The Impact of the COVID-19 Pandemic on Vulnerable People Suffering from Depression: Two Studies on Adults in France

This study investigated the difficulties experienced by people suffering from depression in coping with the stressful context of the COVID-19 pandemic and the lockdown. Two large samples of the French population were classified on the basis of their depressive symptoms and completed an online questionnaire on their emotions and their behaviors during the lockdown. Results showed that, compared to participants with no or mild mental health-related symptoms, participants with moderate to severe depressive symptoms suffered from greater psychological effects of the pandemic and the lockdown (fear, anxiety, sadness, sleep quality, loss of daily routine). However, health risk behaviors (smoking, drinking, non-compliance with lockdown and barrier gestures) and perceived vulnerability did not differ between the participant groups, although more severely depressed participants tended to be less respectful of health guidelines. In addition, the most heightened effects on the depressed participants were boredom and the feeling of social isolation, which was not compensated by the search for social affiliation. Supporting people with depression should be a public health priority because they suffer psychologically more than others from the pandemic and the lockdown.

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The impact of adolescent spirituality on depressive symptoms and health risk behaviors

Journal of Adolescent Health ◽

10.1016/j.jadohealth.2004.07.017 ◽

2005 ◽

Vol 36 (6) ◽

pp. 529 ◽

Cited By ~ 100

Author(s):

Sian Cotton ◽

Elizabeth Larkin ◽

Andrea Hoopes ◽

Barbara A. Cromer ◽

Susan L. Rosenthal

Keyword(s):

Depressive Symptoms ◽

Health Risk ◽

Risk Behaviors ◽

Health Risk Behaviors ◽

Adolescent Spirituality ◽

The Impact

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Stress and Health Outcomes: Do Meaningful Work and Physical Activity Help?

Journal of Career Development ◽

10.1177/0894845317741370 ◽

2017 ◽

Vol 46 (3) ◽

pp. 251-264 ◽

Cited By ~ 4

Author(s):

Suzanne H. Lease ◽

Christina L. Ingram ◽

Emily L. Brown

Keyword(s):

Physical Activity ◽

Depressive Symptoms ◽

Health Outcomes ◽

Risk Behaviors ◽

Perceived Stress ◽

Meaningful Work ◽

Health Risk Behaviors ◽

Negative Effects ◽

Stress And Health

The negative effects of stress and burnout on mental and physical health are widely known, as are the beneficial effects of physical activity. While the organizational literature emphasizes the value of meaningful work for employers and employees alike, the stress-buffering role of meaningful work in combination with physical activity is not known. The present study examined the (a) mediating role of burnout in the relationships between perceived stress and health risk behaviors (i.e., poor diet, tobacco use, and alcohol use) and depressive symptoms and (b) moderating roles of meaningful work and physical activity on the relationships between perceived stress and health outcomes. Participants were 229 employed adults. Perceived stress predicted physical health risk behaviors and depressive symptoms, but the direct and indirect negative effects of stress were stronger when meaningful work scores were lower. Findings offer support for the development of interventions that include enhancing work meaningfulness.

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Preventive Health Behaviors, Health-Risk Behaviors, Physical Morbidity, and Health-Related Role Functioning Impairment in Veterans with Post-Traumatic Stress Disorder

Military Medicine ◽

10.7205/milmed.169.7.536 ◽

2004 ◽

Vol 169 (7) ◽

pp. 536-540 ◽

Cited By ~ 67

Author(s):

Todd C. Buckley ◽

Susannah L. Mozley ◽

Michele A. Bedard ◽

Anne-Cecile Dewulf ◽

Jennifer Greif

Keyword(s):

Risk Behaviors ◽

Post Traumatic Stress Disorder ◽

Traumatic Stress ◽

Stress Disorder ◽

Health Risk Behaviors ◽

Role Functioning ◽

Post Traumatic Stress ◽

Preventive Health Behaviors ◽

Health Related ◽

Post Traumatic

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Association of health-risk behaviors and depressive symptoms and anxiety symptoms: a school-based sample of Chinese adolescents

Journal of Public Health ◽

10.1093/pubmed/fdz115 ◽

2019 ◽

Vol 42 (3) ◽

pp. e189-e198

Author(s):

Xiaomin Luo ◽

Yu Zhou ◽

Ruimin Zheng ◽

Xin Li ◽

Yue Dai ◽

...

