Commentary

A Section 508–conformant HTML version of this article
is available at https://doi.org/10.1289/EHP556.

Opportunities and Challenges for Personal Heat Exposure Research
Evan R. Kuras,1,2 Molly B. Richardson,3 Miriam M. Calkins,4 Kristie L. Ebi,4,5 Jeremy J. Hess,4,5,6 Kristina W. Kintziger,7
Meredith A. Jagger,8 Ariane Middel,9 Anna A. Scott,10 June T. Spector,4,6 Christopher K. Uejio,11 Jennifer K. Vanos,12
Benjamin F. Zaitchik,9 Julia M. Gohlke,3 and David M. Hondula1,9
1

Center for Policy Informatics, Arizona State University, Phoenix, Arizona, USA
Department of Environmental Conservation, University of Massachusetts, Amherst, Massachusetts, USA
3
Department of Population Health Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
4
Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
5
Department of Global Health, University of Washington, Seattle, Washington, USA
6
Department of Medicine, University of Washington, Seattle, Washington, USA
7
Department of Public Health, University of Tennessee, Knoxville, Tennessee, USA
8
Public Health Division, Oregon Health Authority, Portland, Oregon, USA
9
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona, USA
10
Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA
11
Department of Geography, Florida State University, Tallahassee, Florida, USA
12
Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, California, USA
2

BACKGROUND: Environmental heat exposure is a public health concern. The impacts of environmental heat on mortality and morbidity at the population scale are well documented, but little is known about specific exposures that individuals experience.
OBJECTIVES: The first objective of this work was to catalyze discussion of the role of personal heat exposure information in research and risk assessment. The second objective was to provide guidance regarding the operationalization of personal heat exposure research methods.
DISCUSSION: We define personal heat exposure as realized contact between a person and an indoor or outdoor environment that poses a risk of
increases in body core temperature and/or perceived discomfort. Personal heat exposure can be measured directly with wearable monitors or estimated
indirectly through the combination of time–activity and meteorological data sets. Complementary information to understand individual-scale drivers
of behavior, susceptibility, and health and comfort outcomes can be collected from additional monitors, surveys, interviews, ethnographic approaches,
and additional social and health data sets. Personal exposure research can help reveal the extent of exposure misclassification that occurs when individual exposure to heat is estimated using ambient temperature measured at fixed sites and can provide insights for epidemiological risk assessment
concerning extreme heat.
CONCLUSIONS: Personal heat exposure research provides more valid and precise insights into how often people encounter heat conditions and when,
where, to whom, and why these encounters occur. Published literature on personal heat exposure is limited to date, but existing studies point to opportunities to inform public health practice regarding extreme heat, particularly where fine-scale precision is needed to reduce health consequences of
heat exposure. https://doi.org/10.1289/EHP556

Introduction
Environmental heat is a natural and anthropogenically enhanced
hazard with well-documented adverse impacts on human health
and well-being (Gasparrini et al. 2015; Parsons 2014). Despite
decades of physiological, epidemiological, and climatological
research about high temperatures, heat waves, and hot indoor and
outdoor environments, information about the actual thermal conditions people experience as they go about their daily lives is
scarce, leaving the potential for under-informed risk assessments,
policies, and intervention measures concerning heat exposure and
health. The recognition that personal exposure can differ substantially from fixed-point measurements has led to more valid and
precise understandings of, and solutions for, the impacts of other
environmental hazards on people, particularly the effects of air

Address correspondence to D.M. Hondula, School of Geographical Sciences
and Urban Planning, Arizona State University, Tempe, AZ 85281 USA.
Telephone: (480) 965-4794. Email: David.Hondula@asu.edu
J.H. has served as a scientific consultant to Stratus/Abt and to the Natural
Resources Defense Council.
All other authors declare they have no actual or potential competing
financial interests.
Received 24 May 2016; Revised 17 January 2017; Accepted 20 January
2017; Published 1 August 2017.
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Environmental Health Perspectives

pollution (e.g., Payne-Sturges et al. 2003; Steinle et al. 2013).
Analogous information about heat could become an important
tool for designing better strategies to monitor exposure and
reduce heat-related illness and death.
Heat illness and death occur when body core temperature
exceeds a tolerable range for physiological functioning. Because
biometric body temperature information is not widely collected
and shared, researchers commonly seek proxy indicators for personal heat stress. The data that are often used to reflect indoor
and outdoor conditions that can become dangerous for various
types of activities and populations are generally well documented
and accepted (Parsons 2014). Less understood is how often individual people encounter these conditions and how long exposures
last; when, where, to whom, and why these encounters occur;
how recurrent exposures may be associated with physiological
impacts; and how best to reduce their frequency, duration, and
severity.
Meteorological observations and climate model projections
indicate that warm-season temperatures and the frequency and severity of extreme heat events have increased in recent decades
and that this trend will continue (IPCC 2013). This warming will
be exacerbated in many cities owing to the impacts of the built
environment on local atmospheric conditions (Oke 1982). As a
consequence of both global- and urban-scale processes, increases
in heat exposure for some populations are expected. In the absence of additional adaptation—physiological, behavioral, or
technological adjustment to actual or expected climate and its
effects—adverse heat-related health events may become more
common (IPCC 2014; Hondula et al. 2015). The challenges associated with current weather patterns and future warming have
motivated many researchers to perform a variety of detailed heat-

