Actigraphic monitoring of sleep and circadian rhythms

Handbook of Clinical Neurology, Vol. 98 (3rd series) Sleep Disorders, Part 1 P. Montagna and S. Chokroverty, Editors # 2011 Elsevier B.V. All rights reserved

Chapter 4

Actigraphic monitoring of sleep and circadian rhythms EUS J.W. VAN SOMEREN * Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Society of Arts and Sciences; Department of Integrative Neurophysiology, VU University and Leiden Institute for the Clinical and Experimental Neuroscience of Sleep, Leiden University Medical Center, The Netherlands

INTRODUCTION Although polysomnography, the continuous monitoring of multiple physiological parameters during sleep, as described in Chapter 2, is the golden standard for the objective assessment of sleep and its disturbances, there may be circumstances that ask for a different approach. For example, one may want to evaluate a large number of nights, or subjects who comply poorly with wearing electrodes for hours, as may be the case in children, or in dementia. Actigraphy provides a cost-effective method of estimating the occurrence of periods of sleep and wakefulness from information on the timing, duration, and intensity of movements for multiple days, weeks, or even months. Actigraphy is the continuous long-term assessment of activity-induced acceleration by means of a small solid-state recorder. Technical progress has enabled the integration of an acceleration sensor, amplifier, filter, microprocessor, and digital memory into a case the size of a wristwatch. Like a wristwatch, these so-called actigraphs are usually worn on the wrist. After the first report on the relation of wrist movement to sleep (Kupfer et al., 1974), the first actigraphs were soon described (Colburn et al., 1976; McPartland et al., 1976) and validated for use in sleep research (Kripke et al., 1978; Mullaney et al., 1980; Webster et al., 1982). Since then, actigraphs of decreasing size and increasing capacity have become commercially available, of which an example is shown in Figure 4.1. The present chapter discusses their application in clinical

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and experimental research on sleep and its day–night rhythm.

APPLICATIONS Actigraphy has been applied in a variety of clinical and research fields which include sleep disorders, obesity, depression, hyperactivity, and movement disorders, including periodic leg movements during sleep (reviewed in Tryon, 1991). The most extensive use has been in sleep research in healthy subjects, where it has even been suggested as an alternative for the costly and time-consuming gold standard of polysomnography. The reliability of actigraphy in the clinical evaluation of sleep disorders is a matter of debate, mostly focusing on the question whether actigraphy can replace polysomnography (Pollak et al., 2001; Tryon, 2004). There is no doubt, however, that actigraphic recordings can give valuable insights into a patient’s sleep and sleep–wake rhythms, whether or not a further investigation with polysomnography is required. Practice parameters for the use of actigraphy in the clinical assessment of sleep disorders have been published by the Board of Directors of the American Academy of Sleep Medicine in 1995 (Sadeh et al., 1995). In 2003, the practice parameters were updated (Littner et al., 2003), with an accompanying review paper on the role of actigraphy in the study of sleep and circadian rhythms (Ancoli-Israel et al., 2003). The present chapter focuses on the use of actigraphy in estimating sleep parameters and in obtaining the rest– activity rhythm over multiple days.

Correspondence to: Prof. Eus J.W. Van Someren, Head Dept. Sleep and Cognition, Netherlands Institute for Neuroscience, Meibergdreef 33, 1105 AZ Amsterdam, The Netherlands. Tel: þ 31 20 566 5500, Fax: þ 31 20 6961006, E-mail: e.van. [email protected]

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Fig. 4.1. Example of an actigraph worn on the wrist (Actiwatch, Cambridge Neurotechnology, Cambridge, UK).

THE ACCELERATION SIGNAL: MOVEMENT AND ARTIFACT The movement-induced signal that actigraphs utilize is picked up by a piezoelectric element, which generates small voltages if accelerations occur. It is important to realize that actigraphy data may contain artifacts. Artifacts that may affect the signal mostly during wakefulness include externally imposed movement from riding in vehicles (Ancoli-Israel et al., 1997; Pollak et al., 2001). An artifact that may be of more importance during sleep is that very sensitive accelerometers can pick up chest movements associated with breathing, if the wrist is positioned on the chest. In addition to these artifactual signalgenerating events, there may be artifactual absence of signal if an actigraph has (temporarily) not been worn. The artifacts mentioned above generate faults in the presence or absence of activity. In addition, there is the risk of an artifact that strongly affects the strength, i.e., amplitude, of the movement-induced acceleration signal. This artifact is due to the earth’s gravitational field. More specifically, the mere rotation of the wrist from upwards to downwards will induce an acceleration signal change of 2 g. This signal is a strong overestimate of the energy involved in the arm movement, because it would take much more muscle effort to induce a signal of 2 g with a wrist movement that does not change the orientation of the accelerometer in the gravitational field. The frequency range that is most affected by such gravitational artifacts depends on the speed of rotation of the wrist. Detailed investigations have demonstrated that most of these artifacts occur in the frequency range below 0.5 Hz (Van Someren et al., 1996b). These artifacts have led early studies to suggest that most of the activity-induced accelerations occur around 0.25 Hz (Redmond and Hegge, 1985), which resulted in low-pass filtering at 2 Hz in early actigraphs. However, later work demonstrated that frequency components of up to about 11 Hz are prominently present in movementinduced acceleration signals, while relatively few truly movement-induced accelerations occur below 0.5 Hz

(Van Someren et al., 1996b). Thus, although it is not possible to prevent gravitational artifacts completely with single-site accelerometer signal, a band-pass filter of 0.5–11 Hz is presently advocated and will yield a more acceptable estimate than the early filter settings of 0.25–2 Hz. After filtering, a data reduction step is necessary to allow for storage of long-term activity data in the limited memory of actigraphs. This may be accomplished in several ways, and some actigraphs leave the choice to the user. The following methods have been applied. First, one may reduce the data by measuring the time that the acceleration signal exceeds a certain threshold just above the noise floor of the device, generating a “time above threshold” number, to be stored in every 30-second or 1-minute epoch. Longer epochs are not advocated for reliable sleep detection. Alternatively, one may integrate the acceleration signal over the time it exceeds the threshold, generating a so-called area under the curve. Yet another approach is to count the number of threshold crossings, often referred to as “zero crossings.” For the remainder of this chapter we will refer to any such output as “activity level.” Depending on the mode of recording, it may be necessary to fine-tune the algorithms used to derive estimates of sleep and wakefulness from the 30-second or 1-minute epochs of activity levels.

PLACEMENT OF THE ACTIGRAPH Actigraphs have mostly been placed on the nondominant wrist, but may also be placed on the dominant wrist, the ankles, or the trunk. During active daytime wakefulness, the dominant wrist shows most motor activity (Middelkoop et al., 1997). The effect of using the dominant versus nondominant wrist on the validity of the nocturnal sleep–wake estimates is equivocal (Van Hilten et al., 1993; Nagels et al., 1996). Assessment from other places on the body generally gives results that differ only marginally from wrist-assessed movements (Meijer et al., 1992; Middelkoop et al., 1997). However, in sleep–wake rhythm research, the dominant wrist may be the preferred site in subjects who are virtually nonambulatory and sedentary, as is the case in some demented elderly patients. In conclusion, placement on the wrist is recommended, and whereas the effect of placement on the dominant or nondominant site is equivocal, it should be standardized for all subject groups within a study.

ESTIMATING SLEEP^WAKE STATE AND SLEEP PARAMETERS During sleep, the activity level is low and periods of immobility last much longer than during quiet wakefulness. Based on these simple premises, algorithms have

