pii: sp-00410-13
http://dx.doi.org/10.5665/sleep.4264
NEW MEASURE OF SLEEP FRAGMENTATION IN CHILDREN
Movement Distribution: A New Measure of Sleep Fragmentation in Children with
Upper Airway Obstruction
Scott Coussens, BSc(Hons)1,2; Mathias Baumert, PhD3,4; Mark Kohler, PhD4; James Martin, MD2; Declan Kennedy, MD2,4; Kurt Lushington, PhD5;
David Saint, PhD1; Yvonne Pamula, PhD2
School of Medical Sciences, University of Adelaide, Adelaide, Australia; 2Department of Respiratory and Sleep Medicine, Children, Youth and Women’s
Health Service, North Adelaide, Australia; 3Cardiovascular Research Centre, Royal Adelaide Hospital and School of Medicine, University of Adelaide,
Adelaide, Australia; 4Children’s Research Centre, University of Adelaide, Adelaide, Australia; 5School of Psychology, University of South Australia,
Adelaide, Australia
1
Study Objectives: To develop a measure of sleep fragmentation in children with upper airway obstruction based on survival curve analysis of
sleep continuity.
Design: Prospective repeated measures.
Setting: Hospital sleep laboratory.
Participants: 92 children aged 3.0 to 12.9 years undergoing 2 overnight polysomnographic (PSG) sleep studies, 6 months apart. Subjects were
divided into 3 groups based on their obstructive apnea and hypopnea index (OAHI) and other upper airway obstruction (UAO) symptoms: primary
snorers (PS; n = 24, OAHI < 1), those with obstructive sleep apnea syndrome (OSAS; n = 20, OAHI ≥ 1) and non-snoring controls (C; n = 48,
OAHI < 1).
Interventions: Subjects in the PS and OSAS groups underwent tonsillectomy and adenoidectomy between PSG assessments.
Measurements and Results: Post hoc measures of movement and contiguous sleep epochs were exported and analyzed using Kaplan-Meier
estimates of survival to generate survival curves for the 3 groups. Statistically significant differences were found between these group curves for
sleep continuity (P < 0.05) when using movement events as the sleep fragmenting event, but not if stage 1 NREM sleep or awakenings were used.
Conclusion: Using conventional indices of sleep fragmentation in survival curve analysis of sleep continuity does not provide a useful measure
of sleep fragmentation in children with upper airway obstruction. However, when sleep continuity is defined as the time between gross body
movements, a potentially useful clinical measure is produced.
Keywords: sleep fragmentation, sleep continuity, movement, children, upper airway obstruction
Citation: Coussens S, Baumert M, Kohler M, Martin J, Kennedy D, Lushington K, Saint D, Pamula Y. Movement distribution: a new measure of
sleep fragmentation in children with upper airway obstruction. SLEEP 2014;37(12):2025-2034.
INTRODUCTION
Body movements are a normal feature of sleep, show distinct
ontogenetic changes, and are altered in various medical and
sleep disorders.1-4 Too many or even too few nocturnal body
movements have been shown to reduce sleep quality, and in the
case of excessive body movements can significantly fragment
sleep.5,6 Furthermore, adverse physiological or psychosocial
events during infancy or childhood have been found to be associated with an abnormal pattern of body movements during
sleep in adulthood.7 Given these findings, it is surprising that
comparatively little research has focused on nocturnal body
movements as a marker of sleep disturbance, despite the advent of more sophisticated polysomnographic monitoring techniques including digital video recording.
Restless sleep is a common presenting complaint in children
with sleep disorders. Sleep-related body movements may be
a more specific marker of sleep disturbance in children compared to adults due to developmental and age-specific differences in both sleep structure and arousal processes.8-10 Studies
Submitted for publication June, 2013
Submitted in final revised form May, 2014
Accepted for publication May, 2014
Address correspondence to: Mr. Scott Coussens, Department of Respiratory & Sleep Medicine, Women’s and Children’s Hospital, 72 King William
Road, North Adelaide 5000, Australia; Tel: +61 8 8161-6037; Fax: +61 8
8161-7050; E-mail: [email protected]
SLEEP, Vol. 37, No. 12, 2014
have demonstrated that abnormalities in nocturnal body movements can occur with respect to their frequency, timing, and
sleep stage distribution in association with various diseases
impacting sleep.11,12 These findings suggest that altered body
movements during sleep may be an indirect marker of central
nervous system (CNS) or arousal dysfunction.13
Sleep-related upper airway obstruction (UAO) is a common
disorder during childhood with a spectrum of severity ranging
from primary snoring (PS) to obstructive sleep apnea syndrome
(OSAS). Primary snoring, characterized by frequent snoring
in the absence of gas exchange abnormalities, is estimated to
occur in 5% to 10% of children, while the prevalence of OSAS,
which causes intermittent hypoxia and sleep fragmentation, is
between 1% and 4%.14 It is now well documented that OSAS
in children is associated with a range of neurocognitive and
behavioral deficits including problems with attention, memory,
learning, executive function, hyperactivity, and aggression.15,16
During childhood, peak presentation of OSAS is observed between 2-5 years of age, which is an important period of cognitive
development.17 Initial efforts to understand the pathophysiological mechanisms underlying these deficits focused largely on
the role of hypoxia resulting from the apneas and hypopneas
characteristic of OSAS. However, emerging evidence now
suggests that children with PS or with disturbed sleep arising
from medical conditions not associated with hypoxia, such as
eczema and juvenile arthritis, also demonstrate reduced neurocognitive performance.18,19 This has focused debate on the relative contribution of hypoxia versus sleep fragmentation to the
2025
Movement Distribution in Children—Coussens et al.
observed neurocognitive sequelae of UAO.20 However, while
sleep fragmentation is a cardinal feature of many sleep disorders, it has proven difficult to accurately measure and quantify.
Restless sleep is frequently reported in children with UAO,
but overnight polysomnography often demonstrates that the
macrostructure of sleep is relatively preserved, with relatively
fewer sleep stage changes and awakenings compared to adults
with UAO.21-25 Furthermore, in children comparatively fewer apneas and hypopneas are terminated by cortical arousal.26,27 While
frequent movement during sleep has been reported in children
with OSAS,22,28,29 conventional sleep scoring rules have tended
to disregard these events. Recently, researchers have noted that
many physiological processes can be described by exponential models,30 including the length of continuous sleep bouts.
Norman et al., using an exponential model of sleep continuity,
utilized survival curve analysis (SCA) to measure sleep continuity in adults with OSAS.31 This novel approach to analysing
sleep fragmentation demonstrated that individuals with OSAS
had less stable sleep than controls, even though conventional
sleep architecture measures did not differ between the groups.
Furthermore, SCA was sensitive enough to show dose-dependent differences in sleep continuity between controls, those with
mild UAO, and the moderate to severe OSAS group.31
This aim of this study was to use SCA to measure sleep continuity in children with UAO before and after adenotonsillectomy. However, while Norman used the occurrence of wake and
stage 1 sleep as their marker of sleep fragmentation, we used
the occurrence of body movements as the indication of a sleep
fragmenting event and to define the end of a sleep period. Several studies have demonstrated increased NREM stage 1 sleep
and decreased stage 2 NREM sleep bout duration in children
with UAO.32-34 However, only two small studies, with results
reported in conference proceedings have directly investigated
using stage 1 NREM sleep as a marker of sleep fragmentation
in children with UAO, and both failed to distinguish between
disease-based groups.35,36 We hypothesized that Norman’s findings using wake and stage 1 NREM sleep as the marker of sleep
fragmentation would not be replicated in children with UAO,
as children show relatively less wake and stage 1 NREM sleep
than adults with UAO. Instead, we hypothesized that children
with UAO would have an altered distribution of body movements during sleep compared to normal controls. Lastly, we
hypothesized that following treatment of children with UAO
by adenotonsillectomy, the pattern of body movements postsurgery would approximate that of the control children.
METHODS
Participants
As part of a broad-ranging study looking at neurocognitive
deficits in children with UAO that used a prospective repeated
measures design, overnight polysomnography (PSG) was performed in children with UAO awaiting adenotonsillectomy at
baseline and 6 months following surgery.16 Non-snoring control children matched for age and gender underwent PSG at the
same time points. Children with UAO were those with a history of frequent snoring who were scheduled for adenotonsillectomy for suspected obstructive sleep apnea as diagnosed by
an experienced pediatric otorhinolaryngologist at the Adelaide
SLEEP, Vol. 37, No. 12, 2014
Women’s and Children’s Hospital prior to being recruited in
this study. It is routine clinical practice to diagnose OSAS in
children based on clinical history and examination findings in
these circumstances. The decision to operate was made prior to
recruitment to the current study on factors other than objective
sleep measures. The PSG results played no role in the decision
to operate on the participants, with PSG results not available
prior to scheduled adenotonsillectomy in most cases.
Children were aged between 3 and 12.9 years of age at baseline. Children were excluded from participating in the study if
they had undergone previous ear, nose, or throat, or craniofacial
surgery, had a medical condition (other than UAO) associated
with hypoxia or sleep fragmentation, or were taking medication
known to affect sleep or cardiorespiratory physiology. Control
children were recruited through the recommendation of parents
of the participating UAO children and from advertisements in
local newspapers and schools. The same exclusion criteria were
applied to controls with the additional requirement that they did
not snore on more than two nights per week as determined from
parental report. Parental consent and child assent was obtained
from all participants. This study was approved by the Women’s
and Children’s Hospital Human Ethics Committee.
Overnight Polysomnography
Overnight PSG was conducted without sedation or reported
sleep deprivation and began close to each child’s usual bedtime with a parent present throughout the procedure. Polysomnography was only performed if children reported as well
on the night and free of any recent illness including respiratory infection. The following parameters were measured and
recorded continuously using a commercially available computerized PSG system (Compumedics S-Series Sleep System,
Melbourne, Australia): electroencephalogram (EEG; C3-A2 or
C4-A1), left and right electroocculogram (EOG), submental
and diaphragmatic electromyogram (EMG) with skin surface
electrodes; leg movements by piezoelectric motion detection;
heart rate by electrocardiogram (ECG); oro-nasal airflow by
thermistor and nasal pressure; respiratory movements of the
chest and abdominal wall using uncalibrated respiratory inductive plethysmography (RIP); arterial oxygen saturation (SpO2)
by pulse oximetry (Nellcor N-595; 2-3 second averaging time);
and transcutaneous CO2 (TcCO2) using a heated (43°C) transcutaneous electrode (TINA, Radiometer Pacific). All data were
digitized and stored for subsequent analysis. Each child was
monitored continuously overnight via infrared camera by a pediatric sleep technician who also documented observations of
sleep behavior including the presence or absence of snoring.
