ArticleJMIR mHealth and uHealth2016
Benefits of Mobile Phone Technology for Personal Environmental Monitoring.
Article in JMIR mHealth and uHealth, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Fluorescence-Based Portable Assays for Detection of Biological and Chemical Analytes.Sensors (Basel, Switzerland) · 2023Review
- Microenvironment Tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation using smartphone data.Journal of exposure science & environmental epidemiology · 2023Article
- Investigating where adolescents engage in moderate to vigorous physical activity and sedentary behaviour: An exploratory study.PloS one · 2022Article
- Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study.JMIR mHealth and uHealth · 2021Observational
- Mobile devices and wearable technology for measuring patient outcomes after surgery: a systematic review.NPJ digital medicine · 2021Article
- Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey.Journal of global health · 2020Article
- Built Environment Interventions to Increase Active Travel: a Critical Review and Discussion.Current environmental health reports · 2019Review
- Assessing Older Adults' Daily Mobility: A Comparison of GPS-Derived and Self-Reported Mobility Indicators.Sensors (Basel, Switzerland) · 2019Article
- Development of an environmental health tool linking chemical exposures, physical location and lung function.BMC public health · 2019Article
- Comparative analysis of positioning accuracy of Samsung Galaxy smartphones in stationary measurements.PloS one · 2019Article
- Advanced Smartphone-Based Sensing with Open-Source Task Automation.Sensors (Basel, Switzerland) · 2018Article
- Living Close to Natural Outdoor Environments in Four European Cities: Adults' Contact with the Environments and Physical Activity.International journal of environmental research and public health · 2017Article
- Predictors of Daily Mobility of Adults in Peri-Urban South India.International journal of environmental research and public health · 2017Article
- How Sensors Might Help Define the External Exposome.International journal of environmental research and public health · 2017Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
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.
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