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Fancello G, Vallée J, Sueur C, van Lenthe FJ, Kestens Y, Montanari A, Chaix B. Micro urban spaces and mental well-being: Measuring the exposure to urban landscapes along daily mobility paths and their effects on momentary depressive symptomatology among older population. ENVIRONMENT INTERNATIONAL 2023; 178:108095. [PMID: 37487375 DOI: 10.1016/j.envint.2023.108095] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 06/29/2023] [Accepted: 07/11/2023] [Indexed: 07/26/2023]
Abstract
The urban environment plays an important role for the mental health of residents. Researchers mainly focus on residential neighbourhoods as exposure context, leaving aside the effects of non-residential environments. In order to consider the daily experience of urban spaces, a people-based approach focused on mobility paths is needed. Applying this approach, (1) this study investigated whether individuals' momentary mental well-being is related to the exposure to micro-urban spaces along the daily mobility paths within the two previous hours; (2) it explored whether these associations differ when environmental exposures are defined considering all location points or only outdoor location points; and (3) it examined the associations between the types of activity and mobility and momentary depressive symptomatology. Using a geographically-explicit ecological momentary assessment approach (GEMA), momentary depressive symptomatology of 216 older adults living in the Ile-de-France region was assessed using smartphone surveys, while participants were tracked with a GPS receiver and an accelerometer for seven days. Exposure to multiple elements of the streetscape was computed within a street network buffer of 25 m of each GPS point over the two hours prior to the questionnaire. Mobility and activity type were documented from a GPS-based mobility survey. We estimated Bayesian generalized mixed effect models with random effects at the individual and day levels and took into account time autocorrelation. We also estimated fixed effects. A better momentary mental wellbeing was observed when residents performed leisure activities or were involved in active mobility and when they were exposed to walkable areas (pedestrian dedicated paths, open spaces, parks and green areas), water elements, and commerce, leisure and cultural attractors over the previous two hours. These relationships were stronger when exposures were defined based only on outdoor location points rather than all location points, and when we considered within-individual differences compared to between-individual differences.
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Affiliation(s)
- Giovanna Fancello
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012 Paris, France.
| | - Julie Vallée
- UMR 8504 Géographie-cités (CNRS, Université Paris 1 Panthéon-Sorbonne, Université Paris Cité, EHESS), France
| | - Cédric Sueur
- UMR 7178 (CNRS, Unistra, Institut Pluridisciplinaire Hubert Curien), France; Anthropolab, ETHICS - EA 7446, Catholic University of Lille, Lille, France
| | - Frank J van Lenthe
- Department of Public Health, Erasmus MC, P.O. Box 2040, 3000 CA Rotterdam, Netherlands
| | - Yan Kestens
- Montreal Université, École de santé publique - Département de médecine sociale et preventive, Canada
| | - Andrea Montanari
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012 Paris, France
| | - Basile Chaix
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012 Paris, France
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Kouis P, Michanikou A, Galanakis E, Michaelidou E, Dimitriou H, Perez J, Kinni P, Achilleos S, Revvas E, Stamatelatos G, Zacharatos H, Savvides C, Vasiliadou E, Kalivitis N, Chrysanthou A, Tymvios F, Papatheodorou SI, Koutrakis P, Yiallouros PK. Responses of schoolchildren with asthma to recommendations to reduce desert dust exposure: Results from the LIFE-MEDEA intervention project using wearable technology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 860:160518. [PMID: 36573449 DOI: 10.1016/j.scitotenv.2022.160518] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/14/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
Current public health recommendations for desert dust storms (DDS) events focus on vulnerable population groups, such as children with asthma, and include advice to stay indoors and limit outdoor physical activity. To date, no scientific evidence exists on the efficacy of these recommendations in reducing DDS exposure. We aimed to objectively assess the behavioral responses of children with asthma to recommendations for reduction of DDS exposure. In two heavily affected by DDS Mediterranean regions (Cyprus & Crete, Greece), schoolchildren with asthma (6-11 years) were recruited from primary schools and were randomized to control (business as usual scenario) and intervention groups. All children were equipped with pedometer and GPS sensors embedded in smartwatches for objective real-time data collection from inside and outside their classroom and household settings. Interventions included the timely communication of personal DDS alerts accompanied by exposure reduction recommendations to both the parents and school-teachers of children in the intervention group. A mixed effect model was used to assess changes in daily levels of time spent, and steps performed outside classrooms and households, between non-DDS and DDS days across the study groups. The change in the time spent outside classrooms and homes, between non-DDS and DDS days, was 37.2 min (pvalue = 0.098) in the control group and -62.4 min (pvalue < 0.001) in the intervention group. The difference in the effects between the two groups was statistically significant (interaction pvalue < 0.001). The change in daily steps performed outside classrooms and homes, was -495.1 steps (pvalue = 0.350) in the control group and -1039.5 (pvalue = 0.003) in the intervention group (interaction pvalue = 0.575). The effects on both the time and steps performed outside were more profound during after-school hours. To summarize, among children with asthma, we demonstrated that timely personal DDS alerts and detailed recommendations lead to significant behavioral changes in contrast to the usual public health recommendations.
