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Background
Actigraphy testing consists of a small portable device (actigraph) that senses physical motion and stores the resulting information. Actigraphy testing has been predominantly used in research studies to evaluate rest-activity cycles in patients with sleep disorders, to determine circadian rhythm activity cycles, and to determine the effect of a treatment on sleep. The actigraph is most commonly worn on the wrist, but can also be worn on the ankle or trunk of the body. Actigraphy testing is based on the assumption that movement is reduced during sleep compared with wakefulness and that activity level can be used as a diagnostic indicator for sleep disorders.
The Actiwatch™ (Mini-Mitter Co., Inc., Bend, OR) is a battery-operated device that has received 510(k) premarket notification from the U.S. Food and Drug Administration (FDA) to be used to automatically collect and score data for sleep parameters, analyze circadian rhythms, and assess activity in any instance where quantifiable analysis of physical motion is desired. Thus, the manufacturer was not required to submit to the FDA the evidence of efficacy that is necessary to support a premarket approval application.
According to the manufacturer’s website, the Actiwatch utilizes a motion sensor known as an “accelerometer” to monitor the occurrence and degree of motion and produces a small signal. The magnitude and duration of the signal depends on the amount of motion. The activity signals are amplified and digitized and stored as activity counts. Recordings can be conducted for days or weeks on patients in their own homes. When the recording period is complete, the stored movement data can be transferred to a computer for analysis. Data may be expressed graphically as actograms or reported numerically as total activity counts per epoch, thereby estimating sleep latency, total sleep time, number and frequency of awakenings, and “sleep efficiency.” The Actiwatch has been proposed as a diagnostic parameter for a number of sleep disorders including insomnia, restless legs syndrome/periodic limb movement disorder, circadian-rhythm disorders, and sleep apnea.
Methods of assessing sleep complaints have included history from the patient and bed partner, use of sleep history questionnaires, sleep-wake diaries, actigraphy and polysomnography (PSG). However, a review of the literature produced few validation studies that incorporated large sample sizes, typical sleep clinic patients, or comparisons with subjective reports of sleep parameters. There is little agreement among authors concerning methods for effective assessment and subsequent differential diagnosis of sleep disorders (Kushida et al, 2001; Bjorvatn et al, 2001). Furthermore, some of the research studies failed to find relationships between sleep measures and health-related symptoms.
Practice guidelines for actigraphy established by the Standards of Practice Committee of the American Academy of Sleep Medicine (Littner et al, 2003) state that actigraphy testing is reliable and valid for detecting sleep in normal, healthy populations. However, the guidelines state that actigraphy testing is not indicated for the routine diagnosis, assessment, or management of any of the sleep disorders.
According to a review by Sadeh and Acebo (2002), actigraphy is less useful for documenting sleep-wake in persons who have long motionless periods of wakefulness (e.g. insomnia patients) or who have disorders that involve altered motility patterns (e.g. sleep apnea). The authors state the pitfalls of actigraphy testing are: (i) validity has not been established for all scoring algorithms or devices, or for all clinical groups; (ii) actigraphy is not sufficient for diagnosis of sleep disorders in individuals with motor disorders or high motility during sleep; (iii) the use of computer scoring algorithms without controlling for potential artifacts can lead to inaccurate and misleading results.
It is difficult to establish actigraphy testing standards at the present time, given the variety of different actigraphs available, the different technology and algorithms for detecting movement, and the lack of standardized units of activity measures. Thus, it is not clear how actigraphic information would be used in the treatment and management of patients with sleep disorders (Edinger et al, 2004). Patients who lie still but are awake for prolonged periods of time will have their sleep time overestimated. Similarly, patients with excessive movements during sleep may be considered to be awake and have an underestimation of sleep time. Additional research comparing actigraphic methodology is needed to establish standards of actigraphy testing.
The Watch_PAT 100 is a portable device that measures peripheral arterial tonometry, pulse oximetry, and actigraphy. Although there are published studies suggesting that the Watch _PAT may be useful in diagnosing OSA (Pillar et al, 2003; Ayas et al, 2003; and Bar et al, 2003), there is currently insufficient scientific evidence in the medical literature to support its use for the diagnosis of obstructive sleep apnea (OSA).
Ayas et al (2003) assessed the accuracy of the Watch_PAT100 to diagnose OSA. A total of 30 adult subjects with and without suspected OSA simultaneously had a standard in-laboratory PSG and wore the Watch_PAT100 during a full-night recording. PSG sleep and respiratory events were scored according to standard criteria. Watch_PAT data were analyzed with an automated computerized algorithm which calculated the frequency of respiratory events per hour of actigraphy measured sleep using a combination of peripheral arterial tonometry (PAT) signal attenuation, desaturation on pulse oximetry, and changes in heart rate. This yielded a PAT apnea hypopnea index (AHI). Mean age was 47.0 +/- 14.8 years, mean body mass index 31.0 +/- 7.6 kg/m2, mean PSG AHI 23 +/- 23.9 events per hour, and mean PAT AHI 23 +/- 15.9 events per hour. There was a significant correlation between PAT AHI and AHI by PSG (r = 0.87, p < 0.001). To assess sensitivity and specificity of the Watch_PAT, the authors constructed receiver operator characteristic (ROC) curves using a variety of AHI threshold values (10, 15, 20, and 30 events per hour). Optimal combinations of sensitivity and specificity for the various thresholds were 82.6/71.4, 93.3/73.3, 90.9/84.2, and 83.3/91.7, respectively. The authors concluded that the Watch_PAT is a device that can detect OSA with reasonable accuracy. Thus, the Watch_PAT may be a useful method to diagnose OSA. They noted that "[p]rior to widespread use of the device, further studies are needed. These include verification of accuracy and ease of use in an ambulatory setting, studies in other medical centers, and studies including more patients with non-respiratory causes of sleep fragmentation. Nevertheless, the Watch_PAT may become a useful technology to diagnose and manage patients with OSA".
