actigraphic recordings in quantification of periodic leg movements during sleep in children
TRANSCRIPT
Original article
Actigraphic recordings in quantification of periodic leg
movements during sleep in children
Hawley E. Montgomery-Downsa, Valerie M. Crabtreea, David Gozalb,*
aDivision of Pediatric Sleep Medicine, Department of Pediatrics, University of Louisville, 571 South Floyd Street, Suite 439, Louisville, KY 40202, USAbDepartment of Pediatrics, Kosair Children’s Hospital Research Institute, 570 South Preston Street, Suite 204, Louisville, KY 40202, USA
Received 21 October 2004; received in revised form 2 February 2005; accepted 4 February 2005
Abstract
Background: Periodic limb movement disorder (PLMD) has recently emerged as a relatively frequent and markedly underdiagnosed
condition in children that induces arousals and sleep fragmentation and leads to poor learning and behavioral problems. Because a cost-
effective and widely available alternative to pediatric polysomnography is needed for diagnosis of limb movement disorders, this study
sought to examine whether periodic leg movements in children could be reliably identified using recently developed actigraphy software.
Methods: Bilateral actigraphs were worn around the feet by 99 children ages 4–12 years during standard clinical overnight polysomnography,
which included bilateral anterior tibial electromyogram (EMG). Left and right leg movements were scored independently for comparison
purposes.
Results: Agreement between tibial EMG and actigraphy-derived events were initially low, with movement indices being overestimated by
actigraphy. This agreement was improved when a correction factor based on the average number of movements during arousals as measured
by EMG was applied. However, the correction factor itself was found to differ substantially for patients who were diagnosed with PLMD
compared to other patients.
Conclusions: A novel actigraphic approach currently used for detection of PLM events during sleep in adults is insufficiently accurate to
permit reliable estimates in children.
q 2005 Elsevier B.V. All rights reserved.
Keywords: Periodic leg movements; PLM; Children; Actigraphy; Sleep
1. Introduction
Periodic limb movement disorder (PLMD) has recently
emerged as a relatively frequent and markedly under-
diagnosed condition in children. PLMD is especially
prevalent among children with neurobehavioral deficits,
particularly attention-deficit hyperactivity disorder
(ADHD) [1–10]. In addition, children with iron deficiency
[11] and renal failure [12] appear to be at risk for PLMD.
Periodic limb movements of sleep (PLMS) can induce
arousals and sleep fragmentation and in turn may lead to
altered neurobehavioral patterns during the day. Thus,
PLMD may lead to emergence of poor learning and
1389-9457/$ - see front matter q 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.sleep.2005.02.002
* Corresponding author. Tel.: C1 502 852 2323; fax: C1 502 852 2215.
E-mail address: [email protected] (D. Gozal).
inattentive/disruptive behaviors similar to those seen in
ADHD, and such children may be misdiagnosed due to the
decreased awareness of this condition by primary care
practitioners [13].
Since overnight polysomnography (PSG) in the sleep
laboratory, the gold-standard diagnostic method for PLMD,
is costly, labor intensive, and relatively difficult to obtain by
virtue of the limited number of pediatric sleep laboratories
in most countries, new accurate but simple methods for the
home diagnosis of PLMD in children are clearly needed.
Actigraphic devices have been found to be useful for
assessing sleep/wake cycles, sleep quality and maintenance,
insomnia, and locomotor activity levels in children with
sleep and/or psychiatric disturbances [14–17].
Several recently developed methods have enabled
ambulatory monitoring of PLMS using actigraphic devices.
Such approaches have been shown to be a sensitive and
specific measure of PLMS in adults [1,18,19] and have been
used clinically to assess drug response among restless leg
Sleep Medicine 6 (2005) 325–332
www.elsevier.com/locate/sleep
H.E. Montgomery-Downs et al. / Sleep Medicine 6 (2005) 325–332326
syndrome (RLS) patients [20,21] and population screening
[22]. However, the validity of any actigraphy device in the
diagnosis of PLMS has yet to be investigated in children.
