Physiology & Behavior 126 (2014) 30–38

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Physiology & Behavior

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Ambulatory Circadian Monitoring (ACM) based on Thermometry,motor Activity and body Position (TAP): A comparisonwith polysomnography

Elisabet Ortiz‐Tudela a, Antonio Martinez‐Nicolas a, Javier Albares b, Francesc Segarra b,Manuel Campos c, Eduard Estivill b, Maria Angeles Rol a,⁎, Juan Antonio Madrid a

a Chronobiology Laboratory, Department of Physiology, Faculty of Biology, University of Murcia, Spainb Sleep Disorders Unit, Institut Universitari Dexeus, Barcelona, Spainc Department of Computer Science and Systems, University of Murcia, Spain

H I G H L I G H T S

• The integrated variable TAP predicted reliably sleep and wake periods.• Wrist temperature rhythm proved to be more sensible to detect sleep phases than actigraphy.• TAP scores better than actigraphy for several sleep parameters.• TAP could be a valuable tool to assess sleep in screening studies.

⁎ Corresponding author at: Chronobiology LaboratoFaculty of Biology, University of Murcia. Espinardo Ca30100, Spain. Tel.: +34 868 88 3929; fax: +34 868 88 39

E-mail address: angerol@um.es (M.A. Rol).

0031-9384/$ – see front matter © 2014 Elsevier Inc. All rihttp://dx.doi.org/10.1016/j.physbeh.2013.12.009

a b s t r a c t

a r t i c l e i n f o

Article history:Received 22 July 2013Received in revised form 9 October 2013Accepted 23 December 2013

Keywords:SleepAmbulatory recordingsWrist temperatureActigraphyThermometryTAPPolysomnographyHuman circadian rhythms

An integrated variable based on the combination ofwrist Temperature,motor Activity and body Position (TAP)waspreviously developed at our laboratory to evaluate the functioning of the circadian system and sleep–wake rhythmunder ambulatory conditions. However, the reliability of TAP needed to be validated with polysomnography(PSG). 22 subjects suffering from sleep disorders were monitored for one night with a temperature sensor(iButton®), an actimeter (HOBO®) and exploratory PSG. Mean waveforms, sensitivity (SE), specificity (SP),agreement rates (AR) and comparisons between TAP and sleep stages were studied. The TAP variable wasoptimized for SE, SP and AR with respect to each individual variable (SE: 92%; SP: 78%; AR: 86%). These resultsimproved upon estimates previously published for actigraphy. Furthermore, TAP values tended to decreaseas sleep depth increased, reaching the lowest point at phase 3. Finally, TAP estimates for sleep latency(SL: 37 ± 9 min), total sleep time (TST: 367 ± 13 min), sleep efficiency (SE: 86.8 ± 1.9%) and numberof awakenings (NA N 5 min: 3.3 ± .4) were not significantly different from those obtained with PSG(SL: 29 ± 4 min; SE: 89.9 ± 1.8%; NA N 5 min: 2.3 ± .4), despite the heterogeneity of the sleep pathologiesmonitored. The TAP variable is a novel measurement for evaluating circadian system status and sleep–wakerhythmswith a level of reliability better to that of actigraphy. Furthermore, it allows the evaluation of a patient'ssleep–wake rhythm in his/her normal home environment, and at a much lower cost than PSG. Future studies inspecific pathologies would verify the relevance of TAP in those conditions.

© 2014 Elsevier Inc. All rights reserved.

1. Introduction

Nowadays, the study of circadian rhythm impairments and sleepdisorders is a hot topic in clinical research because of their role in thedevelopment and exacerbation of awide range of pathologies, includinghypertension [1], cancer [2–4], aging [5], sleep disorders [6,7]metabolic

ry, Department of Physiology,mpus. Espinardo, Murcia CP.63.

ghts reserved.

syndrome [8] and affective and cognitive impairments [9]. Thus, thedevelopment of non-invasive techniques to evaluate this impairmentin subjects living in their normal home environment has attracted alot of interest.

In 2010, our laboratory developed a new integrated variable, calledTAP, based on the information provided by the Ambulatory CircadianMonitoring (ACM) of wrist Temperature, motor Activity and bodyPosition rhythms [10]. The purpose of this variable was to provide abetter assessment of the status of the human circadian system. Thisvariable was validated as a useful tool for the study of circadianrhythms, allowing the classification of circadian patterns according to

Table 1Characteristics of the subjects participating in the study.

Age (mean ± SEM) (52.3 ± 3.2) y.o.

