LAUSR

PURPOSE. To compare and harmonize accelerometry-based measures of physical activity (PA) to increase the comparability, generalizability, and translation of findings from studies using objective measures of PA. METHODS. High resolution accelerometry data were collected from 655 participants in the Baltimore Longitudinal Study on Aging who wore an ActiGraph GT9X device at wrist continuously for a week. Data were summarized at the minute-level as activity counts (AC; measure obtained from ActiGraph's ActiLife software) and MIMS, ENMO, MAD, and AI (open-source measures implemented in R). The correlation between AC and other measures was quantified both marginally and conditionally on age, sex and BMI. Next, each pair of measures were harmonized using nonparametric regression of minute-level measurements. A freely available SummarizedActigraphy R package with a unified interface for computation of the open-source measures from raw accelerometry data was developed. RESULTS. The study sample had the following characteristics: mean (sd) age of 69.8 (14.2), BMI of 27.3 (5.0) kg/m^2, 54.5% females, and 67.9% white. The marginal participant-specific correlation between AC and MIMS, ENMO, MAD, and AI were 0.988, 0.867, 0.913 and 0.970, respectively. After harmonization, the mean absolute percentage error for predicting TAC from MIMS, ENMO, MAD, and AI was 2.5, 14.3, 11.3 and 6.3, respectively. The accuracy for predicting sedentary minutes based on AC (AC > 1853) using MIMS, ENMO, MAD and AI was 0.981, 0.928, 0.904, and 0.960, respectively. CONCLUSION. Our comparison of accelerometry-based measures of PA enables us to extend the knowledge from the thousands of manuscripts that have been published using ActiGraph activity counts to MIMS and other metrics by demonstrating their high correlation and comparability.