File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A Data-Driven Context-Aware Health Inference System for Children during School Closures

TitleA Data-Driven Context-Aware Health Inference System for Children during School Closures
Authors
Keywordsdata analysis
health inference
risk factor analysis
school closures
Issue Date27-Mar-2022
PublisherAssociation for Computing Machinery (ACM)
Citation
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2022, v. 7, n. 1 How to Cite?
Abstract

Many countries have implemented school closures due to the outbreak of the COVID-19 pandemic, which has inevitably affected children's physical and mental health. It is vital for parents to pay special attention to their children's health status during school closures. However, it is difficult for parents to recognize the changes in their children's health, especially without visible symptoms, such as psychosocial functioning in mental health. Moreover, healthcare resources and understanding of the health and societal impact of COVID-19 are quite limited during the pandemic. Against this background, we collected real-world datasets from 1,172 children in Hong Kong during four time periods under different pandemic and school closure conditions from September 2019 to January 2022. Based on these data, we first perform exploratory data analysis to explore the impact of school closures on six health indicators, including physical activity intensity, physical functioning, self-rated health, psychosocial functioning, resilience, and connectedness. We further study the correlation between children's contextual characteristics (i.e., demographics, socioeconomic status, electronic device usage patterns, financial satisfaction, academic performance, sleep pattern, exercise habits, and dietary patterns) and the six health indicators. Subsequently, a health inference system is designed and developed to infer children's health status based on their contextual features to derive the risk factors of the six health indicators. The evaluation and case studies on real-world datasets show that this health inference system can help parents and authorities better understand key factors correlated with children's health status during school closures.


Persistent Identifierhttp://hdl.handle.net/10722/331388
ISSN
2023 Impact Factor: 3.6
2023 SCImago Journal Rankings: 1.905
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJiang, ZH-
dc.contributor.authorLin, L-
dc.contributor.authorZhang, XC-
dc.contributor.authorLuan, JD-
dc.contributor.authorZhao, RN-
dc.contributor.authorChen, LB-
dc.contributor.authorLam, J-
dc.contributor.authorYip, KM-
dc.contributor.authorSo, HK-
dc.contributor.authorWong, WHS-
dc.contributor.authorIp, P-
dc.contributor.authorNgai, ECH-
dc.date.accessioned2023-09-21T06:55:17Z-
dc.date.available2023-09-21T06:55:17Z-
dc.date.issued2022-03-27-
dc.identifier.citationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2022, v. 7, n. 1-
dc.identifier.issn2474-9567-
dc.identifier.urihttp://hdl.handle.net/10722/331388-
dc.description.abstract<p>Many countries have implemented school closures due to the outbreak of the COVID-19 pandemic, which has inevitably affected children's physical and mental health. It is vital for parents to pay special attention to their children's health status during school closures. However, it is difficult for parents to recognize the changes in their children's health, especially without visible symptoms, such as psychosocial functioning in mental health. Moreover, healthcare resources and understanding of the health and societal impact of COVID-19 are quite limited during the pandemic. Against this background, we collected real-world datasets from 1,172 children in Hong Kong during four time periods under different pandemic and school closure conditions from September 2019 to January 2022. Based on these data, we first perform exploratory data analysis to explore the impact of school closures on six health indicators, including physical activity intensity, physical functioning, self-rated health, psychosocial functioning, resilience, and connectedness. We further study the correlation between children's contextual characteristics (i.e., demographics, socioeconomic status, electronic device usage patterns, financial satisfaction, academic performance, sleep pattern, exercise habits, and dietary patterns) and the six health indicators. Subsequently, a health inference system is designed and developed to infer children's health status based on their contextual features to derive the risk factors of the six health indicators. The evaluation and case studies on real-world datasets show that this health inference system can help parents and authorities better understand key factors correlated with children's health status during school closures.</p>-
dc.languageeng-
dc.publisherAssociation for Computing Machinery (ACM)-
dc.relation.ispartofProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectdata analysis-
dc.subjecthealth inference-
dc.subjectrisk factor analysis-
dc.subjectschool closures-
dc.titleA Data-Driven Context-Aware Health Inference System for Children during School Closures-
dc.typeArticle-
dc.identifier.doi10.1145/3580800-
dc.identifier.scopuseid_2-s2.0-85152486588-
dc.identifier.volume7-
dc.identifier.issue1-
dc.identifier.eissn2474-9567-
dc.identifier.isiWOS:000957429700018-
dc.identifier.issnl2474-9567-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats