What Factors Are Most Closely Associated With Mood Disorders in Adolescents During the Covid-19 Pandemic? A Cross-Sectional Study Based on 1,771 Adolescents in Shandong Province, China

Bibliographic Data

ID15526441
AuthorsZiyuan Ren (0000-0002-0275-141X, Shandong University), Yaodong Xin (Shanghai University of Finance and Economics), Zhong Lin Wang (0000-0002-5530-0380, University of California, Irvine), Dexiang Liu (0000-0001-5023-4874, Shandong University, corresponding author), Roger C M Ho (0000-0001-9629-4493, National University of Singapore), Cyrus S H Ho (0000-0002-7092-9566, National University of Singapore)
Year2021
Volume12
Pages728278-728278
Publication date2021-09-16
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2021.728278
PMID34603106
OpenAlexW3200673329
LanguageEN
Citations received22
References cited23

Background and Aims: COVID-19 has been proven to harm adolescents' mental health, and several psychological influence factors have been proposed. However, the importance of these factors in the development of mood disorders in adolescents during the pandemic still eludes researchers, and practical strategies for mental health education are limited. Methods: We constructed a sample of 1,771 adolescents from three junior high middle schools, three senior high middle schools, and three independent universities in Shandong province, China. The sample stratification was set as 5:4:3 for adolescent aged from 12 - 15, 15 - 18, 18 - 19. We examined the subjects' anxiety, depression, psychological resilience, perceived social support, coping strategies, subjective social/school status, screen time, and sleep quality with suitable psychological scales. We chose four widely used classification models-k-nearest neighbors, logistic regression, gradient-boosted decision tree (GBDT), and a combination of the GBDT and LR (GBDT + LR)-to construct machine learning models, and we utilized the Shapley additive explanations value (SHAP) to measure how the features affected the dependent variables. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves was used to evaluate the performance of the models. Results: The current rates of occurrence of symptoms of anxiety and depression were 28.3 and 30.8% among the participants. The descriptive and univariate analyses showed that all of the factors included were statistically related to mood disorders. Among the four machine learning algorithms, the GBDT+LR algorithm achieved the best performance for anxiety and depression with average AUC values of 0.819 and 0.857. We found that the poor sleep quality was the most significant risk factor for mood disorders among Chinese adolescents. In addition, according to the feature importance (SHAP) of the psychological factors, we proposed a five-step mental health education strategy to be used during the COVID-19 pandemic (sleep quality-resilience-coping strategy-social support-perceived social status). Conclusion: In this study, we performed a cross-sectional investigation to examine the psychological impact of COVID-19 on adolescents. We applied machine learning algorithms to quantify the importance of each factor. In addition, we proposed a five-step mental health education strategy for school psychologists

Anxiety · Logistic regression · Mental health · Mood · Mood disorders · Psychiatry · Child and Adolescent Psychosocial and Emotional Development · Clinical Psychology · COVID-19 and Mental Health · Medicine · Mental Health Research Topics · Psychology · Internal Medicine

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