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A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
Published on: April 21, 2017
Comparing the performance of three generations of ActiGraph accelerometers
Megan P Rothney1, Gregory A Apker, Yanna Song
1Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA. rothneym@niddk.nih.gov
Journal of Applied Physiology (Bethesda, Md. : 1985)
|July 19, 2008
Summary
This study compared three ActiGraph accelerometer generations, finding the GT1M reduced inter-monitor variability but decreased sensitivity for light physical activity. Algorithms for energy expenditure may need updates for the GT1M model.
Area of Science:
- Biomedical Engineering
- Physical Activity Measurement
- Wearable Technology
Background:
- ActiGraph accelerometers are crucial for objective physical activity assessment in research.
- Consistency across different ActiGraph generations is not well-established.
- Understanding generational differences is vital for data comparability.
Purpose of the Study:
- To evaluate the dynamic response and inter-monitor variability of three ActiGraph accelerometer generations (7164, 71256, GT1M).
- To identify measurement differences impacting physical activity data across devices.
- To inform the use of historical and current ActiGraph data.
Main Methods:
- Utilized mechanical oscillations to test accelerometer response across varying radii and frequencies.
- Quantified inter-monitor variability within each of the three ActiGraph generations.
- Compared the dynamic response characteristics and measurement consistency between models.
Main Results:
- The GT1M showed significantly different activity count-radius relationships compared to older models (7164, 71256).
- All generations exhibited non-linear frequency responses, with significant inter-generational differences at certain frequencies.
- The GT1M demonstrated markedly reduced inter-monitor variability but decreased sensitivity in low-frequency detection.
Conclusions:
- The GT1M offers improved inter-monitor consistency but may be less suitable for monitoring sedentary or light-intensity movements.
- Existing energy expenditure prediction algorithms developed on older ActiGraph models may require recalibration for the GT1M.
- Careful consideration of generational differences is necessary when analyzing physical activity data from multiple ActiGraph models.
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