Development and Evaluation of Sedentary Time Cut-Points for the activPAL in Adults Using the GGIR R-Package.

Area of Science:

• Kinesiology
• Biomedical Engineering
• Public Health

Background:

• Wearable accelerometers like activPAL are crucial for objective physical activity assessment.
• Accurate identification of sedentary behavior is essential for understanding health risks.
• Existing sedentary cut-points may require refinement for optimal performance.

Purpose of the Study:

• To develop and validate novel sedentary behavior cut-points for the activPAL device.
• To compare the performance of different accelerometer-derived variables (ENMO, MAD) against a criterion measure.
• To improve the accuracy of classifying sedentary time during free-living conditions.

Main Methods:

• Laboratory-based validation using controlled sedentary and non-sedentary activities.
• Free-living validation with participants wearing activPAL devices.
• Receiver Operator Characteristic (ROC) curve analysis to determine optimal cut-points.
• Statistical comparison using Mean Percent Error (MPE), Mean Absolute Percent Error (MAPE), and Limits of Agreement (LoA).

Main Results:

• Initial ROC analysis suggested optimal cut-points of 26.4 mg for ENMO and 30.1 mg for MAD.
• In free-living, the MAD cut-point demonstrated low error (MPE: 2.2%, MAPE: 6.5%) compared to the criterion.
• A refined ENMO cut-point (ENMOs) after excluding standing achieved improved accuracy (MPE: 3.1%, MAPE: 7.5%).
• Both MAD and ENMOs showed superior discrimination between sedentary and non-sedentary activities compared to the original ENMO.

Conclusions:

• The Mean Amplitude Deviation (MAD) cut-point offers a reliable method for identifying sedentary behavior with the activPAL device.
• A refined Euclidean Norm Minus One (ENMOs) cut-point, excluding standing, also provides accurate sedentary behavior classification.
• These validated cut-points enhance the precision of physical activity monitoring and sedentary behavior research.