1
|
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.
Collapse
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
| |
Collapse
|
2
|
Chaix B, Bista S, Wang L, Benmarhnia T, Dureau C, Duncan DT. MobiliSense cohort study protocol: do air pollution and noise exposure related to transport behaviour have short-term and longer-term health effects in Paris, France? BMJ Open 2022; 12:e048706. [PMID: 35361634 PMCID: PMC8971765 DOI: 10.1136/bmjopen-2021-048706] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
INTRODUCTION MobiliSense explores effects of air pollution and noise related to personal transport habits on respiratory and cardiovascular health. Its objectives are to quantify the contribution of personal transport/mobility to air pollution and noise exposures of individuals; to compare exposures in different transport modes; and to investigate whether total and transport-related personal exposures are associated with short-term and longer-term changes in respiratory and cardiovascular health. METHODS AND ANALYSIS MobiliSense uses sensors of location, behaviour, environmental nuisances and health in 290 census-sampled participants followed-up after 1/2 years with an identical sensor-based strategy. It addresses knowledge gaps by: (1) assessing transport behaviour over 6 days with GPS receivers and GPS-based mobility surveys; (2) considering personal exposures to both air pollution and noise and improving their characterisation (inhaled doses, noise frequency components, etc); (3) measuring respiratory and cardiovascular outcomes (smartphone-assessed respiratory symptoms, lung function with spirometry, resting blood pressure, ambulatory brachial/central blood pressure, arterial stiffness and heart rate variability) and (4) investigating short-term and longer-term (over 1-2 years) effects of transport. ETHICS AND DISSEMINATION The sampling and data collection protocol was approved by the National Council for Statistical Information, the French Data Protection Authority and the Ethical Committee of Inserm. Our final aim is to determine, for communicating with policy-makers, how scenarios of changes in personal transport behaviour affect individual exposure and health.
Collapse
Affiliation(s)
- Basile Chaix
- Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, Nemesis team, INSERM, Paris, France
| | - Sanjeev Bista
- Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, Nemesis team, INSERM, Paris, France
| | - Limin Wang
- Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, Nemesis team, INSERM, Paris, France
| | - Tarik Benmarhnia
- Department of Family Medicine and Public Health & Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA
| | - Clélie Dureau
- Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, Nemesis team, INSERM, Paris, France
| | - Dustin T Duncan
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York, USA
| |
Collapse
|
3
|
Chaix B, Benmarhnia T, Kestens Y, Brondeel R, Perchoux C, Gerber P, Duncan DT. Combining sensor tracking with a GPS-based mobility survey to better measure physical activity in trips: public transport generates walking. Int J Behav Nutr Phys Act 2019; 16:84. [PMID: 31590666 PMCID: PMC6781383 DOI: 10.1186/s12966-019-0841-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 08/16/2019] [Indexed: 11/10/2022] Open
Abstract
Background Policymakers need accurate data to develop efficient interventions to promote transport physical activity. Given the imprecise assessment of physical activity in trips, our aim was to illustrate novel advances in the measurement of walking in trips, including in trips incorporating non-walking modes. Methods We used data of 285 participants (RECORD MultiSensor Study, 2013–2015, Paris region) who carried GPS receivers and accelerometers over 7 days and underwent a phone-administered web mobility survey on the basis of algorithm-processed GPS data. With this mobility survey, we decomposed trips into unimodal trip stages with their start/end times, validated information on travel modes, and manually complemented and cleaned GPS tracks. This strategy enabled to quantify walking in trips with different modes with two alternative metrics: distance walked and accelerometry-derived number of steps taken. Results Compared with GPS-based mobility survey data, algorithm-only processed GPS data indicated that the median distance covered by participants per day was 25.3 km (rather than 23.4 km); correctly identified transport time vs. time at visited places in 72.7% of time; and correctly identified the transport mode in 67% of time (and only in 55% of time for public transport). The 285 participants provided data for 8983 trips (21,163 segments of observation). Participants spent a median of 7.0% of their total time in trips. The median distance walked per trip was 0.40 km for entirely walked trips and 0.85 km for public transport trips (the median number of accelerometer steps were 425 and 1352 in the corresponding trips). Overall, 33.8% of the total distance walked in trips and 37.3% of the accelerometer steps in trips were accumulated during public transport trips. Residents of the far suburbs cumulated a 1.7 times lower distance walked per day and a 1.6 times lower number of steps during trips per 8 h of wear time than residents of the Paris core city. Conclusions Our approach complementing GPS and accelerometer tracking with a GPS-based mobility survey substantially improved transport mode detection. Our findings suggest that promoting public transport use should be one of the cornerstones of policies to promote physical activity. Electronic supplementary material The online version of this article (10.1186/s12966-019-0841-2) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Basile Chaix
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, Nemesis team, Faculté de Médecine Saint-Antoine, 27 rue Chaligny, 75012, Paris, France.
