Journal of Transport and Land Use

How does neighborhood walkability affect obesity? The mediating role of commute mode

Wenyue Yang

South China Agricultural University

Xinyu Zhen

South China Agricultural University

Suhong Zhou

Sun Yat-sen University

DOI: https://doi.org/10.5198/jtlu.2021.1948

Keywords: walkability, commute mode, body mass index (BMI), mediating effect, Multilevel Linear Model (MLM)

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Abstract

The walkability of a neighborhood is closely related to residents’ travel behavior and daily life and, ultimately, their health and wellbeing. Although existing studies in this area have reached some enlightening conclusions, few of them have considered residents’ travel attitudes and preferences, or the mediating role of commute mode. Do travel attitudes and preferences matter in the relationship between neighborhood walkability and residents being obese? How does commute mode work as a mediator? To answer these questions, based on the 2019 travel survey data in Guangzhou, this paper uses the Multilevel Linear Model (MLM) to examine the association between neighborhood walkability and residents’ body mass index (BMI). Furthermore, the Mediation Model is used to identify the mediating role of commute mode in the relationship between walkability and BMI. The results show that (1) travel attitudes and preferences do affect the individual’s BMI through the mediator of commute mode. (2) After controlling the individual socio-demographics and travel attitudes and preferences, neighborhood walkability has a significant negative effect on BMI. Meanwhile, walkability has a significant positive effect on the use of non-private motorized commute modes. Non-private motorized commute modes have a significant negative effect on BMI. (3) The mediating effect of commute mode in the relationship of neighborhood walkability with the individual’s BMI is significant. The proportion of mediation is 32.90%. Insights into the relationship between neighborhood walkability, commute mode, and individual BMI highlight the importance of walkable neighborhoods that encourage people to use healthy commute modes.

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References

Adlakha, D., & Parra, D. C. (2020). Mind the gap: Gender differences in walkability, transportation and physical activity in urban India. Journal of Transport and Health, 18, 100875.

Adlakha, D., Hipp, J. A., & Brownson, R. C. (2016). Neighborhood-based differences in walkability, physical activity, and weight status in India. Journal of Transport and Health, 34, 485–499.

Administration of Sports of Guangdong Province. (2011). Sampling survey of national physique in Guangzhou in 2011. http://tyj.gd.gov.cn/

Alfonzo, M., Guo, Z., Lin, L., & Day, K. (2014). Walking, obesity and urban design in Chinese neighborhoods. Preventive Medicine, 69, S79–S85.

An, R., Shen, J., Yang, Q. Y., & Yang, Y. (2019). Impact of built environment on physical activity and obesity among children and adolescents in China: A narrative systematic review. Journal of Sport and Health Science, 82, 153–169.

Barnes, R., Winters, M., Ste-Marie, N., Mckay, H., & Ashe, M. C. (2016). Age and retirement status differences in associations between the built environment and active travel behavior. Journal of Transport and Health, 43, 513–522.

Black, J. L., Macinko, J., Dixon, L. B., & Fryer, G. E. (2010). Neighborhoods and obesity in New York City. Health and Place, 163, 489–499.

Bodea, T. D., Garrow, L. A., Meyer, M. D., & Ross, C. L. (2009). Socio-demographic and built environment influences on the odds of being overweight or obese: The Atlanta experience. Transportation Research Part A: Policy and Practice, 434, 430–444.

Boisjoly, G., El-Geneidy, A., & Wasf, R. (2018). How much is enough? Assessing the influence of neighborhood walkability on undertaking 10-minute walks. Journal of Transport and Land Use, 11, 143–151.

Bödeker, M., Finne, E., Kerr, J., & Bucksch, J. (2018). Active travel despite motorcar access. A city-wide, GIS-based multilevel study on neighborhood walkability and active travel in Germany. Journal of Transport and Health, 9, 8–18.

Bongiorno, C., Santucci, D., Kon, F., Santi, P., & Ratti, C. (2019). Comparing bicycling and pedestrian mobility: Patterns of non-motorized human mobility in Greater Boston. Journal of Transport Geography, 80, 102501.

Cambra, P., & Moura, F. (2020). How does walkability change relate to walking behavior change? Effects of a street improvement in pedestrian volumes and walking experience. Journal of Transport and Health, 16, 100797.

Cervero, R., & Kockekman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transport Research Part D: Transport and Environment, 23, 199–219.

Chen, C., & Menifield, C. E. (2017). An ecological study on means of transportation to work and obesity: Evidence from U.S. states. Transport Policy, 59, 174–180.

Chinese Center for Disease Prevention. (2015). The report on residents' nutrition and chronic diseases in China. Retreived from http://www.scio.gov.cn/xwfbh/xwbfbh/wqfbh/2015/33038/index.htm

Cohen, J. (1988). Statistical power analysis for the behavioral sciences, 2nd edition. New York: Routledge.

Dai, F., Diao, M. & Sing, T. F. (2020). Effects of rail transit on individual travel mode shares: A two-dimensional propensity score matching approach. Transportation Research Part D: Transport and Environment, 89, 102601.

Day, K. (2016). Built environmental correlates of physical activity in China: A review. Preventive Medicine Report, 3, 303–316.

Egset, K. S., & Nordfjærn, T. (2019). The role of transport priorities, transport attitudes and situational factors for sustainable transport mode use in wintertime. Transport Research Part F: Traffic Psychology and Behavior, 62, 473–482.

Fang, J., Wen, Z. L., Zhang M. Q., & Ren, H. (2014). Analyzing multilevel mediation using multilevel structural equation models. Advances in Psychological Science, 223, 530–539.

Frank, L. D., Iroz-Elardo, N., MacLeod, K. E., & Hong, A. (2019). Pathways from built environment to health: A conceptual framework linking behavior and exposure-based impacts. Journal of Transport and Health, 12, 319–335.

Frank, L. D., Kerr, J., Sallis, J. F., Miles, R., & Chapman, J. (2008). A hierarchy of sociodemographic and environmental correlates of walking and obesity. Preventive Medicine, 47(2), 172–178.

Frank, L. D., Saelens, B. E., Powell, K. E., & Chapman, J. E. (2007). Stepping towards causation: Do built environments or neighborhood and travel preferences explain physical activity, driving, and obesity? Social Science & Medicine, 65(9), 1898–1914.

Frank, L. D., Sallis, J. F., Saelens, B. E., Leary, L., Cain, K., Conway, T. L., & Hess, P. M. (2010). The development of a walkability index: Application to the neighborhood quality of life study. British Journal of Sports Medicine, 44, 924–933.

Freeman, L., Neckerman, K., Schwartz-Soicher, O., Quinn, J., Richards, C., Bader, M. D., … & Konty, K. (2013). Neighborhood walkability and active travel walking and cycling in New York City. Journal of Urban Health, 904, 575–585.

Golan, Y., Wilkinson, N. L., Henderson, J., & Weverka, A. (2019). Gendered walkability: Building a daytime walkability index for women. Journal of Transport and Land Use, 121, 50–526.

Hamidi, Z., & Zhao, C. L. (2020). Shaping sustainable travel behavior: Attitude, skills, and access all matter. Transportation Research Part D: Transport and Environment, 88, 102566.

Hoffmann, C., Abraham, C., Skippon, S. M., & White, M. P. (2018). Cognitive construction of travel modes among high-mileage car users and non-car users – A repertory grid analysis. Transport Research Part A: Policy and Practice, 118, 216–233.

Jones, A. (2015). Residential instability and obesity over time: The role of the social and built environment. Health and Place, 32, 74–82.

Joop, J. H., Mirjam, M., & Rens, S. (2018). Multilevel analysis: Techniques and applications, 3rd edition. New York, USA: Routledge.

Kikuchi, H., Nakaya, T., Hanibuchi, T., Fukushima, N., Amagasa, S., Oka, K., … & Inoue, S. (2018). Objectively measured neighborhood walkability and change in physical activity in older Japanese adults: A five-year cohort study. International Journal of Environment Research and Public Health, 159, 1814–1827.

Kjær, T., Højgaard, B., & Gyrd-Hansen, D. (2019). Physical exercise versus shorter life expectancy? An investigation into preferences for physical activity using a stated preference approach. Health Policy, 123(8), 790–796.

Kowaleski-Jones, L., Zick, C., Smith, K. R., Brown, B., Hanson, H., & Fan, J. (2018). Walkable neighborhoods and obesity: Evaluating effects with a propensity score approach. SSM Population Health, 6, 9–15.

Leslie, E., Coffee, N., Frank, L. D., Owen, N., Bauman, A., & Hugo, G. (2007). Walkability of local communities: Using geographic information systems to objectively assess relevant environmental attribute. Health and Place, 13, 111–122.

Liao, Y., Tsai, H. H., Wang, H. S., Lin, C. P., Wu, M. C., & Chen, J. F. (2016). Travel mode, transportation-related physical activity, and risk of overweight in Taiwanese adults. Journal of Transport and Health, 32, 220–225.

Lindelöw, D., Svensson, A., Brundell-Freij, K., & Hiselius, L. W. (2017). Satisfaction or compensation? The interaction between walking preferences and neighborhood design. Transportation Research Part D, 50, 520–532.

