The dilemma of analyzing physical activity and sedentary behavior with wrist accelerometer data: Challenges and opportunities

Zan Gao, Wenxi Liu, Daniel J McDonough, Nan Zeng, Jung Eun Lee

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations

Abstract

Physical behaviors (e.g., physical activity and sedentary behavior) have been the focus among many researchers in the biomedical and behavioral science fields. The recent shift from hip-to wrist-worn accelerometers in these fields has signaled the need to develop novel approaches to process raw acceleration data of physical activity and sedentary behavior. However, there is currently no consensus regarding the best practices for analyzing wrist-worn accelerometer data to accurately predict individuals’ energy expenditure and the times spent in different intensities of free-living physical activity and sedentary behavior. To this end, accurately analyzing and interpreting wrist-worn accelerometer data has become a major challenge facing many clinicians and researchers. In response, this paper attempts to review different methodologies for analyzing wrist-worn accelerometer data and offer cutting edge, yet appropriate analysis plans for wrist-worn accelerometer data in the assessment of physical behavior. In this paper, we first discuss the fundamentals of wrist-worn accelerometer data, followed by various methods of processing these data (e.g., cut points, steps per minute, machine learning), and then we discuss the opportunities, challenges, and directions for future studies in this area of inquiry. This is the most comprehensive review paper to date regarding the analysis and interpretation of free-living physical activity data derived from wrist-worn accelerometers, aiming to help establish a blueprint for processing wrist-derived accelerometer data.

Original languageEnglish (US)
Article number5951
JournalJournal of Clinical Medicine
Volume10
Issue number24
DOIs
StatePublished - Dec 1 2021

Bibliographical note

Funding Information:
Funding: The work of this study was funded by the CEHD Acceleration Grant 2021 from the University of Minnesota–Twin Cities.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Cut points
  • Deep learning
  • GGIR
  • Machine learning
  • Motion sensors
  • Steps per minute

PubMed: MeSH publication types

  • Journal Article
  • Review

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