Abstract
The COVID-19 pandemic has been making big impact on mental and physical health of youth. Recent research shows that the COVID-19 pandemic has exacerbated existing mental health problems due to the unique combination of public health crises and social isolation. The objective of this study is to integrate and analyze various health data sources to improve health care for youth during the COVID-19 pandemic. The focus of the research is to merge self-assessment data from individuals, data obtained from wearable devices, and health data based on Traditional Chinese Medicine (TCM), utilizing machine learning techniques to gain a comprehensive perspective of youth health. The experiment results showed that the correlation between the TCM-based Health Score (TCMHS) in the TCM dimension and the Wearable Device Stress-based Health Score (WDSHS) in wearable devices was stronger than the correlation between the Self-assessed Subjective Health Score (SSHS) and the WDSHS. On the other hand, activity calorie consumption was the most important feature to both the SSHS and WDSHS while resting heart rate affected the TCMHS most.
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Acknowledgement
The work was supported in part by 2022–2024 Masaru Ibuka Foundation Research Project on Oriental Medicine, 2020–2025 JSPS A3 Foresight Program (Grant No. JPJSA3F20200001), and 2022 JST SPRING (Grant No. JPMJSP2128).
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Wu, J. et al. (2023). Multidimensional Data Integration and Analysis for Youth Health Care During the Covid-19 Pandemic. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2023. Lecture Notes in Computer Science, vol 14029. Springer, Cham. https://doi.org/10.1007/978-3-031-35748-0_11
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DOI: https://doi.org/10.1007/978-3-031-35748-0_11
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