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新冠疫情封锁期间焦虑和睡眠障碍分析的机器学习方法

Machine learning approach for anxiety and sleep disorders analysis during COVID-19 lockdown.

作者信息

Anbarasi L Jani, Jawahar Malathy, Ravi Vinayakumar, Cherian Sherin Miriam, Shreenidhi S, Sharen H

机构信息

School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, 600 127 India.

Leather Process Technology Division, CSIR-Central Leather Research Institute, Adyar, Chennai, 600020 India.

出版信息

Health Technol (Berl). 2022;12(4):825-838. doi: 10.1007/s12553-022-00674-7. Epub 2022 May 30.

DOI:10.1007/s12553-022-00674-7
PMID:35669293
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9148681/
Abstract

The Severe Acute Respiratory Syndrome (SARS)-CoV-2 virus caused COVID-19 pandemic has led to various kinds of anxiety and stress in different strata and sections of the society. The aim of this study is to analyse the sleeping and anxiety disorder for a wide distribution of people of different ages and from different strata of life. The study also seeks to investigate the different symptoms and grievances that people suffer from in connection with their sleep patterns and predict the possible relationships and factors in association with outcomes related to COVID-19 pandemic induced stress and issues. A total of 740 participants (51.3% male and 48.7% female) structured with 2 sections, first with general demographic information and second with more targeted questions for each demographic were surveyed. Pittsburgh Sleep Quality Index (PSQI) and General Anxiety Disorder assessment (GAD-7) standard scales were utilized to measure the stress, sleep disorders and anxiety. Experimental results showed positive correlation between PSQI and GAD-7 scores for the participants. After adjusting for age and gender, occupation does not have an effect on sleep quality (PSQI), but it does have an effect on anxiety (GAD-7). Student community in spite of less susceptible to COVID-19 infection found to be highly prone to psychopathy mental health disturbances during the COVID-19 pandemic. The study also highlights the connectivity between lower social status and mental health issues. Random Forest model for college students indicates clearly the stress induced factors as anxiety score, worry about inability to understand concepts taught online, involvement of parents, college hours, worrying about other work load and deadlines for the young students studying in Universities.

摘要

严重急性呼吸综合征冠状病毒2(SARS-CoV-2)引发的新冠疫情在社会的不同阶层和群体中导致了各种焦虑和压力。本研究的目的是分析不同年龄、不同生活阶层的广泛人群的睡眠和焦虑障碍。该研究还试图调查人们在睡眠模式方面所遭受的不同症状和困扰,并预测与新冠疫情引发的压力和问题相关的结果中可能存在的关系和因素。总共对740名参与者(男性占51.3%,女性占48.7%)进行了调查,问卷分为两个部分,第一部分是一般人口统计学信息,第二部分是针对每个人口统计学特征的更具针对性的问题。使用匹兹堡睡眠质量指数(PSQI)和广泛性焦虑障碍评估量表(GAD-7)来测量压力、睡眠障碍和焦虑。实验结果显示,参与者的PSQI和GAD-7得分之间呈正相关。在对年龄和性别进行调整后,职业对睡眠质量(PSQI)没有影响,但对焦虑(GAD-7)有影响。学生群体尽管感染新冠病毒的易感性较低,但在新冠疫情期间被发现极易出现精神心理方面的健康问题。该研究还强调了社会地位较低与心理健康问题之间的联系。针对大学生的随机森林模型清楚地表明,压力诱发因素包括焦虑得分、担心无法理解在线授课内容、父母的参与情况、上课时间、担心其他工作量以及大学生的截止日期。

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