Methods for classifying physical activities using accelerometer data: a scoping review

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.