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Related Concept Videos

Introduction to R01:11

Introduction to R

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R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
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Related Experiment Video

Updated: Jul 17, 2025

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
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Resilience characterized and quantified from physical activity data: A tutorial in R.

Dario Baretta1, Sarah Koch2, Inés Cobo2

  • 1Institute of Psychology, University of Bern, Fabrikstrasse 8, 3012, Bern, Switzerland.

Psychology of Sport and Exercise
|September 4, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to quantify resilience in physical activity, measuring how well individuals recover from stressors like the COVID-19 lockdown. The approach helps assess and manage the impact of disruptions on consistent exercise habits.

Keywords:
AUCPhysical activityR tutorialResilienceTime seriesWearable devices

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Area of Science:

  • Behavioral Science
  • Biomedical Engineering
  • Sports Science

Background:

  • Consistent physical activity is crucial for health but susceptible to disruptions.
  • Resilience, the ability to recover from stressors and return to baseline activity, is vital for maintaining physical activity.
  • Quantifying resilience is essential for understanding and managing the impact of stressors on physical activity.

Purpose of the Study:

  • To present a novel method for quantifying resilience in physical activity time series data.
  • To adapt existing resilience operationalizations (area under the curve) for physical activity.
  • To provide a methodological guide and R implementation for researchers.

Main Methods:

  • Leveraged the 'area under the curve' concept from psychological resilience research.
  • Applied growth models to physical activity time series (step count) to identify recovery points.
  • Utilized Root Mean Squared Error (RMSE) for model selection and quantified resilience based on recovery time and magnitude.

Main Results:

  • Quantified resilience in step count data for eight participants post-COVID-19 lockdown.
  • Demonstrated a method to identify recovery time and calculate resilience as area under the curve.
  • Extracted additional resilience features to capture diverse aspects of the recovery process.

Conclusions:

  • The developed methodology provides a quantitative measure of resilience in physical activity.
  • This approach can help researchers better understand and track recovery from stressors impacting physical activity.
  • Facilitates further research into resilience for physical activity and its implications for health and well-being.