Heat Waves in the Elderly: Reducing Thermal and Cardiovascular Consequences
Phase: NA
Conditions: Aging, Hyperthermia
Sponsor: University of Texas Southwestern Medical Center

 
Study Protocol and Statistical Analysis Plan 
Heat Waves in the Elderly: Reducing Thermal and Cardiovascular Consequences   
NCT04538144 
August 23rd, 2024 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Experimental Protocol: All experimental trials began between 8:00 and 10:00 to avoid 
diurnal variations. Before experimental trials, participants were instructed to eat a light breakfast 
(without caffeinated beverages) on the morning of testing. Alcoholic beverages and 
aerobic/resistance exercise were prohibited 24 hours before testing. Upon arrival, participants 
provided a urine sample and weighed themselves nude. A euhydrated state before each trial was 
confirmed by a urine specific gravity value ≤1.020 (Atago Inc., Bellevue, WA). If urine specific 
gravity was between 1.020 and 1.024, participants ingested 500 mL of water before testing. If 
their urine specific gravity was ≥1.025, the trial was rescheduled to a later date. Participants rested 
in a temperate environment (~22°C and 40% humidity) for at least one hour while baseline 
measurements (e.g., heart rate, blood pressure, forearm blood flow, etc.) were performed. 
Following baseline measurements, participants entered the environmental chamber set to one of 
the following two extreme heat simulations: 1) DRY (47°C and 15% humidity; reflective of the 
2018 Los Angeles heatwave) and 2) HUMID (41°C and 40% humidity; reflective of the 1995 
Chicago heatwave). The duration of each exposure was 3 hours, which represents the 
approximate duration of peak environmental temperatures during a heatwave (4, 31). Before 
instrumentation, participants’ body mass was measured using a precision balance scale with ±10 
g accuracy (Mettler Toledo, OH). After a brief period (~5-minutes) of instrumentation, participants 
were seated in a semi-recumbent position on a chair with a breathable fabric thereby reducing 
impediments for thermal exchange from the regions in contact with the chair. To reduce the 
influence of clothing on thermoregulatory responses, participants wore athletic shorts (males) or 
athletic shorts and a sports bra (females). To decrease the effect of dehydration, participants 
consumed 3 mL/kg body mass of tap temperature water (14-16°C) every hour during the extreme 
heat simulation. This level of fluid ingestion was based on pilot data and was selected to maintain 
hydration to a level that would not compromise thermoregulatory, perceptual, or cardiovascular 
responses during heat exposure (16, 54, 55). Tap water temperature was used to mimic the water 
available to individuals during an actual heatwave. Following the extreme heat simulation, 

participants were de-instrumented and provided a towel to wipe off excess sweat before they 
weighed themselves nude. 
Insert Figure 1 here 
Metabolic Heat Production: Participants performed seven 5-minute bouts of light physical 
activity (either cycling: n=39 or walking: n=1) dispersed throughout the 3-hour extreme heat 
exposure (Figure 1). The intensity of the activity was maintained at a working metabolic rate of 
~3 METs to mimic activities of daily living such as cooking, cleaning, carrying groceries, moving 
around the home, etc. (1). Metabolic heat generation was verified using indirect calorimetry 
(PARVO Medics True-One Metabolic Measurement System, Parvo Medics, Salt Lake City, UT), 
with metabolic gases being collected during the first two 5-minute periods of exercise. That 
identified workload was used for all subsequent exercise bouts. The average rate of oxygen 
consumption, respiratory exchange ratio, and workload were used to determine metabolic heat 
production, which was calculated using standard formulae (19).  
Instrumentation: Core temperature was measured via rectal temperature using a general-
purpose thermocouple probe inserted 10 cm beyond the anal sphincter (Mon-a-therm, 
Mallinckrodt Medical, St. Louis, MO) (Young: n=17, Older: n=8) or a telemetric pill (e-Celsius 
performance pill ®, BodyCap©, Caen, France) used as a rectal suppository (Young: n=1, Older: 
n=1). If participants did not consent to rectal temperature measurement, or if there were technical 
issues with the rectal thermocouple probe, core temperature was measured using an orally 
ingested telemetric pill (Young: n=2, Older: n=11). In these participants, the ingestible telemetric 
pill was taken no less than 1 hour before beginning the extreme heat exposure. Importantly the 
time following ingestion of the telemetric pill (e.g., between 1 and 12 hours) does not influence 
the validity of pill temperature as an index of core temperature (49). In addition, there is good 
agreeability amongst these methods to measure core temperature (10, 29), and for participants 
who had simultaneous measures of pill and rectal temperature in this study (n=25) the intraclass 
correlation coefficient was 0.87. Mean skin temperature was obtained as the weighted average 

