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Psychiatry Investig > Volume 19(1); 2022 > Article
Lu, Yu, Zhao, and Guo: Mental Health and Related Factors of Adolescent Students During Coronavirus Disease 2019 (COVID-19) Pandemic

Abstract

Objective

Adolescents are at a special stage of physical and mental development, which is a susceptible period for mental disorders. Since the outbreak of coronavirus pneumonia in December 2019, long term stress may have negative effects on the mental health of the adolescents. In the context of the coronavirus disease 2019 (COVID-19), the study was designed to investigate the mental and psychological health of adolescents in China and its possible related factors.

Methods

A cross-sectional study design was adopted using a structured questionnaire which were distributed through the Internet to measure depression, anxiety, life events and stress related factors. Descriptive statistics and multiple regression analyses were conducted to process the data.

Results

The final sample comprised 795 adolescents. The total detection rate of depression was 76.48% and the total detection rate of anxiety was 33.08%. ANOVA showed that there were significant differences in depression scores in terms of gender, anxiety scores, history of mental disorders, COVID-19 knowledge reserve, family and social contradictions (p<0.05). And there were significant differences in anxiety scores in terms of gender, depression scores, mental health knowledge reserves, family and social contradictions (p<0.05). Multiple regression analysis showed that anxiety score, health status and COVID-19 knowledge reserve were positively associated with depression score (p<0.01), and history of psychosocial disorders was negatively associated with depression score (p<0.05); depression score, family and social contradictions were significantly positively correlated with anxiety score (p<0.01), and history of mental disorders was significantly negatively correlated with SDS score (p<0.01).

Conclusion

During the outbreak of COVID-19, adolescent students with better understanding of the pandemic, more complete knowledge of mental health, and better family and social relationship had less impact on their mental health. Therefore, to ensure a sound social support system, elaborate health instruction, and family communication and mutual understanding are conducive to alleviating the psychological stress caused by the epidemic, and it is positive for adolescent students to maintain a good mental health.

INTRODUCTION

Coronavirus disease 2019 (COVID-19) is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has swept through the globe quickly and indiscriminately. Due to the impact that brought by the pandemic, COVID-19 has affected the lives of adolescents through several ways, such as the fear of infection, uncertainty, and potential economic problems [1]. On January 27, 2020, in response to the outbreak of COVID-19, and to protect the life safety of teachers and students, the government of China requested citizens to stay home and reduce unnecessary social activity [2,3]. This may lead to the discouragement of adolescents’ development, deprive opportunities for stress regulation, and lead to social isolation [4].
To recognize the importance of the effects of the COVID-19 pandemic on adolescent mental health, one must first understand the pertinent developmental changes that occur during adolescence. Adolescents are in a particular stage of physical and mental development, which is the critical period for exploring and establishing their identity. During this period, their body, brain, cognition and the surrounding social environment will change, their self- consciousness will be enhanced, their internal conflict will increase, they will feel the curiosity of the opposite sex, they will be psychologically sensitivity, mindful of others’ views, emotionally fluctuated, vulnerable to their surrounding influences, and susceptible to mental disorders [5-7]. As adolescents strive to be independent of their parents and spend more time with their peers, instead of parents, friends gradually become the primary source of interaction and influence, and this means that negative peer interactions during this important developmental period can lead to poor self-concept, a low sense of worth, and subsequent increases in symptoms of anxiety and depression [8,9]. Thus, base on the above development characteristics, adolescents can expose to an increased risk of psychological problems during the COVID-19 pandemic.
Plenty of studies over the past decades have shown that major social event including COVID-19 pandemic exists a detrimental impact on the mental health of individuals [10-14]. Early in 2003, during severe acute respiratory syndrome (SARS) pandemic in China, a study focused on medical students reported that compared with national normal model, the detection rate of anxiety and depression of medical students increased significantly [13]. Another study focused on the impact of Hurricane Katrina on mental health status among United States (US) adults also found that the hurricane adversely impacted the mental health of residents in Katrina-affected states, and the impact was tended to be associated with age, gender, income, and physical health [14]. And the latest studies also consistently point to the detrimental impact on the mental health of COVID-19. During the early stage of the pandemic, a research included 1,210 adults found that more than half (53.8%) of the respondents rated the psychological impact as moderate-to-severe, and about one-third reported moderate-to-severe anxiety [15]. A meta-analysis focused on the mental health among clinical workers which included 13 studies reported that, almost a quarter exhibited elevated COVID-19 related symptoms of anxiety (23.2%) and depression (22.8%), and more than a third (38.9%) experienced problems with insomnia [16].
Due to the particularity of the period of physical and mental development of adolescents, the psychological stress caused by the long-term isolation at home during the pandemic and the differences between online teaching and conventional way may have negative effects on their mental health [17,18]. Yet compared with studies concentrated on adults, few studies have evaluated the impact of the pandemic on mental health in adolescents. A study reported that the increases in generalized anxiety associated with COVID-19, were found to be more pronounced in younger people (<35 years) compared to older age groups [19]. A study from India demonstrated a high prevalence of psychological distress in quarantined children and adolescents due to the COVID-19 pandemic, and these individuals have experienced helplessness (66.11%), worry (68.59%) and fear (61.98%) [20]. Meanwhile, it is reported in China that the children and adolescents aged 3-18 years presented symptoms of inattention, worry and irritability during this pandemic [21].
During the current COVID-19 pandemic, people are being affected in multifaceted ways, having their physical, psychological and emotional well-being compromised, along with social and economic problems [22]. Similarly, COVID-19 has an impact on the mental health of adolescents through multiple factors. During the COVID-19 pandemic, parental psychiatric status significantly influenced the mental health of adolescents [23-25], the relationship between family members is one of the most important factors, and there have been reports of a significant increase in domestic violence around the world [4,26-28], putting adolescents exposed to this environment at increased risk for mental health problems. At the same time, isolation measures may have more impact on the mental health of adolescents and their family environment than one might expect. A systematic review of the psychological impact of isolation during a pandemic noted PTSD symptoms in 28%-34% of isolated populations, and adolescents who experienced isolation had an average PTSD score four times compared to those who did not experience [29,30]. And another study found that older adolescents are anxious regarding cancellation of examinations, exchange programs and academic events [31]. Further, the increased use of internet and social media increased the adolescents’ vulnerability to the mental disorders and was associated with increased depression and anxiety [32].
The COVID-19 pandemic affects the adolescents through multiple perspectives, however, consider from another perspective that, in the face of a current crisis, if challenges can be mastered, successfully managing stress with trauma may help with adolescents’ psychological growth and be a protective factor in coping with future stress [33]. Nevertheless, most of the current research focuses on adults or groups with special diseases, while few of them concentrated on the adolescents. In order to fill this gap, the purpose of this study was to estimate the impact of COVID-19 pandemic on adolescents’ wellbeing, and explore the associations among environment, family, and the COVID-19 relative factors on adolescents’ mental health.