Therefore, the objective of this prospective, cross-sectional
study was to test the validity of Actiwatchw actigraphs as an
assessment tool in measuring PLMS in children. In order to
establish its effectiveness, the unbiased readings derived
from the Actiwatchw were compared with those of
concurrent anterior tibial EMG recordings obtained during
overnight PSG in the sleep laboratory.
2. Methods
The study was approved by the Institutional Review
Boards at the University of Louisville and Kosair Children’s
Hospital; informed consent and, when appropriate, assent
for minors, were obtained.
2.1. Participants
Subjects were clinical patients ages 4–12 years referred for
overnight polysomnographic evaluation at an urban-setting
children’s hospital. Standard overnight multichannel PSG was
performed in the Sleep Medicine Center at Kosair Children’s
Hospital in a quiet, darkened room with an ambient
temperature approximately 24 8C and with a parent or
guardian present. For analyses, recording time was considered
to begin at sleep onset (09:29 p.m.G2.8 h) and end at lights on
(05:42 a.m.G0.8 h); average (SD) recording length was 8.2
(G0.13) h. No medications were used to induce sleep.
2.2. Polysomnography
The following parameters were measured: chest and
abdominal wall movement by respiratory impedance or
inductance plethysmography, heart rate by ECG, and air
flow with a sidestream end-tidal capnograph which also
provided breath-by-breath assessment of end-tidal carbon
dioxide levels (PETCO2; BCI SC-300, Menomonee Falls,
WI), a nasal pressure cannula and a thermistor. Arterial
oxygen saturation (SpO2) was assessed by pulse oximetry
(Nellcor N 100; Nellcor, Inc., Hayward, CA), with
simultaneously recorded pulse waveform. Bilateral electro-
oculogram (EOG), eight channels of electroencephalogram
(EEG), chin electromyogram (EMG), and analog output
from a body position sensor (Braebon Medical Corporation,
New York, NY) were also monitored. Tracheal sound was
monitored with a microphone sensor (Sleepmate, Mid-
lothian, VA) and digital time-synchronized video images
were collected.
2.3. Classification of sleep-disordered breathing
Snoring children with an AHI!1/h TST with no
clinically significant hypoxemia, hypercapnia, or
excessive daytime sleepiness were considered to have
primary snoring (PS). Children with clinically significant
hypoxemia, hypercapnia, daytime sleepiness, and/or
AHIO5/h TST were considered to have clinically-relevant
SDB, and were referred for surgical treatment. Those
children with AHIR1 but %5/h TST were considered to
have mild SDB, and in the vast majority were not referred
for any additional evaluation or therapy. The definition
used for non-clinically significant hypoxemia was the
absence of any events associated with oxyhemoglobin
desaturations O3% and mean SaO2R95%, while that for
hypercapnia required the absence of end-tidal carbon
dioxide tension values O50 mmHg for O3% of total
recording time.
2.4. Arousal detection
Criteria for arousal have not yet been specifically
developed for children [23]; arousals were defined as
recommended by the American Sleep Disorders Association
Task Force report [24], and included respiratory-related
(occurring immediately subsequent to an apnea, hypopnea
or snore), technician-induced, and spontaneous arousals.
The respiratory arousal index was expressed as the total
number of respiratory-related arousals per hour of TST; and
the total arousal index was expressed as the number of all
types of arousal per hour of TST.
2.5. Leg movement detection and scoring
As part of the standard PSG, bilateral anterior tibial EMG
was used to measure leg movements and all measures were
digitized using a commercially available PSG system
(Medcare Diagnostics, Buffalo, NY). Briefly, a limb move-
ment was scored if the EMG burst was between 0.5 and 5 s
in duration and was at least 25% of the burst elicited during
pre-study calibration. A PLM sequence was assigned if
there were four or more leg movements that which were
separated by at least 5, but no more than 1990 s. Scoring was
automated and then visually scored following task force
guidelines for scoring PLM [25].
