A recent study demonstrates that accelerometry can play an important role in detecting PLM in children for both clinical and research purposes.
Sleep and sleep disorders in pediatric and adolescent populations recently have become a topic of interest for clinicians and researchers. Reports of sleep-disordered breathing (SDB) and restless legs syndrome (RLS) associated with symptoms of attention-deficit/hyperactivity disorder (ADHD) have led to speculation that sleep disorders may contribute, or even cause, cognitive and behavioral disorders in children. Evidence shows that in school-aged children even short-term 60-minute alterations in sleep time can significantly affect academic performance. Disorders that disturb sleep such as SDB and RLS are gaining notoriety in the lay press, and this has heightened awareness of the importance of sleep in children.1 As a consequence, sleep diagnostic testing for children is gaining a foothold in the clinical literature and, hopefully, in clinical practice. Unfortunately, recent surveys suggest that no more than 12% of school-aged children who undergo adenotonsillectomy for presumed SDB have the disorder diagnosed with a sleep study prior to the surgical intervention.2
Currently, the standard for diagnosis of many pediatric sleep disorders is an overnight polysomnogram (PSG) performed in a qualified pediatric sleep laboratory. An increase in demand for this type of testing and interpretation may easily overwhelm the few pediatric sleep centers across the country—many of which are already experiencing long patient wait times. In addition, a PSG can be rather costly, uncomfortable, and inconvenient for parents and children. Thus, alternatives to PSG testing, such as validated sleep questionnaires and home-based monitoring, are attractive especially in the pediatric setting. One category of sleep disruption that is particularly amenable to home-based or ambulatory monitoring is nocturnal movement disorders, such as periodic limb movements in sleep.
Periodic limb movements (PLM) are defined as stereotypic nocturnal movements of the legs (and occasionally arms) that last between 0.5 and 5 seconds. A series of four distinct movements separated by 4 to 90 seconds constitutes the periodicity of PLM. Sleep disruption due to PLM is associated with many conditions including RLS, peripheral neuropathy, renal disease, iron deficiency, and ADHD.3 Identification of PLM in the clinical setting relies upon PSG detection of anterior tibialis electromyographic (EMG) activity from a single night of study. For many years, prevailing wisdom suggested that children rarely have PLM, so few attempts were made to characterize them in children. In the mid to late 1990s, a series of PSG-based observational studies in children with ADHD alerted the field to the presence of elevated PLM in this population.4,5 Further investigation in larger population-based and clinic-based samples also demonstrated the presence of PLM in children.6-8 PLM elevations above the threshold set for adults (>5/h) are noted in children with RLS and ADHD.
The detection of PLM in children using a full overnight PSG may not be an adequate method to fully characterize the occurrence of PLM. A single night of study can fail to take into account the variability of PLM from one night to the next. Night-to-night variability of PLM has been demonstrated in adults9-11 and children.12 This variability may preclude an accurate assessment of a subject’s leg movements due to sampling bias. Patients may be erroneously misdiagnosed with or without PLM based on a single night study, and treatment or further clinical investigation may not be applied appropriately. A recent study of 145 RLS and other sleep disordered patients, ranging in age from 21 to 71 years, demonstrated a significant variability in PLM between two consecutive nights of PSG study.13 This variability was most marked in the RLS population but was observed in control subjects as well. In fact, the variability in PLM from one night to the next in 27% of the subjects was so marked that one could conclude opposing clinical diagnoses depending on which night was used for analysis. Thus, multiple nights of leg monitoring are needed to accurately characterize a subject’s leg movements. Ambulatory leg monitoring over a series of nights has been used to provide a more comprehensive understanding of the severity and variability of PLM in children and adults.12,14 In fact, ambulatory monitoring with accelerometry-based devices has been experimentally employed for a number of years to study adults with psychiatric and/or sleep/wake related disorders.15-17
Nocturnal and daytime activity monitoring using wrist and leg actigraphy (also referred to as actometry or accelerometry) has been employed in a number of studies to verify subjective questionnaire-based sleep/wake reports, as well as to replace/augment subjective findings associated with neurological, psychiatric, and medication-induced disorders (akathisia).14,18-22 Actigraph devices differ in their ability to detect accelerations related to individual movements due to sampling rates (usually described in number of samples per second, or Hz), and in the sensitivity of the device to movement in different directions, or axes. Actigraphic devices that sample at low rates (multiple seconds to multiple minutes between measures) and few axes can reveal patterns of activity over a 24-hour day for many days at a time. This is useful for determining larger patterns of sleep/wake behavior that may be associated with circadian disorders or increased sleep fragmentation.23 In order to record individual movements in a reliable fashion, actigraphic devices with high sampling rates (10-32 Hz or more) and multiple axes (triaxial) are necessary.
At least five different actigraphic devices have been tested for validity in measuring PLM against PSG-EMG activity in adults.24-28 The correlation coefficient of actigraphy and PSG for detection of PLM is r = 0.78-0.96. These differences are attributable to multiple factors including the inherent properties of the different devices (sampling rates), positioning of the devices (ankle vs foot vs toe), scoring systems used for PLM (automated vs human), and statistical analysis of the data (correlation of individual movements vs hourly indexes of movement). Studies using a triaxial accelerometer with at least a 10 Hz sampling rate indicate that accelerometry measures are highly correlated with PSG measures for PLM but not other types of movements during sleep.25 Aside from the inherent properties of a particular accelerometer, the use of one or both legs as well as placement on the leg may result in a lack of or enhanced sensitivity to movement when compared to EMG measures. Movement of the leg without registration of surface EMG activity over a single muscle certainly is possible due to the posture of a leg or direction and amplitude of the movement. Therefore, an accelerometer has the advantage of not being confined to activation of any one particular muscle associated with a leg movement. Alternatively, EMG detection of muscle contraction may not always result in a detectable movement, which could lead to underdetection by the accelerometer.