METHODS

Study population

The study adopted a voluntary sampling method after obtaining the students’ informed consent. A structured questionnaire was conducted among 11-19 years old students in China during the period from March 19, 2020 to March 29, 2020 (Figure 1). The inclusion criteria were as follows: those who were 1) full-time student, 2) aged between 11 and 19, 3) willing to cooperate with the investigation, and obtained informed consent online. The exclusion criteria were as follows: those who refused to participate or cooperate (Figure 1).

Design and methods

A cross-sectional study design was adopted using a structured questionnaire to measure depression, anxiety, mental health knowledge, COVID-19 knowledge, and mental health related factors. The questionnaire was divided into 4 sections: 1) self-made general common questionnaire: including general demographic characteristics such as age, sex, school age, origin, health status, current living conditions, mental illness history (according to the participants), etc., and students fill in according to their own conditions; 2) Self-rating Depression Scale (SDS): the self-rating scale includes 20 items, divided into 4-grade rating, and the total score is 80. The standard score <50 is within the normal range, and ≥50 is defined as depression, 50-59 for mild depression, 60-69 for moderate depression, and 70 for severe depression;34,35 3) Self-rating Anxiety Scale (SAS): including 20 items which divides into 4-grade rating, and the total score is 80. The standard score <50 is within the normal range, and ≥50 is defined as anxiety, 50-59 for mild anxiety, 60-69 for moderate anxiety, and 70 for severe anxiety [36,37]; 4) self-made life event questionnaire: composed of 9 items, including the emotion-related factors, emotional impact of online courses, mental health-related knowledge (including understanding and coping with mental disorders, etc.), the understanding of COVID-19 (including the understanding of situation, measures, etc.), as well as the family and social contradictions, and the contradictory factors. Among those items, the emotion-related factors, emotional impact of online courses, family and social contradictory factors were designed as multiple-choice question (2-3 at most). Other items were designed as 5-grade rating question.
The calculation of the simple size was based on the estimation formula Z2 n=Z1-a/2p(1-p)2d2 . According to relevant data [38-40], p=45%, confidence interval 95%, confidence width α=0.05, Z1-α/2=1.96. The calculated sample size was 470. It is estimated that 10% of the ineffective answers (including short time spent on answering questions, incomplete information, logical questions, etc.) will be given. An estimated 10% of the responses were invalid, and the final approved minimum sample size was 522.
The questionnaires were distributed to major mainstream network platforms in China, such as WeChat, QQ and Microblog, etc. The technology was provided by the WJX platform (A professional online questionnaire survey, evaluation, voting platform). Backstage setting each IP address can only be filled once. Online informed consent was accessed in advance. For those whose response time was less than 120 seconds or more than 900 seconds were classified as invalid responses.

Statistical analysis

R project version 3.6.3 was used for data analysis in this study. Statistical significance was set at the 5% significance level. For demographic data, descriptive statistical analysis was used to summarize the statistical characteristics. Analysis of variance (ANOVA) was adopted to investigate the correlation between the total scores and the potential influencing factors (i.e., sex, age, grade, health status, mental disease history, mental health knowledge reserve, understanding of COVID-19, family and social contradiction). A multiple linear regression analysis was performed to analyze the relationships between the SAS scores and SDS scores and the potential influencing factors, which the dependent variables were SDS scores and SAS scores, and the independent variables were sex, age, grade, health status, mental disease history, mental health knowledge reserve, understanding of COVID-19, and family and social contradiction.

Ethics approval

The study was authorized by IRB of Dongfang Hospital Beijing University of Chinese Medicine (JDF-IRB-2019036101).

RESULTS

Demographic characteristics

A total of 813 samples were collected from the study, 18 of which were excluded due to the answer time. Ultimately, 795 cases were included in the study, and the sample efficiency was 97.79%. As shown in Table 1, among the 795 participants at the age of 11-19 in China, 582 were male (73.2%), 213 were female (26.8%), with an average age of 17 (±1.42) years, and the oldest aged 19, the youngest aged 13. Seventy-nine were junior high school students, 554 (69.7%) were senior high school students, and 162 (20.4%) were college students. As of the time of investigation, 791 subjects (99.5%) were healthy uninfected, 1 subject (0.1%) was observed and quarantined, 2 subjects (0.3%) were confirmed COVID-19 and being treated, and 1 subject (0.1%) with COVID-19 was cured. Furthermore, 105 participants had a history of mental diseases such as anxiety and depression (13.2%) (Table 1).

Results of Depression Self Rating Scale (SDS)

As shown in Table 2, of 795 participants, 608 were found to be depressed, with a detection rate of 76.48%, of which 441 were mild (72.53%), 157 were moderate (25.82%) and 10 were severe (1.64%). The results of ANOVA showed that age, grade (education) and mental health knowledge reserve had no statistical difference on the SDS score (p>0.05); gender had a statistical difference on the SDS score (p<0.05); SAS scores, history of psychosocial diseases, the understanding of COVID-19, and family and social contradiction had significant statistical difference on SDS scores (p<0.01).
In the multiple linear regression analysis, the SDS scores of the participants were used as the dependent variable, and the independent variables included age, sex, grade, SAS score, health status, the understanding of COVID-19, mental disease history, mental health knowledge reserve, and family and social contradictions. The results of regression analysis are presented in Table 3, which revealed that SAS score, health status and the understanding of COVID-19 were significantly positively correlated with the SDS score (p<0.01), and the history of psychosocial disorders was negatively correlated with the SDS score (p<0.05).

Results of Anxiety Self Rating Scale (SAS)

As shown in Table 4, the results of Self-rating Anxiety Scale showed that there were 263 participants with anxiety, and the detection rate was 33.08%. Among them, 185 were mild (70.34%), 70 were moderate (26.62%) and 8 were severe (3.04%). ANOVA results showed that age, grade (education), health status, and the understanding of COVID-19 had no statistical difference on the SAS score (p>0.05); gender, the SDS score, mental health knowledge reserve, and family and social contradiction had significant statistical differences on the SAS score (p<0.01).
The results of multiple linear regression analysis are presented in Table 5. The SAS Self-rating Anxiety Scale scores of the participants were taken as dependent variables, and the independent variables included age, sex, grade, the SDS score, health status, the understanding of COVID-19, history of mental disease, mental health knowledge reserve, and family and social contradiction. Regression analysis showed that the SDS score, and family and social contradiction were significantly positively correlated with the SAS score (p<0.01), and the history of mental disorders was significantly negatively correlated with the SDS score (p<0.01).

