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.
Highlights
Physical behaviors (i.e., physical activity (PA) and sedentary behavior) have been the focus among many researchers in the biomedical and behavioral science fields
Since the 1980s, physical behaviors have been quantified through self-reported questionnaires and diaries, which are generally limited in their accuracy due to recall bias and measurement errors
In this paper, we first provide the fundamentals of wristworn accelerometer data, followed by various methods of dealing with these data (e.g., GGIR, machine learning) and discuss opportunities, challenges, and directions for future studies in this area of inquiry
Summary
Physical behaviors (i.e., physical activity (PA) and sedentary behavior) have been the focus among many researchers in the biomedical and behavioral science fields. A number of empirical studies and reviews have identified currently available evidence concerning the use of accelerometers for PA measurement among various clinical populations, including patients with diabetes [10], knee osteoarthritis [11], chronic health conditions [12], and overweight and obesity [13]. To this end, accurately analyzing and interpreting wrist-worn accelerometer data has become a major challenge facing many healthcare clinicians and researchers. In this paper, we first provide the fundamentals of wristworn accelerometer data, followed by various methods of dealing with these data (e.g., GGIR, machine learning) and discuss opportunities, challenges, and directions for future studies in this area of inquiry
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