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Individual differences in normal body temperature: longitudinal big data analysis of patient records

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• Peer review
• Ziad Obermeyer , assistant professor 1 2 ,
• Jasmeet K Samra , research analyst 1 ,
• Sendhil Mullainathan , professor 3
• 1 Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, MA, USA
• 2 Department of Emergency Medicine and Health Care Policy, Harvard Medical School, Boston, MA, USA
• 3 Department of Economics, Harvard University, Boston, MA, USA
• Correspondence to: Z Obermeyer zobermeyer{at}bwh.harvard.edu
• Accepted 23 November 2017

Objective To estimate individual level body temperature and to correlate it with other measures of physiology and health.

Design Observational cohort study.

Setting Outpatient clinics of a large academic hospital, 2009-14.

Participants 35 488 patients who neither received a diagnosis for infections nor were prescribed antibiotics, in whom temperature was expected to be within normal limits.

Main outcome measures Baseline temperatures at individual level, estimated using random effects regression and controlling for ambient conditions at the time of measurement, body site, and time factors. Baseline temperatures were correlated with demographics, medical comorbidities, vital signs, and subsequent one year mortality.

Results In a diverse cohort of 35 488 patients (mean age 52.9 years, 64% women, 41% non-white race) with 243 506 temperature measurements, mean temperature was 36.6°C (95% range 35.7-37.3°C, 99% range 35.3-37.7°C). Several demographic factors were linked to individual level temperature, with older people the coolest (–0.021°C for every decade, P<0.001) and African-American women the hottest (versus white men: 0.052°C, P<0.001). Several comorbidities were linked to lower temperature (eg, hypothyroidism: –0.013°C, P=0.01) or higher temperature (eg, cancer: 0.020, P<0.001), as were physiological measurements (eg, body mass index: 0.002 per m/kg 2 , P<0.001). Overall, measured factors collectively explained only 8.2% of individual temperature variation. Despite this, unexplained temperature variation was a significant predictor of subsequent mortality: controlling for all measured factors, an increase of 0.149°C (1 SD of individual temperature in the data) was linked to 8.4% higher one year mortality (P=0.014).

Conclusions Individuals’ baseline temperatures showed meaningful variation that was not due solely to measurement error or environmental factors. Baseline temperatures correlated with demographics, comorbid conditions, and physiology, but these factors explained only a small part of individual temperature variation. Unexplained variation in baseline temperature, however, strongly predicted mortality.

Introduction

Have you ever felt cold, or warm, in a room where everyone else felt comfortable? This common experience about room temperature has some interesting lessons for body temperature and how we measure it. To know how warm or cold someone feels, we would not look at room temperature alone. Married couples sitting next to each other in the same room routinely disagree about whether to turn the heat up or down. Individuals have different baseline propensities to feel hot or cold—at any given absolute room temperature.

Yet doctors have forgotten this lesson when measuring core body temperature. We would not use absolute room temperature to infer perceived warmth, but we do use absolute body temperature to infer fever.

In medical school, students are taught that humans have a core body temperature as a species, not as individuals. When clinicians take patients’ temperatures in the clinic or hospital, they compare the measurements with the population average. Deviations from this single number help in the diagnosis of acute pathological states, from infections to thyroid disorders.

Why should someone’s physiological state be compared with an absolute standard temperature? Body temperature deviations, after all, can have their roots in individual physiology, such as age 1 2 and circadian, 3 metabolic, 4 and ovulatory cycles. 5 These factors vary dramatically across individuals, raising the possibility that individuals have baseline temperatures that differ systematically from the population average. The same temperature that is normal for one person might be dangerously high for another.

Historically, the use of a population average was partly a data problem: estimating each individual’s baseline body temperature would have been challenging. Any credible effort would need to tease apart individual baseline temperatures from other sources of variation in measured temperatures: ambient conditions when temperature is taken, 6 differences in technique, 7 8 and random error due to intrinsic variability in the measurement process itself. 8 9 Distinguishing signal from noise under these conditions would require large amounts of data: both large numbers of patients and multiple temperature measurements for each patient.

The modern electronic health record stores rich physiological data, including temperature, on large numbers of patients, and many have noted its potential to generate new clinical knowledge. 10 11 We used these data to deal with random errors in individual temperature measurements, by applying statistical techniques that find signal and minimize noise across multiple temperature measurements, taken in a variety of outpatient settings and for a large number of patients. To deal with the many factors known to affect temperature measurements, we used rich contextual data to control for conditions at the time of measurement: ambient temperature, humidity, time of day, date, and body site of measurement. This allowed us to estimate stable baseline temperatures for every patient in our large and diverse sample, as opposed to population averages. We then explored links between individual temperature and a range of other variables: demographics; physiological measures, including vital signs; and mortality.

Individual differences in body temperature might be meaningful in two ways. Firstly, their very existence could open new insights into human physiology and links between body temperature, metabolism, and longevity. 12 13 14 Secondly, individualized “precision temperatures” could allow doctors to tailor testing and treatment decisions to patients’ physiology. At a minimum, they might change one familiar part of the doctor-patient conversation: the eye-rolling that sometimes ensues when patients report that a given temperature, although normal, is “high for me.”

Until recently, large scale databases of temperature measurements were scarce: with some notable exceptions, 15 16 most studies of body temperature in humans date from 1950 or before 17 and have important limitations. Temperatures were measured at varying times of day and in different seasons, using unspecified instruments. 17 Details regarding enrollment procedures were often absent, but—particularly in older studies—there was little apparent concern about achieving a diverse sample of patients for race, sex, and comorbid conditions. 17 Lack of longitudinal data meant that temperature measurements could not be correlated to subsequent outcomes. 15 16 17 Perhaps most importantly, albeit with some exceptions, 15 18 sample sizes were small—often in the 10s to 100s of patients. 16 17

We used a dataset of electronic health records from a large US based academic hospital. The dataset was assembled in two steps: firstly, we identified patients to be included in the cohort—those with one or more visits to the hospital’s emergency and outpatient departments during 2010-12; and, secondly, we obtained data on each of these patient’s outpatient visits from 2009-14, consisting of visits to clinics during which a temperature was measured (Welch Allyn SureTemp Plus digital thermometers were present in most examination rooms).

Since we were interested in individual estimates of normal body temperature in adults, we focused on routine visits during which temperature was expected to be within normal limits. We did not include emergency department visits, during which acute physiological disturbances may affect measured temperatures. We excluded patients aged less than 18 years and those seen on weekends or outside business hours (7 am-6 pm), to avoid selecting patients seen for non-routine problems; those with implausible recorded temperatures (<32°C and >45°C; 0.04% of the total sample); and those visiting clinics for infections, to remove the effect of infection related disturbances in body temperature. We thus excluded visits with ICD-9 (international classification of diseases, ninth revision) codes for infectious diseases, and visits with antibiotics prescribed in the week after the visit (see supplement eTable 1 for cohort construction). Our final sample accounted for roughly 3-5% of the hospital’s annual load of outpatient visits.

Statistical analysis

Estimating individual baseline temperatures.

In the included sample of routine outpatient visits, we used ordinary least squares regression to model measured visit level temperature as a function of external conditions (ie, ambient temperature and dew point, drawn from National Oceanographic and Atmospheric Administration data 19 ), body site (eg, oral, axillary), and time of measurement (hour, day, month, and year).

To estimate baseline temperatures, we modeled individual patients’ deviations from the population mean, controlling for the selected factors. We used fixed effects to ensure that individual temperature effects were approximately normally distributed (see supplement eFigure 1), then we re-estimated the model using random effects. We chose random effects because even in the presence of measurement error they are consistent and efficient estimators of variance. 20 21 These steps allowed us to estimate individual temperature effects as well as features of the population level distribution, such as the variance of individual effects. Using individual level effects necessarily restricted the sample to patients with at least two measured temperatures over the study period. We omitted time invariant attributes of patients (eg, sex, race) since these cannot be estimated alongside individual level effects. Standard errors were clustered at patient level.

We correlated the resulting individual temperature random effects with other variables of interest in the electronic health record, in three groups: demographics, including age, sex, and race; comorbidities, defined using ICD-9 codes over the year before visits, following usual practice 22 ; and physiological measurements (pulse, systolic and diastolic blood pressure, body mass index), using average values over the period spanning from first to last included visit date for each patient—this mirrored the period over which individual level effects were estimated in our dataset. Since we were exploring a range of correlations, we adjusted P values for multiple hypothesis testing using the Holm sequential procedure. 23 Alternative methods of adjustment 24 25 are provided in supplement eTable 3 (results were substantively unchanged).

Relation between individual temperature effects and mortality

Finally, we explored the relation between individual temperature and mortality, using linkage to state social security data. To accurately calculate one year mortality, we addressed a source of bias in these longitudinal cohort data: patients were sampled and included in the cohort because of encounters over 2010-12, up until which they were necessarily alive. Likewise, patients were followed until their last temperature measurement, as late as 2014, at which time they were also alive by construction. Since our primary interest was the correlation between mortality and routine temperature measurements, we calculated mortality in the year after each patient’s last temperature measurement, excluding those whose 2010-12 sampling event occurred after the last temperature measurement (16% of the sample). We then estimated the relation between one year mortality and individual temperature effects by logistic regression, controlling for demographics, comorbidities, and physiological measures. As the hospital is a referral center serving patients from the local community and further away—who require specialized care for complex, serious illnesses with high mortality—we also controlled for log distance between patients’ home zip code and hospital zip code.

Statistical packages

All analyses were performed in STATA (version 14.0) and R (version 3.2.3; Foundation for Statistical Computing).

Patient involvement

No patients were involved in setting the research question or the outcome measures, nor were they involved in developing plans for design or implementation of the study. No patients were asked to advise on interpretation or writing up of results. There are no plans to disseminate the results of the research to study participants or the relevant patient community.

Of 374 306 patients with temperature measurements at outpatient visits, we excluded 130 800 (largely because of infection diagnoses or antibiotic prescriptions; see supplement eTable 1), leaving 243 506 visits (18% of all outpatient visits meeting the other inclusion criteria). Table 1 shows demographics, physiological measurements, comorbidities, and one year mortality. The mean age at time of the visit was 52.9 years, 64% of patients were women, and 41% were of non-white race (including 16% black or African-American patients, 17% Hispanic patients). The most common primary diagnoses at included outpatient visits were osteoarthritis (5.9%), back pain (4.9%), and routine evaluation and examination (4.5%). The clinics most commonly visited were orthopedics (10%) and internal medicine (hospital based clinic: 7.8%, community clinic: 5.7%). One year mortality in this sample of patients receiving care at a tertiary referral hospital was 6.2%, considerably higher than in the general population (<1% in a similar age range).

Demographics, physiological measurements, comorbidities, and one year mortality for full sample. Values are numbers (percentages) unless stated otherwise

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The mean measured temperature was 36.6°C (95% confidence interval 36.6°C to 36.6°C). Each patient had a median 5 (interquartile range 3-9) temperature measurements over a median of 2.1 (0.8-3.8) years, and 19% had more than 10 measurements.

Summary statistics on temperature

Mean 36.6°C (95% range 35.7-37.3°C; 99% range 35.3-37.7°C)

Measurements at different sites (versus oral): temporal: –0.03°C; tympanic: –0.06°C; axillary: –0.26°C

Figure 1 shows the correlation of temporal and environmental factors to measured body temperature, estimated by using random effects regression. Temperature measurements were largely oral (88.2% oral, 3.5% temporal, 3.0% tympanic, 0.1% axillary, 5.2% not recorded), and temporal, tympanic, and axillary temperatures were significantly lower than oral temperatures (by –0.03°C, –0.06°C, and –0.27°C, respectively; all P<0.001). We observed diurnal variation in temperature by hour, with a peak at 4 pm (0.03°C v 12 pm, P<0.001). Higher ambient temperature and dew point were both linked to higher body temperature. Month effects worked to offset these effects—that is, at the same ambient temperature and dew point, summer months were linked to lower body temperature and winter months to higher body temperature. On a median temperature day in our dataset (12.2°C), body temperature was on average 0.08°C lower in July than in February, presumably reflecting the effect of compensatory physiological mechanisms (eg, evaporative cooling, vasoconstriction) by season. Supplement eTable 2 presents the full coefficients from the model.

Relation of temporal and environmental factors to measured body temperature. Coefficients estimated by random effects regression are shown for ambient temperature, dew point, hour, and month, compared with reference categories: median temperature 10th (12.2°C), median dew point 10th (4.7˚C), 12 pm, and April, respectively

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The standard deviation for random effects from this model, denoted as individual baseline temperatures, was 0.15, with 95% range (ie, 2.5–97.5th centile range) 0.60°C (–0.33-0.27°C). In comparison, the standard deviation for raw measured body temperature was 0.42, with 95% range 1.67°C (after subtracting the mean, for comparability: –0.93-0.74°C).

