Evaluation of Wrist Accelerometer Cut-Points for Classifying Physical Activity Intensity in Youth

Abstract

Background: Wrist worn accelerometers are convenient to wear and provide greater compliance. However, methods to transform the resultant output into predictions of physical activity (PA) intensity have been slow to evolve, with most investigators continuing the practice of applying intensity-based thresholds or cut-points. The current study evaluated the classification accuracy of seven sets of previously published youth-specific cut-points for wrist worn ActiGraph accelerometer data.

Methods: Eighteen children and adolescents [mean age (± SD) 14.6 ± 2.4 years, 10 boys, 8 girls] completed 12 standardized activity trials. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the wrist and energy expenditure (Youth METs) was measured directly using the Oxycon Mobile portable calorimetry system. Seven previously published sets of ActiGraph cut-points were evaluated: Crouter regression vertical axis, Crouter regression vector magnitude, Crouter ROC curve vertical axis, Crouter ROC curve vector magnitude, Chandler ROC curve vertical axis, Chandler ROC curve vector magnitude, and Hildebrand ENMO. Classification accuracy was evaluated via weighted Kappa. Confusion matrices were generated to summarize classification accuracy and identify patterns of misclassification.

Results: The cut-points exhibited only moderate agreement with directly measured PA intensity, with Kappa ranging from 0.45 to 0.58. Although the cut-points classified sedentary behavior accurately (> 95%), classification accuracy for the light (3-51%), moderate (12-45%), and vigorous-intensity trials (30-88%) was generally poor. All cut-points underestimated the true intensity of the walking trials, with error rates ranging from 35 to 100%, while the intensity of activity trials requiring significant upper body and/or arm movements was consistently overestimated. The Hildebrand cut-points which serve as the default option in the popular GGIR software package misclassified 30% of the light intensity trials as sedentary and underestimated the intensity of moderate and vigorous intensity trials 75% of the time.

Conclusion: Published ActiGraph cut-points for the wrist, developed specifically for school-aged youth, do not provide acceptable classification accuracy for estimating daily time spent in light, moderate, and vigorous intensity physical activity. The development and deployment of more robust accelerometer data reduction methods such as functional data analysis and machine learning approaches continues to be a research priority.

Keywords: GGIR; adolescents; children; device based monitoring; energy expenditure (EE); placement; threshold methods; wearable sensors.

Figure 1

Kappa statistics for the seven youth-specific ActiGraph cut-points for the wrist.

Figure 2

Heat map confusion matrices for the youth specific ActiGraph cut-points for the wrist. Diagonal = correct predictions; Columns = predictions; Rows = measured by portable calorimetry.

Figure 3

Stacked column bar graphs summarizing the extent to which each cut-point overestimated, correctly classified, or underestimated the physical activity intensity category of each structured activity. (LD = lying down, HW = handwriting, CG = computer game, LN = laundry task, TC = throw and catch, SW = sweeping, CW = comfortable walk, DA = dance, FW = fast walk, TW = treadmill walk, BB = basketball, RU = run).