Nb of subjects

Gender Men 15Women 7

Pathology diagnosed OSA 8Insomnia 6OSA & Insomnia 2Snoring 1DSPD 1PLM 1Not confirmed 3

OSA stands forObstructiveSleepApnea. DSPD stands forDelayed Sleep PhaseDisorder. PLMstands for Periodic Limb Movement disorder. “Not confirmed” represents the 3 subjectsfrom whom no sleep pathology could be detected after the PSG.

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their robustness. Moreover, the TAP variable was also able to providea reliable evaluation of the sleep–wake rhythm [10]. In the study inquestion, TAP was compared to sleep logs, and excellent correlationsand high values of sensitivity, specificity and agreement rates werefound. However, in order to validate the TAP algorithm and improveits accuracy in sleep detection, this tool needed to be compared to the“gold standard” in sleep studies, polysomnography (PSG), and testedwith subjects who demonstrate poor sleep quality.

PSG is the accepted gold standard for analyzing sleep architecture bydetecting and differentiating sleep phases and objectively estimatingwake and sleep timing, and it is also essential for diagnosing a numberof sleep-related pathologies [11]. However, PSG is not easy to perform,as it requires trained specialists, expensive equipment and frequentlya special room set aside for this purpose, and although home recordingis feasible nowadays [12], still it results uncomfortable for patients.Furthermore, the patient is connected to several wires and is awayfrom his/her home environment. Consequently, patients often sufferfrom the “the first night effect”, which makes sleep difficult, especiallyif the patient is experiencing insomnia [13]. For these reasons, alterna-tive procedures to accurately detect sleep–wake cycles without theseconstraints are desirable.

Actigraphy has been proposed as a partial solution to circum-vent these problems. As early as 1997, the American Academy ofSleep Medicine (AASM) advocated for actigraphy as a useful toolfor sleep studies, but with no clear clinical relevance [14]. Later in2007, the AASM declared actigraphy as clinically appropriate forthe study of several sleep-related pathologies and circadian rhythmdisorders, including insomnia, hypersomnia, obstructive sleepapnea, familial advanced sleep phase disorder (FASPD), and de-layed sleep phase disorder (DSPD) [15]. The main advantages tothe use of actigraphy are that it involves practically no constraintsfor the subjects, and it permits long-term recording (weeks oreven months) in the patient's own home, and at a lower cost ascompared to PSG.

In general, actigraphy has proved to be very sensitive for detectingsleep, but its ability to detect wake states during the night (specificity)

is not so high [16,17]. It tends to overestimate sleep, as it considersmotionless moments as sleep. That is the reason why it loses clinicalimportance when studying specific pathologies like insomnia, inwhich the subject tends to lie awake for long periods of time withoutalmost any movement [18,19]. In addition, the movements of a bedpartner, vehicle vibrations and sensor removal can all affect the reliabil-ity of measurements [20].

To counteract the inaccuracy associated with the use of a singlevariable, multivariable recordings under ambulatory conditions haverecently been proposed [10,21]. These ACM procedures integratea combination of variables, such as temperature, activity and bodyposition, which provide complementary information about circadiansystem functionality.

Until now, temperature has yet to be considered as a feasible vari-able for ACM, because it has traditionally been recorded with a rectalprobe, which is not readily accepted by the subjects volunteeringfor these tests. However, in 2008 Sarabia JA et al. [22] showed that theperipheral temperature rhythm measured on the wrist was a robustrhythm with a pattern that is almost identically reverse to that of corebody temperature. Furthermore, evidence suggests that sleepinessmay be more closely linked to increased peripheral skin temperaturethan to a decrease in core temperature [23,24]. To date, wrist tempera-ture has been used to evaluate circadian rhythms under severalphysiological and pathological conditions, such as newborn circadianmaturation [25], metabolic syndrome [26], and obesity [26]; and acorrelation has even been found with clock gene polymorphism [27].However, like core body temperature, peripheral temperature is sub-jected to environmental and physiological influences, includingphysicalactivity, body position, high environmental temperatures and sleepitself [22,28–30].

Body position has rarely been considered for this purpose,because most actimeters are placed on the wrist, and thus theyprovide no information about the horizontal/vertical position ofthe subject. However, when the sensor is appropriately positioned,as has already been suggested [1,10,21], this signal helps depictdaily habits, and permits distinguishing when the subject is lyingdown or sitting (for example, at a computer), in spite of low activitylevels in both cases [10].

Since masking, that is, the influence of external signals that affector mask overt rhythms [10], not only occurs with actigraphy, butalso occurs when studying any single variable (such as peripheraltemperature), as already mentioned, new approaches to overcomepotential artifacts during ambulatory measurements have beendeveloped. These include multivariable recordings [21] and inte-grated variables, such as TAP, as described by Ortiz-Tudela et al.(2010) [10].

Thus, although the TAP algorithm has demonstrated its utility in theevaluation of the rest/activity rhythm when compared to sleep logs inhealthy young subjects, the usefulness of such a variable has yet to bevalidated as a reliable system to record sleep–wake rhythms, as com-pared to PSG (the gold standard for sleep evaluation) and in subjectswith poor sleep quality. Thus, the aim of the present work is to deter-mine the reliability of the TAP algorithm calculated by means ofACM to assess sleep–wake detection in patients with different sleeppathologies.

2. Methods

2.1. Ethics statement

The study performed was approved by the University of Murcia's internal review board and ethics committee and abided by the HelsinkiDeclaration of 1975 (revised in 1983). Besides, it was designed as to meet the ethical standards outlined in Portaluppi et al. (2010) [31]. After fullexplanation of the procedure and before being enrolled in the study, all patients signed an informed consent form.

Fig. 1.Wrist temperature in each sleep phase, during sleep interruptions and after sleep end. A: phase 1 + 2, B: phase 3, C: REM sleep, D: during sleep interruptions, E: after sleep end.n = 9, sampling time: one minute intervals. Values are expressed as mean ± SEM. The gray area indicates the percentage of subjects in each phase (1 + 2, 3, REM, sleep interruptionsor sleep end) per time point, expressed on a unitary scale. Only the first event in the night for phase 1 + 2 and phase 3 and two events for REM phase and sleep interruptions per subjectwere considered.

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2.2. Participants

Twenty-two subjects (15 men and 7 women, 20–80 years of age) were recruited for the study in strict order of arrival at the Sleep Unit of theInstitut Universitari Dexeus in Barcelona (Spain). Room temperaturewasmaintained at 23° ± 1 °C and the same experimental settingswere appliedto all the subjects. These patients complained of sleep problems and were prescribed PSG testing in order to diagnose the underlying problem. Theidentified conditions included sleep apnea, periodic legmovements, conciliation andmaintenance insomnia and delayed sleep phase. No inclusion orexclusion criteria were defined. However, we verified that none of the patients were feverish. Patient's characteristics are detailed in Table 1.

2.3. Study design

Supervised PSG recordings were taken for around 8 h during the patients' stay at the Sleep Unit. Patients also wore temperature and activitysensors during the night on which PSG was performed. Both sensors were placed coinciding with preparation for PSG. All patients wore a temper-ature data logger (iButton Thermochron®, Dallas) on their non-dominant wrist, programmed with a sampling frequency of one measurementevery 10 min. However, 9 of these patients also wore an additional sensor in the same spot, programmed to record temperature every minute.

The activity sensor, an actimeter (HOBO® Pendant G Acceleration Data Logger), was placed on the upper part of the non-dominant arm andprogrammed to sample activity and body position every 30 s.

2.4. TAP, TA, TP and AP computation

Wrist temperature, motor activity and body positionwere combined according to the algorithms described by Ortiz-Tudela et al. (2010) in orderto obtain the integrated TAP variable. The calculation of TA, TP and AP was performed according to the same TAP algorithm, but combining only twovariables at a time (temperature and activity for TA, temperature and position for TP, activity and position for AP). Briefly, each variable was normal-ized between 0 and 1, after removing artifacts identified by visuals inspection of the data (such as those produced by temporarily removing the sen-sors, for example for personal hygiene). In addition, wrist temperature data were inverted in such a way that maximum values for all variables

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occurred at the same time (note that high skin temperatures are found during the night when low activity or position levels are present). We thencalculated themean of the normalized variables (T and A and P, T and A, T and P or A and P) for each subject, obtaining values ranging between 0 and1. Values near 1 indicate a low wrist temperature, a high level of activity and a vertical arm position, suggesting a high level of activation, whereasvalues near 0 correspond to a high wrist temperature, a low level of activity and a horizontal arm position, which is compatible with quiet sleep.

2.5. Polysomnography

A night of conventional in-laboratory PSG testing was used as the standard of reference for assessing the reliability and validity of the TAPalgorithm. Brain activity was recorded by means of EEG surface electrodes placed on the rolandic (C3–M2), frontal (F3–M2) and occipital (O1–M2)areas of the brain in monopolar derivation, based on the 10–20 International system.

