The Daily, Weekly, and Seasonal Cycles of Body Temperature Analyzed at Large Scale

Chronobiology International The Journal of Biological and Medical Rhythm Research

ISSN: 0742-0528 (Print) 1525-6073 (Online) Journal homepage: https://www.tandfonline.com/loi/icbi20

The daily, weekly, and seasonal cycles of body temperature analyzed at large scale

Charles Harding, Francesco Pompei, Samantha F Bordonaro, Daniel C McGillicuddy, Dmitriy Burmistrov & Leon D Sanchez

To cite this article: Charles Harding, Francesco Pompei, Samantha F Bordonaro, Daniel C McGillicuddy, Dmitriy Burmistrov & Leon D Sanchez (2019) The daily, weekly, and seasonal cycles of body temperature analyzed at large scale, Chronobiology International, 36:12, 1646-1657, DOI: 10.1080/07420528.2019.1663863 To link to this article: https://doi.org/10.1080/07420528.2019.1663863

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Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=icbi20 CHRONOBIOLOGY INTERNATIONAL 2019, VOL. 36, NO. 12, 1646–1657 https://doi.org/10.1080/07420528.2019.1663863

ORIGINAL ARTICLE The daily, weekly, and seasonal cycles of body temperature analyzed at large scale Charles Harding a, Francesco Pompeib, Samantha F Bordonaroc, Daniel C McGillicuddyd, Dmitriy Burmistrove, and Leon D Sanchez f,g aStatistical Analyst and Data Scientist, Seattle, Washington, USA; bExergen Corporation, Watertown, Massachusetts, USA; cProfessional Emergency Services, Gates Vascular Institute, Buffalo, NewYork, USA; dDepartment of Emergency Medicine, Saint Joseph Mercy Hospital, Ann Arbor, Michigan, USA; eStatistical Analyst and Data Scientist, Lowell, Massachusetts, USA; fDepartment of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA; gHarvard Medical School, Boston, Massachusetts, USA

ABSTRACT ARTICLE HISTORY We performed large-scale analyses of circadian and infradian cycles of human body temperature, Received 13 August 2019 focusing on changes over the day, week, and year. Temperatures (n= 93,225) were collected using Revised 30 August 2019 temporal artery thermometers from a Boston emergency department during 2009–2012 and were Accepted 2 September 2019 statistically analyzed using regression with cyclic splines. The overall mean body temperature was KEYWORDS 36.7°C (98.1°F), with a 95% confidence interval of 36.7–36.7°C (98.1–98.1°F) and a standard Acrophase; bathyphase; deviation of 0.6°C (1.1°F). Over the day, mean body temperature followed a steady cycle, reaching circadian; circannual; its minimum at 6:00–8:00 and its maximum at 18:00–20:00. Across days of the week, this diurnal circaseptan; orthophase; cycle was essentially unchanged, even though activities and sleeping hours change substantially seasonal; temperature during the weekly cycles of human behavior. Over the year, body temperatures were slightly colder in winter than summer (~0.2°C difference), consistent with most prior studies. We propose these seasonal differences might be due to ambient effects on body temperature that are not eliminated because they fall within the tolerance range of the thermoregulatory system. Over the year, bathyphase (daily time of minimum temperature) appeared to parallel sunrise times, as expected from sunrise’s zeitgeber role in circadian rhythms. However, orthophase (daily time of maximum temperature) and sunset times followed opposite seasonal patterns, with orthophase preceding nightfall in summer and following nightfall in winter. Throughout the year, bathyphase and orthophase remained separated by approximately 12 h, suggesting this interval might be conserved. Finally, although 37.0°C (98.6°F) is widely recognized as the mean or normal human body temperature, analysis showed mean temperature was <37.0°C during all times of day, days of the week, and seasons of the year, supporting prior arguments that the 37.0°C standard has no scientific basis. Overall, this large study showed robust and consistent behavior of the human circadian cycle at the population level, providing a strong example of circadian homeostasis.

