Replication Data for: "Promoting Sustainable Travel Modes Through Health and Active Lifestyle Messaging"

Data and Code Repository This repository contains the anonymized dataset and analysis code associated with the paper titled "Promoting Sustainable Travel Modes Through Health and Active Lifestyle Messaging." Contents anonymized_data.csv: Contains the raw anonymized dataset (1 MB) used in the analysis. analysis_v1.ipynb: Python scripts for analysis, and visualization. (Last Modified July 7, 2025, 5:00PM) README.md: Description of the repository contents and usage. datadictionary.md: A detailed explanation of each variable in the final dataset. 68 unique variables, 4,840 observations. Requirements Packages required to run this analysis are pandas==2.0.3, numpy==1.24.1, statsmodel.api==0.14.1. This code was tested on Python 3.8.13 and 3.9.2 and on macOS Sequoia 15.5 and Google Colab CPUs. Structure of the code The first code block loads the dataset and required packages. The second code block has helper function that generates dataframe for statistical analysis in the later blocks. The third code block has helper variables and functions to load model specifications and formatting model coefficients for analysis in the later blocks Code blocks four and above generate statistical results used in the paper. Output This code package generates the necessary derived data consisting of odds ratios and uncertainty for Figure 1 and Figure 2 in the main document: Main Document Fig. 1 Treatment effects of air quality impacts targeting bus transit and active lifestyle messaging targeting walking or biking. Main Document Fig. 2 Comparative treatment effects of health and active lifestyle messaging for all respondents and those with underlying health conditions. This code package generates the following tables: Main Document Table 1 Heterogeneous treatment effects across key subgroups. Table S4. Treatment effects of air pollution exposure messaging in Experiment 1 (personal and community benefits targeting bus transit) Table S5. Treatment effects of air quality improvement messaging in Experiment 2 (personal and community benefits targeting bus transit) Table S6. Treatment effects of air quality improvement messaging in Experiment 2 (personal gain targeting walking) Table S7. Treatment effects of air quality improvement messaging in Experiment 2 (personal and community benefits targeting walking) Table S8. Treatment effects of air quality improvement messaging in Experiment 2 (personal gain targeting biking) Table S9. Treatment effects of air quality improvement messaging in Experiment 2 (personal and community benefits targeting biking) Table S10. Treatment effects of all active lifestyle messaging in Experiment 3 (personal and community benefits targeting walking and biking) Table S11. Treatment effects of all active lifestyle messaging in Experiment 3 (personal gain targeting biking) Table S12. Treatment effects of active lifestyle messaging in Experiment 3 (personal and community benefits targeting biking) Table S13. Treatment effects of step count messaging in Experiment 3 (personal and community benefits targeting walking) Table S14. Treatment effects of calories burned messaging in Experiment 3 (personal and community benefits targeting walking) Table S15. Treatment effects of heart health messaging in Experiment 3 (personal and community benefits targeting walking and biking) Table S16. Heterogeneous treatment effects of air pollution exposure messaging in Experiment 1 (personal gain targeting bus transit) Table S17. Heterogeneous treatment effects of air pollution exposure messaging in Experiment 1 (personal and community benefits targeting bus transit) Table S18. Heterogeneous treatment effects of air quality improvement messaging in Experiment 2 (personal gain targeting bus transit) Table S19. Heterogeneous treatment effects of air quality improvement messaging in Experiment 2 (personal and community benefits targeting bus transit) Table S20. Heterogeneous treatment effects of active lifestyle messaging in Experiment 3 (personal gain targeting walking) Table S21. Heterogeneous treatment effects of active lifestyle messaging in Experiment 3 (personal and community benefits targeting walking) Table S22. Heterogeneous treatment effects of different active lifestyle messaging in Experiment 3 targeting walking Table S23. Heterogeneous treatment effects of calories burned messaging for commuters with varying daily travel times in Experiment 3 (targeting walking) Replication Supporting replication code is also available here: https://github.com/asensio-lab/health-active-lifestyle. This code package was last replicated on July 7, 2025 by @YifanLiu0304 Declaration of generative AI and AI-assisted technologies in the coding process During the preparation of this work the authors used ChatGPT in order to debug code errors such as KeyError, syntax errors in python scripts that were used to generate statistical tables. After using this tool/service, the researchers reviewed, edited, and replicated all code.