Conceived and designed the experiments: MÁR JAM. Performed the experiments: EOT. Analyzed the data: EOT AMN. Contributed reagents/materials/analysis tools: AMN MC JAM. Wrote the paper: EOT MÁR JAM. Revised the manuscript: MÁR. Supervised the performed experiments, results, and interpretation: MÁR. Supervised mathematical modelling: MC. Supervised all tasks: JAM.
The authors have declared that no competing interests exist.
The disruption of the circadian system in humans has been associated with the development of chronic illnesses and the worsening of pre-existing pathologies. Therefore, the assessment of human circadian system function under free living conditions using non-invasive techniques needs further research. Traditionally, overt rhythms such as activity and body temperature have been analyzed separately; however, a comprehensive index could reduce individual recording artifacts. Thus, a new variable (TAP), based on the integrated analysis of three simultaneous recordings: skin wrist temperature (T), motor activity (A) and body position (P) has been developed. Furthermore, we also tested the reliability of a single numerical index, the Circadian Function Index (CFI), to determine the circadian robustness. An actimeter and a temperature sensor were placed on the arm and wrist of the non-dominant hand, respectively, of 49 healthy young volunteers for a period of one week. T, A and P values were normalized for each subject. A non-parametric analysis was applied to both TAP and the separate variables to calculate their interdaily stability, intradaily variability and relative amplitude, and these values were then used for the CFI calculation. Modeling analyses were performed in order to determine TAP and CFI reliability. Each variable (T, A, P or TAP) was independently correlated with rest-activity logs kept by the volunteers. The highest correlation (r = −0.993, p<0.0001), along with highest specificity (0.870), sensitivity (0.740) and accuracy (0.904), were obtained when rest-activity records were compared to TAP. Furthermore, the CFI proved to be very sensitive to changes in circadian robustness. Our results demonstrate that the integrated TAP variable and the CFI calculation are powerful methods to assess circadian system status, improving sensitivity, specificity and accuracy in differentiating activity from rest over the analysis of wrist temperature, body position or activity alone.
Faced with environmental cycles and daily alternation between light and darkness, organisms have evolved a time measuring mechanism, the biological clocks. Besides following circadian rhythms, all physiological variables must be coordinated with one another, like an orchestra led by a conductor; if the appropriate rhythm is not kept, noise rather than music is produced. In an organism, when this temporal order is disrupted due to aging or shift work, health is compromised. Afflictions include metabolic syndrome, diabetes and cardiovascular diseases, among others, or even worse prognosis of preexisting illnesses like cancer. Since the circadian pacemaker (suprachiasmatic nuclei) is located deep within the brain in humans, the only way to evaluate its function is by assessing the output signals, observing marker rhythms such as the sleep-wake cycle, body temperature or activity. The problem is that isolated variable measurement is not error free. However, we can increase reliability by combining the information from several circadian marker rhythms in an integrated variable that we have called TAP (Temperature, Activity and Position), a methodological approach that has not been used before, that in conjunction with a new index called Circadian Function Index, provides a useful tool for standardizing the status of the circadian system.