Introduction 1800s (Wunderlich 1871). Though Wunderlich’s research used large sample sizes, sometimes invol- In humans, body temperature usually follows a daily ving tens of thousands of individuals, only a few cycle that is lowest in the morning and highest in the large studies of the cycles of human body tempera- afternoon and evening (Mackowiak et al. 1992;Ogle ture have been conducted since (Cross and 1866;Wunderlich1871). This is an aspect of the Anderson 2018; Obermeyer et al. 2017). circadian rhythm and is tied to many body functions Here, we analyzed 93,225 temperature measure- and states, such as sleep, hormone secretion, meta- ments, which makes this one of the largest studies bolic disorders, mood disorders, and the internal of human body temperature. To our knowledge, it clock (Dunlap et al. 2009;Panda2016). The first is also the only large study since Wunderlich that large studies of the cycles in human body tempera- has included body temperatures collected across ture were performed by the pioneering physician all 24 h of the day, and therefore allows evaluation Carl Reinhold August Wunderlich in the mid- of the entire circadian cycle. We evaluated both

CONTACT Leon D Sanchez [email protected] Department of Emergency Medicine, Beth Israel Deaconess Medical Center, One Deaconess Road, W-CC2, Boston, MA 02215, USA Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/icbi. Supplemental data for this article can be accessed here. © 2019 Taylor & Francis Group, LLC CHRONOBIOLOGY INTERNATIONAL 1647 circadian and infradian variation, focusing on hypothermia). The remaining 93,225 (81%) tem- changes over the day, week, and year. peratures were included in the analysis.

Sensitivity analyses Materials and methods To evaluate the sensitivity of our results to these data Data source filtering procedures, we repeated all of our analyses In total, 115,149 temperature measurements were using 60 s and 120 s for the exclusions, instead of 15 collected from September 2009 to March 2012 at s. None of the study findings were changed mean- Beth Israel Deaconess emergency department in ingfully.Asacheckonbiasintroducedbytheemer- Boston, Massachusetts, USA (42.34°N, 71.11°W). gency department setting of our study, we also Temperatures were collected at the time of triage repeated each of our analyses after excluding all tem- ≥ ≥ using temporal artery thermometers that standar- peratures in the fever range ( 38.0°C, 100.4°F), dize measurements to core body temperature which accounted for 2.9% of the dataset (n = 2672). (Exergen Corp., Watertown, MA), and that were The results obtained after excluding these fever-range connected to data-loggers. Local times were ana- temperatures are shown in Supplementary Figures S1 lyzed in this study (as opposed to solar times, for and S2. Excluding fever-range temperatures shifted example). The data are cross-sectional, in that the mean body temperatures downward by 0.1°F temperatures were collected at a single occasion and made the times of minimum and maximum per patient. The study was approved by the insti- body temperature less statistically certain, but did tutional review board of Beth Israel Deaconess not result in any other meaningful differences from medical center, which waived the need for patient the main findings. consent. To preserve patient anonymity, collected tem- Statistical methods peratures were not linked to other medical records or laboratory values. Though this prevented us To analyze daily (circadian), weekly (circaseptan), from analyzing temperature by patient character- and seasonal (circannual) temperature variation, istics, demographic data for overall visits to our we used cyclic spline regressions. We chose to emergency department from September 2009 to use splines because they allow the data to freely August 2011 showed that 54% of presenting indi- determine the shapes (waveforms) of the daily, viduals were women and the median age was 49 weekly, and seasonal cycles without imposing (interquartile range 32–66). Data collection has parametric assumptions on these shapes, as been described in greater detail in a previous pub- would have been imposed by some alternative lication, which used some of the same dataset to approaches, like the cosinor method or harmonic investigate fever-based systems for disease out- regressions of low order (Brown and Czeisler 1992; break detection (Bordonaro et al. 2016). The data- Fernandez et al. 2009; Halberg et al. 1967; Refinetti set is also currently being used in a study of daily et al. 2007). The downside to splines is that they fever variation. require