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health risk assessments that largely aim to inform future adaptation efforts, local- to global-scale climate policy, or both (e.g.,
Gasparrini et al. 2015; Harlan et al. 2012; Vargo et al. 2016;
Xiang et al. 2014).
The intent of our commentary is to catalyze discussion of personal heat exposure among environmental health scientists and
practitioners. In particular, we lay the groundwork for additional
perspectives on how personal heat exposure research could
become a useful addition to the portfolio of techniques used to
assess heat-health risk and ultimately reduce the health burden of
heat. Studies are emerging that involve measuring and estimating
personal heat exposure. Some researchers are capturing the environmental conditions experienced by individuals going about
their daily lives using small, portable, affordable data loggers
(Basu and Samet 2002; Bernhard et al. 2015; Kuras et al. 2015).
Other investigators are estimating personal heat exposure via
simulation models of the positions and travel patterns of large
populations (Glass et al. 2015; Karner et al. 2015; Schlink et al.
2014). We explore how such studies relate to one another and
enhance the precision and validity of knowledge about the
impacts of heat on people. To do so, we first offer a definition
for personal heat exposure. We next share insights from our
own experiences studying and collecting personal heat exposure
data, as well as perspectives gleaned from extant literature.
Subsequently, we review how personal heat exposure research
can advance scientific knowledge and inform public health
practice.

Discussion
Defining Personal Heat Exposure
The word “personal” distinguishes personal heat exposure research
from a large portion of the environmental health literature that
assesses impacts of the thermal environment using a surrogate of
potential exposure measured at one or more fixed geographical
locations. “Personal” shifts the emphasis of the measurement setting from places to people (Ott 1982) and the unit of analysis from
populations to individuals.
The word “heat” is more difficult to precisely define in a practical yet accurate manner. In the physical sciences, heat is defined
as energy transferred from objects with higher temperatures to
objects with lower temperatures. In hot weather, however, it is
not only the transfer of energy from the environment to the body
that is of concern for human health. In some circumstances,
adverse health effects linked to thermal stress may also occur
because the body is unable to adequately transfer energy to the

environment to maintain core body temperature within normal
physiological limits (WHO 2004). We adopt a usage similar to
public and medical communication about hot weather and health:
heat refers to a state of the environment in which the combination
of the four relevant ambient parameters—air temperature, radiation (short- and long-wave), humidity, and air movement
(Parsons 2014)—constrains energy dissipation from the body or
adds energy to the body, thus posing a risk of discomfort, tissue
injury, or a combination of the two secondary to the increase in
core body temperature.
For our purposes, we are defining heat exposure as exposure
that occurs because a person is present at a location where heat
occurs. This exposure can be assessed in terms of magnitude,
duration, and frequency. Consistent with the use of the term
“exposure” in ambient air pollution research (Ott 1982), we are
making a distinction between external exposure and dose. Our
definition does not encompass internal exposure to heat (i.e., an
internal dose of heat) because this results from the combined
effects of environmental heat exposure and internal physiological interactions.
We define personal heat exposure as realized contact between
a human and an indoor or outdoor environment in which the air
temperature, radiative load, atmospheric moisture content, and
air velocity collectively pose a risk of increase in body core temperature, perceived discomfort, or both. Increases in body core
temperature are of clinical concern because of the potential for
tissue injury and metabolic derangement. Perceived thermal discomfort can occur with or without changes to body core temperature and is a function of several factors including skin temperature, sweating rate/condition, psychological state, prior thermal
exposure, and individual expectation of the thermal conditions to
be encountered. Personal heat exposure is an important step of
the conceptual pathway linking climate drivers and indoor and
outdoor environmental conditions to thermal discomfort and
adverse health outcomes (Figure 1).
The definition we propose encompasses traditional considerations of both occupational and environmental exposures and
intentionally focuses on only the exogenous factors that contribute to thermal discomfort and health outcomes. To sufficiently
address an individual’s susceptibility to heat illness or other injuries associated with heat exposure, personal heat exposure information should be combined with information about physiological
susceptibility factors (Figure 1, box D). Physiological factors that
modify the relationship between personal heat exposure and
adverse health events include but are not limited to age, sex, body
mass and surface area, hydration status, metabolic rate, preexisting

Figure 1. Conceptual pathway linking climate drivers (A) to outdoor and indoor ambient heat (B), personal heat exposure (E), and heat-related health and comfort outcomes (F). Behavioral and social factors (C) modify the association between ambient heat and personal heat exposure; physiologic susceptibility factors
(D) modify the association between personal heat exposure and health and comfort outcomes. This pathway considers public health systems (G) and systems
representing the built environment and other infrastructure (H) to be responsive to observed or perceived risks of adverse health or comfort outcomes. These
systems can intervene at various stages of the conceptual pathway to reduce risk.

