Natalia Sytnik

A COMPARISON OF SOME DIFFERENT METHODS FOR PURIFYING CORE TEMPERATURE DATA FROM HUMANS

Nine healthy females were studied about the time of the spring equinox while living in student accommodations and aware of the passage of solar time. After 7 control days, during which a conventional lifestyle was lived under a 24h “constant routine,” the subjects lived 17 × 27h “days” (9h sleep in the dark and 18h wake using domestic lighting, if required). Throughout the experiment, recordings of wrist activity and rectal (core) temperature were taken. The raw temperature data were assessed for phase and amplitude by cosinor analysis and another method, “crossover times,” which does not assume that the data set is sinusoidal. Two different purification methods were used in attempts to remove the masking effects of sleep and activity from the core temperature record and so to measure more closely the endogenous component of this rhythm; these two methods were “purification by categories” and “purification by intercepts.” The former method assumes that the endogenous component is a sinusoid, and that the masking effects can be estimated by putting activity into a number of bands or categories. The latter method assumes that a temperature that would correspond to complete inactivity can be estimated from measured temperatures by linear regression of these on activity and extrapolation to a temperature at zero activity. Three indices were calculated to assess the extent to which exogenous effects had been removed from the temperature data by these purification methods. These indices were the daily variation of phase about its median value; the ratio of this variation to the daily deviation of phase about midactivity; and the relationship between amplitude and the square of the deviation of phase from midactivity. In all cases, the index would decrease in size as the contribution of the exogenous component to a data set fell. The purification by categories approach was successful in proportion to the number of activity categories that was used, and as few as four categories produced a data set with significantly less masking than raw data. The method purification by intercepts was less successful unless the raw data had been “corrected” to reflect the direct effects of sleep that were independent of activity (a method to achieve this being produced). Use of this purification method with the corrected data then gave results that showed least exogenous influences. Both this method and the purification by categories method with 16 categories of activity gave evidence that the exogenous component no longer made a significant contribution to the purified data set. The results were not significantly influenced by assessing amplitude and phase of the circadian rhythm from crossover times rather than cosinor analysis. The relative merits of the different methods, as well as of other published methods, are compared briefly; it is concluded that several purification methods, of differing degrees of sophistication and ease of application to raw data, are of value in field studies and other circumstances in which constant routines are not possible or are ethically undesirable. It is also concluded that such methods are often somewhat limited insofar as they are based on pragmatic or biological, rather than mathematical, considerations, and so it is desirable to attempt to develop models based equally on mathematics and biology. (Chronobiology International, 17(4), 539–566, 2000)

A preliminary investigation into individual differences in the circadian variation of meal tolerance : Effects on mood and hunger

A forced desynchrony methodology was used to assess postprandial blood glucose in 9 female volunteers during a 3-h period following a mixed meal presented at four times of day (08:00, 14:00, 20:00, 02:00). The influence of time of day on the postmeal glucose responses was evaluated by calculating the area under the curve, largest increase, time taken to reach peak, and fasting level. Circadian variations in meal tolerance were found for the area under the curve and largest increase, responses were greater (indicating poorer meal tolerance) in the evening than the morning. Fasting blood glucose exhibited diurnal variation although in the opposite direction to meal tolerance; levels were higher in the morning than the evening. Time taken to reach peak levels was not modulated by circadian rhythmicity. Estimates of the timing of poorest meal tolerance and the magnitude of this intolerance were computed for each subject. Individual differences in the magnitude of meal intolerance were found to influence hunger and self-reported calmness. Subjects with good tolerance had rhythms in both calmness and hunger, which were not found in those with poor tolerance. Subjects with good tolerance also tended to rate themselves as feeling more calm. These mood and hunger effects may result from differences in insulin resistance, which is hypothesized to underlie the circadian variations in meal tolerance.

Time of day effects in, and the relationship between, sleep quality and movement

The study aimed to measure the effects of a 27‐h ‘day’ sleep‐wake regime on actigraphic and subjective sleep variables, and to examine the relationships between these variables. Nine subjects spent 30 days and nights in the laboratory. After sleeping 8 h for each of 8 nights, the subjects had an imposed 27‐h ‘day’, for 18 ‘days’, remaining in bed for 9 h on each sleep period. Sleep periods therefore started 3 h later each day, although subjects’ circadian rhythms stayed entrained to 24 h, because subjects were not isolated from the natural light‐dark cycle. Time asleep, subjective sleep efficiency and subjective sleep quality, but not movement during sleep, were found to be significantly affected by time of going to bed. There were significant decreases in movement during recovery sleeps following each of two episodes of 26 h sleep deprivation. Over the study there were significant within‐subject correlations between subjective sleep quality and subjective sleep efficiency (rav=0.65), movement during sleep and subjective sleep efficiency (rav=−0.48), and movement during sleep and subjective sleep quality (rav=–0.26). We conclude that sleep movement, despite its low within‐and between‐subjects variability, is nevertheless a statistically reliable, but weak, indicator of subjective sleep efficiency and quality.

Modeling the effect of spontaneous activity on core temperature in healthy human subjects

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 sepa-rately 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 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.