For validation purposes, leg movements on EMG were
compared to those recorded using two Mini Mitter
ActiwatchwK64 Monitors. The ActiwatchwK64 is 28!27!10 mm and weighs 17.5 g. These instruments were
placed on the dorsum of each foot prior to sleep onset and
worn throughout the duration of the PSG recording. Inside
the actigraph is an accelerometric sensor that interprets
motion and presents it as a mathematical calculation. This
calculation of the patient movements is subsequently
downloaded through the use of a computerized reader.
Actigraphic leg movement scoring was automated using
Mini Mitter Actiware-PLMS software and following as
closely as possible the ASDA PLM-scoring criteria [25].
However, the Actiware software records in consecutive 2 s
epochs. This method constituted a strictly computer-derived
H.E. Montgomery-Downs et al. / Sleep Medicine 6 (2005) 325–332 327
identification of PLMS which was not user-validated as are
EMG-scored movements on PSG.
The actigraphic method for determining PLM events
examines each leg independently, so for comparison
purposes scoring of anterior tibial EMG was also done
independently for left and right legs. Data from the two
recording methods were compared three different ways:
(a)
Tabl
Desc
Stan
EMG
Acti
Acti
Left and right leg movements during sleep periods were
independently scored on EMG and the resulting indices
were compared with those reported from the actigraph
software analyses.
(b)
It was initially hypothesized that the Actiwatchw woulddetect fewer PLMS than the EMG, consistent with
previous findings [1,18,19], requiring a correction by
lowering the cutoff of the number of PLMS required for
a diagnosis of PLMD. However, because initial results
from the current study showed large discrepancies
between the two recording methods, with PLM indices
much higher on actigraphy, it was thought that motion
artifact during arousals and wake periods which are not
scored as leg movements on EMG may have con-
tributed to the actigraph-recorded indices. Thus, a
subsequent analysis was conducted in which EMG data
were rescored with movements during arousals and
periods of wake retained.
(c)
The final analysis was an attempt to adjust for theover-scoring of PLMs on actigraphy. One group mean
for the number of PLM-qualifying EMG movements
per arousal for left and right legs was calculated and
then multiplied by each individual’s arousal index to
create a correction factor. The resulting left and right
leg correction factors were subtracted from the raw
actigraph-recorded left and right leg counts and
revised indices calculated. Left and right leg acti-
graph indices following correction factor application
were then compared to EMG indices in which
movements during wake were retained and those
during arousals were excluded. Thus, the correction
factor constituted a subtraction method whereby the
greater the number of arousals, the greater the amount
by which the subject’s actigraphy-scored PLM index
was reduced. It is important to note that leg
movements that caused arousals were not included
in the calculation of the number of movements
occurring during arousals.
e 1
riptive statistics for PLMI from EMG with and without movements during a
Mean Med
Left Right Lef
dard EMG 4.0 4.0 1.3
with movements in arousals and wake included 10.9 11.1 7.3
graphy 6.4 7.9 4.1
graphy with correction factor 3.3 3.1 1.4
2.6. Statistical analyses
Analysis of variance was calculated to test the difference
between diagnostic subgroups’ movement-within-arousal
indices. Independent samples t-test was used to compare
overall arousal indices between groups. Data were analyzed
using SPSS version 11.5; a P!0.05 was considered
statistically significant. Bland–Altman plots with delineated
mean difference C2 SD of the Difference (SDD) were
constructed to provide a graphic representation of agree-
ment not provided by correlation coefficients [26]. Scatter-
plots were included in figures to provide visual
representation of the data.
3. Results
One hundred sixty-eight consecutive patients attending
overnight clinical PSG who met age eligibility criteria were
invited to participate in the research study. Fifty potential
subjects declined participation with the remaining 118
agreeing to participate. Nineteen subjects were lost due to
intolerance to wearing the actigraph (7), technician error
(2), or because one (2) or both (8) of the actigraphs was later
found to have become separated from the subject. Those
who withdrew from the study did not differ with regard to
age (7.8 [2.2]), gender (42% male), ethnicity (68%
Caucasian, 32% African-American), or diagnosis (63%
sleep-disordered breathing (SDB), 32% primary snoring,
21% PLMD). Thus, 99 subjects were available for analyses.