Environmental Health Perspectives

085001-2

health conditions, psychological state, and acclimatization (Chan
et al. 2001; Chen and Ng 2012). Elements and consequences of
behavior and social context modify the association between ambient conditions and personal heat exposure (Figure 1, box C) but
can also indirectly modify subsequent risks of adverse health
events through their influence on physiological state. Applying our
definition, two individuals with different physiological states in
the same location would experience the same personal heat exposure but would not necessarily be equally susceptible to increases
in core body temperature or to associated discomfort or adverse
health impacts. Our conceptual pathway considers public health
systems (box G) and systems that manage the built environment
(box H) to be responsive to observed or perceived risks of thermal
discomfort and adverse health events. Modifications to the built
environment and to infrastructure systems can directly alter thermal
environmental conditions (e.g., shade provisioning, air conditioning)
and can alter the association between ambient heat and personal
heat exposure (e.g., adding transit stops to reduce walking times).
Public health systems and interventions can also modify determinants of personal heat exposure (e.g., behavioral change induced by
warning messages) and can additionally modify the impacts of personal heat exposure on health (e.g., through medical care).

Challenges in Current Heat Exposure Assessment
The vast majority of epidemiological studies relating extreme
heat to human health outcomes rely on meteorological observations of outdoor temperature and humidity from weather stations
proximate to the study population as surrogate indicators for personal heat exposure (e.g., Gasparrini et al. 2015). Yet, we know
that personal heat exposure is dependent on outdoor microclimate
variability, indoor heat exposure, and a wide suite of social and
behavioral factors such as individual travel patterns (e.g.,
Bernhard et al. 2015). Furthermore, in outdoor settings, radiative
heat is a main factor influencing an individual’s heat load
(Middel et al. 2016; Thorsson et al. 2007). Thus, heat exposure
misclassification is likely widespread and substantial, particularly
when considering the full human energy balance. The consequences of this misclassification will not be known until more personal exposure studies and fine-scale meteorological data become
available.
Outdoor meteorological measurements from a sparse station
network do not adequately represent the range of conditions experienced by people. In rural areas, populations may live dozens of
miles away from the nearest weather station. Meteorological measurements are influenced by surrounding land cover (e.g., forest,
urban) (Fall et al. 2011) and may be drawn from instruments
deployed for reasons other than estimating representative conditions (e.g., at airports to support aviation). Contrasts exist between
available observations and outdoor conditions in places of interest.
In urban settings, the built environment and land use heterogeneity
alter the exchange of energy and moisture between the surface and
atmosphere, creating local climate variability and the urban heat
island (UHI) effect (Oke 1982).
The scale of misclassification related to weather station siting,
however, is likely to be small when compared with the misclassification associated with the location of exposures, and in particular, if the exposure occurs indoors or outdoors. On average in the
United States and Canada, large-scale survey data indicate that
people spend most of their time (∼80–90%) indoors (e.g., Klepeis
et al. 2001). In most cases, buildings moderate thermal extremes
experienced outdoors by reducing exposure to solar radiation,
high and low temperatures, and wind. Today, many buildings are
climate-controlled to maintain nearly constant temperature and
humidity levels, increasing the divergence between weather station observations and personal heat exposure.
Environmental Health Perspectives

The growing prevalence of air conditioning and divergence
between outdoor and indoor thermal conditions does not imply,
however, that no heat-health hazards exist indoors. In fact, a high
proportion (50–80%) of decedents classified as victims of
extreme heat may perish inside their own homes (Fouillet et al.
2006; CDC 2013). Indoor environments are influenced by the
composition and configuration of the residential matrix, heating,
ventilation, air conditioning, and occupant behavior, preference,
and ability or willingness to pay for thermal modification (IOM
2011). Buildings without adequate climate controls may amplify
heat exposure above outdoor levels owing to the high thermal
mass of many buildings, which absorbs and retains heat and
effectively heats the indoor environment, thus increasing the risk
of heat-related morbidity and mortality (White-Newsome et al.
2012). Other studies have shown only weak correlations between
outdoor and indoor temperatures and moderate correlations
between outdoor and indoor humidity (Smargiassi et al. 2008;
Uejio et al. 2016).
Exposure misclassification is of particular concern for certain
groups of people, including those with limited opportunities to
change their exposures. Workers engaged in agriculture, construction, firefighting, manufacturing, military, or resource extraction
face heightened health risks owing to exertion and prolonged heat
exposures from outdoor conditions, extreme indoor environments,
heat generated by machinery or power equipment, or a combination
of these factors (Xiang et al. 2014). Athletes and others involved in
high-exertion recreational activities may also be at high risk with
limited agency and conflicting motivation to engage in adaptive
behaviors (Casa et al. 2015). Localized, on-site meteorological
monitoring is already recommended or employed in many of the
settings in which such circumstances occur (e.g., Parsons 2006).
Additional personal exposure monitoring could yield even greater
insight into individual-level variability in exposures and time–activity patterns that influence how accurately measures of ambient
conditions reflect personal heat exposure in these settings.

Measuring Personal Heat Exposure
What to Measure and How
As individuals move through time and space, they experience a
range of thermal environmental conditions. Consider the downtown area of a busy city on a hot and humid day. Within ten city
blocks, individuals may be sitting in air-conditioned offices,
walking through shaded parks or on sunny sidewalks, welding
steel many stories up a new construction project, or doing any
number of activities that could affect their environment, clothing,
and physical exertion. In a traditional population-based epidemiological assessment, all of these individuals would be assigned the
same official meteorological statistics collected at the nearest
weather station located ten minutes away. The type of thermal
environmental variability experienced by individuals within this
city is often not well-captured in heat-health risk assessments.
Personal heat exposure approaches address the limitations of current exposure assessment techniques by reducing misclassification at the level of the individual and by increasing precision
through measuring temperature exposure at a finer spatial scale.
These approaches combine information about the thermal environment, which helps describe where and when heat exposure
may occur, with information about human travel patterns and the
behavioral and social factors that help explain to whom and how
heat exposure is realized. To date, and to the best of our knowledge, data about personal exposure to three of the four relevant
parameters (air temperature, radiation, and humidity, but not air
velocity) have been collected directly through personal monitoring approaches in which research participants wear devices that

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log observations as they go about normal activities (e.g.,
Bernhard et al. 2015; Kuras et al. 2015), and indirectly through
simulation, in which meteorological information is merged with
activity or transportation information (e.g., Glass et al. 2015;
Karner et al. 2015). Personal monitoring addresses environmental
conditions and travel patterns simultaneously, whereas simulations address these two components separately (Steinle et al.
2013).
Personal monitoring strategies utilize small, wearable data
loggers that sense environmental conditions and store data internally. Commercially available devices exist to measure temperature, humidity, and sunlight (Table 1). Opportunities to measure
these parameters using open-source electronics, such as Arduino,
and cell phones are rapidly evolving, allowing for lower-cost
monitoring (Muller et al. 2015). Light exposure data are useful in
estimating time spent outside (outdoor light intensity is easily differentiated from lower-intensity indoor lighting even on cloudy
days) and could be useful for estimating radiant heat exposure.
Indirect estimates of sunlight exposure can also be obtained
through matching activity logs completed by research participants to weather data. Short- and long-wave radiation exposure
can be measured using globe thermometers and other sensors that
yield mean radiant temperature, but we are not aware of studies
that have employed these devices at the individual level. Air
movement, typically measured with anemometers at central stations, can be measured at the individual level using globe anemoradiometers (Nakayoshi et al. 2015), but it has not been explicitly
incorporated in direct personal heat-monitoring studies to our
knowledge. Simulation approaches have employed various combinations of the four parameters, obtained through central site
monitors, gridded meteorological data sets, and remote sensing.
Because personal heat exposure depends upon an individual’s
travel patterns through time and space, some aspects of behavior
and social context are implicitly included in individual-level heat
measurements. The missing elements required to estimate risk of
increase in core body temperature, negative health outcomes, and
thermal discomfort include individual physiological condition,
adaptive strategies such as clothing choices, basal metabolic rate,
and physical exertion. When conducting personal monitoring
research, questionnaires, daily logs, phone-based apps, and photos may collect information about clothing, comfort, self-reported
health status, and observations of heat-related symptoms; in addition, a range of biomarkers, monitors, and sensors can quantify
parameters such as heart rate and activity level (e.g., Basu and
Samet 2002; Spector et al. 2015). Beyond informing estimates of
heat strain, these complementary data sources are useful to understand the circumstances in which heat exposure occurs, associated risk, and the extent to which individuals have agency or
motivation to change their exposure.

Operationalizing Direct Exposure Assessment with
Wearable Sensors
When designing data collection protocols to measure heat exposure using wearable sensors, a range of factors should be considered; chief among these factors is sensor placement. Thermal
sensors should be worn outside of bags and clothing, exposed to
atmospheric conditions at all times. Observations from devices
placed on top of participants’ shoes, at the waist, and at the neck
outside the shirt were relatively consistent for both temperature
and relative humidity during no, moderate, and heavy physical
activity (Dumas et al. 2016). For light exposure measurements,
however, sensor placement on the body should remain consistent
among participants because some parts of the body are more
Environmental Health Perspectives

typically exposed to solar radiation than others (Weihs et al.
2013). Viable sensor placement must take into account the
following:
• Convenience and comfort. Placement on clothes or objects
carried by participants yielded reliable data and participant
compliance (Kuras et al. 2015; Uejio et al. 2016). Sensors
(HOBO®, Onset Computer Corporation) clipped to the shoe
resulted in some discomfort among women wearing dress
shoes (Bernhard et al 2015).
• Participants’ physical attributes and anticipated behaviors.
Sweat-inducing activities potentially affect humidity readings.
• Environmental characteristics. Contact with wet or highheat-radiating surfaces and exposure to or submersion in
water should be avoided. Care should also be given to sensor
placement in the context of highly localized point sources of
heat (e.g., welding), which may not substantively reflect the
environmental conditions of interest to the researcher.
• Body heat. The extent to which body heat influences sensor
readings should stay consistent among participants as much
as is possible. The relationship between sensor placement
and heat exposure estimates requires further investigation.
• Safety concerns. In certain occupational or recreational settings, some configurations for sensor placement may not be
available, safe, or preferable for participants (e.g., because
of risks of the sensor getting tangled or interfering with
equipment).
Finally, participants should be encouraged to avoid touching
or repositioning sensors unnecessarily.
Although wearing or carrying a data logger is less intrusive
than other research activities, recruiting individuals to participate
is a limiting factor for study design. We have found that participants usually consider the sensors noted above to be unobtrusive
in their daily lives. In an occupational setting, recruitment may
involve working with an agency, then managers, and finally individual staff. This top-down recruitment of a specific population
may be effective but could limit individual interest and raise concerns about coerced participation, adequate representation of
exposures experienced, and data ownership (Jagger et al. 2016).
In other circumstances, individuals may be recruited directly in a
neighborhood or community context through existing organizations, fliers in businesses, or word of mouth (Kuras et al. 2015).
Partnering with community organizations allowed community
partners to follow up with participants and respond to concerns
about wearing the monitor in various settings (Bernhard et al.
2015). Investigations that involve participants more actively
throughout the research process, including study design, data collection, and data analysis, may also increase participant buy-in
and the relevance of the research to on-the-ground community
concerns. Although such buy-in is critical in all human-subjects
research, tasks inherent in personal monitoring (i.e., remembering
to place sensors on the body) demand a level of commitment
above and beyond that required for methods such as surveys,
interviews, or diaries.

Estimating Personal Heat Exposure Indirectly
There are limits to the scale at which personal heat exposure data
can be collected with wearable sensors. Alternate approaches leverage large-scale data sets to estimate personal heat exposure
indirectly, enabling examination of larger populations while introducing greater uncertainties regarding the accuracy and precision
of environmental and time–activity data. The most comprehensive
method for assessing personal heat exposure indirectly would
involve combining high-resolution thermal environment data
(including temperature, humidity, radiation, and wind speed) for

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Environmental Health Perspectives

085001-5

iButton-basic®

iButton-hygro®

Maxim Integrated

Maxim Integrated

HIH-4000 Integrated
Circuit Humidity
Sensor®
DHT22 TemperatureHumidity Sensor®

TMP36 - Analog
Temperature Sensor®

Honeywell

Analog Devices

Device size
30 mm × 41 mm × 17 mm

Requires additional parts

Storage on mobile device

8K bytes

2 bytes

64K bytes

8K or 64K bytes

Memory
64K bytes

5 mm × 18 mm
18 mm × 16 mm × 2 mm

± 1 C, ± 2%

Requires additional parts

Requires additional parts

27 mm × 59 mm × 13:5 mm Requires additional parts

2.54 mm

40 mm × 40 mm × 10 mm

16 mm diameter (sensor)

16 mm diameter (sensor)

102 mm × 38 mm

± 1 C

± 0:5 C,
± 2–5%

± 5–8%

± 0:3 C,
± 3%

± 0:5 C,
± 5%

± 0:5 C

± 0:2 C,
± 2:5%

58 mm × 33 mm × 23 mm
± 0:5 C,
150–1200 nm

Accuracyb
± 0:2 C

Additional featuresc
Retail cost (USD)
Website
Waterproof to 300 m; solar radiation 133
http://www.onsetcomp.
shield is required
com/products/dataloggers/utbi-001
Waterproof housing for wet or
47 (8K bytes) or http://www.onsetcomp.
underwater use, up to 30 m
64 (64 K bytes)
com/products/dataloggers/ua-002-08
Weatherproof housing; user replace- 145–170
http://www.onsetcomp.
able battery; model vary by tempercom/products/dataature ranges; 003 and 004 can be
loggers/u23-001
immersed in water for extended
periods of time
Alarm triggers
4.40–8.80d
https://www.
maximintegrated.com/
en/products/digital/
ibutton/DS1920.html
d
Logs time, variable measurement
79–149
https://www.
intervals (1 s to 273 hr), delayed
maximintegrated.com/
start, alarm triggers
en/products/digital/dataloggers/DS1923.html
Requires additional components to
49
http://www.icelsius.com/
make sensor wearable, as well as a
store/icelsius_blue_rh
corresponding mobile app, bluetooth compatible with many iOS
and Android devices
Must be connected to a controller to 10–14d
http://sensing.honeywell.
provide power and read in and store
com/HIH-4000-001measurements
Humidity-Sensors
d
See AirBeam as example of weara- 8–10
https://www.adafruit.
ble devices that incorporates this
com/products/385
sensor along with air quality and
noise: http://aircasting.org
Must be connected to a controller to 1.50
https://www.adafruit.
provide power and read in and store
com/products/165
measurements
Must be connected to a controller to 12–15d
https://www.adafruit.
provide power and read in and store
com/product/1899
measurements

Note: °C, degrees Celsius; K, thousand; RH, relative humidity; USD, U.S. dollars.
a
This list is not exhaustive but provides an overview of some of the more frequently used and basic measurement devices available at the time of publication and includes devices with which coauthors have direct experience.
b
Accuracy includes temperature (degrees Celsius), relative humidity (percent), and sunlight (nanometers), as applicable.
c
All sensors require some type of connector and/or communications device, such as a USB connector, unless otherwise noted in this column.
d
Total retail cost depends on quantity.

Measurement Specialities HTU21D-F Temperature
& Humidity Sensor
Breakout Board®

Aosong

iCelsius Blue T/RH®

Aginova

Onset

Onset

Devicea
HOBO TidbiT v2 Water
Temperature Data
Logger®
HOBO Pendant®
Temperature/Light 8K
or 64K Data Logger
HOBO U23 Pro v2
(001–004)
Temperature/Relative
Humidity Data Logger®

Manufacturer
Onset

Table 1. Examples of personal heat exposure measurement devices.

all indoor and outdoor locations occupied by individuals in a population of interest along with detailed time–location records for
each individual. The intersection of these two data sources would
produce simulated personal heat exposure profiles that could be
interrogated to answer various questions of interest to researchers
and practitioners. To date, researchers have combined meteorological data with large-scale census, survey, and/or activity pattern
simulations to estimate exposure in different local environments,
examining particular drivers of risk or certain circumstances associated with exposure (e.g., Glass et al. 2015; Karner et al. 2015;
Schlink et al. 2014).
Environmental data sources for indirect personal heat exposure methods offer trade-offs with respect to representativeness,
spatial coverage, and labor and data processing needs. Citizen
scientist networks of weather stations such as WeatherBug
increase the density of data availability (Muller et al. 2015), but
data may be sparse in low-income urban settings that are most
affected by UHIs, and this approach requires additional quality
control and bias correction. Alternatively, researchers (or public
officials) can implement their own high-density sensor networks
on public property, such as lampposts or trees, or on the private
property of community volunteers (e.g., the Array of Things
Project, Jacob 2015). Intentional transects, in which individuals
move along predefined paths while making meteorological
measurements, provide another strategy for researchers to collect high-density heat information in areas with high microclimatic variability (e.g., Nakayoshi et al. 2015; Tsin et al. 2016).
Enriching the volume and quality of data available regarding
indoor environments is an urgent need. Across outdoor and
indoor environments, collecting measurements that are both of
high quality and representative of the human experience
presents a range of challenges, from microclimatic variability to
device accuracy to sensor placement and response time (Oke
2006).
Many researchers have used remotely sensed (usually
satellite-derived) and model-derived temperature estimates in
analyses of heat impacts or vulnerability assessments (e.g.,
Harlan et al. 2012). Although remotely sensed measurements
offer unprecedented spatial coverage and bypass the challenges
of implementing reliable in situ heat monitoring networks,
researchers must make assumptions when linking remote temperature measurements with those relevant to personal heat exposure. Remotely sensed radiometric temperature estimates are
based on surface emission of thermal infrared radiation. They
correlate strongly with air temperature at a large scale, but
neighborhood-scale correlations are significantly lower because
of variability in surface emissive properties, shadowing effects,
and the influence of wind on air temperature (e.g., Prihodko and
Goward 1997; Nichol et al. 2009). Other shortcomings include
data gaps due to infrequent satellite overpass schedules, lack of
representativeness due to cloud interference, and inadequate spatial resolution for some applications. High-resolution climate
model simulation output (e.g., Heaton et al. 2014) and gridded/
interpolated meteorological data sets (e.g., DayMet, Thornton
et al. 1997) are also viable for indirect methods. In addition to
retrospective analysis, these tools can be used to explore the
impacts of projected or possible changes in the urban landscape
and global climate on personal heat exposure. However, scale
mismatch and bias correction limit the ability of these data
sources to capture extremes in environmental conditions, which
might lead to misrepresentation of exposures of interest (e.g.,
Guentchev et al. 2016).
Information about the thermal environment must be complemented with information about individuals’ locations and activities, particularly time outdoors, distances traveled, and use of
Environmental Health Perspectives

transportation. These data can be collected through tracking devices, including dedicated GPS devices (Kerr et al. 2011), cell
phone–based location information (Khan et al. 2015), lightsensing devices (e.g., Bernhard et al. 2015), or through personal
reports via logs, surveys, time–activity diaries, or interviews
(e.g., Klepeis et al. 2001; McCurdy and Graham 2003). These
approaches require volunteers who are willing to report their
travel patterns or to have them recorded. Recall and reporting
errors can be a problem for self-reported activity logs, and tracking devices need to be carried and activated to be meaningful.
Large-scale simulations of time–activity patterns, often used in
transportation planning, provide an additional type of data about
human mobility (Glass et al. 2015; Karner et al. 2015). Geographic
Information Systems (GIS) may help researchers integrate such information about travel patterns with thermal environment data,
thereby facilitating visualizations and predictions across time and
space.

