365, 1.145) | 1.162 (0.658, 2.053) | 1.054 (0.764, 1.455) | 1.262 (0.912, 1.747) |
| Binge drinking in the past year | *** | *** | *** | *** |
| Chronic disease | *** | ** | ** | ** |
| Medication | ** | *** | *** | *** |
| PHQ-15 | *** | *** | *** | *** |
| Self-rated health | *** | *** | *** | *** |
| Self-efficacy | *** | *** | *** | *** |
| Anti-epidemic related work | 1.060 (0.750, 1.498) | 1.156 (0.767, 1.740) | 1.043 (0.831, 1.308) | ** |
| Cases around | *** | 1.867 (1.137, 3.064)* | ** | *** |
| Cases in the city | 1.035 (0.947, 1.130) | 0.994 (0.893, 1.205) | 1.000 (0.944, 1.059) | 1.040 (0.980, 1.103) |
| Perceived risk of being infected | ** | *** | *** | *** |
| Exercise | 1.003 (0.998, 1.007) | 0.993 (0.961, 1.026) | 1.002 (0.998, 1.006) | 0.994 (0.979, 1.009) |
| Sedentary time | *** | * | * | 1.010 (0.989, 1.032) |
| Going out frequency | 0.958 (0.875, 1.048) | * | 1.045 (0.986, 1.209) | 0.942 (0.887, 1.001) |
| Going out distance | 0.936 (0.861, 1.016) | * | 1.006 (0.954, 1.060) | 0.977 (0.925, 1.032) |
| Screen time | *** | 1.110 (0.993, 1.241) | * | 1.018 (0.957, 1.083) |
| Perceived time needed for infection control | ** | ** | ** | *** |
| Fear of being infected | *** | *** | *** | *** |
| Negative influence ^ | * | ** | *** | *** |
| Variables | AOR (95%CI) | |||
|---|---|---|---|---|
| Depression (PHQ-2) | Anxiety (GAD-2) | Loneliness (UCLA-3: 4β9) | PTSD (3β10) | |
| Single# | 1.635 (0.955, 2.798) | 1.619 (0.844, 3.104) | ** | 1.350 (0.941, 1.937) |
| Separated/divorced/widowed # | 0.854 (0.234, 3.121) | 1.091 (0.220, 5.417) | ** | * |
| Education | 1.068 (0.868, 1.315) | 1.176 (0.909, 1.522) | ** | 0.981 (0.857, 1.123) |
| Hubei | 1.330 (0.678, 2.611) | 1.008 (0.448, 2.266) | ** | 0.723 (0.442, 1.183) |
| Binge drinking in the past year | ** | 1.271 (0.748, 2.160) | 1.210 (0.889, 1.647) | 1.218 (0.899, 1.651) |
| Medication | 0.966 (0.707, 1.320) | 1.138 (0.804, 1.612) | ** | 1.098 (0.881, 1.369) |
| PHQ-15 | *** | *** | *** | *** |
| Self-efficacy | *** | 0.665 (0.464, 0.953)* | *** | ** |
| Anti-epidemic related work | 0.930 (0.606, 1.426) | 0.978 (0.586, 1.634) | 0.987 (0.753, 1.294) | * |
| Perceived risk of being infected | 0.865 (0.663, 1.127) | 1.088 (0.801, 1.476) | 1.086 (0.910, 1.295) | * |
| Going out frequency | 1.077 (0.934, 1.243) | 0.922 (0.772, 1.100) | * | 0.920 (0.842, 1.005) |
| Screen time | * | 1.045 (0.904, 1.208) | 0.990 (0.914, 1.073) | 0.962 (0.888, 1.042) |
| Fear of being infected | * | 1.392 (0.971, 1.994) | 1.209 (0.994, 1.470) | *** |
| Negative influence ^ | 1.148 (0.740, 1.779) | 1.718 (0.975, 3.027) | 1.214 (0.923, 1.596) | ** |
Depressive symptoms (PHQ-2)
One-hundred and sixty-five participants (11.3%) scored β₯3 on PHQ-2, signifying the presence of significant depressive symptoms. In the univariable analysis (Table 2), an increased risk of depressive symptoms was significantly associated with being single, student, currently in Hubei province, binge drinking in the past year, having chronic diseases, more medications, higher PHQ-15 score, having confirmed or suspected cases around, high perceived risk of being infected, longer sedentary and screen time, perceived longer time needed for infection control, fear of being infected, and reported more negative influence due to COVID-19 (p<0.05). A decreased risk of depressive symptoms was significantly related to older age, higher self-efficacy, and better self-rated health (p<0.05).