Height and weight were measured on the night of PSG, and established growth charts corrected for age and gender were used
to determine body mass index (BMI) z-scores.37
Sleep stages were scored visually in 30-s epochs by a single
trained technician (SC) according to the standardized EEG, EOG,
and EMG criteria of Rechtschaffen and Kales.38 Stage 3 and 4
NREM sleep were combined as slow wave sleep (SWS). Epochs
were scored as movement if the EEG and EOG signals were obscured for ≥ 50% of the epoch by muscle tension or artifact associated with movement of the subject.38 Movement time was
scored as a separate category and was not included in either sleep
or wake time. Wake after sleep onset (WASO) refers to time
2026
Movement Distribution in Children—Coussens et al.
spent awake during the recording period after initial sleep onset
and before the final awakening. Sleep efficiency (SE) was calculated as the percentage of time spent in sleep of the time available
for sleep (i.e., from lights out to the final epoch of sleep).
Respiratory variables were scored according to standard
guidelines recommended for pediatric sleep studies.39 The obstructive apnea-hypopnea index (OAHI) was calculated as the
total number of obstructive apneas, mixed apneas, and obstructive hypopneas divided by the total sleep time (TST) and expressed as the number of events per hour of sleep. An OAHI ≥ 1
was considered indicative of UAO. The central apnea hypopnea
index (CAHI) was calculated as the total number of central apneas and central hypopneas divided by the TST and expressed
as the number of events per hour of sleep. The total apnea and
hypopnea index (AHI) was calculated as the total number of
all respiratory events divided by the TST and expressed as the
number of events per hour of sleep.
Cortical arousals were scored according to the criteria of the
American Sleep Disorders Task Force.40 The total arousal index
(AI) represents all arousals combined (excluding arousals caused
by external stimuli) expressed as the number of arousal per
hour of sleep. The spontaneous arousal index (SAI) represents
the total number of spontaneous arousals per hour of sleep. The
respiratory arousal index (RAI) represents the total number of
respiratory arousals per hour of sleep. Periodic limb movements
(PLM) were scored using standard criteria.41 The PLM index
(PLMI) was calculated as the number of PLMS per hour of sleep.
Movement Measures
Two different measures of body movement were analysed
in this study. The first was movement time (MT) as defined by
Rechtschaffen and Kales, which represents sleep epochs where
the PSG signals are obscured or distorted by gross body movements.38 An epoch was scored as MT when the EEG and EOG
signals were obscured > 50% of the epoch by muscle tension
or other artifact associated with movement of the subject.38 Epochs were scored as MT only if the preceding and subsequent
epochs were scored as sleep. Movement time is reported as the
percentage of the TST.
Secondly, we utilized a new measure of movement during
sleep termed movement events (ME). Movement events were
defined as discrete body movements > 3 sec duration and were
scored when there was evidence of movement on ≥ 2 independent PSG channels, but requiring at least one EMG channel
(chin, diaphragmatic, or legs) to show evidence of movement.
Evidence of movement included but was not limited to distortion or artifact in a PSG signal, an increase in EMG activity, generation of a cortical arousal, or a visually discernible increase
in heart rate or respiratory rate above baseline levels. An ME
was scored for an indefinite period until evidence of movement
ceased and the subject remained asleep. Movement events separated by < 0.5 sec were combined into one event. Our definition
of ME is modified from 2 existing measures of body movements:
(1) movement arousals defined by Rechtschaffen and Kales38
and (2) the movement/arousal definition of Mograss et al.29 We
modified these 2 previous definitions to improve inter-scorer reliability and to incorporate additional signals that are now routinely collected during PSG, which provide improved evidence
of body movement. Movement event number and duration were
SLEEP, Vol. 37, No. 12, 2014
Figure 1—The distribution of sleep runs terminated by movement events
(ME) or wake epochs for the baseline PSG (pre) for a single control
subject. The diamonds represent individual sleep runs in seconds and
the proportion of those runs from the single subject’s population of total
sleep runs for that night. The solid black line is the approximated survival
curve for the subject using the Kaplan-Meier method.42
recorded. Movement events included movements of relatively
short duration and intensity to gross body movements accompanying postural shifts. Movement events are reported as the
number of events per hour of sleep for each subject, rectified for
that subject’s TST (movement event index, MEI) and a mean
value then generated for each of the 3 experimental groups.
Measures of Sleep Continuity
Sleep continuity was calculated by measuring the duration
of uninterrupted periods of sleep (contiguous sleep epochs)
throughout the night, each period being termed a “sleep run.”
Two different definitions of sleep runs were used to assess sleep
continuity. In the first method, a sleep run began with a change
from wake to any stage of sleep and was terminated by the appearance of either an epoch of stage 1 NREM sleep or an epoch
of wake as per Norman et al.31 In the second method, a sleep
run began with either a change from wake to any stage of sleep
or following an ME termination in sleep and was terminated by
the appearance of either an ME or an epoch of wake. The occurrence of stage 1 NREM sleep was not used to terminate a sleep
run in the second definition as we wanted to utilize movement
rather than sleep stage lightening as a measure of sleep fragmentation, particularly given that children do not commonly
exhibit much stage 1 sleep compared to adults.
Survival Curve Analysis
For each individual the epoch based sleep staging and all
scored movement measures were exported to MATLAB (Mathworks, USA) for further analysis. Using custom written software sleep runs throughout the night were calculated (for an
example of this process see Figure 1, diamonds), twice for each
individual using the 2 definitions described above. Survival
curves representing sleep continuity were then calculated for
each individual using the Kaplan-Meier method.42
2027
Movement Distribution in Children—Coussens et al.
Table 1—Demographic characteristics for control (C), primary snoring (PS), and obstructive sleep apnea syndrome (OSAS) children at baseline and follow
up polysomnography (PSG).