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Affiliation(s)
- Panayiotis Kouis
- Respiratory Physiology Laboratory, Medical School, University of Cyprus, Nicosia, Cyprus
| | - Antonis Michanikou
- Respiratory Physiology Laboratory, Medical School, University of Cyprus, Nicosia, Cyprus
| | | | | | - Helen Dimitriou
- Medical School, University of Crete, Heraklion, Crete, Greece
| | - Julietta Perez
- Medical School, University of Crete, Heraklion, Crete, Greece
| | - Paraskevi Kinni
- Respiratory Physiology Laboratory, Medical School, University of Cyprus, Nicosia, Cyprus
| | - Souzana Achilleos
- Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia, Cyprus; Cyprus International Institute for Environmental & Public Health, Cyprus University of Technology, Limassol, Cyprus
| | | | | | | | - Chrysanthos Savvides
- Air Quality and Strategic Planning Section, Department of Labour Inspection, Ministry of Labour and Social Insurance, Nicosia, Cyprus
| | - Emily Vasiliadou
- Air Quality and Strategic Planning Section, Department of Labour Inspection, Ministry of Labour and Social Insurance, Nicosia, Cyprus
| | - Nikos Kalivitis
- Department of Chemistry, University of Crete, Heraklion, Crete, Greece
| | | | | | - Stefania I Papatheodorou
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Petros Koutrakis
- Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, USA
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3
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Chatzidiakou L, Krause A, Kellaway M, Han Y, Li Y, Martin E, Kelly FJ, Zhu T, Barratt B, Jones RL. Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution. Environ Health 2022; 21:125. [PMID: 36482402 PMCID: PMC9733291 DOI: 10.1186/s12940-022-00939-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 11/07/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose because pollutant concentrations and inhalation rates vary significantly by location and activity. METHODS We developed and evaluated an automated model to classify major exposure-related microenvironments (home, work, other static, in-transit) and separated them into indoor and outdoor locations, sleeping activity and five modes of transport (walking, cycling, car, bus, metro/train) with multidisciplinary methods from the fields of movement ecology and artificial intelligence. As input parameters, we used GPS coordinates, accelerometry, and noise, collected at 1 min intervals with a validated Personal Air quality Monitor (PAM) carried by 35 volunteers for one week each. The model classifications were then evaluated against manual time-activity logs kept by participants. RESULTS Overall, the model performed reliably in classifying home, work, and other indoor microenvironments (F1-score>0.70) but only moderately well for sleeping and visits to outdoor microenvironments (F1-score=0.57 and 0.3 respectively). Random forest approaches performed very well in classifying modes of transport (F1-score>0.91). We found that the performance of the automated methods significantly surpassed those of manual logs. CONCLUSIONS Automated models for time-activity classification can markedly improve exposure metrics. Such models can be developed in many programming languages, and if well formulated can have general applicability in large-scale health studies, providing a comprehensive picture of environmental health risks during daily life with readily gathered parameters from smartphone technologies.
