ArticleJournal of exposure science & environmental epidemiology2016
Using smartphones to collect time-activity data for long-term personal-level air pollution exposure assessment.
Article in Journal of exposure science & environmental epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Using a smartphone application to capture daily work activities: a longitudinal pilot study in a farming population.Annals of work exposures and health · 2023Article
- Article
- Personal exposure monitoring using GPS-enabled portable air pollution sensors: A strategy to promote citizen awareness and behavioral changes regarding indoor and outdoor air pollution.Journal of exposure science & environmental epidemiology · 2023Article
- Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution.Environmental health : a global access science source · 2022Article
- Imputation of missing time-activity data with long-term gaps: A multi-scale residual CNN-LSTM network model.Computers, environment and urban systems · 2022Article
- Wireless muometric navigation system.Scientific reports · 2022Article
- Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study.JMIR mHealth and uHealth · 2021Observational
- The Impact of Individual Mobility on Long-Term Exposure to Ambient PMInternational journal of environmental research and public health · 2021Article
- Environmental Health Surveillance System for a Population Using Advanced Exposure Assessment.Toxics · 2020Review
- Personal exposure to particulate matter in peri-urban India: predictors and association with ambient concentration at residence.Journal of exposure science & environmental epidemiology · 2020Article
- Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey.Journal of global health · 2020Article
- Quality of hybrid location data drawn from GPS-enabled mobile phones: Does it matter?Transactions in GIS : TG · 2020Article
- Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data.International journal of environmental research and public health · 2019Article
- Derivation of Time-Activity Data Using Wearable Cameras and Measures of Personal Inhalation Exposure among Workers at an Informal Electronic-Waste Recovery Site in Ghana.Annals of work exposures and health · 2019Article
- Genes for Good: Engaging the Public in Genetics Research via Social Media.American journal of human genetics · 2019Article
- Quantifying population exposure to air pollution using individual mobility patterns inferred from mobile phone data.Journal of exposure science & environmental epidemiology · 2019Article
- Population dynamics based on mobile phone data to improve air pollution exposure assessments.Journal of exposure science & environmental epidemiology · 2019Article
- Smartphone GPS accuracy study in an urban environment.PloS one · 2019Article
- How Short Is Long Enough? Modeling Temporal Aspects of Human Mobility Behavior Using Mobile Phone Data.Annals of the American Association of Geographers · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
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 <10 min. Apolux's degree of temporal resolution varied across manufacturers, mobile networks, and the time of day that data collection occurred. The discrepancy between diary points and corresponding Apolux data was 342.3 m (Euclidian distance) and varied across mobile networks. This study's high compliance and feasibility for data collection demonstrates the potential for integrating smartphone-based time-activity data into long-term and large-scale air pollution exposure studies.
Indexed as
Identifiers
25425137What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.