| | - Tarik Benmarhnia
- Department of Family Medicine and Public Health & Scripps Institution of Oceanography, University of California in San Diego, 9500 Gilman Drive #0725, La Jolla, CA, 92093, USA
| | - Yan Kestens
- Department of Social and Preventive Medicine, École de Santé Publique de l'Université de Montréal, Centre de recherche du CHUM, Tour Saint-Antoine, 850 Saint-Denis, S03-280, Montréal, H2X 0A9, Canada.,University of Montreal Hospital Research Centre, Tour Saint-Antoine, 850 Saint-Denis, S03-280, Montréal, H2X 0A9, Canada
| | - Ruben Brondeel
- Department of Social and Preventive Medicine, École de Santé Publique de l'Université de Montréal, Centre de recherche du CHUM, Tour Saint-Antoine, 850 Saint-Denis, S03-280, Montréal, H2X 0A9, Canada.,University of Montreal Hospital Research Centre, Tour Saint-Antoine, 850 Saint-Denis, S03-280, Montréal, H2X 0A9, Canada
| | - Camille Perchoux
- Luxembourg Institute of Socio-Economic Research, Maison des Sciences Humaines, 11 Porte des Sciences, L-4366, Esch-sur-Alzette, Luxembourg
| | - Philippe Gerber
- Luxembourg Institute of Socio-Economic Research, Maison des Sciences Humaines, 11 Porte des Sciences, L-4366, Esch-sur-Alzette, Luxembourg
| | - Dustin T Duncan
- Spatial Epidemiology Lab, Department of Population Health, School of Medicine, New York University, 180 Madison Avenue, New York, NY, 10016, USA
| |
Collapse
|
4
|
Abstract
Public health research has witnessed a rapid development in the use of location, environmental, behavioral, and biophysical sensors that provide high-resolution objective time-stamped data. This burgeoning field is stimulated by the development of novel multisensor devices that collect data for an increasing number of channels and algorithms that predict relevant dimensions from one or several data channels. Global positioning system (GPS) tracking, which enables geographic momentary assessment, permits researchers to assess multiplace personal exposure areas and the algorithm-based identification of trips and places visited, eventually validated and complemented using a GPS-based mobility survey. These methods open a new space-time perspective that considers the full dynamic of residential and nonresidential momentary exposures; spatially and temporally disaggregates the behavioral and health outcomes, thus replacing them in their immediate environmental context; investigates complex time sequences; explores the interplay among individual, environmental, and situational predictors; performs life-segment analyses considering infraindividual statistical units using case-crossover models; and derives recommendations for just-in-time interventions.