Results of self-made life event questionnaire

The study collected stress factors that may affect the psychological and emotional status of the participants during the epidemic. The main contents are as follows: 1) emotional changes during the pandemic; 2) the main causes of contradiction between family and friends; 3) the most worrying things at present; and 4) the influence of online teaching on oneself and its influencing factors. The results are as follows.
Regarding the emotional change, 3.77% of 795 participants felt that their emotions had changed significantly during the pandemic, 17.36% of the participants had changed moderately, 31.32% of the participants thought that the emotional change was mild, 30.31% of the participants felt that their emotions were unaffected, and 17.23% of the participants were not clear about their emotional changes. Meanwhile, 30.44% of the participants believed that their emotional changes might be related to the COVID-19 pandemic and related prevention measures, and 42.77% of the participants believed that they were not related to the above factors (Figure 2A).
As for the causes of the contradiction with family and friends (study, recreation, health, lifestyle, emotion, going out, others; multiple choice questions, and three choices at most), the three most common choices are lifestyle (58.99%), study (57.48%), and recreation (43.27%) (Figure 2D).
When talking about the most concerning things at present (life, academic, recreation, family, friends, the development of COVID-19, health, other, multiple choice question, up to three options), the top three were academic pressure (84.53%), inability to meet recreation needs (30.06%), and family relationships (24.15%) (Tables 6, 7, and Figure 2E).
Online courses are one of the greatest changes that the pandemic has brought to the participants daily life. As shown in Figure 2C, 47.17% of the participants considered that the change brought by online coursing has affected their daily mood, and 35.85% of the participants thought that online teaching has no impact on their mood. In the investigation of the reasons of online courses affecting emotions (unable to communicate with teachers efficiently, network instability, self-discipline, unable to see classmates, concern for the examinations, and others; multiple choice questions, and up to two options), the most common reasons are lack of supervision, poor self-discipline (64.4%) and concern for the future examinations (53.33%). Furthermore, we took the score of SAS and SDS as the dependent variable and the item option as the factor for ANOVA, the result showed that the different reasons for the influence of online class on emotion had a significant statistical difference on both SDS and SAS score (p<0.05) (Tables 6 and 7).

DISCUSSION

Since the outbreak of COVID-19, there has been persistent panic and psychological pressure. At the same time, due to the relevant national policies and the needs of pandemic prevention, the impact on the daily life of the student is much greater than that on the general population. Among them, the adolescent students who are in a special stage of physical and mental development are exposed to triple the stress of suspending classes, changes brought on by online teaching and the entrance examinations. In such an environment, the physical, mental, and behavioral health of the adolescent students has become the focus of attention of schools and colleges. In our study, we find that the pandemic had a practical impact on the adolescents’ mental health, both of the rate of anxiety and depression was higher than usual. The gender, living conditions, relationship with family and friends, mental disease history and knowledge of mental health and COVID-19 plays an important role in adolescent mental health during the epidemic. Besides, we find that, those adolescents who had a certain understanding of mental disorders and COVID-19 pandemic showed a lower rate of depression and anxiety. Furthermore, the life event questionnaire showed that online courses did have an impact on the adolescents’ emotion, mainly for the reason of lack of self-discipline. And the main sources of contradiction and pressure are closely related to study.
Of this study, the total detection rate of depression in adolescent students was 76.48%, 74.91% for males and 80.75% for females, including 441 mild (72.53%), 157 moderate (25.82%) and 10 severe (1.64%); the total detection rate of anxiety symptoms was 33.08%, 29.73% for males and 42.25% for females, including 185 mild (70.34%), 70 moderate (26.62%) and 8 severe (3.04%). Compared with a previous study on depression and anxiety among medical students during the COVID-19 pandemic with the detection rate of 35.53% and 15.41%, the detection rate of our study is relatively high [38]. Our results are similar to another study with the detection rate of 88.6%, which measured the impact of natural disasters on the psychological status of adolescents at school [40]. The differences with the former may mainly because of the immature states of physical and mental development of the adolescent students and the gap in the understanding of the pandemic situation between medical students and ordinary adolescent students. In addition, the adolescent students are also facing the pressures brought on by the entrance examinations, and during the pandemic, the school suspended classes and turned to teaching online, which has few precedents to follow. Therefore, the academic pressure brought on by the entrance examinations may have a more severe impact on the adolescent students’ mood than usual.
Our study found that there was a significant correlation between gender and the scores of anxiety and depression scales, and the detection rate of anxiety and depression in females was higher than that in males. Through the analysis of the entries of SDS and SAS, it can be discovered that most psychosocial symptoms (such as melancholy, crying, dread, panic, anxiety, irritability, and worthlessness) and somatic symptoms (such as insomnia, weight loss, astriction, palpitation, physical pain, and nightmare) are generally higher in women than in men. The prevailing consensus belief is that females are more vulnerable to environmental stressors than males [41], which is consistent with the conclusions of our study. In addition, we found that among the respondents with severe depression assessed by the SDS, symptoms were mostly concentrated in somatic disorders (temporality, abnormal appetite, weight loss, astriction, palpitation, and restless), while those with severe anxiety assessed by the SAS, symptoms were mostly concentrated in anxiety mood (anxiety, dread, madness, and nightmares) (Figures 3 and 4).
At the same time, we observed that family and social contradictions were both significantly correlated with anxiety and depression symptoms of adolescent students, and based on the results of regression analysis, they were significantly negatively correlated with SAS scores. A previous study on the related factors of anxiety and depression in adolescents with congenital dorsal vertebral insufficiency, the family environment was negatively correlated with the depression [42], which was consistent with the conclusion of our study, but the participants in this study had specific characteristics, and their family environment was also different from those respondents who were included in our study, so the bias may be exist compared with the reality. Adolescents are in the period of psychological development, and their antagonistic mentality has an important impact on their mental health. During this period of suspended education and home isolation, the adolescents’ individual freedom was restricted and threatened, which may lead to the aggravation of the antagonistic mentality, thus intensifying the family and social contradiction, and then having a greater negative impact on the mood, thus falling into a vicious circle [43-45].
According to the results of data analysis, it can be found that the knowledge of COVID-19 pandemic and mental health is closely related to the anxiety and depression symptoms of adolescents, and the more abundant the relevant knowledge reserve, the corresponding detection rate of anxiety and depression will also decrease. The results of multiple linear regression analysis proved that the knowledge reserve of COVID-19 was significantly negatively correlated with the SDS score. The above results indicate that it is necessary to popularize COVID-19 and mental health-related knowledge to adolescents during the pandemic so as to enable them to recognize the occurrence of the pandemic and their own emotional changes correctly, establish a positive concept of prevention and treatment, and reduce unnecessary excessive stress reactions.
Meanwhile, the history of mental and psychological diseases is also one of the most important influencing factors in the group mood of adolescent students. As shown in the results, history of psychiatric disorders was significantly associated with anxiety and depressive symptoms. The results of regression analysis showed that it was significantly negatively correlated with SDS and SAS scores, that is, subjects with a history of psychosocial disorders had lower SDS and SAS scores than normal subjects.
In addition, the study investigated subjects’ perceptions of their own emotional changes (significant change, moderate change, unclear, mild change, or no change), and correlated the results with the scores of SDS and SAS. We classified the subjects into those who 1) picked ‘significant change’ or ‘moderate change’ option and with anxiety, depression, or either; 2) picked ‘unclear’ or ‘mild change’ or ‘no change’ option and without anxiety and depression into the category of ‘good insight’. As for subjects who picked ‘significant change’ or ‘moderate change’ without anxiety and depression, and those who picked ‘unclear’ or ‘mild change’ or ‘no change’ with anxiety, depression, or either, they were classified into the category of ‘poor insight’. In this way, the subjects’ insight was roughly estimated. As shown in Tables 8 and 9, anxiety and depression scores of subjects with good insight were lower than those of subjects with poor insight, and the detection rate also decreased significantly. In previous studies on the correlation between different mental disorders and insight, it was found that patients with different mental disorders and various degrees of mental disorders had varying degrees of self-knowledge impairment [46,47]. Another study on the association between depression and insight showed that insight was significantly correlated with depression, and the recovery of insight was synchronized with the improvement of depressive symptoms [48], which is basically consistent with our results of rough estimates of insight.
In the investigation of life events, we found that the main stressors of adolescent students during the period of the COVID-19 pandemic came from the stresses brought on by study, examination and further education, as well as family contradictions which caused by lifestyle, recreation, study and work. Meanwhile, we found that online courses are also one of the factors affecting the emotions of most adolescent students. It focuses on the lack of effective supervision, students’ lack of self-control, and worries about future examinations due to the unstable efficiency of online teaching. Compared with a study on the efficiency of online teaching and classroom teaching of an immunity course which showed that there are no differences between the two [49], the majority of the subjects in our study believed that the efficiency of online teaching is lower than that of classroom teaching. One possible reason may be that the respondents have different ages and learning abilities, and the group of adolescent students in the middle school still need to face the entrance examination, which has a heavier burden than that of the college students. Besides, the differences between the teaching concepts of universities and middle schools may also be the reason for the different result.
In order to deal with the threat that adolescent mental health facing during the COVID-19 pandemic, support from both family and community is necessary. First, it is important for relevant departments to spread clear and correct information about the COVID-19 pandemic to parents and adolescents, including the risks and possible changes in daily life. At the same time, parents and adolescents should be educated on mental health to recognize their emotional changes during the pandemic objectively and reasonable methods of catharsis should be taught to manage their negative sentiment. Second, the communities and schools should provide better online learning and interaction platforms to adolescents in order to meet their needs of education and the needs of communication between their peers [50]. Parents should try to maintain their children’s life rhythm, keep a balance between work and rest, exercise regularly, meanwhile, regular meals, balanced nutrition and adequate sleep should also be ensured. Final, emerging technologies such as remote consultation and diagnosis which are more suitable than face-to-face care should also be widely used, which can help support adolescents’ psychological and behavioral health during the pandemic.

