Inequities in Physical Activity Environments and Leisure-Time Physical Activity in Rural Communities

Kegler, Michelle C.; Gauthreaux, Nicole; Hermstad, April; Arriola, Kimberly Jacob; Mickens, Addison; Ditzel, Kelley; Hernandez, Clarisa; Haardörfer, Regine

doi:10.5888/pcd19.210417

Cited by 14 publications

(8 citation statements)

References 31 publications

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“…Our findings underscore the large health disparity that exists between people living in rural and urban areas in the United States [44]. Residents of rural areas are at a higher risk of geographic isolation which contributes to limited access to healthy food sources [45], have fewer opportunities for physical activity [46], and are at a higher risk of smoking [47,48] compared to their urban counterparts. Rural areas' geographic isolation also extends to inadequate access to health care and healthcare providers [49].…”

Section: Discussionmentioning

confidence: 57%

Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes

Yue

Antonietti²,

Alirezaei

et al. 2022

IJERPH

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Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study is to utilize publicly available Google Street View images as a data source for characterizing built environments and to examine the influence of built environments on chronic diseases and health behaviors in the United States. Data were collected by processing 164 million Google Street View images from November 2019 across the United States. Convolutional Neural Networks, a class of multi-layer deep neural networks, were used to extract features of the built environment. Validation analyses found accuracies of 82% or higher across neighborhood characteristics. In regression analyses controlling for census tract sociodemographics, we find that single-lane roads (an indicator of lower urban development) were linked with chronic conditions and worse mental health. Walkability and urbanicity indicators such as crosswalks, sidewalks, and two or more cars were associated with better health, including reduction in depression, obesity, high blood pressure, and high cholesterol. Street signs and streetlights were also found to be associated with decreased chronic conditions. Chain link fence (physical disorder indicator) was generally associated with poorer mental health. Living in neighborhoods with a built environment that supports social interaction and physical activity can lead to positive health outcomes. Computer vision models using manually annotated Google Street View images as a training dataset were able to accurately identify neighborhood built environment characteristics. These methods increases the feasibility, scale, and efficiency of neighborhood studies on health.

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Section: Discussionmentioning

confidence: 57%

Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes

Yue

Antonietti²,

Alirezaei

et al. 2022

IJERPH

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“…16 Higher leisure activity is consistent with other high-income nations 16 and could be explained by factors such as high levels of urbanization and access to and investment in PA infrastructure. 36,37 Low travel PA likely reflects NZ's prioritization and investment in land transport, which has contributed to one of the highest car ownership rates in the world and low active travel mode share. [38][39][40] When breaking down domain-specific activity by weekly participation and, among participants, median weekly MET-min, we found significant inequalities by gender, age, ethnicity, and area deprivation.…”

Section: Discussionmentioning

confidence: 99%

The Epidemiology of Domain-Specific Physical Activity in New Zealand Adults: A Nationally Representative Cross-Sectional Survey

Gage

Mizdrak

Richards

et al. 2023

Journal of Physical Activity and Health

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Background: Surveillance of domain-specific physical activity (PA) helps to target interventions to promote PA. We examined the sociodemographic correlates of domain-specific PA in New Zealand adults. Methods: A nationally representative sample of 13,887 adults completed the International PA Questionnaire–long form in 2019/20. Three measures of total and domain-specific (leisure, travel, home, and work) PA were calculated: (1) weekly participation, (2) mean weekly metabolic energy equivalent minutes (MET-min), and (3) median weekly MET-min among those who undertook PA. Results were weighted to the New Zealand adult population. Results: The average contribution of domain-specific activity to total PA was 37.5% for work activities (participation = 43.6%; median participating MET-min = 2790), 31.9% for home activities (participation = 82.2%; median participating MET-min = 1185), 19.4% for leisure activities (participation = 64.7%; median participating MET-min = 933), and 11.2% for travel activities (participation = 64.0%; median MET-min among participants = 495). Women accumulated more home PA and less work PA than men. Total PA was higher in middle-aged adults, with diverse patterns by age within domains. Māori accumulated less leisure PA than New Zealand Europeans but higher total PA. Asian groups reported lower PA across all domains. Higher area deprivation was negatively associated with leisure PA. Sociodemographic patterns varied by measure. For example, gender was not associated with total PA participation, but men accumulated higher MET-min when taking part in PA than women. Conclusions: Inequalities in PA varied by domain and sociodemographic group. These results should be used to inform interventions to improve PA.

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“…Leisure time physical activity has been shown to be lower among low-income populations as well as many racial/ethnic minorities [11]. Factors underlying this disparity are complex and likely include disproportionately lower access to quality environments to engage in physical activity, transportation, fitness center membership, parks, and safe streetscapes [12][13][14]. The CDC recommends six evidence-based strategies to increase physical activity, which include: (1) community-wide campaigns; (2) signs encouraging stair use; (3) health-related behavioral change programs that can be adapted to individuals; (4) school physical education programs; (5) interventions to increase social support in community settings; and (6) optimizing access to locations for physical activity combined with informational outreach [15].…”

Section: Modifiable Risk Factors 1physical Activitymentioning

confidence: 99%

Disparities in Breast Cancer Care—How Factors Related to Prevention, Diagnosis, and Treatment Drive Inequity

Wilkerson,

Gentle,

Ortega

et al. 2024

Healthcare

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Breast cancer survival has increased significantly over the last few decades due to more effective strategies for prevention and risk modification, advancements in imaging detection, screening, and multimodal treatment algorithms. However, many have observed disparities in benefits derived from such improvements across populations and demographic groups. This review summarizes published works that contextualize modern disparities in breast cancer prevention, diagnosis, and treatment and presents potential strategies for reducing disparities. We conducted searches for studies that directly investigated and/or reported disparities in breast cancer prevention, detection, or treatment. Demographic factors, social determinants of health, and inequitable healthcare delivery may impede the ability of individuals and communities to employ risk-mitigating behaviors and prevention strategies. The disparate access to quality screening and timely diagnosis experienced by various groups poses significant hurdles to optimal care and survival. Finally, barriers to access and inequitable healthcare delivery patterns reinforce inequitable application of standards of care. Cumulatively, these disparities underlie notable differences in the incidence, severity, and survival of breast cancers. Efforts toward mitigation will require collaborative approaches and partnerships between communities, governments, and healthcare organizations, which must be considered equal stakeholders in the fight for equity in breast cancer care and outcomes.

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Inequities in Physical Activity Environments and Leisure-Time Physical Activity in Rural Communities

Cited by 14 publications

References 31 publications

Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes

Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes

The Epidemiology of Domain-Specific Physical Activity in New Zealand Adults: A Nationally Representative Cross-Sectional Survey

Disparities in Breast Cancer Care—How Factors Related to Prevention, Diagnosis, and Treatment Drive Inequity

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