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Pearson AL, Tribby C, Brown CD, Yang JA, Pfeiffer K, Jankowska MM. Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research. BMJ Open 2024; 14:e077036. [PMID: 38307539 PMCID: PMC10836389 DOI: 10.1136/bmjopen-2023-077036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 01/16/2024] [Indexed: 02/04/2024] Open
Abstract
Global Positioning System (GPS) technology is increasingly used in health research to capture individual mobility and contextual and environmental exposures. However, the tools, techniques and decisions for using GPS data vary from study to study, making comparisons and reproducibility challenging. OBJECTIVES The objectives of this systematic review were to (1) identify best practices for GPS data collection and processing; (2) quantify reporting of best practices in published studies; and (3) discuss examples found in reviewed manuscripts that future researchers may employ for reporting GPS data usage, processing and linkage of GPS data in health studies. DESIGN A systematic review. DATA SOURCES Electronic databases searched (24 October 2023) were PubMed, Scopus and Web of Science (PROSPERO ID: CRD42022322166). ELIGIBILITY CRITERIA Included peer-reviewed studies published in English met at least one of the criteria: (1) protocols involving GPS for exposure/context and human health research purposes and containing empirical data; (2) linkage of GPS data to other data intended for research on contextual influences on health; (3) associations between GPS-measured mobility or exposures and health; (4) derived variable methods using GPS data in health research; or (5) comparison of GPS tracking with other methods (eg, travel diary). DATA EXTRACTION AND SYNTHESIS We examined 157 manuscripts for reporting of best practices including wear time, sampling frequency, data validity, noise/signal loss and data linkage to assess risk of bias. RESULTS We found that 6% of the studies did not disclose the GPS device model used, only 12.1% reported the per cent of GPS data lost by signal loss, only 15.7% reported the per cent of GPS data considered to be noise and only 68.2% reported the inclusion criteria for their data. CONCLUSIONS Our recommendations for reporting on GPS usage, processing and linkage may be transferrable to other geospatial devices, with the hope of promoting transparency and reproducibility in this research. PROSPERO REGISTRATION NUMBER CRD42022322166.
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Affiliation(s)
- Amber L Pearson
- CS Mott Department of Public Health, Michigan State University, Flint, MI, USA
| | - Calvin Tribby
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, California, USA
| | - Catherine D Brown
- Department of Geography, Environment and Spatial Sciences, Michigan State University, East Lansing, Michigan, USA
| | - Jiue-An Yang
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, California, USA
| | - Karin Pfeiffer
- Department of Kinesiology, Michigan State University, East Lansing, Michigan, USA
| | - Marta M Jankowska
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, California, USA
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2
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Wei L, Kwan MP, Vermeulen R, Helbich M. Measuring environmental exposures in people's activity space: The need to account for travel modes and exposure decay. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023; 33:954-962. [PMID: 36788269 DOI: 10.1038/s41370-023-00527-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 01/24/2023] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Accurately quantifying people's out-of-home environmental exposure is important for identifying disease risk factors. Several activity space-based exposure assessments exist, possibly leading to different exposure estimates, and have neither considered individual travel modes nor exposure-related distance decay effects. OBJECTIVE We aimed (1) to develop an activity space-based exposure assessment approach that included travel modes and exposure-related distance decay effects and (2) to compare the size of such spaces and the exposure estimates derived from them across typically used activity space operationalizations. METHODS We used 7-day-long global positioning system (GPS)-enabled smartphone-based tracking data of 269 Dutch adults. People's GPS trajectory points were classified into passive and active travel modes. Exposure-related distance decay effects were modeled through linear, exponential, and Gaussian decay functions. We performed cross-comparisons on these three functional decay models and an unweighted model in conjunction with four activity space models (i.e., home-based buffers, minimum convex polygons, two standard deviational ellipses, and time-weighted GPS-based buffers). We applied non-parametric Kruskal-Wallis tests, pair-wise Wilcoxon signed-rank tests, and Spearman correlations to assess mean differences in the extent of the activity spaces and correlations across exposures to particulate matter (PM2.5), noise, green space, and blue space. RESULTS Participants spent, on average, 42% of their daily life out-of-home. We observed that including travel modes into activity space delineation resulted in significantly more compact activity spaces. Exposure estimates for PM2.5 and blue space were significantly (p < 0.05) different between exposure estimates that did or did not account for travel modes, unlike noise and green space, for which differences did not reach significance. While the inclusion of distance decay effects significantly affected noise and green space exposure assessments, the decay functions applied appear not to have had any impact on the results. We found that residential exposure estimates appear appropriate for use as proxy values for the overall amount of PM2.5 exposure in people's daily lives, while GPS-based assessments are suitable for noise, green space, and blue space. SIGNIFICANCE For some exposures, the tested activity space definitions, although significantly correlated, exhibited differing exposure estimate results based on inclusion or exclusion of travel modes or distance decay effect. Results only supported using home-based buffer values as proxies for individuals' daily short-term PM2.5 exposure.
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Affiliation(s)
- Lai Wei
- Department of Human Geography and Spatial Planning, Utrecht University, Utrecht, The Netherlands.
| | - Mei-Po Kwan
- Department of Human Geography and Spatial Planning, Utrecht University, Utrecht, The Netherlands
- Department of Geography and Resource Management and Institute of Space and Earth Information Science, Chinese University of Hong Kong, Hong Kong, China
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
- Julius Centre for Health Sciences and Primary Care, University Medical Centre, Utrecht University, Utrecht, The Netherlands
| | - Marco Helbich
- Department of Human Geography and Spatial Planning, Utrecht University, Utrecht, The Netherlands
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3
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Jankowska MM, Yang JA, Luo N, Spoon C, Benmarhnia T. Accounting for space, time, and behavior using GPS derived dynamic measures of environmental exposure. Health Place 2023; 79:102706. [PMID: 34801405 PMCID: PMC9129269 DOI: 10.1016/j.healthplace.2021.102706] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 10/19/2022]
Abstract
Time-weighted spatial averaging approaches (TWSA) are an increasingly utilized method for calculating exposure using global positioning system (GPS) mobility data for health-related research. They can provide a time-weighted measure of exposure, or dose, to various environments or health hazards. However, little work has been done to compare existing methodologies, nor to assess how sensitive these methods are to mobility data inputs (e.g., walking vs driving), the type of environmental data being assessed as the exposure (e.g., continuous surfaces vs points of interest), and underlying point-pattern clustering of participants (e.g., if a person is highly mobile vs predominantly stationary). Here we contrast three TWSA approaches that have been previously used or recently introduced in the literature: Kernel Density Estimation (KDE), Density Ranking (DR), and Point Overlay (PO). We feed GPS and accelerometer data from 602 participants through each method to derive time-weighted activity spaces, comparing four mobility behaviors: all movement, stationary time, walking time, and in-vehicle time. We then calculate exposure values derived from the various TWSA activity spaces with four environmental layer data types (point, line, area, surface). Similarities and differences across TWSA derived exposures for the sample and between individuals are explored, and we discuss interpretation of TWSA outputs providing recommendations for researchers seeking to apply these methods to health-related studies.
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Affiliation(s)
| | - Jiue-An Yang
- Population Sciences, Beckman Research Institute, City of Hope, USA
| | - Nana Luo
- Scripps Institute of Oceanography, University of California San Diego, USA
| | - Chad Spoon
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, USA
| | - Tarik Benmarhnia
- Scripps Institute of Oceanography, University of California San Diego, USA
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4
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Lan Y, Roberts H, Kwan MP, Helbich M. Daily space-time activities, multiple environmental exposures, and anxiety symptoms: A cross-sectional mobile phone-based sensing study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 834:155276. [PMID: 35439503 DOI: 10.1016/j.scitotenv.2022.155276] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 03/16/2022] [Accepted: 04/10/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Few mobility-based studies have investigated the associations between multiple environmental exposures, including social exposures, and mental health. OBJECTIVE To assess how exposure to green space, blue space, noise, air pollution, and crowdedness along people's daily mobility paths are associated with anxiety symptoms. METHODS 358 participants were cross-sectionally tracked with Global Positioning System (GPS)-enabled mobile phones. Anxiety symptoms were measured at baseline using the Generalized Anxiety Disorder-7 (GAD-7) questionnaire. Green space, blue space, noise, and air pollution were assessed based on concentric buffers of 50 m and 100 m around each GPS point. Crowdedness was measured by the number of nearby Bluetooth-enabled devices detected along the mobility paths. Multiple linear regressions with full covariate adjustment were fitted to examine anxiety-environmental exposures associations. Random forest models were applied to explore possible nonlinear associations and exposure interactions. RESULTS Regression results showed null linear associations between GAD-7 scores and environmental exposures. Random forest models indicated that GAD-7-environment associations varied nonlinearly with exposure levels. We found a negative association between green space and GAD-7 scores only for participants with moderate green space exposure. We observed a positive association between GAD-7 scores and noise levels above 60 dB and air pollution concentrations above 17.2 μg m-3. Crowdedness was positively associated with GAD-7 scores, but exposure-response functions flattened out with pronounced crowdedness of >7.5. Blue space tended to be positively associated with GAD-7 scores. Random forest models ranked environmental exposures as more important to explain GAD-7 scores than linear models. CONCLUSIONS Our findings indicate possible nonlinear associations between mobility-based environmental exposures and anxiety symptoms. More studies are needed to obtain an in-depth understanding of underlying anxiety-environment mechanisms during daily life.
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Affiliation(s)
- Yuliang Lan
- Department of Human Geography and Spatial Planning, Utrecht University, the Netherlands.
| | - Hannah Roberts
- Department of Human Geography and Spatial Planning, Utrecht University, the Netherlands
| | - Mei-Po Kwan
- Department of Human Geography and Spatial Planning, Utrecht University, the Netherlands; Department of Geography and Resource Management and Institute of Space and Earth Information Science, Chinese University of Hong Kong, Hong Kong, China
| | - Marco Helbich
- Department of Human Geography and Spatial Planning, Utrecht University, the Netherlands
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5
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Roberts H, Helbich M. Multiple environmental exposures along daily mobility paths and depressive symptoms: A smartphone-based tracking study. ENVIRONMENT INTERNATIONAL 2021; 156:106635. [PMID: 34030073 DOI: 10.1016/j.envint.2021.106635] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/07/2021] [Accepted: 05/06/2021] [Indexed: 06/12/2023]
Abstract
Few studies go beyond the residential environment in assessments of the environment-mental health association, despite multiple environments being encountered in daily life. This study investigated 1) the associations between multiple environmental exposures and depressive symptoms, both in the residential environment and along the daily mobility path, 2) examined differences in the strength of associations between residential- and mobility-based models, and 3) explored sex as a moderator. Depressive symptoms of 393 randomly sampled adults aged 18-65 were assessed using the Patient Health Questionnaire (PHQ-9). Respondents were tracked via global positioning systems- (GPS) enabled smartphones for up to 7 days. Exposure to green space (normalized difference vegetation index (NDVI)), blue space, noise (Lden) and air pollution (particulate matter (PM2.5)) within 50 m and 100 m of each residential address and GPS point was computed. Multiple linear regression analyses were conducted separately for the residential- and mobility-based exposures. Wald tests were used to assess if the coefficients differed across models. Interaction terms were entered in fully adjusted models to determine if associations varied by sex. A significant negative relationship between green space and depressive symptoms was found in the fully adjusted residential- and mobility-based models using the 50 m buffer. No significant differences were observed in coefficients across models. None of the interaction terms were significant. Our results suggest that exposure to green space in the immediate environment, both at home and along the daily mobility path, is associated with a reduction in depressive symptoms. Further research is required to establish the utility of dynamic approaches to exposure assessment in studies on the environment and mental health.
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Affiliation(s)
- Hannah Roberts
- Department of Human Geography and Spatial Planning, Utrecht University, the Netherlands.
| | - Marco Helbich
- Department of Human Geography and Spatial Planning, Utrecht University, the Netherlands
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6
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Zuidema C, Stebounova LV, Sousan S, Gray A, Stroh O, Thomas G, Peters T, Koehler K. Estimating personal exposures from a multi-hazard sensor network. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2020; 30:1013-1022. [PMID: 31164703 PMCID: PMC6891140 DOI: 10.1038/s41370-019-0146-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 04/11/2019] [Accepted: 05/10/2019] [Indexed: 05/29/2023]
Abstract
Occupational exposure assessment is almost exclusively accomplished with personal sampling. However, personal sampling can be burdensome and suffers from low sample sizes, resulting in inadequately characterized workplace exposures. Sensor networks offer the opportunity to measure occupational hazards with a high degree of spatiotemporal resolution. Here, we demonstrate an approach to estimate personal exposure to respirable particulate matter (PM), carbon monoxide (CO), ozone (O3), and noise using hazard data from a sensor network. We simulated stationary and mobile employees that work at the study site, a heavy-vehicle manufacturing facility. Network-derived exposure estimates compared favorably to measurements taken with a suite of personal direct-reading instruments (DRIs) deployed to mimic personal sampling but varied by hazard and type of employee. The root mean square error (RMSE) between network-derived exposure estimates and personal DRI measurements for mobile employees was 0.15 mg/m3, 1 ppm, 82 ppb, and 3 dBA for PM, CO, O3, and noise, respectively. Pearson correlation between network-derived exposure estimates and DRI measurements ranged from 0.39 (noise for mobile employees) to 0.75 (noise for stationary employees). Despite the error observed estimating personal exposure to occupational hazards it holds promise as an additional tool to be used with traditional personal sampling due to the ability to frequently and easily collect exposure information on many employees.
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Affiliation(s)
- Christopher Zuidema
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Occupational and Environmental Health Sciences, University of Washington School of Public Health, Seattle, USA
| | - Larissa V Stebounova
- Department of Occupational and Environmental Health, University of Iowa, Iowa City, IA, USA
| | - Sinan Sousan
- Department of Occupational and Environmental Health, University of Iowa, Iowa City, IA, USA
- Department of Public Health, East Carolina University, Greenville, NC, USA
- North Carolina Agromedicine Institute, Greenville, NC, USA
| | - Alyson Gray
- Department of Occupational and Environmental Health, University of Iowa, Iowa City, IA, USA
| | - Oliver Stroh
- Department of Industrial and Systems Engineering, University of Iowa, Iowa City, IA, USA
| | - Geb Thomas
- Department of Industrial and Systems Engineering, University of Iowa, Iowa City, IA, USA
| | - Thomas Peters
- Department of Occupational and Environmental Health, University of Iowa, Iowa City, IA, USA
| | - Kirsten Koehler
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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7
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Goddard FB, Ban R, Barr DB, Brown J, Cannon J, Colford JM, Eisenberg JNS, Ercumen A, Petach H, Freeman MC, Levy K, Luby SP, Moe C, Pickering AJ, Sarnat JA, Stewart J, Thomas E, Taniuchi M, Clasen T. Measuring Environmental Exposure to Enteric Pathogens in Low-Income Settings: Review and Recommendations of an Interdisciplinary Working Group. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:11673-11691. [PMID: 32813503 PMCID: PMC7547864 DOI: 10.1021/acs.est.0c02421] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 08/18/2020] [Accepted: 08/19/2020] [Indexed: 05/06/2023]
Abstract
Infections with enteric pathogens impose a heavy disease burden, especially among young children in low-income countries. Recent findings from randomized controlled trials of water, sanitation, and hygiene interventions have raised questions about current methods for assessing environmental exposure to enteric pathogens. Approaches for estimating sources and doses of exposure suffer from a number of shortcomings, including reliance on imperfect indicators of fecal contamination instead of actual pathogens and estimating exposure indirectly from imprecise measurements of pathogens in the environment and human interaction therewith. These shortcomings limit the potential for effective surveillance of exposures, identification of important sources and modes of transmission, and evaluation of the effectiveness of interventions. In this review, we summarize current and emerging approaches used to characterize enteric pathogen hazards in different environmental media as well as human interaction with those media (external measures of exposure), and review methods that measure human infection with enteric pathogens as a proxy for past exposure (internal measures of exposure). We draw from lessons learned in other areas of environmental health to highlight how external and internal measures of exposure can be used to more comprehensively assess exposure. We conclude by recommending strategies for advancing enteric pathogen exposure assessments.
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Affiliation(s)
- Frederick
G. B. Goddard
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Radu Ban
- Bill and
Melinda Gates Foundation, Seattle, Washington 98109, United States
| | - Dana Boyd Barr
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Joe Brown
- School of
Civil and Environmental Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
| | - Jennifer Cannon
- Centers
for Disease Control and Prevention Foundation, Atlanta, Georgia 30308, United States
| | - John M. Colford
- Division
of Epidemiology and Biostatistics, School of Public Health, University of California−Berkeley, Berkeley, California 94720, United States
| | - Joseph N. S. Eisenberg
- Department
of Epidemiology, University of Michigan
School of Public Health, Ann Arbor, Michigan 48109, United States
| | - Ayse Ercumen
- Department
of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Helen Petach
- U.S. Agency
for International Development, Washington, DC 20004, United States
| | - Matthew C. Freeman
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Karen Levy
- Department
of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98105, United States
| | - Stephen P. Luby
- Division
of Infectious Diseases and Geographic Medicine, Stanford University, California 94305, United States
| | - Christine Moe
- Center
for
Global Safe Water, Sanitation and Hygiene, Rollins School of Public
Health, Emory University, Atlanta, Georgia 30322, United States
| | - Amy J. Pickering
- Department
of Civil and Environmental Engineering, School of Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Jeremy A. Sarnat
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Jill Stewart
- Department
of Environmental Sciences and Engineering, Gillings School of Global
Public Health, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Evan Thomas
- Mortenson
Center in Global Engineering, University
of Colorado Boulder, Boulder, Colorado 80303, United States
| | - Mami Taniuchi
- Division
of Infectious Diseases and International Health, Department of Medicine, University of Virginia, Charlottesville, Virginia 22903, United States
| | - Thomas Clasen
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
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8
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Measuring objective and subjective well-being: dimensions and data sources. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2020. [DOI: 10.1007/s41060-020-00224-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
AbstractWell-being is an important value for people’s lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development.
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Marsh A, Hirve S, Lele P, Chavan U, Bhattacharjee T, Nair H, Campbell H, Juvekar S. Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey. J Glob Health 2020; 10:010602. [PMID: 32426124 PMCID: PMC7211413 DOI: 10.7189/jogh.10.010602] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Introduction Common approaches to measure health behaviors rely on participant responses and are subject to bias. Technology-based alternatives, particularly using GPS, address these biases while opening new channels for research. This study describes the development and implementation of a GPS-based approach to detect health facility visits in rural Pune district, India. Methods Participants were mothers of under-five year old children within the Vadu Demographic Surveillance area. Participants received GPS-enabled smartphones pre-installed with a location-aware application to continuously record and transmit participant location data to a central server. Data were analyzed to identify health facility visits according to a parameter-based approach, optimal thresholds of which were calibrated through a simulation exercise. Lists of GPS-detected health facility visits were generated at each of six follow-up home visits and reviewed with participants through prompted recall survey, confirming visits which were correctly identified. Detected visits were analyzed using logistic regression to explore factors associated with the identification of false positive GPS-detected visits. Results We enrolled 200 participants and completed 1098 follow-up visits over the six-month study period. Prompted recall surveys were completed for 694 follow-up visits with one or more GPS-detected health facility visits. While the approach performed well during calibration (positive predictive value (PPV) 78%), performance was poor when applied to participant data. Only 440 of 22 251 detected visits were confirmed (PPV 2%). False positives increased as participants spent more time in areas of high health facility density (odds ratio (OR) = 2.29, 95% confidence interval (CI) = 1.62-3.25). Visits detected at facilities other than hospitals and clinics were also more likely to be false positives (OR = 2.78, 95% CI = 1.65-4.67) as were visits detected to facilities nearby participant homes, with the likelihood decreasing as distance increased (OR = 0.89, 95% CI = 0.82-0.97). Visit duration was not associated with confirmation status. Conclusions The optimal parameter combination for health facility visits simulated by field workers substantially overestimated health visits from participant GPS data. This study provides useful insights into the challenges in detecting health facility visits where providers are numerous, highly clustered within urban centers and located near residential areas of the population which they serve.
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Affiliation(s)
- Andrew Marsh
- Institute for International Programs, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.,KEM Hospital Research Centre, Sardar Moodliar Road, Rasta Peth, Pune, India
| | | | - Pallavi Lele
- KEM Hospital Research Centre, Sardar Moodliar Road, Rasta Peth, Pune, India
| | - Uddhavi Chavan
- KEM Hospital Research Centre, Sardar Moodliar Road, Rasta Peth, Pune, India
| | - Tathagata Bhattacharjee
- KEM Hospital Research Centre, Sardar Moodliar Road, Rasta Peth, Pune, India.,INDEPTH Network, 40 Mensah Wood Street, East Legon, Accra, Ghana
| | - Harish Nair
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Harry Campbell
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Sanjay Juvekar
- KEM Hospital Research Centre, Sardar Moodliar Road, Rasta Peth, Pune, India.,INDEPTH Network, 40 Mensah Wood Street, East Legon, Accra, Ghana
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10
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Rozylowicz L, Bodescu FP, Ciocanea CM, Gavrilidis AA, Manolache S, Matache ML, Miu IV, Moale IC, Nita A, Popescu VD. Empirical analysis and modeling of Argos Doppler location errors in Romania. PeerJ 2019; 7:e6362. [PMID: 30723631 PMCID: PMC6360076 DOI: 10.7717/peerj.6362] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 12/30/2018] [Indexed: 11/23/2022] Open
Abstract
Background Advances in wildlife tracking technology have allowed researchers to understand the spatial ecology of many terrestrial and aquatic animal species. Argos Doppler is a technology that is widely used for wildlife tracking owing to the small size and low weight of the Argos transmitters. This allows them to be fitted to small-bodied species. The longer lifespan of the Argos units in comparison to units outfitted with miniaturized global positioning system (GPS) technology has also recommended their use. In practice, large Argos location errors often occur due to communication conditions such as transmitter settings, local environment, and the behavior of the tracked individual. Methods Considering the geographic specificity of errors and the lack of benchmark studies in Eastern Europe, the research objectives were: (1) to evaluate the accuracy of Argos Doppler technology under various environmental conditions in Romania, (2) to investigate the effectiveness of straightforward destructive filters for improving Argos Doppler data quality, and (3) to provide guidelines for processing Argos Doppler wildlife monitoring data. The errors associated with Argos locations in four geographic locations in Romania were assessed during static, low-speed and high-speed tests. The effectiveness of the Douglas Argos distance angle filter algorithm was then evaluated to ascertain its effect on the minimization of localization errors. Results Argos locations received in the tests had larger associated horizontal errors than those indicated by the operator of the Argos system, including under ideal reception conditions. Positional errors were similar to those obtained in other studies outside of Europe. The errors were anisotropic, with larger longitudinal errors for the vast majority of the data. Errors were mostly related to speed of the Argos transmitter at the time of reception, but other factors such as topographical conditions and orientation of antenna at the time of the transmission also contributed to receiving low-quality data. The Douglas Argos filter successfully excluded the largest errors while retaining a large amount of data when the threshold was set to the local scale (two km). Discussion Filter selection requires knowledge about the movement patterns and behavior of the species of interest, and the parametrization of the selected filter typically requires a trial and error approach. Selecting the proper filter reduces the errors while retaining a large amount of data. However, the post-processed data typically includes large positional errors; thus, we recommend incorporating Argos error metrics (e.g., error ellipse) or use complex modeling approaches when working with filtered data.
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Affiliation(s)
- Laurentiu Rozylowicz
- Center for Environmental Research and Impact Studies, University of Bucharest, Bucharest, Romania
| | | | - Cristiana M Ciocanea
- Center for Environmental Research and Impact Studies, University of Bucharest, Bucharest, Romania
| | | | - Steluta Manolache
- Center for Environmental Research and Impact Studies, University of Bucharest, Bucharest, Romania
| | - Marius L Matache
- Center for Environmental Research and Impact Studies, University of Bucharest, Bucharest, Romania
| | - Iulia V Miu
- Center for Environmental Research and Impact Studies, University of Bucharest, Bucharest, Romania
| | | | - Andreea Nita
- Center for Environmental Research and Impact Studies, University of Bucharest, Bucharest, Romania
| | - Viorel D Popescu
- Center for Environmental Research and Impact Studies, University of Bucharest, Bucharest, Romania.,Department of Biological Sciences, Ohio University, Athens, OH, USA
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Klous G, Smit LAM, Borlée F, Coutinho RA, Kretzschmar MEE, Heederik DJJ, Huss A. Mobility assessment of a rural population in the Netherlands using GPS measurements. Int J Health Geogr 2017; 16:30. [PMID: 28793901 PMCID: PMC5551017 DOI: 10.1186/s12942-017-0103-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Accepted: 08/04/2017] [Indexed: 12/22/2022] Open
Abstract
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/m2) 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. Electronic supplementary material The online version of this article (doi:10.1186/s12942-017-0103-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gijs Klous
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands. .,Institute for Risk Assessment Sciences (IRAS), Division Environmental Epidemiology and Veterinary Public Health (EEPI-VPH), Utrecht University, Yalelaan 2, 3584 CM, Utrecht, The Netherlands.
| | - Lidwien A M Smit
- Institute for Risk Assessment Sciences (IRAS), Division Environmental Epidemiology and Veterinary Public Health (EEPI-VPH), Utrecht University, Yalelaan 2, 3584 CM, Utrecht, The Netherlands
| | - Floor Borlée
- Institute for Risk Assessment Sciences (IRAS), Division Environmental Epidemiology and Veterinary Public Health (EEPI-VPH), Utrecht University, Yalelaan 2, 3584 CM, Utrecht, The Netherlands.,Netherlands Institute for Health Services Research (NIVEL), Utrecht, The Netherlands
| | - Roel A Coutinho
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.,Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Mirjam E E Kretzschmar
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.,National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Dick J J Heederik
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.,Institute for Risk Assessment Sciences (IRAS), Division Environmental Epidemiology and Veterinary Public Health (EEPI-VPH), Utrecht University, Yalelaan 2, 3584 CM, Utrecht, The Netherlands
| | - Anke Huss
- Institute for Risk Assessment Sciences (IRAS), Division Environmental Epidemiology and Veterinary Public Health (EEPI-VPH), Utrecht University, Yalelaan 2, 3584 CM, Utrecht, The Netherlands
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How Sensors Might Help Define the External Exposome. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 14:ijerph14040434. [PMID: 28420222 PMCID: PMC5409635 DOI: 10.3390/ijerph14040434] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Revised: 03/14/2017] [Accepted: 03/23/2017] [Indexed: 01/23/2023]
Abstract
The advent of the exposome concept, the advancement of mobile technology, sensors, and the “internet of things” bring exciting opportunities to exposure science. Smartphone apps, wireless devices, the downsizing of monitoring technologies, along with lower costs for such equipment makes it possible for various aspects of exposure to be measured more easily and frequently. We discuss possibilities and lay out several criteria for using smart technologies for external exposome studies. Smart technologies are evolving quickly, and while they provide great promise for advancing exposure science, many are still in developmental stages and their use in epidemiology and risk studies must be carefully considered. The most useable technologies for exposure studies at this time relate to gathering exposure-factor data, such as location and activities. Development of some environmental sensors (e.g., for some air pollutants, noise, UV) is moving towards making the use of these more reliable and accessible to research studies. The possibility of accessing such an unprecedented amount of personal data also comes with various limitations and challenges, which are discussed. The advantage of improving the collection of long term exposure factor data is that this can be combined with more “traditional” measurement data to model exposures to numerous environmental factors.
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Donaire-Gonzalez D, Valentín A, de Nazelle A, Ambros A, Carrasco-Turigas G, Seto E, Jerrett M, Nieuwenhuijsen MJ. Benefits of Mobile Phone Technology for Personal Environmental Monitoring. JMIR Mhealth Uhealth 2016; 4:e126. [PMID: 27833069 PMCID: PMC5122720 DOI: 10.2196/mhealth.5771] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 08/11/2016] [Accepted: 08/28/2016] [Indexed: 01/31/2023] Open
Abstract
Background Tracking individuals in environmental epidemiological studies using novel mobile phone technologies can provide valuable information on geolocation and physical activity, which will improve our understanding of environmental exposures. Objective The objective of this study was to assess the performance of one of the least expensive mobile phones on the market to track people's travel-activity pattern. Methods Adults living and working in Barcelona (72/162 bicycle commuters) carried simultaneously a mobile phone and a Global Positioning System (GPS) tracker and filled in a travel-activity diary (TAD) for 1 week (N=162). The CalFit app for mobile phones was used to log participants’ geographical location and physical activity. The geographical location data were assigned to different microenvironments (home, work or school, in transit, others) with a newly developed spatiotemporal map-matching algorithm. The tracking performance of the mobile phones was compared with that of the GPS trackers using chi-square test and Kruskal-Wallis rank sum test. The minute agreement across all microenvironments between the TAD and the algorithm was compared using the Gwet agreement coefficient (AC1). Results The mobile phone acquired locations for 905 (29.2%) more trips reported in travel diaries than the GPS tracker (P<.001) and had a median accuracy of 25 m. Subjects spent on average 57.9%, 19.9%, 9.0%, and 13.2% of time at home, work, in transit, and other places, respectively, according to the TAD and 57.5%, 18.8%, 11.6%, and 12.1%, respectively, according to the map-matching algorithm. The overall minute agreement between both methods was high (AC1 .811, 95% CI .810-.812). Conclusions The use of mobile phones running the CalFit app provides better information on which microenvironments people spend their time in than previous approaches based only on GPS trackers. The improvements of mobile phone technology in microenvironment determination are because the mobile phones are faster at identifying first locations and capable of getting location in challenging environments thanks to the combination of assisted-GPS technology and network positioning systems. Moreover, collecting location information from mobile phones, which are already carried by individuals, allows monitoring more people with a cheaper and less burdensome method than deploying GPS trackers.
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Affiliation(s)
- David Donaire-Gonzalez
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,Pompeu Fabra University (UPF), Barcelona, Spain.,Ciber on Epidemiology and Public Health (CIBERESP), Barcelona, Spain.,Physical Activity and Sports Sciences Department, Fundació Blanquerna, Ramon Llull University, Barcelona, Spain
| | - Antònia Valentín
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,Pompeu Fabra University (UPF), Barcelona, Spain.,Ciber on Epidemiology and Public Health (CIBERESP), Barcelona, Spain
| | - Audrey de Nazelle
- Center for Environmental Policy, Imperial College London, London, United Kingdom
| | - Albert Ambros
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,Pompeu Fabra University (UPF), Barcelona, Spain.,Ciber on Epidemiology and Public Health (CIBERESP), Barcelona, Spain
| | - Glòria Carrasco-Turigas
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,Pompeu Fabra University (UPF), Barcelona, Spain.,Ciber on Epidemiology and Public Health (CIBERESP), Barcelona, Spain
| | - Edmund Seto
- Department of Environmental and Occupational Health Services, University of Washington, Seattle, WA, United States
| | - Michael Jerrett
- Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, United States.,Department of Environmental Health, Fielding School of Public Health, University of California, Los Angeles, CA, United States
| | - Mark J Nieuwenhuijsen
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,Pompeu Fabra University (UPF), Barcelona, Spain.,Ciber on Epidemiology and Public Health (CIBERESP), Barcelona, Spain
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de Müllenheim PY, Chaudru S, Gernigon M, Mahé G, Bickert S, Prioux J, Noury-Desvaux B, Le Faucheur A. Accuracy of a low-cost global positioning system receiver for estimating grade during outdoor walking. Physiol Meas 2016; 37:1741-1756. [PMID: 27653453 DOI: 10.1088/0967-3334/37/10/1741] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The aim of this study was to assess, for the first time, the accuracy of a low-cost global positioning system (GPS) receiver for estimating grade during outdoor walking. Thirty subjects completed outdoor walks (2.0, 3.5 and 5.0 km · h-1) in three randomized conditions: 1/level walking on a 0.0% grade; 2/graded (uphill and downhill) walking on a 3.4% grade; and 3/on a 10.4% grade. Subjects were equipped with a GPS receiver (DG100, GlobalSat Technology Corp., Taiwan; ~US$75). The GPS receiver was set to record at 1 Hz and its antenna was placed on the right shoulder. Grade was calculated from GPS speed and altitude data (grade = altitude variation/travelled distance × 100). Two methods were used for the grade calculation: one using uncorrected altitude data given by the GPS receiver and another one using corrected altitude data obtained using map projection software (CartoExploreur, version 3.11.0, build 2.6.6.22, Bayo Ltd, Appoigny, France, ~US$35). Linear regression of GPS-estimated versus actual grade with R 2 coefficients, bias with 95% limits of agreement (±95% LoA), and typical error of the estimate with 95% confidence interval (TEE (95% CI)) were computed to assess the accuracy of the GPS receiver. 444 walking periods were performed. Using uncorrected altitude data, we obtained: R 2 = 0.88 (p < 0.001), bias = 0.0 ± 6.6%, TEE between 1.9 (1.7-2.2)% and 4.2 (3.6-4.9)% according to the grade level. Using corrected altitude data, we obtained: R 2 = 0.98 (p < 0.001), bias = 0.2 ± 1.9%, TEE between 0.2 (0.2-0.3)% and 1.0 (0.9-1.2)% according to the grade level. The low-cost GPS receiver used was weakly accurate for estimating grade during outdoor walking when using uncorrected altitude data. However, the accuracy was greatly improved when using corrected altitude data. This study supports the potential interest of using GPS for estimating energy expenditure during outdoor walking.
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Affiliation(s)
- Pierre-Yves de Müllenheim
- Movement, Sport and Health Laboratory (EA 1274), Faculty of Sport Sciences, University of Rennes 2, F-35000 Rennes, France
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de Müllenheim PY, Dumond R, Gernigon M, Mahé G, Lavenu A, Bickert S, Prioux J, Noury-Desvaux B, Le Faucheur A. Predicting metabolic rate during level and uphill outdoor walking using a low-cost GPS receiver. J Appl Physiol (1985) 2016; 121:577-88. [PMID: 27402559 DOI: 10.1152/japplphysiol.00224.2016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Accepted: 07/05/2016] [Indexed: 12/26/2022] Open
Abstract
The objective of this study was to assess the accuracy of using speed and grade data obtained from a low-cost global positioning system (GPS) receiver to estimate metabolic rate (MR) during level and uphill outdoor walking. Thirty young, healthy adults performed randomized outdoor walking for 6-min periods at 2.0, 3.5, and 5.0 km/h and on three different grades: 1) level walking, 2) uphill walking on a 3.7% mean grade, and 3) uphill walking on a 10.8% mean grade. The reference MR [metabolic equivalents (METs) and oxygen uptake (V̇o2)] values were obtained using a portable metabolic system. The speed and grade were obtained using a low-cost GPS receiver (1-Hz recording). The GPS grade (Δ altitude/distance walked) was calculated using both uncorrected GPS altitude data and GPS altitude data corrected with map projection software. The accuracy of predictions using reference speed and grade (actual[SPEED/GRADE]) data was high [R(2) = 0.85, root-mean-square error (RMSE) = 0.68 MET]. The accuracy decreased when GPS speed and uncorrected grade (GPS[UNCORRECTED]) data were used, although it remained substantial (R(2) = 0.66, RMSE = 1.00 MET). The accuracy was greatly improved when the GPS speed and corrected grade (GPS[CORRECTED]) data were used (R(2) = 0.82, RMSE = 0.79 MET). Published predictive equations for walking MR were also cross-validated using actual or GPS speed and grade data when appropriate. The prediction accuracy was very close when either actual[SPEED/GRADE] values or GPS[CORRECTED] values (for level and uphill combined) or GPS speed values (for level walking only) were used. These results offer promising research and clinical applications related to the assessment of energy expenditure during free-living walking.
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Affiliation(s)
- Pierre-Yves de Müllenheim
- Movement, Sport and Health Laboratory (EA 1274), Faculty of Sport Sciences, University of Rennes 2, Rennes, France
| | - Rémy Dumond
- Movement, Sport and Health Laboratory (EA 1274), Faculty of Sport Sciences, University of Rennes 2, Rennes, France
| | - Marie Gernigon
- Laboratory for Vascular Investigations and Sports Medicine, University Hospital, Angers, France; Laboratory of Physiology, Institut National de la Santé et de la Recherche Médicale UMR 1083, Centre National de la Recherche Scientifique UMR 6214, Medical School, University of Angers, Angers, France
| | - Guillaume Mahé
- Clinical Investigation Center, Institut National de la Santé et de la Recherche Médicale CIC 1414, Rennes, France; Heart Vessels Imaging Team, University Hospital, Rennes, France; University of Rennes 1, Rennes, France
| | - Audrey Lavenu
- Clinical Investigation Center, Institut National de la Santé et de la Recherche Médicale CIC 1414, Rennes, France; Laboratory of Experimental and Clinical Pharmacology, Medical School, University of Rennes 1, Rennes, France
| | - Sandrine Bickert
- Laboratory for Vascular Investigations and Sports Medicine, University Hospital, Angers, France
| | - Jacques Prioux
- Movement, Sport and Health Laboratory (EA 1274), Faculty of Sport Sciences, University of Rennes 2, Rennes, France; Department of Sport Sciences and Physical Education, Ecole normale supérieure de Rennes, Campus de Ker Lann, Bruz, France
| | - Bénédicte Noury-Desvaux
- Laboratory for Vascular Investigations and Sports Medicine, University Hospital, Angers, France; Laboratory of Physiology, Institut National de la Santé et de la Recherche Médicale UMR 1083, Centre National de la Recherche Scientifique UMR 6214, Medical School, University of Angers, Angers, France; Institute of Physical Education and Sports Sciences (IFEPSA), Université Catholique de l'Ouest, Les Ponts-de-Cé, France; and
| | - Alexis Le Faucheur
- Movement, Sport and Health Laboratory (EA 1274), Faculty of Sport Sciences, University of Rennes 2, Rennes, France; Clinical Investigation Center, Institut National de la Santé et de la Recherche Médicale CIC 1414, Rennes, France; Department of Sport Sciences and Physical Education, Ecole normale supérieure de Rennes, Campus de Ker Lann, Bruz, France;
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Hoi TX, Phuong HT, Van Hung N. Using smartphone as a motion detector to collect time-microenvironment data for estimating the inhalation dose. Appl Radiat Isot 2016; 115:267-273. [PMID: 27451110 DOI: 10.1016/j.apradiso.2016.06.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Revised: 06/10/2016] [Accepted: 06/20/2016] [Indexed: 10/21/2022]
Abstract
During the production of iodine-131 from neutron irradiated tellurium dioxide by the dry distillation, a considerable amount of (131)I vapor is dispersed to the indoor air. People who routinely work at the production area may result in a significant risk of exposure to chronic intake by inhaled (131)I. This study aims to estimate the inhalation dose for individuals manipulating the (131)I at a radioisotope production. By using an application installed on smartphones, we collected the time-microenvironment data spent by a radiation group during work days in 2015. Simultaneously, we used a portable air sampler combined with radioiodine cartridges for grabbing the indoor air samples and then the daily averaged (131)I concentration was calculated. Finally, the time-microenvironment data jointed with the concentration to estimate the inhalation dose for the workers. The result showed that most of the workers had the annual internal dose in 1÷6mSv. We concluded that using smartphone as a motion detector is a possible and reliable way instead of the questionnaires, diary or GPS-based method. It is, however, only suitable for monitoring on fixed indoor environments and limited the targeted people.
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Affiliation(s)
- Tran Xuan Hoi
- Faculty of Natural Science, Phu Yen University, 18 Tran Phu, Tuy Hoa, Vietnam; Faculty of Physics and Engineering Physics, VNUHCM-University of Science, 227 Nguyen Van Cu, Ho Chi Minh City, Vietnam.
| | - Huynh Truc Phuong
- Faculty of Physics and Engineering Physics, VNUHCM-University of Science, 227 Nguyen Van Cu, Ho Chi Minh City, Vietnam.
| | - Nguyen Van Hung
- Training Center, Nuclear Research Institute, 01 Nguyen Tu Luc, Da Lat, Vietnam.
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Mooney SJ, Sheehan DM, Zulaika G, Rundle AG, McGill K, Behrooz MR, Lovasi GS. Quantifying Distance Overestimation From Global Positioning System in Urban Spaces. Am J Public Health 2016; 106:651-3. [PMID: 26890178 DOI: 10.2105/ajph.2015.303036] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To investigate accuracy of distance measures computed from Global Positioning System (GPS) points in New York City. METHODS We performed structured walks along urban streets carrying Globalsat DG-100 GPS Data Logger devices in highest and lowest quartiles of building height and tree canopy cover. We used ArcGIS version 10.1 to select walks and compute the straight-line distance (Geographic Information System-measured) and sum of distances between consecutive GPS waypoints (GPS-measured) for each walk. RESULTS GPS distance overestimates were associated with building height (median overestimate = 97% for high vs 14% for low building height) and to a lesser extent tree canopy (43% for high vs 28% for low tree canopy). CONCLUSIONS Algorithms using distances between successive GPS points to infer speed or travel mode may misclassify trips differentially by context. Researchers studying urban spaces may prefer alternative mode identification techniques.
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Affiliation(s)
- Stephen J Mooney
- Stephen J. Mooney, Daniel M. Sheehan, Garazi Zulaika, Andrew G. Rundle, and Gina Schellenbaum Lovasi are with Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY. Kevin McGill is with State University of New York at New Paltz. Melika R. Behrooz is with Barnard College, New York
| | - Daniel M Sheehan
- Stephen J. Mooney, Daniel M. Sheehan, Garazi Zulaika, Andrew G. Rundle, and Gina Schellenbaum Lovasi are with Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY. Kevin McGill is with State University of New York at New Paltz. Melika R. Behrooz is with Barnard College, New York
| | - Garazi Zulaika
- Stephen J. Mooney, Daniel M. Sheehan, Garazi Zulaika, Andrew G. Rundle, and Gina Schellenbaum Lovasi are with Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY. Kevin McGill is with State University of New York at New Paltz. Melika R. Behrooz is with Barnard College, New York
| | - Andrew G Rundle
- Stephen J. Mooney, Daniel M. Sheehan, Garazi Zulaika, Andrew G. Rundle, and Gina Schellenbaum Lovasi are with Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY. Kevin McGill is with State University of New York at New Paltz. Melika R. Behrooz is with Barnard College, New York
| | - Kevin McGill
- Stephen J. Mooney, Daniel M. Sheehan, Garazi Zulaika, Andrew G. Rundle, and Gina Schellenbaum Lovasi are with Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY. Kevin McGill is with State University of New York at New Paltz. Melika R. Behrooz is with Barnard College, New York
| | - Melika R Behrooz
- Stephen J. Mooney, Daniel M. Sheehan, Garazi Zulaika, Andrew G. Rundle, and Gina Schellenbaum Lovasi are with Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY. Kevin McGill is with State University of New York at New Paltz. Melika R. Behrooz is with Barnard College, New York
| | - Gina Schellenbaum Lovasi
- Stephen J. Mooney, Daniel M. Sheehan, Garazi Zulaika, Andrew G. Rundle, and Gina Schellenbaum Lovasi are with Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY. Kevin McGill is with State University of New York at New Paltz. Melika R. Behrooz is with Barnard College, New York
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Ouidir M, Giorgis-Allemand L, Lyon-Caen S, Morelli X, Cracowski C, Pontet S, Pin I, Lepeule J, Siroux V, Slama R. Estimation of exposure to atmospheric pollutants during pregnancy integrating space-time activity and indoor air levels: Does it make a difference? ENVIRONMENT INTERNATIONAL 2015; 84:161-73. [PMID: 26300245 PMCID: PMC4776347 DOI: 10.1016/j.envint.2015.07.021] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Revised: 07/28/2015] [Accepted: 07/29/2015] [Indexed: 05/19/2023]
Abstract
Studies of air pollution effects during pregnancy generally only consider exposure in the outdoor air at the home address. We aimed to compare exposure models differing in their ability to account for the spatial resolution of pollutants, space-time activity and indoor air pollution levels. We recruited 40 pregnant women in the Grenoble urban area, France, who carried a Global Positioning System (GPS) during up to 3 weeks; in a subgroup, indoor measurements of fine particles (PM2.5) were conducted at home (n=9) and personal exposure to nitrogen dioxide (NO2) was assessed using passive air samplers (n=10). Outdoor concentrations of NO2, and PM2.5 were estimated from a dispersion model with a fine spatial resolution. Women spent on average 16 h per day at home. Considering only outdoor levels, for estimates at the home address, the correlation between the estimate using the nearest background air monitoring station and the estimate from the dispersion model was high (r=0.93) for PM2.5 and moderate (r=0.67) for NO2. The model incorporating clean GPS data was less correlated with the estimate relying on raw GPS data (r=0.77) than the model ignoring space-time activity (r=0.93). PM2.5 outdoor levels were not to moderately correlated with estimates from the model incorporating indoor measurements and space-time activity (r=-0.10 to 0.47), while NO2 personal levels were not correlated with outdoor levels (r=-0.42 to 0.03). In this urban area, accounting for space-time activity little influenced exposure estimates; in a subgroup of subjects (n=9), incorporating indoor pollution levels seemed to strongly modify them.
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Affiliation(s)
- Marion Ouidir
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
| | - Lise Giorgis-Allemand
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
| | - Sarah Lyon-Caen
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
| | - Xavier Morelli
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
| | - Claire Cracowski
- CHU de Grenoble, Clinical Pharmacology Unit, Inserm CIC 1406, Grenoble, France
| | | | - Isabelle Pin
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France; CHU de Grenoble, Pediatric department, Grenoble, France
| | - Johanna Lepeule
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
| | - Valérie Siroux
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
| | - Rémy Slama
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France.
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Huss A, Beekhuizen J, Kromhout H, Vermeulen R. Using GPS-derived speed patterns for recognition of transport modes in adults. Int J Health Geogr 2014; 13:40. [PMID: 25304171 PMCID: PMC4320483 DOI: 10.1186/1476-072x-13-40] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2014] [Accepted: 10/02/2014] [Indexed: 12/03/2022] Open
Abstract
Background Identification of active or sedentary modes of transport is of relevance for studies assessing physical activity or addressing exposure assessment. We assessed in a proof-of-principle study if speed as logged by GPSs could be used to identify modes of transport (walking, bicycling, and motorized transport: car, bus or train). Methods 12 persons commuting to work walking, bicycling or with motorized transport carried GPSs for two commutes and recorded their mode of transport. We evaluated seven speed metrics: mean, 95th percentile of speed, standard deviation of the mean, rate-of-change, standardized-rate-of-change, acceleration and deceleration. We assessed which speed metric would best identify the transport mode using discriminant analyses. We applied cross validation and calculated agreement (Cohen’s Kappa) between actual and derived modes of transport. Results Mode of transport was reliably classified whenever a person used a mode of transport for longer than one minute. Best results were observed when using the 95th percentile of speed, acceleration and deceleration (kappa 0.73). When we combined all motorized traffic into one category, kappa increased to 0.95. Conclusions GPS-measured speed enable the identification of modes of transport. Given the current low costs of GPS devices and the built-in capacity of GPS tracking in most smartphones, the use of such devices in large epidemiological studies may facilitate the assessment of physical activity related to transport modes, or improve exposure assessment using automated travel mode detection. Electronic supplementary material The online version of this article (doi:10.1186/1476-072X-13-40) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Anke Huss
- Institute for Risk Assessment Sciences, Utrecht University, PO Box 80178, Utrecht 3508 TD, The Netherlands.
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Breen MS, Long TC, Schultz BD, Crooks J, Breen M, Langstaff JE, Isaacs KK, Tan YM, Williams RW, Cao Y, Geller AM, Devlin RB, Batterman SA, Buckley TJ. GPS-based microenvironment tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation in central North Carolina. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2014; 24:412-20. [PMID: 24619294 PMCID: PMC4269558 DOI: 10.1038/jes.2014.13] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2013] [Accepted: 12/19/2013] [Indexed: 05/22/2023]
Abstract
A critical aspect of air pollution exposure assessment is the estimation of the time spent by individuals in various microenvironments (ME). Accounting for the time spent in different ME with different pollutant concentrations can reduce exposure misclassifications, while failure to do so can add uncertainty and bias to risk estimates. In this study, a classification model, called MicroTrac, was developed to estimate time of day and duration spent in eight ME (indoors and outdoors at home, work, school; inside vehicles; other locations) from global positioning system (GPS) data and geocoded building boundaries. Based on a panel study, MicroTrac estimates were compared with 24-h diary data from nine participants, with corresponding GPS data and building boundaries of home, school, and work. MicroTrac correctly classified the ME for 99.5% of the daily time spent by the participants. The capability of MicroTrac could help to reduce the time-location uncertainty in air pollution exposure models and exposure metrics for individuals in health studies.
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Affiliation(s)
- Michael S. Breen
- National Exposure Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | - Thomas C. Long
- National Center for Environmental Assessment, US EPA, Research Triangle Park, NC, USA
| | - Bradley D. Schultz
- National Exposure Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | - James Crooks
- National Health and Environmental Effects Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | - Miyuki Breen
- National Health and Environmental Effects Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | - John E. Langstaff
- Office of Air Quality Planning and Standards, US EPA, Research Triangle Park, NC, USA
| | - Kristin K. Isaacs
- National Exposure Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | - Yu-Mei Tan
- National Exposure Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | - Ronald W. Williams
- National Exposure Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | - Ye Cao
- National Center for Environmental Assessment, US EPA, Research Triangle Park, NC, USA
| | - Andrew M. Geller
- Immediate Office of the Assistant Administrator, US EPA, Research Triangle Park, NC, USA
| | - Robert B. Devlin
- National Health and Environmental Effects Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | | | - Timothy J. Buckley
- National Exposure Research Laboratory, US EPA, Research Triangle Park, NC, USA
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Schipperijn J, Kerr J, Duncan S, Madsen T, Klinker CD, Troelsen J. Dynamic Accuracy of GPS Receivers for Use in Health Research: A Novel Method to Assess GPS Accuracy in Real-World Settings. Front Public Health 2014; 2:21. [PMID: 24653984 PMCID: PMC3948045 DOI: 10.3389/fpubh.2014.00021] [Citation(s) in RCA: 82] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2013] [Accepted: 02/21/2014] [Indexed: 12/02/2022] Open
Abstract
The emergence of portable global positioning system (GPS) receivers over the last 10 years has provided researchers with a means to objectively assess spatial position in free-living conditions. However, the use of GPS in free-living conditions is not without challenges and the aim of this study was to test the dynamic accuracy of a portable GPS device under real-world environmental conditions, for four modes of transport, and using three data collection intervals. We selected four routes on different bearings, passing through a variation of environmental conditions in the City of Copenhagen, Denmark, to test the dynamic accuracy of the Qstarz BT-Q1000XT GPS device. Each route consisted of a walk, bicycle, and vehicle lane in each direction. The actual width of each walking, cycling, and vehicle lane was digitized as accurately as possible using ultra-high-resolution aerial photographs as background. For each trip, we calculated the percentage that actually fell within the lane polygon, and within the 2.5, 5, and 10 m buffers respectively, as well as the mean and median error in meters. Our results showed that 49.6% of all ≈68,000 GPS points fell within 2.5 m of the expected location, 78.7% fell within 10 m and the median error was 2.9 m. The median error during walking trips was 3.9, 2.0 m for bicycle trips, 1.5 m for bus, and 0.5 m for car. The different area types showed considerable variation in the median error: 0.7 m in open areas, 2.6 m in half-open areas, and 5.2 m in urban canyons. The dynamic spatial accuracy of the tested device is not perfect, but we feel that it is within acceptable limits for larger population studies. Longer recording periods, for a larger population are likely to reduce the potentially negative effects of measurement inaccuracy. Furthermore, special care should be taken when the environment in which the study takes place could compromise the GPS signal.
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Affiliation(s)
- Jasper Schipperijn
- Research Unit for Active Living, Department of Sport Science and Clinical Biomechanics, University of Southern Denmark , Odense , Denmark
| | - Jacqueline Kerr
- Department of Family and Preventive Medicine, University of California San Diego , San Diego, CA , USA
| | - Scott Duncan
- Human Potential Centre, Auckland University of Technology , Auckland , New Zealand
| | - Thomas Madsen
- Research Unit for Active Living, Department of Sport Science and Clinical Biomechanics, University of Southern Denmark , Odense , Denmark
| | - Charlotte Demant Klinker
- Research Unit for Active Living, Department of Sport Science and Clinical Biomechanics, University of Southern Denmark , Odense , Denmark
| | - Jens Troelsen
- Research Unit for Active Living, Department of Sport Science and Clinical Biomechanics, University of Southern Denmark , Odense , Denmark
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Dons E, Temmerman P, Van Poppel M, Bellemans T, Wets G, Int Panis L. Street characteristics and traffic factors determining road users' exposure to black carbon. THE SCIENCE OF THE TOTAL ENVIRONMENT 2013; 447:72-9. [PMID: 23376518 DOI: 10.1016/j.scitotenv.2012.12.076] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2012] [Revised: 12/10/2012] [Accepted: 12/21/2012] [Indexed: 04/14/2023]
Abstract
Many studies nowadays make the effort of determining personal exposure rather than estimating exposure at the residential address only. While intra-urban air pollution can be modeled quite easily using interpolation methods, estimating exposure in transport is more challenging. The aim of this study is to investigate which factors determine black carbon (BC) concentrations in transport microenvironments. Therefore personal exposure measurements are carried out using portable aethalometers, trip diaries and GPS devices. More than 1500 trips, both by active modes and by motorized transport, are evaluated in Flanders, Belgium. GPS coordinates are assigned to road segments to allow BC concentrations to be linked with trip and road characteristics (trip duration, degree of urbanization, road type, traffic intensity, travel speed and road speed). Average BC concentrations on highways (10.7μg/m(3)) are comparable to concentrations on urban roads (9.6μg/m(3)), but levels are significantly higher than concentrations on rural roads (6.1μg/m(3)). Highways yield higher BC exposures for motorists compared to exposure on major roads and local roads. Overall BC concentrations are elevated at lower speeds (<30km/h) and at speeds above 80km/h, in accordance to vehicle emission functions. Driving on roads with low traffic intensities resulted in lower exposures than driving on roads with higher traffic intensities (from 5.6μg/m(3) for roads with less than 500veh/h, up to 12μg/m(3) for roads with over 2500veh/h). Traffic intensity proved to be the major explanatory variable for in-vehicle BC exposure, together with timing of the trip and urbanization. For cyclists and pedestrians the range in BC exposure is smaller and models are less predictive; for active modes exposure seems to be influenced by timing and degree of urbanization only.
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Affiliation(s)
- Evi Dons
- VITO (Flemish Institute for Technological Research), Boeretang 200, 2400 Mol, Belgium.
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