larger sample sizes than the cosinor method or low-order harmonic regression, but this was not an obstacle for the present study. In more detail, we used cyclic cubic regression Data filtering spline terms in gaussian location-scale regressions We excluded measurements with digital recording (Wood 2006, 2017). For the daily analysis, the errors (1%), any measurements taken immediately splines were set up to capture a smooth, 24-h before another measurement (14%; <15 s apart, cycle to body temperature. For the weekly and presumed to be re-measurements of the same per- seasonal analyses, the splines were set up to cap- son), and values that were aberrantly low (11%; ture a smooth 24-h cycle and also any changes to <35.0°C/<95.0°F, indicating measurement of the form of this cycle that occurred over the week a target other than a person, or a rare case of or year, to the extent that such changes were 1648 C. HARDING ET AL. statistically supported by the data and continued chose to use gaussian location-scale regression to produce a smooth curve. because variation in both the mean and standard The specific, technical set ups of the regression deviation of temperature were seen in the data. models were as follows: For the daily analysis, the The confidence intervals in this article are 95%. linear predictor of the regression model consisted When estimating the times of minimum and max- of the sum of an intercept and a penalized uni- imum temperature, they were obtained by applying variate spline with a day-long (24 h) period. For the percentile bootstrap to 5000 resamples from the the weekly analysis, the linear predictor consisted posterior of the fitted spline regression, unconditional of the sum of an intercept, two main effect terms, on smoothing parameters (Wood 2006, 2017). When and an interaction term. The main effect terms estimating other quantities, they were obtained by were a penalized univariate spline with a day- percentile pairs bootstrap with 10,000 resamples. long period and an unpenalized univariate spline with a week-long (7-day) period, while the interaction term was a bivariate tensor of a penalized Results spline with a day-long period and random effect term for day of week as an unordered categorical Overall body temperature variable. We made two of these choices to allow The overall mean body temperature was 36.7°C for the possibility of sudden changes from one day (98.1°F) with a 95% CI of 36.7–36.7°C (98.1– to the next, such as might occur from the work- 98.1°F) and a standard deviation of 0.6°C (1.1°F). week to the weekend: First, we used an unpena- The 93,225 analyzed body temperature measure- lized spline instead of a penalized spline for the ments followed a roughly Gaussian distribution week-long main effect. Second, in the interaction near the mean temperature value, but had heavy term, we used a random effect for day of week as tails at abnormally low and high-temperature a categorical variable instead of a penalized spline values (Figure 1). of time-of-week as a numeric variable. However, in practice, we found that these allowances barely changed the obtained results. For the yearly analysis, the linear predictor consisted of the sum of an intercept and a bivariate tensor of penalized splines with day-long and year-long periods. All splines were cyclic cubic splines with equi- distant starting knots. Each analysis required linear predictors for both the mean and standard deviation, and the same set ups were used for both. In all regressions, an identity link was used for the mean and a log link for the standard deviation. For the penalized splines, we selected the number of starting knots to be much greater than the effective degrees of freedom of the spline term in the fitted model (Wood 2006). Fitting was performed using restricted maximum likelihood estimation with the R package mgcv (Wood 2017). Gaussian location-scale regression allows both the mean and the standard deviation of the dependent variable (in our case, temperature) to vary with the Figure 1. Distribution of human body temperature measurements. independent variables (in our case, time). In this way, The black arrowhead indicates the mean temperature, which itisamoreflexiblealternativetocommonplacelinear was 36.7°C (98.1°F). The white arrowheads indicate the middle 95% range of temperatures (2.5th to 97.5th percentile), which regression, which assumes that the standard deviation was 35.6°C to 38.0°C (96.0°F to 100.4°F). The standard deviation does not vary with the independent variable. We of temperature was 0.6°C (1.1°F). CHRONOBIOLOGY INTERNATIONAL 1649

