Inertial measurement units (IMUs) provide benefits over the traditional optoelectronic motion capture (OMC) systems in measuring kinematics for the low back pain population. The reliability and validity of IMUs to quantify three-dimensional motion for multiple hip/spine segments have not been systematically evaluated. The purpose of this study was to determine the repeated-measures reliability and concurrent validity of an IMU system for measuring the three-dimensional spine/hip kinematics in six common movement assessments. Seventeen participants (32.3 (14.7) years; 11 female) performed two trials each of six range-of-motion assessments while fitted with four IMUs (T1/T2, T12/L1, L5/S1, and femur). The IMUs showed good–excellent reliability for most of the movements in the primary plane and poor–moderate reliability in the non-primary planes. The IMU and OMC systems showed generally good–excellent agreement in the primary plane and RMSE values between 3.03° and 15.75°. The removal of outliers based on the Bland–Altman analysis resulted in RMSE values between 2.44° and 10.30°. The system agreement in the non-primary planes was generally poor–moderate, and the RMSE values ranged from 2.19° to 45.88°. Anomalies in the proprietary sensor fusion algorithm or calibration may have contributed to the large RMSE values, highlighting the importance of assessing data for physiological relevance. The results suggest that these IMUs may be best suited for population-based studies measuring movement in the primary plane and point toward the need for the development of more robust approaches for broader implementation.

Assessing physical activity is important in the treatment of chronic conditions, including chronic low back pain (cLBP). ActiGraph™, a widely used physical activity monitor, collects raw acceleration data, and processes these data through proprietary algorithms to produce physical activity measures. The purpose of this study was to replicate ActiGraph™ algorithms in MATLAB and test the validity of this method with both healthy controls and participants with cLBP. MATLAB code was developed to replicate ActiGraph™’s activity counts and step counts algorithms, to sum the activity counts into counts per minute (CPM), and categorize each minute into activity intensity cut points. A free-living validation was performed where 24 individuals, 12 cLBP and 12 healthy, wore an ActiGraph™ GT9X on their non-dominant hip for up to seven days. The raw acceleration data were processed in both ActiLife™ (v6), ActiGraph™’s data analysis software platform, and through MATLAB (2022a). Percent errors between methods for all 24 participants, as well as separated by cLBP and healthy, were all less than 2%. ActiGraph™ algorithms were replicated and validated for both populations, based on minimal error differences between ActiLife™ and MATLAB, allowing researchers to analyze data from any accelerometer in a manner comparable to ActiLife™.