ACTIGRAPHIC MONITORING OF SLEEP AND CIRCADIAN RHYTHMS been developed to estimate from a time series of activity counts whether a subject is awake or asleep (Cole et al., 1992; Sadeh et al., 1994). The algorithms require storage of activity level in 30-second or 1-minute intervals, and do not work well if the data have been acquired and stored with a lower time resolution, i.e., aggregated over longer time intervals. In general, the classification of an epoch as representing “sleep” or “wakefulness” is based on a weighted sum of the activity level in the current epoch and of the activity levels and their standard deviation in a time window of a few minutes surrounding the current epoch. If this sum exceeds a certain threshold, the epoch is scored as wakefulness, and if not, as sleep. This results in a sequence of sleep and wake epochs for each recorded night, from which parameters like sleep latency, sleep duration, wakefulness after sleep onset, sleep efficiency, and several fragmentation indexes can be derived. An example of how such algorithms translate activity levels into sleep estimates is shown in Figure 4.2. Several studies (Pilsworth et al., 2001) investigated the reliability of actigraphic sleep estimate by means of a one-to-one comparison of actigraphy epochs classified as sleep versus wakefulness and equivalent polysomnography epochs classified as sleep versus wakefulness by the gold standards of Rechtschaffen

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and Kales (1968). In healthy subjects, actigraphy is a sensitive method: nearly 100% of the epochs classified as sleep by polysomnography are also identified as sleep by actigraphy. The specificity, however, is poor: actigraphy correctly identifies only about 40% of the epochs classified as wakefulness using polysomnography. Because healthy subjects have only a limited amount of wakefulness during their major sleep period, the overall accuracy is still high: about 90% of the epochs obtain the same classification from actigraphy and polysomnography. The reliability and validity of the actigraphy-derived sleep parameters are a matter of debate. As a result of the low sensitivity for wakefulness during the nocturnal sleep period, actigraphy tends to underestimate intermittent wakefulness and overestimate the total sleep time and sleep efficiency (Mullaney et al., 1980; Cole et al., 1992; De Souza et al., 2003). The precision of the sleep parameter estimates, and especially the precision of the sleep onset latency estimate, is very sensitive to even small deviations in the reported times of lights out and getting up, because these times have to be entered into the sleep-scoring software and determine the start and stop time of the analysis. Usually, these times are obtained from a sleep–wake diary the subject is asked to fill out daily. However, even healthy subjects may make considerable mistakes, and a

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Fig. 4.2. Example of the steps taken to derive the sleep–wake state and sleep parameters. The upper panel shows the first 2 days of a typical activity recording, where every bar represents the activity level in 1 minute. The gray part of the time series is zoomed in on in the middle panel, to provide more detail in the alternating periods of activity and rest. Based on a sleep– wake diary, the times of lights out (23:53 hours) and getting out of bed (6:47 hours) have to be entered into the software. They are shown as small dark gray bars just below the second panel. Subsequently an algorithm is run to estimate sleep onset (23:58 hours) and offset (6:47 hours), shown as small light gray bars just below the middle panel, as well as the momentary sleep– wake state over the night, which is shown in the third (thin) panel as alternating gray (wakefulness) and white (sleep) periods. Sleep parameters can be calculated from this sequence. Note, in the present example, that the subject appears to sleep soundly for the first sleep cycle of about 80 minutes, then experiences much wakefulness for more than an hour, after which sleep is once more rather sound. For the example given, a total sleep time of 5:47 hours results, and a sleep efficiency of 84%. Usually, such sleep parameters are calculated and averaged over multiple nights. (Sleep Analysis software, Cambridge Neurotechnology, Cambridge, UK.)

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Fig. 4.3. A bedside monitoring system, consisting of a miniature logger equipped with a light sensor, a pressure-sensitive mat switch, and a software algorithm can be used to determine automatically bedtime, lights-out time, and rise time. The precision of these times determines the precision of actigraphic sleep estimates. Sleep diary times are prone to contain errors (Krahn et al., 1997; Usui et al., 1998; Eissa et al., 2001), which is not surprising because subjects are required to memorize precisely clock times at the very times when their cognitive abilities suffer from high sleep pressure or sleep inertia. The figure gives an example of a 24-hour (ordinate) actigraphic recording combined with the automated bedtime detection system. Black columns represent minute-by-minute activity counts (abscissa, arbitrary units). The light grey area indicates the period during which the subject is in bed and the dark grey area indicates the lights-out period.

precise sleep–wake diary may not even be feasible at all in very young subjects and patients with motor disabilities or limited cognitive capabilities. In these groups, actigraphic sleep estimates may become feasible only by combining actigraphy with a bedside monitor which records bedtimes, lights-out times and get-up times. Such a system has recently been developed in our group (Figure 4.3), allowing for sleep parameter estimates in these subjects as well as for improved reliability of sleep parameter estimates in healthy subjects.