In addition to the variables of PLM detection using PSG or accelerometry, the interpretation and scoring of the data can greatly affect the outcome. For PSG, human scoring using visual detection is standard. Automated scoring algorithms preset to detect or reject movements based on PLM criteria are faster than PSG and generate visual displays for further human interpretation. Data correction following automated PLM scoring has been shown to be very reliable between scorers (r=0.99) and results in high correlations with PSG measures of PLM (r=0.92).25 Statistical analysis of the correlation between accelerometry and PSG-EMG detection of PLM varies from study to study. Bland-Altman techniques for comparison of individual movements between PSG-EMG and accelerometry indicate lower coefficients of correlation (r=0.78) compared to comparison of hourly PLM indices (r=0.92). The one-to-one correlation of individual movements may underestimate the correlation of the techniques to detect PLM due to variables inherent in the previously mentioned strengths of the two technologies. Therefore, as a matter of practicality and clinical significance, the correlations of PLM indices that are routinely utilized for sleep laboratory diagnostic purposes arguably represent the measure of importance. Accelerometry and PSG-EMG are highly correlated for PLM on that measure which is relevant to the clinical diagnosis—the PLM index.
Figure 1. A leg monitor is attached to the right leg of a 9-year-old girl.
Ambulatory Measurement of PLM in Children With and Without RLS
Recently, our laboratory investigated the clinical utility of accelerometer-based detection of PLM in children suffering with sensory symptoms of RLS.12 The study included seven boys and 13 girls, ages 2 to 17, recruited from an academic pediatric sleep clinic. Each child was assessed for symptoms of RLS using the four standard criteria including: 1. Uncomfortable leg sensations or urges to move the legs (U); 2. a worsening of symptoms while at rest (R); 3. relief from symptoms with movement of the affected limb (M); and 4. a worsening of symptoms in the evening hours (E). Children were classified as positive for RLS symptoms if they had three to four of the U, R, M, and E symptoms; possible for RLS if they had one or two of the U, R, M, and E symptoms; or negative if they had none of these symptoms. Every child was studied over a 5-night period using an eight-ounce triaxial accelerometer with a 10 Hz sampling rate (see Figure 1, page 50). The monitors were attached each night via Velcro® bands to both ankles. A log of sleep and wake times was kept to assist in defining sleep and wake onset.
Figure 2. Data downloaded with automated PLM scoring parameters. Individual movements from one leg appear as vertical displacements. Larger vertical displacements denote more forceful movements. X-axis is time denoted by 1 minute bars; Y-axis is force denoted by g-force.
Data was downloaded and analyzed in a blinded fashion with software that uses standard PLM criteria for scoring (Figure 2, page 50). Visual inspection of the data in concert with the sleep logs was used to determine sleep onset and wake, as well as arousals from sleep and motion artifacts. A PLM index (PLMi) was calculated for PLM per hour for each night and each leg. An average PLMi for the 5 nights was determined for each leg, and the higher number of the two legs used for comparison between subjects. A Student t test was used to compare the average PLMi between positive, possible, and negative RLS symptom groups.
Figure 3. Summary of PLMi in 20 children with and without RLS symptoms. X-axis includes subjects numbered 1-20. Subjects 1-9 were categorized as positive for RLS symptoms; 10-15 were categorized as possible for RLS symptoms; 16-20 were categorized as negative for RLS symptoms. Y-axis indicates PLMi in increments of 5/h. The average 5-night PLMi for each subject appears along each bar as the midpoint. The range of PLMi over the 5 nights is represented by the length of the bar (more variability from night to night is depicted as a longer bar).
The data from this study are depicted in Figure 3, page 50. Nine children were included in the positive RLS symptom group, six in the possible RLS group, and five in the negative RLS group. Night-to-night variability of PLM was identified in all groups, but the more symptomatic subjects demonstrated the greatest variability. The 5-night average PLMi by group was 8.15 (positive RLS), 6.67 (possible RLS), and 2.20 (negative RLS). This shows a statistically significant difference between the positive and negative group (P=0.0017) and the possible and negative group (P=0.0045). In addition, there is not a statistically significant difference between the positive and possible groups (P=0.33).
Interpretation of this data suggests a number of interesting conclusions. First of all, this study demonstrates that PLM detection using accelerometry-based leg monitoring in children as young as 2 years old is possible. Also, PLM captured in this way demonstrate many of the same characteristics (such as night-to-night variability) found in similar studies of adults. In fact, the variability in PLMi from night to night in children is quite marked, and illustrates the need to use multiple nights of recording to accurately quantify PLMi. PLMi averages are significantly higher in children with RLS symptoms compared to children without these symptoms; however, there is variability of PLM within the symptomatic groups. This indicates that some children who are more affected by RLS symptoms (positive RLS group) may not be greatly affected by PLM during sleep. The opposite can also be seen in that some children less affected by RLS symptoms (possible RLS group) may be greatly affected by PLM during sleep. Until the neurophysiology and etiology of RLS and PLM are better understood, we can only speculate that children present with RLS, PLM, or a combination of these symptoms based on factors such as disease progression and variable expression of a complex genetic trait. As this study demonstrates, accelerometry can play an important role in detection of PLM in children for both clinical and research purposes.
Jeffrey S. Durmer, MD, is assistant professor at the Department of Neurology, Emory University School of Medicine, Atlanta.
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