Table 2 shows demographics, vital signs, and comorbidities by fifth of individual baseline temperature, along with regression coefficients of baseline temperatures on the variable. Baseline temperatures declined with age (–0.02°C every decade, P<0.001). African-American women had the highest temperature (0.052°C higher than white men, P<0.001). Baseline temperature also varied significantly as a function of comorbid conditions. Cancer was linked to higher temperature (0.02°C, P<0.001), whereas hypothyroidism was linked to lower temperature (–0.01°C, P=0.01; the relation was linear for mean thyroid stimulating hormone level over the span of the data (see supplement eFigure 2A). The total number of comorbidities was not statistically significantly linked to baseline temperature over and above all individual included comorbidities.

Coefficients from regression of individual temperature effect on demographics, comorbidities, and physiological measurements in 20 718 participants

Table 2 shows the relation between individual baseline temperatures and average physiological measurements over the study period. Controlling for demographic factors and comorbidities, higher temperatures were linked to increased body mass index (0.002°C per m/kg 2 , P<0.001). Higher temperature was also linked to higher pulse (4.0×10 −5 °C per beats per minute, P=0.17) and increased diastolic blood pressure (1.2×10 −4 °C per mm Hg, P=0.01). Figure 2 shows mean vital signs over the study period and their relation to baseline temperatures by sex.

Physiological measurements and relation to temperature effects, by sex. Dots represent centiles of individual temperature effect and vertical bar 95% confidence intervals

Despite the many statistically significant relations identified between individual baseline temperatures and demographics, comorbidities, and physiological measures, these factors collectively accounted for only 8.2% of variation (adjusted R 2 ) in temperature. Residual variation was greater for women than for men (residual sum of squares: 283 v 170), raising the possibility that at least part of the residual difference was driven by hormonal cycles (not measured). However, most variation in baseline temperatures remained unexplained by commonly measured health variables in both sexes.

Table 3 shows the relation between individual baseline temperature and mortality. Controlling for age, sex, race, vital signs, and comorbidities, a 1°C increase in temperature translated into 3.5% higher mortality (P = 0.014). For example, a 1 SD increase in temperature (0.15°C) would translate into a 0.52% absolute increase in one year mortality. Compared with a mean mortality of 6.2% in our sample, this represented an 8.4% relative increase in mortality risk.

Regression of one year mortality on individual temperature, controlling for demographics, comorbidities, and physiological measures in 15 821 participants

To our knowledge, this is the first attempt to demonstrate meaningful variation in individual body temperature, separately from random measurement error and the influence of external factors, and to correlate them to a range of patient factors and outcomes in a diverse patient population.

These results illustrate a way in which “big data” can serve to generate new medical knowledge. Here we used these data not to answer a causal question (eg, to discover side effects of drugs) or to predict an outcome (eg, to create an early warning system for a given condition), but to discover previously unsuspected and potentially important patterns in human physiology. It is unlikely that any one practitioner could have noticed these patterns, despite their fairly large magnitude connections with mortality. Rather, large datasets and statistical methods are needed to bring out these patterns from the empirical record.

Our most noteworthy result was the connection between temperature and mortality. This fits with a larger body of research showing that reduction in body temperature increases longevity and delays aging in a range of (ectothermic) experimental models, including Drosophila and Caenorhabditis elegans , 26 as well as transgenic (homeothermic) mice engineered to have lower temperatures. 12 This observation raises a set of questions that may be worth answering through further research. What is the biological basis for an individual’s baseline temperature? And how might these factors teach us more about subtle but important physiological patterns that might lead to good or poor outcomes? Two sets of results from our analysis were suggestive along these lines.

Firstly, individual temperatures were highly correlated with measured patient characteristics, particularly those related to metabolism and obesity. These differences may have their roots in obvious thermodynamic factors: bodies with larger mass dissipate heat less rapidly, leading to higher temperatures. Fat (which is correlated with mass) could also act as an insulator, independent of mass, leading to higher heat retention in people with more fat. Other explanations, however, are also plausible. It is well known that caloric restriction through fasting leads to down-regulation of temperature, presumably to conserve energy 27 ; indeed, reduced temperature is now considered a biomarker for caloric restriction. 13 14 Although there are few trials of caloric supplementation, the existing literature suggests that individuals vary widely in their ability to dissipate excess energy from overfeeding. 28 Given the strong links between resting metabolic rate and body temperature, 4 it is possible that higher resting body temperature could be a response serving to dissipate excess energy from caloric intake. We found that a raised temperature correlated with both body mass index and activation of the sympathetic nervous system (ie, increased pulse rate and diastolic blood pressure). Thus temperature could be another variable in the cluster of traits linking obesity and activation of the sympathetic nervous system. 29 30 31 Interestingly, while our temperature effects were estimated over longer periods, some small studies have found that hypothermia is linked to increased mortality for acute events (eg, in hip fractures), 32 potentially implying a different physiology in short term versus long term temperature regulation.

Secondly, we found a large correlation between individual baseline temperature and mortality that was not explained by measured patient characteristics. What factors might this temperature variation be picking up on that confer an 8.4% mortality disadvantage? It is tempting to speculate. We did identify a correlation between diagnosed cancers and temperature. This has been noted in the literature previously, either because of the direct metabolic demands of the cancer itself, 33 or because of the body’s immune response. 34 Subclinical infections or rheumatological diseases could exert a similar effect. If higher temperature reflected undiagnosed cancers or other illnesses, this would generate the correlation we observed between the unexplained component of temperature variation and subsequent mortality from these same illnesses. Another potential explanation is that higher temperature reflected a pro-inflammatory milieu; however, we found no clear connection between individual temperature and the inflammatory marker reactive protein (see supplement eFigure 2b). Ultimately, further study is required. We could imagine studies that estimated individual temperature effects using similar methods, then subjected those with higher temperatures to additional diagnostic studies to identify undiagnosed illnesses.

The finding that measured temperature was, other things being equal, lower in hot months and higher in cold months may reflect engagement of well known compensatory adaptations (eg, plasma volume, evaporative cooling, vasoconstriction, shivering) to temperatures experienced over longer periods, as opposed to the short term direct effects of higher or lower temperature. 35 Related recent work has shown, for example, that drinking warm beverages on warm days results in heat loss from increased sweat output. 36

Finally, our estimate of population mean temperature differed from other studies—for example, it was lower than in a sample of primarily young, healthy participants, 16 and higher than in a population of older adults seen in healthcare settings. 15 One potential advantage of our approach is that it adjusted for environmental and temporal factors at the time of measurement, which was not possible in other studies. Although this might enhance the generalizability of our estimate, it would be difficult for any one study of core temperature to ensure that estimates are valid for an entire species. However, it may help with clinical decision making for populations seeking healthcare in similar settings.

Limitations of this study

Our study had several limitations. We considered patients at one academic center and measured temperature using similar equipment, which can have correlated errors in measurement. Our methods were designed to estimate robust deviations in individual temperature separately from measurement error but might not generalize to other equipment, although we would guess that stringent Food and Drug Administration standards for clinical electronic thermometers offer some guarantees that the relative magnitude of effects should be similar. Our data came from one climate zone. Although we controlled for the substantial variation in environmental conditions within this zone, temperature and compensatory mechanisms may vary across climactic zones. We excluded patients with infection and those prescribed antibiotics, but it is possible that some patients had infections that were undiagnosed and untreated by their physicians. However, since this would have to be a consistent finding over multiple visits for the same patients, and since most infections are by contrast transient, this is unlikely to have affected our estimated correlation between individual baseline temperature and mortality. We sampled patients based on visits to a hospital emergency department and clinics. This resulted in an ethnically and medically diverse sample over multiple years of data, but it also selected a sicker set of patients, with a higher comorbidity burden and mortality than the general population. An advantage is that our sample was representative of the population of patients using healthcare today.

Conclusions

We found that individuals have body temperature baselines that correlate with a range of demographic factors, comorbid conditions, and physiological measurements. Since the unexplained variation in temperature is large and correlates with mortality, it may be an interesting and important area for further study.

What is already known on this topic

A long tradition of research on human core body temperature, starting in the 19th century, has focused on establishing average temperature in a population

Temperature is known to be influenced by many factors that differ widely across patients (eg, age and circadian, metabolic, and ovulatory cycles) raising the possibility that individual baseline body temperatures might vary systematically

What this study adds

Individual baseline temperatures are correlated with specific demographic factors, with older people the coldest and African-American women the hottest

Particular medical conditions were also statistically significantly linked to lower or higher temperature, as were physiological measurements, but these factors explained only 8.2% of variation in individual baseline temperatures

The remaining unexplained variation was a large and significant predictor of subsequent mortality, nearly 8.4% higher mortality for a 1 SD increase in temperature

Acknowledgments

We thank Philip Mackowiak for comments on an early draft of this manuscript.

Contributors: ZO and SM designed the study and wrote the manuscript. ZO obtained funding. JKS and ZO analyzed the data. All authors had full access to all of the data (including statistical reports and tables) in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. ZO acts as guarantor.

Funding: This study was supported by a grant from the office of the director of the National Institutes of Health (DP5 OD012161) to ZO. This research was independent from funders. The funder had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: This study was approved by the institutional review boards of Partners HealthCare, the parent organization of the hospital where the research was conducted.

Data sharing: No additional data available.

Transparency: The lead author (ZO) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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Normal Body Temperature: A Systematic Review

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Ivayla I Geneva, Brian Cuzzo, Tasaduq Fazili, Waleed Javaid, Normal Body Temperature: A Systematic Review, Open Forum Infectious Diseases , Volume 6, Issue 4, April 2019, ofz032, https://doi.org/10.1093/ofid/ofz032

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PubMed was searched from 1935 to December 2017 with a variety of search phrases among article titles. The references of the identified manuscripts were then manually searched. The inclusion criteria were as follows: (1) the paper presented data on measured normal body temperature of healthy human subjects ages 18 and older, (2) a prospective design was used, and (3) the paper was written in or translated into the English language. Thirty-six articles met the inclusion criteria. This comprised 9227 measurement sites from 7636 subjects. The calculated ranges (mean ± 2 standard deviations) were 36.32–37.76 (rectal), 35.76–37.52 (tympanic), 35.61–37.61 (urine), 35.73–37.41 (oral), and 35.01–36.93 (axillary). Older adults (age ≥60) had lower temperature than younger adults (age <60) by 0.23°C, on average. There was only insignificant gender difference. Compared with the currently established reference point for normothermia of 36.8°C, our means are slightly lower but the difference likely has no physiological importance. We conclude that the most important patient factors remain site of measurement and patient’s age.

Human body temperature is well established as one of the key vital signs. It is measured at regular intervals in the medical setting and often at home to try estimate the degree of “sickness” of an individual [ 1 ]. It had been used since antiquity [ 2–5 ], yet its interpretation had been, and still is, actively debated in the clinical setting [ 1 , 6 , 7 ]. The first step towards understanding the relationship between temperature and disease is to define “normal” body temperature, from where deviations can be measured. Indeed, many attempts had been made to this end, including the 1868 seminal paper by Wunderlich [ 8 ], who is believed to be the first to establish a link between fever and clinical diagnosis. He was also the first to apply a thermometer experimentally to measure human body temperature. Using a large sample size, Wunderlich [ 8 ] concluded that the average axillary temperature was 37.0°C, with the upper limit of normal defined as 38.0°C. However, newer studies challenged Wunderlich’s [ 8 ] “normothermia” [ 6 ]. Furthermore, research had shown that body temperature is a nonlinear function of several variables such as age, state of health, gender, environmental temperature, time of the diurnal cycle, among many others [ 9 , 10 ]. To make the best use of the currently available literature, we reviewed and herein present an analysis of previously published human body temperature studies using healthy individuals, with the goal of better understanding the variables that determine normal body temperature.

The peer-reviewed literature was searched using PubMed ( Table 1 ). The time period ranged from 1935 to December 2017. The following search phrases among article titles were used: “normal body temperature”, “body temperature AND review”, “body temperature AND adult”, “body temperature AND gender”, “human body temperature”, “core body temperature”, “hypothermia AND elderly”, “body temperature AND measurement”, “tympanic body temperature AND measurement”, “rectal body temperature AND measurement”, and “oral body temperature AND measurement”. Furthermore, the references of the above-identified papers were manually searched for additional useful articles. To be included in our analysis, papers had to meet the following inclusion criteria: (1) the paper presented data on measured normal body temperature of healthy human subjects ages 18 and older, (2) a prospective design was used, and (3) the paper was written in or translated into the English language. Using the data from the articles that met our inclusion criteria, we calculated mean temperatures and ranges before and after stratifying the data by gender, age (less than 60 years old vs 60 years old or older), and site of measurement (oral, axillary, temporal, rectal, urine) or by both variables.

Summary of the Literature Data Search Grouped by Search Phrase

Pooled standard deviations were calculated using the pooled standard deviation formula:

For equal sample sizes, the formula was simplified as follows:

For the data in which standard deviation for the measured temperatures was not reported in the original articles, the standard deviation was estimated via extrapolation from a plot of the known standard deviations and the corresponding sample sizes. Table 2 shows the available and missing standard deviations (8 of the 36 articles that met our inclusion criteria did not report standard deviations for at least some portion of their data).

Data Summary From the Articles That Met the Inclusion Criteria

Abbreviations: N, number of participants; NH, New Hampshire; SD, standard deviation.