Circadian system disruption in humans has been associated with the development of chronic illnesses and worsening of pre-existing conditions such as cancer, premature ageing, metabolic syndrome, cardiovascular diseases, cognitive impairment and mood disorders (for a review, see
The circadian system consists of a set of structures involved in the generation of circadian rhythms in behavioral, physiological and biochemical variables, as well as in the external and internal synchronization of these variables to environmental cues and to each other, respectively. Three different approaches have been developed to determine circadian system function in humans. Many researchers have measured the output of the hypothalamus's major circadian clock, the suprachiasmatic nuclei (SCN), after trying to eliminate all influence from the external factors (masking factors) that may affect the variable being measured. One example may be the use of constant 48-h routine protocols in which the subjects were kept under constant light, temperature and body position, and frequently fed isocaloric snacks
Theoretically, most overt rhythms controlled by the circadian clock, which can be measured easily and with minimal subject discomfort, can be used as marker rhythms to assess circadian system function
Core body temperature rhythm (CBT) is frequently used as a circadian marker rhythm because it is relatively easy to record and the data can be analysed immediately. However, the most frequent way to measure CBT, rectal temperature, causes subject discomfort. Recently, eatable telemetric pills are also available for core temperature recordings. Nevertheless, the recordings are limited by the duration and dependent on intestinal transit. In addition, the rhythm is also masked by factors like posture, physical activity, meals, environmental light and temperature in both cases
Rest-activity rhythm measurement by actimetry is a simple, non-invasive method for indirectly evaluating the sleep-wake cycle. Therefore, it can be considered a marker rhythm. But as occurs with other methods, actimetry is subjected to masking and artifacts, such as difficulties related to differentiating between the onset of nocturnal rest and sensor removal for bathing before going to bed, bed partner movements, sleeping when travelling in a car or train, etc.
Recently, our group proposed wrist skin temperature as a possible alternative method for evaluating circadian system status in humans under normal living conditions
The existence of artifacts and different masking factors for all the rhythmic variables considered to be circadian markers led us to propose the use of a combination of three rhythmic variables for ambulatory monitoring. Therefore, we propose, for the first time, to integrate skin temperature, along with actimetry and body position data into a single variable to evaluate the status of the human circadian system under normal living conditions. This simple, non-invasive and practical approach will encourage clinicians to treat patients with circadian rhythm disorders and facilitate chronotherapy individualization.
Forty-nine subjects volunteered for this study. They included 25 women and 24 men ranging from eighteen to forty years of age (21.30±4.44). All participants received appropriate information about the study characteristics and signed an informed consent form before their inclusion in the study. The study was approved by the Ethics Committee of the University of Murcia.
Participants were recruited from among Biology and Medical students. They were all healthy and presented no physical conditions that disturbed their sleep (e.g. sleep apnea, asthma, periodic limb movement, diabetes, etc.). Furthermore, they were encouraged to maintain their normal life style during the week of the study and were monitored under free-living conditions.
Throughout the study, all subjects were instructed to keep a sleep and food diary designed by the Chronobiology Lab at the University of Murcia
The wrist temperature rhythm was assessed continuously for 7 days using a temperature sensor (Thermochron iButton DS1921H, Dallas, Maxim) with a sensitivity of 0.1°C and programmed to sample every 10 minutes. It was attached to a double-sided cotton sport wrist band, and the sensor surface was placed over the inside of the wrist on the radial artery of the non-dominant hand (
The activity data logger is placed in a sport band, on the upper non-dominant arm and the temperature data logger on the wrist of the non-dominant hand. Here it is shown the three variables selected for the study and a simplistic representation of their behaviour. Motor activity and body position show their higher levels during day-time, when the subject is active. Wrist temperature (T) profile, on the contrary, show its higher levels during the night, when the subject is resting. Then, to calculate TAP, we reversed T profile.
The body position and rest-activity rhythm was assessed over the same 7 days using an actimeter (Hobo Pendant G Acceleration Data Logger, Massachusetts, USA) placed on the non-dominant arm by means of a sports band, with its X-axis parallel to the humerus bone (
The sensor was programmed to record data every 30 seconds. The information stored in the actimeter was transferred through an optical USB Base Station (MAN-BASE-U-4, HOBO) to a personal computer using the software provided by the manufacturer (HOBOware 2.2).
From the information provided by the actimeter, we defined 2 variables: motor activity (A) and body position (P). Motor activity, expressed as degrees of change in position, was calculated at 30-s intervals as the sum of the first derivative of the angle formed between the current sensor position and its position 30 s before, taking into account the X, Y and Z axes. Body position was calculated as the angle between the X-axis of the actimeter and a horizontal plane. Thus, P oscillated between 0° for maximum horizontality and 90° for maximum verticality.
