agcounts: An R Package to Calculate ActiGraph Activity Counts From Portable Accelerometers

Abstract

Portable accelerometers are used to capture physical activity in free-living individuals with the ActiGraph being one of the most widely used device brands in physical activity and health research. Recently, in February 2022, ActiGraph published their activity count algorithm and released a Python package for generating activity counts from raw acceleration data for five generations of ActiGraph devices. The nonproprietary derivation of the ActiGraph count improved the transparency and interpretation of accelerometer device-measured physical activity, but the Python release of the count algorithm does not integrate with packages developed by the physical activity research community using the R Statistical Programming Language. In this technical note, we describe our efforts to create an R-based translation of ActiGraph's Python package with additional extensions to make data processing easier and faster for end users. We call the resulting R package agcounts and provide an inside look at its key functionalities and extensions while discussing its prospective impacts on collaborative open-source software development in physical behavior research. We recommend that device manufacturers follow ActiGraph's lead by providing open-source access to their data processing algorithms and encourage physical activity researchers to contribute to the further development and refinement of agcounts and other open-source software.

Keywords: measurement; open-source; physical activity; wearable devices.

Figure 1 —

Average time elapsed running the calibrated reader functions. Microbenchmark was used to run 100 iterations on a 42-MB GT3X file.

Figure 2 —

The raw acceleration data module of the agcounts Shiny app. ENMO = Euclidean norm minus one; MAD = mean amplitude deviation.