Analyzing Personal Heat Exposure Data
Strategies for analyzing personal heat exposure data have used a
variety of aggregating and statistical techniques with the goal of
understanding exposure patterns. Several issues have been
explored, including the following:
• Correlations between data from personal heat exposure sampling, often at relatively high frequencies (e.g., 1–5-min
intervals), and data from stationary monitors with lower
sampling frequencies (e.g., hourly), for periods of up to 1 wk
(Basu and Samet 2002; Bernhard et al. 2015; Kuras et al.
2015).
• Appropriate sampling periods to capture exposures that may
lead to heat stress. Physiologically, capturing exposure at
relatively short intervals (e.g., every 15 minutes) may be
most appropriate in extreme heat settings. Karner et al.
(2015) generated an Extreme Degree-Minute metric that
aggregates the time simulated individuals spent above a certain heat threshold while participating in nonmotorized
transport.
• Statistical approaches for managing personal exposure data
and linking exposure data from different sources, for example, estimating the relationship between outdoor conditions
and personal heat exposure by incorporating a random effect
term into a linear mixed model (Bernhard et al. 2015).
Despite the progress that has been made, several issues
remain, including temporal autocorrelation, diurnal cycles, and
within- and between-participant behaviors and physiology.
Future personal heat exposure analyses should utilize and refine
more sophisticated statistical approaches, such as random forest
models (Brokamp et al. 2017) or two-stage semi-parametric
regression models (Deffner et al. 2016), that have been employed
in air pollution personal exposure data analyses to build prediction models that include the contributions of outdoor, indoor, and
home characteristics and behaviors to personal exposure. More
work is also needed to understand the time-lagged relationship
between personal exposure and the onset of physiological symptoms, including increasing body core temperature (e.g., Figure 1
of Koehler and Peters 2015). Future work should also leverage
prior research establishing thresholds for characterizing dangerous exposures based on generalized physiological principles
and observations, such as charts of physiologically equivalent
temperature (e.g., Matzarakis et al. 1999), exertion–rest thresholds based on the wet-bulb globe temperature (WBGT) index
(e.g., Parsons 2006), and thresholds and triggers informed
through analyses of specific populations and health end points
(e.g., Petitti et al. 2015). More detailed analyses will be useful

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for determining optimal sampling frequencies and frames, sample sizes, and metrics for exploring associations with various
health outcomes.

Implications of Personal Heat Exposure Research
We identify four broad areas in which personal heat exposure
research can have a positive impact on research and practice:
developing a more valid and precise understanding of the human
experience with heat, more effective targeting of intervention
measures, evaluation of interventions and spatiotemporal trends,
and community engagement and outreach. More generally, we
envision that personal heat exposure data could become a fundamental component of how we think about and respond to societal
challenges associated with extreme heat. Achieving this broad goal
will require a large community of practice comprising collaborators
from disciplines including atmospheric science, computer science,
epidemiology, anthropology, medicine, engineering, environmental health, geography, and urban planning.
By measuring the thermal environment as experienced by
individuals, personal heat exposure approaches may illuminate
fine-scale differences in attributes, behaviors, preferences, and
access to cooling resources within or between populations.
Although this knowledge does not complete the pathway from
environmental hazard to health outcome, it may illuminate mechanisms and patterns through which suspected risk factors (e.g.,
urban form, poverty, social or linguistic isolation, housing quality) and capacity for resilience (e.g., adaptive behavior, heatmitigating infrastructure) are realized into exposure, particularly
when appropriate qualitative methods are involved to provide
necessary context. Further, personal exposure approaches may
both facilitate early diagnosis and intervention related to clinical
heat illness and provide a more complete understanding of the
total health burden of heat, a topic of long-standing debate in the
literature, given that administrative health records do not often
provide sufficient information regarding exposure history (e.g.,
Shen et al. 1998; Whitman et al. 1997).
If available on a larger scale, personal heat exposure data
could be incorporated into epidemiological and geographical
research aiming to pinpoint social and environmental determinants of risk and vulnerable populations. The surrogate exposure
variables used in current time series and spatial models for heat
morbidity and mortality, for example, could be supplemented by
or replaced with personal heat exposure estimates, highlighting
exposure-driven differences in outcomes that have not yet been
explored. The primary opportunity we see at present for personal
exposure research is to inform the modeling approaches that output exposure data for use in subsequent epidemiological analyses.
Recently published research, for example, demonstrates that
fixed-point airport temperature data may underestimate the effect
of temperature on mortality when compared with a higherresolution, satellite-derived, spatially continuous estimate of the
same variable (Lee et al. 2016). We propose that these positive
developments can be even further accelerated with guidance
from personal heat exposure studies. Personal monitoring data
and time–activity assessment have been similarly leveraged in air
pollution epidemiology for at least the past decade (e.g., Nethery
et al. 2008; Özkaynak et al. 2013; Baxter et al. 2013).
Personal heat exposure research offers a promising means of
informing decision making and targeting intervention measures
because it can substantially enhance our knowledge of the who,
why, where, and when dimensions of exposure-related health
risks. Reports of personal heat exposure studies should clearly
communicate methodological limitations and uncertainties in
study findings to help policy makers and decision makers
Environmental Health Perspectives