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Affiliation(s)
- Lia Chatzidiakou
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
| | - Anika Krause
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
- Institute for Chemistry, University of Potsdam, Karl-Liebknecht-Straße 24-25, 14476 Potsdam, Germany
| | | | - Yiqun Han
- Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, W12 0BZ London, UK
| | - Yilin Li
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
| | - Elizabeth Martin
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
| | - Frank J. Kelly
- Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, W12 0BZ London, UK
| | - Tong Zhu
- BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Center for Environment and Health, Peking University, 100871 Beijing, China
| | - Benjamin Barratt
- Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, W12 0BZ London, UK
| | - Roderic L. Jones
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
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Quinn C, Anderson GB, Magzamen S, Henry CS, Volckens J. Dynamic classification of personal microenvironments using a suite of wearable, low-cost sensors. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2020; 30:962-970. [PMID: 31937850 PMCID: PMC7358126 DOI: 10.1038/s41370-019-0198-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 09/04/2019] [Accepted: 10/29/2019] [Indexed: 05/13/2023]
Abstract
Human exposure to air pollution is associated with increased risk of morbidity and mortality. However, personal air pollution exposures can vary substantially depending on an individual's daily activity patterns and air quality within their residence and workplace. This work developed and validated an adaptive buffer size (ABS) algorithm capable of dynamically classifying an individual's time spent in predefined microenvironments using data from global positioning systems (GPS), motion sensors, temperature sensors, and light sensors. Twenty-two participants in Fort Collins, CO were recruited to carry a personal air sampler for a 48-h period. The personal sampler was retrofitted with a GPS and a pushbutton to complement the existing sensor measurements (temperature, motion, light). The pushbutton was used in conjunction with a traditional time-activity diary to note when the participant was located at "home", "work", or within an "other" microenvironment. The ABS algorithm predicted the amount of time spent in each microenvironment with a median accuracy of 99.1%, 98.9%, and 97.5% for the "home", "work", and "other" microenvironments. The ability to classify microenvironments dynamically in real time can enable the development of new sampling and measurement technologies that classify personal exposure by microenvironment.
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Affiliation(s)
- Casey Quinn
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, 80523, USA
| | - G Brooke Anderson
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, 80523, USA
| | - Sheryl Magzamen
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, 80523, USA
| | - Charles S Henry
- Department of Chemistry, Colorado State University, Fort Collins, CO, 80523, USA
| | - John Volckens
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, 80523, USA.
- Department of Mechanical Engineering, Colorado State University, Fort Collins, CO, 80523, USA.
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5
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Hirve S, Marsh A, Lele P, Chavan U, Bhattacharjee T, Nair H, Campbell H, Juvekar S. Concordance between GPS-based smartphone app for continuous location tracking and mother's recall of care-seeking for child illness in India. J Glob Health 2018; 8:020802. [PMID: 30410742 PMCID: PMC6209739 DOI: 10.7189/jogh.08.020802] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Background Traditionally, health care-seeking behaviour for child illness is assessed through population-based national demographic and health surveys. GPS-based technologies are increasingly used in human behavioural research including tracking human mobility and spatial behaviour. This paper assesses how well a care-seeking event to a health care facility for child illness, as recalled by the mother in a survey setting using questions sourced from Demographic and Health Surveys, concurs with one that is identified by TrackCare, a GPS-based location-aware smartphone application. Methods Mothers residing in the Vadu HDSS area in Pune district, India having at least one young child were randomly assigned to receive a GPS-enabled smartphone with a pre-installed TrackCare app configured to record the device location data at one-minute intervals over a 6-month period. Spatio-temporal parameters were derived from the location data and used to detect a care-seeking event to any of the health care facilities in the area. Mothers were asked to recall a child illness and if, where and when care was sought, using a questionnaire during monthly visits over a 6-month period. Concordance between the mother's recall and the TrackCare app to identify a care-seeking event was estimated according to percent positive agreement. Results Mean concordance for a care-seeking event between the two methods (mother's recall and TrackCare location data) ranged up to 45%, was significantly higher (P-value <0.001) for care-seeking at a hospital as compared to a clinic and for a health care facility in the private sector compared to that in the public sector. Overall, the proportion of disagreement for a care-seeking event not detected by TrackCare but reported by mother ranged up to 77% and was significantly higher (P-value <0.001) compared to those not reported by mother but detected by TrackCare. Conclusions Given the uncertainty and limitations in use of continuous location tracking data in a field setting and the complexity of classifying human activity patterns, additional research is needed before continuous location tracking can serve as a gold standard substitute for other methods to determine health care-seeking behaviour. Future performance may be improved by incorporating other smartphone-based sensors, such as Wi-Fi and Bluetooth, to obtain more precise location estimates in areas where GPS signal is weakest.
