{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Division 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",
    "McKing Consulting Corporation, Atlanta, Georgia, United States of America",
    "Oak Ridge Institute for Science and Education Research Participation Program, Oak Ridge, Tennessee, United States of America",
    "Epidemic Intelligence Service, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America",
    "Geospatial Research, Analysis, and Services Program, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America",
    "Scripps Institution of Oceanography, University of California San Diego, San Diego, California, United States of America",
    "Office 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"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tiffany J. Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Miriam E. Van Dyke"
    },
    {
      "affs": [
        2
      ],
      "name": "Graycie W. Soto"
    },
    {
      "affs": [
        3
      ],
      "name": "Bryant J. Webber"
    },
    {
      "affs": [
        4
      ],
      "name": "Kelly Fletcher"
    },
    {
      "affs": [
        5
      ],
      "name": "Sarah Rockhill"
    },
    {
      "affs": [
        6
      ],
      "name": "Michael D. Garber"
    },
    {
      "name": "Akimi Smith"
    },
    {
      "name": "Geoffrey P. Whitfield"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f39a8th5xryh1.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0359535",
  "fulltext_url": null,
  "id": "f39a8th5xryh1",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0359535",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3313|Urban Transport and Accessibility"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Biological locomotion/Walking"
    ],
    [
      "plos",
      "/Earth sciences/Geography/Human geography/Human mobility"
    ],
    [
      "plos",
      "/Engineering and technology/Transportation"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Disease surveillance"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Public and occupational health"
    ],
    [
      "plos",
      "/Medicine and health sciences/Public and occupational health/Physical activity"
    ],
    [
      "plos",
      "/Research and analysis methods/Research design/Survey research/Surveys"
    ],
    [
      "plos",
      "/Social sciences/Human geography/Human mobility"
    ]
  ],
  "openalex": "W7220700531",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359535&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/f39a8th5xryh1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pone.0359535",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0359535"
    }
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  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "County-level associations between passively collected walking and bicycling and self-reported nonoccupational physical activity data",
  "title_text": "County-level associations between passively collected walking and bicycling and self-reported nonoccupational physical activity data",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:36Z",
  "url": "https://research.draconitic.com/paper/f39a8th5xryh1",
  "venue": "PLOS One",
  "versions": []
}