Since no marked differences by gender were detected (Student's t test), male and female data were pooled and analysed together (note that the number of male and female was balanced).
Firstly, data were filtered in order to eliminate erroneous measurements, such as those produced by temporarily removing the sensors. In order to obtain the same sampling frequency for all variables for the purpose of computing TAP values, motor activity and body position data were added up and averaged, respectively, in 10-minute intervals (i.e., the sampling rate of wrist temperature). However, motor activity was expressed as degrees of position change per minute by dividing these previous values by 10.
Rest log data were converted into a binary code, in which 1 corresponded to a declared resting period and 0 to an activity period.
In order to obtain TAP we first normalized the 3 variables (T, A and P) by calculating the 95th and 5th percentiles for each variable and volunteer. In a typical wrist temperature rhythm, the highest values are seen at night when the subject is asleep and the lowest values during the day when the subject is awake, whereas the opposite occurs in the case of motor activity and body position. Normalized wrist temperature values were therefore inverted, so that the maximum values for all 3 variables occurred at the same time of the day.
In a third step, we calculated the mean of the 3 normalized variables. Thus, 0 corresponded to complete rest and sleep, and 1 to periods of high arousal and movement.
TAP calculation was performed by using the next formula:
In order to objectively evaluate whether TAP improved the accuracy of rest-activity deduction as compared to each variable considered separately, we analyzed their correlation to rest probability calculated from the rest-activity diaries. In order to do so, we first had to determine the thresholds, for TAP and each separate variable, above and below which a subject was considered to be awake and resting, respectively. To that end, we calculated the different thresholds for each of 6 randomly chosen subjects by iteratively changing the threshold value in order to maximize the agreement rate between the prediction and the corresponding rest-activity diary. The values thus obtained for each variable were then averaged in order to calculate the final thresholds that were later applied to the whole group. If the value of a variable was above its threshold, it scored 0 (meaning awake), and if the value was under that threshold, it was assigned a score of 1 (resting). Furthermore, specificity, sensitivity and agreement rates were also calculated. Sensitivity reflected the probability of finding an actual resting individual when the TAP score indicated rest. On the contrary, specificity tried to find the probability of agreement when a subject was actually active and our TAP variable also scored a non-rest period. Finally, agreement rates represent the proportion of periods scored as “rest” by TAP that are truly “rest” periods based on the sleep logs analysis.
Linear regressions were performed for TAP and each single variable with respect to the rest periods indicated by the subjects. Significant differences in these correlations were established using a specific test (
In order to characterize the circadian pattern for TAP and each single variable, we performed a non-parametric analysis (as previously described
In addition, we generated weekly representations for all variables studied, as well as mean waveforms for every subject and the group as a whole.
Besides, in order to further analyse CFI, its scores were correlated to parametrical tests such as power content of the first harmonic and percentage of variance explained by the rhythm in a cosinor analysis, performed by the program El Temps (Diez Noguera, 1999).
CFI incorporates three parameters, IV, IS and RA, from the TAP variable. IV values were inverted and normalized between 0 and 1, with 0 being a noise signal, and 1 a perfect sinusoid. Finally, CFI was calculated as the average of these three parameters. Consequently, CFI oscillates between 0 (absence of circadian rhythmicity) and 1 (a robust circadian rhythm).
In this paper CFI has been calculated only for TAP. However, it can be calculated for other variables such as temperature, activity or position separately.
In order to determine whether the TAP and CFI accurately described circadian function, we performed a computational simulation of TAP with different levels of noise and instability in the rest-activity ratio. This kind of methodological approach allows verifying whether CFI varies as expected depending on noise and instability increases, as well as obtaining the corresponding waveform of TAP.
On the one hand, TAP was simulated from a squared wave, considering the activity phase as 66% of the total time of day. A kind of noise present in biological signals, the fractal noise was included in different percentages (from 0 to 100%) to simulate a continuous gradient between normal and extreme situations.
Secondly, we performed these same simulations for a sinusoidal waveform since many rhythms adjust to this kind of wave with two levels of noise (0 and 60%).