Moreover, a technology assessment on portable monitoring devices for diagnosing OSA prepared for the Agency for Healthcare Research and Quality (AHRQ, 2004) evaluated the evidence on the clinical value of Watch_PAT. It found that the quality of evidence to be fair for the study by Bar et al (2003), while the quality of evidence is poor for the studies by Pillar et al (2003) and Ayas et al (2003). It concluded that the new body of evidence does not materially change earlier findings regarding in-home devices for diagnosing OSA - there is inadequate to support the use of unattended portable multi-channel sleep testing for the diagnosis of OSA. Furthermore, Acebo and LeBourgeois (2006) stated that although actigraphy maybe suitable for documenting and evaluating some sleep disorders, its role in clinical diagnosis is limited.
In a prospective randomized study with blinded analysis, Garcia-Diaz et al (2007) ascertained the utility and reliability of a respiratory polygraphy (RP) device with actigraphy in the diagnosis of sleep apnea-hypopnea syndrome (SAHS). A total of 62 patients with suspected SAHS were enrolled in the following two RP studies: (i) one in the sleep laboratory (sleep laboratory RP [LRP]), simultaneously with polysomnography; and (ii) the other at home (home RP [HRP]). To study the inter-observer reliability of RP, two manual analyses were carried out by two different researchers. In LRP, when the respiratory disturbance index was calculated using the total sleep time estimated by actigraphy (RDI) as a denominator, the sensitivity ranged between 94.6 % and 100 %, and the specificity between 88 % and 96.7 % for the different cutoff points of the apnea-hypopnea indexes studied. When the respiratory disturbance index was calculated according to the total recording time (RDITRT), the sensitivity was slightly lower (91.6 % to 96.9 %) and the specificity was similar (92 % to 96.7 %). In HRP, the sensitivity of the RDI ranged between 83.8 % and 95.8 %, and the specificity between 92 % and 100 %, whereas, when the RDITRT was used, the sensitivity was between 83.8 % and 87.5 %, and the specificity was between 94.7 % and 100 %. With regard to inter-observer reliability, the intra-class correlation coefficient for the RDI of the two analyses of the RP was 0.99 for both LPR and HPR. The authors concluded that HPR is an effective and reliable technique for the diagnosis of SAHS, although it is less sensitive than LRP. Furthermore, wrist actigraphy improves the results of HRP only slightly.
Paquet and associates (2007) assessed the ability of actigraphy compared to PSG to detect wakefulness in subjects submitted to 3 sleep conditions with different amounts of wakefulness: a nocturnal sleep episode and 2 day-time recovery sleep episodes, one with placebo and one with caffeine (200 mg). A second objective was to compare the ability of 4 different scoring algorithms (2 threshold algorithms and 2 regression analysis algorithms) to detect wake in the 3 sleep conditions. A total of 15 healthy subjects aged between 20 and 60 years (7 males and 8 females) were included in this study. An epoch-by-epoch comparison between actigraphy and PSG showed a significant decrease in actigraphy accuracy with increased wakefulness in sleep conditions due to the low sleep specificity of actigraphy (generally < 50 %). Actigraphy over-estimated total sleep time and sleep efficiency more strongly in conditions involving more wakefulness. Compared to the 2 regression algorithms, the 2 threshold algorithms were less able to detect wake when the sleep episode involved more wakefulness, and they tended to alternate more between wake and sleep in the scoring of long periods of wakefulness resulting in an over-estimation of the number of awakenings. The authors concluded that the very low ability of actigraphy to detect wakefulness casts doubt on its validity to measure sleep quality in clinical populations with fragmented sleep or in situations where the sleep-wake cycle is challenged, such as jet-lag and shift-work.
Sitnick and colleagues (2008) compared actigraphy with videosomnography in preschool-aged children, with special emphasis on the accuracy of detection of night-time awakenings. A total of 58 subjects wore an actigraph for 1 week and were videotaped for 2 nights while wearing the actigraph. Participants were solitary sleepers, studied in their homes. One group (n = 22) was diagnosed with autism, another group (n = 11) had developmental delays without autism, and a third group (n = 25) were typically developing children; age ranged from 28 to 73 months (mean age of 47 months); 29 boys and 29 girls. Nocturnal sleep and wakefulness were scored from simultaneously recorded videosomnography and actigraphy. The accuracy of actigraphy was examined in an epoch-by-epoch comparison with videosomnography. Findings were 94 % overall agreement, 97 % sensitivity, and 24 % specificity. Statistical corrections for overall agreement and specificity resulted in an 89 % weighted-agreement and 27 % adjusted specificity. The authors concluded that actigraphy has poor agreement for detecting nocturnal awakenings, compared with video observations, in preschool-aged children.
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