This paper is in the following e-collection/theme issue:

Innovations and Technology for Physical Activity Education (926) Novel Sensors and Data Acquisition Methods in Cardiology (225) Digital Biomarkers and Digital Phenotyping (284) Wearable Devices and Sensors (1133)

Published on in Vol 24, No 7 (2022): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34669, first published .
High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study

High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study

High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study

Authors of this article:

Weizhuang Zhou1 Author Orcid Image ;   Yu En Chan1 Author Orcid Image ;   Chuan Sheng Foo1 Author Orcid Image ;   Jingxian Zhang1 Author Orcid Image ;   Jing Xian Teo2 Author Orcid Image ;   Sonia Davila2, 3, 4 Author Orcid Image ;   Weiting Huang5 Author Orcid Image ;   Jonathan Yap5, 6 Author Orcid Image ;   Stuart Cook4 Author Orcid Image ;   Patrick Tan2, 7, 8, 9 Author Orcid Image ;   Calvin Woon-Loong Chin5, 6 Author Orcid Image ;   Khung Keong Yeo2, 5, 6 Author Orcid Image ;   Weng Khong Lim2, 3, 7 Author Orcid Image ;   Pavitra Krishnaswamy1 Author Orcid Image
Article Authors Cited by (14) Tweetations (10) Metrics

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