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International Journal of Data Science and Analytics

Erschienen in:

15.06.2018 | Regular Paper

Modeling behaviors and lifestyle with online and social data for predicting and analyzing sleep and exercise quality

verfasst von: Mehrdad Farajtabar, Emre Kıcıman, Girish Nathan, Ryen W. White

Erschienen in: International Journal of Data Science and Analytics | Ausgabe 4/2019

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Abstract

While recent data studies have focused on associations between sleep and exercise patterns as captured by digital fitness devices, it is known that sleep and exercise quality are affected by a much broader set of factors not captured by these devices, such as general lifestyle, eating, and stress. Here, we conduct a large-scale data study of exercise and sleep effects through an analysis of 8 months of exercise and sleep data for 20 k users, combined with search query logs, location information and aggregated social media data. We analyze factors correlated with better sleep and more effective exercise, and confirm these relationships through causal inference analysis. Further, we build linear models to predict individuals’ sleep and exercise quality. This analysis demonstrates the potential benefits of combining online and social data sources with data from health trackers, and is a potentially rich computational benchmark for health studies. We discuss the implications of our work for individuals, health practitioners and health systems.

Vorheriger Artikel Accurate classification of socially generated medical discourse

Nächster Artikel Multi-domain and multi-view networks model for clustering hospital admissions from the emergency department

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Titel: Modeling behaviors and lifestyle with online and social data for predicting and analyzing sleep and exercise quality
verfasst von: Mehrdad Farajtabar
Emre Kıcıman
Girish Nathan
Ryen W. White
Publikationsdatum: 15.06.2018
Verlag: Springer International Publishing
Erschienen in: International Journal of Data Science and Analytics / Ausgabe 4/2019
Print ISSN: 2364-415X
Elektronische ISSN: 2364-4168
DOI: https://doi.org/10.1007/s41060-018-0136-8

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