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International Journal of
Environmental Research
and Public Health
Article
Building Vulnerability in a Changing Climate: Indoor
Temperature Exposures and Health Outcomes in
Older Adults Living in Public Housing during an
Extreme Heat Event in Cambridge, MA
Augusta A. Williams 1, John D. Spengler 1, Paul Catalano 2, Joseph G. Allen 1and
Jose G. Cedeno-Laurent 1, *
1Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA 02115, USA
2Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA
*Correspondence: memocedeno@mail.harvard.edu; Tel.: +617-384-5260
Received: 14 May 2019; Accepted: 29 June 2019; Published: 4 July 2019
Abstract:
In the Northeastern U.S., future heatwaves will increase in frequency, duration, and
intensity due to climate change. A great deal of the research about the health impacts from extreme
heat has used ambient meteorological measurements, which can result in exposure misclassification
because buildings alter indoor temperatures and ambient temperatures are not uniform across cities.
To characterize indoor temperature exposures during an extreme heat event in buildings with and
without central air conditioning (AC), personal monitoring was conducted with 51 (central AC, n=24;
non-central AC, n=27) low-income senior residents of public housing in Cambridge, Massachusetts in
2015, to comprehensively assess indoor temperatures, sleep, and physiological outcomes of galvanic
skin response (GSR) and heart rate (HR), along with daily surveys of adaptive behaviors and health
symptoms. As expected, non-central AC units (T
mean
=25.6
◦
C) were significantly warmer than
those with central AC (T
mean
=23.2
◦
C, p<0.001). With higher indoor temperatures, sleep was
more disrupted and GSR and HR both increased (p<0.001). However, there were no changes in
hydration behaviors between residents of different buildings over time and few moderate/several
health symptoms were reported. This suggests both a lack of behavioral adaptation and thermal
decompensation beginning, highlighting the need to improve building cooling strategies and heat
education to low-income senior residents, especially in historically cooler climates.
Keywords:
health; heat; vulnerability; built environment; public housing; indoor environmental
quality; temperature
1. Introduction
Extreme heat events are a significant public health threat that are increasing in frequency, duration,
and severity with climate change [
1
,
2
]. Globally, people were exposed to an average of 1.4 more
heatwave (HW) days in from 2000 to 2017 than from 1986 to 2005, and 18 million more extreme heat
exposure events occurred in 2017 than in 2016 [
3
]. In Boston, Massachusetts (MA), there was an average
of 11 days above 90
◦
F per year from 1971 to 2000, but it is expected to experience up to 40 of these
hot days by the year 2030, and up to 90 hot days by 2070, depending on greenhouse gas emission
trajectories [4].
As temperatures increase, the human body becomes less effective at thermoregulation, which
has direct and indirect health impacts on cardiovascular, respiratory, renal, pancreatic, digestive,
cerebrovascular, and cognitive functions that result in significant morbidity and mortality. Extreme
heat events, such as the 2003 European HW and the 2015 HW in India, result in extraordinary loss of
Int. J. Environ. Res. Public Health 2019,16, 2373; doi:10.3390/ijerph16132373 www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2019,16, 2373 2 of 15
life [
4
,
5
]. However, temperatures that are currently designated as extreme will become more common
with climate change, and are expected to result in significantly higher heat-related mortality and
morbidity in the U.S. and throughout the world by the end of the century [6–8].
The impacts of extreme heat have the potential to widen existing disparities in economically
disadvantaged and health-compromised populations in the U.S., as those who are most vulnerable
will be most impacted by extreme heat with climate change. Human vulnerability to extreme heat
arises from many risk factors, including, but not limited to, infants and the elderly, social isolation, low
income, and low education [
9
]. Semenza et al. found that during the Chicago HW of 1995, there were
higher odds of death for those with pre-existing medical conditions [
10
], and chronic disease has been
found to be a risk factor for heat illness across the U.S. [
11
]. Pre-existing disease can alter sympathetic
nervous system response, preventing cardiac responses that allow for thermoregulation adjustments
during extreme heat exposure [12].
Older adults are more susceptible to adverse health outcomes during extreme heat events due to
high prevalence of pre-existing disease, medications, and autonomic nervous system impairments
affecting the thermoregulation and perception of extreme temperature exposure [
13
]. Skin-related
vasoconstriction and vasodilation have both been found to be diminished in older adults, even when
considering younger individuals at similar fitness and hydration levels [
14
]. The number of elderly
individuals who are at least 65 years old in the U.S. is expected to more than double by mid-century,
and will make up one-quarter of our population by 2060 [
15
], resulting in a larger group of susceptible
individuals that will be vulnerable to the health impacts of HWs.
Air conditioning (AC) has been widely adopted as the main adaptation strategy to mitigate
extreme heat exposures indoors and is extremely important in reducing the incidence of poor health
outcomes during extreme heat [
10
,
16
]. Quinn et al. (2017) found that central AC is more effective as
overcoming internal thermal loads during extreme heat and cooling indoor environments compared to
portable or window AC units [
17
]. A 2011 study in New York City has found that, of seniors or adults
in poor health, 34% surveyed did not own or use AC during extreme heat events, 30% were unaware
of heat warnings, and many did not perceive themselves to be at risk of the extreme temperature
exposures [
18
]. Between a fifth and a third of Massachusetts residents have central AC at home, while
20% of households lack any type of AC [19,20].
Despite the dependence on AC for cooling during extreme heat, one of the many protective factors
often studied with regard to heat stress, the role of buildings is less well-understood. Depending on
the building architecture, design, orientation, materials, and ventilation, extreme temperatures may be
exacerbated indoors, as compared to outdoor temperatures during extreme heat events due to internal
heat loads and the building’s thermal mass. Individuals who are most vulnerable to the health impacts
of extreme heat are less likely to have access to or utilize AC. While upwards of 90% of residents in
New York City were found to have some form of AC, roughly only 50% of public housing residents
have access to AC [
21
]. Even in cities with widespread AC use, such as in Maricopa County, Arizona,
nearly 40% of heat-related deaths happen indoors due to a variety of factors, including non-functioning
ACs, lack of electricity, and disabling ACs to avoid high financial costs [22].
In the United States, adults can spend up to 90% of their time indoors [
23
]. In buildings without
adequate cooling systems, people may be exposed to elevated indoor temperatures, thus increasing the
risk of experiencing heat-related health effects. A great deal of the previous research about the health
impacts resulting from extreme heat exposure has utilized ambient meteorological measurements to
classify heat exposures, which can result in exposure misclassification in two ways: from buildings
altering temperatures indoors where people are primarily exposed and from fine spatial differences in
ambient temperatures that are not uniform across a city.
There are also a wide range of adaptive behaviors an individual can take that could mitigate or
exacerbate heat exposure and poor health outcomes as a result. A past study in Detroit, Michigan
found that behaviors like opening windows/doors, using fans or AC, and leaving the house have been
used more than behaviors like changing clothes, showering, and going to the basement/porch/yard for
Int. J. Environ. Res. Public Health 2019,16, 2373 3 of 15
urban-dwelling adults [
24
]. However, these behaviors are most frequently enacted in more moderate
temperatures (23.8
◦
C–26.6
◦
C), and least used when indoor temperatures exceed 32.2
◦
C [
24
]. While
there was a large majority (>90%) of older adults surveyed in Australia who reported wearing cooler
clothing, closing curtains/shades, and drinking more fluids during hot days, less than 14% reported
going to a cooler place [
25
]. During a 2013 HW in the UK, the elderly were the least likely to take
similar protective measures against the heat, while those with higher income and education reported
taking these measures often/always [
26
]. As is demonstrated here, the use of heat-mitigating adaptive
behaviors varies widely.
Several of the risk factors associated to weakened thermoregulation during periods of heat stress
are aggravated in older adults: lower capillary blood flow for radiative and convective cooling, lower
sensitivity to thermal stimuli and core temperature-activated vasodilation, and maximal skin blood
flow; all of these are detrimentally impacted by dehydration [
27
]. Drinking water during hot periods
in urban areas has been found to be associated with a decreased risk of heatstroke [
28
]. Given the
physiologic importance of vasodilation and its dependence on water intake, hydration is a key adaptive
behavior that impacts human thermoregulation during periods of extreme heat.
In Cambridge, MA, the location of this study, buildings have been designed based on historical
climate (i.e., colder winters and ocean-moderated milder summers). With warming summer
temperatures, buildings without adequate cooling systems have the potential to retain heat, even during
non-extreme events, in the future. This study characterized the indoor environmental quality (i.e.,
temperature, relative humidity, carbon dioxide, and noise) in the apartments of older adults residing in
public housing in Cambridge, MA during an extreme heat event. In addition, personal physiological
parameters were tracked with wearable devices, capturing daily adaptive behaviors, physiological
responses to heat, and health symptoms. The use of affordable sensors and personalized monitoring
provided a more comprehensive and representative documentation of participants’ exposures and
experiences over the course of several summer days when an extreme heat event occurred compared to
relying on ambient meteorological stations placed throughout a city. Improving the understanding of
the exposures experienced by low-income seniors at home during extreme heat events has the potential
to inform more equitable adaptation strategies that increase the resilience of this vulnerable population.
2. Materials and Methods
2.1. Study Design and Participants
A cohort of low-income seniors, living in public housing in Cambridge, MA, participated during
the summer of 2015. This study took place over three periods: 29 June–3 July, 6 July–14 July, and
28 July–2 August, deploying study instruments when weather was predicted to be especially hot.
Participants were recruited from two public housing units in Cambridge, MA. The first residence was
a 180-unit multi-story high-rise building with five- and twelve-story sections that was originally built
in 1973 and was renovated in 2013, and all residents had central AC (n=24). The second residence
was a 180-unit, cast-concrete 19-story high-rise building originally constructed in 1976 (non-central
AC, n=27) and had mixed-use AC with residents using either efficiently working window AC units,
less efficiently working window AC units, or no window AC unit at all. In both buildings, residents
received full energy subsidies to cover energy costs.
Prospective participants received details of the study objectives and protocols at informational
meetings held at each of the study sites. Recruitment occurred on a rolling basis until recruitment
targets (at least n=20 for each building) were met; the research team only required that groups from
both building types were balanced in terms of participant age and sex. Inclusion criteria required that
the participant was at least 55 years of age, resided in either of the study buildings, and met a set of
predetermined health conditions (not using oral or intravenous antibiotics or chemotherapy, was not
using prednisone or nonsteroidal anti-inflammatory drugs, and did not currently have acute infectious
disease (cold/flu, gastroenteritis, etc.)). There were no significant differences in the prevalence of
Int. J. Environ. Res. Public Health 2019,16, 2373 4 of 15
pre-existing health conditions between participants living in these two buildings (Table 1). It was
found that 92.6% and 79.2% of participants in the non-central AC and central AC groups reported that
energy costs did not limit their use of AC (p=0.88, Table 1), which is a common and important reason
for not cooling a home environment in other low-income populations. Therefore, we assumed that
these populations were comparable and living in either building was independent of the participant’s
demographics, health status, energy financing restrictions, and potential health outcomes.
Table 1.
Descriptive statistics for demographics, pre-existing medical diagnoses, and indoor
environmental quality of study participants living in public housing with and without central air
conditioning (AC) in Cambridge, MA.
Descriptive Statistics Non-central AC
(n=27)
Central AC
(n=24) p-Value
Demographic information
Age, mean (SD) years 65.3 (7.9) 65.5 (7.5) 0.90
Sex, n(%) male 11 (45.8) 11 (40.7) 0.71
Race, n(%) non-white 7 (29.2) 10 (37.0) 0.83
Born in the United States, n(%) 16 (66.7) 21 (77.8) 0.37
Good +self-assessment of health, n(%) 19 (70.4) 13 (54.1) 0.23
Ever smoker, n(%) 15 (62.5) 21 (77.8) 0.23
Energy costs (do not limit AC use), n(%) 25 (92.6) 19 (79.2) 0.88
Have a heat action plan, n(%) 20 (74.1) 13 (54.2) 0.08
Indoor environmental quality
Temperature, mean (SD) (◦C) 25.6 (2.28) 23.2 (1.8) <0.001
Relative humidity, mean (SD) (%) 57.9 (7.3) 67.0 (6.7) <0.001
Absolute humidity, mean (SD) (g/m3)0.0140 (0.002) 0.0141 (0.001) 0.5356
Vapor pressure, mean (SD) (hPa) 1939.7 (343.9) 1935.9 (209.0) <0.001
Noise, mean (SD) (dB) 54.3 (8.1) 48.0 (8.1) <0.001
Carbon dioxide, mean (SD) (ppm) 559 (176.9) 546 (161.2) <0.001
Pre-existing medical diagnosis, n(%)
Chronic migraines 7 (25.9) 7 (29.2) 0.80
Severe headaches 5 (19.2) 7 (29.2) 0.41
Asthma 8 (30.8) 3 (12.5) 0.12
Chronic bronchitis 6 (23.1) 5 (20.8) 0.85
Allergies 12 (44.4) 12 (50.0) 0.69
Eczema 3 (11.1) 3 (12.5) 0.88
Hives 2 (7.7) 3 (12.5) 0.57
Sleep apnea 9 (34.6) 8 (33.3) 0.92
ADD/ADHD 3 (11.1) 5 (20.8) 0.34
Hearing loss 5 (18.5) 4 (16.7) 0.86
Thyroid 5 (18.5) 4 (16.7) 0.86
Diabetes 9 (33.3) 7 (29.2) 0.75
Heart disease 5 (16.7) 4 (16.7) 0.81
Chronic fatigue/Fibromyalgia 2 (7.4) 4 (16.7) 0.31
Depression 7 (25.9) 10 (41.7) 0.23
Anxiety 8 (29.6) 12 (50.0) 0.14
COPD 4 (14.8) 2 (8.3) 0.74
All subjects gave their informed consent for inclusion before they participated in the study. The
study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved
by the Institutional Review Board (IRB) at the Harvard T.H. Chan School of Public Health (IRB15-1435).
2.2. Survey Instruments
Consented participants completed a baseline survey to assess their personal demographics
(i.e., age, gender, height, weight, smoking, race, and ethnicity). The baseline survey also included
information on sleep habits, as well as perception and satisfaction with existing indoor environmental
quality (IEQ, i.e., thermal comfort, indoor air quality, acoustics, and lighting). Two key metrics assessed
via the baseline survey were the number of pre-existing conditions each participant had been previously
diagnosed with by a healthcare professional, and whether the participant had an appropriate heat
Int. J. Environ. Res. Public Health 2019,16, 2373 5 of 15
action plan, defined by having at least one adaptive measure that they would do during an HW (turn
on AC/fan, remove clothing, increase hydration, seek medical attention, etc.). The baseline survey is
available in Table S1 (Supplementary Materials).
Participants were instructed to complete a self-administered survey every morning after waking
for the duration of the study, which inquired about the previous day’s activities, IEQ, sleep quality
and duration, and adaptive behaviors (i.e., beverage consumption, physical activity, and window
opening/closing). We chose to also specifically assess hydration given its importance in cooling core
body temperature via enhanced intracellular fluids to allow for enhanced vasodilation. Additionally,
the other adaptive behavior questions (window opening/closing) were not consistently reported by
study participants, while hydration (number of glasses of water) was consistent throughout the study
period. We used the following symptom groups previously identified by the U.S. Environmental
Protection Agency Building Assessment Survey and Evaluation (BASE) study as the most representative
health outcomes associated to IEQ: neurocognitive (dizziness, nausea, headaches, and thirstiness);
allergies (skin rash and sneezing); lower respiratory (coughing, breathing problems, and wheezing);
irritation (nose bleeds, eye irritation, and sore throat); upper respiratory (ear pain, nasal drip,
common cold, and sinusitis), mental health symptoms (tiredness, anxiety, irritation, and depression),
and heat stress (nausea, numbness in hands/feet, dry skin, rash, sweating, and clammy skin) [
29
].
Musculoskeletal symptoms were not included in our survey as they were not a focus of this particular
study. Instead, we expanded the number of symptoms associated with mental health disorders.
Recognizing that neurocognitive health symptoms may also result from heat stress [
30
], even in
young, healthy adult populations [
31
], these symptoms were combined with heat stress symptoms
to assess symptoms experienced by the study participants. The daily survey is available in Table S2
(Supplementary Materials).
2.3. Environmental Measures
IEQ monitors (Netatmo, France) were installed in each participant’s bedroom to measure indoor
dry-bulb temperature (
◦
C), relative humidity (%), carbon dioxide concentration (CO
2
, ppm), and noise
(dBa). The monitors were installed following a standardized protocol, ensuring they were away from
sources of heat (computer screen, direct insolation, etc.) or drafts (e.g., windows and AC vents). Before
deployment, CO
2
was referenced to outdoor air (~400 ppm) to eliminate a drift error. CO
2
drift and
gain errors during deployment were estimated by collocating the IEQ monitors next to a recently
calibrated instrument (Q-trak 7575, TSI Instruments, Shoreview, MN USA) inside a chamber, following
ten stepwise increments from 400 to 3000 ppm. Values from the calibrated instrument were used
as a reference to produce monitor-specific adjustment curves to match the experimentally derived
values. Hourly outdoor weather variables were obtained from the local airport weather station (Logan
International Airport, KBOS), located approximately five miles away from the study site. Indoor
temperature exposures were analyzed as continuous measurements (5-min intervals) of daily means
respective to each participant’s residential unit.
