Considerations When Using Individual GPS Data in Food Environment Research: A Scoping Review of ‘Selective (Daily) Mobility Bias’ in GPS Exposure Studies and Its Relevance to the Retail Food Environment
Advancements in geospatial technologies including geographic information systems and global positioning system (GPS) devices have provided insights on how the retail food environment might be contributing to the ongoing obesity epidemic. Caution has been raised, however, around the potential for research that uses GPS-captured activity spaces to overestimate the impact that exposure to food retailers has on food choices and behaviour. This phenomenon, where it is difficult to discern whether an individual is passively exposed to a space or actively seeks it out, is referred to as a ‘selective (daily) mobility bias’. Researchers’ understanding of this bias is relatively new and understudied, particularly in the food environment literature, where the bias could have serious implications. This chapter reviews 14 peer-reviewed papers and two doctoral theses to identify and critique the methods proposed for handling this bias and offer recommendations to consider as the use of GPS-activity space studies continues to grow.
KeywordsGPS Built environment Selective (daily) mobility bias Environment Health
Fast food retailer
Geographical information systems
Global positioning system
Highly processed food
Selective daily mobility bias
Selective mobility bias
- Boone-Heinonen, J., Gordon-Larsen, P., Kiefe, C. I., Shikany, J. M., Lewis, C. E., & Popkin, B. M. (2011). Fast food restaurants and food stores: Longitudinal associations with diet in young adults: The CARDIA Study. Archives of Internal Medicine, 171(13), 1162–1170. https://doi.org/10.1001/archinternmed.2011.283.CrossRefGoogle Scholar
- Bramer, W. M., Rethlefsen, M. L., Kleijnen, J., & Franco, O. H. (2017). Optimal database combinations for literature searches in systematic reviews: A prospective exploratory study. Systematic Reviews, 6. https://doi.org/10.1186/s13643-017-0644-y.
- Burgoine, T., Jones, A. P., Namenek Brouwer, R. J., & Benjamin Neelon, S. E. (2015). Associations between BMI and home, school and route environmental exposures estimated using GPS and GIS: Do we see evidence of selective daily mobility bias in children? International Journal of Health Geographics, 14, 8. https://doi.org/10.1186/1476-072X-14-8.CrossRefGoogle Scholar
- Byrnes, H. F., Miller, B. A., Morrison, C. N., Wiebe, D. J., Remer, L. G., & Wiehe, S. E. (2016). Brief report: Using global positioning system (GPS) enabled cell phones to examine adolescent travel patterns and time in proximity to alcohol outlets. Journal of Adolescence, 50, 65–68. https://doi.org/10.1016/j.adolescence.2016.05.001.CrossRefGoogle Scholar
- Cebrecos, A., Díez, J., Gullón, P., Bilal, U., Franco, M., & Escobar, F. (2016). Characterizing physical activity and food urban environments: A GIS-based multicomponent proposal. International Journal of Health Geographics, 15. https://doi.org/10.1186/s12942-016-0065-5.
- Chaix, B., Kestens, Y., Perchoux, C., Karusisi, N., Merlo, J., & Labadi, K. (2012). An interactive mapping tool to assess individual mobility patterns in neighborhood studies. American Journal of Preventive Medicine, 43(4), 440–450. https://doi.org/10.1016/j.amepre.2012.06.026.CrossRefGoogle Scholar
- Chaix, B., Méline, J., Duncan, S., Merrien, C., Karusisi, N., Perchoux, C., et al. (2013). GPS tracking in neighborhood and health studies: A step forward for environmental exposure assessment, a step backward for causal inference? Health & Place, 21, 46–51. https://doi.org/10.1016/j.healthplace.2013.01.003.CrossRefGoogle Scholar
- Crézé, C., Notter-Bielser, M.-L., Knebel, J.-F., Campos, V., Tappy, L., Murray, M., & Toepel, U. (2018). The impact of replacing sugar- by artificially-sweetened beverages on brain and behavioral responses to food viewing – An exploratory study. Appetite, 123, 160–168. https://doi.org/10.1016/j.appet.2017.12.019.CrossRefGoogle Scholar
- Giskes, K., van Lenthe, F., Avendano-Pabon, M., & Brug, J. (2011). A systematic review of environmental factors and obesogenic dietary intakes among adults: Are we getting closer to understanding obesogenic environments? Obesity Reviews, 12(5), e95–e106. https://doi.org/10.1111/j.1467-789X.2010.00769.x.CrossRefGoogle Scholar
- Hager, E. R., Cockerham, A., O’Reilly, N., Harrington, D., Harding, J., Hurley, K. M., & Black, M. M. (2017). Food swamps and food deserts in Baltimore City, MD, USA: Associations with dietary behaviours among urban adolescent girls. Public Health Nutrition, 20(14), 2598–2607. https://doi.org/10.1017/S1368980016002123.CrossRefGoogle Scholar
- Harrison, F., Burgoine, T., Corder, K., van Sluijs, E. M., & Jones, A. (2014). How well do modelled routes to school record the environments children are exposed to?: A cross-sectional comparison of GIS-modelled and GPS-measured routes to school. International Journal of Health Geographics, 13(1), 5. https://doi.org/10.1186/1476-072X-13-5.CrossRefGoogle Scholar
- Health Canada. (2013, October 9). Measuring the Food Environment in Canada [research]. Retrieved May 3, 2018, from https://www.canada.ca/en/health-canada/services/food-nutrition/healthy-eating/nutrition-policy-reports/measuring-food-environment-canada.html.
