Psychiatric and Mental Health Commons^™

The Acute And Persisting Impact Of Covid-19 On Trajectories Of Adolescent Depression: Sex Differences And Social Connectedness, Sabrina R. Liu, Elyssia Poggi Davis, Anton M. Palma, Curt A. Sandman, Laura M. Glynn

Psychology Faculty Articles and Research

Background

The COVID-19 era is a time of unprecedented stress, and there is widespread concern regarding its short- and long-term mental health impact. Adolescence is a sensitive period for the emergence of latent psychopathology vulnerabilities, often activated by environmental stressors. The present study examined COVID-19′s impact on adolescent depression and possible influences of different domains of social connectedness (loneliness, social media use, social video game time, degree of social activity participation).

Methods

A community sample of 175 adolescents (51% boys, mean age = 16.01 years) completed questionnaires once before and twice during the COVID-19 pandemic. Piecewise growth modeling examined the …

A Predictable Home Environment May Protect Child Mental Health During The Covid-19 Pandemic, Laura M. Glynn, Elyssia Poggi Davis, Joan L. Luby, Tallie Z. Baram, Curt A. Sandman

Psychology Faculty Articles and Research

Objective

Information about the adverse effects of the COVID-19 pandemic on adolescent and adult mental health is growing, yet the impacts on preschool children are only emerging. Importantly, environmental factors that augment or protect from the multidimensional and stressful influences of the pandemic on emotional development of young children are poorly understood.

Methods

Depressive symptoms in 169 preschool children (mean age 4.1 years) were assessed with the Preschool Feelings Checklist during a state-wide stay-at-home order in Southern California. Mothers (46% Latinx) also reported on externalizing behaviors with the Strengths & Difficulties Questionnaire. To assess the role of environmental factors in …

Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae

Psychology Faculty Articles and Research

Background

As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Methods

Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.

Results

A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …