Polysomnography and Self-reported Sleep, Pain, Fatigue, and Anxiety
in Children with Active and Inactive Juvenile Rheumatoid Arthritis
Teresa M. Ward,
PHD RN, Patricia Brandt,
PHD RN, Kristen Archbold,
PHD RN, Martha Lentz,
RN, Sarah Ringold,
MD, Carol A. Wallace,
MD and Carol A. Landis,
DNSc RN FAAN
Biobehavioral Nursing and Health Systems,
Department of Family and Child Nursing, School of Nursing, and
Department of Pediatrics, Immunology, Rheumatology & Infect Disease, School of Medicine, University of
Objective To compare polysomnography (PSG) and self-reported sleep, symptoms (pain and fatigue), and
anxiety between children with active and inactive juvenile rheumatoid arthritis (JRA) and examine relations
among sleep, symptoms, and anxiety. Methods Two consecutive nights of PSG, self-reported sleep, and
symptoms were obtained in 70 children 6–11 years of age with active (n¼35) or inactive (n¼35)
JRA. Results On the second (study) night, PSG and self-reported sleep variables were not different, but
pain and fatigue were significantly higher (both p<.02) in children with active compared to inactive disease.
In a stepwise regression, age, medications, disease status, anxiety, evening pain, total sleep time, and arousals
explained 36% of the variance in fatigue and age, disease status, and evening pain were significant (all p<.04)
predictors of fatigue. All children showed longer sleep latency and reduced sleep efficiency on the first night
in the laboratory. Conclusions Sleep was not altered in children with active JRA, however, the ‘‘first night
effect’’ suggests that valid laboratory sleep assessments require an adaptation night.
Key words fatigue, pain, anxiety; juvenile rheumatoid arthritis; polysomnography; sleep; sleep disturbance.
Juvenile rheumatoid arthritis (JRA), also termed juvenile
idiopathic arthritis, is one of the most common rheumato-
logic conditions in children; estimated to affect approxi-
mately 300,000 children in the United States (Arthritis
Foundation, 2007). JRA is divided into three main subtypes
based on the number of joints involved during the first 6
months of illness and the presence or absence of any
systemic features. These subtypes include: oligoarticular
(arthritis in less than four joints); polyarticular (arthritis in
more than five joints); and systemic (arthritis in association
with spiking fever, and other systemic features, including
rash, hepatosplenomegaly, lymphadenopathy, and serosi-
tis) (International League of Associations for Rheumatology
Classification, 2004). JRA is an important cause of short-
and long-term disability in children (Cassidy & Perry,
2006). The course of JRA is unpredictable with fluctuating
periods of active and inactive disease. Children with JRA
fatigue easily, experience joint inflammation and swelling,
pain and tenderness, morning stiffness, and limited
mobility. They also report sleep disturbances including
difficulty falling asleep, fragmented sleep with more nightly
awakenings, and daytime sleepiness, (Amos, Curry, Drutz,
Frost, & Warren, 1997; Bloom et al., 2002; Labyak, Stein,
Bloom, Owens & Lunsford, 2001; Passarelli et al., 2006;
Zamir, Press, Tal, & Tarasiuk, 1998). Parents often report
on questionnaires symptoms suggestive of sleep disorders,
including insomnia (difficulty falling asleep, frequent
nighttime, and early morning awakenings), parasomnias
(sleep terrors, walking), sleep-disordered breathing, and
daytime sleepiness (Bloom et al., 2002; Labyak et al., 2001).
Further, greater disease severity has been associated with
parental reports of more pain and greater interference with
daily activities (Bloom et al., 2002).
Disturbed sleep in JRA may be associated with anxiety
and pain, and negatively impact fatigue and a child’s ability
to engage in daily physical and social activities, but few
studies have obtained measures of these symptoms in
All correspondence concerning this article should be addressed to Carol A. Landis, DNSc, RN, FAAN, Professor, Box
357266, Biobehavioral Nursing and Health Systems, University of Washington, Seattle, WA, 98195-7266, USA.