The search hits are summarized in Table 1 . A total of 36 articles met our inclusion criteria and the extracted raw data is shown in Table 2 . The sample sizes for all of these studies were plotted against the year in which the studies were published in Figure 1A . Of the identified articles, 33 reported oral temperatures, 13 reported rectal temperatures, 9 reported tympanic temperatures, 6 reported urine temperatures, and 5 reported axillary temperatures. Seventeen of the studies reported temperatures in younger adults (age <60 years) and 19 reported temperatures in older adults (age ≥60 years). There were a total of 7636 healthy subjects, 1992 of which were identified as female and 2102 were identified as male, and the rest did not have their gender reported. There were a total of 9227 individual measurement sites used, where 5257 adults provided oral measurements, 2462 provided tympanic measurements, 618 provided rectal measurements, 551 provided axillary measurements, and 339 adults provided urine measurements. Our statistical analysis ( Table 3 ) showed that the average body temperature among all subjects in all 36 studies and combining the data from all measurement sites was 36.59 ± 0.43 (standard deviation).

Summary of Normal Body Temperature Ranges Stratified by the Modifying Factors Measurement Site, Age, and Gender

Literature search results and the determinants of normothermia. (A) Number of studies and their sizes over the search time period. (B) The dependence of body temperature on measurement site. (C) The dependence of body temperature on age, shown stratified by measurement site. (D) The dependence of body temperature on gender, shown stratified by measurement site.

The average temperatures per measurement site, in decreasing order, were rectal at 37.04 ± 0.36, tympanic at 36.64 ± 0.44, urine at 36.61 ± 0.5, oral at 36.57 ± 0.42, and axillary at 35.97 ± 0.48 ( Figure 1B , Table 3 ). Overall, when using the data from all of the measurement sites, the average body temperature of younger adults (<60 years of age) was higher (36.69 ± 0.34) than the average body temperature of older adults ( ≥60 years of age), which was 36.5 ± 0.48. The same age-related trend held true for all individual measurement sites ( Figure 1C , Table 3 ). When looking at gender differences, we found that when using all reported measurements, the average body temperature of females was slightly lower (36.65 ± 0.46) compared with males (36.69 ± 0.43), but this trend was not pronounced when looking at the individual measurement sites, except for the urine measurement site ( Figure 1D , Table 3 ).

The quest for understanding human body temperature and defining normothermia is ongoing, as is evidenced by the steady number of published prospective studies depicted in Figure 1A . To the best of our knowledge, our systematic review, where we analyzed 36 separate prospective studies, is the largest of its kind. When using the data from all measurement sites and all included studies, we calculated the overall mean body temperature to be 36.59°C, which is lower than the currently acceptable mean of 36.8, as published in one of the most respected medical reference books, Harrison’s Principles of Internal Medicine [ 46 ]. However, the latter number from the reference book is not based on an all-inclusive meta-analysis, and therefore our average is likely more accurate. Of course, it should be kept in mind that there is no single number that defines normothermia; instead, there is a range for normal temperature, with corresponding standard deviation and standard error. As such, the 0.2°C difference in the mean when we compare our mean temperature with the Harrrison’s is likely not of much physiological relevance. In that respect, our calculated overall range (mean ± 2 standard deviations) is 36.16–37.02°C, which is narrower than the range of 33.2–38.3°C reported by Sund-Levander et al [ 42 ], which is an older systematic review comprising of only 20 studies, all of which were also part of our analysis. The tighter range is most likely due to bigger sample size used in our report, which validates our results further.

Knowing that body temperature is influenced by the measurement site, we calculated average temperatures, in decreasing order, rectal at 37.04°C, tympanic at 36.64°C, urine at 36.61°C, oral at 36.57°C, and axillary at 35.97°C. The trend is similar to the one reported by Sund-Levander et al [ 42 ]; however, the latter systematic review did not contain measurements of urine temperature. In addition, all of our site-specific calculated temperatures, except for axillary, were higher compared with the Sund-Levander et al [ 42 ] report. Furthermore, it is intriguing that we found such a large difference between what is considered the body core temperatures: rectal (37.04°C) and urine (urine at 36.61°C). This likely reflects a fault in the measurement in earlier studies from the 1970s and 1980s, which constitute a significant portion of the analyzed data and in which the measurements of urine temperature were not done invasively, eg, via a monotherm system. Therefore, these urine temperatures are fundamentally different from what we should consider core body temperature, which is temperature measured inside the human body.

With regards to age, our analysis confirmed that, on average, healthy elderly people have lower body temperature ( Table 3 and Figure 1B ) compared with younger adults. This was true for both the total average as well as for the individual measurements sites, except for urine temperatures because there were no studies reporting such measurements among younger adults. The decrease in body temperature with age is believed to be a phenomenon arising from a slowing of the human metabolic rate coupled with a decline in the ability to regulate body temperature in response to environmental changes such as seasonal changes, which had been previously studied [ 17 , 19 , 22 , 47 , 48 ]. These age-related changes are of particular clinical importance because elderly patients are often not capable of mounting a strong inflammatory response to infection and disease, with their temperature failing to reach the temperature range of what is traditionally considered the fever temperature range. Moreover, there is evidence to suggest that the presence of a robust fever response carries prognostic value when considering such infectious disease processes [ 49 ]. In the elderly, who may not be able to mount such a thermal response, we may similarly have to readjust our outlook on temperature-based prognostication. However, until we have research data to specifically address this question, clinicians should use lower normal temperature ranges as reference in the elderly, such as the ones presented in our systematic review.

Finally, our analysis demonstrated only a trivial difference in body temperature between the genders ( Table 2 and Figure 1C ), with women’s temperature being slightly lower when using all measurements from all measurement sites. However, when grouping the results by measurement site, in some cases (tympanic site) females’ body temperature is in fact higher compared with their male counterparts, whereas in other cases there is no difference (oral and rectal sites). There had been a disagreement in the literature as well, with some studies reporting that females have higher body temperature [ 6 , 8 , 16 , 31 ], whereas others reported no differences among the genders [ 39 ]. Gender differences in body temperature had been suspected to relate to a difference in body fat percentage between women and men. Those studies revealed that women have a comparably larger percentage of body fat distribution subcutaneously, which in turn correlates with lower average skin temperatures [ 50 , 51 ]. It had also been theorized that body temperature differences relate to female hormone levels, and yet, even in the studies that report statistically significant differences, the actual difference is fairly small and thus not likely to be of any clinical significance. Our large sample size from 36 individual studies is expected to reflect the true temperature variable in the human population and supports the lack of clinical significance of gender-based body temperature difference even if it could be measured.

Human body temperature is a highly variable vital sign and known to be influenced by several variables, most prominently the person’s age and the site of measurement. Our systematic review is the largest of its kind and provides clinicians with evidence-based normal temperature ranges to guide their evaluation of patients with possible fever or hypothermia.

Potential conflicts of interest.   All authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest.

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• body temperature
• older adult
• normothermia

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• Published: 07 June 2021

An initial study on the agreement of body temperatures measured by infrared cameras and oral thermometry

• Scott Adams   ORCID: orcid.org/0000-0001-6466-0444 1 ,
• Tracey Bucknall   ORCID: orcid.org/0000-0001-9089-3583 2 , 3 , 4   na1 &
• Abbas Kouzani   ORCID: orcid.org/0000-0002-6292-1214 1   na1  

Scientific Reports volume  11 , Article number:  11901 ( 2021 ) Cite this article

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• Biomedical engineering
• Population screening

The COVID-19 pandemic has led to the rapid adoption and rollout of thermal camera-based Infrared Thermography (IRT) systems for fever detection. These systems use facial infrared emissions to detect individuals exhibiting an elevated core-body temperature, which is present in many symptomatic presentations of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Despite the rollout of these systems, there is little independent research supporting their efficacy. The primary objective of this study was to assess the precision and accuracy of IRT screening solutions in a real-world scenario. The method used was a single-centre, observational study investigating the agreement of three IRT systems compared to digital oral thermometer measurements of body temperature. Over 5 days, 107 measurements were taken from individuals wearing facial masks. During each entry, two measurements of the subject’s body temperature were made from each system to allow for the evaluation of the measurement precision, followed by an oral thermometer measurement. Each participant also answered a short demographic survey. This study found that the precision of the IRT systems was wider than 0.3 °C claimed accuracy of two of the systems. This study also found that the IRT measurements were only weakly correlated to those of the oral temperature. Additionally, it was found that demographic characteristics (age, gender, and mask-type) impacted the measurement error. This study indicates that using IRT systems in front-line scenarios poses a potential risk, where a lack of measurement accuracy could possibly allow febrile individuals to pass through undetected. Further research is required into methods which could increase accuracy and improve the techniques viability.

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Introduction.

Core body temperature is one of the four key vital signs, which is regularly assessed by healthcare settings, alongside respiration rate, blood pressure and heart rate 1 . In an in-patient setting, core body temperature can be assessed from different body locations using oral, rectal, tympanic or temporal artery thermometers, or even through urinary or pulmonary artery catheters with in-built temperature sensors 2 . The accuracy, precision, advantages and disadvantages of these temperature measurement devices in clinical settings has been well established 2 , 3 , 4 .

In addition, in modern medical practice, every device must be assessed against national and international regulations. To ensure that a device meets appropriate levels of quality, accuracy and safety, strict medical equipment certification standards must be met by the device prior to its use in a clinical setting. The bodies administering these standards for medical devices include the Therapeutic Goods Authority in Australia, the Food and Drug Administration in the USA and the Medicines and Healthcare products Regulatory Agency in the UK 5 . The use of evidence-based assessment to evaluate technologies for use in a hospital setting is a common and critical part of modern healthcare, ensuring patient safety and assisting in the delivery of high-quality care 6 .

The COVID-19 pandemic has led to an unprecedented adoption and rollout of new fever detection technologies 7 . As fever is present in a significant proportion of symptomatic SARS-CoV-2 cases, the goal of the screening is to identify individuals exhibiting an elevated temperature, isolate them, and refer them for a more comprehensive assessment to a health practitioner 8 . Currently, fever screening technologies are typically installed in high-traffic areas, such as train-stations or airports, and also at the entrance of high-risk sites, such as hospitals, where the consequences of an outbreak could be catastrophic. Many of the deployed fever screening solutions have not yet been assessed by regulatory agencies.

The ideal screening technology must be accurate, rapid, widely available, and operate in a way that keeps both the test administrator and subject safe from viral transmission. In addition, an ideal solution would operate without consumables to deal with global supply chain shortages of critical resources, such as consumables, personal protective equipment (PPE) and medical devices, which have been experienced during the COVID-19 pandemic 9 . When compared to this ideal screening technology, it is clear that traditional measurement techniques have a variety of limitations which restrict them from being highly suited for use as mass-screening tools. Traditional hospital-grade, contact-based measurement techniques all require close proximity between the test administrator and the subject, and some methods are too invasive, too slow, or too expensive to be widely used. This has led to the increased adoption of infrared thermal detection systems for fever screening applications.

Infrared thermal detection systems operate through the measurement of thermal radiation emitted in the infra-red wavelengths, of the electromagnetic spectrum 10 . The thermal radiation is converted through a transducer to an electrical signal which can be interrogated and measured on-board of the device. In fever screening applications, detection systems fall into two main categories: handheld Non-Contact Infrared Thermometry (NCIT) devices, and Infrared Thermography (IRT) systems. These systems meet many of the criteria for a successful mass screening system, they are non-contact, require no consumables, are rapid, and in the case of IRT systems the operator can be physically distanced from the subject. While the use of NCIT devices has been explored in a hospital setting through a number of clinical trials and research articles, and that many of NCIT devices have achieved appropriate medical device approvals, these are not yet established for most IRT systems 11 , 12 , 13 .

Despite the wide-spread use of IRT systems, there remains limited independent evidence demonstrating their efficacy and accuracy when measuring body temperature for fever screening. Some clinical trials have been conducted, but the results have been mixed, and there is a lack of consensus in the literature on the effectiveness of IRT systems. A 2015 study in Singapore found that one system was able to achieve a high level of sensitivity and specificity (89.7% and 92%, respectively) 14 , and a similar result found from a study conducted in the USA in 2010, reporting a sensitivity of 91.0% and specificity of 86.0%. However, these results were not found to be broadly repeatable, as revealed by three experiments performed in Hong-Kong and New Zealand between 2011 and 2013 15 , 16 , 17 . The most recent of these studies only reported a maximum sensitivity and specificity of 64% and 86%, respectively, when measurements were taken in a comparative manner 15 . International standards such as the ISO/TR 13154:2017 18 which are used to explain and outline current best-practice approaches suggest a number of considerations to be taken into account (e.g. measurement location, number of subjects who can be measured simultaneously, and recommended distance to subject) when performing fever screening. The existing studies did not report that any of the systems which were installed according to these standards. This is likely due to the fact that the finalised version of the ISO/TR 13154:2017 standard was released after the studies were conducted.

As such, this study, conducted in Australia during the COVID-19 pandemic, evaluates the precision and accuracy of three different types of IRT system for human temperature measurements when installed in a real-world scenario with a mask-wearing population. This was performed to determine their efficacy as screening systems, when compared with a certified benchmark temperature measurement device commonly used in hospitals.

