Related Experiment Video
Updated: Jan 20, 2026

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption
Published on: June 20, 2025
Physical Activity and Sedentary Behavior Assessment: A Laboratory-Based Evaluation of Agreement between Commonly Used
Shohei Yano1, Mohammad Javad Koohsari2,3,4, Ai Shibata5
1Institute for Sport Sciences, Waseda University, Saitama 359-1192, Japan. shoheiyano2018@gmail.com.
Abstract:
Different models of accelerometer have the potential to provide a different estimate of the same physical activity or sedentary behavior. Our study compared the outputs of the Active Style Pro (ASP) and ActiGraph (AG) devices in assessing predicted metabolic equivalents (METs) for specific activities under laboratory conditions. Thirty healthy young adults wore two hip accelerometers (ASP and AG), simultaneously while performing twenty-two activities (eight sedentary, eight household, and six ambulatory activities) in a controlled laboratory setting. For the AG, predicted METs for each activity was calculated using four equations based on vertical-axis and vector magnitude data. Separate paired t-tests and Bland-Altman analysis examined the difference and agreement in METs between AG using four commonly used equations and ASP measurements for each activity. AG devices using different equations calculated significantly different outcomes for most activities compared with ASP devices. The smallest differences in predicted METs estimates between ASP and AG were observed for ambulatory activities. Ambulatory activities demonstrated the best agreement between ASP and AG regardless of which AG equation was used. Our findings can be used to assist researchers in their selection of accelerometer and output estimation equations for measuring physical activity and sedentary behavior in adults.
Related Concept Videos
08:45Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption
06:51Physical Activity Measurement in Children Accepting Table Tennis Training
05:59Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
07:47Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:24A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
06:55Developing a Behavioral Box for Assessing Prepulse Inhibition and Neural Activity in Psychiatric Animal Models