†
C
PS
OSAS
PSG 1
N
Age (years)
Gender, N (% male)
BMI z-score
48
7.7 ± 2.6
22 (46)
0.29 ± 0.9
24
6.4 ± 2.2
15 (60)
0.54 ± 1.2
20
6.8 ± 3.0
13 (68)
1.21 ± 1.4
PSG 2
N
Age (years)
Gender, N (% male)
BMI z-score †
48
8.4 ± 2.8
22 (46)
0.53 ± 1.1
24
7.8 ± 2.5
15 (60)
0.50 ± 1.0
20
8.4 ± 2.6
13 (68)
1.01 ± 1.2
Post Hoc (ANOVA)
NS
NS
OSAS > PS *; OSAS > C *
NS
NS
NS
Analysis performed using transformed values. * P < 0.05. NS, not significant; BMI, body mass index; ANOVA, analysis of variance.
Survival curves of sleep runs between sleep disrupting
events (e.g., ME) were computed for each participant based
on empirical cumulative distribution functions and exponential
regression functions fitted (see Figure 1 for an example, solid
black line). Assumptions of normality were not valid for survival curve exponent values and a square root transform [√ x ]
was used to correct for skewness when performing some statistical tests. For a more detailed explanation of the methods
involved in performing survival curve analysis see Norman et
al.31 For each subject, sleep continuity is modelled on the equation y = m− θt where m is a constant and t is sleep run length;
thus each subject has a theta (θ, survival curve exponent) value
that characterizes the distribution of sleep disrupting events.
Statistics
For analysis between groups a one way analysis of variance (ANOVA) test was utilized. For correlation calculations
between the various sleep fragmentation measures, Pearson r
method was employed. Kolmogorov-Smirnov statistic, with
a Lilliefors significance level, was utilized in testing the normality of variables.43 Assumptions of normality were valid for
all PSG variables with the exception of PLMI, RAI, frequency
of SpO2 desaturations ≥ 3%/h TST, percentage of sleep time
with SpO2 < 95%, TcCO2 > 50 mm Hg, OAHI, and AHI. Inverse
transformation [1/(x + 1)] was required for these variables to
correct skewness. Post hoc testing was conducted using Tukey
Honestly Significant Difference (HSD) method for multiple
comparisons. Within group changes were tested using a general
linear models multiple measures test and paired samples t-test.
All P values reported are 2-tailed, with statistical significance
determined at α = 0.05. Correlations between normal variables
was performed using the Pearson r method and for non-normal
variables via the Spearman rho test. Data are presented as
mean ± standard deviation unless stated otherwise.
RESULTS
Subjects
Forty four children with sleep-related UAO and 48 control children underwent both baseline and follow-up PSG.
SLEEP, Vol. 37, No. 12, 2014
Children with UAO were divided into 2 groups based on
their OAHI: PS (OAHI < 1, N = 24) and those with OSAS
(OAHI ≥ 1, N = 20). Demographic characteristics for the 92
children are presented in Table 1. Children with OSAS had a
significantly greater BMI compared to both PS and control
so BMI was entered as a covariate where appropriate. There
were no other significant differences in demographic variables
between the 3 groups or within the groups between baseline
and follow-up PSG.
Polysomnography
Baseline PSG data are presented in Table 2. There were no
significant differences between the 3 groups with respect to
TST, the amount of time spent in all sleep stages, REM sleep
latency, spontaneous arousals and PLM index. As expected,
children with OSAS had higher OAHI, higher AHI, higher
RAI, greater number of oxygen desaturations ≥ 3%, and lower
nadir oxygen saturation compared to both the PS and control
groups. Standard PSG measures of sleep disturbance (shaded
variables in Table 2) including sleep efficiency, number of
awakenings and frequency of sleep stage shifts were not significantly different between the three groups of children during
baseline measurement.
The mean time between baseline and follow-up polysomnography was 29.4 ± 5.9 weeks. The mean time between adenotonsillectomy and follow-up polysomnography for children with
UAO was 27.5 ± 6.0 weeks. In the follow-up PSG (Table 3),
there were no significant differences between the 3 groups with
respect to TST, the amount of time spent in all sleep stages,
REM sleep latency, spontaneous arousals, or PLMI. Following
adenotonsillectomy the OSAS children still had a higher mean
OAHI and AHI and a greater number of oxygen desaturations ≥ 3% than both the PS and control groups and a lower
nadir oxygen desaturation compared to the PS group suggesting
the presence of residual OSA. As in the first PSG standard measures of sleep disturbance including sleep efficiency, number of
awakenings, and frequency of sleep stage shifts were not significantly different between the 3 groups of children. However,
at the second PSG, the OSAS children spent more time awake
after sleep onset than controls.
2028
Movement Distribution in Children—Coussens et al.
Table 2—Baseline polysomnography results for control (C), primary snoring (PS), and obstructive sleep apnea syndrome (OSAS) children.
