LAUSR

The proliferation of approaches for analyzing accelerometer data using raw acceleration or novel analytic approaches like machine learning (‘novel methods’) outpaces their implementation in practice. This may be due to lack of accessibility, either because authors do not provide their developed models or because these models are difficult to find when included as supplementary material. Additionally, when access to a model is provided, authors may not include example data or instructions on how to use the model. This further hinders use by other researchers, particularly those who are not experts in statistics or writing computer code. Objective: We created a repository of novel methods of analyzing accelerometer data for the estimation of energy expenditure and/or physical activity intensity and a framework and reporting guidelines to guide future work. Approach: Methods were identified from a recent scoping review. Available code, models, sample data, and instructions were compiled or created. Main Results: Sixty-three methods are hosted in the repository, in preschoolers (n = 6), children/adolescents (n = 20), and adults (n = 42), using hip (n = 45), wrist (n = 25), thigh (n = 4), chest (n = 4), ankle (n = 6), other (n = 4), or a combination of monitor wear locations (n = 9). Fifteen models are implemented in R, while 48 are provided as cut-points, equations, or decision trees. Significance: The developed tools should facilitate the use and development of novel methods for analyzing accelerometer data, thus improving data harmonization and consistency across studies. Future advances may involve including models that authors did not link to the original published article or those which identify activity type.