Daily cycle of body temperature Weekly cycle of body temperature Mean body temperature varied cyclically over the day, Even though peoples’ schedules, activities, diets, following the well-known pattern of lower tempera- and lifestyles change across the week, the diurnal tures in the morning and higher temperatures in the cycle of mean body temperature remained consis- afternoon and evening (Figure 2a). It rose from tent (Figure 2b). Temperatures were slightly a minimum of 36.5°C (97.8°F) at 6:00–8:00 AM to reduced on weekend evenings and potentially on a maximum of 36.8°C (98.3°F) at 6:00–8:00 PM. Sunday mornings, but no other deviation from the Notably, the time from the minimum to the max- overall diurnal cycle was supported by the data. imum temperature was nearly equal to the time from the maximum to the minimum temperature (11 h 53 Seasonal cycle of body temperature min vs. 12 h 7 min; difference = 14 min), even though this equality is not an inherent feature of complex The diurnal cycle of body temperature was largely oscillators like the circadian cycle. The midline esti- maintained across the seasons of the year, though mating statistics of rhythm (MESOR) temperature body temperatures were somewhat higher during (Refinetti et al. 2007) was 36.7°C (98.0°F). warmer months and somewhat lower during

Figure 2. Daily, weekly, and seasonal variation in mean body temperature. (a) Over the day, human body temperature varied from a minimum of 36.5°C (97.8°F) at 6:00–8:00 AM to a maximum of 36.8°C (98.3° F) at 6:00–8:00 PM. Mean values and 95% confidence intervals are shown for each hour of the day (points and error bars), while the red curve is a regression fit to the data. (b) Analyzing body temperature by day of the week demonstrates the consistency of the diurnal cycle of mean body temperature, providing a powerful example of circadian homeostasis. In this panel, the black curve is the regression fit to the data, while the dashed red curve is a repetition of the daily analysis (the red curve from panel A), shown for comparative purposes. (c) Evaluating body temperature over multiple years allowed seasonal variation to be considered. Body temperatures were slightly higher in the warmer summer months and lower in the colder winter months. As previously, the black curves are the regression fit to the data, while the dashed red curves are repetitions of the daily analysis (panel A), shown for comparative purposes. Arrowheads indicate average sunrise and sunset times. 1650 C. HARDING ET AL. colder months (Figure 2c; overall seasonal varia- one another, which, as noted previously, is not an tion of approximately ±0.1°C). Given the large inherent feature of complex oscillators. seasonal differences in daylight hours in the study setting (Boston, USA) and the well-known connections between daylight and circadian tim- Mean body temperature was always <37.0°C ing, we also investigated seasonal variation in (<98.6°F) bathyphase (the time of the minimal value taken Though 37.0°C (98.6°F) is often said to be the by the diurnal cycle) and orthophase (the time of normal body temperature, mean body temperature the maximal value taken by the diurnal cycle) was less than 37.0°C (98.6°F) during all times of (Figure 3). Bathyphase appeared to track sunrise the day, days of the week, and seasons of the year times across seasons of the year, though results (Figure 2). were somewhat obscured by broad confidence intervals. On the other hand, orthophase followed an opposing pattern to sunset times, crossing over Discussion the time of sunset as the seasons changed: ortho- Daily cycle of body temperature phase occurred significantly before sunset during summer, but significantly after sunset during win- In this study, we evaluated daily, weekly, and sea- ter. Throughout the year, bathyphase and ortho- sonal cycles of human body temperature. Over phase remained spaced at roughly 12 h apart from the day, mean body temperature followed the well-

