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

mHealth for Data Collection and Research (772) Wearable Devices and Sensors (733) mHealth for Wellness, Behavior Change and Prevention (2523) Focus Groups and Qualitative Research with Users (660) Fitness Trackers and Smart Pedometers/Accelerometers (513) Electronic Health Records (867) Big Data (187) Data Science (162) Clinical Informatics (701) E-Health / Health Services Research and New Models of Care (315)

Published on in Vol 9, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31618, first published .
Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: Multi-Method Study

Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: Multi-Method Study

Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: Multi-Method Study

Authors of this article:

Sylvia Cho 1 Author Orcid Image ;   Chunhua Weng 1 Author Orcid Image ;   Michael G Kahn 2 Author Orcid Image ;   Karthik Natarajan 1 Author Orcid Image
Article Authors Cited by (5) Tweetations (2) Metrics

Sylvia Cho   1 , PhD ;   Chunhua Weng   1 , PhD ;   Michael G Kahn   2 , MD, PhD ;   Karthik Natarajan   1 , PhD

1 Department of Biomedical Informatics, Columbia University, New York, NY, United States

2 Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States

Corresponding Author: