The Impact of Quantifying Human Locomotor Activity on Examining Sleep-Wake Cycle

Actigraphy quantifies human locomotor activity by measuring wrist acceleration with wearable devices at relatively high rates and converting it into lower-temporal-resolution activity values; however, the computational implementations of this data compression differ substantially across manufacturers. Building on our previous work, where we ex-amined how dissimilarly the various activity determination methods we generalized can quantify the same movements through correlation analysis, we investigated here how these methods (e.g., digital filtering, data compression) influence nonparametric circadian rhythm analysis and sleep–wake scoring. In addition to our generalized actigraphic framework, we also emulated the use of specific devices commonly employed in such sleep-related studies by applying their methods to raw actigraphic acceleration data we collected to demonstrate, through concrete real-life examples, how methodological choices may shape analytical outcomes. Additionally, we assessed whether nonparametric indi-cators could be derived directly from acceleration data without compressing them into ac-tivity values. Overall, our analysis revealed that all these analytical approaches of the sleep-wake cycle can be substantially affected by the manufacturer dependent actigraphic methodology, with the observed effects traceable to distinct steps of the signal processing pipeline, underscoring the necessity of cross manufacturer harmonization from a clini-cally oriented perspective.