Figure 3. Seasonal variations in the bathyphase and orthophase of the diurnal cycle of body temperature, as compared with daylight hours. Bathyphase (hollow points) refers to the time of the minimum value in the diurnal cycle of body temperature. Orthophase (solid points) refers to the time of the maximum value. Over the year, bathyphase appeared to vary in conjunction with times of sunrise, while orthophase varied in an opposing pattern to times of sunset. Notably, orthophase occurred before nightfall during the summer and after nightfall during the winter, remaining spaced at roughly 12-h difference from bathyphase throughout the year. Confidence intervals are 95%. The displayed times of night, twilight, and day are the average values that occurred across the years of the study period (total study duration: 30 months). The times of night, twilight, and day shift sharply in middle March and early November because the daylight saving time system is used at the study location (Boston, United States). In the daylight saving time system, clock times are advanced by 1 h on the second Sunday of March and this change is undone on the first Sunday of November. Consequently, times of sunrise and sunset increase by 1 h in middle March and decrease by 1 h in early November. CHRONOBIOLOGY INTERNATIONAL 1651 known circadian pattern of lower temperatures in including across seasons and in studies that sought the morning and higher temperatures in the eve- to isolate the endogenous circadian cycle of body ning (Figure 2). There was also a slight hump in temperature by requiring individuals to live body temperature during the afternoon. We can- according to constant routines or extended >24-h not determine the cause of this hump from the days (Minors et al. 1996; Waterhouse et al. 1999; data analyzed in the present study, but it might Wever 1985). However, the evidentiary value of reflect increases in body temperature associated our observations of previous research is unclear with metabolism of lunch. Mild increases in body from a statistical perspective given the relatively temperature after meals have been noted in the small sample sizes of the previous studies and the medical literature since at least the mid-1800s, fact that none of them reported confidence inter- when they were reported by Ogle (1866). vals for both bathyphase and orthophase. In our analyses of diurnal body temperature It should also be noted that a half-wavelength variation, the time interval from bathyphase to trough-to-peak time and a half-wavelength ultra- orthophase (the trough-to-peak time) and the dian cycle (i.e., a semicircadian or circasemidian time interval from orthophase to bathyphase (the cycle) are mathematically different phenomena, in peak-to-trough time) were both about 12 h, or half that the half-wavelength trough-to-peak time may the wavelength of the circadian cycle. This half- be conserved with or without a semicircadian cycle wavelength separation was present throughout the being present, and a semicircadian cycle may be daily, weekly, and seasonal analyses of body tem- present with or without a conserved half- perature (Figure 2 and 3), but is not present for wavelength trough-to-peak time. The possibility several other circadian parameters, like melatonin of ultradian cycles has been investigated repeatedly or cortisol levels. Based on these observations, the in previous studies (Refinetti et al. 2007) and has half-wavelength interval between orthophase and been reported for wrist temperatures (Sarabia et al. bathyphase might be a conserved attribute of the 2008), but to our knowledge the possibility of diurnal cycle of body temperature at the popula- a conserved trough-to-peak time has not been tion level. A half-wavelength separation between examined. The topic may not have been examined minima and maxima is a characteristic of simple previously because very large sample sizes are sine and cosine curves, which are sometimes used needed to obtain statistically reliable estimates of to statistically analyze the circadian cycle (Refinetti trough and peak times: Our experience in the et al. 2007). However, the half-wavelength separa- current study has shown us that the diurnal cycle tion is not an inherent feature of more complex of body temperature approaches its trough and oscillatory processes, like those that are believed to peak values gradually (Figure 2), and therefore drive the circadian cycle, and we also observed that that the specific times of the trough and peak are the diurnal variation of body temperature does not subject to considerable statistical uncertainty, form a sine or cosine curve in other aspects of its which can only be ameliorated with very large shape (Figure 2). Further research would be sample sizes (Figure 3). needed to determine whether the half-wavelength trough-to-peak time of body temperature is con- Weekly cycle of body temperature served in some manner and, if so, to characterize Over the week, the diurnal cycle of body tempera- the responsible mechanisms and identify whether ture was largely unchanged in our dataset, show- they are physiological, societal, or involve some ing evidence of almost no day-to-day differences combination of the two. We examined several except for slightly reduced body temperatures on prior studies (Cisse et al. 1991; Honma et al. weekend nights and Sunday mornings (Figure 2b). 