These final subjects were 7.8 (G2.2) years of age
(4.2–12.6), 62% male, 70% Caucasian and 22% African-
American. Fifty-four percent were diagnosed with sleep-
disordered breathing, 38% had primary snoring or normal
results on PSG, and 15% were diagnosed with PLMD.
When periods of movement during arousals and wake
were excluded from EMG recordings, consistent with
standard scoring practices, the mean difference (SD of
differences (SDD)) between the EMG and actigraphy-
recorded PLMS for the left leg was 2.41 (7.31) with 12% of
subjects falling O1 SD from the mean difference and 34%
of subjects falling O.5 SD from the mean difference. For the
right leg, the mean difference (SDD) was 2.05 (5.74) with
20% falling O1 SDD from the mean difference and 41%
falling O.5 SDD from the mean difference (Table 1, Fig. 1).
rousals included, and from actigraphy with and without correction factor
ian SD IQR Range
t Right Left Right Left Right Left Right
1.5 7.4 5.8 5.1 5.8 51.0 28.0
7.9 10.7 9.0 9.1 11.2 75.5 43.6
3.9 6.1 5.5 8.7 6.9 31.1 23.7
1.3 5.7 5.3 5.1 5.6 31.5 25.1
-60
-40
-20
0 0
20
40
60
-60
-40
-20
20
40
60
Dif
fere
nce
in P
LM
I (a
ctig
raph
y
-
EM
G)
Dif
fere
nce
in P
LM
I (a
ctig
raph
y
-
EM
G)
0
10
20
30
40
50
PLM
Ind
ex f
rom
Act
igra
phy
PLM Index from EMG0 10 20 30 40 50
(a)
Average PLMI by actigraphy and EMG Average PLMI by actigraphy and EMG
0 10 20 30 40 50 0 10 20 30 40 50
(c) (d)
PLM
Ind
ex f
rom
Act
igra
phy
0
10
20
30
40
50
PLM Index from EMG0 10 20 30 40 50
(b)
Fig. 1. (a) Scatterplot for periodic leg movement indices recorded by actigraphy or standard EMG for left leg, (b) scatterplot for periodic leg movement indices
recorded by actigraphy or standard EMG for right leg, (c) Bland–Altman plot for agreement between periodic leg movement indices measured by actigraphy or
standard EMG for the left leg, (d) Bland–Altman plot for agreement between periodic leg movement indices measured by actigraphy or standard EMG for the
right leg.
H.E. Montgomery-Downs et al. / Sleep Medicine 6 (2005) 325–332328
When periods of movement during arousals and wake
were included in the EMG scoring of PLM, mean difference
(SDD) for agreement for left leg was K4.5 (9.25) with 25%
falling O1 SDD from the mean difference and 40% falling
O.5 SDD from the mean difference. Mean difference (SDD)
for the right leg was K4.62 (7.03) with 21% falling O1
SDD from the mean difference and 53% falling O.5 SDD
from the mean difference (Table 1, Fig. 2).
The average (standard error of the mean (SE); range)
number of PLM-qualifying EMG movements during
arousals in this group of subjects for left and right legs
was 0.241 (.013; 0.016–0.582) and 0.243 (.013; 0.021–
0.606) movements per arousal, respectively. This value,
multiplied by the number of arousals in each subject’s
study, constituted a correction factor to eliminate the
influence of motion artifact from the actigraphy-recorded
PLMS index. Correction factor-applied actigraph-recorded
PLMS indices were compared to EMG-recorded PLMS
index from which movements during arousals (but not
wake) were excluded. With correction factor application
to actigraphy, the mean difference (SDD) for agreement
for left leg was K.69 (6.93) with 17% falling O1 SDD
from the mean difference and 34% falling O.5 SDD from
the mean difference. Mean difference (SDD) for the right
leg was K.91 (5.51) with 20% falling O1 SDD from the
mean difference and 45% falling O.5 SDD from the mean
difference (Table 1, Fig. 3).
Subjects were divided into three groups based on
diagnosis: sleep-disordered breathing (SDB), primary snor-
ing or normal (normal), or periodic limb movement disorder
(PLMD). There was a significant difference between groups
with regard to subjects’ movement-in-arousal indices used
to create the correction factor. Subjects who had been
diagnosed with PLMD had significantly higher numbers of
leg movements during arousals than those diagnosed with
clinical SDB, mild SDB, who had primary snoring, or were
normal. Average (SE) total arousal indices for PLMD-
diagnosed and other subjects were 11.8 (.74) and 13.3 (.32),
respectively, and did not differ significantly (tZ.65,
PZ.52) (Table 2).
4. Discussion
Through recording of concurrent anterior tibialis EMG
and bilateral actigraphy in a relatively large cohort of
children with various diagnostic entities, we found that
although there was a visually evident relationship between
the two recording methodologies, there was also gross
inflation of PLM events recorded with actigraphy. Agree-
ment between the two methods was assessed using Bland–
Altman plots and application of a correction factor was used
in an attempt to improve validity of this application in the
evaluation of PLMD in pediatric populations. Despite this,
0
10
20
30
40
50
-60
-40
-20
0
20
40
60
PLM
Ind
ex f
rom
Act
igra
phy
PLM Index from EMG
Average PLMI by actigraphy and EMG
Dif
fere
nce
in P
LM
I (a
ctig
raph
y
-
EM
G)
0 10 20 30 40 50
0
10
20
30
40
50
PLM
Ind
ex f
rom
Act
igra
phy
0 10 20 30 40 50
PLM Index from EMG
0 10 20 30 40 50
(a) (b)
(c) (d)
-60
-40
-20
0
20
40
60
Average PLMI by actigraphy and EMG
Dif
fere
nce
in P
LM
I (a
ctig
raph
y
-
EM
G) 0 10 30 40 5020
Fig. 2. (a) Scatterplot for periodic leg movement indices recorded by actigraphy or EMG when movements during Arousals and Wake were included for left
leg, (b) scatterplot for periodic leg movement indices recorded by actigraphy or EMG when movements during Arousals and Wake were included for right leg,
(c) Bland–Altman plot for agreement between periodic leg movement indices measured by actigraphy, or by EMG when movements during Arousals and
Wake were included, for the left leg, (d) Agreement Bland–Altman plot for agreement between periodic leg movement indices measured by actigraphy, or by
EMG when movements during Arousals and Wake were included, for the right leg.
H.E. Montgomery-Downs et al. / Sleep Medicine 6 (2005) 325–332 329
the major finding of the present study is that a currently
available actigraphic approach is insufficiently accurate to
permit use of this method for reliable estimates of PLM
events in sleeping children. Indeed, visual examination of
the agreement plots indicates a triangular distribution such
that the higher the PLM index, the greater the discrepancy
between the methods.
Several methodological issues merit comment. The
population approached for the current study was typical of
the cross-sectional distribution of patients usually evaluated
at pediatric sleep centers. Indeed, there were no major
differences between the overall diagnostic characteristics of
our cohort and those of patients being evaluated prior to and
subsequent to completion of the study. Sub-analyses in
patients with PLMD failed to improve the validity of the
actigraphic method; however, even if the correction factor
had succeeded in PLMD subjects, it is important to consider
the fact that the diagnosis of PLMD involves the evaluation
of patients for whom no information about their PLM index
is known a priori. Furthermore, any reliable diagnostic
method should be able to detect the disease condition of
interest even when such is not suspected prior to testing.
These requirements were not met when the actigraphic
device approach was used, and as mentioned use of the latter
led to substantial increases in the numbers of PLM events
when compared to the tibial EMG-derived PLM indices.
This actigraphic inflation was despite the difference in
scoring epoch-length; movements less than 2 s were not
scored by the actigraph, while movements as short as .5 s
were scored on EMG. Thus, an inflation on the part of EMG
was originally expected, as has been found by others [1,18,
19].
To determine whether this inflation was due to move-
ments during arousals and wake periods, comparison was
then made when such movements were retained during
analysis of the polysomnographically recorded EMG.
Furthermore, to adjust for this motion artifact, the average
number of leg movements during arousals was used to
create a correction factor that was applied to each
subject’s actigraphically-recorded PLM index. Application
of the correction factor improved the agreement between
actigraphy and polysomnographically-derived PLM indices,
with fewer subjects falling further from the mean difference.
However, there were significant group differences in the
movement indices used to establish the correction factor.
Indeed, subjects with PLMD had seven times more leg
-5
5
15
25
35
45
-5
5
15
25
35
45
0
PLM Index from EMG
-60
-40
-20
0
20
40
60
-10 0 10 20 30 40 50
-60
-40
-20
0
20
40
60
-10 0 10 20 30 40 50
PLM Index from EMG
PL
M I
ndex
fro
m A
ctig
raph
yD
iffer
ence
in P
LM
I (a
ctig
raph
y
- E
MG
)
Diff
eren
ce in
PL
MI
(act
igra
phy
-
EM
G)
PL
M I
ndex
fro
m A
ctig
raph
y
Average PLMI by actigraphy and EMG Average PLMI by actigraphy and EMG
0 10 20 30 40 50 10 20 30 40 50
(a) (b)
(c)(d)
Fig. 3. (a) Scatterplot for periodic leg movement indices recorded by actigraphy with application of a correction factor or standard EMG when movements
during wake (but not arousals) were included for the left leg, (b) scatterplot for periodic leg movement indices recorded by actigraphy with application of a
correction factor or standard EMG when movements during wake (but not arousals) were included for the right leg, (c) Bland–Altman plot for agreement
between periodic leg movement indices measured by actigraphy with application of a correction factor or by standard EMG when movements during wake (but
not arousals) were included, for the left leg, (d) Agreement Bland–Altman plot for agreement between periodic leg movement indices measured by actigraphy
with application of a correction factor, or by standard EMG when movements during wake (but not arousals) were included, for the right leg.
Table 2
Leg movements in arousals index for subjects with SDB, PLMD, and who
were normal
Left leg Right leg
N (subjects) Mean (SE) N (subjects) Mean (SE)
SDB 55 2.71 (.51) 55 3.64 (.82)
PLMD 10 15.79 (5.34) 10 8.90 (2.20)
Normal 34 2.65 (.77) 34 3.13 (.90)
FZ19.1 FZ4.3
P!.001 PZ.017
H.E. Montgomery-Downs et al. / Sleep Medicine 6 (2005) 325–332330
movements during arousal compared to children who had
SDB or to those children who had no evidence of either
SDB or PLMD.
The initial hypothesis was that a correction factor would
improve the usefulness of actigraphy in home-based
recordings of PLM in children. However, agreement between
recording methods was still low after correction factor
application; at best, the standard deviation of PLM index for
agreement was over 5. Again, without a priori knowledge of
the specific patient’s diagnosis (i.e. PLMD or no PLMD) it
would be impossible to determine the appropriate correction
factor for use, nullifying the intrinsic value of actigraphy as a
diagnostic tool. For the same reason, this device could not be
considered appropriate for the follow-up evaluation of
treatment after PSG-diagnosed PLMD in children since the
purpose of clinical treatment is to change the group into
which the subject falls (i.e. from PLMD to normal).
It is important to note that in developing the correction
factor for the current study, periods of movement during
wake were not included. After applying the correction
factor, the resulting actigraphy PLM index would include
movements during wake, and thus were compared to EMG
indices in which movements were removed during arousals,
but retained during wake. The rationale for this approach
relied on published evidence supporting the validity of
actigraphy in determination of sleep/wake cycles [27], and
therefore such detection could be used prior to analysis of
PLM events to extract periods of waking from the
subsequent derivation of the PLMS index.
The observation of increased leg movements during
arousals in children with PLMD is intriguing. We suspect
that there might be a restless leg syndrome component that
is emerging during arousals from sleep in children with
PLMD. Indeed that while they do not have increased
numbers of arousals per hour of sleep compared to other
clinical groups, their arousals may be of longer duration,
which would account for the differences in movement
indices between these subjects and those without PLMD.