2.4. Physiologic Measures
Participants wore an actigraphy-based sleep tracker (Basis Peak watch, Intel, USA) on their
non-dominant wrist and were instructed to wear it at all times, especially during sleep, and except
when bathing/swimming. Tosses and turns during sleep were quantified by the tracker. The tracker
used photoplethysmography to measure the heart rate (HR) in beats per minute (bpm), and galvanic
skin response (GSR) in microsiemens at a 1-min resolution. Hourly HR and GSR means were utilized
in statistical analyses.
2.5. Statistical Analyses
Building-level characteristics were analyzed with Mann–Whitney–Wilcoxon tests (IEQ variables),
Student t-tests (demographic traits), binomial test of proportions (IEQ threshold comparisons), and
Int. J. Environ. Res. Public Health 2019,16, 2373 6 of 15
paired t-tests (hydration). A binary variable having reported at least one moderate or severe heat- or
non-heat-related health symptom in a given day was considered as a primary outcome. Generalized
additive mixed models were used to evaluate the influence of mean hourly indoor temperatures and
physiologic markers. GSR was log-transformed. To investigate the effect of the indoor temperatures
on both heat- and non-heat-related self-reported health outcomes, we conducted a mixed-effects
logistic regression model with a binomial distribution. Participant, nested within a building, was
treated as a random effect in all models to account for repeated measurements within each subject and
non-time-varying covariates, such as age, gender, U.S.-born status, and pre-existing health conditions,
as well as other unquantifiable differences that may exist between buildings. Exploratory analyses for
time spent outdoors and maximum daytime ambient temperature yielded few differences between or
within individuals and groups, so these were excluded from the models. The final model evaluated
the association of indoor maximum temperature with having at least one self-reported health outcome
(binary) while controlling for that having at least two pre-existing conditions (binary) or having an
appropriate heat action plan (binary). R (Version 3.5.0, R Core Team, Vienna, Austria) was used for all
statistical analysis.
3. Results
3.1. Descriptive Results
There were no significant differences in demographic characteristics (age, gender, ethnicity, etc.)
or in the prevalence of pre-existing health conditions between participants living in these two buildings
(Table 1). The number of participants with a heat action plan was marginally different (p=0.08)
with more participants without central AC reporting protective measures. The average daily survey
completion rates for the study were 79.0% for the central AC building and 77.5% for the non-central AC
building throughout the study period. Therefore, we assumed that these populations were comparable
and living in either building was independent of the participant’s demographics, health status, or
potential health outcomes.
As expected, mean indoor values of temperature, vapor pressure, noise, and CO
2
were significantly
higher in the non-central AC residences than in the central AC residences (p<0.001). The mean indoor
relative humidity was significantly higher in the central AC group than in the non-central AC building
(p<0.001), but absolute humidity was similar between building groups (p=0.5356) (Table 1). Indoor
temperatures of the non-central AC residences closely followed the ambient temperatures, but as
outdoor temperatures rose, the indoor temperatures of the central AC residences dissociated from
this correlation with ambient temperatures (Figure 1). In both the central AC and non-central AC
buildings, the daytime differences between indoor and outdoor temperatures (AC:
−
3.7
◦
C, non-central
AC:
−
1.1
◦
C) were significantly different than the night-time differences between indoor and outdoor
temperature (AC:
−
3.7
◦
C, non-central AC:
−
1.5
◦
C) (p=0.029 and p=0.024 for daytime and night-time
situations, respectively) (Figure 2). The difference in nighttime indoor-outdoor temperature difference
between buildings was also statistically significant (p<0.001). Even though mean indoor temperatures
were cooler than ambient temperatures for both buildings, 45.7% (central AC) and 63.3% (non-central
AC) of daytime hours and 64.5% (central AC) and 82.8% (non-central AC) of overnight hours had
mean indoor temperatures that exceeded ambient temperatures, which were significantly different
between buildings (p<0.001).
Int. J. Environ. Res. Public Health 2019,16, 2373 7 of 15
Int. J. Environ. Res. Public Health 2019, 16, x 7 of 15
Figure 1. Indoor temperature distribution (boxplots) of non-central AC (red) and central AC (blue)
groups; daily range of ambient temperature (grey shading).
Figure 2. Difference between hourly indoor and outdoor temperatures (boxplots) of non-central AC
(red) and central AC (blue) groups during both day (7 am–7 pm) and night (7 pm–7 am) periods.
3.3. Physiology and Sleep
The relationship between tosses and turns and indoor temperature was modeled as a Poisson
model of the toss and turn counts per night and viewed as a dependent variable, and mean
temperature overnight and building were regarded as independent variables. When analyzing the
reported duration of sleep from the daily surveys or the watches, there was no significant association
with indoor temperature or building. However, the number of tosses and turns recorded by the
watches during sleep, as quantified by the personal sensors, increased with indoor mean
temperatures (Figure 3). No significant effect of building was observed (p = 0.496).
Figure 1.
Indoor temperature distribution (boxplots) of non-central AC (red) and central AC (blue)
groups; daily range of ambient temperature (grey shading).
Int. J. Environ. Res. Public Health 2019, 16, x 7 of 15
Figure 1. Indoor temperature distribution (boxplots) of non-central AC (red) and central AC (blue)
groups; daily range of ambient temperature (grey shading).