Limitations

In addition to the observed findings above, some study limitations which may be addressed by future research should be taken in consider. The first limitation concerns the sampling error. Due to the limitation during the pandemic, our study was based on the internet platform. Though the internet penetration rate reached 70.4% by the end of December 2020 according to the Statistical report on the development of Internet in China from China Internet Network Information Center (CNNIC), there are still some parts of populations that were not covered by our study [51]. The second limitation concerns the absence of a direct assessment and observation of adolescents. The sole use of the self-rate scales is confine to participants’ capabilities to evaluate themselves and may biased by semantic understanding of the scales [52]. Third, though a primary study confirmed that the difference in children’s symptoms between areas identified by different levels of pandemic risk was not statistically significant [21], our sample mostly lived in eastern provinces of China, thus, we cannot assume that our findings can be generalized to the whole China national population.
In conclusion, this study investigated the mental and psychological health of the adolescent students during the period of the COVID-19 pandemic and their possible related factors. Due to limitations during the epidemic, the authenticity of the questionnaire may be biased by using the online questionnaire as the medium of choice for the questionnaire. Although random sampling was adopted in this study, due to the different populations using different networks and social software, specific bias may exist in gender, and further follow-up studies are needed to expand the sample size.

Notes

Availability of Data and Material

All data, models generated or used during the study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: Rongjuan Guo. Data curation: Tian’ge Lu. Formal analysis: Yao Yu. Investigation: Tian’ge Lu, Yao Yu, Zhonghui Zhao. Methodology: Tian’ge Lu, Yao Yu, Zhonghui Zhao. Project administration: Tian’ge Lu. Resources: Rongjuan Guo, Tian’ge Lu. Supervision: Rongjuan Guo. Validation: Zhonghui Zhao. Visualization: Tian’ge Lu. Writing—original draft: Tian’ge Lu. Writing—review & editing: all authors.

Funding Statement

None

ACKNOWLEDGEMENTS

We are grateful for Tianyi Lu’s great help in data collection. And we are also very appreciative of Jin Zhao and Hejing Lu who provided the linguistic assistance during the preparation of this manuscript. Besides, we thank Lianhan Shang and Yuze Liu for their suggestions on the submitting process.