understand how this type of information might complement existing research or decision support tools already in use. Such transparency is particularly important at present, when personal heat
exposure studies are limited in number and scope. A potential
policy application of this research is providing guidance for the
targeted deployment of heat mitigation strategies such as cool
roofs or green infrastructure. Additionally, individuals may learn
more about their own exposure patterns and might be able to take
preventative actions in advance of dangerous increases in core
temperature if able to interact with their own exposure data. We
envision that users of their own personal heat exposure data
would likely come from particular niches such as athletics, the
military, or certain outdoor occupations, sectors where methods
of providing real-time physiological information related to heat
stress are already being explored (e.g., Wickwire et al. 2012).
Enhancing decision-support tools for institutions and individuals
with personal exposure data is consistent with goals to increase
the adoption of the evidence-based public health framework for
climatic hazards and climate change (Hess et al. 2014).
Just as personal heat exposure research enables better targeting
of extreme heat intervention measures, so too can it facilitate the
evaluation of their effectiveness by reducing the potential for misclassification related to exposure. Such an improvement would
come at a critical time: in a recent review, Boeckmann and Rohn
(2014) reported that “concrete evidence for the effectiveness” of
strategies such as heat warning systems “is lacking,” citing challenges in defining a counterfactual and the absence of true control
groups as major hindrances to rigorous evaluative research.
Ecological epidemiological studies reporting declines in heatrelated mortality coincident with the deployment of heat warning
systems have been unable to draw conclusions based on process
and mechanism (Boeckmann and Rohn 2014), complicating
claims of causality. Used in appropriate study designs, direct measurements of personal heat exposure could dramatically improve
our understanding of whether and why certain adaptation measures
work. For example, Longo et al. (2017) found that homeless individuals enrolled in a residential program at a local shelter experienced reduced heat exposure compared with homeless individuals
not in the program, which may help explain differential health outcomes between those two groups. At a larger scale, examining exposure patterns more rigorously can help us understand why
spatial and temporal patterns in heat-related deaths and illness
have emerged, as well as predict the impact of future climate
change on heat-related deaths and illnesses (Hondula et al. 2015).
Personal heat exposure research creates new opportunities for
community and educational outreach. Several authors of this paper have engaged communities during and after personal heat exposure studies by organizing lectures at local libraries, hosting a
temperature-themed bingo game, leading a thermal historical
walking tour, and delivering reports and presentations to study
participants with their exposure data. Similarly to the ways in
which participatory research with air pollution sensors have
empowered some communities to pursue environmental justice
and reduce health disparities related to poor air quality (e.g.,
Grineski 2006), we can also imagine—and have begun to observe
in some of our own projects—that personal heat exposure data
can be leveraged for communities to advocate for and achieve
socioenvironmental changes.

Limitations
Personal heat exposure methods have limitations worth noting
even if their application leads to more valid and precise exposure
estimates. An overarching challenge that researchers face at present, and will continue to face for some time, is a lack of standards

085001-7

and best practices, which will hinder the comparability of studies
and could limit interpretation and uptake by decision makers and
policy makers. Similar to other developments that leverage technology to gather individualized data (e.g., precision medicine),
other potential issues include privacy, selection bias, data ownership, data quality, and cost. Given the interest in linking personal
heat exposure with location and activity data, data collection
could be seen as intrusive or burdensome, constraining generalizability. Similarly, there may be issues with employers or other
gatekeepers regarding correlation of personal heat exposure and
activity data and regarding data ownership. Many of these issues
are clearly surmountable with the use of appropriate techniques
and safeguards; nevertheless, they should be mentioned. A final
caveat involves the potential for overemphasizing precision in
heat exposure estimates at the expense of investment in other important heat-health research questions. Determining adequate precision in exposure estimates required to support evaluation of
interventions is likely the most effective way to advance both
research and practice goals.

Conclusions
In this commentary, we defined personal heat exposure as realized contact between a person and an indoor or outdoor environment that poses a risk of increases in body core temperature,
perceived discomfort, or both. Focusing on personal heat exposure using novel methods and devices for data collection presents
opportunities to increase both validity and precision in heat exposure assessment. These methods also allow researchers and public health practitioners to gain important insights into human
behavior and environmental conditions. Although the published
literature is limited, this approach highlights individual aspects of
the heat-health relationship that may be key to understanding
how to best reduce the frequency, duration, and severity of exposure. Knowledge gained can be applied to developing a better
understanding of how people experience hot weather, to designing and evaluating interventions, to analyzing spatiotemporal
trends, and to targeting community engagement and outreach
activities to reduce the health burden of heat.

Acknowledgments
The authors acknowledge K. Brown, J. Brown-Saracino, M. Chester,
M. Evans, C. Gronlund, S. Harlan, E. Johnson, E. Johnston, M.
O’Neill, B. Ruddell, B. Stone, S. Threadgill Matthews, S. Tyson,
and other collaborators for their contributions to prior and ongoing
personal heat exposure research efforts, some of which are referenced in this commentary.
E.K. and D.H. were partially supported by the National Science
Foundation (NSF) through the Central Arizona-Phoenix LongTerm Ecological Research (CAP LTER) program (BCS1026865). M.B. was supported in part by the National Institutes of
Health (NIH) (T32 HL105349) and the Nutrition Obesity Research
Center (P30DK0563360). M.B., J.G., A.S., and B.Z. are partially
supported by NIH grant R01 ES023029. J.H. was partially
supported by NIH grant R21TW009535. A.S. was supported in
part by NSF Integrative Graduate Education and Research
Traineeship Program (IGERT) DGE-1069213. J.S. was supported
in part by Centers for Disease Control and Prevention/National
Institute for Occupational Safety and Health (CDC/NIOSH) grant
5K01OH010672-02. D.H. was partially supported by the Virginia
G. Piper Trust Health Policy Informatics Initiative and NSF grant
SES-1520803. J.V., A.M., and D.H. were partially supported by
NSF Sustainability Research Network (SRN) Cooperative Agreement
1444758. The opinions expressed are those of the authors and do
Environmental Health Perspectives

not necessarily represent those of the NIH, NSF, CDC, NIOSH, or
any other organization.

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