Lin, T., Wang, D. G., & Guan, X. D. (2017). The built environment, travel attitude, and travel behavior: Residential self-selection or residential determination? Journal of Transport Geography, 65, 11–122.

Mavoa, S., Eagleson, S., Badland, H. M., & Gunn, L. (2018). Identifying appropriate land-use mix measures for use in a national walkability index. Journal of Transport and Land Use, 11, 681–700.

Marquez, L., Macea, L. F., & Soto, J. J. (2019). Willingness to change car use to commute to the UPTC main campus, Colombia: A hybrid discrete choice modeling approach. Journal of Transport and Land Use, 121, 335–353.

Matozinhos, F. P., Gomes, C. S., Andrade, A. C. D. S., Mendes, L. L., Pessoa, M. C., Friche, A. A. D. L., & Velasquez-Melendez, G. (2015). Neighborhood environments and obesity among adults: A multilevel analysis of an urban Brazilian context. Preventive Medicine Report, 2, 337–341.

Matsumura, L. C., Garnier, H. E., & Spybrook, J. (2013). Literacy coaching to improve student reading achievement: A multi-level mediation model. Learning and Instruction, 25, 35–48.

Moniruzzaman, M., Páez, A., Nurul, H. K. M., & Morency, C. (2013). Mode use and trip length of seniors in Montreal. Journal of Transport Geography, 30, 89–99.

Munshi, T. (2016). Built environment and mode choice relationship for commute travel in the city of Rajkot, India. Transportation Research Part D: Transport and Environment, 44, 239–253.

Mytton, O., Ogilvie, D., Griffin, S., Brage, S., Wareham, N., & Panter, J. (2018). Associations of active commuting with body fat and visceral adipose tissue: A cross-sectional population-based study in the UK. Preventive Medicine, 106, 86–93.

Nichani, V., Turley, L., Vena, J. E., & McCornack, G. R. (2020). Associations between the neighborhood characteristics and body mass index, waist circumference, and waist-to-hip ratio: Findings from Alberta's Tomorrow Project. Health and Place, 64, 102357.

Pendola, R., & Gen, S. (2007). BMI, auto use, and the urban environment in San Francisco. Health and Place, 132, 551–556. https://doi.org/10.1016/j.healthplace.2006.02.004

Peugh, J. L. (2010). A practical guide to multilevel modeling. Journal of School Psychology, 481, 85–112.

Sallis, J. F., Frank, L. D., Saelens, B. E., & Kraft, M. K. (2004). Active transportation and physical activity: Opportunities for collaboration on transportation and public health research. Transport Research Part A: Policy Practice, 384, 249–268.

Sarkar, C, Gallacher, J., & Webster, C. (2013). Built environment configuration and change in body mass index: The Caerphilly Prospective Study (CaPS). Health and Place, 19, 33–44.

Schoner, J., Chapman, J., Brookes, A., MacLeod, K. E., Fox, E. H., Iroz-Elardo, N., & Frank, L. D. (2018). Bringing health into transportation and land use scenario planning: Creating a National Public Health Assessment Model N-PHAM. Journal of Transport and Health, 10, 401–418.

Schneider, R. J., & Stefanich, J. (2018). Exploring the importance of detailed environment variables in neighborhood commute mode share models. Journal of Transport and Land Use, 11, 921–938.

Shashank, A., & Schuurman, N. (2019). Unpacking walkability indices and their inherent assumptions. Health and Place, 55, 145–154.

Smargiassi, A., Plante, C., Morency, P., Hatzopoulou, M., Morency, C., Eluru, N., … & Requia, W. (2020). Environmental and health impacts of transportation and land use scenarios in 2061. Environmental Research, 187, 109622.

Smart, M. J. (2018). Walkability, transit, and body mass index: A panel approach. Journal of Transport and Health, 8, 193–201.

Stark, J., Singleton, P. A., & Uhlmann, T. (2019). Exploring children’s school travel, psychological well-being, and travel-related attitudes: Evidence from primary and secondary school children in Vienna, Austria. Travel Behavior and Society, 16, 118–130.

Stephen, W. R., & Anthony, S. B. (2002). Hierarchical linear models: applications and data analysis methods, 2nd edition. London: SAGE Publications.

Su, S. L., Pi, J. H., Xie, H., Cai, Z. L., & Weng, M. (2017). Community deprivation, walkability, and public health: Highlighting the social inequalities in land use planning for health promotion. Land Use Policy, 67, 315–326.

Sugiyama, T., Niyonsenga, T., Howard, N. J., Coffee, N. T., Paquet, C., Taylor, A. W., & Daniel, M. (2016). Residential proximity to urban centers, local-area walkability and change in waist circumference among Australian adults. Preventive Medicine, 93, 39–45.