Study design

The investigation was designed as a single-centre observational study comparing the accuracy of three IRT systems to core-temperature measurements taken using certified oral thermometers. Additionally, the precision of each IRT system was determined through repeat measurements.

The study was conducted at a University during a 5-day period in August 2020 from 9 a.m. to 5 p.m.

Sampling and eligibility criteria

The study was performed on a community sample and used a convenience method for participant recruitment. The study aims, participation requirements, and methods of consent and requirements of participation were provided in an email to staff and students in the building. Verbal consent was gathered on the day. These measures were performed to ensure that social distancing guidelines were able to be maintained during the study. Every employee and student who attended the building during the study period was invited to participate. Due to the nature of the facility, the participants were all over 18 years of age.

Ethical considerations

This study was approved by the Deakin Human Ethics Advisory Group (Health) (approval number: HEAG-H 154-2020) at Deakin University (Australia). All methods were performed in accordance with the recommendations of the Ethics Advisory Group as well as the relevant guidelines and regulations. Informed consent was gathered from every participant, prior to their inclusion in the study and all data was anonymised before being stored in a secure double identifier password protected REDCap (Research Electronic Data Capture) database administered by Deakin University with access limited to the study investigators.

IRT systems

Three IRT systems were selected for use which represented three of the main types of systems that are being sold in the Australian market. These were as follows:

System 1—A dual-camera system with a 40 °C external reference temperature device (blackbody), advertised to be able to measure up to 30 subjects simultaneously while performing facial recognition tasks.

System 2—A single camera system with laser-assisted autofocus which operates without a blackbody.

System 3—A single camera system with a 35 °C blackbody which is deployed in-line with the guidance provided in the ISO/TR 13154:2017 technical standard (apart from the guidance on masks) 18 .

The specifications for each system are included in Table 1 :

Experimental setup

Each of the three IRT systems were loaned to the researchers by the manufacturing companies for the purpose of conducting this experiment. To ensure the independence of this trial, each company provided the equipment free of charge and signed a research services waiver giving the researchers the right to publish the results without commercial input or oversight. The IRT systems were all setup according to the manufacturer’s directions. The researchers were provided training on the assembly, deployment and operation of the IRT systems to ensure correct operation. Each manufacturer confirmed the configurations of the systems, the setup environment and the experimental methodology prior to the beginning of the trial through video conference but were otherwise uninvolved in the experiment.

The setup conditions of the three IRT systems are visualised in Fig.  1 .

Experimental setup of the three IRT systems. In each case, the location of the subject being measured is indicated by a cross.

The conditions of the experimental environment were as follows:

The area was not directly under any active Heating, Ventilating, and Air Conditioning (HVAC) system.

The room in which the trial was located was temperature and humidity controlled.

No lights or thermal radiation sources were directly in view of any of the cameras, and the cameras were not pointed at any reflective surfaces.

Each IRT system was allowed 30 min of temperature stabilization each day prior to the first measurement being taken.

The room had no direct entry from the outside, each doorway had an airgap which had to be traversed prior to entering the experimental area.

Systems 2 and 3 were focussed at the beginning of the trial, and focus was checked four times per day (the systems remained in focus throughout the trial). The manufacturer of System 1 indicated that manual focussing was not required.

Systems 2 and 3 were directly in-line with the subject’s face. The manufacturer of System 1 indicated that this was not necessary.

TV Screens were setup for Systems 2 and 3 so that subjects could orient themselves within the camera targeting area. System 1 used a web-based application and a laptop to display the data.

Experimental procedure

Every step of this experiment was strictly conducted according to socially distancing guidelines between the researcher and the subject.

As a potential participant entered the building, they were approached by the researcher who confirmed their knowledge of the study (information which had been provided through email) and asked for their verbal consent. If the answer was in the affirmative, the participant then verbally completed a questionnaire of demographic data (age, gender, and mask-type), and also was queried as to their current health status, in particular:

“Have you experienced any fever symptoms in the last 24 h?”

“If yes, did you take any medications to treat your fever in the last 4 h?”

“Have you experienced any symptoms in the last 24 h of sore throat, cough, runny nose, loss of taste or smell?”

Have you had a hot or cold drink in the last 30 min?

This data was entered into a case report form for each individual in a secure REDCap (Research Electronic Data Capture) database.

The participant was then questioned to if they had been outside the building in the past 5 min, if the answer was in the affirmative, they were asked to wait for 5 min to acclimatise. The purpose of this was to avoid readings from being impacted by the exterior environment. The participants were also asked to remove hats or glasses to ensure accurate readings.

All participants were wearing masks during the trial, apart from when using the oral thermometers, as during the SARS-CoV-2 pandemic in Victoria, Australia, there was a state-wide mandate that all individuals must wear masks when outside their own homes, this means that any screening technology used during this time would be required to operate with masked participants 19 .

Each participant was then asked to spend 5 s standing in front of each IRT system facing directly into each camera at a distance of 1.5 m (indicated by a mark on the floor). This was then repeated, to give two readings for each time an individual participated in the experiment. At the conclusion of these measurements (6 in total), the participant was provided with a DT-01B oral thermometer (Measurement accuracy: ± 0.1 °C [35.5–42.0 °C]) and requested to stand in an isolated location (indicated by a mark on the floor), where they proceeded to take an oral thermometer reading. The two readings from each IRT system and the single oral thermometer reading were then entered into the secure REDCap database case report form.

The RStudio integrated development environment for R (version 1.3.1056, R version 4.0.2) was used for the statistical analysis, and for all tests, a 0.05 level of significance was used. The frequency distribution (reported as mean and standard deviation), was calculated for the participant’s age, the number and percentage distribution of the participant’s gender, and mask-type were also calculated.

An investigation into the measurement precision was then performed to analyse the difference between the first and second measurements of each IRT system and to determine if the claimed 0.3 °C of accuracy was observed in a multi-measurement precision test. This precision error was also reported as a boxplot to observe the distribution of the quartiles as well as to identify outliers. The frequency distribution of the precision error was then calculated and reported as mean and standard deviation.

The measurements of the IRT systems were then compared against the reading of the oral thermometer, and the mean and standard deviation were calculated. The Pearson's correlation coefficient (ρ) was also calculated to determine the correlation between the measurements. The thermal camera measurements, in comparison to the oral thermometer readings were then fitted to a linear model, and the coefficient of determination for each system was calculated. The error of each measurement for the three systems was then calculated. Finally, the accuracy was assessed in relation to each of the demographic attributes (age, gender, mask type), the mean and standard deviation of the error was calculated, and tests of statistical significance between the systems were calculated using Welch’s t-test (as the sample sizes were unequal 20 ).

Participant characteristics

Over the 5-day period of the study, a total of 107 measurements were taken from the participating subjects within the building. Each participant was measured twice by each system in the manner described in the Experimental Procedure, giving a total number of thermography measurements as 214. As individuals were able to take part in the study on subsequent days, the number of unique participants was seventy-one. Table 2 details the demographic attributes of the study participants. All participants wore face masks as mandated by the Victorian Department of Health and Humans Services 19 .

In this study, no participants reported having a fever in the past 24 h, or experiencing other COVID-19 related symptoms (such as sore throat, cough, runny nose etc.) which is due to a level of selection-bias, as these symptoms would have precluded their entrance to the campus.

IRT system precision test

The first test performed on the IRT systems was to determine if the systems were able to achieve a precision of within ± 0.3 °C through a repeated measurement precision test, the results are shown in Fig.  2 and Table 3 . As each participant in the study was measured twice by each system within 30 s, the ideal system would have had a temperature difference of 0 °C between the two measurements. From our results it was clear that none of the systems achieved a precision within ± 0.3 °C. The mean and standard deviation of precision error for each system were as follows: System 1: 0.024 ± 0.183 °C, System 2: 0.019 ± 0.194 °C, and System 3: 0.044 ± 0.214 °C. The mean errors were low in this case as all systems experienced both positive and negative precision errors which caused error cancellation. Using the 2-standard deviation confidence interval, the systems were found to have precisions of: ± 0.34 °C for System 1, ± 0.37 °C for System 2 and ± 0.38 °C for System 3. From the box-plot in Fig.  2 B, it can be clearly observed that System 2 was the system with the most measurements within ± 0.3 °C measurement precision, however it still recorded a number of outliers.

Repeated measurements precision test results. ( A ) A bubble-plot which displays the first measurement against the second. The dotted lines is the range in which a system with 0.1 °C of precision would fit, the continuous lines are the range in which a system with 0.3 °C of precision would fit. ( B ) A boxplot of the difference between the first and second measurement clearly displaying the quartile ranges and the outliers from each system.

IRT system accuracy and correlation test

The second test compared the IRT system readings against the oral thermometer to determine how correlated the readings from each system were to core body temperature. While not required to be exact, an accurate system for detection of elevated body temperature should have a strong degree of correlation with the oral thermometry readings, with an increase in core temperature resulting in an increased IRT measurement. The results of this test were tabulated and are displayed in Table 3 . The oral thermometer measurements were similar to those found in previous studies 3 , 4 . Firstly, there were significant differences found between the IRT systems and the oral thermometer measurements with the mean difference in System 1 being 0.26 °C, System 2 being − 1.90 °C, and System 3 being − 1.31 °C. In addition, the correlation coefficients calculated were weak (< 0.5), with System 3 being the most correlated to the oral thermometer results (ρ = 0.49) and System 1 being the least (ρ = 0.33).

A linear model was developed for each of the systems relative to the oral thermometer results and is displayed as a bubble-plot in Fig.  3 . Each of the systems had a generally increasing trend in their reported temperature readings as the oral thermometer readings increased. However, the results from this analysis agree with the results in Table 3 with the three IRT systems only presenting weak coefficient of determination’s (r 2 ), with the strongest being System 3 (r 2  = 0.24) and the weakest being System 1 (r 2  = 0.11).

Bubble-plot of the IRT measurements vs the oral thermometer measurements.

Participant characteristics and IRT system error

In addition to examining the participants in aggregate, a set of analyses was also performed to determine if any of the recorded demographic attributes had an impact on the measurement error experienced by the IRT systems compared to the oral thermometer. For each of the recorded characteristics (age, gender, and mask type), the mean and standard deviation of the error and p-values were calculated, using Welch’s t-test (as the sample sizes are unequal 20 ). These results were tabulated and are displayed in Table 4 . From the data, it can be seen that the IRT systems errors were impacted by the demographic factors, however, each of the systems was not impacted by the same demographic factors. System 1 was impacted by the age of the subject as well as by the mask type. System 2 and 3 were impacted by the participants gender.

Validation of System 2 against external temperature reference devices

System 2 was checked against two separate blackbody reference devices (35 ± 0.1 °C and 40 ± 0.1 °C) and was found to report the correct temperature within 0.1 °C, demonstrating its high degree of measurement accuracy.

IRT systems are being installed in a wide variety of locations worldwide in response to the COVID-19 pandemic. Yet, there is limited evidence available in the literature reporting on their accuracy or efficacy in these application spaces, in particular on how they correlate to core-body temperature measurements. In order to address this research gap, the current study investigated the use of three different IRT systems from different manufacturers in a real-world setting, with a community mask-wearing population.

Firstly, the precision of each of the three systems was determined through repeated measurements taken a short time apart (< 30 s). Each of the systems were found to have a precision wider than the 0.3 °C accuracy claimed by two of the systems. Secondly, in regard to measurement accuracy, our results suggest that the IRT systems each experience a deviation from the core temperature measurements. Thirdly, this study has shown that in our sample, the IRT systems measurements only have a weak correlation to the oral temperature measurements, with System 3 having the highest ρ and r 2 with values of 0.49 and 0.24, respectively. Fourthly, the participant characteristics were associated with changes in the error between oral measurements and IRT systems measurements, however, these were not consistent across all three IRT systems.

Some existing studies have found IRT systems to be sensitive and specific in assessing the febrile status of subjects 14 , 21 . These studies were also conducted with participants > 18 years of age and with multiple IRT systems. However, neither of these studies were conducted with a mask-wearing population, and nor with IRT systems installed at the first stage of entry to a building as in our study, which is a common use-case for these systems in hospitals and workplaces in 2020. In addition, the study by Nguyen et al. 21 which found the IRT systems to be sensitive and specific, was performed on individuals after they had been registered in the emergency department, which may have allowed for an extended period of acclimatisation time when compared to a regular building entry.

Our study is the only recent study reporting on repeated measurement precision of the IRT systems, this is a significant result as it describes the repeatability of the system when measuring the same person multiple times. From our results it seems unlikely that a 0.3 °C level of accuracy would be achievable with such a wide measurement precision in each of the systems. Indeed, all 3 of the systems reported measurements with differences of > 0.5 °C on the same individuals only 30 s apart.

The research conducted by Ghassemi et al. 22 , and the ISO/TR 13154:2017 standard suggest that using an external reference device (blackbody) increases the accuracy of the measurements. Our study results show that this is a good recommendation; System 3, which used a blackbody reference, had a mean difference 0.59 °C closer to the core temperature measurement than System 2, which used a similar camera without a blackbody. In addition, System 3 was found to have a greater correlation to core temperature than System 2, with their respective correlation coefficients being 0.491 and 0.407.