Electromyographic (EMG) activity was measured by applying electrodes to the submental region, bilateral tibialis anterior muscles and bodyposition sensor. Electrocardiography (ECG) was also recorded. Ocular movements (EOG) were recorded by means of two electrodes placed on theleft eye (LOC-M2) and on the right eye (ROC-M2). Nasal and mouth thermal resistances, as well as abdominal and thoracic induction strips, wereused to analyze respiratory dynamics. Nasal flow and blood oxygen saturation (SaO2) were recorded by means of pulse oximetry.

PSG data were acquired simultaneously from 15 different channels at 30 s per page, during approximately 8 h using the Nicolet™ Sleep System(Natus Medical Incorporated, San Carlos, CA, USA).

Based on PSG data scoring, sleep stages (1, 2, 3 and REM) and wakefulness were categorized according to the American Academy of SleepMedicine (AASM) criteria.

In order to match PSG results (one page every 30 s) to TAP recordings (one measurement of wrist temperature every 10 min for 22 subjects),we calculated the mode for every 10 min of PSG results (20 PSG pages) for each time period studied. In the case of the 9 subjects who wore theextra temperature sensor programmed at 1-minute intervals, one PSG data out-of-two were selected in order to match the TAP values scored in1-minute intervals. This way, only the TAP value which timing matched the one from PSG was chosen.

2.6. Detection of polysomnographic events by TAP analysis

For this analysis, only the subjects (n = 9) who wore the additional temperature sensor programmed for one-minute recordings wereconsidered, in order to evaluate changes in all variables over shorter periods of time.

To calculatemean TAP values corresponding to different sleep stages, all subjectswere synchronizedwith one another using the start of one of thedifferent sleep events as a reference. This reference could be the time when PSG marked the entrance into phase 1–2, phase 3 or REM phase, thebeginning of a sleep interruption and waking up time. Since phase 1 has a very short duration and represents a very light and reversible state ofsleep, we considered both phases 1 and 2 together; thus sleep started at either phase 1 or 2, and was denoted as “phase 1 + 2”. Once the subjectswere event-synchronized, the mean and standard error of the mean per time point were calculated.

To avoid bias and for only this analysis, phase 1 + 2 and phase 3 were only analyzed once per subject, choosing the first event in the night.However, due to the variability of the REM phase and sleep interruptions among subjects, two events from these phases were evaluated per subject.

To avoid individual activity level biaswhen averaging the subjects' activity, this variablewas recalculated as a dichotomic variable (movement vs. nomovement). Thus,≥10 Δ°/minwas considered as a period ofmovement and defined accordingly as “1”. On the contrary, a time periodwith an activityvalue b10 Δ°/min was considered “0”. Afterwards, these values were averaged among the subjects, thus obtaining the percentage of subjects movingmore than 10° per time period and phase.

2.7. Sensitivity, specificity and agreement rates

Sensitivity and specificity were defined as follows:

Sensivity ¼ Number of periods scored as sleep by variable and PSGNumber of periods scored as sleep by variable and PSG þ number of periods scored as wake by variable but as sleep by PSG

Sensivity ¼ Number of periods scored as wake by both variable and PSGNumber of periods scored as sleep by both variable and PSG þ Number of periods scored as sleep for variable but as wake by PSG

where variable was considered as T, A, P, TA, TP, AP or TAP. Finally, agreement rates measure the coincidence of both systems in evaluating an event(wake or sleep). The three measurements are expressed as percentages, with 100% indicating maximum coincidence and 0% indicating maximumdisagreement.

In order to calculate sensitivity, specificity and agreement rates for each variable used to differentiate sleep and wakefulness (T, A, P, TA, TP, APor TAP), we first needed to establish a threshold under which to define sleep periods. To do this, we manually increased and empirically verifiedthresholds starting at .10 until we found the optimal threshold for every subject, at which agreement rate values were maximized. These individualvalues (from the 22 subjectsmonitored every 10-minutes) were then averaged to obtain a single threshold thatwas applied to the entire population.All episodes of sleep were considered to this purpose.

In order to compare agreement rate, specificity and sensitivity for each variable and TAPwe performed aWilcoxon Rank Test andwe set statisticalsignificance at p b .05.

2.8. Phase detection

Comparisons between the “wake” state (defined as body position inferior to 45° and scored as wakefulness in PSG record), and phase 1 + 2,phase 3 and REM were made by means of a repeated measures ANOVA followed by post hoc pairwise comparisons using a Bonferroni test. Forthese analyses, all episodes from each sleep phase (22 subjects monitored every 10-minutes) were considered.

