Kang, Mingyu; Moudon, Anne V.; Hurvitz, Philip M.; Saelens, Brian E. (2018). Capturing Fine-Scale Travel Behaviors: A Comparative Analysis between Personal Activity Location Measurement System (PALMS) and Travel Diary. International Journal Of Health Geographics, 17(1).
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Abstract
BackgroundDevice-collected data from GPS and accelerometers for identifying active travel behaviors have dramatically changed research methods in transportation planning and public health. Automated algorithms have helped researchers to process large datasets with likely fewer errors than found in other collection methods (e.g., self-report travel diary). In this study, we compared travel modes identified by a commonly used automated algorithm (PALMS) that integrates GPS and accelerometer data with those obtained from travel diary estimates.MethodsSixty participants, who made 2100 trips during seven consecutive days of data collection, were selected from among the baseline sample of a project examining the travel behavior impact of a new light rail system in the greater Seattle, WA (USA) area. GPS point level analyses were first conducted to compare trip/place and travel mode detection results using contingency tables. Trip level analyses were then performed to investigate the effect of proportions of time overlap between travel logs and device-collected data on agreement rates. Global performance (with all subjects' data combined) and subject-level performance of the algorithm were compared at the trip level.ResultsAt the GPS point level, the overall agreement rate of travel mode detection was 77.4% between PALMS and the travel diary. The agreement rate for vehicular trip detection (84.5%) was higher than for bicycling (53.5%) and walking (58.2%). At the trip level, the global performance and subject-level performance of the PALMS algorithm were 46.4% and 42.4%, respectively. Vehicular trip detection showed highest agreement rates in all analyses. Study participants' primary travel mode and car ownership were significantly related to the subject-level mode agreement rates.ConclusionsThe PALMS algorithm showed moderate identification power at the GPS point level. However, trip level analyses found lower agreement rates between PALMS and travel diary data, especially for active transportation. Testing different PALMS parameter settings may serve to improve the detection of active travel and help expand PALMS's applicability in geographically different urbanized areas with a variety of travel modes.
Keywords
Transportation Planning; Public Health; Accelerometers; Global Positioning System; Voyages & Travels; Cycling; Algorithms; Accelerometer; Automated Algorithm; Gis; Gps; Places; Trips; Global Positioning Systems; Physical-activity; Data-collection; Health Research; Gps Data; Accelerometry; Validity
Buszkiewicz, James; Rose, Chelsea; Gupta, Shilpi; Ko, Linda K.; Mou, Jin; Moudon, Anne, V; Hurvitz, Philip M.; Cook, Andrea; Aggarwal, Anju; Drewnowski, Adam. (2020). A Cross-Sectional Analysis of Physical Activity and Weight Misreporting in Diverse Populations: The Seattle Obesity Study III. Obesity Science & Practice, 6(6), 615 – 627.
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Abstract
Background: In-person assessments of physical activity (PA) and body weight can be burdensome for participants and cost prohibitive for researchers. This study examined self-reported PA and weight accuracy and identified patterns of misreporting in a diverse sample. Methods: King, Pierce and Yakima county residents, aged 21-59 years (n= 728), self-reported their moderate-to-vigorous PA (MVPA) and weight, in kilograms. Self-reports were compared with minutes of bout-level MVPA, from 3 days of accelerometer data, and measured weights. Regression models examined characteristics associated with underreporting and overreporting of MVPA and weight, the potential bias introduced using each measure and the relation between perceived and measured PA and weight. Results: MVPA underreporting was higher among males and college educated participants; however, there was no differential MVPA overreporting. Weight underreporting was higher among males, those age 40-49 years and persons with obesity. Weight overreporting was higher among Hispanic participants and those reporting stress, unhappiness and fair or poor health. The estimated PA-obesity relation was similar using measured and self-reported PA but not self-reported weight. Perceived PA and weight predicted measured values. Conclusion: Self-reported PA and weight may be useful should objective measurement be infeasible; however, though population-specific adjustment for differential reporting should be considered.