Keyword(s):

Depressive Symptoms ◽

Health Risk ◽

Risk Behaviors ◽

Descriptive Analysis ◽

Health Risk Behaviors ◽

Anxiety Symptoms ◽

Chinese Adolescents ◽

School Students ◽

Epidemiological Characteristics ◽

Multiple Health

Abstract Background Depressive symptoms and anxiety symptoms of adolescents not only affect youth but also have wide-ranging impacts on the health of adults. The study was carried out to determine the epidemiological characteristics of depressive symptoms and anxiety symptoms and the associations between the two and health-risk behaviors in Chinese adolescents. Methods Participants were recruited from the junior and senior high schools in China. Data were collected by self-designed questionnaires. The questionnaires included questions about demographic characteristics, depressive symptom scales, anxiety symptom scales and nine categories of health-risk behaviors. Descriptive analysis and binary logistic regression were performed by SPSS 21.0 software. Results There were 4.4% of the participants with depressive symptoms. Approximately 32.0% of the participants had anxiety symptoms. Girls and general senior school students were risk factors for depressive symptoms and anxiety symptoms. Multiple health-risk behaviors were associated with depressive symptoms and anxiety symptoms in Chinese adolescents. Conclusion Depressive symptoms and anxiety symptoms were prevalent in Chinese adolescents. Their distribution was affected by certain health-risk behaviors. Multiple health-risk behaviors were associated with depressive symptoms and anxiety symptoms in Chinese adolescents.

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The Role of Campus Data in Representing Depression Among College Students: Exploratory Research (Preprint)

10.2196/preprints.12503 ◽

2018 ◽

Author(s):

Guang Mei ◽

Weisheng Xu ◽

Li Li ◽

Zhen Zhao ◽

Hao Li ◽

...

Keyword(s):

Academic Performance ◽

Depressive Symptoms ◽

Correlation Analysis ◽

Personal Information ◽

Influencing Factor ◽

Depression Scale ◽

Machine Learning Algorithms ◽

Daily Routine ◽

Exploratory Research ◽

Online Questionnaire

BACKGROUND Depression is a predominant feature of many psychological problems leading to extreme behaviors and, in some cases, suicide. Campus information systems keep detailed and reliable student behavioral data; however, whether these data can reflect depression and we know the differences in behavior between depressive and nondepressive students are still research problems. OBJECTIVE The purpose of this paper is to investigate the behavioral patterns of depressed students by using multisource campus data and exploring the link between behavioral preferences and depressive symptoms. The campus data described in this paper include basic personal information, academic performance, poverty subsidy, consumption habit, daily routine, library behavior, and meal habit, totaling 121 features. METHODS To identify potentially depressive students, we developed an online questionnaire system based on a standard psychometric instrument, the Zung Self-Rating Depression Scale (SDS). To explore the differences in behavior of depressive and nondepressive students, the Mann-Whitney U test was applied. In order to investigate the behavioral features of different depressive symptoms, factor analysis was used to divide the questionnaire items into different symptom groups and then correlation analysis was employed to study the extrinsic characteristics of each depressive symptom. RESULTS The correlation between these factors and the features were computed. The results indicated that there were 25 features correlated with either 4 factors or SDS score. The statistical results indicated that depressive students were more likely to fail exams, have poor meal habits, have increased night activities and decreased morning activities, and engage less in social activities (eg, avoiding meal times with friends). Correlation analysis showed that the somatic factor 2 (F4) was negatively correlated with the number of library visits (r=–.179, P<.001), and, compared with other factors, had the greatest impact on students’ daily schedule, eating and social habits. The biggest influencing factor to poor academic performance was cognitive factor F1, and its score was found to be significantly positively correlated with fail rate (r=.185, P=.02). CONCLUSIONS The results presented in this study indicate that campus data can reflect depression and its symptoms. By collecting a large amount of questionnaire data and combining machine learning algorithms, it is possible to realize an identification method of depression and depressive symptoms based on campus data.