Fig. 2.Motor activity in each sleep phase, during sleep interruptions and after sleep end. A: phase 1 + 2, B: phase 3, C: REM sleep, D: during sleep interruptions, E: after sleep end. n = 9,sampling time: one minute intervals. Values are expressed as mean ± SEM. The gray area indicates the percentage of subjects in each phase (1 + 2, 3, REM, sleep interruptions or sleepend) per time point, expressed on a unitary scale. Only the first event in the night for phase 1 + 2 and phase 3 and two events for REM phase and sleep interruptions per subject wereconsidered.

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2.9. Sleep parameters

In order to calculate sleep onset latency, total sleep time, sleep efficiency and the number of awakenings of more than 5 min (NA N 5), twodifferent thresholds were used. The threshold employed to detect the moment of beginning and ending of sleep was the TAP value that maximizedagreement rates, as already described. Similarly, nocturnal awakenings were identified by finding the individual threshold that optimized thedetection of sleep interruptions according to PSG for each of the 22 subjects whose wrist temperature was sampled every 10 min. For this analysis,all episodes from each sleep phasewere taken into account. Secondly, these individual thresholdswere averaged and the value obtainedwas appliedto the population as a whole.

To assess whether differences between TAP and PSG predictions were statistically significant a Student's T test was employed, except for thenumber of awakenings for which a U Mann–Whitney non-parametrical test was used.

All data were expressed as mean ± SEM, and all statistical analyses were performed with SPSS version 15.0 (SPSS, Chicago, Illinois, USA). Valuesof p b .05 were considered to be statistically significant.

3. Results

3.1. Event synchronization based on sleep phases

Whenwrist temperaturewas analyzed considering each sleep phaseas defined by PSG (Fig. 1), we found that during phase 1 + 2 (Fig. 1A)wrist temperature increases, anticipating sleep by approximately10 min. Immediately before phase 3 (Fig. 1B), the temperaturecontinues to rise slightly and seems to stabilize 10 min later. However,whenREMsleep starts (Fig. 1C), the temperature dropsmoderately. Dur-ing sleep interruptions (Fig. 1D), wrist temperature seems to decrease,

but most interruptions are too short to appreciate any significant chang-es in temperature. Nevertheless, when sleep ends (Fig. 1E), the temper-ature drops up to 1 °C in about 5 min.

With regard to motor activity (Fig. 2), the percentage of subjectsmoving falls to zero when they enter the first phase 1 + 2 (Fig. 2A).During phase 3 (Fig. 2B) and the REM phase (Fig. 2C), the frequency ofmovements is low, but some short episodes of activity can still be appre-ciated. Conversely, following a sleep interruption (Fig. 2D) and at sleepend (Fig. 2E), activity increases sharply.

As expected, the integrated variable TAP (Fig. 3), which combinesthe information of the other 3 variables, follows the same dynamics.

Fig. 3. TAP in each sleep phase, during sleep interruptions and after sleep end. A: phase 1 + 2, B: phase 3, C: REM sleep, D: during sleep interruptions, E: after sleep end. n = 9, samplingtime: one minute intervals. Values are expressed as mean ± SEM. The gray area indicates the percentage of subjects in each phase (1 + 2, 3, REM, sleep interruptions or sleep end) pertime point, expressed on a unitary scale. Only the first event in the night for phase 1 + 2 and phase 3 and two events for REM phase and sleep interruptions per subject were considered.

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For phase 1 + 2 (Fig. 3A), TAP values decreased in advance of sleeponset. As early as phase 3 (Fig. 3B) and during REM (Fig. 3C) it tendsto stabilize. Finally, during interruptions (Fig. 3D) and after sleep hasended, but the subject is still lying in bed (Fig. 3E), its values increasedue to higher levels of activity and lower temperature values.

3.2. Sensitivity, specificity and agreement rates

The ability of each variable to detect sleep (sensitivity), wakeful-ness (specificity) and the percentage of agreement (agreement

Table 2Agreement rates, sensitivity and specificity for the optimal thresholds in wrist temperature(T), activity (A), body position (P), and the combined variables TA, TP, AP or TAP.

