Abstract:
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The use of open-source accelerometry-based PA measures allows for reproducible and generalizable data collection, processing and analysis. However, their comparability with widely used proprietary measures such as ActiGraph activity counts (AC) is unknown.
We used subsecond-level accelerometry data collected from 655 participants in the Baltimore Longitudinal Study on Aging who wore ActiGraph GT9X Link device at wrist continuously for a week. Data were summarized at the minute level as ActiGraph AC and open-source MIMS, ENMO, MAD, and AI. Each pair of measurements were harmonized using nonparametric regression.
The marginal participant-specific correlation between AC and MIMS, ENMO, MAD, and AI were 0.988, 0.867, 0.913 and 0.970, respectively; significant yet small size effects of age, BMI, and sex on the correlations were observed. After harmonization, the mean absolute percentage error for predicting total activity counts from MIMS, ENMO, MAD, and AI was 2.5, 14.3, 11.3 and 6.3, respectively.
This measurement harmonization can be used to compare results from different studies and improve the translation and generalizability of accelerometry studies.
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