The current ISO standards also recommend that measurements should be taken from the inner canthus of the eye rather than the forehead or general facial measurements 18 , 22 . Our study found that System 1 which was not specifically taking measurements from the inner canthus in fact reported measurements which were closest to the core body temperature measurements (mean difference increase of 0.26 °C). This was an unexpected result, as the literature suggests that the facial skin temperature is generally expected to be lower than the core-body temperature 16 , 23 . This suggests that System 1 is employing a correction algorithm on the measurement results, which may shift the measurements into a more “acceptable” range. Additionally, the measurements from System 1, which were not taking measurements from the inner canthus were found to have the smallest coefficient of determination of any of the systems to the core body temperature (r 2  = 0.11). This suggests that the use of the algorithm does not significantly improve the efficacy of whole-face temperature measurement, and that the current guidance is correct in recommending measurements be taken from the inner-canthus region over a general facial measurement.

The correlation found in our study (maximum being ρ = 0.49) is generally in agreement with the existing literature, which reported values between 0.3 and 0.5 for well-functioning systems 15 , 21 , 24 . However, the study by Chan et al. 15 found that the correlation is higher among febrile populations with core-temperatures ≥ 38 °C, which we were not able to include in our study, so the correlation may have been improved with a population including a large cohort of febrile individuals.

The experiment involving the validation of System 2 against external temperature reference devices (blackbodies) demonstrated that this system is capable of measuring emitted thermal radiation with a high degree of accuracy in the experimental environment. When measuring these near-ideal sources, System 2 reported a measurement value within 0.1 °C of the expected temperature, which is within the margin of error of the reference device. This suggests that the measurement error observed in experiments with human subjects is likely due to the physiological link between core-temperature and facial temperature, rather than inherent technological error.

Existing research into normal body temperatures has found that the normothermic difference between different demographics (age, gender, etc.) is smaller than the error range of our measurement devices (0.3 °C) 4 , thus a study into the temperature differences of demographics would be of limited use. Thus, this work investigated how these demographic factors impact the accuracy of the measurements taken when using IRT measurement systems. Our study found that demographic characteristics had a significant impact on the measurement error of the systems, however, this was not consistent across the three IRT systems. System 1 which measured the whole face temperature exhibited an increased error on subjects ≥ 40 years of age, and those who were wearing thinner masks. Systems 2 and 3 which measured the inner canthus of the eye had an increase in error on subjects who identified as female. This is in agreement with existing literature which suggests that gender and age are factors which impact measurement accuracy of infrared thermography systems 15 , 21 .

To the authors’ knowledge, this is the first IRT study which has reported face-mask type as a demographic factor and investigated its impact on measurement error. Our earlier study using NCIT devices in a hospital setting found that demographic factors (age, skin tone and gender) also impacted measurement results, so it appears that there is scope for future research to determine their precise impact on the performance of infrared measurement systems in fever-screening scenarios 12 . Finally, we do not believe that the use of masks had a significant impact on the effectiveness and efficacy of the measurements being taken. As the systems were not targeting regions of the face covered by the mask it is unlikely that the inclusion of a mask altered these temperatures, particularly the systems that were measuring the inner canthus region. From observing the systems during operation it was clear that each system was able to quickly and correctly identify the facial location to be measured, indicating that the masks did not make an impact on the facial positioning algorithms being used. This could be confirmed through a specific research study.

Strengths and limitations

To the authors’ knowledge, this the first study of this type which has been conducted on a mask-wearing population, which was a mandated intervention in Victoria, Australia during parts of the COVID-19 pandemic. The limitations of the study were as follows: this was a convenience selected community sample, with no febrile subjects, there were no subjects > 65, the duration of the camera loan agreements and facility agreements dictated a one week timeframe which restricted the sample size. Additionally, the variations in height of individuals impacted the measurement process as some subjects had to bend, lean, or use chairs in order to be in focus in the measurement. Also, this study had a larger population of male (71.96%) than female (28.04%) participants, this may impact the translation of the results. The inclusion of an NCIT device could have allowed for the exploration of the source of the measurement error, and finally the lack of febrile individuals made it impossible to assess the sensitivity and specificity for individual febrile detection.

Future work

There is a clear need to expand this study into a setting with more febrile individuals to allow for the assessment of sensitivity and specificity of fever detection, however, the low correlation values found between each of the measurement sources raises doubt to the efficacy of these systems at detecting individuals with low-grade fevers (37.5–38 °C). Also, there is clearly scope for future research into investigating the impact of camera characteristics on measurement results. This would involve creating a study where multiple cameras with varying characteristics from multiple manufacturers would be tested in the same setup conditions to determine the impact of the technical characteristics on the measurement results. Another avenue of investigation for future research would be a quantitative determination of the impact of masks on IRT system measurements. A study in which each participant is measured both masked and un-masked and the associated results compared, would be a great addition to the literature and allow for a quantitative determination of the impact of masks on IRT measurements. In addition, there is clear scope to perform further investigations into the impact of age and gender on the measurement results. Finally, there is clearly an interesting avenue of investigation in relation to improving the precision of these measurement techniques for mass screening ( Supplementary Information ).

This paper presented the first study on assessing the capabilities of IRT systems in a face-masked population in a real-world mass screening scenario. This system was tested outside of a hospital setting, at the entrance to a research facility within a building, mimicking the installation scenario of many currently operating IRT systems. Our results show that using the systems as a front-line intervention for fever-screening poses a potential risk, where the lack of measurement repeatability could negatively impact sensitivity and specificity, possibly allowing febrile individuals to pass through undetected. Although these systems are currently seeing widespread use due to the COVID-19 pandemic, our results show that there is still further research required to improve their precision and accuracy so that users can be confident in their operation. There remains an opportunity for new technology to meet this gap.

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Acknowledgements

We sincerely thank Bridey Saultry (Deakin University, Australia) and Dr Andrew Valentine (University of Queensland, Australia) for their contributions towards the conduct of the study and for critical commentary at various stages of this work.

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S.A., A.K., & T.B. made substantial contributions to the conception and design of the work. S.A., A.K. & T.B. developed the experimental methodology. S.A. performed the experimentation, data analysis and visualization. S.A. A.K. & T.B. performed the data interpretation. S.A. drafted the work. A.K. & T.B. critically reviewed the work, and revised the final manuscript.

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Adams, S., Bucknall, T. & Kouzani, A. An initial study on the agreement of body temperatures measured by infrared cameras and oral thermometry. Sci Rep 11 , 11901 (2021). https://doi.org/10.1038/s41598-021-91361-6

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• Xue Q Liu Y Breda J Springston M Iyer V Patel S (2024) Thermal Earring Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 10.1145/3631440 7 :4 (1-28) Online publication date: 12-Jan-2024 https://dl.acm.org/doi/10.1145/3631440
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Circadian Clock and Body Temperature

• First Online: 18 September 2024

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• Takahito Miyake 10 ,
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Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1461))

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The circadian fluctuation of body temperature is one of the most prominent and stable outputs of the circadian clock and plays an important role in maintaining optimal day–night energy homeostasis. The body temperature of homothermic animals is not strictly constant, but it shows daily oscillation within a range of 1–3 °C, which is sufficient to synchronize the clocks of peripheral tissues throughout the body. The thermal entrainment mechanisms of the clock are partly mediated by the action of the heat shock transcription factor and cold-inducible RNA-binding protein—both have the ability to affect clock gene expression. Body temperature in the poikilotherms is not completely passive to the ambient temperature change; they can travel to the place of preferred temperature in a manner depending on the time of their endogenous clock. Based on this behavior-level thermoregulation, flies exhibit a clear body temperature cycle. Noticeably, flies and mice share the same molecular circuit for the controlled body temperature; in both species, the calcitonin receptors participate in the formation of body temperature rhythms during the active phase and exhibit rather specific expression in subsets of clock neurons in the brain. We summarize knowledge on mutual relationships between body temperature regulation and the circadian clock.

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Miyake, T., Inoue, Y., Maekawa, Y., Doi, M. (2024). Circadian Clock and Body Temperature. In: Tominaga, M., Takagi, M. (eds) Thermal Biology. Advances in Experimental Medicine and Biology, vol 1461. Springer, Singapore. https://doi.org/10.1007/978-981-97-4584-5_12

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Decreasing human body temperature in the United States since the Industrial Revolution

• Myroslava Protsiv
• Catherine Ley
• Joanna Lankester
• Trevor Hastie

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Introduction

Materials and methods, data availability, article and author information.

In the US, the normal, oral temperature of adults is, on average, lower than the canonical 37°C established in the 19 th century. We postulated that body temperature has decreased over time. Using measurements from three cohorts—the Union Army Veterans of the Civil War (N = 23,710; measurement years 1860–1940), the National Health and Nutrition Examination Survey I (N = 15,301; 1971–1975), and the Stanford Translational Research Integrated Database Environment (N = 150,280; 2007–2017)—we determined that mean body temperature in men and women, after adjusting for age, height, weight and, in some models date and time of day, has decreased monotonically by 0.03°C per birth decade. A similar decline within the Union Army cohort as between cohorts, makes measurement error an unlikely explanation. This substantive and continuing shift in body temperature—a marker for metabolic rate—provides a framework for understanding changes in human health and longevity over 157 years.

In 1851, the German physician Carl Reinhold August Wunderlich obtained millions of axillary temperatures from 25,000 patients in Leipzig, thereby establishing the standard for normal human body temperature of 37°C or 98.6 °F (range: 36.2–37.5°C [97.2- 99.5 °F]) ( Mackowiak, 1997 ; Wunderlich and Sequin, 1871 ). A compilation of 27 modern studies, however ( Sund-Levander et al., 2002 ), reported mean temperature to be uniformly lower than Wunderlich’s estimate. Recently, an analysis of more than 35,000 British patients with almost 250,000 temperature measurements, found mean oral temperature to be 36.6°C, confirming this lower value ( Obermeyer et al., 2017 ). Remaining unanswered is whether the observed difference between Wunderlich’s and modern averages represents true change or bias from either the method of obtaining temperature (axillary by Wunderlich vs. oral today) or the quality of thermometers and their calibration ( Mackowiak, 1997 ). Wunderlich obtained his measurements in an era when life expectancy was 38 years and untreated chronic infections such as tuberculosis, syphilis, and periodontitis afflicted large proportions of the population ( Murray et al., 2015 ; Tampa et al., 2014 ; Richmond, 2014 ). These infectious diseases and other causes of chronic inflammation may well have influenced the ‘normal’ body temperature of that era.

The question of whether mean body temperature is changing over time is not merely a matter of idle curiosity. Human body temperature is a crude surrogate for basal metabolic rate which, in turn, has been linked to both longevity (higher metabolic rate, shorter life span) and body size (lower metabolism, greater body mass). We speculated that the differences observed in temperature between the 19 th century and today are real and that the change over time provides important physiologic clues to alterations in human health and longevity since the Industrial Revolution.

In men, we analyzed: a) 83,900 measurements from the Union Army Veterans of the Civil War cohort (UAVCW) obtained between 1862 and 1930, b) 5998 measurements from the National Health and Nutrition Examination Survey I cohort (NHANES) obtained between 1971 and 1975, and c) 230,261 measurements from the Stanford Translational Research Integrated Database Environment cohort (STRIDE) obtained between 2007 and 2017 ( Table 1 ). We also compared temperature measurements in women within the two later time periods (NHANES, 9303 measurements; and STRIDE, 348,006 measurements).

Demographic characteristics (N (%)) and mean (SD)) of cohort participants included in the analyses.

SD: standard deviation; UAVCW: Union Army Veterans of the Civil War; NHANES: National Health and Nutrition Examination Survey I; STRIDE: Stanford Translational Research Integrated Database Environment; BMI: body mass index. * Mean (SD). 1 Between one and four temperature measurements were available per person. 2 UAVCW included men only.

Table 1—source code 1

R code for Table 1 .

Overall, temperature measurements were significantly higher in the UAVCW cohort than in NHANES, and higher in NHANES than in STRIDE ( Figure 1 ; Figure 1—figure supplement 1 ). In each of the three cohorts, and for both men and women, we observed that temperature decreased with age with a similar magnitude of effect (between −0.003°C and −0.0043°C per year of age, Figure 1 ). As has been previously reported ( Eriksson et al., 1985 ), temperature was directly related to weight and inversely related to height, although these associations were not statistically significant in the UAVCW cohort. Analysis using body mass index (BMI) and BMI adjusted for height produced similar results ( Figure 1—figure supplement 2 ) and analyses including only white and black subjects ( Figure 1—figure supplement 3 ) showed similar results to those including subjects of all ethnicities.

Body temperature measurements by age as observed in three different time periods: 1860–1940 (UAVCW), 1971–1975 (NHANES 1), and 2007–2017 (STRIDE).