Figure 1.
Distribution of samples in provinces and autonomous regions of China.
pi-2020-0416f1.jpg
Figure 2.
Results of self-made life event questionnaire Link text: A: The emotional changes during the pandemic. B: Whether the online courses had an impact on mood during the pandemic. C: The main reasons for the influence of online class on emotion. D: The main factors causing unhappiness and conflicts with others. E: Main sources of pressure.
pi-2020-0416f2.jpg
Figure 3.
Heatmap of Symptom Distribution on SDS. Link text: In the compare of the entries of the SDS, most of psychosocial symptoms (such as melancholy, easy to cry, worthless) and somatic symptoms (such as sleep disorders, weight loss, astriction, palpitation, fatigue) are generally higher in Females than in males. In terms of decreased sexual interest, ability decline, difficulty thinking, despair, males scored higher than female. And among the respondents assessed as severe depression, symptoms were more likely to concentrated in somatic disorders (temporality, abnormal appetite, weight loss, astriction, palpitation, and restless). SDS, Selfrating Depression Scale.
pi-2020-0416f3.jpg
Figure 4.
Heatmap of Symptom Distribution on SAS. Link text: For the entries of the SAS, most of psychosocial symptoms (anxiety, dread, panic, madness) and somatic symptoms (physical pain, fatigue, palpitation, dizziness, syncope, numbness, stomachache or indigestion, flush, limbs shaking) are generally higher in Females than in males. In terms of akathisia, sweat, sleep disorders, presentiment, males scored higher than female. Among the respondents assessed as severe anxiety, symptoms were more likely to concentrated in anxiety mood (anxiety, dread, madness, and nightmares). SAS, Self-rating Anxiety Scale.
pi-2020-0416f4.jpg
Table 1.
Demographic characteristics of the adolescent students
Junior (N=79) Senior (N=554) College (N=162) Overall (N=795)
Sex
 Male 54 (68.4) 431 (77.8) 97 (59.9) 582 (73.2)
 Female 25 (31.6) 123 (22.2) 65 (40.1) 213 (26.8)
Age (years) 15±0.76 17±0.98 19±0.36 17±1.42
Health status
 Healthy uninfected 79 (100) 55 2(99.6) 160 (98.8) 791 (99.5)
 Observation isolation 0 (0) 0 (0) 1 (0.6) 1 (0.1)
 COVID-19 infected (in treatment) 0 (0) 2 (0.4) 0 (0) 2 (0.3)
 COVID-19 infected (cured) 0 (0) 0 (0) 1 (0.6) 1 (0.1)
Mental disease history
 Yes 68 (86.1) 494 (89.2) 128 (79.0) 690 (86.8)
 No 11 (13.9) 60 (10.8) 34 (21.0) 105 (13.2)

Data are presented as mean±standard deviation or N (%). SD, standard deviation

Table 2.
ANOVA between adolescent students’ SDS scores and potential related factors
Normal (N=187) Mild (N=441) Moderate (N=157) Severe (N=10) p-value
Sex 0.015
Male 146 (78.1) 328 (74.4) 100 (63.7) 8 (80.0)
Female 41 (21.9) 113 (25.6) 57 (36.3) 2 (20.0)
Age (years) 17±1.3 17±1.4 17±1.4 17±1.3 0.086
Grade 0.308
Junior 12 (9.6) 50 (11.3) 16 (11.3) 1 (10.0)
Senior 138 (73.8) 300 (68.0) 109 (69.4) 7 (70.0)
College 37 (19.8) 91 (20.6) 32 (20.4) 2 (20.0)
SDS score 45±4.3 54±2.7 62±2.3 74±5.7
SAS score 43±6.4 47±7.2 52±8.7 57±13 <0.001
Health status 0.066
Healthy uninfected 185 (98.9) 440 (99.8) 156 (99.4) 10 (100)
Observation isolation 1 (0.5) 0 (0) 0 (0) 0 (0)
COVID-19 infected (in treatment) 1 (0.5) 0 (0) 1 (0.6) 0 (0)
COVID-19 infected (cured) 0 (0) 1 (0.2) 0 (0) 0 (0)
Mental disease history <0.001
Yes 12 (6.4) 50 (11.3) 39 (24.8) 4 (40.0)
No 175 (93.6) 391 (88.7) 118 (75.2) 6 (60.0)
Mental health knowledge reserve 0.395
Good 27 (14.4) 67 (15.2) 25 (15.9) 2 (20.0)
Average 115 (61.5) 299 (67.8) 101 (64.3) 7 (70.0)
Poor 45 (24.1) 75 (17.0) 31 (19.7) 1 (10.0)
Understanding of COVID-19 <0.001
Good 46 (24.6) 162 (36.7) 65 (41.4) 5 (50.0)
Average 133 (71.1) 257 (58.3) 85 (54.1) 4 (40.0)
Poor 8 (4.3) 22 (5.0) 7 (4.5) 1 (10.0)
Family and social contradiction <0.001
Very severe 6 (3.2) 18 (4.1) 16 (10.2) 0 (0)
Severe 23 (12.3) 84 (19.0) 37 (23.6) 4 (40.0)
Moderate 22 (11.8) 36 (8.2) 14 (8.9) 2 (20.0)
Mild 54 (28.9) 156 (35.4) 53 (33.8) 4 (40.0)
Not present 82 (43.9) 147 (33.3) 37 (23.6) 0 (0)

Data are presented as mean±standard deviation or N (%). SDS, Self-rating Depression Scale; SAS, Self-rating Anxiety Scale

Table 3.
Regression coefficients and standard errors for major factors associated with SDS score
B SE OR (95% CI) p value
SAS score 0.338 0.029 0.28, 0.40 <0.001
Health status 0.352 0.130 0.10, 0.61 0.007
Understanding of COVID-19 -0.753 0.285 -1.31, -0.19 0.008
Mental disease history -0.198 0.084 -0.36, -0.33 0.019
Mental health knowledge reserve -0.378 0.215 -0.80, 0.04 0.079
Grade -0.446 0.392 -1.21, 0.32 0.255
Gender 0.389 0.492 -0.58, 1.35 0.430
Family and social contradiction -0.143 0.185 -0.51, 0.22 0.440
Age 0.139 0.371 -0.59, 0.87 0.709

B, B-value; SE, standard error; OR, odds ratio; CI, confidence interval; SDS, Self-rating Depression Scale; SAS, Self-rating Anxiety Scale