1992; Kleitman and Ramsaroop 1948; Levendosky Given the comparatively large changes in peoples’ et al. 1991; Minors et al. 1996; Waterhouse et al. schedules and behaviors over the week, we view 1999; Wehr et al. 1995, 1993; Wever 1985), and the consistency of the diurnal cycle as a strong observed an overall tendency of the published example of circadian homeostasis. However, this temperature rhythms to have trough-to-peak consistency also poses a puzzle because the phase intervals that were half-wavelength in duration, and amplitude of the circadian cycle are widely 1652 C. HARDING ET AL. known to vary in response to changes in peoples’ Senegal (Cisse et al. 1991), a study of a single schedules and behaviors (Dunlap et al. 2009; individual who took her own temperature regu- Honma et al. 2003), such as those related to sleep larly over a period of 30 years in Canada and the interruptions, daylight savings (Harrison 2013), United States (Cross and Anderson 2018), a study and jet lag (Waterhouse et al. 2007). of 10 healthy men in Sapporo, Japan (Honma et al. One possible explanation of these apparently 1992), and other research (Kleitman and contrasting results is that the within-week changes Ramsaroop 1948). On the other hand, some stu- in schedules and behaviors do affect the circadian dies have found slightly reduced body tempera- cycle of body temperature, but that these effects tures in summer (Levendosky et al. 1991), or are too small to be evident in our analyses. This different seasonal patterns of body temperature explanation is supported by research on adoles- in men and women (Horne and Coyne 1975), cents, among whom large differences in sleep and leading to disagreement about the true nature of lifestyle between school days and weekends only seasonal body temperature variation in humans. translate into small differences (averaging <1 h) in In humans, the autonomic thermoregulatory sys- acrophase (Crowley and Carskadon 2010). tem maintains core temperature to within Additionally, this explanation is supported by an a tolerance of about ±0.1°C of the core temperature experimental study in which 16 young adults set point. Larger deviations in core temperature delayed weekend wake-up times by about 3 h, trigger sweating, vasoconstriction, and shivering resulting in an average circadian phase shift of responses that push core temperature back into only <1 h (Taylor et al. 2008). A phase shift of the ±0.1°C tolerance range (Lopez et al. 1994). In this size would not be evident in our analyses. our research and all of the abovementioned studies, Alternatively, a more conjectural explanation of the magnitude of seasonal body temperature devia- the weekly consistency of the diurnal cycle is that tion was smaller than or similar to the tolerance the body could partially adjust to repeating aspects range. Therefore, we suggest it is possible that the of weekly schedule and behaviors, to largely main- seasonal body temperature differences do not result tain the daily circadian cycle across the entire from an annual physiological rhythm, but instead week. For example, an individual who goes to represent changes in response to ambient tempera- sleep at 9 PM on weekdays and 2 AM on weekends ture and other external stimuli that are small for several weeks in a row might have a largely enough to avoid elimination by the thermoregula- maintained circadian body temperature variation tory system. For example, a person will sweat to throughout the week, even though greater circa- reduce their core temperature in hot summer dian disruption might be experienced by an indi- weather, but will not sweat once their core tempera- vidual who regularly goes to sleep at 9 PM ture is about 0.1°C above the core temperature set every day of the week and then stays awake until point. On the other hand, a person will vasocon- 2 AM during an unusual weekend. We have not strict to raise their core temperature in cold winter seen this possibility excluded in the previous weather, but will not vasoconstrict once their core research and it might make a worthwhile topic temperature is about 0.1°C below the set point. So, for future investigations. the existence of the tolerance range could leave a roughly 0.2°C core temperature difference between summer and winter that is unacted upon Seasonal cycle of body temperature by the thermoregulatory system. In this way, the Over the year, mean body temperature increased tolerance range might explain the entirety of somewhat in warmer seasons (Figure 2). This pro- observed seasonal differences in body temperature vides confirmation for the minor body tempera- without any annual physiological rhythm being ture increases during summer that have been present. reported by previous, smaller studies set in other Our suggestion can explain several previously climates that also have seasonal variations in ambi- reported aspects of the seasonal differences in ent temperature and/or daylight hours, including body temperature: First, it can explain why most a study of 103 young men in the Sahelian area of studies have reported higher body temperatures in CHRONOBIOLOGY INTERNATIONAL 1653 summer than in winter. Second, given the variable measure temperature over a core site, the temporal ambient temperatures that are observed in life, our artery, though at a location where it is near the suggestion provides a plausible explanation for surface. why a minority of studies have reported higher In addition to the changes in mean body tem- body temperatures in winter than in summer. perature, changes in bathyphase and orthophase Third, our suggestion can explain the magnitudes were also observed in our study. Bathyphase of seasonal body temperature changes that have appeared to track sunrise times, which is expected been reported (generally ≤0.2°C). Fourth, it can from the conclusion of animal studies that daylight explain an apparent conflict between previous is the main zeitgeber (clock-setter) of the mamma- results: On the one hand, seasonal changes in lian circadian cycle (Dunlap et al. 2009). Across mean body temperature appear to have the same seasons, orthophase remained separated from size in climates that have smaller and larger sea- bathyphase by a 12-h interval in our study, as sonal changes in ambient temperature. This has discussed above. Overall, the large-scale results of led researchers to suspect that the seasonal cycle of our study are consistent with prior evidence that mean body temperature does not result from sunlight times remain a zeitgeber in contemporary ambient temperatures themselves, and is instead