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Affiliation(s)
- Siddhivinayak Hirve
- KEM Hospital Research Centre, Pune, India.,Joint first author with equal contributions
| | - Andrew Marsh
- KEM Hospital Research Centre, Pune, India.,Institute for International Programs, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.,Joint first author with equal contributions
| | | | | | | | - Harish Nair
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland, UK
| | - Harry Campbell
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland, UK.,Joint last author with equal contributions
| | - Sanjay Juvekar
- KEM Hospital Research Centre, Pune, India.,INDEPTH Network, East Legon, Accra, Ghana.,Joint last author with equal contributions
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6
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Effects of Individual and Environmental Factors on GPS-Based Time Allocation in Urban Microenvironments Using GIS. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8102007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Time-activity patterns are an essential part of personal exposure assessment to various environmental factors. People move through different environments during the day and they have different daily activity patterns which are significantly influenced by individual characteristics and the residential environment. In this study, time spent in different microenvironments (MEs) were assessed for 125 participants for 7 consecutive days to evaluate the impact of individual characteristics on time-activity patterns in Kaunas, Lithuania. The data were collected with personal questionnaires and diaries. The global positioning system (GPS) sensor integrated into a smartphone was used to track daily movements and to assess time-activity patterns. The study results showed that behavioral and residential greenness have a statistically significant impact on time spent indoors. These results underline the high influence of the individual characteristics and environmental factors on time spent indoors, which is an important determinant for exposure assessment and health impact assessment studies.
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7
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Hazlehurst MF, Spalt EW, Curl CL, Davey ME, Vedal S, Burke GL, Kaufman JD. Integrating data from multiple time-location measurement methods for use in exposure assessment: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2017; 27:569-574. [PMID: 28120831 DOI: 10.1038/jes.2016.84] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 12/06/2016] [Indexed: 06/06/2023]
Abstract
Tools to assess time-location patterns related to environmental exposures have expanded from reliance on time-location diaries (TLDs) and questionnaires to use of geospatial location devices such as data-logging Global Positioning System (GPS) equipment. The Multi-Ethnic Study of Atherosclerosis and Air Pollution obtained typical time-location patterns via questionnaire for 6424 adults in six US cities. At a later time (mean 4.6 years after questionnaire), a subset (n=128) participated in high-resolution data collection for specific 2-week periods resulting in concurrent GPS and detailed TLD data, which were aggregated to estimate time spent in various microenvironments. During these 2-week periods, participants were observed to spend the most time at home indoors (mean of 78%) and a small proportion of time in-vehicle (mean of 4%). Similar overall patterns were reported by these participants on the prior questionnaire (mean home indoors: 75%; mean in-vehicle: 4%). However, individual micro-environmental time estimates measured over specific 2-week periods were not highly correlated with an individual's questionnaire report of typical behavior (Spearman's ρ of 0.43 for home indoors and 0.39 for in-vehicle). Although questionnaire data about typical time-location patterns can inform interpretation of long-term epidemiological analyses and risk assessment, they may not reliably represent an individual's short-term experience.
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Affiliation(s)
- Marnie F Hazlehurst
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Elizabeth W Spalt
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Cynthia L Curl
- Department of Community and Environmental Health, Boise State University, Boise, Idaho, USA
| | - Mark E Davey
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland
| | - Sverre Vedal
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Gregory L Burke
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Joel D Kaufman
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
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8
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Lee B, Lim C, Lee K. Classification of indoor-outdoor location using combined global positioning system (GPS) and temperature data for personal exposure assessment. Environ Health Prev Med 2017; 22:29. [PMID: 29165131 PMCID: PMC5664917 DOI: 10.1186/s12199-017-0637-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 01/24/2017] [Indexed: 11/10/2022] Open
Abstract
Objectives The objectives of this study was to determine the accuracy of indoor-outdoor classification based on GPS and temperature data in three different seasons. Methods In the present study, a global positioning system (GPS) was used alongside temperature data collected in the field by a technician who visited 53 different indoor locations during summer, autumn and winter. The indoor-outdoor location was determined by GPS data alone, and in combination with temperature data. Results Determination of location by the GPS signal alone, based on the loss of GPS signal and using the used number of satellites (NSAT) signal factor, simple percentage agreements of 73.6 ± 2.9%, 72.9 ± 3.4%, and 72.1 ± 3.1% were obtained for summer, autumn, and winter, respectively. However, when temperature and GPS data were combined, simple percentage agreements were significantly improved (87.9 ± 3.3%, 84.1 ± 2.8%, and 86.3 ± 3.1%, respectively). A temperature criterion for indoor-outdoor determination of ~ Δ 2°C for 2 min could be applied during all three seasons. Conclusion The results showed that combining GPS and temperature data improved the accuracy of indoor-outdoor determination.
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Affiliation(s)
- B Lee
- Department of Environmental Health science, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro Gwanak-gu, Seoul, 08826, Republic of Korea
| | - C Lim
- Department of Environmental Health science, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro Gwanak-gu, Seoul, 08826, Republic of Korea
| | - K Lee
- Department of Environmental Health science, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro Gwanak-gu, Seoul, 08826, Republic of Korea.
