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Updated: Mar 16, 2026

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption
Published on: June 20, 2025
An Activity Index for Raw Accelerometry Data and Its Comparison with Other Activity Metrics
Jiawei Bai1, Chongzhi Di2, Luo Xiao3
1Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, United States of America.
A new Physical Activity Index (AI) summarizes accelerometer data better than traditional Activity Counts (AC) and ENMO. AI accurately measures sedentary to vigorous physical activity, especially in older adults.
Area of Science:
- Biomedical Engineering
- Public Health
- Gerontology
Background:
- Accelerometers are common in public health studies, collecting high-resolution data.
- Current summarized metrics like Activity Count (AC) lack transparent formulas and vary by device.
- This limits accurate interpretation and comparability of physical activity data.
Purpose of the Study:
- To introduce a novel metric, the Physical Activity Index (AI), for summarizing raw tri-axial accelerometry data.
- To compare the AI with existing metrics (AC and ENMO) against energy expenditure.
- To evaluate the sensitivity of AI, AC, and ENMO across different physical activity intensities.
Main Methods:
- Data from 194 women (60-91 years) performing 9 lifestyle activities were used.
- Tri-axial accelerometers (ActiGraph GT3X+) and portable calorimeters (Oxycon) were employed.
- Raw accelerometry data were converted into AI, AC, and ENMO; oxygen uptake was measured in METs.
Main Results:
- AI and ENMO showed higher sensitivity for moderate to vigorous activities than AC.
- AI demonstrated superior sensitivity for sedentary and light activities compared to ENMO.
- AI achieved the highest coefficient of determination for METs (0.72) and outperformed AC and ENMO in classifying physical activity intensity.
Conclusions:
- The proposed Physical Activity Index (AI) offers a transparent and effective method for summarizing accelerometry data.
- AI serves as a valuable alternative to AC, particularly for studies involving older adults.
- AI's enhanced sensitivity in detecting sedentary and light activities provides a significant advantage in geriatric research.
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