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Updated: Jan 25, 2026
Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption
Published on: June 20, 2025
A closer look at the relationship among accelerometer-based physical activity metrics: ICAD pooled data
Soyang Kwon1, Lars Bo Andersen2, Anders Grøntved3
1Ann & Robert H. Lurie Children's Hospital of Chicago Stanley Manne Children's Research Institute, 225 E Chicago Ave, Box 157, Chicago, IL, 60611, USA. skwon@luriechildrens.org.
Insights
Understanding child physical activity (PA) metrics is crucial. Accelerometer data reveals moderate to high correlations between different PA measures, highlighting the need to address collinearity in statistical models.
Area of Science:
- Pediatrics
- Exercise Physiology
- Biomedical Data Science
Background:
- Accelerometers are standard tools for measuring child physical activity (PA).
- Analyzing accelerometer data yields various PA metrics.
- Understanding relationships between these metrics enhances PA pattern comprehension and cross-study comparability.
Purpose of the Study:
- To investigate the interrelationships among diverse accelerometer-derived PA metrics in children.
- To inform the selection and interpretation of PA metrics in research and health studies.
Main Methods:
- Utilized data from 24,316 children (aged 5-18 years) from the International Children's Accelerometer Database (ICAD) 2.0.
- Calculated correlation coefficients between wear time, sedentary behavior (SB), light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), moderate- and vigorous-intensity PA (MVPA), and total activity counts (TAC).
Main Results:
- Total activity counts (TAC) increased with longer wear time, but moderate- and vigorous-intensity PA (MVPA) remained consistent across wear durations (≥8 h/day).
- MVPA showed a very high correlation with TAC (r=0.91).
- Wear time-adjusted correlations revealed a very high inverse relationship between sedentary behavior (SB) and light-intensity PA (LPA) (r=-0.96), and a moderate correlation between VPA and MPA (r=0.58).
Conclusions:
- Total activity counts (TAC) are influenced by both MVPA and wear time.
- MVPA is a reliable metric across different wear durations (≥8 h/day), facilitating cross-study comparisons.
- The significant correlations among PA metrics necessitate addressing potential collinearity in statistical models to avoid bias in health effect analyses.
Background:
Accelerometers are widely used to assess child physical activity (PA) levels. Using the accelerometer data, several PA metrics can be estimated. Knowledge about the relationships between these different metrics can improve our understanding of children's PA behavioral patterns. It also has significant implications for comparing PA metrics across studies and fitting a statistical model to examine their health effects. The aim of this study was to examine the relationships among the metrics derived from accelerometers in children.
Methods:
Accelerometer data from 24,316 children aged 5 to 18 years were extracted from the International Children's Accelerometer Database (ICAD) 2.0. Correlation coefficients between wear time, sedentary behavior (SB), light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), moderate- and vigorous-intensity PA (MVPA), and total activity counts (TAC) were calculated.
Results:
TAC was approximately 22X103 counts higher (p < 0.01) with longer wear time (13 to 18 h/day) as compared to shorter wear time (8 to < 13 h/day), while MVPA was similar across the wear time categories. MVPA was very highly correlated with TAC (r = .91; 99% CI = .91 to .91). Wear time-adjusted correlation between SB and LPA was also very high (r = -.96; 99% CI = -.96, - 95). VPA was moderately correlated with MPA (r = .58; 99% CI = .57, .59).
Conclusions:
TAC is mostly explained by MVPA, while it could be more dependent on wear time, compared to MVPA. MVPA appears to be comparable across different wear durations and studies when wear time is ≥8 h/day. Due to the moderate to high correlation between some PA metrics, potential collinearity should be addressed when including multiple PA metrics together in statistical modeling.
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