Suppr超能文献

新冠疫情下的大学生研究:通过手机传感视角看新冠疫情期间大学生的一年生活

COVID Student Study: A Year in the Life of College Students during the COVID-19 Pandemic Through the Lens of Mobile Phone Sensing.

作者信息

Nepal Subigya, Wang Weichen, Vojdanovski Vlado, Huckins Jeremy F, daSilva Alex, Meyer Meghan, Campbell Andrew

机构信息

Dartmouth College, Hanover, NH, USA.

Biocogniv Inc., Burlington, VT, USA.

出版信息

Proc SIGCHI Conf Hum Factor Comput Syst. 2022 Apr;2022. doi: 10.1145/3491102.3502043. Epub 2022 Apr 28.

Abstract

The COVID-19 pandemic continues to affect the daily life of college students, impacting their social life, education, stress levels and overall mental well-being. We study and assess behavioral changes of N=180 undergraduate college students one year prior to the pandemic as a baseline and then during the first year of the pandemic using mobile phone sensing and behavioral inference. We observe that certain groups of students experience the pandemic very differently. Furthermore, we explore the association of self-reported COVID-19 concern with students' behavior and mental health. We find that heightened COVID-19 concern is correlated with increased depression, anxiety and stress. We evaluate the performance of different deep learning models to classify student COVID-19 concerns with an AUROC and F1 score of 0.70 and 0.71, respectively. Our study spans a two-year period and provides a number of important insights into the life of college students during this period.

摘要

新冠疫情持续影响着大学生的日常生活,冲击着他们的社交生活、教育、压力水平以及整体心理健康。我们以疫情爆发前一年N = 180名本科大学生的行为变化作为基线进行研究和评估,然后在疫情第一年使用手机传感和行为推断进行研究。我们观察到,某些学生群体对疫情的体验截然不同。此外,我们探讨了自我报告的对新冠疫情的担忧与学生行为和心理健康之间的关联。我们发现,对新冠疫情的高度担忧与抑郁、焦虑和压力的增加相关。我们评估了不同深度学习模型对学生新冠疫情担忧程度进行分类的性能,其受试者工作特征曲线下面积(AUROC)和F1分数分别为0.70和0.71。我们的研究跨越两年时间,为这一时期大学生的生活提供了许多重要见解。

相似文献

引用本文的文献

3
MoodCapture: Depression Detection Using In-the-Wild Smartphone Images.情绪捕捉:利用自然场景下的智能手机图像进行抑郁症检测
Proc SIGCHI Conf Hum Factor Comput Syst. 2024 May;2024. doi: 10.1145/3613904.3642680. Epub 2024 May 11.
4
Mental Health and Mobile Communication Profiles of Crowdsourced Participants.众包参与者的心理健康与移动通信概况。
IEEE J Biomed Health Inform. 2024 Dec;28(12):7683-7692. doi: 10.1109/JBHI.2024.3436654. Epub 2024 Dec 5.
8
Human behavior in the time of COVID-19: Learning from big data.新冠疫情期间的人类行为:从大数据中学习
Front Big Data. 2023 Apr 6;6:1099182. doi: 10.3389/fdata.2023.1099182. eCollection 2023.

本文引用的文献

1
Predicting Brain Functional Connectivity Using Mobile Sensing.利用移动传感技术预测脑功能连接性
Proc ACM Interact Mob Wearable Ubiquitous Technol. 2020 Mar;4(1). doi: 10.1145/3381001. Epub 2020 Mar 18.
6
Analysis of mobility data to build contact networks for COVID-19.利用移动数据构建 COVID-19 接触网络分析。
PLoS One. 2021 Apr 15;16(4):e0249726. doi: 10.1371/journal.pone.0249726. eCollection 2021.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验