We further speculate that these subjects’ increased
H.E. Montgomery-Downs et al. / Sleep Medicine 6 (2005) 325–332 331
movements during arousals may be associated with
hyperactivity or attention deficit disorder or other move-
ment disorders, although the status of this set of clinical
subjects with regard to ADD/ADHD or movement disorders
is unknown.
It is important to note that EMG and actigraphy measure
different physiological events; EMG assesses electrical
activity as an output from muscle activation and
the actigraph is an accelerometer and detects generalized
movement. This may explain some of the discrepancies
between the methods but also supports the notion that
actigraphy’s use as a tool for screening for nocturnal
evidence of RLS might be a future application of this
technology.
There were four extreme outliers on the discrepancy
between the two recording methods. These were hetero-
geneous and unremarkable: three were female; two were
African-American, one Hispanic, and one White; they
ranged from 4 to 11 years of age, and had diagnoses of OSA
(2), PLMD (1), and primary snoring (1).
Our findings are inconsistent with others’ validation
studies testing actigraphic-detection of PLMS in adults.
Kazenwadel and colleagues found that the MOVOPORT
actigraphy device was accurate screening tool for PLMS [1]
and have applied this method for use in clinical drug trials for
treatment of RLS [20,21] and actigraphy has been applied to
screen for presence of PLMS in a wide age range among a
general population in the UK.[22] Two groups have
performed validation studies using the same device tested
here and have reported positive results, although neither has
reported agreement between the methods as opposed to
correlations [18,19]. We expect that this discrepancy may be
due in part to the ages of the subjects studied.
The current validation study looked at the comparison of
EMG and actigraphy recordings of leg movements on a
macro level; further examination using a more closely
synchronized approach, and possibly development of a
method whereby the actigraphic device could recognize
arousals through an increase in baseline movements is
clearly called for at this time. Further enhancement of the
validity of this method might be achieved by allowing user-
validation of raw signal as is the standard practice with
EMG. While this study suggests that further research be
performed to explore the possibility of using actigraphy to
identify PLMs in children, at this time it does not support
the method currently used for PLM detection in adults for
use in children for the purposes of diagnosing or assessing
the treatment response of PLMD.
Acknowledgements
The authors are grateful to the subjects and their families
who participated in the study. David Davis, PSGT, Renee
Ferguson, PSGT, Pam Gerges, RPSGT, Carrie Klaus,
RPSGT, Allison Parker, PSGT, and Tonya Thornton,
PSGT provided technical assistance with subject recruit-
ment and data collection.
This work was supported in part by US Department of
Education Grant H324E011001, Centers for Disease Con-
trol and Prevention Grant E11/CCE 422081-01, and
National Institutes of Health Grant F32 HL-074591. The
study was also supported by Mini Mitter Co, Inc. through
the provision of two Actiwatchesw, the Actiwatchw reader
with cable and adaptor, and the Actiwarew—Sleep software.
References
[1] Kazenwadel J, Pollmacher T, Trenkwalder C, et al. New actigraphic
assessment method for periodic leg movements (PLM). Sleep 1995;
18:689–97.
[2] Konofal E, Lecendreux M, Bouvard M, Mouren-Simeoni M. High
levels of nocturnal activity in children with attention-deficit
hyperactivity disorder: a video analysis. Psychiatry Clin Neurosci
2001;55:97–103.
[3] Picchietti DL, Walters AS. Restless legs syndrome and periodic limb
movement disorder in children and adolescents: comorbidity with
attention-deficit hyperactivity disorder. Child Adolescent Psychiatric
Clin North Am 1996;5:729–40.
[4] Picchietti DL, England SJ, Walters AS, Willis K, Verrico T. Periodic
limb movement disorder and restless legs syndrome in children with
attention-deficit-hyperactivity disorder. J Child Neuro 1998;13:
588–94.
[5] Picchietti DL, Walters AS. Moderate to severe periodic limb
movement disorder in childhood and adolescence. Sleep 1999;22:
297–300.