COMPARISON WITH POLYSOMNOGRAPHY Actigraphy has some disadvantages as compared to polysomnography. Although a reasonable estimate of being awake or asleep is feasible from actigraphy recordings in healthy subjects under normal conditions, the reliability in, for example, insomniac and elderly patients may be worse: these subjects show an increase in the number of epochs where no movements are made, yet wakefulness is present (Hauri and Wisbey, 1992). Also, actigraphy cannot discriminate between sleep stages. In case of screening for sleep apnea, polysomnography can easily be extended to include sensors obtaining respiratory effort, oronasal airflow, and blood oxygen desaturation. Obviously, this is not within the scope of actigraphy. On the other hand, actigraphy also has a number of advantages as compared to polysomnography. Actigraphy is cost-effective, easily applied, less demanding for the subject, and allows several nights of recording continuously. This makes sleep studies feasible in a larger number of clinical and experimental investigations. For example, whereas polysomnography may be difficult

to attain in demented elderly individuals, actigraphy is usually well tolerated. The advantage of being able to record for several nights continuously deserves attention. It is known that two polysomnographic recordings obtained over subsequent nights may show considerable differences. This has been referred to as a “first-night effect.” In a study on the first-night effect in actigraphic recordings, no systematic difference for the first night could be found (Van Hilten et al., 1993). However, there was a considerable within-subject variation over the six nights recorded. This indicates that, in addition to systematic first-night effects, there may also be a considerable variability in sleep parameters as obtained over several nights. Acebo and colleagues (1999) have provided estimates of the reliability of sleep scores based on 1–7 nights in children. We have recently investigated the day-today variability in a systematic empirical way in elderly insomniacs and demented elderly subjects: the reliability of sleep parameter estimates continues to increase if the number of recorded nights is extended, even up to 10 nights of sleep (Van Someren, 2007). Thus, an advantage of actigraphy over polysomnography is that it is much more feasible to do such long-term investigations that allow for improved sleep parameter estimates as well as insight into the variability of the sleep parameters. This advantage has not yet been fully exploited, since clinicians and researchers have often relied on three nights of recording, the minimum advised in the practice parameters for the use of actigraphy in the clinical assessment of sleep disorders, as published by the Board of Directors of the American Academy of Sleep Medicine (Littner et al., 2003). Figure 4.4 shows how the reliability of an actigraphic estimate of the percentage of wakefulness after sleep

Absolute difference (mean±s.e.m.) of two estimates of % wake after sleep onset (WASO)

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Fig. 4.4. The reliability of an actigraphic estimate of the percentage of wakefulness after sleep onset (WASO) in a group of 12 demented elderly individuals improves with the number of recorded days. Subjects were actigraphically recorded for 20 days continuously, and actigraphic WASO estimates were derived in pairs from the day 1–10 period and from the day 11–20 period. Pairs resulted from calculating WASO twice for a single day (day 1 and day 11), twice over a period of 2 days (days 1–2 and 11–12), twice over 3 days (days 1–3 and 11–13), up to twice over 10 days (days 1–10 and 11–20). The resulting WASO estimates were averaged over the number of days. The figure shows how the average ( SEM, abscissa) absolute difference between two separate actigraphic estimates of WASO, derived from assessments only 10 days apart, decreases with the number of days (ordinate) included to obtain the estimate.

onset (WASO) in a group of 12 demented elderly individuals improves with the number of recorded days.