Optimal threshold Agreement rate Sensitivity Specificity

T .43 79.57 71.73 85.87A .10 61.08 95.01 22.07P .29 76.93 82.57 65.09TA .34 86.71 93.98 77.62TP .37 83.45 89.56 73.54AP .23 77.80 84.49 65.91TAP .30 85.70 92.29 77.82

Data were obtained from the 22 patients with a sampling time interval of 10 min. See theMethods section for details.Wilcoxon Rank test confirmed that TAP yielded better agreement rate, sensitivity andspecificity scores than A (p b .001 for the 3 comparisons between TAP and A) and thatTA and TAP yielded similar scores when compared to PSG (p N .05 for the 3 comparisonsbetween TAP and TA). For details, see the Results sections.

rates) compared to PSG is shown in Table 2. In terms of individualvariables, activity showed the lowest specificity (22%) whereaswrist temperature exhibited the highest specificity (86%). By con-trast, sensitivity was higher for activity (95%) than for wrist temper-ature (72%). Intermediate sensitivity (83%) and specificity values(65%) were found for body position. Note that the composite oftemperature and activity (TA) and TAP presented the highest valuesfor these three parameters when the optimal threshold for eachsubject was applied. This was confirmed at statistical level sinceTAP presented statistically higher agreement rates and specificitywith PSG than A (p b .001 for both comparisons), P (p b .001 forboth comparisons), TP (p b .01 for both comparisons) or AP(p b .001 for both comparisons) but with similar scores for agree-ment rates (p = .144) and specificity (p = .074) and lower sensi-tivity (p = .012) when compared with T. However, TAP and TAwere not significantly different from each other (p = .943 for agree-ment rates, p = .118 for sensitivity and p = .879 for specificity).

3.3. Sleep phase detection

Since TA and TAP showed the highest agreement rates in theprevious analyses, both of these were tested for sleep phase detection,as was every individual variable (T, A and P).

According to our results (Fig. 4A), TAP can discriminate betweensleep and wake states during nighttime (ANOVA's F = 17.070;p b .001; eta = .729). Even a tendency toward lower values in deepersleep phases that usually rise in REM sleep was found. Similar results

Fig. 4.Mean values for TAP (A), TA (B), motor activity (C), wrist temperature (D) and body position (E), according to different phases. Data, obtained from 10-minute interval recordings,are expressed as mean ± SEM (n = 22). Repeated measures ANOVA and post hoc Bonferroni test were used. Different letters indicate significant differences (p b .05).

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(ANOVA's F = 20.691; p b .001; eta = .756)were found for TA (Fig. 4B).However, with regard to motor activity (Fig. 4C), no differences amongsleep phases were observed, but it was successful in distinguishingsleep from wakefulness (ANOVA's F = 23.270; p b .001; eta = .777).The picture is somewhat different in relation to wrist temperature, asthis variable allowed for differentiating between phases 2 and 3(ANOVA's F = 11.338; p b .001; eta = .630) (Fig. 4D). In this case,REM sleep once again seemed to be an intermediate state betweenwake and deep sleep stages. Body position (Fig. 4E) seemed to discrimi-nate less accurately between wakefulness and phase 1 + 2 than WT ormotor activity (ANOVA's F = 3.396; p = .038; eta = .338).

Finally, when only considering two possible states: wake in bedand sleep (independently of the phase) for all variables, significant

Table 3Comparison between the sleep latency, total sleep time, sleep efficiency and number of awake

Sleep latency (min) Total sleep time (min)

TAP PSG TAP PSG

37 ± 9 29 ± 4 367 ± 13 382 ± 9

Datawere calculated for the 22patientswith a sampling time interval of 10 min. No significant dsleep latency, total sleep time and sleep efficiency or Mann–Whitney U Test for the number of

differences (Student T test, p b .05) were found for wrist tempera-ture (34.05 ± .15 °C vs. 34.56 ± .15 °C, respectively), motor activi-ty (18.28 ± 1.79 Δ°/min vs. 7.23 ± 1.05 Δ°/min, respectively),body position (5.20 ± 1.23° vs. 1.60 ± .51°), and TAP (.37 ± .02A.U. vs. sleep .22 ± .02 A.U., respectively).

3.4. Sleep parameters

To quantify sleep parameters such as sleep latency, total sleeptime, sleep efficiency and number of awakenings (Table 3), twothresholds were established for TAP recordings, as described in theMethods section.

nings, as calculated by TAP or PSG.

Sleep efficiency (%) Number of awakenings

TAP PSG TAP PSG

86.8 ± 1.9 89.9 ± 1.8 3.3 ± .4 2.3 ± .4

ifferences between TAP andPSG estimationswere found (according to a Student's T test forawakenings).