[6] Picchietti DL, Underwood DJ, Farris WA, et al. Further studies on
periodic limb movement disorder and restless legs syndrome in
children with attention-deficit-hyperactivity disorder. Mov Disord
1999;14:1000–7.
[7] Thorpy MJ. International classification of sleep disorders: diagnostic
and coding manual. Rochester, MN: American Sleep Disorders
Association; 1990 [p. 69–71].
[8] Chervin RD, Hedger KM. Clinical prediction of periodic leg
movements during sleep in children. Sleep Med 2001;2:501–10.
[9] Walters S, Mandelbaum DE, Lewin DS, et al. Dopaminergic therapy
in children with restless legs/periodic limb movements in sleep and
ADHD. Pediatr Neurol 2000;22:182–6.
[10] Crabtree VM, Ivanenko A, O’Brien LM, Gozal D. Periodic limb
movement disorder of sleep in children. J Sleep Res 2003;12(1):
73–81.
[11] Simakajornboon N, Gozal D, Vlasic V, et al. Periodic limb
movements in sleep and iron status in children. Sleep 2003;26(6):
735–8.
[12] Hanly PJ, Gabor JY, Chan C, Pierratos A. Daytime sleepiness in
patients with CRF: impact of nocturnal hemodialysis. Am J Kidney
Diseases 2003;41(2):403–10.
[13] O’Brien LM, Ivanenko A, Crabtree VM, et al. Sleep disturbances in
children with attention deficit hyperactivity disorder. Pediatr Res
2003;54(2):237–43.
[14] Glod CA, Teicher MH, Hartman CR, Harakal T. Increased nocturnal
activity and impaired sleep maintenance in abused children. J Am
Acad Child Adolescent Psychiatry 1997;36:1236–43.
[15] Sadeh A, Hauri PJ, Kripke DF, Lavie P. The role of actigraphy in the
evaluation of sleep disorders. Sleep 1995;18:288–302.
[16] Teicher MH. Actigraphy and motion analysis: new tools for
psychiatry. Harvard Rev Psychiatry 1995;3:18–35.
H.E. Montgomery-Downs et al. / Sleep Medicine 6 (2005) 325–332332
[17] Teicher MH, Glod CA, Harper D, et al. Locomotor activity in
depressed children and adolescents: I circadian dysregulation. J Am
Acad Child Adolescent Psychiatry 1993;32:760–9.
[18] Sack RL, Pires ML, Brandes RW, deJongh E. Actigraphic detection of
periodic leg movements: a validation study. Sleep Abstract Suppl
2001;24:A405.
[19] Gschliesser V, Frauscher B, Kunz K, et al. Actigraphy for PLM
detection: a validation study with polysomnography. Sleep Abstract
Suppl 2004;660:A295.
[20] Trenkwalder C, Stiasny K, Pollmacher T, Wetter T, et al. pa therapy of
uremic and idiopathic restless legs syndrome: a double-blind
crossover trial. Sleep 1995;18(8):681–8.
[21] Collado-Seidel V, Kazenwadel J, Wetter TC, et al. A controlled study
of additional sr-L-dopa in L-dopa-responsive restless legs syndrome
with late-night symptoms. Neurology 1999;52(2):285–90.
[22] Morrish E, King MA, Pilsworth SN, et al. Periodic limb movement in
a community population detected by a new actigraphy technique.
Sleep Med 2002;3(6):489–95.
[23] Bandla HPR, Gozal D. Dynamic changes in EEG spectra during
obstructive apnea in children. Pediatr Pulmonol 2000;(29):359–65.
[24] Sleep Disorders Atlas Task Force. EEG arousals: scoring and rules
and examples. In: Guilleminault C, editor. Sleep, vol. 15, 1992. p.
173–84.
[25] Atlas Task Force. Recording and scoring leg movements. Sleep 1993;
16(8):748–59.
[26] Bland JM, Altman DG. Statistical methods for assessing agreement
between two methods of clinical measurement. Lancet 1986;i:
307–10.
[27] Standards of Practice Committee. Practice parameters for the use of
actigraphy in the clinical assessment of sleep disorders. Sleep 1995;
18(4):285–7.