- Hebebrand, J., Albayrak, Ö., Adan, R., Antel, J., Dieguez, C., de Jong, J., et al. (2014). “Eating addiction”, rather than “food addiction”, better captures addictive-like eating behavior. Neuroscience & Biobehavioral Reviews, 47, 295–306. https://doi.org/10.1016/j.neubiorev.2014.08.016.CrossRefGoogle Scholar
- Laska, M. N., Hearst, M. O., Lust, K., Lytle, L. A., & Story, M. (2015). How we eat what we eat: Identifying meal routines and practices most strongly associated with healthy and unhealthy dietary factors among young adults. Public Health Nutrition, 18(12), 2135–2145. https://doi.org/10.1017/S1368980014002717.CrossRefGoogle Scholar
- McCrorie, P. R., Fenton, C., & Ellaway, A. (2014). Combining GPS, GIS, and accelerometry to explore the physical activity and environment relationship in children and young people - a review. International Journal of Behavioral Nutrition and Physical Activity, 11(1), 93. https://doi.org/10.1186/s12966-014-0093-0.CrossRefGoogle Scholar
- Minaker, L. M. (2016). Retail food environments in Canada: Maximizing the impact of research, policy and practice. Canadian Journal of Public Health = Revue Canadienne De Sante Publique, 107.(Suppl 1, 5632.Google Scholar
- Mitchell, C. (2016). Children’s physical activity and the built environment: The impact of neighbourhood opportunities and contextual environmental exposure. Electronic Thesis and Dissertation Repository. Retrieved from https://ir.lib.uwo.ca/etd/3524
- Nederkoorn, C., & Jansen, A. (2002). Cue reactivity and regulation of food intake. Eating Behaviors, 3(1), 61–72. https://doi.org/10.1016/S1471-0153(01)00045-9.
- Perchoux, C., Chaix, B., Brondeel, R., & Kestens, Y. (2016). Residential buffer, perceived neighborhood, and individual activity space: New refinements in the definition of exposure areas – The RECORD Cohort Study. Health & Place, 40(Suppl C), 116–122. https://doi.org/10.1016/j.healthplace.2016.05.004.CrossRefGoogle Scholar
- Ridder, D. D., Manning, P., Leong, S. L., Ross, S., Sutherland, W., Horwath, C., & Vanneste, S. (2016). The brain, obesity and addiction: An EEG neuroimaging study. Scientific Reports, 6(34122). https://doi.org/10.1038/srep34122.
- Scully, J. Y. (2016). Human Mobility, Exposure to the Built Environment, and Health (Thesis). Retrieved from https://digital.lib.washington.edu:443/researchworks/handle/1773/36862.
- Spook, J. E., Paulussen, T., Kok, G., & Empelen, P. V. (2013). Monitoring dietary intake and physical activity electronically: Feasibility, usability, and ecological validity of a mobile-based ecological momentary assessment tool. Journal of Medical Internet Research, 15(9), e214. https://doi.org/10.2196/jmir.2617.CrossRefGoogle Scholar
- Steele, E. M., Baraldi, L. G., Louzada, M. L. d. C., Moubarac, J.-C., Mozaffarian, D., & Monteiro, C. A. (2016). Ultra-processed foods and added sugars in the US diet: Evidence from a nationally representative cross-sectional study. BMJ Open, 6(3), e009892. https://doi.org/10.1136/bmjopen-2015-009892.CrossRefGoogle Scholar
- Tang, D. W., Fellows, L. K., Small, D. M., & Dagher, A. (2012). Food and drug cues activate similar brain regions: A meta-analysis of functional MRI studies. Physiology & Behavior, 106(3), 317–324. https://doi.org/10.1016/j.physbeh.2012.03.009.
- Teixeira, P. J., Carraça, E. V., Marques, M. M., Rutter, H., Oppert, J.-M., De Bourdeaudhuij, I., et al. (2015). Successful behavior change in obesity interventions in adults: A systematic review of self-regulation mediators. BMC Medicine, 13, 84. https://doi.org/10.1186/s12916-015-0323-6.CrossRefGoogle Scholar
- Thompson, D. (2017, June 20). The Golden Age of Restaurants Is Stranger Than It Seems. Retrieved July 30, 2018, from https://www.theatlantic.com/business/archive/2017/06/its-the-golden-age-of-restaurants-in-america/530955/.
- WHO | Obesity and overweight. (n.d.). Retrieved November 29, 2017, from http://www.who.int/mediacentre/factsheets/fs311/en/.
- Widener, M. J., Minaker, L. M., Reid, J. L., Patterson, Z., Ahmadi, T. K., & Hammond, D. (2018). Activity space-based measures of the food environment and their relationships to food purchasing behaviours for young urban adults in Canada. Public Health Nutrition, 21, 1–14. https://doi.org/10.1017/S1368980018000435.CrossRefGoogle Scholar
- Zientek, L. R., Werner, J. M., Campuzano, M. V., & Nimon, K. (n.d.). The use of Google Scholar for research and research dissemination. New Horizons in Adult Education and Human Resource Development, 30(1), 39–46. https://doi.org/10.1002/nha3.20209.