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9
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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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10
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Paz-Soldan VA, Reiner RC, Morrison AC, Stoddard ST, Kitron U, Scott TW, Elder JP, Halsey ES, Kochel TJ, Astete H, Vazquez-Prokopec GM. Strengths and weaknesses of Global Positioning System (GPS) data-loggers and semi-structured interviews for capturing fine-scale human mobility: findings from Iquitos, Peru. PLoS Negl Trop Dis 2014; 8:e2888. [PMID: 24922530 PMCID: PMC4055589 DOI: 10.1371/journal.pntd.0002888] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2013] [Accepted: 04/10/2014] [Indexed: 01/28/2023] Open
Abstract
Quantifying human mobility has significant consequences for studying physical activity, exposure to pathogens, and generating more realistic infectious disease models. Location-aware technologies such as Global Positioning System (GPS)-enabled devices are used increasingly as a gold standard for mobility research. The main goal of this observational study was to compare and contrast the information obtained through GPS and semi-structured interviews (SSI) to assess issues affecting data quality and, ultimately, our ability to measure fine-scale human mobility. A total of 160 individuals, ages 7 to 74, from Iquitos, Peru, were tracked using GPS data-loggers for 14 days and later interviewed using the SSI about places they visited while tracked. A total of 2,047 and 886 places were reported in the SSI and identified by GPS, respectively. Differences in the concordance between methods occurred by location type, distance threshold (within a given radius to be considered a match) selected, GPS data collection frequency (i.e., 30, 90 or 150 seconds) and number of GPS points near the SSI place considered to define a match. Both methods had perfect concordance identifying each participant's house, followed by 80-100% concordance for identifying schools and lodgings, and 50-80% concordance for residences and commercial and religious locations. As the distance threshold selected increased, the concordance between SSI and raw GPS data increased (beyond 20 meters most locations reached their maximum concordance). Processing raw GPS data using a signal-clustering algorithm decreased overall concordance to 14.3%. The most common causes of discordance as described by a sub-sample (n=101) with whom we followed-up were GPS units being accidentally off (30%), forgetting or purposely not taking the units when leaving home (24.8%), possible barriers to the signal (4.7%) and leaving units home to recharge (4.6%). We provide a quantitative assessment of the strengths and weaknesses of both methods for capturing fine-scale human mobility.
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Affiliation(s)
- Valerie A. Paz-Soldan
- Global Health Systems and Development Department, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Robert C. Reiner
- Department of Entomology, University of California, Davis, Davis, California, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Amy C. Morrison
- Department of Entomology, University of California, Davis, Davis, California, United States of America
- Graduate School of Public Health, San Diego State University, San Diego, California, United States of America
| | - Steven T. Stoddard
- Department of Entomology, University of California, Davis, Davis, California, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Uriel Kitron
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
- Department of Environmental Studies, Emory University, Atlanta, Georgia, United States of America
| | - Thomas W. Scott
- Department of Entomology, University of California, Davis, Davis, California, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - John P. Elder
- Graduate School of Public Health, San Diego State University, San Diego, California, United States of America
| | | | | | - Helvio Astete
- U.S. Navy Medical Research Unit No. 6, Iquitos, Peru
| | - Gonzalo M. Vazquez-Prokopec
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
- Department of Environmental Studies, Emory University, Atlanta, Georgia, United States of America
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Nethery E, Mallach G, Rainham D, Goldberg MS, Wheeler AJ. Using Global Positioning Systems (GPS) and temperature data to generate time-activity classifications for estimating personal exposure in air monitoring studies: an automated method. Environ Health 2014; 13:33. [PMID: 24885722 PMCID: PMC4046178 DOI: 10.1186/1476-069x-13-33] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2013] [Accepted: 04/28/2014] [Indexed: 05/22/2023]
Abstract