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In order to detect sleep onset and offset, the optimal threshold thatmaximized agreement rates (.3) was employed. A second thresholdidentified sleep interruptions. In this case, we averaged the individualvalues that optimized the number of awakenings as evaluated by TAPand PSG. This threshold was set at .45. Thus, during the period betweensleep onset and offset, TAP values over .45 were identified as awaken-ings and values under .45 were coded as sleep periods.

No differences have been found in these parameters as calculated byeither PSG or TAP (according to Student's T orMann–WhitneyU Test, asappropriate).

4. Discussion

Based on the comparison to PSG, our results show that TAP, or TAalgorithms when position constraints exist, as it occurs when a patientis compulsory lying in bed, show the highest specificity, sensitivityand agreement rates of all variables tested in discriminating betweensleep and wakefulness. In addition, wrist temperature by itself, apartfrom anticipating sleep, can also differentiate among phase 2, 3 andREM stages, although it fails to detect short sleep interruptions. Howev-er, sleep interruptions can be effectively evaluated when activity andposition data are also considered. In addition, TAP is able to reliably cal-culate several sleep parameters, such as total sleep time or sleepefficiency. Thus, TAP, in conjunctionwith information on sleep architec-ture provided by skin temperature, constitutes a reliable and moreinformative alternative to actigraphy to evaluate the sleep–wake cyclein an ambulatory manner.

PSG represents the “gold standard” technique in sleep studies. Itsreliability and efficacy in detecting sleep pathologies [11] beyond dis-cussion. However, its methodological complexities and expensiveequipment, although nowadays in-home PSG is also available [12],have led researchers to develop non-specialized, user-friendly devicesfor monitoring sleep–wake rhythms in the home environment.

Actigraphy has been extensively studied and has been proposed as areliable method to evaluate sleep quality and discern sleep and wakestates [11,32]. However, every individual variable studied is subjectedto a certainmasking of the results due to potential artifacts. For instance,actigraphy tends to overestimate sleep, as it considers motionlessperiods as sleep [18,33]. This can lead to the misinterpretation of datafrom insomniac patients who are experiencing trouble falling asleep,but who are lying quietly in bed [20,34]. In this sense, in the literatureto date, actigraphy has proven to be quite accurate in detecting sleepperiods during rest spans, as compared to PSG. However, the specificityof wakefulness detection is weaker [16,17]. Our study also supports thefinding of higher sensitivity, but low specificity for actigraphy (Table 2).Furthermore, activity underwent no changes between sleep phases inour patients, proving its inability to discern sleep depth by itself(Fig. 4C). Although previous studies seem to have found differencesbetweenNREMandREM sleep in subjectswho experience sleep pathol-ogies such as periodic limb movements [35], this capacity could belimited to a condition that specifically affects patient movements.

It has been suggested that temperature plays an important role insleep initiation, and the distal–proximal skin temperature gradient hasbeen defined as a good predictor of sleep onset latency [36]. In ourstudy, temperature seems to anticipate light sleep, as its values beginto increase about 10 min before phases 1 and 2. Physiologically, acentral temperature drop is associated with sleep onset. This is preced-ed by the activation of peripheral vasodilation, which causes peripheraltemperature increases [24], the result being a peripheral temperaturerhythm that anticipates that of the central temperature [22,37,38].Surprisingly, similar towhat happensmoments before and immediatelyafter waking up, temperature drops at the beginning of the REM phase.This result shows the resemblance of the REM phase to the wake state,not only with respect to the electrical activity of the brain [39], but alsoin relation to the wrist temperature rhythm. In addition, temperatureshowed the highest specificity of all the variables studied (single and

composite), and it was also the only variable whose values variedsignificantly among the sleep phases (Table 2 and Fig. 4D).

In our study, subjects had to maintain a horizontal position due tothe obligation to stay in bed during PSG recording; thus informationabout body position does not contribute any information to sleep–wake detection. Nevertheless, when considering both nighttime anddaytime, it permits the identification of rest periods during daytime[10], and therefore it has been proposed as a key tool for the correctidentification of dipping and non-dipping circadian blood pressurepatterns [1] by personalizing resting periods. Accordingly, body positionshowed the smallest wake detection (specificity values around 65%)and only medium values of sleep detection (around 83%), proving thatin situations in which body position is restricted to horizontality, thisvariable contributes little to the sleep detection (Table 2 and Fig. 4E).

