SEARCHCOHORTepidemiology, public health

cohort·epidemiology, public health, nutrition, endocrinology, research methods·PMC10651929

Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations

NPJ Digital Medicine · 10 authors, 10 centres

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TL;DR

This study used smartphone mobility data to track visits to fast food outlets in Los Angeles County and found that aggregated visit measures strongly correlated with self-reported fast food intake, obesity, and diabetes in a population survey. Mobility-based measures were better predictors of obesity and diabetes than self-reported intake.

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The study linked aggregated smartphone mobility data on visits to fast food outlets to a population health survey (LACHS) in Los Angeles County. Mobility-derived visit variables were stronger predictors of obesity and diabetes than individual self-reported FF intake, after adjusting for sociodemographic factors. Sensitivity analyses indicated the findings were robust to the time gap between datasets and controlling for general mobility behavior.

STUDY TYPE

cohort

SPECIALTY

epidemiology

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ORIGINAL PAPER

PMC10651929

Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations

NPJ Digital Medicine

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ABSTRACT

The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people’s homes. As smartphone ownership increases, large-scale data on human mobility (i.e., smartphone geolocations) represents a promising resource for studying dynamic food environments that people have access to and visit as they move throughout their day. This study investigates whether mobility data provides meaningful indicators of diet, measured as FF intake, and diet-related disease, evaluating its usefulness for food environment research. Using a mobility dataset consisting of 14.5 million visits to geolocated food outlets in Los Angeles County (LAC) across a representative sample of 243,644 anonymous and opted-in adult smartphone users in LAC, we construct measures of visits to FF outlets aggregated over users living in neighborhood. We find that the aggregated measures strongly and significantly correspond to self-reported FF intake, obesity, and diabetes in a diverse, representative sample of 8,036 LAC adults included in a population health survey carried out by the LAC Department of Public Health. Visits to FF outlets were a better predictor of individuals’ obesity and diabetes than their self-reported FF intake, controlling for other known risks. These findings suggest mobility data represents a valid tool to study people’s use of dynamic food environments and links to diet and health.

CITATION EXPORT

Abigail L. Horn, Brooke M. Bell, Bernardo García Bulle Bueno et al.. Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations. NPJ Digital Medicine. (2023). https://doi.org/10.1038/s41746-023-00949-x

AUTHORS

10 · 10 CENTRES

Abigail L. Horn, Brooke M. Bell, Bernardo García Bulle Bueno, Mohsen Bahrami, Burçin Bozkaya, Yan Cui, John P. Wilson, Alex Pentland, Esteban Moro, Kayla de la Haye

1https://ror.org/03taz7m60grid.42505.360000 0001 2156 6853Information Sciences Institute and Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA USA
2https://ror.org/03taz7m60grid.42505.360000 0001 2156 6853Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
3https://ror.org/03v76x132grid.47100.320000 0004 1936 8710Department of Chronic Disease Epidemiology, Yale School of Public Health, Yale University, New Haven, CT USA
4https://ror.org/042nb2s44grid.116068.80000 0001 2341 2786Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA USA
5https://ror.org/049asqa32grid.5334.10000 0004 0637 1566Sabanci Business School, Sabanci University, Istanbul, Turkey
6https://ror.org/017dm4063grid.416097.d0000 0004 0428 8718Los Angeles County Department of Public Health, Los Angeles, CA USA
7https://ror.org/03taz7m60grid.42505.360000 0001 2156 6853Spatial Sciences Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA USA
8https://ror.org/03taz7m60grid.42505.360000 0001 2156 6853Departments of Civil & Environmental Engineering and Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA USA
9https://ror.org/03ths8210grid.7840.b0000 0001 2168 9183Departamento de Matemáticas & GISC, Universidad Carlos III de Madrid, Leganés, Spain
10https://ror.org/03taz7m60grid.42505.360000 0001 2156 6853Institute for Food System Equity, Center for Economic and Social Research, University of Southern California, Los Angeles, CA USA