Figure 2. Difference between hourly indoor and outdoor temperatures (boxplots) of non-central AC
(red) and central AC (blue) groups during both day (7 am–7 pm) and night (7 pm–7 am) periods.
3.3. Physiology and Sleep
The relationship between tosses and turns and indoor temperature was modeled as a Poisson
model of the toss and turn counts per night and viewed as a dependent variable, and mean
temperature overnight and building were regarded as independent variables. When analyzing the
reported duration of sleep from the daily surveys or the watches, there was no significant association
with indoor temperature or building. However, the number of tosses and turns recorded by the
watches during sleep, as quantified by the personal sensors, increased with indoor mean
temperatures (Figure 3). No significant effect of building was observed (p = 0.496).
Figure 2.
Difference between hourly indoor and outdoor temperatures (boxplots) of non-central AC
(red) and central AC (blue) groups during both day (7 am–7 pm) and night (7 pm–7 am) periods.
3.2. Physiology and Sleep
The relationship between tosses and turns and indoor temperature was modeled as a Poisson
model of the toss and turn counts per night and viewed as a dependent variable, and mean temperature
overnight and building were regarded as independent variables. When analyzing the reported
duration of sleep from the daily surveys or the watches, there was no significant association with indoor
temperature or building. However, the number of tosses and turns recorded by the watches during
Int. J. Environ. Res. Public Health 2019,16, 2373 8 of 15
sleep, as quantified by the personal sensors, increased with indoor mean temperatures (Figure 3). No
significant effect of building was observed (p=0.496).
Int. J. Environ. Res. Public Health 2019, 16, x 8 of 15
Figure 3. Relationship between indoor temperatures and number of tosses and turns during sleep
periods. The gray shading represents the 95% confidence interval.
The objective physiologic parameters, as measured by the watches, showed that the maximum
hourly indoor temperature was a significant predictor (p < 0.001) of mean hourly HR after adjusting
for building and had a non-linear relationship. An optimum of HR and GSR was found at around 24
°C (~75.2 °F). Both GSR and HR were found to increase once temperatures exceeded approximately
this optimum threshold of 24 °C. (Figure 4). Both of these objective parameters demonstrated that as
indoor temperatures were above or below this threshold, there were significant decrements in these
physiologic markers. This threshold is higher than the threshold documented in a study using similar
methodology for assessing the impact of heat on students’ cognitive functioning (~22 °C) [31].
(a) (b)
Figure 4. Non-linear association between hourly indoor temperatures and mean hourly heart rate
(HR), after adjusting for building (a). Non-linear association between hourly indoor temperatures and
mean hourly log-transformed galvanic skin response (GSR), after adjusting for building, and HR (b).
The dotted lines represent the 95% confidence intervals.
Figure 3.
Relationship between indoor temperatures and number of tosses and turns during sleep
periods. The gray shading represents the 95% confidence interval.
The objective physiologic parameters, as measured by the watches, showed that the maximum
hourly indoor temperature was a significant predictor (p<0.001) of mean hourly HR after adjusting
for building and had a non-linear relationship. An optimum of HR and GSR was found at around
24
◦
C (~75.2
◦
F). Both GSR and HR were found to increase once temperatures exceeded approximately
this optimum threshold of 24
◦
C. (Figure 4). Both of these objective parameters demonstrated that as
indoor temperatures were above or below this threshold, there were significant decrements in these
physiologic markers. This threshold is higher than the threshold documented in a study using similar
methodology for assessing the impact of heat on students’ cognitive functioning (~22 ◦C) [31].
Int. J. Environ. Res. Public Health 2019, 16, x 8 of 15
Figure 3. Relationship between indoor temperatures and number of tosses and turns during sleep
periods. The gray shading represents the 95% confidence interval.
The objective physiologic parameters, as measured by the watches, showed that the maximum
hourly indoor temperature was a significant predictor (p < 0.001) of mean hourly HR after adjusting
for building and had a non-linear relationship. An optimum of HR and GSR was found at around 24
°C (~75.2 °F). Both GSR and HR were found to increase once temperatures exceeded approximately
this optimum threshold of 24 °C. (Figure 4). Both of these objective parameters demonstrated that as
indoor temperatures were above or below this threshold, there were significant decrements in these
physiologic markers. This threshold is higher than the threshold documented in a study using similar
methodology for assessing the impact of heat on students’ cognitive functioning (~22 °C) [31].
(a) (b)
Figure 4. Non-linear association between hourly indoor temperatures and mean hourly heart rate
(HR), after adjusting for building (a). Non-linear association between hourly indoor temperatures and
mean hourly log-transformed galvanic skin response (GSR), after adjusting for building, and HR (b).
The dotted lines represent the 95% confidence intervals.
Figure 4.
Non-linear association between hourly indoor temperatures and mean hourly heart rate (HR),
after adjusting for building (
a
). Non-linear association between hourly indoor temperatures and mean
hourly log-transformed galvanic skin response (GSR), after adjusting for building, and HR (
b
). The
dotted lines represent the 95% confidence intervals.
Int. J. Environ. Res. Public Health 2019,16, 2373 9 of 15
3.3. Perception and Self-Reported Health Symptoms
As expected, the number of participants who reported their unit was too hot increased as the
study period went on, which follows increases in temperature (Figure S1, Supplementary Materials).
The impact of thermal conditions at home on daily activities (Figure 5a) and on sleep (Figure 5b),
as assessed through the daily survey, worsened throughout the study period. Approximately equal
proportions of residents were satisfied, dissatisfied, or neither satisfied or dissatisfied between the
non-central and central AC buildings at baseline (Figure S2, Supplementary Materials). However,
almost 75% of participants in the non-central AC building indicated that their apartment’s temperature
was too hot, while less than 25% of central AC building occupants felt too hot at baseline (Figure S2,
Supplementary Materials).