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The Impact of COVID-19 on U.S. Adolescents: Loss of Basic Needs and Engagement in Health Risk Behaviors

10.31234/osf.io/zbp7k ◽

2021 ◽

Author(s):

Akash Shroff ◽

Julia Fassler ◽

Kathryn Fox ◽

Jessica L. Schleider

Keyword(s):

Risk Behaviors ◽

Regression Models ◽

Healthcare Providers ◽

Basic Needs ◽

The United States ◽

Health Risk Behaviors ◽

Coping Behaviors ◽

Maladaptive Coping ◽

Demographic Differences ◽

The Impact

Across the United States, the COVID-19 pandemic created myriad challenges for youth and families, including losses of basic needs which may be associated with increased use of maladaptive coping behaviors. In a sample of 2,491 U.S. youth (ages 13-16), demographic differences in loss of basic needs and maladaptive coping were assessed using regression models. More than 21% of adolescents endorsed losing one or more basic needs and 83% endorsed use of maladaptive coping strategies. Minoritzed youth reported losing more basic needs (ps < 0.05), and greater reliance on maladaptive coping behaviors (ps < 0.05), relative to non-minoritized peers. Healthcare providers and teachers must consider basic need losses while structuring support for youth during and beyond the COVID-19 pandemic.

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The Role of Campus Data in Representing Depression Among College Students: Exploratory Research

JMIR Mental Health ◽

10.2196/12503 ◽

2020 ◽

Vol 7 (1) ◽

pp. e12503 ◽

Cited By ~ 2

Author(s):

Guang Mei ◽

Weisheng Xu ◽

Li Li ◽

Zhen Zhao ◽

Hao Li ◽

...

Keyword(s):

Academic Performance ◽

Depressive Symptoms ◽

Correlation Analysis ◽

Personal Information ◽

Influencing Factor ◽

Depression Scale ◽

Machine Learning Algorithms ◽

Daily Routine ◽

Exploratory Research ◽

Online Questionnaire

Background Depression is a predominant feature of many psychological problems leading to extreme behaviors and, in some cases, suicide. Campus information systems keep detailed and reliable student behavioral data; however, whether these data can reflect depression and we know the differences in behavior between depressive and nondepressive students are still research problems. Objective The purpose of this paper is to investigate the behavioral patterns of depressed students by using multisource campus data and exploring the link between behavioral preferences and depressive symptoms. The campus data described in this paper include basic personal information, academic performance, poverty subsidy, consumption habit, daily routine, library behavior, and meal habit, totaling 121 features. Methods To identify potentially depressive students, we developed an online questionnaire system based on a standard psychometric instrument, the Zung Self-Rating Depression Scale (SDS). To explore the differences in behavior of depressive and nondepressive students, the Mann-Whitney U test was applied. In order to investigate the behavioral features of different depressive symptoms, factor analysis was used to divide the questionnaire items into different symptom groups and then correlation analysis was employed to study the extrinsic characteristics of each depressive symptom. Results The correlation between these factors and the features were computed. The results indicated that there were 25 features correlated with either 4 factors or SDS score. The statistical results indicated that depressive students were more likely to fail exams, have poor meal habits, have increased night activities and decreased morning activities, and engage less in social activities (eg, avoiding meal times with friends). Correlation analysis showed that the somatic factor 2 (F4) was negatively correlated with the number of library visits (r=–.179, P<.001), and, compared with other factors, had the greatest impact on students’ daily schedule, eating and social habits. The biggest influencing factor to poor academic performance was cognitive factor F1, and its score was found to be significantly positively correlated with fail rate (r=.185, P=.02). Conclusions The results presented in this study indicate that campus data can reflect depression and its symptoms. By collecting a large amount of questionnaire data and combining machine learning algorithms, it is possible to realize an identification method of depression and depressive symptoms based on campus data.

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