Journal of Pediatric Psychology pp. 1–10,2007
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conjunction with polysomnography (PSG) and self-report
measures of sleep from children (Labyak et al., 2001;
Passarelli et al., 2006; Zamir et al., 1998). A recent PSG
study by Passarelli and colleagues (2006) found that
compared to healthy controls, children with JRA had
reduced total sleep time (TST), more transient electro-
encephalogram (EEG) arousals (brief shifts in the EEG to
fast frequency without an awakening), and increased limb
movements. Morning stiffness was associated with
increased limb movements and pain was associated with
a higher number of brief arousals during sleep. Compared
to healthy controls, Zamir and colleagues (1998) also found
more arousals and a higher number of awakenings in
children with JRA. Previous studies based on daily diary
reports have shown associations among anxiety, pain, and
fatigue in children with JRA (Schanberg, Anthony, Gil, &
Maurin, 2003). However, relations among sleep distur-
bance, symptoms (pain and fatigue), and anxiety in children
with JRA have been not been well studied.
The aims of this cross-sectional study were (a) to
compare PSG and self-reported sleep, symptoms (pain
and fatigue) and anxiety between children with active and
inactive JRA, and (b) to examine relations among sleep,
symptoms, and anxiety. Based on previous studies of
children with JRA, we hypothesized that compared to
children with inactive disease, children with active disease
would show more disturbed sleep [e.g., reduced TST,
longer sleep latency, decreased sleep efficiency (SE), more
awakenings of longer duration WASO (wake after sleep
onset), and more frequent arousals], and have greater
pain, fatigue, and anxiety. As regards the secondary aim,
based on a preliminary examination of bivariate correla-
tions, we hypothesized that anxiety and evening pain
would explain a significant portion of the variance in
disturbed sleep manifested by arousals, and that
disturbed sleep (e.g., TST, arousals) would explain a
significant portion of the variance in fatigue.
As children age, they sleep less and have reduced slow
wave sleep. Based on previous published reports of
developmental effects on sleep in healthy preadolescent
children (Bes, Schultz, Navelet, & Salzarulo, 1991;
Gaudreau, Carrier, & Montplaisir, 2001; Montgomery-
Downs, O’Brien, Gulliver, & Gozal, 2006), and because we
found inverse correlations between age and TST and Non-
rapid eye movement (NREM) sleep stages 3 and 4 (i.e., slow
wave sleep), we explored possible interactions among sleep,
age, and disease status. Both cognitive and physical
developmental changes occur during preadolescence, yet
there is a paucity of data about age-related changes in sleep
of children with JRA.
Approval for this study was obtained from the Institutional
Review Board at the Children’s Hospital and Regional
Medical Center (CHRMC) in Seattle, WA, USA. From April
2004 through January 2007, a convenience sample of 73
children (64 girls) 6–11 years of age with active or inactive
JRA (pauciarticular, polyarticular, and systemic), and their
parents were recruited from the Pediatric Rheumatology
Clinic at CHRMC. During a routine clinic visit, a
rheumatologist informed a parent and child about the
study, and if interested, a trained research coordinator
explained the purpose of the study and scheduled them for
a visit to the Clinical Research Center (CRC) at CHRMC and
to the Sleep Research Laboratory at the University of
Washington. Children were excluded if they had a
diagnosis of a psychiatric condition, diabetes, asthma,
cancer; a sleep disorder or family history of narcolepsy in
the first-degree relative.
Of the 135 families approached, 62 declined to
participate; 48% cited parental time constraints, 52%
declined for various reasons (e.g., home residence at a
great distance, child was afraid of staying the laboratory,
or lack of interest). Of the 73 children enrolled in the
study, PSG was obtained on 70 children. Three children
(i.e., two children with inactive disease and one with
active disease) did not complete the PSG portion of the
study because of scheduling conflicts.
On the first day of the study, children were admitted to the
CRC where height and weight measures and a blood sample
were obtained, and the children and parents completed
questionnaires. A pediatric rheumatologist examined the
child and rated disease activity (physician global rating)
according to standard clinic procedures: active disease was
defined as inflammation of one or more joints with
swelling, limited range of motion, or tenderness (1ona
scale of 0–10); inactive disease was defined as a lack of
inflammation, limited range of motion, or tenderness (0 on
a scale of 0–10) (Schanberg et al., 2003).