Table 4.
ANOVA between adolescent students’ SAS scores and potential related factors
Normal (N=532) Mild (N=185) Moderate (N=70) Severe (N=8) p-value
Sex <0.001
Male 409 (76.9) 128 (69.2) 40 (57.1) 5 (62.5)
Female 123 (23.1) 57 (30.8) 30 (42.9) 3 (37.5)
Age (years) 17±1.4 17±1.4 17±1.4 17±1.7 0.824
Grade 0.852
Junior 51 (9.6) 21 (11.4) 6 (8.6) 1 (12.5)
Senior 373 (70.1) 124 (67.0) 50 (71.4) 7 (87.5)
College 108 (20.3) 40 (21.6) 14 (20.0) 0 (0)
SAS score 43±3.9 54±2.9 63±2.2 73±5.0
SDS score 52±6.5 56±5.2 60±7.0 61±7.4 <0.001
Health status 0.702
Healthy uninfected 529 (99.4) 185 (100) 70 (100) 7 (87.5)
Observation isolation 1 (0.2) 0 (0) 0 (0) 0 (0)
COVID-19 infected (in treatment) 1 (0.2) 0 (0) 0 (0) 1 (12.5)
COVID-19 infected (cured) 1 (0.2) 0 (0) 0 (0) 0 (0)
Mental disease history <0.001
Yes 41 (7.7) 33 (17.8) 26 (37.1) 5 (62.5)
No 491 (92.3) 152 (82.2) 44 (62.9) 3 (37.5)
Mental health knowledge reserve 0.008
Good 98 (18.4) 16 (8.6) 6 (8.6) 1 (12.5)
Average 331 (62.2) 142(76.8) 46 (65.7) 3 (37.5)
Poor 103 (19.4) 27 (14.6) 18 (25.7) 4 (50.0)
Understanding of COVID-19 0.291
Good 191 (35.9) 59 (31.9) 24 (34.3) 4 (50.0)
Average 321 (60.3) 114 (61.6) 42 (60.0) 2 (25.0)
Poor 20 (3.8) 12 (6.5) 4 (5.7) 2 (25.0)
Family and social contradiction <0.001
Very severe 11 (2.1) 15 (8.1) 11 (15.7) 3 (37.5)
Severe 77 (14.5) 42 (22.7) 29 (41.4) 0 (0)
Moderate 45 (8.5) 23 (12.4) 5 (7.1) 1 (12.5)
Mild 173 (32.5) 74 (40.0) 17 (24.3) 3 (37.5)
Not present 226 (42.5) 31 (16.8) 8 (11.4) 1 (12.5)

Data are presented as mean±standard deviation or N (%). ANOVA, analysis of variance; SAS, Self-rating Anxiety Scale; SDS, Self-rating Depression Scale

Table 5.
Regression coefficient sand standard errors for major factors associated with SAS score
B SE OR (95% CI) p value
SDS score 0.434 0.037 0.36, 0.51 <0.001
Family and social contradiction -1.545 0.203 -1.94, -1.15 <0.001
Mental disease history -3.825 0.755 -5.31, -2.34 <0.001
Grade 0.742 0.444 -0.13, 1.61 0.095
Mental health knowledge reserve 0.351 0.243 -0.13, 0.83 0.149
Understanding of COVID-19 0.468 0.324 -0.17, 1.11 0.149
Gender 0.778 0.557 -0.32, 1.87 0.163
Age -0.514 0.420 1.34, 0.31 0.221
Health status 0.139 0.371 -0.59, 0.87 0.709

B, B-value; SE, standard error; OR, odds ratio; CI, confidence interval; SAS, Self-rating Anxiety Scale; SDS, Self-rating Depression Scale

Table 6.
Results of self-made questionnaire (by the results of SDS)
Normal (N=187) Mild (N=441) Moderate (N=157) Severe (N=10) Overall (N=795)
Whether consider the online courses during the epidemic had an impact on mood
Yes 83 (44.4) 215 (48.8) 69 (43.9) 8 (80.0) 375 (47.2)
No 73 (39.0) 154 (34.9) 56 (35.7) 2 (20.0) 285 (35.8)
Not clear 31 (16.6) 72 (16.3) 32 (20.4) 0 (0.0) 135 (17.0)
The main reasons for the influence of online class on emotion
Unable to communicate with teachers effectively 35 (18.7) 83 (18.8) 27 (17.2) 2 (20.0) 147 (18.5)
Unstable network affects the teaching efficiency 40 (21.4) 110 (26.8) 43 (27.4) 4 (40.0) 197 (24.8)
Lack of effective supervision 135 (72.2) 279 (63.3) 94 (59.9) 4 (40.0) 512 (64.4)
Unable to meet with classmates and friends 18 (9.6) 70 (15.9) 25 (15.9) 3 (30.0) 116 (14.6)
Worry about the future tests 102 (54.5) 231 (52.4) 84 (53.5) 7 (70.0) 424 (53.3)
The main factors causing unhappiness and conflicts with others
Study and work 103 (55.1) 250 (57.1) 98 (62.4) 6 (60.0) 457 (57.5)
Recreation and entertainment 83 (44.4) 185 (42.0) 68 (43.3) 8 (80.0) 344 (43.3)
Health and exercise 33 (17.6) 70 (16.3) 28 (17.8) 3 (30.0) 134 (16.9)
Time table and lifestyle 108 (57.8) 261 (59.2) 93 (59.2) 7 (70.0) 469 (59.0)
Emotional problems 15 (8.0) 34 (7.7) 17 (11.0) 1 (10.0) 67 (8.4)
Going out 4 (2.1) 32 (7.3) 9 (5.7) 0 (0.0) 45 (5.7)
Main sources of pressure
Life pressure 38 (20.3) 82 (18.6) 27 (17.2) 3 (30.0) 150 (18.9)
Academic pressure 159 (85.0) 374 (84.8) 130 (82.8) 9 (90.0) 672 (84.5)
Unable to meet the need for entertainment and relaxation 62 (33.2) 125 (28.3) 47 (29.9) 5 (50.0) 239 (30.1)
Relationship with family 36 (19.3) 105 (23.8) 47 (29.9) 4 (40.0) 192 (24.2)
Relationship with friends 15 (8.0) 29 (6.6) 14 (8.9) 1 (10.0) 59 (7.4)
The progression of COVID-19 23 (12.3) 80 (18.1) 33 (21.0) 1 (10.0) 137 (17.2)
Self and family health 19 (10.2) 53 (12.0) 26 (16.7) 0 (0.0) 98 (12.3)

