Accessibility and use of novel methods for predicting physical activity and energy expenditure using accelerometry: a scoping review.

Use of raw acceleration data and/or 'novel' analytic approaches like machine learning for physical activity measurement will not be widely implemented if methods are not accessible to researchers.: This scoping review characterizes the validation approach, accessibility and use of novel analytic techniques for classifying energy expenditure and/or physical activity intensity using raw or count-based accelerometer data.: Three databases were searched for articles published between January 2000 and February 2021.

Methods for estimating physical activity and energy expenditure using raw accelerometry data or novel analytical approaches: a repository, framework, and reporting guidelines.

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.