CIRCADIAN AND DIURNAL RHYTHMS Circadian rhythms, i.e., rhythms with a period of about 24 hours, are present in most physiological and behavioral parameters, including the vigilance state (sleep versus wakefulness) and activity level. Such rhythms are usually described in terms of the phase, period, and amplitude of a sinusoidal curve fitted to the data. In experimental protocols, the functionality of the circadian timing system is usually evaluated by measuring alterations in the period, phase, and/or amplitude of this curve after imposing shifts in the environmental light–dark cycle, or by putting animals or human subjects in a constantly lit environment without any time cues for a period of up to weeks or months. In the latter situation a rhythm that may deviate from 24 hours emerges, and this is called the free-running rhythm. The majority of actigraphic studies, however, are obtained under unrestrained conditions in the subject’s normal environment. Yet, information on the

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functionality of the circadian timing system can be extracted from actigraphic recordings assessed in the subject’s usual environment. When the actigraphic data are plotted as a time series, a clear circadian rhythm can be seen, and several variables can be calculated for a quantitative description of the rhythm. A traditional way of quantifying circadian rhythms is by fitting a single or dual harmonic cosine function on the data, thus summarizing it in a mesor (a measure for the mean of a circular function), the phase of the peak, the amplitude, and the period of the rhythm. This ‘cosinor’ method of data reduction has successfully been applied to quantify the specific time course of body temperature and hormone levels. However, because the rest–activity rhythm is far from sinusoidal, the goodness of fit of such functions is usually unacceptable for application to activity data. Nonparametric methods to describe the activity time series have therefore been proposed. They outperformed several frequently used parametric variables in a comparative study on their sensitivity to the effect of bright daylight – the primary input to the biological clock of the brain – on the circadian rest–activity rhythm (Van Someren et al., 1999), and appeared sensitive as well as in other treatment studies (Van Someren et al., 1998). In addition to nonparametric equivalents of the timing and level of the peak and trough of the rest–activity rhythms, and the amplitude that results from their difference, two variables deserve some additional description. First, in most healthy subjects, the activity profiles from different 24-hour periods resemble each other to a reasonable extent. In some diseases, notably in Alzheimer patients, subsequent days may lose any such typical pattern (Figure 4.5). This phenomenon can be quantified using the interdaily stability (IS) value (Van Someren et al., 1999; Carvalho-Bos et al., 2007), essentially a normalized 24-hour value from a periodogram (Sokolove and Bushell, 1978). IS gives an indication of the strength of coupling between the rest– activity rhythm and supposedly stable environmental cues with a 24-hour pattern, also known as Zeitgebers. Second, in most healthy subjects, sleep and wakefulness are both confined to one major period of time each. If one takes a nap during the daytime, sleep and wakefulness both occur in two instead of one periods of time during 24 hours. In some diseases, notably in Alzheimer patients, periods of high and low vigilance, and consequently high and low amounts of activity, may alternate even more frequently, resulting in a fragmented rhythm (Figure 4.5). The nonparametric variable intradaily variability (IV) (Van Someren et al., 1999) gives an indication of the fragmentation of the rhythm, i.e., the frequency and extent of transitions between rest and activity.

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Fig. 4.5. Examples of 7-day activity plots in Alzheimer patients. Each bar represents the activity counts in 1 hour. The top panel (A) shows a rhythm that does not significantly differ from rhythms of control subjects. Panel B shows the rhythm of a patient with a low interdaily stability (IS), panel C a patient with a high intradaily variability (IV), and the bottom panel (D) a patient with both low IS and high IV. (Reproduced from Van Someren et al. (1996a), with permission.)

It should be noted that, under the conditions of everyday life, the measured rest–activity rhythm does not strictly represent the function of the endogenous biological clock of the brain, located in the hypothalamic suprachiasmatic nucleus (SCN). The measurements in fact at best represent the interaction of the endogenous biological clock with the environmental 24-hour time structure, which includes social demands and the light– dark cycle – the primary input to the SCN. Such conditions are referred to as entrained conditions. Rhythms obtained under such conditions are usually referred to as diurnal rhythms, whereas rhythms obtained under experimental

conditions in the absence of any time cues are referred to as circadian rhythms. If one wants to obtain an indication of clock function in the absence of entrainment, one needs to apply dedicated laboratory protocols like constant routine and enforced sleep–wake cycles of considerably shorter or longer duration than 24 hours (ultrashort sleep–wake cycles, forced desynchrony).