Total sleep time (min)
Stage 1 (% TST)
Stage 2 (% TST)
SWS (% TST)
REM (% TST)
REM latency (min)
PLMI, median (range) †
OAHI †
CAI
AHI †
SpO2 nadir
SpO2 > 3% dips/h TST
RAI †
SAI
Sleep efficiency
WASO (min) †
WASO (% TST)
Awakenings/h TST
Sleep stage shifts/h TST
C
447.0 ± 35.2
3.3 ± 1.8
44.6 ± 5.9
31.7 ± 5.6
20.4 ± 4.1
91.8 ± 21.4
1.4 (0.0–22.6)
0.16 ± 0.2
1.41 ± 1.3
0.86 ± 0.9
93.1 ± 1.8
0.8 ± 0.8
0.5 ± 0.5
9.4 ± 2.9
81.7 ± 6.8
40.2 ± 28.7
9.3 ± 7.1
0.78 ± 0.5
12.2 ± 2.8
PS
442.9 ± 52.0
2.6 ± 2.0
43.3 ± 5.6
35.1 ± 7.0
19.0 ± 5.2
86.0 ± 30.3
2.0 (0.0–24.7)
0.30 ± 0.3
1.36 ± 1.0
0.88 ± 0.6
93.1 ± 1.6
0.8 ± 0.5
0.6 ± 0.7
8.8 ± 2.7
82.0 ± 7.5
40.9 ± 35.5
9.9 ± 9.2
0.73 ± 0.7
12.8 ± 2.8
OSAS
429.2 ± 45.9
3.2 ± 2.1
41.4 ± 5.2
33.6 ± 5.3
21.9 ± 5.2
94.6 ± 27.4
1.5 (0.0–14.9)
12.37 ± 12.3
4.13 ± 5.3
15.23 ± 14.4
85.6 ± 6.9
12.8 ± 14.2
7.6 ± 7.6
8.2 ± 2.5
81.3 ± 8.7
50.6 ± 45.3
12.8 ± 12.4
0.85 ± 0.6
13.1 ± 2.9
Post Hoc
NS
NS
NS
NS
NS
NS
NS
OSAS > C ***; OSAS > PS ***
OSAS > C *; OSAS > PS *
OSAS > C ***; OSAS > PS ***
OSAS < C ***; OSAS < PS ***
OSAS > C ***; OSAS > PS ***
OSAS > PS ***; OSAS > C ***
NS
NS
NS
NS
NS
NS
The shaded rows indicate traditional measures of sleep fragmentation. TST, total sleep time; REM, rapid eye movement sleep; SWS, slow wave sleep;
WASO, wake time after sleep onset; PLMI, periodic limb movement index; SAI, spontaneous arousal index; RAI, respiratory arousal index; OAHI, obstructive
apnea-hypopnea index; CAHI, central apnea-hypopnea index; AHI, apnea-hypopnea index; NS, not significant. † Analysis performed using transformed
values. * P < 0.05, ** P < 0.01, *** P < 0.001.
Table 3—Follow-up polysomnography results for control (C), primary snoring (PS), and obstructive sleep apnea syndrome (OSAS) children.
Total Sleep Time (min)
Stage 1 (% TST)
Stage 2 (% TST)
SWS (% TST)
REM (% TST)
REM latency (min)
PLMI, median (range) †
OAHI †
CAI
AHI †
SpO2 nadir
SpO2 > 3% dips/h TST
RAI †
SAI
Sleep Efficiency
WASO (min) †
WASO (% TST)
Awakenings/h TST
Sleep stage shifts h TST
C
451.8 ± 54.0
3.1 ± 2.3
46.7 ± 5.1
30.0 ± 4.9
20.3 ± 4.2
92.5 ± 25.4
0.8 (0.0–11.9)
0.34 ± 0.5
1.10 ± 0.9
0.89 ± 0.9
93.1 ± 1.8
0.8 ± 0.8
0.6 ± 0.6
9.4 ± 2.6
82.9 ± 8.1
39.4 ± 36.7
9.9 ± 11.9
0.67 ± 0.6
12.1 ± 2.9
PS
453.8 ± 45.2
2.4 ± 1.5
43.3 ± 7.2
33.8 ± 5.8
20.5 ± 3.1
93.4 ± 19.1
0.6 (0.0–26.4)
0.23 ± 0.3
1.42 ± 0.8
1.02 ± 0.7
91.4 ± 2.5
1.1 ± 0.6
0.6 ± 0.4
9.0 ± 3.1
82.0 ± 6.8
45.4 ± 39.7
10.9 ± 10.7
0.59 ± 0.3
11.9 ± 2.9
OSAS
443.5 ± 63.9
3.8 ± 3.1
42.0 ± 6.9
33.5 ± 6.3
20.7 ± 5.1
93.1 ± 30.0
3.9 (0.0–20.0)
1.50 ± 1.2
1.66 ± 1.6
2.66 ± 2.0
90.3 ± 3.1
2.1 ± 1.3
1.4 ± 2.1
9.8 ± 3.3
81.0 ± 10.0
61.9 ± 51.2
15.7 ± 14.8
0.82 ± 0.5
13.9 ± 3.3
Post Hoc
NS
NS
NS
NS
NS
NS
NS
OSAS > C ***; OSAS > PS ***
NS
OSAS > C ***; OSAS > PS ***
OSAS < C **; PS < C *
OSAS > C ***; OSAS > PS ***
NS
NS
NS
OSAS > C *
NS
NS
NS
The shaded rows indicate traditional measures of sleep fragmentation. TST, total sleep time; REM, rapid eye movement sleep; SWS, Slow wave sleep;
WASO, wake time after sleep onset; PLMI, periodic limb movement index; SAI, spontaneous arousal index; RAI, respiratory arousal index; OAHI, obstructive
apnea-hypopnea index; CAHI, central apnea-hypopnea index; AHI, apnea-hypopnea index; NS, not significant. † Analysis performed using transformed
values. * P < 0.05, ** P < 0.01, *** P < 0.001.
SLEEP, Vol. 37, No. 12, 2014
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Movement Distribution in Children—Coussens et al.
Table 4—Summary of movement measures for controls (C), primary snorers (PS), and children with clinically defined obstructive sleep apnea syndrome
(OSAS) at baseline (1) and follow up (2) polysomnography (PSG).
