Using smartphones to collect time–activity data for long-term personal-level air pollution exposure assessment

Because of the spatiotemporal variability of people and air pollutants within cities, it is important to account for a person’s movements over time when estimating personal air pollution exposure. This study aimed to examine the feasibility of using smartphones to collect personal-level time–activit...

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Bibliographic Details
Published in:Journal of exposure science & environmental epidemiology 2016-06, Vol.26 (4), p.356-364
Main Authors: Glasgow, Mark L, Rudra, Carole B, Yoo, Eun-Hye, Demirbas, Murat, Merriman, Joel, Nayak, Pramod, Crabtree-Ide, Christina, Szpiro, Adam A, Rudra, Atri, Wactawski-Wende, Jean, Mu, Lina
Format: Article
Language:English
Subjects:
692/700/478
704/172
Adult
Air pollution
Air Pollution - analysis
Air quality
Comparative analysis
Data collection
Data Collection - methods
Data Collection - standards
Data entry
Diaries
Environmental aspects
Environmental health
Environmental monitoring
Environmental Monitoring - methods
Epidemiology
Evaluation
Exposure
Feasibility
Female
Geographic Information Systems
Health aspects
Humans
Male
Medicine
Medicine & Public Health
Methods
Middle Aged
Mobile Applications - standards
Mobile Applications - statistics & numerical data
New York
original-article
Pollutants
Self Report
Smart phones
Smartphone
Smartphones
Technology application
Temporal resolution
Time
Time of use
Young Adult
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Summary:Because of the spatiotemporal variability of people and air pollutants within cities, it is important to account for a person’s movements over time when estimating personal air pollution exposure. This study aimed to examine the feasibility of using smartphones to collect personal-level time–activity data. Using Skyhook Wireless’s hybrid geolocation module, we developed “Apolux” (Air, Pollution, Exposure), an Android TM smartphone application designed to track participants’ location in 5-min intervals for 3 months. From 42 participants, we compared Apolux data with contemporaneous data from two self-reported, 24-h time–activity diaries. About three-fourths of measurements were collected within 5 min of each other (mean=74.14%), and 79% of participants reporting constantly powered-on smartphones ( n =38) had a daily average data collection frequency of
ISSN:1559-0631
1559-064X
DOI:10.1038/jes.2014.78