Collapse
Affiliation(s)
- Basile Chaix
- Nemesis Team, Pierre Louis Institute of Epidemiology and Public Health, UMR-S 1136 (Inserm, Sorbonne Universités), 75012, Paris, France;
| |
Collapse
|
5
|
Weisberg-Shapiro P, Devine C. Food Activity Footprint: Dominican Women’s Use of Time and Space for Food Procurement. JOURNAL OF HUNGER & ENVIRONMENTAL NUTRITION 2019. [DOI: 10.1080/19320248.2019.1613276] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
| | - Carol Devine
- Division of Nutritional Sciences, Cornell University, Ithaca, NY
| |
Collapse
|
6
|
Kang M, Moudon AV, Hurvitz PM, Saelens BE. Capturing fine-scale travel behaviors: a comparative analysis between personal activity location measurement system (PALMS) and travel diary. Int J Health Geogr 2018; 17:40. [PMID: 30509275 PMCID: PMC6278002 DOI: 10.1186/s12942-018-0161-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 11/26/2018] [Indexed: 01/08/2023] Open
Abstract
Background Device-collected data from GPS and accelerometers for identifying active travel behaviors have dramatically changed research methods in transportation planning and public health. Automated algorithms have helped researchers to process large datasets with likely fewer errors than found in other collection methods (e.g., self-report travel diary). In this study, we compared travel modes identified by a commonly used automated algorithm (PALMS) that integrates GPS and accelerometer data with those obtained from travel diary estimates. Methods Sixty participants, who made 2100 trips during seven consecutive days of data collection, were selected from among the baseline sample of a project examining the travel behavior impact of a new light rail system in the greater Seattle, WA (USA) area. GPS point level analyses were first conducted to compare trip/place and travel mode detection results using contingency tables. Trip level analyses were then performed to investigate the effect of proportions of time overlap between travel logs and device-collected data on agreement rates. Global performance (with all subjects’ data combined) and subject-level performance of the algorithm were compared at the trip level. Results At the GPS point level, the overall agreement rate of travel mode detection was 77.4% between PALMS and the travel diary. The agreement rate for vehicular trip detection (84.5%) was higher than for bicycling (53.5%) and walking (58.2%). At the trip level, the global performance and subject-level performance of the PALMS algorithm were 46.4% and 42.4%, respectively. Vehicular trip detection showed highest agreement rates in all analyses. Study participants’ primary travel mode and car ownership were significantly related to the subject-level mode agreement rates. Conclusions The PALMS algorithm showed moderate identification power at the GPS point level. However, trip level analyses found lower agreement rates between PALMS and travel diary data, especially for active transportation. Testing different PALMS parameter settings may serve to improve the detection of active travel and help expand PALMS’s applicability in geographically different urbanized areas with a variety of travel modes.
Collapse
Affiliation(s)
- Mingyu Kang
- Urban Form Lab, Department of Urban Design and Planning, University of Washington, 1107 NE 45th St, Suite 535, Seattle, WA, 98195, USA.
| | - Anne V Moudon
- Urban Form Lab, Department of Urban Design and Planning, University of Washington, 1107 NE 45th St, Suite 535, Seattle, WA, 98195, USA
| | - Philip M Hurvitz
- Urban Form Lab, Department of Urban Design and Planning, University of Washington, 1107 NE 45th St, Suite 535, Seattle, WA, 98195, USA
| | - Brian E Saelens
- Department of Pediatrics, Seattle Children's Research Institute, University of Washington, 2001 Eighth Avenue, Suite 400, Seattle, WA, 98121, USA
| |
Collapse
|
7
|
Spatial access to food: Retiring the food desert metaphor. Physiol Behav 2018; 193:257-260. [PMID: 29454842 DOI: 10.1016/j.physbeh.2018.02.032] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Revised: 02/06/2018] [Accepted: 02/15/2018] [Indexed: 01/08/2023]
Abstract
The food desert metaphor has been widely used over the past few decades as a way to identify regions as being at risk for having little or no access to healthy food. While the simplicity of the metaphor is attractive, this article argues that its usefulness to researchers interested in understanding the relationship between the geography of healthy food opportunities and dietary behaviours is limited. More nuanced approaches to incorporating geography into food access studies, like including transportation, economic factors, and time use, in addition to considering other dimensions of accessibility, are warranted.
Collapse
|