Predicting Psychological State Among Chinese Undergraduate Students in the COVID-19 Epidemic: A Longitudinal Study Using a Machine Learning

Fenfen Ge,1,* Di Zhang,2,* Lianhai Wu,2 Hongwei Mu2 1Clinical Psychology Department, Qingdao Municipal Hospital, Qingdao 266000, Shandong, People’s Republic of China; 2Mental Health Education and Counseling Center, Ocean University of China, Qingdao 266100, Shandong, People’s Rep...

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Autores principales:	Ge F, Zhang D, Wu L, Mu H
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2020
Materias:	covid-19 anxiety insomnia cohort machine learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/15d5fd89eb29440a86397e77ea306b87
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spelling	oai:doaj.org-article:15d5fd89eb29440a86397e77ea306b872021-12-02T10:18:40ZPredicting Psychological State Among Chinese Undergraduate Students in the COVID-19 Epidemic: A Longitudinal Study Using a Machine Learning1178-2021https://doaj.org/article/15d5fd89eb29440a86397e77ea306b872020-09-01T00:00:00Zhttps://www.dovepress.com/predicting-psychological-state-among-chinese-undergraduate-students-in-peer-reviewed-article-NDThttps://doaj.org/toc/1178-2021Fenfen Ge,1,* Di Zhang,2,* Lianhai Wu,2 Hongwei Mu2 1Clinical Psychology Department, Qingdao Municipal Hospital, Qingdao 266000, Shandong, People’s Republic of China; 2Mental Health Education and Counseling Center, Ocean University of China, Qingdao 266100, Shandong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hongwei MuMental Health Education and Counseling Center, Ocean University of China, Qingdao 266100, Shandong, People’s Republic of ChinaTel +86-0532-66782024Email mhw.1230@163.comBackground: The outbreak of the 2019 novel coronavirus disease (COVID-19) not only caused physical abnormalities, but also caused psychological distress, especially for undergraduate students who are facing the pressure of academic study and work. We aimed to explore the prevalence rate of probable anxiety and probable insomnia and to find the risk factors among a longitudinal study of undergraduate students using the approach of machine learning.Methods: The baseline data (T1) were collected from freshmen who underwent psychological evaluation at two months after entering the university. At T2 stage (February 10th to 13th, 2020), we used a convenience cluster sampling to assess psychological state (probable anxiety was assessed by general anxiety disorder-7 and probable insomnia was assessed by insomnia severity index-7) based on a web survey. We integrated information attained at T1 stage to predict probable anxiety and probable insomnia at T2 stage using a machine learning algorithm (XGBoost).Results: Finally, we included 2009 students (response rate: 80.36%). The prevalence rate of probable anxiety and probable insomnia was 12.49% and 16.87%, respectively. The XGBoost algorithm predicted 1954 out of 2009 students (translated into 97.3% accuracy) and 1932 out of 2009 students (translated into 96.2% accuracy) who suffered anxiety and insomnia symptoms, respectively. The most relevant variables in predicting probable anxiety included romantic relationship, suicidal ideation, sleep symptoms, and a history of anxiety symptoms. The most relevant variables in predicting probable insomnia included aggression, psychotic experiences, suicidal ideation, and romantic relationship.Conclusion: Risks for probable anxiety and probable insomnia among undergraduate students can be identified at an individual level by baseline data. Thus, timely psychological intervention for anxiety and insomnia symptoms among undergraduate students is needed considering the above factors.Keywords: COVID-19, anxiety, insomnia, cohort, machine learningGe FZhang DWu LMu HDove Medical Pressarticlecovid-19anxietyinsomniacohortmachine learningNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol Volume 16, Pp 2111-2118 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	covid-19 anxiety insomnia cohort machine learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
spellingShingle	covid-19 anxiety insomnia cohort machine learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 Ge F Zhang D Wu L Mu H Predicting Psychological State Among Chinese Undergraduate Students in the COVID-19 Epidemic: A Longitudinal Study Using a Machine Learning
description	Fenfen Ge,1,* Di Zhang,2,* Lianhai Wu,2 Hongwei Mu2 1Clinical Psychology Department, Qingdao Municipal Hospital, Qingdao 266000, Shandong, People’s Republic of China; 2Mental Health Education and Counseling Center, Ocean University of China, Qingdao 266100, Shandong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hongwei MuMental Health Education and Counseling Center, Ocean University of China, Qingdao 266100, Shandong, People’s Republic of ChinaTel +86-0532-66782024Email mhw.1230@163.comBackground: The outbreak of the 2019 novel coronavirus disease (COVID-19) not only caused physical abnormalities, but also caused psychological distress, especially for undergraduate students who are facing the pressure of academic study and work. We aimed to explore the prevalence rate of probable anxiety and probable insomnia and to find the risk factors among a longitudinal study of undergraduate students using the approach of machine learning.Methods: The baseline data (T1) were collected from freshmen who underwent psychological evaluation at two months after entering the university. At T2 stage (February 10th to 13th, 2020), we used a convenience cluster sampling to assess psychological state (probable anxiety was assessed by general anxiety disorder-7 and probable insomnia was assessed by insomnia severity index-7) based on a web survey. We integrated information attained at T1 stage to predict probable anxiety and probable insomnia at T2 stage using a machine learning algorithm (XGBoost).Results: Finally, we included 2009 students (response rate: 80.36%). The prevalence rate of probable anxiety and probable insomnia was 12.49% and 16.87%, respectively. The XGBoost algorithm predicted 1954 out of 2009 students (translated into 97.3% accuracy) and 1932 out of 2009 students (translated into 96.2% accuracy) who suffered anxiety and insomnia symptoms, respectively. The most relevant variables in predicting probable anxiety included romantic relationship, suicidal ideation, sleep symptoms, and a history of anxiety symptoms. The most relevant variables in predicting probable insomnia included aggression, psychotic experiences, suicidal ideation, and romantic relationship.Conclusion: Risks for probable anxiety and probable insomnia among undergraduate students can be identified at an individual level by baseline data. Thus, timely psychological intervention for anxiety and insomnia symptoms among undergraduate students is needed considering the above factors.Keywords: COVID-19, anxiety, insomnia, cohort, machine learning
format	article
author	Ge F Zhang D Wu L Mu H
author_facet	Ge F Zhang D Wu L Mu H
author_sort	Ge F
title	Predicting Psychological State Among Chinese Undergraduate Students in the COVID-19 Epidemic: A Longitudinal Study Using a Machine Learning
title_short	Predicting Psychological State Among Chinese Undergraduate Students in the COVID-19 Epidemic: A Longitudinal Study Using a Machine Learning
title_full	Predicting Psychological State Among Chinese Undergraduate Students in the COVID-19 Epidemic: A Longitudinal Study Using a Machine Learning
title_fullStr	Predicting Psychological State Among Chinese Undergraduate Students in the COVID-19 Epidemic: A Longitudinal Study Using a Machine Learning
title_full_unstemmed	Predicting Psychological State Among Chinese Undergraduate Students in the COVID-19 Epidemic: A Longitudinal Study Using a Machine Learning
title_sort	predicting psychological state among chinese undergraduate students in the covid-19 epidemic: a longitudinal study using a machine learning
publisher	Dove Medical Press
publishDate	2020
url	https://doaj.org/article/15d5fd89eb29440a86397e77ea306b87
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Predicting Psychological State Among Chinese Undergraduate Students in the COVID-19 Epidemic: A Longitudinal Study Using a Machine Learning

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