Pathway analysis of relationships among community development, active travel behavior, body mass index, and self-rated health

Abstract

A higher Body Mass Index (BMI) is often associated with higher risks for health-related issues (e.g., arthritis, cancer). Lifestyle behaviors (e.g., healthy diet) are believed to be important factors closely related to people’s BMI and health. Among the behaviors, active travel modes (e.g., walking and bicycling) may be an important factor correlated with BMI. Such travel behaviors can somehow be affected by community design (e.g., walkability) and environmental features (e.g., air quality). These factors exhibit complex relationships with a person’s health as they may either directly or indirectly correlate with health through travel behaviors. To this end, this study employed a pathway analysis approach to unveil potential health benefits relating to community living environments and travel behaviors to BMI and health. Specifically, this study integrated national-level health and travel behavior datasets and developed rigorous multi-level hierarchical models to explore and explain how the health pathways are related to community living environments, travel behaviors, and BMI. The modeling results showed that limited walking facilities and unhealthy air quality could significantly associate with people’s reduced willingness to engage in active travel modes, which correlated with subsequent higher obesity risk, and a higher risk of poor health. More importantly, if walking or bicycling facilities were not fully utilized, health benefits gained from these facilities were found to be substantially reduced. Other significant health-related behaviors (e.g., sleep/exercise time, fruit consumption, smoking) are also discussed in detail.

Keywords:

• Pathway analysis
• walkability index
• body mass index
• self-rate health
• hierarchical modeling

Acknowledgements

The authors would like to present their heartful thanks to the National Cancer Institute for sharing the complete HINTS data, which significantly improves the study quality. We would also like to thank Matthew Shrode Hargis and Jessica Neese, who helped a lot with the editing of this paper.

Authors contribution

The authors confirm contribution to the paper as follows: study conception and design: Xiaobing Li, Jun Liu, Qinglin Hu; data collection: Xiaobing Li, Qinglin Hu, Abhay Lidbe; analysis and interpretation of results: Xiaobing Li, Jun Liu, Qinglin Hu, Shashi Nambisan; draft manuscript preparation: Xiaobing Li, Jun Liu, Qinglin Hu, Shashi Nambisan, Asad J. Khattak, Hee Yun Lee. All authors reviewed the results and approved the final version of the manuscript.