A GPS-Based Methodology to Analyze Environment-Health Associations at the Trip Level: Case-Crossover Analyses of Built Environments and Walking

Chaix, Basile; Kestens, Yan; Duncan, Dustin T.; Brondeel, Ruben; Méline, Julie; Aarbaoui, Tarik El; Pannier, Bruno; Merlo, Juan

doi:10.1093/aje/kww071

Cited by 57 publications

(71 citation statements)

References 32 publications

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“…Using a methodology similar to the one described in our study, these researchers measured NDVI levels at 30-second epochs and found a 34% increased odds of momentary moderate-vigorous physical activity (95% CI 1.30, 1.38) for a 0.11 increase in NDVI. As part of the Residential Environment and Coronary Heart Disease (RECORD) GPS Study, Chaix et al gathered seven days of GPS and accelerometry data on 227 participants in Paris (41). Analyses demonstrated that the probability of walking during a trip was 16% higher (95% CI 2%, 28%) when the trip originated in the top quartile of green space density and 20% higher (95% CI 7%, 33%) when the trip ended in the top quartile of green space density versus the bottom quartile.…”

Section: Discussionmentioning

confidence: 99%

GPS-Based Exposure to Greenness and Walkability and Accelerometry-Based Physical Activity

James

Hart

Hipp

et al. 2017

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Background Physical inactivity is a risk factor for cancer that may be influenced by environmental factors. Indeed, dense and well-connected built environments and environments with natural vegetation may create opportunities for higher routine physical activity. However, studies have focused primarily on residential environments to define exposure and self-reported methods to estimate physical activity. The current study explores the momentary association between minute-level global positioning systems (GPS)-based greenness exposure and time-matched objectively measured physical activity. Methods Adult women were recruited from sites across the US. Participants wore a GPS device and accelerometer on the hip for 7 days to assess location and physical activity at minute-level epochs. GPS records were linked to 250m resolution satellite-based vegetation data and Census Block Group-level EPA Smart Location Database walkability data. Minute-level generalized additive mixed models were conducted to test for associations between GPS measures and accelerometer count data, accounting for repeated measures within participant and allowing for deviations from linearity using splines. Results Among 360 adult women (mean age of 55.3 ± 10.2 years), we observed positive nonlinear relationships between physical activity and both greenness and walkability. In exploratory analyses, the relationship between environmental factors and physical activity were strongest among those who were White, had higher incomes, and who were middle-aged. Conclusions Our results indicate that higher levels of physical activity occurred in areas with higher greenness and higher walkability. Impact Findings suggest that planning and design policies should focus on these environments to optimize opportunities for physical activity.

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Section: Discussionmentioning

confidence: 99%

GPS-Based Exposure to Greenness and Walkability and Accelerometry-Based Physical Activity

James

Hart

Hipp

et al. 2017

Cancer Epidemiology, Biomarkers &Amp; Prevention

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“…There are a total of 22 studies in children, 17 in youth [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] and 20 in adults. 28, [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] Sample sizes ranged from 12 to 1053; 39% were small (N ≤ 100).…”

Section: Risk Of Bias Appraisalmentioning

confidence: 99%

“…Several studies examined or reported exclusively on time spent in active transportation. [61][62][63][64]74 Commuting/active transportation accounted for between 33% and 68% of total daily MVPA. 61,63,74 Chaix et al 63 found that in general transportation accounted for a median of 13% of ST, but public transportation trips were associated with significantly more ST compared to personalized motor vehicle trips.…”

Section: Location-based Findings For Adults (≥ 18 Years)mentioning

confidence: 99%

Where are children and adults physically active and sedentary? – a rapid review of location-based studies

Prince

Butler

Rao

et al. 2019

Health Promot Chronic Dis Prev Can

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Introduction Geographical positioning systems (GPS) have the capacity to provide further context around where physical activity (PA) and sedentary time (ST) are accrued especially when overlaid onto objectively measured movement. The objective of this rapid review was to summarize evidence from location-based studies which employed the simultaneous use of GPS and objective measures of PA and/or ST. Methods Six databases were searched to identify studies that employed the simultaneous use of GPS and objective measures of PA or ST to quantify location of movement. Risk of bias was assessed, and a qualitative synthesis completed. Results Searching identified 3446 articles; 59 were included in the review. A total of 22 studies in children, 17 in youth and 20 in adults were captured. The active transportation environment emerged as an important location for moderate-to-vigorous intensity physical activity (MVPA) in children, youth and adults. In children and youth, the school is an important location for MVPA, especially the schoolyard for children. Indoor locations (e.g., schools, homes) appear to be greater sources of lighter intensities of PA and ST. The review was limited by a lack of standardization in the nomenclature used to describe the locations and methods, as well as measures of variance. Conclusion Evidence suggests that the active transportation environment is a potentially important contributor of MVPA across an individual’s lifespan. There is a need for future location-based studies to report on locations of all intensity of movement (including minutes and proportion) using a whole-day approach in larger representative samples.

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“…To the best of our knowledge, there are few previous studies with such extensive datasets. Most studies that focus on GPS measurements included fewer than 300 participants [14,41]. Few larger studies with GPS measurements (Schuessler and Axhausen 2008 N = 4882 and Bohte and Maat 2009 N = 1104) [42,43], did not evaluate characteristics that explain observed differences in mobility patterns.…”

Section: Strengthsmentioning

confidence: 99%

Mobility assessment of a rural population in the Netherlands, using GPS measurements

Klous*¹,

Coutinho²,

Kretzschmar³

et al. 2016

ISEE Conference Abstracts

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Background: The home address is a common spatial proxy for exposure assessment in epidemiological studies but mobility may introduce exposure misclassification. Mobility can be assessed using self-reports or objectively measured using GPS logging but self-reports may not assess the same information as measured mobility. We aimed to assess mobility patterns of a rural population in the Netherlands using GPS measurements and self-reports and to compare GPS measured to self-reported data, and to evaluate correlates of differences in mobility patterns. Method: In total 870 participants filled in a questionnaire regarding their transport modes and carried a GPS-logger for 7 consecutive days. Transport modes were assigned to GPS-tracks based on speed patterns. Correlates of measured mobility data were evaluated using multiple linear regression. We calculated walking, biking and motorised transport durations based on GPS and self-reported data and compared outcomes. We used Cohen's kappa analyses to compare categorised self-reported and GPS measured data for time spent outdoors. Results: Self-reported time spent walking and biking was strongly overestimated when compared to GPS measurements. Participants estimated their time spent in motorised transport accurately. Several variables were associated with differences in mobility patterns, we found for instance that obese people (BMI > 30 kg/m 2) spent less time in non-motorised transport (GMR 0.69-0.74) and people with COPD tended to travel longer distances from home in motorised transport (GMR 1.42-1.51). Conclusions: If time spent walking outdoors and biking is relevant for the exposure to environmental factors, then relying on the home address as a proxy for exposure location may introduce misclassification. In addition, this misclassification is potentially differential, and specific groups of people will show stronger misclassification of exposure than others. Performing GPS measurements and identifying explanatory factors of mobility patterns may assist in regression calibration of self-reports in other studies.

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A GPS-Based Methodology to Analyze Environment-Health Associations at the Trip Level: Case-Crossover Analyses of Built Environments and Walking

Cited by 57 publications

References 32 publications

GPS-Based Exposure to Greenness and Walkability and Accelerometry-Based Physical Activity

GPS-Based Exposure to Greenness and Walkability and Accelerometry-Based Physical Activity

Where are children and adults physically active and sedentary? – a rapid review of location-based studies

Mobility assessment of a rural population in the Netherlands, using GPS measurements

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