Data are presented as N (%). SDS, Self-rating Depression Scale

Table 7.
Results of self-made questionnaire (by the results of SAS)
Normal (N=532) Mild (N=185) Moderate (N=70) Severe (N=8) Overall (N=795)
Whether consider the online courses during the epidemic had an impact on mood
Yes 221 (41.5) 108 (58.4) 41 (58.6) 5 (62.5) 375 (47.2)
No 291 (54.7) 48 (25.9) 16 (22.9) 2 (25) 285 (35.8)
Not clear 92 (17.3) 29 (15.7) 13 (18.6) 1 (12.5) 135 (17.0)
The main reasons for the influence of online class on emotion
Unable to communicate with teachers effectively 99 (18.6) 34 (18.4) 13 (18.6) 1 (12.5) 147 (18.5)
Unstable network affects the teaching efficiency 130 (24.4) 50 (27.0) 16 (22.9) 1 (12.5) 197 (24.8)
Lack of effective supervision 353 (66.4) 109 (58.9) 46 (65.7) 4 (50.0) 512 (64.4)
Unable to meet with classmates and friends 75 (14.1) 30 (16.2) 8 (11.4) 3 (37.5) 116 (14.6)
Worry about the future tests 283 (53.2) 97 (52.4) 40 (57.1) 4 (50.0) 424 (53.3)
The main factors causing unhappiness and conflicts with others
Study and work 302 (56.8) 110 (59.5) 42 (60.0) 3 (37.5) 457 (57.5)
Recreation and entertainment 212 (39.8) 95 (51.4) 35 (50.0) 2 (25.0) 344 (43.3)
Health and exercise 81 (15.2) 39 (21.1) 14 (20.0) 0 (0.0) 134 (16.9)
Time table and lifestyle 304 (57.1) 114 (61.6) 47 (67.1) 4 (50.0) 469 (59.0)
Emotional problems 32 (6.0) 16 (8.6) 17 (24.3) 2 (25.0) 67 (8.4)
Going out 29 (5.5) 10 (5.4) 5 (7.1) 1 (12.5) 45 (5.7)
Main sources of pressure
Life pressure 79 (14.8) 49 (26.5) 20 (28.6) 2 (25.0) 150 (18.9)
Academic pressure 452 (85.0) 159 (85.9) 56 (80.0) 5 (62.5) 672 (84.5)
Unable to meet the need for entertainment and relaxation 152 (28.6) 59 (31.9) 25 (35.7) 3 (37.5) 239 (30.1)
Relationship with family 108 (20.3) 53 (28.6) 29 (41.4) 2 (25.0) 192 (24.2)
Relationship with friends 37 (7.0) 12 (6.5) 9 (12.9) 1 (12.5) 59 (7.4)
The progression of COVID-19 89 (16.7) 32 (17.3) 13 (18.6) 3 (37.5) 137 (17.2)
Self and family health 59 (11.1) 23 (12.4) 15 (21.4) 1 (12.5) 98 (12.3)

Data are presented as N (%). SAS, Self-rating Anxiety Scale

Table 8.
Correlation between the insight and the SDS score
Insight Normal
Affected
p value
Frequency Mean±SD Frequency Mean±SD
Good 149 (29.92) 45.17±4.62 349 (70.08) 56.50±4.78 <0.01
Poor 38 (12.79) 46.45±2.43 259 (87.21) 57.09±5.05

SD, standard deviation; SDS, Self-rating Depression Scale

Table 9.
Correlation between the insight and the SAS score
Insight Normal
Affected
p value
Frequency Mean±SD Frequency Mean±SD
Good 408 (74.77) 42.61±4.12 139 (25.41) 56.90±5.76 <0.01
Poor 124 (50.00) 43.09±3.27 124 (50.00) 56.75±5.47

SD, standard deviation; SAS, Self-rating Anxiety Scale

REFERENCES

1. Sher L. The impact of the COVID-19 pandemic on suicide rates. QJM 2020;113:707-712.
crossref pmid
2. The Notice of the Ministry of Education on Delaying the Opening of Spring Semester in 2020. Available at: http://www.moe.gov.cn/jyb_xwfb/gzdt_gzdt/s5987/202001/t20200127_416672.html. Accessed May 29, 2020.

3. The Guiding Opinions on Organizing and Managing Online Teaching in Higher Education Institutes during the Period of Epidemic Prevention and Control. Available at: http://www.moe.gov.cn/jyb_xwfb/gzdt_gzdt/s5987/202002/t20200205_418131.html. Accessed May 29, 2020.

4. Fegert JM, Vitiello B, Plener PL, Clemens V. Challenges and burden of the Coronavirus 2019 (COVID-19) pandemic for child and adolescent mental health: a narrative review to highlight clinical and research needs in the acute phase and the long return to normality. Child Adolesc Psychiatry Ment Health 2020;14:20.
crossref pmid pmc
5. Blakemore SJ. Adolescence and mental health. Lancet 2019;393:2030-2031.
crossref pmid
6. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005;62:593-602.
crossref pmid
7. Hollenstein T, Lougheed JP. Beyond storm and stress: typicality, transactions, timing, and temperament to account for adolescent change. Am Psychol 2013;68:444-454.
crossref pmid
8. Meuwese R, Cillessen A, Guroglu B. Friends in high places: a dyadic perspective on peer status as predictor of friendship quality and the mediating role of empathy and prosocial behavior. Soc Develop 2017;26:503-519.
crossref
9. Somerville LH. Special issue on the teenage brain: Sensitivity to social evaluation. Curr Dir Psychol Sci 2013;22:121-127.
pmid pmc
10. Ren JH, Chiang CL, Jiang XL, Luo BR, Liu XH, Pang MC. Mental disorders of pregnant and postpartum women after earthquakes: a systematic review. Disaster Med Public Health Prep 2014;8:315-325.
crossref pmid
11. Nukui H, Midorikawa S, Murakami M, Maeda M, Ohtsuru A. Mental health of nurses after the Fukushima complex disaster: a narrative review. J Radiat Res 2018;59(suppl_2):ii108-ii113.
crossref pmid pmc
12. Chen F, Zheng D, Liu J, Gong Y, Guan Z, Lou D. Depression and anxiety among adolescents during COVID-19: a cross-sectional study. Brain Behav Immun 2020;88:36-38.
crossref pmid pmc
13. Liu SF, Ma JD, Wei P, kjia FJ. Mental state of medical college students during SARS. J Health Psychol 2004;117-118.

14. An R, Qiu Y, Xiang X, Ji M, Guan C. Impact of hurricane Katrina on mental health among US adults. Am J Health Behav 2019;43:1186-1199.
crossref pmid
15. Wang C, Pan R, Wan X, Tan Y, Xu L, Ho CS, et al. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. Int J Environ Res Public Health 2020;17:1729.
crossref pmc
16. Pappa S, Ntella V, Giannakas T, Giannakoulis VG, Papoutsi E, Katsaounou P. Prevalence of depression, anxiety, and insomnia among healthcare workers during the COVID-19 pandemic: a systematic review and meta-analysis. Brain Behav Immun 2020;88:901-907.
crossref pmid pmc
17. Yin G, Xu F, Yu P, Feng H. A study on the pressure source and pressure of college students facing SARS in the epidemic period. Stud Psychol Behav 2003;1:236-239.

18. Liu Z, Huang Y, Dang W, Liu M, Li S. Analysis of the influencing factors of students’ psychological status in three universities in Beijing during the epidemic period of SARS. Chin J Epidemiol 2004;48-51.