Int. J. Environ. Res. Public Health 2019, 16, x 9 of 15
3.3. Perception and Self-Reported Health Symptoms
As expected, the number of participants who reported their unit was too hot increased as the
study period went on, which follows increases in temperature (Figure S1, Supplementary Materials).
The impact of thermal conditions at home on daily activities (Figure 5a) and on sleep (Figure 5b), as
assessed through the daily survey, worsened throughout the study period. Approximately equal
proportions of residents were satisfied, dissatisfied, or neither satisfied or dissatisfied between the
non-central and central AC buildings at baseline (Figure S2, Supplementary Materials). However,
almost 75% of participants in the non-central AC building indicated that their apartment’s
temperature was too hot, while less than 25% of central AC building occupants felt too hot at baseline
(Figure S2, Supplementary Materials).
(a) (b)
Figure 5. (a) Percent of respondents indicating the impact the thermal conditions of their apartment
have on their self-reported daily activities. (b) Percent of respondents indicating the impact the
thermal conditions of their apartment have on their self-reported sleep.
Comparing hydration throughout the study period, the number of glasses of water consumed
on hot days during the study (TMax > 32.2 °C (90 °F)) was not significantly different than on days below
this threshold for either building (central AC: p = 0.48; non-central AC: p = 0.64). Hydration was also
not significantly different on days that were warm but did not meet extreme heat thresholds (>29.4
°C (85 °F)) (central AC: p = 0.89; non-central AC: p = 0.21). Thus, despite an enhanced perception of
hotter indoor living conditions and reporting greater impact of these thermal conditions on routine
living functions, the study participants did not drink more water, which many highlighted as a key
action to take to protect themselves from heat stress.
During the study period, 9 participants reported 177 moderate and severe health symptoms,
with 44 of those symptoms being heat-related. The number of daily self-reported health symptoms
in any category, as well as those related to heat, was highest in the units at the highest indoor
temperature quartiles for those with and without pre-existing conditions. The final models for
experiencing at least one self-reported health outcome, which follows on previously methodology in
Quinn and Shaman [32], controlled for mean daily indoor temperature and having either pre-existing
conditions or a personal heat action plan. Given the relative rarity of these outcomes being reported
in the study period, models examining the interaction between these covariates or with additional
covariates were not assessed because of lack of statistical power (Table S3 and Table S4,
Supplementary Materials; Equations (1) and (2)).
4. Discussion
Many studies have shown the association between heat exposure and health outcomes through
the use of laboratory-controlled settings and/or through ambient exposure metrics. This study
comprehensively characterized individual temperature exposures at home and IEQ using personal
Figure 5.
(
a
) Percent of respondents indicating the impact the thermal conditions of their apartment
have on their self-reported daily activities. (
b
) Percent of respondents indicating the impact the thermal
conditions of their apartment have on their self-reported sleep.
Comparing hydration throughout the study period, the number of glasses of water consumed on
hot days during the study (T
Max
>32.2
◦
C (90
◦
F)) was not significantly different than on days below
this threshold for either building (central AC: p=0.48; non-central AC: p=0.64). Hydration was also
not significantly different on days that were warm but did not meet extreme heat thresholds (>29.4
◦
C
(85
◦
F)) (central AC: p=0.89; non-central AC: p=0.21). Thus, despite an enhanced perception of hotter
indoor living conditions and reporting greater impact of these thermal conditions on routine living
functions, the study participants did not drink more water, which many highlighted as a key action to
take to protect themselves from heat stress.
During the study period, 9 participants reported 177 moderate and severe health symptoms, with
44 of those symptoms being heat-related. The number of daily self-reported health symptoms in any
category, as well as those related to heat, was highest in the units at the highest indoor temperature
quartiles for those with and without pre-existing conditions. The final models for experiencing at
least one self-reported health outcome, which follows on previously methodology in Quinn and
Shaman [
32
], controlled for mean daily indoor temperature and having either pre-existing conditions
or a personal heat action plan. Given the relative rarity of these outcomes being reported in the study
period, models examining the interaction between these covariates or with additional covariates were
not assessed because of lack of statistical power (Table S3 and Table S4, Supplementary Materials;
Equations (1) and (2)).
Int. J. Environ. Res. Public Health 2019,16, 2373 10 of 15
4. Discussion
Many studies have shown the association between heat exposure and health outcomes through
the use of laboratory-controlled settings and/or through ambient exposure metrics. This study
comprehensively characterized individual temperature exposures at home and IEQ using personal
sensors and wearable devices. Simultaneously, it also incorporated environmental and behavioral
factors that influence resilience and adaptive capacity during extreme heat events, which influence an
individual’s susceptibility to poor health outcomes during an extreme heat event. The results showed
that indoor temperatures alter physiology, increasing both GSR and HR, with an optimum range for
these physiologic markers centered at around 24
◦
C. Simultaneously, participants’ perception of indoor
temperatures and the impact of these exposures on their activities and sleep worsened. However, these
changes did not result in a significant change in hydration, signaling a lack of enacting an important
adaptive behavior and thermal decompensation beginning as the body is unable to thermoregulate.
While outdoor temperatures are associated with public health outcomes, these analyses underscore
the importance of characterizing exposures to temperatures indoors and accounting for building
conditions for understanding health risks during and after extreme heat events. All of these residents,
given their close proximity, were exposed to the same ambient conditions, but their residential building
played a role in either exacerbating or mitigating heat exposures. It also demonstrated the complex
nature of heat vulnerability, pre-existing conditions, adaptive behaviors, and access to and use of AC
(either well-functioning window units or less efficient window units for those in the non-central AC
building). The success of using wearable sensors devices to monitor indoor and personal exposures
demonstrated the feasibility of resolving a previous research limitation [
27
,
28
]. As sensor technology
develops further and becomes more widespread, it may be possible to intervene before someone
experiences life-threatening heat stress.
