Mental health and resilience during the coronavirus pandemic: A machine learning approach.
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ABSTRACT: Objective
This study explored risk and resilience factors of mental health functioning during the coronavirus disease (COVID-19) pandemic.Methods
A sample of 467 adults (M age = 33.14, 63.6% female) reported on mental health (depression, anxiety, posttraumatic stress disorder [PTSD], and somatic symptoms), demands and impacts of COVID-19, resources (e.g., social support, health care access), demographics, and psychosocial resilience factors.Results
Depression, anxiety, and PTSD rates were 44%, 36%, and 23%, respectively. Supervised machine learning models identified psychosocial factors as the primary significant predictors across outcomes. Greater trauma coping self-efficacy and forward-focused coping, but not trauma-focused coping, were associated with better mental health. When accounting for psychosocial resilience factors, few external resources and demographic variables emerged as significant predictors.Conclusion
With ongoing stressors and traumas, employing coping strategies that emphasize distraction over trauma processing may be warranted. Clinical and community outreach efforts should target trauma coping self-efficacy to bolster resilience during a pandemic.
SUBMITTER: Samuelson KW
PROVIDER: S-EPMC8657346 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundResilience is a process that allows recovery from or adaptation to adversities. The aim of this study was to evaluate state resilience during the COVID-19 pandemic in psychiatric patients (PP), unaffected relatives (UR) and community controls (CC).MethodsThis study is part of the Barcelona ResIlience Survey for Mental Health COVID-19 (BRIS-MHC) project. Logistic regression models were performed to identify mental health outcomes associated with bad state resilience and predictors of good state resilience. The association between state resilience and specific affective temperaments as well as their influence on the association between depressive symptoms and state resilience were verified.ResultsThe study recruited 898 participants that took part in the survey. The presence of depressive symptoms was a predictor of bad state resilience in PP (β=0.110, OR=1.117, p=0.028). No specific mental health outcome was associated with bad state resilience in UR and CC. Predictors of good state resilience in PP were having pursued hobbies/conducted home tasks (β=1.261, OR=3.528, p=0.044) and level of organization in the family (β=0.986, OR=2.682, p=0.008). Having a controlling family was inversely associated with good state resilience in CC (β=-1.004, OR=0.367, p=0.012). The association between bad state resilience and depressive symptoms was partially mediated by affective temperaments.LimitationsParticipants self-reported their psychiatric diagnoses, their relatives' diagnoses or the absence of a psychiatric disorder, as well as their psychiatric symptoms.ConclusionsEnhancing resilience and coping strategies in the face of the COVID-19 pandemic might have important implications in terms of mental health outcomes.
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Project description:Adolescent mental health problems are rising rapidly around the world. To combat this rise, clinicians and policymakers need to know which risk factors matter most in predicting poor adolescent mental health. Theory-driven research has identified numerous risk factors that predict adolescent mental health problems but has difficulty distilling and replicating these findings. Data-driven machine learning methods can distill risk factors and replicate findings but have difficulty interpreting findings because these methods are atheoretical. This study demonstrates how data- and theory-driven methods can be integrated to identify the most important preadolescent risk factors in predicting adolescent mental health. Machine learning models examined which of 79 variables assessed at age 10 were the most important predictors of adolescent mental health at ages 13 and 17. These models were examined in a sample of 1176 families with adolescents from nine nations. Machine learning models accurately classified 78% of adolescents who were above-median in age 13 internalizing behavior, 77.3% who were above-median in age 13 externalizing behavior, 73.2% who were above-median in age 17 externalizing behavior, and 60.6% who were above-median in age 17 internalizing behavior. Age 10 measures of youth externalizing and internalizing behavior were the most important predictors of age 13 and 17 externalizing/internalizing behavior, followed by family context variables, parenting behaviors, individual child characteristics, and finally neighborhood and cultural variables. The combination of theoretical and machine-learning models strengthens both approaches and accurately predicts which adolescents demonstrate above average mental health difficulties in approximately 7 of 10 adolescents 3-7 years after the data used in machine learning models were collected.
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Project description:BackgroundThe effect of social distancing due to the COVID-19 pandemic on the mental health of pregnant women is of particular concern, given potential effects on physical health, family functioning, and child development.MethodsPregnant women were recruited for the "Implications of and Experiences Surrounding being Pregnant during the COVID-19 Pandemic" study at Woman's Hospital in Baton Rouge, Louisiana. Participants enrolled at any point during their pregnancy and surveys were delivered weekly until the participant indicated that she had delivered her baby; a postpartum survey followed four weeks after delivery. This analysis includes 1037 participants with baseline, 596 with follow-up, and 302 with postpartum surveys. Questions on social distancing behaviors were asked at baseline and grouped based on whether they involved social distancing from work, friends and family, or public places. Symptoms of anxiety, stress, depression, and pregnancy-related anxiety were measured. Each type of social distancing was examined as a predictor of mental health using linear model with control for confounders.ResultsThe study population was largely white (84.1%), married (81.8%), and educated (76.2% with a bachelor's or higher degree). Women who were younger, Black, unmarried, or had less education or income reported fewer social distancing behaviors. Mean anxiety score in the highest quartile of overall social distancing was 8.3 (SD 5.6), while in the lowest quartile it was 6.0 (SD 5.0) (p < 0.01), while perceived stress postpartum and pregnancy-related stress were not associated with social distancing. Associations were substantially diminished when controlled for baseline levels of anxiety symptoms.ConclusionsGreater social distancing was associated with more anxiety symptoms, but worse mental health, particularly anxiety, may also have contributed to greater social distancing behaviors.
