DigiWELL
Published October 9, 2025 | Version v1
Journal article Open

Predicting recovery after stressors using step count data derived from activity monitors

Authors/Creators

  • 1. Institute of Psychology, University of Bern, Bern, Switzerland
  • 2. University of Basel, Basel, Switzerland
  • 3. ISGlobal, Barcelona, Spain
  • 4. CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
  • 5. Universitat Pompeu Fabra (UPF), Barcelona, Spain
  • 6. University of Ostrava, Ostrava, Czechia
  • 7. Family Health Centers of San Diego, San Diego, CA, USA
  • 8. UC San Diego, San Diego, CA, USA
  • 9. Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
  • 10. Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail), Rennes, France

Description

Abstract

This study examines the stressor-response process in physical activity among 226 participants across four countries. We analyzed their step count collected via activity monitors before and after a significant stressor: the COVID-19 lockdown. Results showed that a ‘local dynamic complexity’ metric significantly predicts the rate of recovery to pre-COVID levels of physical activity. These findings provide new opportunities for just-in-time interventions to support physical activity recovery after disruptive stressors.

Data availability

The data used in the analysis are available at https://osf.io/gsmhk/.

Code availability

The R scripts used for the analysis are available at https://osf.io/gsmhk/.

Files

Baretta_npjDigitalMedicine_2025.pdf

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Additional details

Funding

European Union
Research of Excellence on Digital Technologies and Wellbeing CZ. 02.01.01/00/22_008/0004583

Software

Repository URL
https://osf.io/gsmhk/
Programming language
R