of local temperatures measured via the chest (22%), upper back (21%), lower back (19%), 
abdomen (14%), anterior thigh (13%), and calf (11%) (58).  
Forearm blood flow, as a surrogate of changes in skin blood flow, was measured from brachial 
artery blood velocity and diameter values obtained by duplex ultrasonography (iE33/EPIQ 7, 
Philips Medical Systems, Andover, MA). A linear transducer (L12-3, Philips Medical Systems, 
Andover, MA), operating at an insonation angle of 60°, was placed proximal to the brachial artery 
bifurcation where the best spatial resolution of the artery could be obtained. Imaging was 
performed by the same sonographer throughout each heat exposure, and to aid in consistency 
between measurements sonographers used anatomical landmarks (e.g., ~9 cm proximal to the 
medical epicondyle) to ensure similar probe placement. Blood velocity was measured using time-
averaged mean velocity from pulsed-wave Doppler ultrasound. Offline 2D images of the brachial 
artery were used to measure vessel diameter at end-diastole. Brachial artery diameter and blood 
flow velocity were measured from recordings of 5 consecutive cardiac cycles at baseline and in 
the final 15 minutes of extreme heat exposure. Blood flow velocity and brachial artery diameter 
measurements were used to calculate forearm blood flow using a standard formula:  
𝐹𝑙𝑜𝑤 (𝑚𝐿/𝑚𝑖𝑛𝑢𝑡𝑒) = 𝑣∙𝜋∙(𝑑2
4 ) ∙60 
Eq 1 
Where v is velocity in m/s and d is diameter in cm. 
Heart rate was obtained from a 6-lead electrocardiogram (GE Medical Systems, Madison, 
WI). Blood pressure was measured by automated auscultation of the brachial artery (Tango+, 
SunTech Medical, Morrisville, NC, USA). Local sweat rate was measured on the upper back using 
a ventilated capsule technique where anhydrous compressed nitrogen was passed through the 
capsule at a flow rate of ~300 mL/min. The water content of the nitrogen gas was then measured 
using capacitance hygrometers (Vaisala, Woburn, WA, USA) and normalized for the area under 
the capsule. 

Thermal perception was measured using an 8-point scale, with 0.5 increments ranging from 
0.0 (‘unbearably ‘cold’’), 4 (‘comfortable’), to 8.0 (‘‘unbearably hot’’). At the start and end of each 
extreme heat exposure, participants were given a modified environmental symptom questionnaire 
(53) to assess symptoms commonly associated with environmental stress such as “I feel 
lightheaded”, “I have a headache”, “I feel dizzy”, “I feel thirsty”, “I feel weak”, “I feel grumpy”, “It is 
hard to breathe”, “I have a muscle cramp”, “I feel tired”, “I feel nauseous”, “I feel hot”, “I have 
trouble concentrating”, “I feel ‘goose bumps’ or chills”, and “I can play at my best”, on a scale of 
1 (“not at all”) to 6 (“extreme”). 
Blood Sampling and Analysis: Blood was taken from an arm vein after 30 minutes of supine 
rest at baseline and the end of the extreme heat simulations. We measured hematocrit 
(microcapillary technique) and hemoglobin (ABL90 Flex, Radiometer, Brønshøj, Denmark) to 
calculate changes in plasma volume using the Dill and Costill (22) equation. Blood samples were 
centrifuged to isolate plasma and aliquots of plasma were sent to a nearby laboratory (Texas 
Health Presbyterian, Dallas TX) to determine plasma osmolality (Abbott Alinity, IL, USA). 
Calculations: We calculated mean body temperature as: 
Mean body temperature (°C) = 0.8𝑇𝑐𝑜𝑟𝑒× 0.2𝑇𝑠𝑘𝑖𝑛 
Eq 2 
Where Tcore and Tskin are core temperature and mean skin temperature, respectively.  
We calculated mean arterial pressure (MAP) as:  
MAP (mmHg) = 𝑆𝐵𝑃
3
+ (𝐷𝐵𝑃× 2
3) 
Eq 3 
Where SBP and DBP are systolic and diastolic blood pressure, respectively.  
We calculated forearm vascular conductance by dividing forearm blood flow by MAP. We 
calculated body mass loss as the difference in nude body weight (Mettler Toledo, OH) between 
pre- and post-extreme heat exposure, and whole body sweat loss (WBSL) after correcting for fluid 
ingestion and urine output, which was collected and measured for volume. We calculated whole 
body sweat rate by dividing the whole body sweat loss by total heat exposure time.  

Data Acquisition: Rectal temperature, skin temperature, local sweat rate, and heart rate were 
sampled at 250 Hz (Biopac MP150, Santa Barbara, CA) and converted into 1-minute averages 
for data analysis. Core temperature measured via the telemetric pill (both ingested and as a 
suppository) was sampled every 30 seconds, and blood pressure was measured every 15 
minutes during heat exposures. 
Statistical Analyses: A power calculation was performed to detect a 0.5°C difference in the 
increase in core temperature between groups, with a standard deviation of that increase being 
0.5°C. Based upon these estimates, and with an alpha of 0.05, 20 participants (10 participants 
per age group) would provide power of >0.80 to detect differences between age groups in the 
change in core temperature (power analyses from: NCSS PASS 2019). However, we enrolled 40 
participants (20 participants per age group, with each age group comprising 10 males and 10 
females) to account for possible attrition/missing data and to increase the capability to detect 
differences in other outcome variables that may have a greater degree of variability than core 
temperature. Due to technical difficulties, we were unable to obtain some measurements (e.g., 
skin temperature, local sweat rate, blood pressure, and limb blood flow) at either baseline or at 
the end of the extreme heat exposures in some participants. Similarly, we were unable to obtain 
metabolic heat production data in one young and one older participant. These missing data were 
excluded from final analyses, and the number of participants included for each variable is 
indicated in text, tables, and figures where appropriate. Before analyses, data were assessed to 
confirm that they met model assumptions (i.e., normality, equality of variance). Within each 
extreme heat simulation, we analyzed data using linear mixed effect models with main effects of 
time (within factor) and group (between factor; older vs young) or unpaired t-tests (two-tailed), as 
appropriate. Statistical analyses were performed in R studio (Version 2022.07.1), and graphs 
were generated using GraphPad Prism 9.4 (GraphPad Software Inc., La Jolla, CA, USA). Data in 
text, tables, and figures are presented as means ± standard deviations. Statistical significance 
was set a priori to p < 0.05.