Sun, B. D., & Yin, C. (2018). Relationship between multi-scale urban built environments and body mass index: A study of China. Applied Geography, 94, 230–240.

Sun, B. D., Yan, H., & Zhang, T. L. (2017). Built environmental impacts on individual mode choice and BMI: Evidence from China. Journal of Transport Geography, 63, 11–21.

Tuckel, P., & Milczarski, W. (2015). Walk score TM, perceived neighborhood walkability, and walking in the US. American Journal of Health Behavior, 392, 241–255.

Turrell, G., Hewitt, B. A., Rachele, J. N., Giles-Corti, B., & Brown, W. J. (2018). Prospective trends in body mass index by main transport mode, 2007–2013. Journal of Transport and Health, 8, 183–192.

van Cauwenberg, J., Van Holle, V., De Bourdeaudhuij, I., Van Dyck, D., & Deforche, B. (2016). Neighborhood walkability and health outcomes among older adults: The mediating role of physical activity. Health and Place, 37, 16–25.

van de Coevering, P., Maat, K., & van Wee, B. (2018). Residential self-selection, reverse causality and residential dissonance. A latent class transition model of interactions between the built environment, travel attitudes and travel behavior. Transportation Research Part A: Policy and Practice, 118, 466–479.

Wang D., & Lin T. (2014). Residential self-selection, built environment, and travel behavior in the Chinese context. Journal of Transport and Land Use, 7(3), 5–14.

Wang, F. H., Wen, M., & Xu, Y. Q. (2013). Population-adjusted street connectivity, urbanicity and risk of obesity in the U.S. Applied Geography, 41, 1–14.

Wang, H., & Yang, Y. Q. (2019). Neighborhood walkability: A review and bibliometric analysis. Cities, 93, 43–61.

Wasfi, R. A., Dasgupta, K., Eluru, N., & Ross, N. (2016). Exposure to walkable neighborhoods in urban areas increases utilitarian walking: Longitudinal study of Canadians. Journal of Transport and Health, 34, 440–447.

Watson, K. B., Whitfield, G. P., Thomas, J. V., Berrigan, D., Fulton, J. E., & Carlson, S. A. (2020). Associations between the National Walkability Index and walking among US adults — National Health Interview Survey, 2015. Preventive Medicine, 137, 106122.

Wee, B. & Ettema, D. (2016). Travel behavior and health: A conceptual model and research agenda. Journal of Transport & Health, 3(3), 240–248.

Weng, M., Ding, N., Li, J., Jin, X. F., Xiao, H., He, Z. M., & Su, S. L. (2019). The 15-minute walkable neighborhoods: Measurement, social inequalities and implications for building healthy communities in urban China. Journal of Transport and Health, 13, 259–273.

Wey, W. M., & Chiu, Y. H. (2013). Assessing the walkability of pedestrian environment under the transit-oriented development. Habitat International, 38, 106–118.

Xie, Y. (2013). Regression analysis, revised edition. Beijing: Social Science Academic Press.

Xu, Y. Q., & Wang, F. H. (2015). Built environment and obesity by urbanicity in the U.S. Health and Place, 34, 19–29.

Xu, Y. Q., Wen, M., & Wang, F. H. (2015). Multilevel built environment features and individual odds of overweight and obesity in Utah. Applied Geography, 60, 197–203.

Yang, W., Chen, H., & Wang, W. (2020). The path and time efficiency of residents’ trips of different purposes with different travel modes: An empirical study in Guangzhou, China. Journal of Transport Geography, 88, 102829.

Yang, W. Y., & Cao, X. S. (2018). Examining the effects of the neighborhood built environment on CO2 emissions from different residential trip purposes: A case study in Guangzhou, China. Cities, 81, 24–34.

Yang, W. Y., Zhen, X. Y., Gao, W., & Ouyang, S. S. (2020). An examination of the impact of neighborhood walking environments on the probability of overweight or obesity among residents of dense urban areas. Canadian Geographer, 64(4), 619–633.

Yencha, C. (2019). Valuing walkability: New evidence from computer vision methods. Transport Research Part A: Policy Practice, 130, 689–709.

Zhao, P. J. (2013). Too complex to be managed? New trends in peri-urbanization and its planning in Beijing. Cities, 30, 68–76.

Zhao, Z., & Kaestner, R. (2010). Effects of urban sprawl on obesity. Journal of Health Economics, 296, 779–787.

Zhou, M., Tan, S. K., Tao, Y. H., Lu, Y, Z., Zhang, Z., Zhang, L., & Yan, D. P. (2017). Neighborhood socioeconomics, food environment and land use determinants of public health: Isolating the relative importance for essential policy insights. Land Use Policy, 68, 246–253.