society, and that the circadian cycle entrains to a physiological rhythm that is tied to the season- dawn (Roenneberg et al. 2007; Roenneberg and ality of daylight hours. On the other hand, Merrow 2007). Yet, in our body temperature a seasonal cycle of mean body temperature has results, bathyphase and orthophase only showed also been reported a setting that has large seasonal associations with sunrise times, and did not have differences in ambient temperature but almost no any evident correspondence with either sunset seasonal differences in daylight hours (Cisse et al. times or total daylight hours. This appears to con- 1991), which has led other researchers to conclude trast with proposals that, in contemporary society, that the seasonal cycle of mean body temperature the human circadian clock continues to entrain is attributable to ambient temperatures alone. Our and otherwise modify substantially in response to suggestion reconciles the prior evidence by sunset (Wehr et al. 2001), the time from dawn to demonstrating how the seasonal differences in dusk (the solar photoperiod) (Wehr 1996), or the mean body temperature can be attributed to integrated natural light exposure over the course changes in ambient temperature without there of a day. being any need for larger changes in ambient Several previous studies have also examined seaso- temperature to cause larger changes in body nal variations in circadian timing. Our findings are temperature. consistent with some of these studies, but contrast It seems likely that most autonomically con- with others. Consistent with our findings, a large sur- trolled parameters have some tolerance range, vey of residents of Central European (n ≈ 55,000) whether currently known or unknown. Therefore, found that self-reported midsleep times correlated we suggest that it is important to consider toler- with dawn (not dusk) and that people on average ance ranges when investigating seasonal cycles in had later (not earlier) chronotypes in winter any physiological parameter. Additionally, mea- (Kantermann et al. 2007; Roenneberg et al. 2013). In surement methods can play a role in seasonal a study of 10 young men in Sapporo, Japan, the start cycles because some measurement sites are more and end of the “valley” (hypothermal) portion of the affected by ambient conditions than others. For circadian cycle of body temperature both occurred body temperature, distant body sites like the later in winter (Honma et al. 1992), which agrees wrist (Sarabia et al. 2008) are most affected by with our results for bathyphase and orthophase. In ambient conditions, while core and central sites astudyoffivemeninCzechoslovakia,melatonin like the pulmonary artery and rectum are the rhythms phase-delayed from summer to winter, but least affected, and near-surface sites like the the duration of melatonin elevations was unchanged mouth and the area near the tympanic membrane (Illnerová et al. 1985), which is also consistent with are intermediately affected (Zehner and Terndrup our observation that the phase of body temperature 1991). The thermometers used in this study rhythms was delayed in winter, in correspondence 1654 C. HARDING ET AL. with dawn, but the bathyphase-orthophase interval normal, a large range of temperatures appear to did not change. In contrast to our results, however, be common: the middle 95% of temperatures analyses of body temperature and other circadian ranged 35.6–38.0°C (96.0–100.4°F) in our study, timing markers in 21 healthy men living in roughly 36.0–37.5°C (96.9–99.5°F) in Mackowiak Washington, DC (USA) showed similar circadian et al.’s(1992) study of healthy participants, and cycle phases in summer and winter, suggesting that 35.7–37.3°C (96.3–99.1°F) in Obermeyer et al.’s daylight has been replaced by artificial lighting and (2017) study of individuals visiting a hospital fixed work hours in its role as the main zeitgeber for at without infections or antibiotics prescriptions. In least some groups of people (Wehr et al. 1995). our dataset, the range of the middle 95% of Furthermore, analyses of individuals camping in temperatures changed only moderately over rural Colorado, USA without artificial light or fixed the day, following the general pattern of lower schedules showed that durations of melatonin morning temperatures and higher evening tem- elevation shifted to match times of sunrise and sunset peratures that is expected from the circadian once individuals had transitioned from usual society cycle (35.4–37.8°C [95.7–100.0°F] at 6:00–9:59; life to camping (n = 8) (Wright et al. 2013), and 35.6–38.3°C [96.1–100.9°F] at 18:00–21:59). thereafter were expanded or contracted in proportion to the seasonal length of night (natural scotoperiod) – (n = 6 14) (Stothard et al. 2017). Altogether, our Limitations analyses and previous studies paint the following pic- ture: sunlight has maintained its role as a major zeit- Study limitations include the emergency department geber in the morning, but the role of sunset has largely (i.e., non-healthy) population. However, it is imprac- been supplanted by artificial light and modern sche- tical to collect temperatures from large numbers of dules in the evening. healthy individuals at all hours of the day for a period of years, as can be seen from the enrollment of less than 50 individuals in almost all chronobiology studies, and the enrollment of less than 10 individuals in 37.0°C (98.6°F) has no scientific relevance many such studies. Practically speaking, we think The mean body temperature in the current study was that the only alternative is to study a medical popu- 36.7°C (98.1°F), which is substantially less than the lation, and to accept that some bias may result as the commonly heard, supposed normal body tempera- price of a large sample size, which can provide sta- ture of 37.0°C (98.6°F). Nonetheless, the mean body tistically backed insights that simply are not possible temperature that we have reported is consistent with with smaller