PERSPECTIVES The presently available actigraphs and the accompanying software are useful tools to provide clinicians

ACTIGRAPHIC MONITORING OF SLEEP AND CIRCADIAN RHYTHMS and researchers with objective indices of sleep. They should not be regarded as a replacement for polysomnography. As has been described, actigraphy has both shortcomings and advantages as compared to polysomnography. This final section discusses a number of recent and ongoing developments that promise a further improvement of actigraphic estimates of sleep parameters. First, optimization of the estimates may be accomplished by adapting the algorithm for sleep estimates to the specific group of subjects under study. For example, lowering the activity threshold that should be surpassed in order to score wakefulness may improve sleep estimates in elderly subjects (Colling et al., 2000). While lying awake in bed elderly subjects may move less than young subjects do. Important for clinical neurology, sleep recordings in patients suffering from Parkinson’s disease may require even more significant adaptations. Thresholds may have to be lowered even more than is already the case in their matching healthy elderly control subjects, because movement-induced accelerations are of a lower amplitude (Eichhorn et al., 1996). In addition, if patients show tremor, it is important to discriminate high levels of activity due to “healthy” movements from those resulting from tremor. An actigraph doing just this has recently been developed and validated for tremor recording (Van Someren et al., 2006). Such devices are likely to provide more detailed insight into activity rhythms originating from the alteration of periods of sleep and wakefulness, and those associated with fluctuating amounts of tremor. A common characteristic of the present generation of actigraphs is that the accompanying sleep analysis software utilizes only one activity measure, be it time above threshold, area under the curve, or zero crossings. However, movements related to wakefulness and sleep, possibly even sleep stages, may differ in more than one signal dimension. Movements may differ in frequency, vigor, fragmentation, and duration. It has been noted, for example, that limb movements during rapid eye movement (REM) sleep are brief, rapid, and jerky (Chase and Morales, 1990) and Aserinsky (1986) has shown that the acceleration characteristics of eye movements are different in REM sleep and wakefulness, suggesting that twitches resulting in wrist movements associated with REM sleep might have a different acceleration profile than the awake wrist movement acceleration profile. A recent advance in the online data reduction algorithm and storage capacity of an actigraph has made it possible to obtain multiple dimensions of the acceleration signal simultaneously, i.e., the amplitude, duration, and repetitiveness (Van Someren et al., 2006). Although this novel

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actigraph has been developed to allow for online discrimination of pathological tremor from normal movements in Parkinson’s disease, for example (Van Someren et al., 1993), it would be of considerable interest to evaluate how the different dimensions of the acceleration signal vary across wakefulness and sleep stages and could be of value in improving their discrimination. An unpublished study indeed found that amplitude, frequency, number, average duration, and total duration of movements differed significantly across wake and sleep stages. Related to the single activity measure mentioned above is the fact that actigraphy utilizes only one type of signal (activity) to estimate sleep and wakefulness, whereas the gold standard of polysomnography utilizes multiple signals. Since the multiple signals of polysomnography are not redundant, it is somewhat unlikely a priori that sleep parameter estimates derived from the single signal of actigraphy could ever reach complete agreement with polysomnographic sleep parameter estimates (Tryon, 2004). Actigraphs may be used to obtain movement signals on other sites, and process them in different ways. The most successful example of this approach is the use of actigraphs to assess periodic leg movements during sleep (King et al., 2005). Alternatively, actigraphs have been developed to obtain other measurements in addition to movement signals, e.g., heart rate and skin temperature. Heart rate variability measures may improve estimates of sleep depth (Otzenberger et al., 1997) and support the screening for obstructive sleep apnea (Roche et al., 1999) and periodic limb movements during sleep (Winkelman, 1999). A development that goes beyond actigraphy is that several research programs are presently utilizing the ongoing miniaturization of sensors and microelectronics to integrate measurement systems within the bedding of the subject under study. These developments will ultimately allow for unobtrusive assessment of signals reflecting heart rate, breathing, gross movements, and skin temperature, which together are likely to provide even better and more detailed estimates of sleep.

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