Biological Rhythm Research 2001, Vol. 32, No. 5, pp. 511–528
0929-1016/01/3205-511$16.00 © Swets & Zeitlinger
Modeling the Effect of Spontaneous Activity on Core Temperature in Healthy Human Subjects J. Waterhouse1, A. Nevill2, D. Weinert3, S. Folkard4, D. Minors5, G. Atkinson1, T. Reilly1, I. Macdonald4, D. Owens4, N. Sytnik4 and P. Tucker4 1
Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, UK; 2School of Sport, Performing Arts and Leisure, University of Wolverhampton, Walsall, UK; 3Department of Zoology, University of Halle, Halle, Germany; 4Body Rhythms and Shiftwork Centre, University of Wales Swansea, Swansea, UK; 5School of Biological Sciences, University of Manchester, Manchester, UK.
Abstract Nine healthy females were studied about the time of the spring equinox, while living in student accommodation and aware of the passage of solar time. After 7 control days, during which a conventional lifestyle was lived, subjects underwent a 24-h ‘constant routine’, followed by 17 ‘days’ on a 27-h ‘day’ (9 h sleep and 18 h wake). Throughout the experiment, regular recordings of (non-dominant) wrist activity (every 30 s) and rectal temperature (every 6 min) were made. Only the control and 27-h (experimental) ‘days’ have been analysed in the present report. From each subject, 24-h profiles of raw temperature (consisting of 240 points) were obtained: one (control days), by averaging the control days; the other (experimental days), by conflating 16 consecutive 27-h ‘days’. Activity data were first collected into 240 points by summing them over 6-min intervals; they were then converted into three data sets (each of 240 points) for control and, separately, for experimental days. These data sets were summed activities in the previous 18 min (A18), summed activity over the previous 18–30 min (A30), and summed activity over the previous 30–42 min (A42). The raw temperature data sets for control and experimental days were separately analysed by ANCOVA using two time-of-day factors: ‘hours’ (24 levels) and ‘six-minute-intervals’ (10 levels). The covariate was the three activity data sets; in order to make the analysis more versatile, a cubic polynomial model was used, with a linear, quadratic and cubic term for each of these activity data sets. Moreover, the effects of activity upon core temperature were separately assessed
Address correspondence to: Dr. J. Waterhouse, Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Trueman Building, Henry Cotton Campus, 15–21 Webster Street, Liverpool L3 2ET, UK. Fax: 0151-231-4353; E-mail:
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for four 6-h sections of the 24-h profile, centred on its low, rising, high and falling phases. The main results were as follows: 1. All three activity data sets made significant contributions to the model, but that by the A30 data set was the most powerful of the three. This supports the use of activity files covering the previous 30 min in other ‘purification’ methods. 2. Although the linear term was the one that was significant most frequently, quadratic and (negative) cubic terms were also present on several occasions. This result indicates that the effect of activity upon core temperature can be approximated by a linear function (as has been done in other ‘purification’ models), but that, with wider ranges of activity, a sigmoid curve would be more accurate, indicative of the process of thermoregulation. 3. During the experimental days, the effect of activity upon temperature was greater in the rising than the falling temperature phase, and greater in the low than in the high phase. These results are predicted from current understanding of the circadian rhythm of thermoregulation. 4. During control days, the effects of activity were more complex, probably due to the factors that were present at some, but not all, phases — factors such as sleep, meals, changes in posture, lighting, and so on. 5. The ANCOVA also enabled the temperature profiles, corrected for the effects of activity (and, therefore, to be considered as ‘purified’), to be displayed. We conclude that the use of ANCOVA to tackle the problem of ‘purifying’ raw temperature data is a promising one. So far, it has produced results that accord with those from other ‘purification’ methods and with predictions based on our current understanding of the circadian rhythm of thermoregulation. Keywords: Core temperature, activity, circadian rhythms, masking, purification, analysis of covariance.