About 37% of all public housing residents in the U.S. are seniors over age 62, while 48% of
public housing residents in Massachusetts are seniors [
32
]. By 2030, it is projected that 20% of the U.S.
population will be seniors [
15
] and almost 90% of seniors have reported that they want to stay in their
homes, living independently, as long as possible [
33
]. Living environments that are adequately cooled
to meet the needs of older adults and effective personal heat mitigation strategies will be crucial to
protect the health of seniors in the future in a warmer climate. Harnessing available technologies to
track the IEQ of living environments, as well as the health conditions, of this population in real time,
has the potential for greater independence while aging in place.
The suite of measurements utilized in this study revealed several interesting features of the indoor
environments of public housing senior residents. Even though the majority of those without central
AC did have access to window AC units, at a variety of efficiency and frequency of use preferences,
these residences were continually warmer than those with central AC. This follows on findings from
Quinn et al. (2017) that apartments cooled by window and portable AC are warmer than those with
central AC and retain the heat for upwards of 1 day after ambient heat subsides [
17
]. In this study,
window AC units were of a variety of ages and conditions, or were not used regularly or soon
enough by the participants, yielding increasingly hotter temperatures indoors that persist as outdoor
temperatures rise and then subside.
During the overnight hours, the differences between the indoor and outdoor temperatures were
most pronounced across all units, and the building without central AC was significantly warmer than
the building with central AC. Researchers have recently preferred a shift from the thermal comfort
model where the percentage of satisfied occupants is based on climate chamber experiments to a thermal
health model that involves not only environmental parameters but also individual physiological and
psychological factors that are specific to vulnerable populations, like low-income seniors, to best protect
public health [34–36].
Not being able to identify adaptive behaviors that would be protective during extreme heat events
has been found to be a marker of increased susceptibility to heat exposure. Analyses suggested that
having a heat action plan did increase the odds of reporting any or heat-related health symptoms,
Int. J. Environ. Res. Public Health 2019,16, 2373 11 of 15
although not significantly. When assessing these results in the context of hydration, or the lack thereof,
the result suggested that although residents were aware of protective actions during heat, they did not
implement them in effective ways. This highlights an important area for intervention.
Takahashi et al. (2015) demonstrated that adaptive cooling behaviors were improved in elderly
residents in Japan that received heat wave warnings in tandem with water distribution [
37
]. Although a
great deal of the existing literature on the effectiveness of hydration during thermal stress has been done
in the field of sports medicine and/or with adults under physical exertion and the study population
here is largely sedentary, water levels within the bodies of older adults and thirst responses decrease
with age [
27
], so thermoregulatory responses are weakened in these individuals. Marked changes in
GSR as temperatures increased demonstrated that the study participants were losing water at a greater
rate as it got hotter indoors, without increasing hydration to replenish body fluids. Future studies
should examine hydration, in quantity and quality, in coordination with other adaptive behaviors for
the most comprehensive description of adaptive responses.
These results provide evidence that not all vulnerability metrics, e.g., not having central AC,
older age, low income, and pre-existing conditions, are equally determinant of poor health during
extreme heat exposures. Åström et al. (2011) have noted the importance of studying non-fatal events
and how housing modifies these outcomes in the elderly [
38
]. While the association between indoor
temperatures, pre-existing conditions, and all health outcomes was not significant when modeled,
the results of this study suggested areas of further investigation with a higher sample size, extended
assessment periods, and more robust measurements of these outcomes.
While increasing utilization of AC is an obvious response to increasing heat, it will increase
energy demand and shed more heat to ambient environments. AC should not be the only adaptation
considered, in part because of the inherent economic disparities about who can afford to purchase
AC and pay for the energy. Despite AC units becoming more efficient over time [
39
], utilizing AC
with our present energy infrastructure contributes to positive feedback cycles that further propagate
climate change and ambient air pollution emissions on hot days when electricity is generated by fossil
fuels [
40
,
41
]. The exhaust heat from current air-cooling systems has also been found to exacerbate the
problem of urban heat island effect [
42
]. Further, the refrigerants used in ACs, hydrofluorocarbons,
are potent greenhouse gases that contribute to climate change. Thus, even though AC is effective in
mitigating heat exposures indoors and protecting public health, additional adaptation strategies, such
as adopting alternative refrigerants as proposed in the Kigali Amendment [
43
], designing our buildings
and cities to be less thermally absorptive, and rapidly expanding renewable energy uptake, must be
incorporated into our long-term solutions to reduce health-damaging air pollutants and greenhouse
gas emissions that contribute to climate change, and to make climate adaptation more economically
attainable for all sectors of the population, especially those who are most vulnerable.
Modeling studies have found that in the UK, external shutters, especially when combined with
energy efficient upgrades to buildings, reduced heat-related mortality from 30% to 52% [
44
], while
less heat-absorbent materials incorporated into buildings can significantly reduce indoor heat stress
risk [
45
]. Energy-intensive AC will become more prevalent in homes across the world in coming
decades and will be a crucial strategy in protecting populations from heat exposures. However, as
discussed by Kwok and Rajkovich (2010), thermal comfort standards should be re-evaluated to better
balance the mitigation of greenhouse gas emissions and provide healthful indoor environments for
vulnerable populations to best equip our built environment for climate change [
46
]. Their point is
underscored by the fact that current thermal comfort standards are not based on elderly populations
with more underlying health risks.
Alternative adaptive/coping strategies need to be available for a growing number of vulnerable
people in case cooling is not always available, like for example, during a power outage. Designing for
the passive habitability of buildings, which would allow “habitable indoor conditions without power
for limited amounts of time” [
47
,
48
] creates greater resilience among our building stock and better
protects the individuals inside.
