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

. 2025 Oct 09 ; 8 (1) : 606. [epub] 20251009

Status PubMed-not-MEDLINE Jazyk angličtina Země Anglie, Velká Británie Médium electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid41068331

Grantová podpora
R01 HL136769 NHLBI NIH HHS - United States
MSCA-IF (10.3030/840513) European Commission
FJC2021-046458-I Juan de la Cierva Formación

Odkazy

PubMed 41068331
PubMed Central PMC12511605
DOI 10.1038/s41746-025-01998-0
PII: 10.1038/s41746-025-01998-0
Knihovny.cz E-zdroje

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.

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Warburton, D. E. R. & Bredin, S. S. D. Health benefits of physical activity: a systematic review of current systematic reviews. PubMed

Scheffer, M. et al. Quantifying resilience of humans and other animals. PubMed PMC

Hill, Y., Kiefer, A. W., Oudejans, R. R. D., Baetzner, A. S. & Den Hartigh, R. J. R. Adaptation to stressors: Hormesis as a framework for human performance.

Spence, J. C. & Lee, R. E. Toward a comprehensive model of physical activity.

Den Hartigh, R. J. R. & Hill, Y. Conceptualizing and measuring psychological resilience: What can we learn from physics?.

Carver, C. S. Resilience and thriving: issues, models, and linkages.

Baretta, D. et al. Resilience characterized and quantified from physical activity data: a tutorial in R. PubMed

Nahum-Shani, I. et al. Just-in-Time Adaptive Interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. PubMed PMC

Heino, M. T. J., Proverbio, D., Marchand, G., Resnicow, K. & Hankonen, N. Attractor landscapes: a unifying conceptual model for understanding behaviour change across scales of observation. PubMed PMC

Kéfi, S., Dakos, V., Scheffer, M., Van Nes, E. H. & Rietkerk, M. Early warning signals also precede non-catastrophic transitions.

Scheffer, M. et al. Early-warning signals for critical transitions. PubMed

Helmich, M. A. et al. Early warning signals and critical transitions in psychopathology: challenges and recommendations. PubMed

Chevance, G. et al. Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior. PubMed PMC

Baretta, D., Mazéas, A., Chalabaev, A., Inauen, J. & Chevance, G. Early warning signals of sudden changes in walking behavior. 10.31219/osf.io/wpge7_v1 (2025).

Wu, Z. et al. The COVID-19 pandemic and daily steps in the general population: meta-analysis of observational studies. PubMed PMC

Paluch, A. E. et al. Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts. PubMed PMC

Lee, I.-M., Keadle, S. K. & Matthews, C. E. Fitness trackers to guide advice on activity prescription. PubMed

Schiepek, G. & Strunk, G. The identification of critical fluctuations and phase transitions in short term and coarse-grained time series—a method for the real-time monitoring of human change processes. PubMed

Evenson, K. R. & Spade, C. L. Review of Validity and Reliability of Garmin Activity Trackers. PubMed PMC

Feehan, L. M. et al. Accuracy of fitbit devices: systematic review and narrative syntheses of quantitative data. PubMed PMC

Tedesco, S. et al. Validity evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in free-living environments in an older adult cohort. PubMed PMC

Elavsky, S. et al. Physical activity in an air-polluted environment: behavioral, psychological and neuroimaging protocol for a prospective cohort study (Healthy Aging in Industrial Environment study—Program 4). PubMed PMC

Henriksen, A., Johannessen, E., Hartvigsen, G., Grimsgaard, S. & Hopstock, L. A. Dataset of consumer-based activity trackers as a tool for physical activity monitoring in epidemiological studies during the COVID-19 Pandemic. PubMed PMC

kogevinas, M. et al. Ambient air pollution in relation to SARS-CoV-2 infection, antibody response, and COVID-19 disease: a cohort study in Catalonia, Spain (COVICAT Study). PubMed PMC

Jin, D. et al. Self-tracking behaviour in physical activity: a systematic review of drivers and outcomes of fitness tracking.

Mansour-Assi, S. J. et al. Social Mobile Approaches to Reducing Weight (SMART) 2.0: protocol of a randomized controlled trial among young adults in university settings. PubMed PMC

Costello, V. L. et al. Impact of the COVID-19 pandemic on objectively measured physical activity and sedentary behavior among overweight young adults: yearlong longitudinal analysis. PubMed PMC

Vládní usnesení související s bojem proti epidemii—rok 2020. https://vlada.gov.cz/cz/epidemie-koronaviru/dulezite-informace/vladni-usneseni-souvisejici-s-bojem-proti-epidemii-koronaviru–rok-2020-186999/.

Bull, F. C. et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. PubMed PMC

Goldberg, X. et al. Mental health and COVID-19 in a general population cohort in Spain (COVICAT study). PubMed PMC

Wood, S. N.

Simpson, G. L. gratia: An R package for exploring generalized additive models.

Olthof, M. et al. Critical fluctuations as an early-warning signal for sudden gains and losses in patients receiving psychotherapy for mood disorders.

Matthews, C. E., Hagströmer, M., Pober, D. M. & Bowles, H. R. Best practices for using physical activity monitors in population-based research. PubMed PMC

Tudor-Locke, C. et al. How many days of pedometer monitoring predict weekly physical activity in adults?. PubMed

Hart, T. L., Swartz, A. M., Cashin, S. E. & Strath, S. J. How many days of monitoring predict physical activity and sedentary behaviour in older adults?. PubMed PMC

Kang, M. et al. How many days are enough? a study of 365 days of pedometer monitoring. PubMed

Turrisi, T. B. et al. Seasons, weather, and device-measured movement behaviors: a scoping review from 2006 to 2020. PubMed PMC

Clemes, S. A., Hamilton, S. L. & Griffiths, P. L. Summer to winter variability in the step counts of normal weight and overweight adults living in the UK. PubMed

Hasselman, F., Olthof, M. & Cui, J. casnet: A toolbox for studying Complex Adaptive Systems and NETworks (0.2.2), https://github.com/FredHasselman/casnet (2022).

Gómez, Maravall, V. & Estimation, A. Prediction, and Interpolation for Nonstationary Series with the Kalman Filter.

Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4.

Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: Keep it maximal. PubMed PMC

Chakrabarti, A. & Ghosh, J. K. in

Obón-Santacana, M. et al. GCAT|Genomes for life: a prospective cohort study of the genomes of Catalonia. PubMed PMC

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