JMIR mHealth and uHealth (Nov 2016)

Benefits of Mobile Phone Technology for Personal Environmental Monitoring

  • Donaire-Gonzalez, David,
  • Valentín, Antònia,
  • de Nazelle, Audrey,
  • Ambros, Albert,
  • Carrasco-Turigas, Glòria,
  • Seto, Edmund,
  • Jerrett, Michael,
  • Nieuwenhuijsen, Mark J

DOI
https://doi.org/10.2196/mhealth.5771
Journal volume & issue
Vol. 4, no. 4
p. e126

Abstract

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BackgroundTracking 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. ObjectiveThe 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. MethodsAdults 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). ResultsThe 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). ConclusionsThe 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.