Variable
Movement time (% TST)
PSG
1
2
C
2.1 ± 1.0
2.1 ± 1.0
PS
2.1 ± 0.9
1.9 ± 0.8
OSAS
2.2 ± 1.0
2.0 ± 1.0
ANOVA Between Groups
NS
NS
Movement event index
(TST) †
1
2
9.4 ± 3.9
9.3 ± 3.7
10.5 ± 4.6
9.0 ± 3.9
16.4 ± 18.3
10.8 ± 4.9
OSAS > C **
NS
NREM sleep movement
event index †
1
2
6.7 ± 3.1
6.7 ± 3.5
8.3 ± 4.5
6.6 ± 4.1
15.1 ± 25.1
8.3 ± 4.4
OSAS > C **
NS
REM sleep movement
event index †
1
2
8.7 ± 5.7
8.3 ± 4.2
7.9 ± 6.0
6.8 ± 5.0
14.9 ± 17.7
9.2 ± 7.0
OSAS > C **; OSAS > PS **
NS
Mean duration of ME
(sec) in TST
1
2
10.6 ± 1.9
10.6 ± 1.6
10.2 ± 1.7
10.0 ± 1.6
9.6 ± 1.4
9.9 ± 1.4
NS
NS
Mean duration of ME
(sec) in NREM sleep
1
2
9.2 ± 1.8
9.0 ± 1.8
8.8 ± 1.7
8.5 ± 1.3
8.3 ± 1.4
8.8 ± 1.3
NS
NS
Mean duration of ME
(sec) in REM sleep †
1
2
8.6 ± 3.0
9.2 ± 2.2
7.4 ± 2.9
9.0 ± 3.0
8.1 ± 1.7
7.9 ± 3.7
NS
NS
TST, total sleep time; ME, movement events; NS, not significant. † Analysis performed on transformed data. * P < 0.05, ** P < 0.01, *** P < 0.001.
that the total number of body movements per hour of sleep
(MEI) was significantly higher in children with OSAS than
controls for TST (TST), NREM sleep, and REM sleep (P < 0.01,
Table 4). The MEI was also significantly higher in children with
OSAS than PS during REM sleep (P < 0.01, Table 4). The MEI
did not differ between PS and controls.
During the follow-up PSG, a total of 6,590 ME met inclusion
criteria for analysis. Following adenotonsillectomy in the children with UAO, there were no longer any significant differences
between the groups with respect to the frequency of body movements (Table 4). The mean duration of ME did not differ between
groups during either the first or the follow-up PSG (Table 4).
Survival Curve Analysis
The survival curve exponent, θ, represents the cumulative
distribution of the length of sleep runs for the night; the higher
the value of θ, the more sharply the survival curve drops, indicating a higher frequency of shorter sleep runs. There was no
effect of age, gender, BMI, or PLM index on θ values in any
subject group.
Figure 2—The exponential coefficient (theta, θ) of the distribution of
sleep runs terminated by NREM stage 1 sleep (stage 1) or wake for
the baseline PSG (pre) in the control (C), primary snoring (PS), and
obstructive sleep apnea syndrome (OSAS) groups. Box ends represent
the 25th and 75th percentile points, the center line represents the mean
value for each group and the whiskers represent the range of the data.
No significant differences between groups were found.
Movement Measures
Descriptive statistics for the 2 movement measures are summarized in Table 4. There was no effect of age, gender, BMI, or
PLM index on any of the movement measures. Movement time
(epochs obscured by gross body movements) was not significantly different between controls and children with UAO at the
baseline or follow-up PSG.
During the first PSG a total of 7,152 ME from all subjects
met the inclusion criteria for analysis. These data demonstrated
SLEEP, Vol. 37, No. 12, 2014
Stage 1 NREM Sleep and Awakening as Markers of Sleep
Fragmentation
When using a shift in sleep stage to stage 1 NREM sleep or
an awakening as the marker of sleep fragmentation there was
no significant difference (P > 0.05) between groups at baseline
for θ, the survival curve exponent value (Figure 2). The same
result was observed in the follow-up PSG (θ = 0.97 ± 0.2 for
controls, 0.98 ± 0.1 for PS and 0.96 ± 0.1 for OSAS, P > 0.05).
2030
Movement Time as a Marker of Sleep Fragmentation
Utilizing movement time as a marker of sleep fragmentation
showed no significant difference (P > 0.05) between groups in
the survival curve exponent values (Figure 3). The same result
Movement Distribution in Children—Coussens et al.
Figure 3—The exponential coefficient (θ, theta) of the distribution of
sleep runs terminated by movement epochs for the baseline PSG (pre)
in the control (C), primary snoring (PS), and obstructive sleep apnea
syndrome (OSAS) groups. Box ends represent the 25th and 75th percentile
points, the center line represents the mean value for each group and
the whiskers represent the range of the data. No significant differences
between groups were found.
Figure 4—The exponential coefficient (θ, theta) of the distribution of
sleep runs terminated by movement events (ME) for the baseline PSG
(pre) in the control (C), primary snoring (PS) and obstructive sleep apnea
syndrome (OSAS) groups. Box ends represent the 25th and 75th percentile
points, the center line represents the mean value for each group, and the
whiskers represent the range of the data. *** P < 0.0001, * P < 0.05.
was observed during the follow-up PSG (θ = 1.03 ± 0.3 for
controls, 0.96 ± 0.1 for PS, and 0.97 ± 0.1 for OSAS, P > 0.05).
