Prediction of human active mobility in rural areas: development and validity tests of three different approaches.

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Abstrakt

Tytuł:: Prediction of human active mobility in rural areas: development and validity tests of three different approaches.
Autorzy:: Klous G; Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands. .; Institute for Risk Assessment Sciences, Division Environmental Epidemiology and Veterinary Public Health, Utrecht University, Utrecht, The Netherlands. .
Kretzschmar MEE; 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.
Coutinho RA; Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.
Heederik DJJ; Institute for Risk Assessment Sciences, Division Environmental Epidemiology and Veterinary Public Health, Utrecht University, Utrecht, The Netherlands.
Huss A; Institute for Risk Assessment Sciences, Division Environmental Epidemiology and Veterinary Public Health, Utrecht University, Utrecht, The Netherlands.
Źródło:: Journal of exposure science & environmental epidemiology [J Expo Sci Environ Epidemiol] 2020 Nov; Vol. 30 (6), pp. 1023-1031. Date of Electronic Publication: 2019 Nov 26.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
Język:: English
Imprint Name(s):: Original Publication: New York, NY : Nature Pub. Group, c2006-
MeSH Terms:: Air Pollutants*/analysis
Bicycling ; Environmental Exposure ; Humans ; Netherlands ; Transportation
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Contributed Indexing:: Keywords: Active mobility; Assessment; Biking; Exposure; GPS validation; Mobility estimation method; Walking
Substance Nomenclature:: 0 (Air Pollutants)
Entry Date(s):: Date Created: 20191128 Date Completed: 20201223 Latest Revision: 20210113
Update Code:: 20240104
DOI:: 10.1038/s41370-019-0194-6
PMID:: 31772295
: Czasopismo naukowe

Background/aim: Active mobility may play a relevant role in the assessment of environmental exposures (e.g. traffic-related air pollution, livestock emissions), but data about actual mobility patterns are work intensive to collect, especially in large study populations, therefore estimation methods for active mobility may be relevant for exposure assessment in different types of studies. We previously collected mobility patterns in a group of 941 participants in a rural setting in the Netherlands, using week-long GPS tracking. We had information regarding personal characteristics, self-reported data regarding weekly mobility patterns and spatial characteristics. The goal of this study was to develop versatile estimates of active mobility, test their accuracy using GPS measurements and explore the implications for exposure assessment studies.
Methods: We estimated hours/week spent on active mobility based on personal characteristics (e.g. age, sex, pre-existing conditions), self-reported data (e.g. hours spent commuting per bike) or spatial predictors such as home and work address. Estimated hours/week spent on active mobility were compared with GPS measured hours/week, using linear regression and kappa statistics.
Results: Estimated and measured hours/week spent on active mobility had low correspondence, even the best predicting estimation method based on self-reported data, resulted in a R 2 of 0.09 and Cohen's kappa of 0.07. A visual check indicated that, although predicted routes to work appeared to match GPS measured tracks, only a small proportion of active mobility was captured in this way, thus resulting in a low validity of overall predicted active mobility.
Conclusions: We were unable to develop a method that could accurately estimate active mobility, the best performing method was based on detailed self-reported information but still resulted in low correspondence. For future studies aiming to evaluate the contribution of home-work traffic to exposure, applying spatial predictors may be appropriate. Measurements still represent the best possible tool to evaluate mobility patterns.

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