Finally, the integrated TAP variable proved to be more sensitive,more specific and had higher agreement rates with PSG than thosealready published for actigraphy and than isolated actigraphic record-ings obtained in our own study, particularlywhen considering a hetero-geneous population of patients with different sleep pathologies [16,17].Thus, as previously described [10], composite variables are moreefficient in detecting sleep and wake states than isolated variables,since individual artifacts can be corrected. Among sleep phases, TAP isnot significantly modified. However, and contrary to what happenswith actigraphy, there is a progressive trend for TAP to decrease withdepth of sleep. In the REM phase, TAP increases slightly, suggesting astate more similar to the wake state [39] (Fig. 4A).

TA behaves in the same manner as TAP (Fig. 4B), suggesting onceagain that body position fails to contribute relevant information dueto the conditions of this particular study, i.e., short time recording(only one night) and forced horizontally during the night (even tourinate). On the other hand, high agreement levels have been foundbetween TAP and PSG for sleep parameters such as total sleep time,sleep efficiency and number of awakenings, showing the reliabilityand usefulness of the former method in this field (Table 3).

This integrated approach has also been recently applied byO'DriscollDM (2012) [40] using the SenseWear Pro3 Armband(™) sensor(Bodymedia Inc.), which incorporates an accelerometer, a heatflux sen-sor, a galvanic skin response sensor, a skin temperature sensor and anear-body ambient temperature sensor. However, in this particularstudy, which was performed on patients suffering from obstructivesleep apnea, the authors found specificity values of only 50%, sensitivityof 89% and agreement rates of 80%, while in our case we obtained 78%for specificity, 92.3% for sensitivity and 85.7% for agreement rates.Differences could be attributed to differences in sleep pathologies,signal processing and to the choice of proximal temperature, which isnot subjected to the same regulatory processes as distal temperature[41].

It must be recognized that our study has certain limitations. Normal-ly, circadian rhythms and their alterations require longer studies [42,43]and the inclusion of the daytime periodwould certainly have contribut-ed to even more informative results, since considering individualdiurnal activity levels would allow relativizing nocturnal restlessnessthreshold, and thus could improve the evaluation of sleep quality and,at the same time, circadian robustness. In addition, under ambulatoryconditions, body position would be an important asset to detect restperiods during daytime hours and to help depict nocturnal awakeningsaccompanied by standing or semi-standing positions (like resting on acouch). In spite of the heterogeneous and not gender-balanced sampleof subjects, the results presented here have proven the usefulness ofTAP with a heterogeneous sample of patients, with different agesand pathologies were recruited, thus demonstrating its suitability forpopulation studies.

In summary, TAP has proven to be clinically useful to measure sleeppatterns objectively and as a complement to PSG, and it provides betterresults than actigraphy. PSGwould still remain essential for the diagno-sis of certain sleep pathologies, such as obstructive sleep apnea, but TAP

38 E. Ortiz‐Tudela et al. / Physiology & Behavior 126 (2014) 30–38

should be a useful tool for prior screening of subjects suspected to havesleep problems, and to detect sleep circadian pathologies. This wouldhelp decrease the long waiting lists for Sleep Units and reduce medicalcosts. Future research should focus on the development of TAP-basedalgorithms to differentiate specific sleep pathologies and fine-tunespecific thresholds applicable to these populations. Furthermore, TAPshould be specially useful in those groups of population experiencingat the same time, sleep and circadian disturbances as occurs in cancerpatients.

Financial disclosure

This was not an industry-sponsored study. The public fundingfor this study (by the Spanish Ministry of Science and Innovation, theSpanish Ministry of Economy and Competitiveness and the Institutode Salud Carlos III) has played no role in the study design, datacollection and analysis, the decision to publish or the preparation ofthe manuscript. The authors declare no conflicts of interest.

Competing interest

The authors do declare that no competing interests exist.

Acknowledgments

Wewish to thank M. Martínez for kindly reviewing the manuscript.Funding: The authors wish to thank the Instituto de Salud Carlos III,

the Ministry of Science and Innovation and the Ministry of Economyand Competitivity for their financial support of this study through theRed de Investigación Cooperativa en Envejecimiento y Fragilidad [TheAgeing and Frailty Cooperative Research Network], RETICEF (RD06/0013/0019, and RD12/0043/0011), BFU 2010-21945-CO1, and IPT-2011-0833-900000, the two latter ones including FEDER cofundinggranted to J. A. Madrid. Furthermore, the authors wish to thank theMinistry of Education and Science for the research fellowship awardedto E. Ortiz-Tudela (AP2008-2850) and to the University of Murcia forthe research fellowship awarded to A. Martinez-Nicolas.

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