datasets. We think that the consistency the results of previous large studies, including of the body temperature cycles that we have reported Mackowiak et al.’s(1992) study of oral temperatures is indicative of a physiological basis, especially given and Obermeyer et al.’s(2017) study of temperatures the consistency that was observed across days of the collected at a mixture of body locations, which found week, despite large variations in human behavior means of 36.8°C (98.2°F) and 36.6°C (97.9°F), and activities. Additionally, as a check for biases respectively. that could be introduced by the emergency depart- In the current study, mean body temperature ment setting, we repeated the study analyses after was less than 37.0°C (98.6°F) during all times of excluding temperatures in the fever range (≥38.0°C, the day, days of the week, and seasons of the year ≥100.4°F; 2.9% of temperatures). This shifted the (Figure 2). Our results therefore provide a new, mean temperature values downward by about 0.1°F large-scale source of support for the argument by for the study population and made the times of Mackowiak et al. (1992), Mackowiak et al. (1997) minimum and maximum body temperature less sta- and others (Horvath et al. 1950; Sund-Levander tistically certain, but otherwise did not substantively et al. 2002) that the commonly heard normal change any of our results (Supplementary Figures S1 body temperature of 37.0°C (98.6°F) has no and S2). The very small effects of removing all meaningful basis and should be abandoned. recorded fevers suggests that the observed cycles of Instead of one value being representative of body temperature are physiological, rather than CHRONOBIOLOGY INTERNATIONAL 1655 medical. However, it should be noted that there is no circadian homeostasis. The findings of the current way for us to prove this in the current study. study also suggest the possibility that there may be Thecurrentstudyisalsolimitedbyourinabilityto a conserved half-wavelength spacing between the evaluate temperature variations according to the char- bathyphase and orthophase of the population body acteristics of study participants, as a consequence of temperature rhythm, though additional research is data anonymity requirements. Obermeyer et al. needed to provide more probative evidence for (2017) have recently published a large-scale evalua- this suggestion. Finally, our results suggest that tion of associations between many characteristics and the well-known, ostensible normal body tempera- body temperature in a hospital setting, and we recom- ture of 37.0°C (98.6°F) has no scientific basis, since mend it to interested readers. As compared with our mean body temperature was less than this value research, Obermeyer et al.’sstudyhastheimportant during all times of day, days of the week, and benefit of providing detailedinformationonpartici- seasons of the year. The circadian rhythm may pant characteristics, but the important drawback of also have medical relevance because of its robust only including temperatures collected during work and predictable effects on body temperature. In hours(7AMto6PM)and,therefore,didnotallow particular, we are working on an additional inves- analysis of the majority of the circadian cycle. tigation that examines the effects of the circadian Another limitation to the current study is its use cycle on fever detection in this dataset and of temporal artery thermometers only, which may a confirmatory data source. affect the generalizability of the findings because temperature readings can differ somewhat between thermometer types (Barnason et al. 2011; Acknowledgments Obermeyer et al. 2017; Sund-Levander et al. We thank Dr. Janette Lee, Jason Jarboe, and Roger Nelson 2002). Finally, our seasonal results are specific to (Exergen, Corp.) for their substantial contributions to device the latitude and climate of the study setting design and data collection. (Boston, USA). We cannot determine how well or poorly our seasonal results would generalize to other latitudes and climates. Author contributions Besides the current study, several previous stu- Concept and design: All authors. dies have also traded the addition of some poten- Acquisition, analysis, or interpretation of data: All authors. tial biases for the advantages that are offered by Drafting of the manuscript: Harding. very large sample sizes. Most prominently, this has Critical revision of the manuscript: Sanchez, Pompei. taken the form of surveys that seek insight into the Statistical analysis: Harding, Burmistrov. circadian cycle by asking about sleeping hours, Supervision: Sanchez, Bordonaro, McGillicuddy. thereby trading the potential for survey response biases for the considerable advantage of having Disclosure of interest tens of thousands of respondents (Roenneberg et al. 2007; Roenneberg and Merrow 2007). In Harding and Burmistrov received payment from Exergen for another tradeoff between potential bias and sam- work on this investigation and related research. Pompei is foun- ple size, Stowie et al. (2015) used regional power der and CEO of Exergen, and holds patents related to the study topic. None of the other authors has any conflicts to declare. grid loads as an indirect measure of human activity periods, which were then analyzed as a surrogate of circadian cycles at the population Funding level, including about 43 million individuals. This work was supported by Exergen, Corp., which designed the thermometer system that was used, and provided ther- Conclusions mometers, technical support, and funding, including for the participation of Harding and Burmistrov. Exergen played Overall, this large study showed robust and con- a role in the design, collection, analysis, and interpretation sistent behavior of the human circadian cycle at of data; in the writing of the manuscript; and in the decision the population level, providing a strong example of to submit for publication. 1656 C. HARDING ET AL.

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