Cleaning and pre-processing of actigraphy data for physical activity and sleep research: a scoping review

<i>Objective</i>. Numerous studies examine the link between health and sleep-wake patterns to understand etiology, establish preventive algorithms, or develop therapeutics. The use of actigraphy to measure physical activity (PA) and sleep is increasing, partly because of its non-invasive nature and its ability to continuously monitor PA and sleep in free-living settings. There are several actigraphy data cleaning and pre-processing methods, but there is no consensus on how to define PA metrics or standardized cleaning procedures to enable comparison across research studies. This scoping review examined existing literature on cleaning and pre-processing of actigraphy data.<i>Approach.</i>The PubMed (US National Library of Medicine), Scopus (Elsevier), and Web of Science: Core Collection (Clarivate Analytics) databases were searched for original studies published in English from 2017-2024. Using Covidence, two reviewers independently screened each article and collected data.<i>Results.</i>A total of 102 studies were included for the final analysis. Our results showed substantial heterogeneity in actigraphy devices, data cleaning and pre-processing methods, with some studies using their own algorithmic approaches to generate PA and sleep variables. While some studies used well-established algorithms like Freedson or Cole-Kripke, a large proportion either developed custom methods or did not report sufficient detail to allow replication. This variability highlights the urgent need for standardized reporting and consensus-based protocols in actigraphy data cleaning and pre-processing to allow replication and comparison of findings across studies.<i>Significance.</i>This scoping review is the first to differentiate, in a standardized way, between<i>cleaning</i>and<i>pre-processing</i>practices in actigraphy research and to quantify reporting practices across multiple device types and data processing strategies. Our findings show a critical gap in standardized reporting and offer actionable guidance for both high- and low-resource research settings.