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Project description:Following the COVID-19 pandemic, many countries worldwide have put lockdowns in place to prevent the virus from spreading. Evidence shows that lockdown measures can affect mental health; it is, therefore, important to identify the psychological characteristics making individuals more vulnerable. The present study aimed, first, to identify, through a cluster analysis, the psychological attributes that characterize individuals with similar psychological responses to the COVID-19 home confinement; second, to investigate whether different psychological characteristics, such as personality traits, alexithymia, and resilience, specifically influence anxiety, stress, and depression, depending on the scope of the confinement. We analyzed data from 393 participants who completed an online survey on their experiences during two different phases of the Italian lockdown, characterized by more or less strict measures of confinement. Two clusters were identified which included participants reporting a better (+ER) and worse (-ER) emotional response to the lockdown, respectively. Individuals in the -ER group showed lower emotional stability, resilience, and higher alexithymia. Moreover, even if lifting part of the restrictions decreased psychological distress among all participants, a reduction in perceived stress was observed only among individuals with high resilience. Finally, personality traits, alexithymia, and resilience differently affected depression, anxiety, and stress. Our results suggest that different psychological interventions should be planned depending on the context: mental health professionals should focus on enhancing the individuals' coping strategies to alleviate stress in emergencies, while long-term intervention aiming at alleviating anxiety and depressive symptoms should focus on alexithymic tendencies and personality constructs.
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Project description:IntroductionUniversity students have been extensively affected with psychological problems due to outbreak of COVID-19 pandemic given their special position and status. In this study, we intend to examine the prevalence of depression, anxiety and stress among a group of students.MethodsThis cross-sectional study was performed in medical Sciences universities of Mazandaran Province. 352 students were recruited by simple available sampling method. Data collection tools were Standard Mental Health Questionnaire (DASS-21) and demographic information questionnaire. Data analysis was done using SPSS software through descriptive and analytical statistics.ResultsThe mean age of students was 22.44 ± 3.4 and 54.3% of them were females. 33.6, 28.4, and 27.3% of students had moderate to extremely severe levels of symptoms of depression, anxiety, and stress, respectively. There was a significant relationship between total mental health score with physical activity (p < 0.04) and with smoking (p < 0.02). There was also a significant relationship between smoking and depression (p < 0.01).ConclusionConsidering the fact that anxiety, stress and depression are common among medical sciences students of Mazandaran Province, necessary measures must be taken to improve their mental health status.
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Project description:AimThe COVID-19 pandemic has forced the education system to undergo changes, which have also affected universities. E-learning became the main form of education, reducing interpersonal contacts, which could affect the mental wellbeing of students. The aim of this study was to investigate the prevalence of depressive symptoms and the level of perceived stress during e-learning among Polish students and to identify the factors for predicting higher levels of depression symptoms.MethodsThe study included 753 participants with a mean age of 22.47 (±4.02) years. The Perception of Stress Questionnaire (PSQ) and Beck Depression Inventory (BDI-II) were used to measure the severity of stress and level of depression. Furthermore, our own survey was used to assess the impact of e-learning on various aspects of life. To examine how much stress can explain a statistically significant amount of variance in depression, three-step hierarchical multiple regression was used. In addition, our own questionnaire was used to assess the impact of e-learning on education, social contacts and technical abilities.ResultsA total of 58% of the students characterized by an increased level of stress. 56% show symptoms of depression and 18% of the participants had suicidal thoughts. The most significant predictor of depression is high stress levels and factors related to e-learning: isolation from friends and acquaintances, negative impact on level of knowledge, reduced motivation to learn, and worsening grades. This predictors may explain about 66% of the variance of depression.ConclusionUniversities should implement interventions and educational programmes, providing ad hoc assistance in the form of individual or group meetings with a psychologist (also in a remote form) and organizing workshops and webinars on strategies for managing stress.
| S-EPMC9152265 | biostudies-literature
Project description:Background: Associations between personality traits and mental health outcomes (depression, anxiety, loneliness, and stress) have rarely been assessed in a population-representative sample of a high-income country during the COVID-19 pandemic. Additionally, as far as we know, the role of health and social behaviors as well as resilience in the personality-mental health relationship has yet to be explored. Methods: A representative sample of 1,828 residents of Luxembourg filled in validated scales to assess personality traits and resilience, depressive symptoms, generalized anxiety, loneliness, and stress, indicating mental health, in mid-April 2020. Results: Approximately 21% of the participants scored above the cut-off for moderate depression and moderate loneliness. Moderate anxiety and moderate stress were present in 6.2 and 0.3% of the participants, respectively. Higher-educated respondents and those living in higher-value housing reported better mental health. Agreeableness and conscientiousness were most consistently associated with better mental health; neuroticism was most consistently associated with worse mental health. Spending more time on social media was also associated with elevated levels of all four mental health outcomes. Social and health behaviors did not change the personality-mental health relationships. Resilience moderated some of the personality-mental health associations, most consistently in neuroticism. Conclusions: Findings suggest educational and socioeconomic inequalities in mental health in a nationally representative sample during the COVID-19 confinement measures. Personality traits, particularly agreeableness, conscientiousness, and low neuroticism were associated with mental health. The moderating role of resilience in the personality-mental health relationship suggests intervention potential to improve mental health during periods of confinement.
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