Personal Sensors for Temperature Exposure Assessments: A Validation Study

Authors: Elizabeth Bailey*, Appalachian State University, Margaret Sugg, Appalachian State University , Christopher Fuhrmann, Mississippi State University , Jennifer Runkle, North Carolina State University
Topics: Medical and Health Geography, Climatology and Meteorology, Quantitative Methods
Keywords: Heat, Personal Temperature Sensors, Validation
Session Type: Poster
Day: 4/4/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded


Heat exposure is a leading weather-related cause of death in the United States. The impacts of heat on human health has sparked research on different approaches to map and predict heat exposure at more accurate and precise spatiotemporal scales. This has led to a variety of personal heat sensor studies that have identified many factors, both physical and social, that contribute to an increased risk for heat vulnerability. Personal heat sensor studies rely on small sensors that can continuously measure ambient temperatures as individuals move through time and space. The accuracy and quantification of differences between these sensors have yet to be fully researched. This study applies a similar methodology to that utilized throughout air quality sensor validation studies to assess the validity of personal ambient temperature sensors. Data collection included a sample size of thirty-eight participants in Boone, NC, and Starkville, MS who wore multiple sensors during their daily routines for week-long periods during the summer months of July through September. HOBOs, iButtons, Hygrochrons, and Kestrel DROP D3FW Fire weather monitors were also attached to weather stations in each location to quantify the difference between sensor and weather station ambient temperature measurements. Bland-Altman analysis, correlation coefficients, and index of agreement statistics are used to identify and quantify where differences between sensors and weather station data occur. Results suggest significant differences in measured temperatures based on the location of sensors on participant’s bodies, sun angle, sensor type, and activity type.

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