Finally, we introduced a new parameter in the simulations according to the instability in the ratio activity/rest for a squared waveform. We chose a 20% in instability for two percentages of noise, 0 and 60%.
Simulations were performed using the Syntesi program by Diez-Noguera (Barcelona, 2007).
A representative individual record from a normal-living subject is shown in
Wrist temperature (A) in red, motor activity (B) in green, body position (C) in orange and TAP (D) in violet of a subject taken as an example, on the left. On the right it is represented the mean waveform of every variable for the same subject. Shaded blue areas coincide with sleep declared by subjects. On the right every variable is represented as value ± SEM.
The integrated TAP variable is shown in
On the left it is shown the weekly evolution of each variable and on the right its correspondent mean waveform. Wrist temperature (A) is represented in red, motor activity (B) in green, body position (C) in orange and TAP (D) in violet. On the right, every point is represented as value ± SEM. Please note that scales vary in mean waveforms representations on the right and representations on the left lack of SEM for a better understanding.
The weekly average TAP data (
In order to characterize the rhythmic patterns presented here, a non-parametric analysis was performed, the results for which are provided in
T | A | P | TAP | |
mean ± sem | mean ± sem | mean ± sem | mean ± sem | |
0.44±0.02 | 0.41±0.01 | 0.50±0.02 | 0.55±0.02 | |
0.19±0.01 | 0.74±0.01 | 0.29±0.02 | 0.23±0.01 | |
0.51±0.02 | 0.69±0.02 | 0.56±0.02 | 0.56±0.02 | |
4:16±0:13 | 4:10±0:09 | 4:49±0:23 | 4:15±0:09 | |
17:21±0:18 | 16:25±0:16 | 15:09±0:24 | 16:45±0:14 | |
0.20±0.01 | 0.10±0.01 | 0.20±0.01 | 0.14±0.03 | |
0.60±0.01 | 0.52±0.01 | 0.72±0.01 | 0.50±0.08 |
Every result is expressed as value ± SEM. T represents wrist temperature results, A motor activity, P body position. IS refers to interdaily stability, IV to intradaily variability, RA to relative amplitude, L5 to the time when the minimum 5 hours average for every variable is found, M10 to the time when the maximum 10 hours average is found for every variable. VL5 and VM10 refer to every variable value for L5 and M10. All variables except L5 and M10 are expressed in arbitrary units.
The time of day (within a period of 5 consecutive hours) when the variables presented the lowest values (L5) was very similar for all of them. As expected, the midpoint of L5 took place during the night, between 04:10 (for A) and 04:49 h (for P). This time can be considered as a valuable phase reference for the circadian system, and coincided with the maximum value of T and the center of the sleep period. M10, the midpoint of the 10 consecutive hour period (when the variable presents maximum values) and a phase marker for the temporal location of the center of the activity period, occurred at mid-day, although with wide dispersion among the different variables (from 15:09 h for P to 17:21 h for T).
In order to objectively evaluate whether TAP improved the accuracy of rest-activity prediction with respect to each variable considered separately, we analyzed the correlation between rest probability, as reported by the subjects in their logs, and temperature, motor activity and body position (
Wrist temperature (A), motor activity (B), body position (C) and TAP (D). Please note correlations coefficients and its probability value on the upper right of every pannel.
In order to determine whether TAP is a reliable marker capable of accurately predicting rest-activity periods, we calculated agreement rates and specificity and sensitivity tests comparing the prediction from each variable (including TAP) and the rest periods reported by the subjects (
Sensitivity | Specificity | Agreement rates | |
0.5500 | 0.7600 | 0.8160 | |
0.5800 | 0.7700 | 0.8261 | |
0.6300 | 0.7900 | 0.8425 | |
0.7400 | 0.8700 | 0.9043 |
T refers to wrist temperature, A to motor activity, P to body position and TAP to the integrated variable. For further information in statistical differences among these parameters, please see tables of contingency in the supporting file information's section.