Keywords
Self-reported Weight; Sedentary Behavior; Validation; Accuracy; Height; Adults; Health Disparity; Obesity; Physical Activity; Self-reported Outcomes
Rhew, Isaac C.; Guttmannova, Katarina; Kilmer, Jason R.; Fleming, Charles B.; Hultgren, Brittney A.; Hurvitz, Philip M.; Dilley, Julia A.; Larimer, Mary E. (2022). Associations of Cannabis Retail Outlet Availability and Neighborhood Disadvantage with Cannabis Use and Related Risk Factors Among Young Adults in Washington State. Drug & Alcohol Dependence, 232.
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Abstract
Background: This study examined associations of local cannabis retail outlet availability and neighborhood disadvantage with cannabis use and related risk factors among young adults. Methods: Data were from annual cross-sectional surveys administered from 2015 to 2019 to individuals ages 18-25 residing in Washington State (N = 10,009). As outcomes, this study assessed self-reported cannabis use at different margins/frequencies (any past year, at least monthly, at least weekly, at least daily) and perceived ease of access to cannabis and acceptability of cannabis use in the community. Cannabis retail outlet availability was defined as the presence of at least one retail outlet within a 1-kilometer road network buffer of one's residence. Sensitivity analyses explored four other spatial metrics to define outlet availability (any outlet within 0.5-km, 2-km, and the census tract; and census tract density per 1000 residents). Census tract level disadvantage was a composite of five US census variables. Results: Adjusting for individual- and area-level covariates, living within 1-kilometer of at least one cannabis retail outlet was statistically significantly associated with any past year and at least monthly cannabis use as well as high perceived access to cannabis. Results using a 2-km buffer and census tract-level metrics for retail outlet availability showed similar findings. Neighborhood disadvantage was statistically significantly associated with at least weekly and at least daily cannabis use and with greater perceived acceptability of cannabis use. Conclusions: Results may have implications for regulatory and prevention strategies to reduce the population burden of cannabis use and related harms.
Keywords
Outlet Stores; Young Adults; Neighborhoods; Older People; Sensitivity Analysis; Washington (state); Cannabis; Cannabis Retail Outlets; Neighborhood Disadvantage; Alcohol-use; Marijuana Use; Density; Proximity; Health; Norms
The Urban Form Lab (UFL) research aims to affect policy and to support approaches to the design and planning of more livable environments. The UFL specializes in geospatial analyses of the built environment using multiple micro-scale data in Geographic Information Systems (GIS). Current research includes the development of novel GIS routines for performing spatial inventories and analyses of the built environment, and of spatially explicit sampling techniques. Projects address such topics as land monitoring, neighborhood and street design, active transportation, non-motorized transportation safety, physical activity, and access to food environments.
Research at the UFL has been supported by the U.S. and Washington State Departments of Transportation, the Centers for Disease Control and Prevention, the Robert Wood Johnson Foundation, the National Institutes of Health, and local agencies.
The Urban Form Lab is directed by Anne Vernez Moudon, Dr es Sc, a leading researcher and educator in quantifying the properties of the built environment as related to health and transportation behaviors. Philip M. Hurvitz, PhD, a veteran of geographic information science and data processing, leads data management and GIS work.
The Urban@UW initiative brings together labs that study urban issues from across the University of Washington. Urban@UW works with scholars, policymakers, and community stakeholders in order to strengthen the connection between research and solutions to urban issues through cross-disciplinary and cross-sector collaborative research. Key functions of Urban@UW include amplifying public awareness of ongoing projects, connecting researchers with outside constituencies, providing staff and administrative support services, and providing pilot funding and fundraising assistance. Multiple BE labs are involved, including the Northwest…
The University of Washington Data Collaborative (UWDC) is now offering services to researchers across campus, including BE researchers Gregg Colburn at the Runstad Department of Real Estate and the Urban Form Lab. Housed at the Center for Studies in Demography & Ecology, UWDC provides infrastructure to access restricted data in a secure and sophisticated computing environment. Data sets available to researchers cover health records, polling data, business and consumer data, and real estate data. Researchers interested in accessing these data…