In the multivariable logistic regression model (Table 3), an increased level of depressive symptoms was independently and significantly associated with binge drinking in the past year (OR = 1.835, 95%CI: 1.188β2.835), higher PHQ-15 score (OR = 1.188, 95%CI: 1.136β1.242), low self-efficacy (OR = 0.551, 95%CI: 0.410β0.740), longer screen time (OR = 1.151, 95%CI: 1.017β1.301), and fear of being infected (OR = 1.442, 95%CI: 1.071β1.942).
Anxiety symptoms (GAD-2)
One hundred and ten (7.6%) participants were screened positive for GAD-2. In the univariable analysis (Table 2), anxiety symptoms were associated with younger age, being single, being a student, past-year residence in a rural area, current location in Hubei province, binge drinking, more chronic diseases, more medications taken regularly, higher PHQ-15, lower self-rated health, lower self-efficacy, cases around, higher perceived risk of being infected, longer sedentary time, going out more frequently and farther, longer perceived control time, fear of being infected, and overall negative influence due to COVID-19 (p<0.05).
In the multivariable regression analysis (Table 3), anxiety was still significantly associated with higher PHQ-15 scores (OR = 1.226, 95%CI: 1.165β1.290) and worse self-efficacy (OR = 0.665, 95%CI: 0.464β0.953).
PTSD symptoms
A total of 494 (33.9%) participants had significant PTSD symptoms. In Table 2, many factors were related to PTSD symptoms, including younger age, single, separated/divorced/widowed, binge drinking in the past year, more chronic disease, more medication, higher PHQ-15, worse self-rated health, lower self-efficacy, anti-epidemic related work, cases around, higher risk of being infected, perceived longer time needed for infection control, more fear of being infected, and negative influence (p<0.05).
In multivariable regression (Table 3), separated/divorced/widowed (OR = 0.309, 95%CI: 0.113β0.847), PHQ-15 (OR = 1.128, 95%CI: 1.090β1.167), self-efficacy (OR = 0.767, 95%CI: 0.636β0.924), anti-epidemic related work (OR = 1.339, 95%CI: 1.026β1.749), perceived risk of being infected (OR = 1.197, 95%CI: 1.009β1.420), fear of being infected (OR = 1.433, 95%CI: 1.182β1.737), negative influence (OR = 1.456, 95%CI: 1.106β1.918) remained significant.
Discussion
Although the public health measures had successfully slowed down the dissemination of the epidemic in China one month later after the outbreak, these strict measures might continue to affect the mental health of the residents. Our study explored the possible immediate mental health effects on loneliness, depressive and anxiety symptoms, and PTSD and possible associated factors from multiple dimensions during the peak of the COVID-19 outbreak in China. Our results showed that many factors were associated with loneliness, depressive symptoms, anxiety symptoms, and PTSD in this particular situation.
Fear of being infected, high perceived risk of being infected, the self-rated overall negative influence due to COVID-19, and taking part in anti-epidemic related work by oneself or family members were related to PTSD symptoms in our study. People may repeatedly think of or dream about the COVID-19, and avoid COVID-19 related events. PTSD is a common phenomenon during the outbreak of infectious diseases, which has been observed during the epidemic of SARS [24β26] and current studies in the COVID-19 epidemic [7, 27]. The prevalence of PTSD symptoms was over 30% in the current study. Among SARS survivors, PTSD symptoms were the most prevalent and would exist in the long term, as well as depressive symptoms [28]. Therefore, during and after the COVID-19 epidemic, more attention should be paid to residentsβ mental health, especially PTSD symptoms.
High self-efficacy is found to be a protective factor for all mental health outcomes. This self-efficacy seemed also to play a role in protecting from worse mental health during COVID-19. Our findings were consistent with previous studies that low self-efficacy was a predictor of loneliness and psychological distress [29β31], and high self-efficacy may be an independent protective factor for loneliness and other mental health problems. The self-efficacy theory of depression explained the independent association between low self-efficacy and high risk of depression [32]. Low self-efficacy may affect mental health through the following ways: people may feel or believe that they are unable to achieve satisfying performance, develop satisfying relationships with others, and control disturbing depressive ruminations [32]. In our study, we mainly measured their confidence to deal efficiently with unexpected events. Its content was consistent with satisfying performance. Those people who showed higher self-efficacy in the unexpected COVID-19 outbreak might have more knowledge and resources in a difficult situation. Future studies might take a closer eye on people who might show higher self-efficacy and how to build up self-efficacy, in preparing people with readiness for future events and situations.
