County-level associations between passively collected walking and bicycling and self-reported nonoccupational physical activity data
7 affiliations
- 1Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
- 2McKing Consulting Corporation, Atlanta, Georgia, United States of America
- 3Oak Ridge Institute for Science and Education Research Participation Program, Oak Ridge, Tennessee, United States of America
- 4Epidemic Intelligence Service, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
- 5Geospatial Research, Analysis, and Services Program, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
- 6Scripps Institution of Oceanography, University of California San Diego, San Diego, California, United States of America
- 7Office on Smoking and Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
Abstract
Passively collected location-based services data on walking and bicycling trips could potentially complement traditional public health surveillance systems as related but distinct sources for physical activity data. Starting with a sample of 298 counties, we explored county-level associations between 2019 StreetLight location-based services measures and 2017 and 2019 Behavioral Risk Factor Surveillance System self-reported nonoccupational physical activity measures using Spearman’s rank correlation coefficients (rho), following Cohen’s interpretation (low: < 0.3; moderate: 0.3 to <0.5; strong: ≥ 0.5). Then, we explored stratified associations for the two strongest correlated StreetLight–Behavioral Risk Factor Surveillance System pairs of measures to understand how these associations differed by county characteristics. Of 72 StreetLight–Behavioral Risk Factor Surveillance System measure combinations, 4 pairs were moderately correlated (rho 0.3 to <0.5). The remaining pairs had low correlations (rho < 0.3). Select stratified measure pairs had strong correlations (rho ≥ 0.5) for medium and small metro counties and counties in the middle tertile of social vulnerability. Location-based services measures and Behavioral Risk Factor Surveillance System physical activity measures mostly showed limited convergent validity. The usefulness of location-based services data for representing nonoccupational physical activity may be context-sensitive to certain types of counties, where the constructs may have more in common.
Related
- Who infected the reported cases? Evidence from 678,482 COVID-19 cases with identified infector collected in routine surveillance in the Netherlands, 2020-2022.
- Stage-Specific Associations of Automated RPR With Treponema pallidum qPCR Positivity and Cycle Threshold in Early Symptomatic Syphilis: A Multicenter Study Using Specimens Collected During Routine Care
- Restrictive Eating Patterns and Eating Disorder Risk in Female Ballet Dancers: Exploratory Associations with Self-Reported Workplace Bullying and Possible Mobbing
- Marital satisfaction and parent-reported emotional competence in Chinese preschoolers: serial indirect associations through mindful parenting and parental involvement
- Tobacco and alcohol use in Germany: a cross-sectional study about associations with the self-reported ability to find, understand and judge health information
BibTeX
@article{chen2026countylevel,
title = {{County-level associations between passively collected walking and bicycling and self-reported nonoccupational physical activity data}},
author = {Tiffany J. Chen and Miriam E. Van Dyke and Graycie W. Soto and Bryant J. Webber and Kelly Fletcher and Sarah Rockhill and Michael D. Garber and Akimi Smith and Geoffrey P. Whitfield},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0359535},
url = {https://doi.org/10.1371/journal.pone.0359535},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - County-level associations between passively collected walking and bicycling and self-reported nonoccupational physical activity data
AU - Tiffany J. Chen
AU - Miriam E. Van Dyke
AU - Graycie W. Soto
AU - Bryant J. Webber
AU - Kelly Fletcher
AU - Sarah Rockhill
AU - Michael D. Garber
AU - Akimi Smith
AU - Geoffrey P. Whitfield
PY - 2026
DA - 2026/10/06
JO - PLOS One
DO - 10.1371/journal.pone.0359535
UR - https://doi.org/10.1371/journal.pone.0359535
AB - Passively collected location-based services data on walking and bicycling trips could potentially complement traditional public health surveillance systems as related but distinct sources for physical activity data. Starting with a sample of 298 counties, we explored county-level associations between 2019 StreetLight location-based services measures and 2017 and 2019 Behavioral Risk Factor Surveillance System self-reported nonoccupational physical activity measures using Spearman’s rank correlation coefficients (rho), following Cohen’s interpretation (low: < 0.3; moderate: 0.3 to <0.5; strong: ≥ 0.5). Then, we explored stratified associations for the two strongest correlated StreetLight–Behavioral Risk Factor Surveillance System pairs of measures to understand how these associations differed by county characteristics. Of 72 StreetLight–Behavioral Risk Factor Surveillance System measure combinations, 4 pairs were moderately correlated (rho 0.3 to <0.5). The remaining pairs had low correlations (rho < 0.3). Select stratified measure pairs had strong correlations (rho ≥ 0.5) for medium and small metro counties and counties in the middle tertile of social vulnerability. Location-based services measures and Behavioral Risk Factor Surveillance System physical activity measures mostly showed limited convergent validity. The usefulness of location-based services data for representing nonoccupational physical activity may be context-sensitive to certain types of counties, where the constructs may have more in common.
SN - 1932-6203
ER -