Login / Signup

Improving the criterion validity of the activPAL in determining physical activity intensity during laboratory and free-living conditions.

Yanlin WuJarrett A JohnsJustine PoitrasDerek S KimmerlyMyles William O'Brien
Published in: Journal of sports sciences (2020)
The activPAL is a valid measure of step counts and posture, but its ability to determine physical activity intensity is unclear. This study tested the criterion validity of the activPAL using its built-in linear cadence-metabolic equivalents (METs) equation (activPAL-linear) versus an individualized height-adjusted curvilinear cadence-METs equation (activPAL-curvilinear) to estimate intensity-related physical activity. Forty adults (25±6 years, 23.3±4.1 kg/m2) wore an activPAL during a 7-stage progressive treadmill walking protocol (criterion: indirect calorimetry). A sub-sample (n=32) wore the device during free-living conditions for 7-days (criterion: PiezoRxD monitor). In the laboratory, the activPAL-linear overestimated METs during slow walking (1.5-3.0 miles•hour-1) but underestimated METs during fast walking (3.5-4.5 miles•hour-1) (all, p<0.001). In the free-living condition, the activPAL-linear overestimated time in light-intensity activity and underestimated moderate-intensity activity (both, p<0.001), but did not register any vigorous-intensity activity. In contrast, the activPAL-curvilinear estimated values statistically equivalent to indirect calorimetry for treadmill stages 1-6 (1.5-4.0 miles•hour-1) and to the PiezoRxD determined light- and moderate-intensity activity during free-living. We present a simple, data processing technique that uses an alternative curvilinear cadence-MET equation that improves the ability of the activPAL to measure intensity-related physical activity in both laboratory and free-living settings.
Keyphrases
  • physical activity
  • high intensity
  • body mass index
  • blood pressure
  • multiple sclerosis
  • magnetic resonance
  • magnetic resonance imaging
  • depressive symptoms
  • sleep quality
  • computed tomography
  • deep learning
  • big data