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Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
Published on: March 7, 2019
Methods for estimating physical activity and energy expenditure using raw accelerometry data or novel analytical
Kimberly A Clevenger1, Alexander H K Montoye2, Cailyn A Van Camp3
1Utah State University, Department of Kinesiology and Health Science, United States of America.
Physiological Measurement
|August 15, 2022
Summary
A new repository offers accessible novel methods for analyzing accelerometer data to estimate physical activity and energy expenditure. This resource aims to improve data consistency and encourage wider adoption of advanced analytical techniques.
Area of Science:
- Biomedical Engineering
- Physical Activity Epidemiology
- Data Science
Background:
- Novel analytical methods for accelerometer data, including machine learning, are advancing rapidly.
- Implementation in practice lags due to accessibility issues with models, code, and instructions.
- Lack of standardized resources hinders adoption by researchers, especially non-experts.
Purpose of the Study:
- To establish a centralized repository of novel methods for accelerometer data analysis.
- To provide a framework and reporting guidelines for future research in this area.
- To facilitate the estimation of energy expenditure and physical activity intensity.
Main Methods:
- Identified novel methods from a recent scoping review.
- Compiled and created accessible code, models, sample data, and usage instructions.
- Organized methods by age group (preschoolers, children/adolescents, adults) and monitor wear location.
Main Results:
- A repository of 63 novel methods for accelerometer data analysis has been created.
- Methods are available for various age groups and body locations (hip, wrist, chest, etc.).
- Fifteen models are implemented in R, with 48 provided as cut-points, equations, or decision trees.
Conclusions:
- The developed repository and framework will enhance the use and development of novel accelerometer analysis methods.
- This initiative promotes improved data harmonization and consistency across research studies.
- Future work may incorporate methods not initially linked to publications or those identifying specific activity types.
