ArticleEnvironmental monitoring and assessment2018
Assessing and enhancing the utility of low-cost activity and location sensors for exposure studies.
Article in Environmental monitoring and assessment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Recommendations for determining the validity of consumer wearable and smartphone step count: expert statement and checklist of the INTERLIVE network.British journal of sports medicine · 2021Guideline
- Bridging the life-course exposome approach with a life-cycle perspective in safe and sustainable by design (SSbD) for chemical risk.Human genomics · 2026Article
- A New Wearable System for Personal Air Pollution Exposure Estimation: Pilot Observational Study.JMIR mHealth and uHealth · 2025Observational
- 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
- DigitalExposome: quantifying impact of urban environment on wellbeing using sensor fusion and deep learning.Computational urban science · 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
- A Scoping Review on Wearable Devices for Environmental Monitoring and Their Application for Health and Wellness.Sensors (Basel, Switzerland) · 2022Article
- The Validity of the Energy Expenditure Criteria Based on Open Source Code through two Inertial Sensors.Sensors (Basel, Switzerland) · 2022Article
- The Exposome and Toxicology: A Win-Win Collaboration.Toxicological sciences : an official journal of the Society of Toxicology · 2022Review
- Neighbourhood and path-based greenspace in three European countries: associations with objective physical activity.BMC public health · 2021Article
- A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology.Environment international · 2019Article
- Enhancing the effectiveness of AHP for environmental performance assessment of Thailand and Taiwan's food industry.Environmental monitoring and assessment · 2018Article
- Wearables and the medical revolution.Personalized medicine · 2018Review
- Wearable Wireless Sensors for Measuring Calorie Consumption.Journal of medical signals and sensorsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
Nowadays, the advancement of mobile technology in conjunction with the introduction of the concept of exposome has provided new dynamics to the exposure studies. Since the addressing of health outcomes related to environmental stressors is crucial, the improvement of exposure assessment methodology is of paramount importance. Towards this aim, a pilot study was carried out in the two major cities of Greece (Athens, Thessaloniki), investigating the applicability of commercially available fitness monitors and the Moves App for tracking people's location and activities, as well as for predicting the type of the encountered location, using advanced modeling techniques. Within the frame of the study, 21 individuals were using the Fitbit Flex activity tracker, a temperature logger, and the application Moves App on their smartphones. For the validation of the above equipment, participants were also carrying an Actigraph (activity sensor) and a GPS device. The data collected from Fitbit Flex, the temperature logger, and the GPS (speed) were used as input parameters in an Artificial Neural Network (ANN) model for predicting the type of location. Analysis of the data showed that the Moves App tends to underestimate the daily steps counts in comparison with Fitbit Flex and Actigraph, respectively, while Moves App predicted the movement trajectory of an individual with reasonable accuracy, compared to a dedicated GPS. Finally, the encountered location was successfully predicted by the ANN in most of the cases.
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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.