PSG Sleep Recordings
All children and their parents slept in the sleep research
laboratory for 2 consecutive nights and arrived at the
laboratory 3 hr prior to the child’s usual bedtime. The
first night served as an adaptation night to the laboratory
and the second night was the study night. A parent
stayed overnight in a separate bed in the same room
with the child, or on occasion in an adjoining room with
2Ward et al.
a connecting door. Parents did not interact with their
child during the night, unless the child required their
assurance. A schedule for bedtime and rise time was
established based on a child’s usual schedule for a school
night, except during summer months when most children
followed a similar schedule every night of the week.
Electrodes to record the EEG, electro-oculogram,
electrocardiogram, electromyelogram, leg movements, and
devices for respiratory (nasal air flow, chest and abdominal
movement, oxygen saturation, and snoring sensor) mon-
itoring were placed according to standard criteria (American
Academy of Sleep Medicine Task Force, 1999;
Rechtschaffen & Kales, 1968). Electrophysiological signals
were recorded and digitized by the EMBLA Somnologica
data acquisition recording system (A10, MedCare,
Reykjavic, Iceland) and displayed and stored on a desktop
(Dell Pentium III) computer. Data were continuously
displayed in 30 s intervals during each recording.
Sleep Stage Scoring and Variables
Sleep recordings from both laboratory nights were scored
manually into wake and sleep stages by one technologist
according to standard criteria (Rechtschaffen & Kales,
1968). Apneas (absence of airflow for at least two
breaths) and hyponeas (50% decrease in nasal airflow
with a corresponding 3% decrease in oxygen saturation
and/or associated arousal) were scored according to
published criteria for children (Montgomery-Downs
et al., 2006), and expressed as an apnea/hyponea (AHI)
index/hour of TST. Periodic leg movements (PLM, more
than four leg movements, of 0.5–5 s duration with an
interval of 5–90 s) (American Sleep Disorders Association
& Sleep Research Society [ASDA], 1993), and arousals
(shift to a fast EEG frequency lasting 3–15 s (ASDA,
1992) were scored manually and each expressed as an
index/hour of TST.
Standard sleep variables were calculated. The amount
of time in each NREM (1, 2, 3, and 4) and REM sleep stage
and WASO were expressed as percentages of sleep period
time (SPT) (time from sleep onset until final awakening).
TST was the amount of time in NREM stages 1–4 and REM.
Sleep latency was the time from lights out to first epoch of
NREM stage 2. SE was expressed as a ratio of TST/time in
bed. Finally, a fragmentation index (number of times a
change from any sleep stage to stage 1 or wake) was
expressed per hour of TST (Landis et al., 2001).
Self-report Laboratory Sleep
Self-reported sleep was assessed each morning on
awakening with a 9-item rise time questionnaire modified
from our previous laboratory studies in adults
(Landis, Lentz, Tsuji, Buchwald, & Shaver, 2004). Sleep
quality was rated on a 5-point Likert scale from (1)
‘‘much better’’ to (5) ‘‘much worse’’ in response to the
statements ‘‘how did your sleep last night compare to
your usual sleep at home’’, ‘‘how do you think your sleep
last night will affect the way you feel today’’, and ‘‘did
you wakeup during the night’’. Children also were asked
how long it took them to fall asleep, the number of night
awakenings, and how long they thought they were asleep.
Laboratory staff assisted the children to complete this
Sleep Self Report (SSR)
Children completed the SSR in the CRC and those
<9-years old were interviewed to obtain responses. The
SSR is a 26-item retrospective survey of a child’s sleep
behavior over the previous week and has established
reliability in school-aged children (Bloom et al., 2002;
Owens, Spirito, McGuinn, & Nobile 2000). We used five
items (‘‘do you think you have trouble sleeping’’; ‘‘do
you fall asleep in about 20 minutes’’; ‘‘do you wake up at
night when your parents think you’re asleep’’; ‘‘do you
have trouble falling back asleep if you wake up during the
night?’’; ‘‘do you feel rested after a night’s sleep’’) that
were similar to items in the laboratory questionnaire.