Movement Events as a Marker of Sleep Fragmentation
When using body movement as a sleep fragmentation event,
significant differences between groups in the mean θ value
were observed. At baseline, children with OSAS had a significantly higher exponent value (θ = 1.00 ± 0.12) than both
normal controls (θ = 0.92 ± 0.06, P < 0.001) and the PS group
(θ = 0.93 ± 0.09, P < 0.05, Figure 4). There was no significant
difference in the distribution of sleep run duration between controls and children with PS (P > 0.05). Control children had the
longest average sleep run duration (median length of 105 sec,
range 4 to 5,483 sec), followed by the primary snorers (median
of 69.4 sec, range 3 to 4,819 sec), while the OSA group had
the shortest average sleep runs (median of 57.9 sec, range 3 to
5,912.4 sec).
Following adenotonsillectomy, children in the OSAS group
continued to have a higher survival curve exponent value
(θ = 0.99 ± 0.07) than the PS group (θ = 0.94 ± 0.04, P < 0.01)
and normal controls (θ = 0.95 ± 0.04, P < 0.05), indicating that
despite surgical treatment they still had an altered distribution
of sleep duration, which included a higher number of shorter
sleep runs. No difference was seen between the PS and control
groups (P > 0.05). Once again the control group had the longest average sleep runs (median 96 sec, range 3 to 4,819 sec)
compared to primary snorers (median 73 sec and range 4 to
4,910 sec) and the OSAS children (median 62 sec, range 3 to
5,120 sec). There were no significant differences in θ for each
subject group between the first and second PSG (P > 0.05).
Sleep Fragmentation and AHI
The relationship between the degree of sleep fragmentation
and disease severity (AHI) was evaluated for the OSAS group
SLEEP, Vol. 37, No. 12, 2014
only as the AHI in the primary snorers and controls was less
than one. In the OSAS children (N = 20), the survival curve coefficient was significantly correlated with the apnea-hypopnea
index (r = 0.62, P < 0.01; Figure 5), indicating that as the AHI
increased, sleep run duration decreased. Following adenotonsillectomy this association was no longer significant (r = 0.11,
P > 0.05). In addition, the amount of change in the survival
curve coefficient from baseline to follow-up PSG was positively
correlated with the respective change in AHI (r = 0.58; P < 0.01;
Figure 6). Thus the children who showed the greatest reduction
in sleep-related obstruction following adenotonsillectomy also
demonstrated bigger improvements in sleep consolidation.
DISCUSSION
This study has demonstrated significantly increased sleep
fragmentation in children with OSAS compared to primary
snorers and healthy controls when ME, as defined by this research protocol, are used as the marker of sleep disturbance.
Furthermore, utilizing survival curve analysis to assess sleep
continuity (and hence fragmentation), as indicated by the exponent of movement event distribution (θ), we found that θ was
significantly correlated with the severity of UAO. In fact, 38%
of the variance in θ was explained by the AHI. Additionally the
extent to which sleep fragmentation “improved” (as indicated
by decreases in θ) following adenotonsillectomy was correlated
with the level of improvement in the AHI. However, the use
of conventional measures of sleep fragmentation (movement
time, stage 1 NREM sleep, or awakenings) in SCA failed to
distinguish children with UAO from controls. These latter results are consistent with previous findings that children with
OSAS show relatively preserved sleep when examining traditional measures of sleep fragmentation such as the number
of cortical arousals or sleep stage shifts. Our findings demonstrate three important points: (1) traditional measures of sleep
2031
Movement Distribution in Children—Coussens et al.
Figure 5—Correlation between the exponential coefficient (θ, theta) of
movement events (ME) and apnea-hypopnea index (AHI) for the children
in the OSAS group at baseline PSG. (AHI is presented on a logarithmic
scale axis as the data was log transformed to correct for a skewed
distribution in calculations). r2 = 0.349, P < 0.01.
Figure 6—Correlation between changes in movement event (ME)
exponential coefficient (Θ, theta) between studies and changes in AHI
between studies in the OSAS group (The AHI change is presented on
a logarithmic scale axis as the data was log transformed to correct for a
skewed distribution in calculations). r2 = 0.376, P < 0.01.
fragmentation do not adequately describe the degree of sleep
disturbance in children with OSAS; (2) children with OSAS
have an altered arousal response compared to normal, healthy
controls; and (3) movement distribution rather than movement
frequency may be a more precise measure of sleep fragmentation and its impact.
Frequent body movements are commonly observed in children with OSAS but these often normalize following adenotonsillectomy. In a video-recording study of sleep in children with
OSAS, Stradling reported that 65% of the children spent more
than 8% of sleep time moving compared to only 4% postoperatively.28 In our study there were no differences in the percentage
of sleep epochs obscured by movement or in the mean duration
of ME between control children and those with PS or OSAS.
However the OSAS children had a significantly higher number
of movements compared to controls in NREM sleep and primary snorers in REM sleep. In addition, when using movement
as a marker of sleep disruption, children with OSAS showed
an altered distribution of sleep periods, demonstrating a higher
frequency of short sleep runs. Following adenotonsillectomy
for the children with UAO, all three subject groups showed a
similar number of movements during sleep. However, in spite
of this normalization in movement frequency, the OSAS children still demonstrated a higher θ value than the control and PS
children, indicating an ongoing alteration in the distribution of
their movements during sleep. This result is in keeping with
the finding that this group of children still showed evidence of
residual OSAS following surgery as demonstrated by a mildly
elevated OAHI. It is noteworthy that these children also continued to show significant neurocognitive deficits six months
post adenotonsillectomy.16
A recent actigraphy study by Suratt et al. examined the relationship between nocturnal body movements and both reaction time and cognitive performance in children with suspected
OSAS.44 They found that a higher frequency of body movements during the sleep period was significantly correlated with
slower reaction times, while children whose body movements
occurred in close clusters had reduced performance on vocabulary and memory tasks. Furthermore, it appeared that the
distribution of movements during the night had a significantly
greater impact on cognitive performance than movement frequency. In our study, the OSAS children had a similar number
of movements during sleep post- adenotonsillectomy as the
controls but a higher number of shorter sleep runs. This means
that the OSAS children must have had a combination of both
greater consolidation of movements (i.e., greater clustering
of ME) and correspondingly longer, uninterrupted periods of
sleep. This is a similar pattern of movement distribution as
found by Suratt,44 and thus both of these studies suggest that it
is not just movement per se that may be an important marker
of sleep fragmentation but also how movement is distributed
throughout the sleep period.