( A ) Unadjusted data (local regression) for temperature measurements, showing a decrease in temperature across age in white men, black men, white women, and black women, in the three cohorts. ( B ) Coefficients and standard errors from multivariate linear regression models for each cohort including age, weight, height, ethnicity group and time of day as available. Yellow cells are statistically significant at a p value of < 0.01, orange cells are of borderline significance (p<0.1 but>0.05), and remaining uncolored cells are not statistically significant. ( C ) Expected body temperature for 30 year old men and women with weight 70 kg and height 170 cm in each time period/cohort.

Figure 1—source code 1

R code for Figure 1 .

In both STRIDE and a one-third subsample of NHANES, we confirmed the known relationship between later hour of the day and higher temperature: temperature increased 0.02°C per hour of the day in STRIDE compared to 0.01°C in NHANES ( Figure 1 , Figure 1—figure supplement 2 , Figure 1—figure supplement 4 ). The month of the year had a relatively small, though statistically significant, effect on temperature in all three cohorts, but no consistent pattern emerged ( Figure 1—figure supplement 5 ). Using approximated ambient temperature for the date and geographic location of the examination in UAVCW and STRIDE, a rise in ambient temperature of one degree Celsius correlated with 0.001 degree (p<0.001) and 0.0004 degree (p=0.013) increases in body temperature in UAVCW and STRIDE, respectively. Because the seasonal and climatic effects were small and the independent variables were unavailable for many measurements, we omitted month and estimated ambient temperature from further models.

We explored whether chronic infectious diseases—even in the absence of a diagnosis of fever—might raise temperature in the UAVCW cohort, by assessing the temperatures of men reporting a history of malaria (N = 2,203), syphilis (N = 465), or hepatitis (N = 24), or with active tuberculosis (N = 738), pneumonia (N = 277) or cystitis (N = 1,301). Only those currently diagnosed with tuberculosis or pneumonia had elevated temperatures compared to the remainder of the UAVCW population [37.22°C (95% CI: 37.20–37.24°C) and 37.06°C (95% CI: 37.03–37.09°C), respectively compared to 37.02 (95% CI: 36.52–37.53)] ( Supplementary file 1 ).

One possible reason for the lower temperature estimates today than in the past is the difference in thermometers or methods of obtaining temperature. To minimize these biases, we examined changes in body temperature by birth decade within each cohort under the assumption that the method of thermometry would not be biased on birth year. Within the UAVCW, we observed a significant birth cohort effect, with temperatures in earlier birth decades consistently higher than those in later cohorts ( Figure 2 ). With each birth decade, temperature decreased by −0.02°C. We then assessed change in temperature over the 197 birth-year span covered by the three cohorts. We observed a steady decrease in body temperature by birth cohort for both men (−0.59°C between birth decades from 1800 to 1997; −0.030°C per decade) and women (−0.32°C between 1890 and 1997; −0.029°C per decade). Black and white men and women demonstrated similar trends over time ( Figure 3 ).

Temperature trends within birth cohorts of the UAVCW, 1860–1940 (black and white men).

( A ) Smoothed unadjusted data (local regression) for temperature measurement trends within birth cohorts. The different colors represent different birth cohorts (green: 1820s, blue: 1830s, orange: 1840s). ( B ) Coefficients (and standard errors) from multivariate linear regression including age, body weight, height and decade of birth (1820–1840) (these coefficients do not correspond to the graph as here the trajectories are approximated by linear functions). Only the three birth cohorts with more than 8000 members are included. * and ** indicate significance at the 90%, and 99% level, respectively. ( C ) Expected body temperature (and associated 95% confidence interval) for 30 year old men with body weight 70 kg and height 170 cm in each birth cohort. These values derive from the regression models presented in B.

Figure 2—source code 1

R code for  Figure 2 .

Modeled body temperature over time in three cohorts by birth year (black and white ethnicity groups).

( A ) Body temperature decreases by birth year in white and black men and women. No data for women were available for the birth years from 1800 to 1890. ( B ) Coefficients (and standard errors) used for the graph from multivariate linear regression including age, body weight, height and birth year. All cells are significant at greater than 99% significance level.

Figure 3—source code 1

R code for  Figure 3 .

In this study, we analyzed 677,423 human body temperature measurements from three different cohort populations spanning 157 years of measurement and 197 birth years. We found that men born in the early 19 th century had temperatures 0.59°C higher than men today, with a monotonic decrease of −0.03°C per birth decade. Temperature has also decreased in women by −0.32°C since the 1890s with a similar rate of decline (−0.029°C per birth decade). Although one might posit that the differences among cohorts reflect systematic measurement bias due to the varied thermometers and methods used to obtain temperatures, we believe this explanation to be unlikely. We observed similar temporal change within the UAVCW cohort—in which measurement were presumably obtained irrespective of the subject's birth decade—as we did between cohorts. Additionally, we saw a comparable magnitude of difference in temperature between two modern cohorts using thermometers that would be expected to be similarly calibrated. Moreover, biases introduced by the method of thermometry (axillary presumed in a subset of UAVCW vs. oral for other cohorts) would tend to underestimate change over time since axillary values typically average one degree Celsius lower than oral temperatures ( Sund-Levander et al., 2002 ; Niven et al., 2015 ). Thus, we believe the observed drop in temperature reflects physiologic differences rather than measurement bias. Other findings in our study—for example increased temperature at younger ages, in women, with increased body mass and with later time of day—support a wealth of other studies dating back to the time of Wunderlich ( Wunderlich and Sequin, 1871 ; Waalen and Buxbaum, 2011 ).

Resting metabolic rate is the largest component of a typical modern human’s energy expenditure, comprising around 65% of daily energy expenditure for a sedentary individual ( Heymsfield et al., 2006 ). Heat is a byproduct of metabolic processes, the reason nearly all warm-blooded animals have temperatures within a narrow range despite drastic differences in environmental conditions. Over several decades, studies examining whether metabolism is related to body surface area or body weight ( Du Bois, 1936 ; Kleiber, 1972 ), ultimately, converged on weight-dependent models ( Mifflin et al., 1990 ; Schofield, 1985 ; Nelson et al., 1992 ). Since US residents have increased in mass since the mid-19 th century, we should have correspondingly expected increased body temperature. Thus, we interpret our finding of a decrease in body temperature as indicative of a decrease in metabolic rate independent of changes in anthropometrics. A decline in metabolic rate in recent years is supported in the literature when comparing modern experimental data to those from 1919 ( Frankenfield et al., 2005 ).

Although there are many factors that influence resting metabolic rate, change in the population-level of inflammation seems the most plausible explanation for the observed decrease in temperature over time. Economic development, improved standards of living and sanitation, decreased chronic infections from war injuries, improved dental hygiene, the waning of tuberculosis and malaria infections, and the dawn of the antibiotic age together are likely to have decreased chronic inflammation since the 19 th century. For example, in the mid-19 th century, 2–3% of the population would have been living with active tuberculosis ( Tiemersma et al., 2011 ). This figure is consistent with the UAVCW Surgeons' Certificates that reported 737 cases of active tuberculosis among 23,757 subjects (3.1%). That UAVCW veterans who reported either current tuberculosis or pneumonia had a higher temperature (0.19°C and 0.03°C respectively) than those without infectious conditions supports this theory ( Supplementary file 1 ). Although we would have liked to have compared our modern results to those from a location with a continued high risk of chronic infection, we could identify no such database that included temperature measurements. However, a small study of healthy volunteers from Pakistan—a country with a continued high incidence of tuberculosis and other chronic infections—confirms temperatures more closely approximating the values reported by Wunderlich (mean, median and mode, respectively, of 36.89°C, 36.94°C, and 37°C) ( Adhi et al., 2008 ).

Reduction in inflammation may also explain the continued drop in temperature observed between the two more modern cohorts: NHANES and STRIDE. Although many chronic infections had been conquered before the NHANES study, some—periodontitis as one example ( Capilouto and Douglass, 1988 )— continued to decrease over this short period. Moreover, the use of anti-inflammatory drugs including aspirin ( Luepker et al., 2015 ), statins ( Salami et al., 2017 ) and non-steroidal anti-inflammatory drugs (NSAIDs) ( Lamont and Dias, 2008 ) increased over this interval, potentially reducing inflammation. NSAIDs have been specifically linked to blunting of body temperature, even in normal volunteers ( Murphy et al., 1996 ). In support of declining inflammation in the modern era, a study of NHANES participants demonstrated a 5% decrease in abnormal C-reactive protein levels between 1999 and 2010 ( Ong et al., 2013 ).

Changes in ambient temperature may also explain some of the observed change in body temperature over time. Maintaining constant body temperature despite fluctuations in ambient temperature consumes up to 50–70% of daily energy intake ( Levine, 2007 ). Resting metabolic rate (RMR), for which body temperature is a crude proxy, increases when the ambient temperature decreases below or rises above the thermoneutral zone, that is the temperature of the environment at which humans can maintain normal temperature with minimum energy expenditure ( Erikson et al., 1956 ). In the 19 th century, homes in the US were irregularly and inconsistently heated and never cooled. By the 1920s, however, heating systems reached a broad segment of the population with mean night-time temperature continuing to increase even in the modern era ( Mavrogianni et al., 2013 ). Air conditioning is now found in more than 85% of US homes ( US Energy Information Administration, 2011 ). Thus, the amount of time the population has spent at thermoneutral zones has markedly increased, potentially causing a decrease in RMR, and, by analogy, body temperature.

Some factors known to influence body temperature were not included in our final model due to missing data (ambient temperature and time of day) or complete lack of information (dew point)( Obermeyer et al., 2017 ). Adjusting for ambient temperature, however, would likely have amplified the changes over time due to lack of heating and cooling in the earlier cohorts. Time of day at which measurement was conducted had a more significant effect on temperature ( Figure 1—figure supplement 4 ). Based on the distribution of times of day for temperature measurement available to us in STRIDE and NHANES, we estimate that even in the worst case scenario, that is the UAVCW measurements were all were obtained late in the afternoon, adjustment for time of day would have only a small influence (<0.05°C) on the −0.59°C change over time.

In summary, normal body temperature is assumed by many, including a great preponderance of physicians, to be 37°C. Those who have shown this value to be too high have concluded that Wunderlich’s 19 th century measurements were simply flawed ( Mackowiak, 1997 ; Sund-Levander et al., 2002 ). Our investigation indicates that humans in high-income countries have changed physiologically over the last 200 birth years with a mean body temperature 1.6% lower than in the pre-industrial era. The role that this physiologic ‘evolution’ plays in human anthropometrics and longevity is unknown.

We compared body temperature measurements from three cohorts. Cohort 1 : The Union Army Veterans of the Civil War, 1860–1940 (UAVCW) is a database from the ‘Early Indictors of Later Work Levels, Disease and Death Study’, initiated by the late Nobel Laureate, Robert Fogel in 1978 ( Fogel and Wimmer, 1992 ) and continuing today. The study abstracted the Compiled Military Service Records, the Pension Records, Carded Medical Records, the Surgeons' Certificates (detailed medical records) and information from the US Federal Census for a cluster sample of Union Army companies in the US Civil War. In total, 331 companies of white and 52 companies of black Union Army veterans were included in the dataset. The Surgeons’ Certificates were obtained at locations throughout the US for veterans seeking pension benefits. These certificates include comprehensive medical histories and physical examinations. Body temperatures in Fahrenheit were hand-written on 83,900 Surgeons' Certificates from 23,710 individuals (mean: 3.53 examinations per individual; Table 1 ). Whether the temperatures were taken orally or in the axilla is unknown; both methods were employed in the 19 th century although oral temperature was more common ( Salinger and Kalteyer, 1900 ). Precision of the instruments is also unknown. Inspection of the distribution of reads, however, suggest that it is no better than 0.2 degrees Fahrenheit, consistent with the hashmarks on mercury thermometers ( Figure 1—figure supplement 1 ). The UAVCW data—including birth date, temperature, height, weight, location and date of the medical visit, medical history, ongoing medical complaints and findings of physical examinations —are freely available on-line in digital format (The Colored Troops (USCT) original and expanded datasets;  Fogel et al., 2000 ; Costa, 2019 ). Cohort 2 : The National Health and Nutrition Examination Survey (NHANES I) is a multistage, national probability survey conducted between 1971 and 1975 in the US civilian population. A subset of subjects, aged 1 to 74 years (N = 23,710) underwent a medical examination (ICPSR study No. 8055), including 15,301 adults. The major focus of NHANES I was nutrition, and persons with low income, pregnant women and the elderly were consequently oversampled ( Centers for Disease Control, National Center for Health Statistics, 1975 ). Data abstracted included weight, height, sex, ethnicity, and month and geographic region of examination and, as available, time of day the temperature was obtained. In NHANES, mercury thermometers were used and temperatures were taken orally. Precision, as with the UAVCW cohort, is assumed to be 0.2 °F. The medical examination was performed by a physician with the help of a nurse. Cohort 3 : The Stanford Translational Research Integrated Database Environment (STRIDE) extracts electronic medical record information from patient encounters at Stanford Health Care (Stanford, CA). All adult outpatient encounters at Stanford Health Care from 2007 to 2017 with recorded temperature measurements in the electronic medical record are included in this study (N = 578,522 adult outpatient encounters). Temperature measurements were obtained orally with annually-calibrated, digital thermometers with precision of 0.1 °F and extracted from the dataset along with age, sex, weight, height, primary concern at the visit, prescribed medications, other conditions in the health record with ICD10 codes, and year and time of day the temperature was obtained (mean: 3.85 examinations per individual; Table 1 ).