Introduction Measurements in subjects living a conventional lifestyle show a circadian rhythm in core temperature, with an inclined plateau from about 14:00 to 20:00 h and a minimum at about 05:00 h, just after the mid-point of sleep. This rhythm reflects the combined effects of sleep, physical and mental activities, and the endogenous circadian pacemaker (Minors & Waterhouse, 1981). Normal physical activity in the daytime imposes a variable heat load upon the body, but the tendency for core temperature to rise is limited by thermoregulatory mechanisms that originate in the hypothalamus. If these loads are small, they are dealt with by changes in the blood flow through the cutaneous vasculature of the forehead and limbs, particularly their extremities. By contrast, sleep results in a fall of core temperature, this being due to a combination of inactivity, changes in posture and
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neurophysiological activity, and an attenuation (but not loss) of thermoregulatory control (Haskell et al., 1981; Sagot et al., 1987; Refinetti & Menaker, 1991). In practice, the fall in core temperature during sleep is limited not only by these thermoregulatory mechanisms but also by the subject’s choice of a comfortable micro-environment (blankets, duvet, for example). The changes in core temperature which are due to the individual’s lifestyle and environment are called ‘exogenous’ changes. When temperatures are measured in subjects living normally, these exogenous effects are superimposed upon, and so tend to hide (‘mask’), ‘endogenous’ changes that originate from activities of the endogenous circadian pacemaker, situated in the suprachiasmatic nuclei at the base of the hypothalamus (Moore, 1995; Refinetti & Menaker, 1991). The conventional way to establish the profile of the endogenous rhythm is to use a ‘constant routine’ protocol. In this, sleep is prevented, and physical and mental activities, the timing and composition of meals, and environmental temperature and humidity, are all maintained as constant as possible (Czeisler et al., 1985; Mills et al., 1978). This protocol minimises any exogenous components of a rhythm, thus making clearer (‘unmasking’) the endogenous component. Experiments have shown that this endogenous component shows a minimum at 05:00 h and a more clearly defined maximum, at about 17:00 h, than do raw data (Krauchi & Wirz-Justice, 1994; Marotte & Timbal, 1982; Minors & Waterhouse, 1981, 1984). In protocols such as the constant routine, where activity and sleep are controlled, the origin of the temperature rhythm cannot be a mechanism that changes the heat load, but must instead result from changes in the loss of heat through the skin of the forehead and extremities (Krauchi & Wirz-Justice, 1994). Thus, in subjects resting in a thermoneutral environment, the cutaneous blood flow and temperature of these areas were highest around 21:00–24:00 h, when core temperature was being reduced, and lowest around 07:00–10:00 h, when it was being raised (Aschoff & Heise, 1972; Refinetti & Menaker, 1991; Smolander et al., 1993). Therefore, the same thermoregulatory reflexes play two roles — generating the endogenous circadian rhythm of core temperature and dissipating the effects of a small heat load. As a result of this dual role, there might be an interaction between them, with the size of the temperature changes produced by spontaneous activity depending upon the circadian phase when that activity took place. Two specific predictions are as follows. First, when the circadian system is in the ‘heat gain’ mode (about 08:00–11:00 h), with a tendency towards cutaneous vasoconstriction, the dissipation of body heat produced by spontaneous activity will be least rapid and, therefore, the rise of core temperature produced by spontaneous activity will be fastest. Conversely, the response to spontaneous activity when in the ‘heat loss’ mode (21:00–24:00 h), when there is a tendency towards cutaneous vasodilatation, will be that heat dissipation will be most rapid and core temperature will rise least quickly. The second prediction is that, when an individual is in the ‘ergotropic’ or active phase (when core temperature is high), spontaneous activity will produce comparatively small rises in temperature, due to the effectiveness of the thermoregulatory reflex mechanisms at this time. By contrast, when the individual is in the ‘trophotropic’ or resting phase (associated with sleep), the damped thermoregulatory
