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

mHealth for Data Collection and Research (981) Wearable Devices and Sensors (903) mHealth for Wellness, Behavior Change and Prevention (2922) Focus Groups and Qualitative Research for Human Factors Research (880) Fitness Trackers and Smart Pedometers/Accelerometers (571) Electronic Health Records (1068) Big Data (245) Data Science (278) Clinical Informatics (1050) E-Health / Health Services Research and New Models of Care (542)

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 Cho1 Author Orcid Image ;   Chunhua Weng1 Author Orcid Image ;   Michael G Kahn2 Author Orcid Image ;   Karthik Natarajan1 Author Orcid Image
Article Authors Cited by (8) Tweetations (2) Metrics

Journals

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  7. Halabi R, Selvarajan R, Lin Z, Herd C, Li X, Kabrit J, Tummalacherla M, Chaibub Neto E, Pratap A. Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World Settings. Sensors 2024;24(19):6246 View
  8. Yang L, Ren M, Sun S, Lu J, Wu Y. Investigation on the preferences for data quality assessment indicators of electronic health records: user-oriented perspective. JAMIA Open 2024;7(4) View