BACKGROUND Personal exposure studies of air pollution generally use self-reported diaries to capture individuals' time-activity data. Enhancements in the accuracy, size, memory and battery life of personal Global Positioning Systems (GPS) units have allowed for higher resolution tracking of study participants' locations. Improved time-activity classifications combined with personal continuous air pollution sampling can improve assessments of location-related air pollution exposures for health studies. METHODS Data was collected using a GPS and personal temperature from 54 children with asthma living in Montreal, Canada, who participated in a 10-day personal air pollution exposure study. A method was developed that incorporated personal temperature data and then matched a participant's position against available spatial data (i.e., road networks) to generate time-activity categories. The diary-based and GPS-generated time-activity categories were compared and combined with continuous personal PM2.5 data to assess the impact of exposure misclassification when using diary-based methods. RESULTS There was good agreement between the automated method and the diary method; however, the automated method (means: outdoors = 5.1%, indoors other =9.8%) estimated less time spent in some locations compared to the diary method (outdoors = 6.7%, indoors other = 14.4%). Agreement statistics (AC1 = 0.778) suggest 'good' agreement between methods over all location categories. However, location categories (Outdoors and Transit) where less time is spent show greater disagreement: e.g., mean time "Indoors Other" using the time-activity diary was 14.4% compared to 9.8% using the automated method. While mean daily time "In Transit" was relatively consistent between the methods, the mean daily exposure to PM2.5 while "In Transit" was 15.9 μg/m3 using the automated method compared to 6.8 μg/m3 using the daily diary. CONCLUSIONS Mean times spent in different locations as categorized by a GPS-based method were comparable to those from a time-activity diary, but there were differences in estimates of exposure to PM2.5 from the two methods. An automated GPS-based time-activity method will reduce participant burden, potentially providing more accurate and unbiased assessments of location. Combined with continuous air measurements, the higher resolution GPS data could present a different and more accurate picture of personal exposures to air pollution.
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Affiliation(s)
- Elizabeth Nethery
- Water and Air Quality Bureau, HECSB, Health Canada, 269 Laurier Avenue West, AL 4903C, Ottawa, Ontario K1A 0 K9, Canada
| | - Gary Mallach
- Water and Air Quality Bureau, HECSB, Health Canada, 269 Laurier Avenue West, AL 4903C, Ottawa, Ontario K1A 0 K9, Canada
| | | | - Mark S Goldberg
- Department of Medicine, McGill University, Montreal, Canada
- Division of Clinical Epidemiology, McGill University Health Center, Montreal, Canada
| | - Amanda J Wheeler
- Water and Air Quality Bureau, HECSB, Health Canada, 269 Laurier Avenue West, AL 4903C, Ottawa, Ontario K1A 0 K9, Canada
- School of Natural Science, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA 6027, Australia
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Han D, Lee K, Kim J, Bennett DH, Cassady D, Hertz-Picciotto I. Development of Time-location Weighted Spatial Measures Using Global Positioning System Data. ENVIRONMENTAL HEALTH AND TOXICOLOGY 2013; 28:e2013005. [PMID: 23700565 PMCID: PMC3657713 DOI: 10.5620/eht.2013.28.e2013005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2012] [Accepted: 02/08/2013] [Indexed: 06/02/2023]
Abstract
OBJECTIVES Despite increasing availability of global positioning system (GPS), no research has been conducted to analyze GPS data for exposure opportunities associated with time at indoor and outdoor microenvironments. We developed location-based and time-weighted spatial measures that incorporate indoor and outdoor time-location data collected by GPS. METHODS Time-location data were drawn from 38 female subjects in California who wore a GPS device for seven days. Ambient standard deviational ellipse was determined based on outdoor locations and time duration, while indoor time weighted standard deviational ellipse (SDE) was developed to incorporate indoor and outdoor times and locations data into the ellipse measure. RESULTS Our findings indicated that there was considerable difference in the sizes of exposure potential measures when indoor time was taken into consideration, and that they were associated with day type (weekday/weekend) and employment status. CONCLUSIONS This study provides evidence that time-location weighted measure may provide better accuracy in assessing exposure opportunities at different microenvironments. The use of GPS likely improves the geographical details and accuracy of time-location data, and further development of such location-time weighted spatial measure is encouraged.
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Affiliation(s)
- Daikwon Han
- Department of Epidemiology and Biostatistics, Texas A&M School of Rural Public Health, College Station, TX, USA
| | - Kiyoung Lee
- Department of Environmental Health and Institute of Health and Environment, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Jongyun Kim
- Department of Environmental Health and Institute of Health and Environment, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Deborah H. Bennett
- Department of Public Health Sciences, School of Medicine, University of California at Davis, Davis, CA, USA
| | - Diana Cassady
- Department of Public Health Sciences, School of Medicine, University of California at Davis, Davis, CA, USA
| | - Irva Hertz-Picciotto
- Department of Public Health Sciences, School of Medicine, University of California at Davis, Davis, CA, USA
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