In addition to being a sensitive marker of sleep fragmentation, movement may also contribute to the adverse sequela
observed in children with OSAS. Compared to isolated movements, clustered body movements represent a more sustained
level of activation of the CNS.44 Increasing evidence now
supports the view that sleep plays a pivotal role in both brain
development and in the formation and consolidation of memories, thereby underpinning a significant component of neurocognitive performance. Periods of sustained arousal during
sleep may impact adversely on CNS activities that occur only
during sleep or may also alter other physiological regulatory
processes. Support for this argument comes from a study by
Loredo et al., who found that in adults with OSAS, movement
arousals (defined similarly to our ME) but not cortical arousals
correlated with awake sympathetic nervous system activity
as measured by plasma norepinephrine.45 Such up-regulation
of the systems controlling sleep could make sleep inherently
less stable in the face of perturbations, as found by Bianchi et
al. where adults with OSAS demonstrated an increased instability in sleep architecture dynamics.46 The increased periods
SLEEP, Vol. 37, No. 12, 2014
2032
Movement Distribution in Children—Coussens et al.
of reduced motor activity seen in the OSAS children in our
study in addition to the greater number of shorter sleep runs
may reflect such sleep instability.
A recent study has proposed that chronic sleep disruption
can generate long-term changes in the neuroendocrine stress response system.47 It has also been proposed that similar changes
would be seen in children with sleep chronically disrupted by
the common symptoms of UAO such as respiratory arousals,
transient hypoxic episodes, obstructive respiratory events,
snoring, and end-apneic snorts.48 The main components of
this stress response system are the autonomic nervous system
(ANS) and the hypothalamic-pituitary-adrenal axis (HPAA).
Activation of this stress system leads to elevated plasma levels
of stress hormones such as adrenaline and cortisol. Sleep would
normally suppress the stress systems, and so sleep disruption
abnormally maintains the activity of these systems at a significantly higher level, with one study showing that children with
sleep disordered breathing (SDB) had an odds ratio of 3.48 for
excessive autonomic activation compared to children without
SDB.48 A study in sleep deprived rats showed a change to underresponsiveness in the slower acting HPAA and an altered,
up-regulated ANS response.47,49 Sleep deprivation and sleep
fragmentation are believed to result in similar physiological
responses and thus a similar adaptive change to that seen in
the chronically sleep deprived rats could potentially explain the
result seen in the children with UAO. The UAO subjects in this
study showed an increased arousal threshold as indicated by the
longer periods of uninterrupted sleep, which may reflect underresponsiveness of the HPAA. In addition they also demonstrated clustered periods of short sleep runs which may reflect
up-regulation of the ANS resulting in an increased propensity
for sleep-wake transitions. This conclusion if correct provides
a plausible mechanism by which sleep disruption arising from
UAO in childhood could lead to increased cardiovascular morbidity in adulthood, as the link between increased stress system
reactivity (e.g. increased cortisol) and cardiovascular disease is
well established.50
Movement metrics are sometimes reported in clinical sleep
study results but are often considered of lower importance
than other PSG-derived measures, such as the apnea-hypopnea
index. Our study, among others over many years suggests that
the evaluation of movement measures should be considered in
the routine clinical analysis of PSG results.29,55
DISCLOSURE STATEMENT
This was not an industry supported study. This study was
supported by grants from the Australian Research Council and
the National Health & Medical Research Council Australia. Dr.
Baumert was supported by the Australian Research Council
(grant #DP0663345). Dr. Kohler was supported by the National
Health & Medical Research Council Australia (grant #453669).
The other authors have indicated no financial conflicts of interest. The work was performed at the Women’s and Children’s
Hospital Adelaide, Australia.
CONCLUSION
Several studies in adults with UAO have found novel associations between sleep parameters and disease severity when
using survival curve analysis to model sleep-wake transitions.52-54 Using the appearance of stage 1 NREM sleep and
awakenings as markers of sleep disruption Norman, et al.
showed large differences in sleep continuity between groups
of adults with varying levels of OSAS.31 Using this same approach, we were not able to replicate these findings in a group
of children with mild to moderate OSAS. However when we
used the occurrence of movement as the marker of sleep fragmentation, we found significant differences in sleep continuity
in the children with OSAS. Even though there is some overlap
of this measure between groups, the movement measure exponent (θ) still does have potential clinical utility. As can be seen
from Figure 5, all subjects in the OSAS group with a θ of > 1.0
were abnormal with AHI values > 10 (considered in the severe
range for children), and furthermore, no subject in the control
group had a θ > 1.0. The results of our study therefore suggest
that movement, when appropriately analyzed, could be an important indicator of sleep fragmentation in children with UAO.
SLEEP, Vol. 37, No. 12, 2014
2033
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