Sensitivity | Specificity | Agreement rates | ||||
χ2 = 1680.330 (p<0.00005) | χ2 = 1419.116 (p<0.00005) | χ2 = 1414.011 (p<0.00005) | ||||
10054 | 8372+ | 27064 | 8372+ | 37118 | 11699+ | |
11014 | 7912+ | 26564 | 7912+ | 37578 | 11239+ | |
11991 | 7166 | 26333 | 7166+ | 38324 | 10493+ | |
12776+ | 4388 | 28669+ | 4388 | 41445+ | 7372 |
T refers to wrist temperature, A to motor activity, P to body position and TAP to the integrated variable, (see
One step further into the study of the human circadian system by means of non-invasive techniques was to devise a new quantitative Circadian Function Index (CFI), consisting of three parameters calculated based on the TAP non-parametric analysis. CFI scores were then calculated for all subjects. Furthermore, all simulated TAP series were also subjected to non-parametric analysis and CFI calculation. Results are shown in
On the first column, TAP patterns obtained from simulations performed on Syntesi (Diez-Noguera, Barcelona, 2007) are shown. These simulations are drawn on black and subject's real TAP pattern on red. The noise percentage employed for modelisation (% noise) and percentage of instability in rest-activity ratio (R-A Ins) are shown in the second and third columns, respectively. Calculations of interdaily stability (IS), intradaily variability (IV), relative amplitude (RA) and circadian function index (CFI) for every model or real case are also indicated. Note that IS, IV, RA and CFI's units are AU. Green shadow indicates simulations performed on squared waves under different noise levels (while the same R-A Inst is maintained) and subject's real TAP pattern. Yellow shadow indicates simulations performed on squared waves under a 20% of R-A Inst and two extreme noise levels. Orange shadow indicates simulations performed on sinusoidal waves under two different noise levels (while the same R-A Inst is maintained).
TAP patterns, non-parametrical analyses and CFI for two real subjects (scoring the highest and the lowest CFI, respectively) have been inserted in the figure between the simulations. CFI scores perfectly matched TAP waveforms, with CFI values being inversely related to the noise level. When focusing on the real subject with CFI = 0.73, we can appreciate how his/her CFI coincided with the 60% noise simulation. IS and IV values were similar in both cases, whereas RA was much higher for the subject. Small differences were found between the second real subject and the 80% noise simulation. However, the graphic representations for both matched quite closely.
As can be seen from this figure, the real subjects scored CFIs between 0.73 (best) and 0.43 (worst). This shows a narrow band within which a homogeneous real population should lie.
Also, we wanted to know whether CFI behaved as expected for other types of waveforms and so we performed these simulations on sinusoidal waves concerning only noise quantity. Again, CFI decreases when the percentage of noise increases, proving that it is very sensitive to circadian disturbances in the waveforms tested.
Finally, we performed correlations between CFI and two parametrical tests that assumed a sinusoidal adjustment: power content of the first harmonic and percentage of variance explained by the rhythm in a cosinor analysis. We only found a strong correlation between CFI and first harmonic (r = 0.532, p<0.001). Correlation between CFI and percentage of variance was r = 0.014, p = 0.919).
Circadian chronodisruption constitutes a heterogeneous group of circadian system impairments, but no objective examination methods are currently being used for their clinical diagnosis in humans. Thus, a new quantitative strategy for assessing circadian system status will facilitate the development of clinical applications of Chronobiology. The present study describes for the first time a new variable, TAP, which integrates the simultaneous information from: wrist temperature, motor activity and body position, in addition to the implementation of a quantitative index for scoring circadian robustness (CFI). TAP provides a reliable and accurate assessment of human circadian system status and the ability to detect rest-activity cycles in large populations under ambulatory conditions.
