Methods for Classifying Physical Activities Using Accelerometer Data: A Scoping Review

Methods for Classifying Physical Activities Using Accelerometer Data: A Scoping Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Methods for Classifying Physical Activities Using Accelerometer Data: A Scoping Review Kiyan Sadeghi Janbahan, Osvaldo Espin Garcia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8007639/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Accurate classification of physical activity from accelerometer data is critical for health research and large-scale population studies. However, the wide variety of computational methods used to derive activity types and intensity levels has led to inconsistencies in implementation, validation, and reproducibility. This scoping review aimed to identify and categorise methods used to classify physical activities using accelerometer data, with a particular focus on implementation, simplicity, validation strategies, and feasibility for application in large-scale datasets such as the All of Us Research Program. We searched PubMed, Web of Science, and SPORTDiscus for studies published between 2015 and 2025. Studies were included if they used accelerometer data to classify specific activities or general activity levels and reported a validation strategy. A total of 1,670 records were screened; 158 met the inclusion criteria. Data were extracted on study characteristics, classification methods, whether validation was performed, device use, specifications, and tool availability. Machine-learning techniques were the most frequently applied classification method (n = 81), followed by deep learning (n = 63), hybrid models (n = 23), rule-based or threshold approaches (n = 22), and unsupervised or other novel methods (n = 12). Walking (n = 97), sitting (n = 79), and standing (n = 68) were the most commonly studied activities. Most studies employed lab-based protocols and used k-fold or leave-one-subject-out validation. Only 16 studies provided public code or tools, and just a couple (n = 2) considered seasonality or population diversity. This review highlights substantial variation in activity classification methods and reporting practices. Open-source tool availability and validation in real-world conditions remain limited. There is a need for simpler, validated, and reproducible approaches, particularly for use in population-scale datasets like All of Us and the UK Biobank. Biological sciences/Computational biology and bioinformatics Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Supplementary Files PublicCodeStudiesTable.docx PRISMAFilled1.pdf SearchStringsAppendix1.docx KSJDataExtractionSheetSubmission1.00.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Jan, 2026 Reviews received at journal 29 Dec, 2025 Reviews received at journal 28 Dec, 2025 Reviews received at journal 26 Dec, 2025 Reviewers agreed at journal 21 Dec, 2025 Reviews received at journal 20 Dec, 2025 Reviewers agreed at journal 20 Dec, 2025 Reviews received at journal 12 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 07 Dec, 2025 Reviewers invited by journal 07 Dec, 2025 Editor assigned by journal 05 Nov, 2025 Submission checks completed at journal 04 Nov, 2025 First submitted to journal 01 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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