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. Objective: 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. Approach: Three databases were searched for articles published between January 2000 and February 2021. Use of each method was coded from a list of citing articles compiled from Google Scholar. Authors’ provision of access to the model (e.g., by request, sample code) was recorded. Main Results: Studies (N=168) included adults (n=143), and/or children (n=38). Model use ranged from 0 to 27 uses/year (average 0.83) with 101 models that have never been used. Approximately half of uses occurred in a free-living setting (52%) and/or by other authors (56%). Over half of included articles (n=107) did not provide complete access to their model. Sixty-one articles provided access to their method by including equations, coefficients, cut-points, or decision trees in the paper (n=48) and/or by providing access to code (n=13). Significance: The proliferation of approaches for analyzing accelerometer data outpaces the use of these models in practice. As less than half of the developed models are made accessible, it is unsurprising that so many models are not used by other researchers. We encourage researchers to make their models available and accessible for better harmonization of methods and improved capabilities for device-based physical activity measurement.

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.

Given the multifaceted nature of physical activity behavior in children and adolescents, researchers have conducted myriad intervention studies designed to increase physical activity across many populations, study designs, contexts, and settings. This narrative review overviews the characteristics, conclusions, and research gaps/future directions indicated in prior reviews of interventions to promote physical activity in youth and identifies potential knowledge gaps. Seven databases were searched for articles published between January 2012 and September 2022. A predetermined list of characteristics of included reviews was extracted. Reviews (n = 68) concluded that interventions were generally effective. Little attention was paid to implementation, theoretical framework was only addressed in about half of reviews, and only a quarter specifically examined individuals from underrepresented groups. Family, community, and policy work are needed, and overarching reviews such as this study should occasionally occur given the high number of reviews focusing on specific populations or settings.

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