Proceedings of the 2024 5th International Conference on Education, Knowledge and Information Management (ICEKIM 2024）

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Integrating Multi-modal Features for Evaluating and Predicting Sleep Status

Authors
Xujie Huang1, 2, Huixia Zhou1, 2, *, Xiangyang Zhang1, 2, *
1CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
2Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
*Corresponding author. Email: zhouhx@psych.ac.cn
*Corresponding author. Email: zhangxy@psych.ac.cn
Corresponding Authors
Huixia Zhou, Xiangyang Zhang
Available Online 31 August 2024.
DOI
10.2991/978-94-6463-502-7_23How to use a DOI?
Keywords
depression levels; ruminative thinking; sleep status prediction; multi-modal; mediating effects
Abstract

This study investigates the link between depression, rumination, and sleep among Uyghur high school students in Kashgar, Xinjiang. Utilizing data from 680 students across three randomly selected high schools, it finds significant correlations: depression positively correlates with both sleep issues and rumination, while rumination also positively correlates with sleep problems. Mediation analysis reveals that rumination partially mediates the relationship between depression and sleep. Furthermore, a novel multi-modal deep learning model, integrating self-reported data and numerical evaluations, effectively predicts students’ sleep status. These findings underscore the importance of addressing depression and rumination to enhance students’ sleep quality and propose innovative approaches for student health management and education.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2024 5th International Conference on Education, Knowledge and Information Management (ICEKIM 2024）
Series
Atlantis Highlights in Computer Sciences
Publication Date
31 August 2024
ISBN
978-94-6463-502-7
ISSN
2589-4900
DOI
10.2991/978-94-6463-502-7_23How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Xujie Huang
AU  - Huixia Zhou
AU  - Xiangyang Zhang
PY  - 2024
DA  - 2024/08/31
TI  - Integrating Multi-modal Features for Evaluating and Predicting Sleep Status
BT  - Proceedings of the 2024 5th International Conference on Education, Knowledge and Information Management (ICEKIM 2024）
PB  - Atlantis Press
SP  - 212
EP  - 223
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-502-7_23
DO  - 10.2991/978-94-6463-502-7_23
ID  - Huang2024
ER  -