Evidence map›Paper›PMID 40811585›Full record

ArticlePloS one2025

Exploring the association between smartphone-based place visitation data and neighborhood-level coronary heart disease in the United States.

Temitope Akinboyewa, Zhenlong Li, Huan Ning, M Naser Lessani, Louisa M Holmes, Shan Qiao

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Measuring "third places": Comparing neighborhood data for cognitive health research.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Temitope AkinboyewaGeoinformation and Big Data Research Laboratory, Department of Geography, The Pennsylvania State University, University Park, Pennsylvania, United States of America.ORCID https://orcid.org/0009-0009-7187-7911
Zhenlong LiGeoinformation and Big Data Research Laboratory, Department of Geography, The Pennsylvania State University, University Park, Pennsylvania, United States of America.ORCID https://orcid.org/0000-0002-8938-5466
Huan NingGeoinformation and Big Data Research Laboratory, Department of Geography, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
M Naser LessaniGeoinformation and Big Data Research Laboratory, Department of Geography, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
Louisa M HolmesDepartments of Geography and Demography, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
Shan QiaoDepartment of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States of America.ORCID https://orcid.org/0000-0003-1834-1834

Funding

The impacts of HIV-related service interruptions during COVID-19 pandemic in South CarolinaR01AI174892 · NIAID · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI Shan Qiao · 2023 to 2026
$2.8M
Visitation-based obesogenic environment measurement: A novel instrument using Big Data approachR21MD018666 · NIMHD · PENNSYLVANIA STATE UNIVERSITY, THE · PI LI, ZHENLONG, QIAO, SHAN · 2024 to 2025
$399k
NIAID NIH HHS R01 AI174892NIMHD NIH HHS R21 MD018666
6 · The paper itself

Abstract

Coronary Heart Disease (CHD) is the leading cause of death in the United States, affecting over 20.5 million adults. Previous studies link health behaviors - such as dietary behavior, physical activity, smoking, and alcohol consumption - to CHD risk. These studies typically use surveys and interviews, which, despite their benefits, are resource-intensive and limited by small sample sizes. Using large-scale national level anonymized smartphone-based location data, our study examines whether health behaviors that are proxy measured by place visitation are associated with CHD prevalence across US census tracts. This study utilized data from multiple sources, including demographic and socioeconomic characteristics, health outcomes, and smartphone-based place visitation data. Health behavior measures were derived from aggregated smartphone location data at the census tract level, focusing on categories such as food retails, drinking places, and physical activity locations. Three sets of regression analyses were conducted: one using only demographic variables, the second including socioeconomic variables, and another incorporating the derived health behavior measures. Linear and spatial regression analyses were employed to assess the relationship between neighborhood-level CHD prevalence and these behaviors. Findings indicate a significant association between health behaviors that are proxy measured by place visitation data and the prevalence of CHD at the neighborhood level. The models incorporating these behaviors demonstrated improved fitness and highlighted specific behavioral factors such as increased visits to physical activity facilities and healthy food retail associated with lower CHD rates. Conversely, higher visits to less healthy food retail were associated with increased CHD rates. Smartphone-based visitation data offers a novel method to assess health behaviors at a large scale, providing valuable insights for targeting CHD interventions more effectively at the neighborhood level. This approach could enhance our understanding and management of CHD, informing public health strategies and interventions to mitigate this major health challenge.

Indexed as

Coronary DiseaseNeighborhood CharacteristicsSmartphoneAdultAgedExerciseFemaleHealth BehaviorHumansMaleMiddle AgedPrevalenceResidence CharacteristicsSocioeconomic FactorsUnited States

Identifiers

PMID40811585
PMCPMC12352783

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.