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Understanding health inequalities is essential for improving social justice. Intersectionality refers to a theoretical framework for studying the intersection of multiple social categorizations that create unique experiences and related social inequalities. Currently, the majority of the intersectional studies in the physical activity field have a qualitative design; thus, there is a need for quantitative intersectional studies. This commentary aims to explore primary obstacles impeding intersectional quantitative research and provide recommendations for overcoming these obstacles in physical activity research. In the commentary, we discuss that the lack of accessibility of large-scale and diverse data sets, and suboptimal social categorizations and intersectionality-related questions may contribute to the scarcity of intersectional quantitative research in the field. To facilitate intersectional quantitative analyses, we advocate for making large-scale data sets accessible for intersectional secondary analyses, diverse sampling, standardizing questions and categories related to intersectionality, promoting inclusive research designs and methods, and using the appropriate questions and social categorization that reflect the distinct experiences of each subgroup. By addressing these challenges, researchers may gain new insights into health disparities, making physical activity research more inclusive and contributing to more equitable health outcomes.

Objective: Previous studies that looked at comparability of accelerometer data focused on epoch or recording level comparability. Our study aims to provide insight into the comparability at raw data level. Methods: We performed five experiments with accelerometers attached to a mechanical shaker machine applying movement along a single axis in the horizontal plane. In each experiment, a 1-min no-movement condition was followed by nineteen 2-min shaker frequency conditions (30–250 rpm). We analyzed accelerometer data from Axivity, ActiGraph, GENEActiv, MOX, and activPAL devices. Comparability between commonly used brands and dynamic ranges was assessed in the frequency domain with power spectra and in the time domain with maximum lagged cross-correlation analyses. The influence of sampling rate on magnitude of acceleration across brands was explored visually. All data were published open access. Results: Magnitude of noise in rest was highest in MOX and lowest in ActiGraph. The signal mean power spectral density was equal between brands at low shaker frequency conditions (<3.13 Hz) and between dynamic ranges within the Axivity brand at all shaker frequency conditions. In contrast, the cross-correlation coefficients between time series across brands and dynamic ranges were higher at higher shaking frequencies. Sampling rate affected the magnitude of acceleration most in Axivity and least in GENEActiv. Conclusions: The comparability of raw acceleration signals between brands and/or sampling rates depends on the type of movement. These findings aid a more fundamental understanding and anticipation of differences in behavior estimates between different implementations of raw accelerometry.