For the UAVCW and STRIDE datasets, any observations having a diagnosis of fever at the time of the medical examination were excluded. From all three datasets, any extreme values of temperature (<35°C and >39°C) were also excluded from the analysis either because they were implausible or because they indicated a diagnosis of fever and would otherwise have been excluded. Improbable values of both body weight (<30 kg and >200 kg) and height (<120 cm and >220 cm) were also removed. In the UAVCW, we also excluded veterans born after 1850, because they were unlikely to have served in the Union Army.

The use of the STRIDE data was approved as an expedited protocol by the Stanford Institutional Review Board (protocol 40539) and informed consent was waived since the only personal health information abstracted was month of clinic visit. Anonymized data from NHANES and the data from UAVCW are freely available on-line for research use.

Data analysis

Ethnicity categories were defined differently across cohorts. UAVCW included only white and black men. For comparability, we restricted analyses between the UAVCW and other cohorts to men in these two ethnicity groups. Asians were categorized as ‘Other’ in NHANES and as ‘Asian’ in STRIDE, so were considered as ‘Other ethnicity’ in combined analyses. We performed analyses stratified by sex to account for known temperature differences between men and women. The NHANES study uses sample weights to account for its design; these were incorporated into models including NHANES data ( Centers for Disease Control, National Center for Health Statistics, 1975 ).

To estimate the average body temperature during each of the three time periods, we modeled temperature within each cohort using multivariate linear regression, simultaneously assessing the effects of age, body weight, and height. Measurements in men and women were analyzed separately, by white and black ethnicity groups. We also conducted mixed effects modeling to account for the repeated temperature measurements from some individuals. Because the coefficients were almost identical to those of the linear regression models, we chose to present this more simple statistical method. We also assessed the effects of geography, that is location at which temperature was obtained, on temperature ( Figure 1—figure supplement 6 ).

To evaluate temperature changes over time, we predicted body temperature using multivariate linear regression including age, body weight, height and birth decade in the UAVCW cohort (the timeframe of NHANES and STRIDE spanned relatively few years, with insufficient variability to evaluate birth cohort effects within these datasets). To assess change in temperature over the 197 birth-year span covered by the three cohorts (between years 1800 and 1997 for men, and between 1890 and 1997 for women), we used linear regression with temperature as the outcome and age, weight, height, and birth decade as independent variables, stratifying by ethnicity and sex. The UAVCW cohort was further investigated for reported infectious conditions that might affect temperature. Diagnoses of infectious conditions, either in the medical history (malaria, syphilis, hepatitis) or active at the time of examination (tuberculosis, pneumonia or cystitis), were included in regression models if fever was not listed as part of that record.

Some models included time of day, ambient temperature and month of year. Time of day at which temperature was taken was available for STRIDE and a subset of NHANES. For individuals without time of day, we imputed the time to be 12:00 PM (noon). We accounted for ambient temperature using the date and geographic location of examination (available in UAVCW and STRIDE) based on data from the National Centers for Environmental Information ( NOAA National Centers for Environmental Information, 2018 ). We used the month of year when each measurement was taken as a random effect. To assess the robustness of our result to the chosen methodology, we repeated the analyses using linear mixed effect modeling, adjusting for multiple measurements.

Within the UAVCW, minimum ages varied across birth cohorts due to the bias inherent in the cohort structure (for example, it is impossible to be younger than 30 years of age at the time of the pension visit, be born in 1820s, and be a veteran of the Civil War). To avoid instability in the analysis due to having too few people within specific age groups per birth decade, we excluded the lowest 1% of observations in each birth cohort according to age.

All analyses were performed using R statistical software version 3.3.0. and packages easyGgplot2, lme4, merTools, and ggplot2 for statistical analysis and graphs ( www.r-project.org ).

All data generated or analysed during this study are included in the manuscript and supporting files.

• United States Department of Health and Human Services
• National Center for Health Statistics
• Google Scholar
• Capilouto ML
• Douglass CW
• Centers for Disease Control, National Center for Health Statistics
• Andersen KL
• Scholander PF
• Svärdsudd K
• Wilhelmsen L
• Rosenberg I
• Scrimshaw N
• Frankenfield D
• Roth-Yousey L
• Heymsfield SB
• Pietrobelli A
• Mackowiak PA
• Mavrogianni A
• Oreszczyn T
• Summerfield A
• Daugherty SA
• Schraufnagel DE
• Hopewell PC
• Weinsier RL
• Laupland KB
• NOAA National Centers for Environmental Information
• Obermeyer Z
• Mullainathan S
• Valero-Elizondo J
• Blankstein R
• Blumenthal RS
• Lloyd-Jones D
• Salinger JL
• Kalteyer FJ
• Schofield WN
• Sund-Levander M
• Georgescu SR
• Tiemersma EW
• van der Werf MJ
• Borgdorff MW
• Williams BG
• Nagelkerke NJ
• US Energy Information Administration
• Wunderlich CA

Author details

Contribution, competing interests.

• Department of Statistics, Stanford University, Stanford, United States
• Department of Biomedical Data Science, Stanford University, School of Medicine, Stanford, United States
• Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, School of Medicine, Stanford, United States
• Division of Epidemiology, Department of Health Research and Policy, Stanford University, School of Medicine, Stanford, United States

For correspondence

Stanford center for clinical and translational research and education (spectrum award).

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Professor Dora Costa, University of California, Los Angeles and Dr Louis Nguyen, Harvard Medical School for sharing their expertise and knowledge of the Union Army data. Thank you also to Dr. Philip Mackowiak, University of Maryland, for providing feedback on study design, analysis and interpretation. We also thank Michelle Bass, PhD, and Yelena Nazarenko for their support of the STRIDE clinical databases.

Human subjects: The use of the STRIDE data was approved as an expedited protocol by the Stanford Institutional Review Board (protocol 40539) and informed consent was waived since the only personal health information abstracted was month of clinic visit. Anonymized data from NHANEs and the data from UAVCW are freely available on-line for research use.

© 2020, Protsiv et al.

This article is distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use and redistribution provided that the original author and source are credited.

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PKR activation-induced mitochondrial dysfunction in HIV-transgenic mice with nephropathy

HIV disease remains prevalent in the USA and chronic kidney disease remains a major cause of morbidity in HIV-1-positive patients. Host double-stranded RNA (dsRNA)-activated protein kinase (PKR) is a sensor for viral dsRNA, including HIV-1. We show that PKR inhibition by compound C16 ameliorates the HIV-associated nephropathy (HIVAN) kidney phenotype in the Tg26 transgenic mouse model, with reversal of mitochondrial dysfunction. Combined analysis of single-nucleus RNA-seq and bulk RNA-seq data revealed that oxidative phosphorylation was one of the most downregulated pathways and identified signal transducer and activator of transcription (STAT3) as a potential mediating factor. We identified in Tg26 mice a novel proximal tubular cell cluster enriched in mitochondrial transcripts. Podocytes showed high levels of HIV-1 gene expression and dysregulation of cytoskeleton-related genes, and these cells dedifferentiated. In injured proximal tubules, cell-cell interaction analysis indicated activation of the pro-fibrogenic PKR-STAT3-platelet-derived growth factor (PDGF)-D pathway. These findings suggest that PKR inhibition and mitochondrial rescue are potential novel therapeutic approaches for HIVAN.

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The effect of stress on core and peripheral body temperature in humans

Affiliation.

• 1 Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences and Rudolf Magnus Institute of Neuroscience, Utrecht University, Utrecht, the Netherlands. [email protected]
• PMID: 23790072
• DOI: 10.3109/10253890.2013.807243

Even though there are indications that stress influences body temperature in humans, no study has systematically investigated the effects of stress on core and peripheral body temperature. The present study therefore aimed to investigate the effects of acute psychosocial stress on body temperature using different readout measurements. In two independent studies, male and female participants were exposed to a standardized laboratory stress task (the Trier Social Stress Test, TSST) or a non-stressful control task. Core temperature (intestinal and temporal artery) and peripheral temperature (facial and body skin temperature) were measured. Compared to the control condition, stress exposure decreased intestinal temperature but did not affect temporal artery temperature. Stress exposure resulted in changes in skin temperature that followed a gradient-like pattern, with decreases at distal skin locations such as the fingertip and finger base and unchanged skin temperature at proximal regions such as the infra-clavicular area. Stress-induced effects on facial temperature displayed a sex-specific pattern, with decreased nasal skin temperature in females and increased cheek temperature in males. In conclusion, the amplitude and direction of stress-induced temperature changes depend on the site of temperature measurement in humans. This precludes a direct translation of the preclinical stress-induced hyperthermia paradigm, in which core temperature uniformly rises in response to stress to the human situation. Nevertheless, the effects of stress result in consistent temperature changes. Therefore, the present study supports the inclusion of body temperature as a physiological readout parameter of stress in future studies.

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Walker HK, Hall WD, Hurst JW, editors. Clinical Methods: The History, Physical, and Laboratory Examinations. 3rd edition. Boston: Butterworths; 1990.

Clinical Methods: The History, Physical, and Laboratory Examinations. 3rd edition.

Chapter 218 temperature.

Victor E. Del Bene .

Normal body temperature is considered to be 37°C (98.6°F); however, a wide variation is seen. Among normal individuals, mean daily temperature can differ by 0.5°C (0.9°F), and daily variations can be as much as 0.25 to 0.5°C. The nadir in body temperature usually occurs at about 4 a.m. and the peak at about 6 p.m. This circadian rhythm is quite constant for an individual and is not disturbed by periods of fever or hypothermia. Prolonged change to daytime-sleep and nighttime-awake cycles will effect an adaptive correction in the circadian temperature rhythm. Normal rectal temperature is typically 0.27° to 0.38°C (0.5° to 0.7°F) greater than oral temperature. Axillary temperature is about 0.55°C (1.0°F) less than the oral temperature.

For practical clinical purposes, a patient is considered febrile or pyrexial if the oral temperature exceeds 37.5°C (99.5°F) or the rectal temperature exceeds 38°C (100.5°F). Hyperpyrexia is the term applied to the febrile state when the temperature exceeds 41.1°C (or 106°F). Hypothermia is defined by a rectal temperature of 35°C (95°F) or less.

Measurement of temperature along with other vital signs should be made with each new patient visit and on a fixed schedule during hospitalization. The glass thermometer is probably the instrument used most frequently. For cooperative patients, the oral glass thermometer is recommended because of its convenience and patient acceptance.

The oral temperature is measured with the probe placed under the tongue and the lips closed around the instrument. The patient should not have recently smoked or ingested cold or hot food or drink. Oxygen delivered by nasal cannula does not affect the accuracy of the measurement. Three minutes is the time commonly quoted for accurate temperature measurement, but it is wise to wait at least 5 minutes. If the reading is abnormal, the thermometer should be replaced for 1-minute intervals until the reading stabilizes.

Rectal thermometers are indicated in children and in patients who will not or cannot cooperate fully. Continuous, frequent temperature measurements can be made by rectal probe and thermocouple connected to a recording device or by repeated glass thermometer measurements in axilla or groin folds. Rectal temperature is measured with a lubricated blunt-tipped glass thermometer inserted 4 to 5 cm into the anal canal at an angle 20° from the horizontal with the patient lying prone. Three minutes dwell time is required.

Electric digital thermometers are more convenient than glass instruments because the probe cover is disposable, response time is quicker (allowing accurate measurements within 10 to 20 seconds), and there is a signal when the rate of change in temperature becomes insignificant.

Reset the glass or electric device to below 35°C (95°F) before each measurement. When hypothermia is suspected, a rectal probe and thermocouple capable of measuring as low as 25°C is essential.

In certain circumstances, it might be important to observe the patient continuously for 15 minutes before and during the measurement of temperature. This would help eliminate the possibility of artifactual readings caused by cold or hot substances taken orally, by smoking, or by surreptitious manipulation of the thermometer. Measurements made by electric devices are less easily influenced by manipulation of the instrument.

Palpation of the skin in the diagnosis of fever is highly unreliable. The presence of fever is underestimated by palpation in 40% of individuals, even when the measured temperature is as high as 39°C (102.2°F).

Patients with fever usually exhibit warm, flushed skin, tachycardia, involuntary muscular contractions or rigors, and sweating or night sweats. Piloerection and positioning of the body in an attempt to minimize exposed surface area are also seen. Occasionally these signs are absent or minimal, and dry, cold skin or extremities are detected in spite of a significant rise in core temperature.

• Basic Science

A fine balance between heat production and loss is maintained imperceptibly in the normal individual. In health, the hypothalamic thermoregulatory center monitors internal temperature changes from core thermoreceptors and surface changes from skin thermoreceptors. The center responds to any changes in heat production or ambient temperature that would cause minor deviations from the body temperature "set point" of 37°C (98.6°F). Production of body heat is primarily the result of conversion of chemical energy in foods to heat by metabolic and mechanical mechanisms. Cellular oxidative metabolism produces a constant, stable source of heat. Mechanical muscular contraction results in bursts of heat when needed. Heat produced is conserved by vasoconstriction and diversion of blood flow away from the skin.