Int. J. Environ. Res. Public Health 2019,16, 2373 12 of 15
While the findings of this research present valuable information for public health practitioners and
building managers, there were several limitations that are important to recognize. There were short
gaps in the study period due to weather forecasts predicting only 1–2 days of extreme heat in mid-July
when our objective was to characterize a longer period of extreme heat and its impact on indoor
environments, and therefore the instruments were only deployed when weather was predicted to be
especially hot. It would have been preferable to have a clearer HW signal to create more delineated
ambient exposure periods with lower temperatures during the other study days, but unfortunately
this was not possible with this type of study outside of a laboratory setting. However, the magnitude
of these results may increase given even more extreme ambient temperature conditions.
The small sample size (n=51) and rarity of the reporting of moderate/severe health symptoms
(only 9 participants) may present limitations and limit our analysis of the impact of indoor heat on
health symptoms in this study. Additionally, the daily surveys were self-administered at home, which
is less controlled than if they were recorded by the study team. Even though we lacked information on
the functionality and efficiency of the window AC units that were present, personal questions about
thermal conditions of the apartments and participant activities were corroborated with environmental
and personal monitors that recorded indoor temperatures, which reinforce the reliability of these
measurements. Participants used time-activity logs but their data on hourly location was not very
complete across the study period. Given the associations found between indoor temperature exposure
and sleep and physiology, as well as with poor health symptoms, it is plausible to assess whether or
not sleep is a mediating factor between indoor temperatures and poor health symptoms. We were
unable to assess this here, due to the rarity of health symptoms reported, but are eager to explore in
future studies.
The amount of time indoor and outdoor was consistent (about 2–3 hours/day outdoors) between
all participants, regardless of building, as well as over time, so indoor temperatures were used as the
exposure of interest since people were spending the majority of their time indoors and at home. Even
though outdoor temperature exposures are not characterized at the individual level, the vast amount
of indoor temperature exposure data utilized decreases the amount of exposure misclassification that
may exist in other studies. However, some exposure misclassification may still exist if participants
were outside or not at home for the entirety of the day
The limited, older age range of the participants may mean that the results are not generalizable
to younger, healthier populations who may be less susceptible to extreme heat exposures. However,
given the severity of climate change and future extreme heat scenarios, compounded by an aging
population in the United States, there are likely still important lessons to be learned from this analysis
for an increasing proportion of our population. Further research should examine the influence of the
building in modifying health outcome and physiologic measures in other demographic populations of
interest, as well as to other geographic locations, potentially over a longer period of times to account
for multiple and recurring extreme heat events.
5. Conclusions
In this study, sensors were used to quantify indoor temperatures during an extreme heat event,
reducing temperature exposure misclassification, and associated increases in HR and GSR of 51 older
adults living in public housing. Assessment of adaptive behaviors demonstrated that hydration did
not increase and the reporting of subclinical health symptoms was rare during the same time period.
This signals that despite thermal decomposition occurring, even during a heat event that was not
severe, older adults are not effectively implementing adaptive behaviors, like drinking more fluids.
With warming summer temperatures, buildings without adequate cooling systems have the
potential to retain heat, even during non-extreme events, and individuals who are most vulnerable
to the health impacts of extreme heat are less likely to have access to or utilize AC. Understanding
how building elements modify indoor temperatures and monitoring indoor temperature conditions
in real time is essential knowledge for identifying vulnerable indoor environments and informing
Int. J. Environ. Res. Public Health 2019,16, 2373 13 of 15
adaptation and mitigation strategies to reduce the impact of excessive heat. Strategies that do not
solely depend on AC, like enhanced shading/shutters and less heat-absorbed materials, will be vital
adaptation solutions to buildings that maintain elevated indoor temperatures during extreme heat.
Further, local heat action plans and HW programing by public health practitioners should account
for the indoor temperature exposures that may be exacerbated by the built environment to improve
messaging, public health campaigns, and distribution of cooling resources (hydration, transportation
to cooling shelters, energy subsidies for AC, etc.).
Supplementary Materials:
The following are available online at http://www.mdpi.com/1660-4601/16/13/2373/s1,
Figure S1: Satisfaction and perception of indoor thermal conditions at the baseline of the study period, Figure S2:
Percent of respondents indicating their perception of the temperature of their apartment during the study period.
Table S1: Baseline survey used to assess personal demographics, sleeping habits, perception/satisfaction with IEQ,
and pre-existing demographics, Table S2: Daily survey used to assess the previous day’s activities, IEQ, sleep
quality, adaptive behaviors, health symptoms, Table S3: Generalized logistic mixed effect regression output Odds
Ratios (OR) for any health symptom or heat-related health symptoms adjusting for daily mean indoor temperature
and having at least 2 preexisting conditions (model 1) or and having a personal heat action plan (model 2), with
participant as a random effect, Table S4: Generalized logistic mixed effect regression models.
Author Contributions:
Conceptualization, J.G.C.-L. and J.D.S.; methodology, A.A.W., J.G.C.-L., and J.D.S.;
software, A.A.W. and J.G.C.-L.; validation, J.G.C.-L.; formal analysis, A.A.W., J.G.C.-L., and P.C.; investigation,
A.A.W. and J.G.C.-L.; resources, J.G.C.-L.; data curation, A.A.W. and J.G.C.-L.; writing of original draft preparation,
A.A.W.; writing of review and editing, all authors.; visualization, A.A.W., P.C., and J.G.C.-L.; supervision, J.D.S.
and J.G.A.; project administration, J.G.C.-L.; funding acquisition, J.G.C.-L.
Funding:
This research was funded by the Harvard University Climate Change Solutions Fund, awarded in 2015
to J.G.C.-L. and the Harvard University Joint Center for Housing Studies.
Acknowledgments:
The authors wish to thank Skye Flanigan, Anna-Kate Hard, and Alex Hem for their assistance
in preparing the study materials.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.
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