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reflexes will result in larger temperature rises for a given amount of spontaneous activity. There is some evidence to support these predictions. First, a bout of sub-maximal exercise taken at 08:00 h (during the rising phase of resting core temperature) produced a more rapid rise of temperature and a more marked increase of skin blood flow than one of the same intensity taken in the late afternoon, when the resting temperature was at its peak (Aldemir et al., 2000). Second, the ‘sensitivity’ to the temperature-raising effects of spontaneous activity (assessed by calculating the gradient of the line relating core temperature to the amount of activity) was found to be inversely related to the purified core temperature, both in humans (Waterhouse et al., 1999) and in mice (Weinert & Waterhouse, 1998). Moreover (see the first prediction), in mice there was also a transient rise in sensitivity during the rising phase of the endogenous temperature rhythm, and a transient fall during the falling phase. In testing the above two predictions, the approach which we have adopted in the present report is one that is essentially statistically-based, covariance analysis. With this method, it is possible to assess the contribution that spontaneous activity makes to the observed temperature rhythm, and to investigate if it depends upon the phase of the temperature rhythm when the activity is taking place. The method also enables the temperature profile in the absence of effects due to activity to be calculated. Other methods — ‘purification’ methods — have been used previously to assess the above predictions (see Waterhouse et al., 1999; Weinert & Waterhouse, 1998), and the relationship between these and the ANCOVA method will be considered in the Discussion. However, if the data have been collected from subjects living normally, then all the effects associated with going to sleep (including changes in neurophysiological activity, posture and the individual’s microclimate) will be present in addition to changes in the amount of spontaneous activity, and these will complicate an interpretation of the results. There are two protocols which enable these difficulties to be addressed: the ‘constant routine’ and ‘forced desynchronisation’ protocols. The constant routine protocol has been described above. Although it enables effects due to sleep and to the environment to be removed, it suffers in the present context from the disadvantage that the amount of activity is severely restricted, and that subjects have to be laboratory-bound. Forced desynchronisation is a method (for one of the first examples of the use of this method, see Kleitman & Kleitman, 1953) that is based on the observation that the body clock is not able to adjust to an imposed lifestyle whose period differs substantially from 24 h. In the present experiment, the imposed period was 27 h and the data span that has been analysed lasted for 16 ¥ 27-h ‘days’. Since, as shown in a previous analysis of these data (Waterhouse et al., 1999), the body clock, and, therefore the endogenous rhythm, retained a period of 24 h (due to the effects of natural zeitgebers), 16 experimental (27-h) ‘days’ equalled 18 cycles of the (24-h) endogenous rhythm. This means that the sleep-wake cycle moved progressively out of phase and back into phase with the endogenous rhythm for two whole ‘beat cycles’. As a
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result, when the whole span of 18 ¥ 24 h of data was conflated to a single cycle of 24 h, any phase of this endogenous rhythm was mixed with all phases of the imposed (27-h) sleep-wake cycle. That is, provided that the sleep-wake cycle was similar day by day, any effects directly or indirectly due to sleep or waking activity would be cancelled out. Therefore, this protocol allows a direct investigation of the effect of spontaneous activity upon temperature to be assessed at all phases of the endogenous temperature rhythm, without complications caused by the phase of the sleep-wake cycle. Moreover, by comparing such results with those obtained during the control days, it then becomes possible to investigate effects caused by the sleep-wake cycle per se.
Methods Subjects, protocol, and measurements Nine healthy female subjects (aged 18–22 years) were studied together in University accommodation at about the time of the Spring equinox in 1995. The general protocol (Owens et al., 1996; Owens et al., 2000) consisted of three sections: first, for days 1–7, a normal routine was followed, with sleep from midnight to 08:00 h and meals at conventional times; second, a constant routine lasting 26 h (from day 8, 08:00 h to day 9, 10:00 h), was carried out; and, third, a series of 27-h ‘days’ was undertaken (starting at 10:00 h, day 9), with 9 h of sleep and 18 h of awake. For the purposes of analysis, only the first and third sections of the protocol have been used, and these have been called ‘Control days’ and ‘Experimental days’, respectively. Throughout the protocol, clocks reading solar time were used to indicate times for retiring and rising, and for eating meals. Considering the experiment as a whole, the subjects were studied at all phases of their menstrual cycles. Sleep was always taken in the dark (