Dissipation of heat depends on vasomotor changes that regulate blood flow to the skin and mucous membranes and sweating. Heat is lost at the skin surfaces by the mechanisms of convection, radiation, and evaporation. Dissipation by convection is more efficient when ambient wind current is increased; evaporation is the primary mechanism in high ambient temperatures, unless the atmosphere is saturated with water vapor. Some heat is dissipated by breathing (panting). Heat loss either by conduction through the gastrointestinal (GI) tract via ingestion of cold food and drink or by immersion in cold water is not normally an important mechanism.

A decrease in metabolism, an abnormality in mechanical muscular function, or exposure to ambient temperatures below the normal body temperature may result in hypothermia . At a temperature of 32°C (89.6°F), oxygen consumption decreases as a function of hypometabolism, the oxygen dissociation curve shifts to the left so that less oxygen is given up to the tissues, and there is a generalized inhibition of enzyme activity.

Excessive exposure to high ambient temperatures, an increase in heat production (either by increased metabolism or, more often, by increased muscular work) or loss of the ability to dissipate sufficient body heat may result in hyperthermia . The hypothalamic "set point" is not disturbed in persons suffering from hyperthermia. The problem is one of overwhelming heat production or inadequacy of heat loss mechanisms. Exercise and heavy work may be responsible for production of heat that raises core temperature 1 to 1.5°C (2 to 3°F), but the temperature usually returns to normal within 30 minutes of cessation of exertion. Over-insulation or exposure to ambient temperatures greater than 37.8°C (100°F), especially in conditions of 100% water vapor pressure and dehydration, interfere with the normal mechanisms for heat dissipation.

Fever , or pyrexia, is the result of the thermoregulatory mechanisms" response to an elevated set point. The set point is raised when acted on by endogenous pyrogen, a substance liberated by leukocytes when they interact with exogenous pyrogens such as microorganisms, nonmicrobial antigens, or certain steroid hormones. Endogenous pyrogen is a protein of 15,000 daltons produced by neutrophils, eosinophils, monocytes, Kupffer cells, and alveolar macrophages, when they are exposed to exogenous pyrogens. Endogenous pyrogen is closely related or identical to lymphokines such as interleukin 1, leukocyte activating factor, and leukocyte endogenous mediator. When endogenous pyrogen is liberated into the bloodstream, it interacts with the preoptic regions of the anterior hypothalamus and raises the thermoregulatory set point to a variable degree, but usually not greater than 41.1°C (106°F). If endogenous pyrogen is placed directly into the cerebral ventricles, high fevers can be induced with concentrations 10- to 100-fold less. Hyperpyrexial states (greater than 41.1°C) may be produced by this direct mechanism. Endogenous pyrogen causes increased firing of hypothalamic, thermosensitive neurons, resulting in the augmentation of heat conservation and production mechanisms, with resultant fever. Moderate increases in the set point are satisfied by heat-seeking behavior, peripheral vasoconstriction, and increased metabolic rate. For marked increases in set point, these mechanisms of heat production and conservation are augmented by mechanical conversion of chemical energy to heat by muscular shivering (rigors). Chilliness felt by the patient whose fever is rising is probably caused by a central perception both of a demand to raise central core temperature and of cold receptors in the skin due to peripheral vasoconstriction.

The molecular mechanisms that mediate the interaction of endogenous pyrogen, the hypothalamus, and effector mechanisms resulting in fever are not completely understood. Prostaglandins of the E series are thought to play a role in excitation of thermosensitive neurons of the hypothalamus. Monoamines are present in high concentrations at that thermosensitive site. Cyclic nucleotides have also been implicated as intermediates induced or released by endogenous pyrogen.

Even during febrile states, the normal diurnal fluctuations in temperature are maintained, although sometimes by extreme mechanisms. For instance, marked muscular activity (rigors) may herald the late afternoon or evening temperature spike in the febrile person, while profuse soaking sweats may be required to achieve the early morning nadir of the circadian temperature rhythm.

• Clinical Significance

Closely regulated temperature is imperative for normal and efficient functioning of organ systems. Drastic and irreparable changes in organ structure and function can occur when body temperature falls below 32.2°C (90°F) or rises above 41.1°C (106°F). Lesser changes result in confusion, delirium, seizures, or cardiorespiratory embarrassment, depending on the physical status and age of the host. However, one could point to remarkable recovery of patients with temperature documented below 18°C (65°F) or above 44.5°C (112°F).

Hypothermia can be due to infection and bacteremia, ethanol or drug ingestion, exposure, a central nervous system event, cachexia from malignancy or malnutrition, gastrointestinal bleeding, or endocrine deficiencies such as panhypopituitarism, myxedema, Addison's disease, uremia, and hypoglycemia. In some patients with these diseases, the febrile state might not be recognized because they will raise their temperature to less than 37.2°C (99°F). The early stage of hypothermia (35° to 32.8°C; 95° to 91°F) is marked by an attempt to react against chilling including shivering, increased blood pressure and pulse, vasoconstriction, and diuresis. An intermediate stage (32.2° to 24°C; 90° to 75°F) is characterized by decrease in metabolism; drop in pulse, blood pressure, and respiration; muscular rigidity; a fine tremor; and respiratory and metabolic acidosis. At a third stage, when all attempts at compensation by the temperature regulatory center fail, the body loses heat like an inanimate object. Most patients with hypothermia exhibit tachycardia, tachypnea, hypotension, leukocytosis, acidosis, increased pulmonary wedge pressure, and right atrial pressure; patients with hypothermia caused by infection with bacteremia have much lower systemic vascular resistance and higher cardiac index than nonbacteremic patients with hypothermia.

Although most increases in body temperature seen in the clinic are fevers, changing lifestyles, old age, and medical treatments are responsible for an increasing number of patients with hyperthermia. Joggers and road runners are particularly prone to hyperthermia, especially during the summer months in areas where temperature is above 32.2°C (90°F) and humidity above 90%. Those at most risk fail to wear proper clothing, wear impermeable sweatsuits, or exercise after taking phenothiazines, anticholinergic drugs, or alcohol. Older people and those who previously have suffered from hyperthermia are particularly prone to hyperthermia because of the inability to respond normally to a heat load. Vacationers unaccustomed to saunas and hot tubs are another high-risk group for hyperthermia. Patients might not pay close enough attention to early signs of hyperthermia, such as headache, piloerection, and chills; and, at times, the first manifestations recognized (by others) are changes in gait, speech, or mental status. Transient paralysis or convulsions may be the first symptoms or signs.

There is no direct evidence in humans of a beneficial effect of fever in infectious states, but evolutionary arguments indicate that fever is important in enhancing inflammation. Additional evidence is available for the salutary effect of fever in cancer therapy. In contrast, the stress of a 13% increase in metabolic rate per degree of temperature Celsius (7% per degree of temperature Fahrenheit) is imposed on febrile patients. This stress may be detrimental to the elderly with cardiovascular disease, or to those with restrictive lung disease. Prolonged fever for over a week is often accompanied by a negative nitrogen balance and dehydration. Herpes simplex exacerbations (fever blisters), febrile convulsions, delirium, and albuminuria are also common.

Classically described fever patterns can be helpful in indicating the possible causes of infections. The graphic temperature chart can be indispensable to the clinician approaching the febrile patient. Remittant is the term used to describe a fever that fluctuates more than 1.1 °C (2°F) daily but never returns to normal. Most fevers caused by infection are of this type. Intermittent , or quotidian, fever is characterized by wide swings in temperature each day with the peak usually in the afternoon and nadir in early morning. The temperature may be normal during the early and mid portion of the day. Intermittent fevers are often accompanied by rigors and profuse night sweats. Intermittent fevers are seen in patients with malaria, abscesses, and cholangitis. When characterized by very wide swings in temperature, intermittent fevers are termed septic or hectic fevers and indicate established deep abscess.

Sustained or continued fever, characterized by temperature elevation with little (less than 1°C) diurnal fluctuation, is seen in patients with pneumococcal pneumonia and typhoid fever. Relapsing indicates that bouts of fever are interspersed with afebrile periods of days to weeks. Examples of relapsing fever can be seen in Hodgkin's disease (Pel-Epstein fever), P. vivax malaria, Borrelia infection, and rat-bite fever due to Spirillum minus . In modern medicine, febrile pattern recognition is often unreliable because of purposeful or inadvertent use of analgesics, steroids, anti-inflammatory agents, and cooling devices that alter physiologic fever responses. In addition, uremics and quadraplegics may have blunted febrile responses, whereas patients with extensive surface burns or dermatologic conditions may have exaggerated and sustained fevers.

Inspection of the temperature chart should always include careful attention to the pulse and respiratory rate pattern. With most infections, the pulse rate will increase about 10 beats per minute for each 0.5 degree Celsius (each degree Fahrenheit) of temperature increase. The respiratory rate will also be above the normal 12 to 14 per minute, usually at 18 to 20 breaths per minute at rest. Three infectious diseases in which a relative bradycardia occurs are mycoplasma pneumonia, psittacosis, and typhoid fever.

Two special circumstances are often confusing to the clinician investigating a patient with fever. They are those patients with "factitious" fever and drug fever. Factitious, or feigned, fever is produced by the patient who, for reasons of secondary gain, is trying to simulate an organic illness. Two types of patients with factitious fever may be distinguished. One is the patient who is inducing fever by a self-inflicted disease such as a bacteremia or endotoxemia by injecting contaminated foreign debris. The other is the patient who has a spuriously high temperature due to manipulation of the thermometer. Clues for a "factitious" fever due to thermometer manipulation include a temperature of more than 41.1°C (106°F) in a patient who looks well, has no chills or rigors, shows no diurnal variations of temperature, has no tachycardia or tachypnea, and has no increase in the temperature of a freshly voided urine specimen. Patients with self-induced fever due to administration of a pyrogenic substance are often in the health care profession, appear well, have no weight loss, and have a normal physical examination between febrile episodes. Similarly, patients with drug (especially antibiotic) fever are confusing, especially when the antibiotic was appropriate and effective in eliminating the infection. These patients usually have sustained (sometimes intermittent) fever, appear entirely well, have an excellent appetite, and are inquiring about discharge from the hospital. Occasional findings supporting the diagnosis of drug fever include pruritus, the feeling of tingling or burning skin, and eosinophilia.

Most infectious causes of fever are naturally self-limited (such as viral diseases) or easily diagnosed and treatable bacterial diseases of the pharynx, ears, sinuses, upper respiratory airways, skin, and urinary tract. These usually are circumscribed illnesses of less than 7 to 10 days that do not require hospitalization and result in no long-term sequelae. Rarely is marked fever present for more than 3 to 4 days in these patients.

Fever persisting for more than 2 weeks, especially when it is accompanied by malaise, anorexia, and weight loss, requires thorough consideration and investigation. Often this will take place within a hospital setting with relatively invasive and expensive tests. Prerequisite to the development of an effective diagnostic plan is a detailed history and careful and complete physical examination with frequent, often daily, reassessment of selected parameters. In addition, a methodical and sequential laboratory evaluation is essential.

The special category of fever of unknown origin (FUO) refers to febrile diseases that have eluded diagnosis for 2 or 3 weeks despite a reasonably complete evaluation by physical examination, chest x-rays, routine blood tests, and cultures. The cause of prolonged fever in these patients will be determined about 90% of the time. Most will be common diseases that have manifested in an unusual way. Infections account for about one-third of FUOs. Miliary tuberculosis, bacterial endocarditis, biliary tract disease including liver abscess and viral hepatitis, pyelonephritis, abdominal abscess, osteomyelitis, and brucellosis are the major infections encountered. In adults, neoplasms are the cause of FUO in about 20% of cases. The fever is due to the neoplasm itself, and not to complicating infection. Lymphomas (Hodgkin's and non-Hodgkin's) and neoplasms (hepatoma, atrial myxoma, and hypernephroma) are frequent causes. Among the Hodgkin's lymphoma cases, those of the lymphocyte-depletion type in elderly patients are most likely to present as FUO. Collagen vascular diseases such as systemic lupus erythematosus, "juvenile" rheumatoid arthritis (Still's disease), polyarteritis, temporal arteritis, and polymyalgia rheumatica account for about 15% of FUO cases. Other causes of prolonged fever include granulomatous diseases such as sarcoidosis, idiopathic hepatitis, and inflammatory bowel disease.

An interesting group of patients who have perplexed clinicians are young individuals, especially women, with slightly exaggerated circadian rhythm, producing long-term daily afternoon temperatures of about 0.5°C higher than the normal. These women usually have no other associated manifestations of disease and are best served by long-term observation by one physician, who can provide reasonable periodic evaluation and reassurance to the patient and family.

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• Cite this Page Del Bene VE. Temperature. In: Walker HK, Hall WD, Hurst JW, editors. Clinical Methods: The History, Physical, and Laboratory Examinations. 3rd edition. Boston: Butterworths; 1990. Chapter 218.
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