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

Fitness Trackers and Smart Pedometers/Accelerometers (492) New Methods (666) mHealth for Wellness, Behavior Change and Prevention (2271)

Published on in Vol 7 , No 3 (2019) :March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11075, first published .
Physical Activity Trend eXtraction: A Framework for Extracting Moderate-Vigorous Physical Activity Trends From Wearable Fitness Tracker Data

Physical Activity Trend eXtraction: A Framework for Extracting Moderate-Vigorous Physical Activity Trends From Wearable Fitness Tracker Data

Physical Activity Trend eXtraction: A Framework for Extracting Moderate-Vigorous Physical Activity Trends From Wearable Fitness Tracker Data

Authors of this article:

Louis Faust 1, 2 Author Orcid Image ;   Cheng Wang 2, 3 Author Orcid Image ;   David Hachen 2, 3 Author Orcid Image ;   Omar Lizardo 4 Author Orcid Image ;   Nitesh V Chawla 1, 2 Author Orcid Image
Article Authors Cited by (5) Tweetations (6) Metrics

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  1. Turicchi J, O'Driscoll R, Finlayson G, Duarte C, Palmeira A, Larsen S, Heitmann B, Stubbs R. Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study. JMIR mHealth and uHealth 2020;8(9):e17977 View
  2. Rykov Y, Thach T, Dunleavy G, Roberts A, Christopoulos G, Soh C, Car J. Activity Tracker–Based Metrics as Digital Markers of Cardiometabolic Health: Cross-Sectional Study. JMIR mHealth and uHealth 2020;8(1):e16409 View

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