Somatic symptoms were a strong independent risk factor for loneliness and all other mental health outcomes in this study, including depressive and anxiety symptoms, and PTSD. Taking more regular medications was also associated with loneliness. However, other physical health factors were not associated with mental health outcomes. The relationship has been confirmed in previous studies that somatic symptoms were closely related to loneliness, depression, and anxiety before COVID-19 [33β36]. However, one of these studies proposed that somatic symptoms frequently originate from mental illness because somatic symptoms had little association with physical diseases [33]. Further analysis should confirm their causal relationship and explore if it was the epidemic that affected somatic symptoms, for example, people could have been more aware of body reactions in face of monitoring possible symptoms related to infection. On the other hand, in Chinese culture, people tend to somaticize mental health problems, meaning they express somatic symptoms instead of mental problems in clinical consultations [37, 38]. Health professionals may need to pay special attention to mental health problems when a client presents somatic symptoms without indicated impaired physical health.
The COVID-19 epidemic and the accompanying control measures had a certain impact on loneliness. Longitudinal studies indicated that there was a significant increase in loneliness after the COVID-19 outbreak [39]. In the current study, almost 40% of the participants reported loneliness during the COVID-19 epidemic. Loneliness may be relieved after the epidemic is controlled and mitigation measures are relaxed. However, if social isolation continues, attention and measures may be needed to reduce loneliness. In this study, people living in Hubei province were less likely to feel lonely. This may be mainly explained by the nationwide attention, support, and encouragement during the epidemic, through formal and informal online and offline channels, that many social forces were helping Hubei to control the epidemic with charitable donations, volunteer activities, and medical assistance [4]. This should have been a good example of how social support could mitigate loneliness during the pandemic, for people in the most affected areas. Overall, with the epidemic and infection control measures continue, mental health services should be provided to the general population affected by the COVID-19 epidemic [40, 41].
The prevalence of depressive and anxiety symptoms of 11.3% and 7.6% were lower than that reported in Huangβs study, where the prevalence of depression and anxiety were 18.1% and 34.0% [9]. Our study was conducted in late February, about two weeks after Huangβs study. The lower prevalence is probably because the epidemic in China was under control to some extent by then, or due to a large proportion of non-Hubei samples or more higher-educated people in our sample. A recent national survey found that a self-developed comprehensive index of mental health was significantly associated with younger age, female, higher education, occupation such as migrant workers, and middle region of China [7]. Another study found that higher trust in doctors, perceived survival possibility, low risk of infection, health information satisfaction, and personal preventive measures were the protective factors for poor mental health [42]. In our study, fear of being infected and more daily screen time in the past two weeks were related to depression symptoms. Many people were advised to stay at home or work online at home during the epidemic, thus increased the use of electronic products. A previous systematic review found that frequent mobile phone use was a risk factor for depressive symptoms among adult populations [43]. It was not clear why these people were more depressed, though it was likely due to more exposure to negative information on the internet, or being less active for other activities especially outdoor activities.
Strengths and limitations
This study provided a comprehensive view of mental health problems during the COVID-19 epidemic. We also explored the risk factors for mental health problems from various aspects, such as COVID-19 related factors, self-efficacy, physical health, lifestyle, and demographic characteristics. The current study also has several limitations. First, this was an online survey using convenience and snowball sampling, which may hardly include people who do not have access to the internet. Although the results in this study may not be representative of the general population of Chinese adults, this should have not influenced our conclusions on the associated risk factors. Second, to ensure the study feasibility and response rate, we used PHQ-2, GAD-2, and part of the PTSD scale to screen depressive symptoms, anxiety symptoms, and PTSD symptoms instead of more accurate and detailed scales although some of these scales have been well validated [18, 19]. These were to avoid bringing too much burden to the respondents as the compressed questionnaire still took ten minutes to finish and may be longer for some others. However, PHQ-2 and GAD-2 showed high sensitivity and specificity in screening depression and anxiety, respectively [18, 44].
In summary, loneliness and other mental health problems during the COVID-19 epidemic in China were associated with many factors, including gender, marriage, location, binge drinking, medication, somatic symptoms, screen time, self-efficacy, COVID-19 related factors (including anti-epidemic related work, perceived risk of being infected, going out frequency, fear of being infected, and perceived overall COVID-19 influence). The results can help identify high-risk groups for poor mental health and promote the screening of mental health problems, as well as providing information for more targeted interventions when mental health resources are scarce.