Related Experiment Video
Updated: Oct 22, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
Published on: February 25, 2013
Predicting physical distancing over time during COVID-19: testing an integrated model
Martin S Hagger1,2,3, Stephanie R Smith3, Jacob J Keech3,4
1Social and Health Psychology Behavioral Research for Prevention and Promotion (SHARPP) Lab, Department of Psychological Sciences and Health Sciences Research Institute, University of California Merced, Merced, California, USA.
Abstract:
We applied an integrated social cognition model to predict physical distancing behavior, a key COVID-19 preventive behavior, over a four-month period. A three-wave longitudinal survey design. Australian and US residents (N = 601) completed self-report measures of social cognition constructs (attitude, subjective norm, moral norm, perceived behavioral control [PBC]), intention, habit, and physical distancing behavior on an initial occasion (T1) and on two further occasions one week (T2) and four months (T3) later. A structural equation model revealed that subjective norm, moral norm, and PBC, were consistent predictors of physical distancing intention on all three occasions. Intention and habit at T1 and T2 predicted physical distancing behavior at T2 and T3, respectively. Intention at T2 mediated effects of subjective norm, moral norm, and PBC at T2 on physical distancing behavior at T3, and habit at T1 and T2 mediated effects of behavior at T1 and T2 on follow-up behavior at T2 and T3, respectively. Normative (subjective and moral norms) and capacity (PBC) constructs were consistent predictors of physical distancing intention, and intention and habit were consistent predictors of physical distancing behavior. Interventions promoting physical distancing should target change in normative and personal capacity beliefs, and habit.Supplemental data for this article is available online at https://doi.org/10.1080/08870446.2021.1968397 .
Related Concept Videos
Steps in Outbreak Investigation
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models
Model Approaches for Pharmacokinetic Data: Physiological Models
Mechanistic Models: Compartment Models in Individual and Population Analysis

