Physical Activity and Life Satisfaction Data from the 4HAIE Study for Modelling Nonlinear Dynamics
Year: 2026
Type: dataset
Abstract: This repository contains the dataset and accompanying code used in the manuscript: "Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data." The dataset originates from the 4HAIE (Healthy Aging in Industrial Environment - Program 4) study, a 12-month intensive longitudinal study designed to examine within-person dynamics of health behaviors and well-being in everyday life (see corresponding protocol paper Elavsky et al., 2021). Dataset Contents The repository includes: Merged analysis dataset linking daily physical activity with corresponding end-of-day life satisfaction assessments. Daily physical activity data collected continuously over approximately 12 months using Fitbit Charge 3 and Fitbit Charge 4 devices. Physical activity is represented by daily step counts. Ecological Momentary Assessment (EMA) data collected during four 14-day measurement bursts distributed across the 12-month study period. Participants completed an end-of-day survey assessing life satisfaction using two items adapted from the Satisfaction with Life Scale (SWLS). Illustrative analysis code demonstrating the generalized additive modeling (GAM) approach used to investigate nonlinear relationships between physical activity and life satisfaction over time. Study Design Participants were monitored continuously for physical activity throughout the study while completing repeated intensive EMA assessments during four separate two-week measurement bursts. This design enabled examination of both long-term behavioral patterns and short-term fluctuations in subjective well-being within individuals. Variables The repository includes variables related to: Participant identifier (de-identified) Date of observation Daily step count (Fitbit) End-of-day life satisfaction ratings (two EMA items) Additional temporal variables required for longitudinal modeling Variable definitions and coding information are provided in the accompanying documentation. Purpose The dataset is intended to illustrate statistical approaches for analyzing intensive longitudinal health behavior data, with particular emphasis on nonlinear modeling of within-person associations using generalized additive models (GAMs). Rather than serving solely as an empirical example, the dataset demonstrates a methodological framework for capturing complex temporal relationships that may not be adequately represented using traditional linear approaches. Associated Publication This dataset accompanies the manuscript: Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data. If you use this dataset or the accompanying analysis code, please cite both this Zenodo repository and the associated publication. References: ELavsky, S., Brabec, M., Maly, M. (in review). Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data. Elavsky S, Jandačková V, Knapová L, Vašendová V, Sebera M, Kaštovská B, Blaschová D, Kühnová J, Cimler R, Vilímek D, Bosek T, Koenig J, Jandačka D. 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). BMC Public Health. 2021 Jan 12;21(1):126. doi: 10.1186/s12889-021-10166-4. PMID: 33435943; PMCID: PMC7801866. Elavsky S, Brabec M, Maly M, Knapova L, Kastovska B, Sebera M, Ely M, Jandackova VK, Keller J, Pavel M. The temporal dynamics of the association between daily physical activity and life satisfaction. Ann Behav Med. 2025 Jan 4;59(1):kaaf079. doi: 10.1093/abm/kaaf079. PMID: 41224246; PMCID: PMC12757008.
Authors: Brabec, Marek, Malý, Marek, Steriani, Elavsky
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7167023782