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

Reviews in Medical Informatics (110) Wearables and MHealth Reviews (307) Tools, Programs and Algorithms (368) Fitness Trackers and Smart Pedometers/Accelerometers (654) Wearables and MHealth Viewpoints (88) Wearable Devices and Sensors (1115)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41153, first published .
Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review

Authors of this article:

Claudio Diaz1 Author Orcid Image ;   Corinne Caillaud2 Author Orcid Image ;   Kalina Yacef1 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

Journals

  1. Nawrin S, Inada H, Momma H, Nagatomi R. Examining physical activity clustering using machine learning revealed a diversity of 24-hour step-counting patterns. Journal of Activity, Sedentary and Sleep Behaviors 2024;3(1) View
  2. Wang B, Xiao Y, Yang X, Zhang X. An interpretable adaptive edge-weighted graph convolutional networks model for physical fitness assessment of primary school student in high-altitude regions. Scientific Reports 2025;15(1) View
  3. Dibben G, Santillan C, Brage S, Buman M, Duncan E, Granat M, Hillsdon M, Martin A, Matthews C, McCrorie P, Taylor R, Vasankari T, Foster C. Reporting of Accelerometry in Health Research: A Scoping Review of Current Guidance. Scandinavian Journal of Medicine & Science in Sports 2025;35(10) View

Books/Policy Documents

  1. Torriani-Pasin C, dos Santos Palma G, da Silva L, Geed S. Locomotion and Posture in Older Adults. View