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Review
. 2021 Dec 18;10(24):5951.
doi: 10.3390/jcm10245951.

The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities

Zan Gao  1 , Wenxi Liu  2 , Daniel J McDonough  3 , Nan Zeng  4 , Jung Eun Lee  5
Affiliations
Review

The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities

Zan Gao et al. J Clin Med. .

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.

Keywords: GGIR; cut points; deep learning; machine learning; motion sensors; steps per minute.

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Conflict of interest statement

The authors declare that there is no conflict of interests regarding the publication of this paper.

Figures

Figure 1
Figure 1
Visual outputs of daily PA from ActiLife v6.13.4 (ActiGraph software).
Figure 2
Figure 2
GGIR Visual outputs of PA, wearing time and sleep variables.
Figure 3
Figure 3
Daily PA and sleep patterns.
Figure 4
Figure 4
Modeling process diagram ([94]; permission from Li et al., 2020). Note: SED—sedentary; LPA—light physical activity; MPA—moderate physical activity; VPA—vigorous physical activity.
See this image and copyright information in PMC

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