19. Wang C, Pan R, Wan X, Tan Y, Xu L, McIntyre RS, et al. A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain Behav Immun 2020;87:40-48.
crossref pmid pmc
20. Saurabh K, Ranjan S. Compliance and psychological impact of quarantine in children and adolescents due to COVID-19 pandemic. Ind J Pediatr 2020;87:532-536.
crossref
21. Jiao WY, Wang LN, Liu J, Fang SF, Jiao FY, Pettoello-Mantovani M, et al. Behavioral and emotional disorders in children during the COVID-19 epidemic. J Pediatr 2020;221:264-266.e1.
crossref pmid pmc
22. Esterwood E, Saeed SA. Past epidemics, natural disasters, COVID19, and mental health: learning from history as we deal with the present and prepare for the future. Psychiatr Q 2020;91:1121-1133.
crossref pmid
23. Hölling H, Schlack R, Petermann F, Ravens-Sieberer U, Mauz E. Psychopathological problems and psychosocial impairment in children and adolescents aged 3-17 years in the German population: prevalence and time trends at two measurement points (2003-2006 and 2009-2012): results of the KiGGS study: first follow-up (KiGGS Wave 1). Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz 2014;57:807-819.
crossref pmid
24. Riley AW, Valdez CR, Barrueco S, Mills C, Beardslee W, Sandler I, et al. Development of a family-based program to reduce risk and promote resilience among families affected by maternal depression: theoretical basis and program description. Clin Child Fam Psychol Rev 2008;11:12-29.
crossref pmid
25. Rasic D, Hajek T, Alda M, Uher R. Risk of mental illness in offspring of parents with schizophrenia, bipolar disorder, and major depressive disorder: a meta-analysis of family high-risk studies. Schizophr Bull 2014;40:28-38.
crossref pmid
26. A Gender Lens on COVID-19: Pandemics and Violence against Women and Children. Available at: https://www.cgdev.org/blog/gender-lens-covid-19-pandemics-and-violenceagainst-women-and-children. Accessed May 29, 2020.

27. Lockdowns around the world bring rise in domestic violence. Available at: https://www.theguardian.com/society/2020/mar/28/lockdowns-world-rise-domestic-violence. 2020. Accessed Feb 2, 2021.

28. UN chief calls for domestic violence ‘ceasefire’ amid ‘horrifying global surge’. Available at: https://news.un.org/en/story/2020/04/1061052. 2020. Accessed Feb 2, 2021.

29. Taylor MR, Agho KE, Stevens GJ, Raphael B. Factors influencing psychological distress during a disease epidemic: data from Australia’s first outbreak of equine influenza. BMC Public Health 2008;8:347.
crossref pmid pmc
30. Sprang G, Silman M. Posttraumatic stress disorder in parents and youth after health-related disasters. Disaster Med Public Health Prep 2013;7:105-110.
crossref pmid
31. Lee J. Mental health effects of school closures during COVID-19. Lancet Child Adolesc Health 2020;4:421.
crossref pmid pmc
32. Ellis WE, Dumas TM, Forbes LM. Physically isolated but socially connected: psychological adjustment and stress among adolescents during the initial COVID-19 crisis. Can J Behav Sci 2020;52:177-187.
crossref
33. Zoellner T, Maercker A. Posttraumatic growth in clinical psychology - a critical review and introduction of a two component model. Clin Psychol Rev 2006;26:626-653.
crossref pmid
34. Zung WW. A Self-Rating Depression Scale. Arch Gen Psychiatry 1965;12:63-70.
crossref pmid
35. Jokelainen J, Timonen M, Keinänen-Kiukaanniemi S, Härkönen P, Jurvelin H, Suija K. Validation of the Zung self-rating depression scale (SDS) in older adults. Scand J Prim Health Care 2019;37:353-357.
crossref pmid pmc
36. Zung WW. A rating instrument for anxiety disorders. Psychosomatics 1971;12:371-379.
crossref pmid
37. Yu M, Chasson GS, Wang M, Zhu Y, Xu Q, Wang J. The latent profile analysis of Chinese adolescents' anxiety: examination and validation. J Anxiety Disord 2018;59:74-81.
crossref pmid
38. Zhang X, Jia W, Duan L. Investigation and analysis of the psychological status of 1486 medical students in the period of COVID-19. J Inn Mong Med Univ 2020;42:128-130.

39. Zhang X, Ye T, Yao L, Song L, Wu Z. Psychological and behavioral investigation of medical students during the outbreak of COVID-19. J Trop Med 2020;20:576-580.

40. Pu J, Yang Y, Han F, Dong Z, Jiao F. An analysis of the effects of natural disasters on depressive symptoms and psychological elasticity of school adolescents: a case study of 1174 school adolescents in changning county, Yunnan province. Soft Sci Health 2020;34:78-82.

41. Skaff MM, Finney JW, Moos RH. Gender differences in problem drinking and depression: different “vulnerabilities?”. Am J Community Psychol 1999;27:25-54.
crossref pmid
42. Bellin MH, Zabel TA, Dicianno BE, Levey E, Garver K, Linroth R, et al. Correlates of depressive and anxiety symptoms in young adults with spina bifida. J Pediatr Psychol 2010;35:778-789.
crossref pmid
43. Shen L, Dillard JP. Psychometric properties of the Hong psychological reactance scale. J Pers Assess 2005;85:74-81.
crossref pmid
44. Seibel CA, Dowd ET. Personality characteristics associated with psychological reactance. J Clin Psychol 2001;57:963-969.
crossref pmid
45. Grandpre J, Alvaro EM, Burgoon M, Miller CH, Hall JR. Adolescent reactance and anti-smoking campaigns: a theoretical approach. Health Commun 2003;15:349-366.
crossref pmid
46. Weiler MA, Fleisher MH, McArthur-Campbell D. Insight and symptom change in schizophrenia and other disorders. Schizophr Res 2000;45:29-36.
crossref pmid
47. de Assis da Silva R, Mograbi DC, Silveira LA, Nunes AL, Novis FD, Landeira-Fernandez J, et al. Insight across the different mood states of bipolar disorder. Psychiatr Q 2015;86:395-405.
crossref pmid
48. Zhai J. Analysis of factors related to the insight in depression patients. J Clin Psychol Med 2001;11:22-24.

49. Porter AL, Pitterle ME, Hayney MS. Comparison of online versus classroom delivery of an immunization elective course. Am J Pharm Educ 2014;78:96.
crossref pmid pmc
50. Ebrahim SH, Ahmed QA, Gozzer E, Schlagenhauf P, Memish ZA. Covid-19 and community mitigation strategies in a pandemic. BMJ 2020;368:m1066.
crossref pmid
51. Statistical report on the development of Internet in China No.47. Available at: http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/202102/t20210203_71361.htm. Accessed April 23, 2021.

52. Schwarz N. Self-reports: how the questions shape the answers. Am Psychol 1999;54:93-105.
crossref
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