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Examining physical activity clustering using machine learning revealed a diversity of 24-hour step-counting patterns.

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Title: Examining physical activity clustering using machine learning revealed a diversity of 24-hour step-counting patterns.
Authors: Nawrin, Saida Salima1 (AUTHOR), Inada, Hitoshi1,2 (AUTHOR) hinada@med.tohoku.ac.jp, Momma, Haruki3 (AUTHOR), Nagatomi, Ryoichi1,3 (AUTHOR) nagatomi@med.tohoku.ac.jp
Source: Journal of Activity, Sedentary & Sleep Behaviors. 8/12/2024, Vol. 3 Issue 1, p1-12. 12p.
Database: Academic Search Ultimate
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  Data: Examining physical activity clustering using machine learning revealed a diversity of 24-hour step-counting patterns.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Activity%2C+Sedentary+%26+Sleep+Behaviors%22">Journal of Activity, Sedentary & Sleep Behaviors</searchLink>. 8/12/2024, Vol. 3 Issue 1, p1-12. 12p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=178968980
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        Value: 10.1186/s44167-024-00059-3
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        Text: English
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              Text: 8/12/2024
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