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Resilience characterized and quantified from physical activity data: A tutorial in R

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dc.contributor.author Baretta, Dario
dc.contributor.author Koch, Sarah
dc.contributor.author Cobo, Inés
dc.contributor.author Castaño Vinyals, Gemma
dc.contributor.author Cid Ibeas, Rafael de
dc.contributor.author Carreras, Anna
dc.contributor.author Buekers, Joren
dc.contributor.author García Aymerich, Judith
dc.contributor.author Inauen, Jennifer
dc.contributor.author Chevance, Guillaume
dc.date.accessioned 2023-01-13T06:56:41Z
dc.date.available 2023-01-13T06:56:41Z
dc.date.issued 2023
dc.identifier.citation Baretta D, Koch S, Cobo I, Castaño-Vinyals G, de Cid R, Carreras A, Buekers J, Garcia-Aymerich J, Inauen J, Chevance G. Resilience characterized and quantified from physical activity data: A tutorial in R. Psychology of Sport and Exercise. 2023;65:102361. DOI: 10.1016/j.psychsport.2022.102361
dc.identifier.issn 1469-0292
dc.identifier.uri http://hdl.handle.net/10230/55263
dc.description.abstract Consistent physical activity is key for health and well-being, but it is vulnerable to stressors. The process of recovering from such stressors and bouncing back to the previous state of physical activity can be referred to as resilience. Quantifying resilience is fundamental to assess and manage the impact of stressors on consistent physical activity. In this tutorial, we present a method to quantify the resilience process from physical activity data. We leverage the prior operationalization of resilience, as used in various psychological domains, as area under the curve and expand it to suit the characteristics of physical activity time series. As use case to illustrate the methodology, we quantified resilience in step count time series (length = 366 observations) for eight participants following the first COVID-19 lockdown as a stressor. Steps were assessed daily using wrist-worn devices. The methodology is implemented in R and all coding details are included. For each person’s time series, we fitted multiple growth models and identified the best one using the Root Mean Squared Error (RMSE). Then, we used the predicted values from the selected model to identify the point in time when the participant recovered from the stressor and quantified the resulting area under the curve as a measure of resilience for step count. Further resilience features were extracted to capture the different aspects of the process. By developing a methodological guide with a step-by-step implementation, we aimed at fostering increased awareness about the concept of resilience for physical activity and facilitate the implementation of related research.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof Psychology of Sport and Exercise. 2023;65:102361
dc.rights © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Resilience characterized and quantified from physical activity data: A tutorial in R
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1016/j.psychsport.2022.102361
dc.subject.keyword Resilience
dc.subject.keyword Physical activity
dc.subject.keyword Time series
dc.subject.keyword R tutorial
dc.subject.keyword AUC
dc.subject.keyword Wearable devices
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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