We selected wrist temperature as part of our TAP variable because it is the result of internal and external influences and provides integrated information about the master pacemaker function and internal and external
Actimetry has been proposed as a substitute for other complex, expensive methodologies such as polysomnography or core body temperature to evaluate circadian system status in humans
Furthermore, to the best of our knowledge, body position has never been used in the assessment of human circadian functionality, mainly because most actimeters are placed on the wrist. Since we placed the actimeter aligned with the arm, we were able to differentiate between when the subject is in and out of bed. Analyzed separately, motor activity and body position showed higher correlations and agreement rates (as well as specificity and sensitivity values) with the rest-activity periods reported by subjects than wrist temperature did. This is not surprising, considering that both variables respond to sleep and their values vary dramatically at the exact moment of waking up or lying down. These variables are less dependent on the endogenous component of the circadian system than the wrist temperature rhythm.
However, each variable individually introduces specific artifacts, as being influenced by several external signals. Thus, the use of an integrative variable combining the study of several variables allows to correct mistakes attributable to the interpretation of single variables. For example, the masking effects of sleep on temperature can be eliminated by taking into account motor activity
When considering non-parametric analysis, it should be stressed that the timing of L5 was very stable for all variables studied. It took place around 4 AM, during the second half of the night, when REM sleep (characterized by minimum muscle tone
Despite the fact that motor activity and body position showed good rates of agreement and a high correlation with rest as reported by the subjects, these results improved even further for the integrated TAP variable (with an agreement rate of 90.43% and r = 0.993), which would indicate that TAP is able to deduct rest-activity periods more reliably than any of the separate variables by themselves. As also expected, sensitivity and specificity results were better for TAP than for single variables. Other authors have found high levels of sensitivity (the ability to detect sleep) but low levels of specificity (the ability to detect wake states)
All correlation factors, agreement rates and specificity-sensitivity values were compared to the rest-activity diaries. These logs had the advantage of allowing subjects to record information at the same time that the event actually took place. However, they did not always prove to be an objective measure of sleep timing, and they were dependent upon the subject's willingness to complete them correctly
Another factor to consider is that in order to improve the ability of actigraphy to predict sleep periods, most commercial brands of actimeters have developed complex algorithms adapted to a specific population; however, the use of highly specific algorithms impairs sleep detection when studying other age groups or patients with different medical conditions, making inter-group comparisons very difficult. For example, a decrease in the accuracy of actimetry for sleep detection was seen in a study of patients with neurological and other medical conditions associated with ageing
The TAP variable allows us to predict rest-activity periods very accurately. In this sense, we strongly believe that our method allow us getting rid of sleep logs, subjected to volunteers' degree of compliance.
Dichotomy indexes such as I<O, I>O and autocorrelation indexes used as indicators of the circadian system status have been successfully correlated to pathological states such as cognitive deficits
CFI proved to be very accurate when trying to define the subjects' circadian status and responds as expected (decreasing) when instability and noise of the rest-activity ratio increases. Next step in the study of TAP will imply populations with circadian disturbances, such as the elderly, shift workers, cancer patients,…where a reduced amplitude and higher fragmentation in their TAP rhythm would be expected.
In conclusion, our results show that the TAP variable, which combines information from temperature, actimetry and position, constitutes a step forward in the ambulatory evaluation of circadian system status in humans. TAP provides information about the status of the circadian system because it includes a variable with a large endogenous component (temperature), but also variables that are more reactive to behavioral demands, such as motor activity and body position. Furthermore, CFI allows for the quantitative classification of populations and provides important information about the circadian timing system, which facilitates the objective evaluation of the efficacy of treatments to improve chronodisruption.
We wish to acknowledge the contributions made by our Chronobiology and Endocrinology and Metabolism students, and we thank them for their interest and motivation they demonstrated as they took part in these experiments. In addition, we would like to thank Imanol Martínez for his kind revision of this manuscript. We are very grateful to Julia and Laura Mena García and Inés Martínez for their work on the representative image of this work. Finally, we would like to thank Manuel Canteras Jordana for his patient advice on statistical procedures.