Effect of Raw Acceleration Filtering Methods on the Relationship Between Accelerometer Outputs and Energy Expenditure

Abstract

Despite the ubiquity of accelerometers in physical activity research, there remains no consensus method for processing raw acceleration data into end-user appropriate metrics. One aspect of accelerometer data processing that has received limited attention is the manner in which raw acceleration data are filtered to remove non-physical noise prior to computing summary outputs. PURPOSE: To evaluate how raw acceleration filtering methods affect the relationship between accelerometer outputs and measured energy expenditure in children and adolescents. METHODS: 122 children and adolescents (61 boys, 13.0 ± 4.3 years, 22.4 ± 6.2 kg/m2) wore an ActiGraph GT3X+ triaxial accelerometer (80 Hz) on their non-dominant wrist while performing jumping jacks (JJ), dribbling a basketball (DB), and ambulating on a treadmill at 2.4 (T1), 4.8 (T2), and 7.2 km/h (T3). Raw data for each axis were filtered nine times using 4th order Butterworth bandpass filters defined by all combinations of three lower (0.15, 0.25, and 0.35 Hz) and upper (2.5, 4.5, and 8.5 Hz) cutoff frequencies. Average signal vector magnitude (SVM) was calculated each second for all nine filters and the average 1 s value for the last 2 min of each 5 min activity trial was retained. Oxygen uptake (VO2) was measured by the COSMED K4b2 system concurrent with accelerometry measures. METs were derived by dividing mass relative VO2 by resting energy expenditure (Schofield equation estimate). Pearson correlations were used to quantify associations between SVM and METs for each activity/filter combination. Meng’s Z-test was used to evaluate differences between dependent correlations within each activity. RESULTS: Correlations between SVM and METs across the evaluated filters for JJ, DB, T1, T2, and T3 ranged from 0.43–0.55, 0.10–0.25, 0.11–0.15, 0.19–0.32, and 0.11–0.22, respectively. Significant between-filter differences (p < 0.05) in the magnitude of association between SVM and METs were noted for JJ (n = 20), DB (n = 20), T2 (n = 9), and T3 (n = 8). CONCLUSION: Filter choice can significantly influence the relationship between an accelerometer’s output and measured energy expenditure. Further study is needed to identify filtering methods which produce accelerometer outputs that best reflect the energy cost of human movement. Support: NIH NICHD 1R21HD073807-01A1