Survey items are rated on a 3-point scale ranging from
0 to 3 (i.e., 0–1 ¼‘‘rarely’’, 2–4 ¼‘‘sometimes’’, and
5–7 ¼‘‘usually’’); higher scores indicate more disturbed
Daily Symptom Diary
With the assistance from laboratory staff, each morning
and evening, children completed questions about their
symptoms for the previous night or day in a diary
(Labyak et al., 2001). Instruments for reporting pain
intensity and location and fatigue were included in the
Pain Intensity and Location
Pain intensity was measured with the Oucher Faces Rating
Pain Scale (Beyer, Denyes, & Villarruel, 1992); a series of
six faces that range from (0) ‘‘doesn’t hurt at all’’ to (10)
‘‘hurts as much as you can imagine.’’ Children placed an
‘‘X’’ on a face that best described their pain. Reported
reliability and validity for the Oucher scale are adequate
in children 3–12 years of age (Beyer et al., 1992; Beyer &
Aradine 1988) and the Cronbach a-coefficient in this
sample was.91. Pain location was measured by an
investigator-developed skeletal figure, (Mr Bones).
Sleep and Symptoms in Children with JRA 3
Children circled the joints on Mr Bones that corre-
sponded to the location of their pain (Labyak et al.,
2001). The number of joints circled was summed to yield
a total ‘‘joint hurt’’ score for each morning and each
Children completed the fatigue scale each evening before
they went to bed. The Child Fatigue Scale (CFS) is a
14-item, two-part instrument that measures both fatigue
frequency (‘‘yes’’ or ‘‘no’’) and ‘‘bothersome’’ (intensity,
5-point Likert scale from ‘‘not at all’’ to ‘‘a lot’’)
(Hockenberry et al., 2003). Frequency scores range
from 0 to 14 and ‘‘bothersome’’ total scores, range
from 0 to 70; higher scores indicate greater amounts of
fatigue. In previous studies of children 7 to 10-years old
with cancer, Cronbach a-coefficient was.73 for frequency
and .84 for intensity (Hockenberry et al., 2003; Hinds &
Hockenberry, 2001). In this study, the Cronbach
a-coefficient was.72 for frequency and.98 for intensity.
Children completed the Revised Children’s Manifest
Anxiety Scale (RCMAS) (Reynolds & Richmond, 1997),
a 37-item self-report scale that assesses the level and
nature of anxiety in children from 6 to 19 years of age, in
the CRC prior to coming to the sleep laboratory. Raw
scores were converted to scaled scores based on the
child’s age and sex, and higher scores are indicative of
increased anxiety. The RCMAS total anxiety score was
used in this study. Reliability and validity of the RCMAS
has been well established in a number of pediatric studies
and in this study the Cronbach a-coefficients was.89 for
Parents completed the Childhood Health Assessment
Questionnaire (CHAQ) (Singh, Athreya, Fries, &
Goldsmith, 1994), a 30-item scale of disease-related
functional status, in the CRC prior to coming to the sleep
laboratory. The CHAQ measures level of difficulty in
performing tasks in eight domains: dressing, arising,
eating, walking, hygiene, reach, grip, and daily activities
on a scale of 0 (no difficulty) to 3 (unable to do).
The final score for the CHAQ, termed the disability index,
is the average of the scores for each domain and was used
as a measure of functional disability in this study
(Dempster, Porepa, Young, & Feldman, 2001). The
reliability and validity of the CHAQ instrument was
previously established in children with JRA (Singh et al.,
1994) and in this study the Cronbach a-coefficient
was.80 for the total CHAQ scale.
Parents completed a daily diary of medications their
child received. Medications were classified into categories:
(a) NSAIDS (nonsteriodal anti-inflammatory drugs);
(b) Corticosteroids; (c) Folic Acid Pathway Inhibitors
(Methotrexate, Arava); (d) TNF alpha inhibitors (enbrel,
humira, remicade); and Other (i.e. vitamins) and None.
Each medication category was scored as ‘‘yes’’ or ‘‘no’’
depending upon whether the child received a medication
in that category anytime during the study.
Disease Duration was measured from the time the
child was first diagnosed with JRA. This information was
obtained through a chart review and confirmed by a clinic
Erythrocyte sedimentation rate (ESR) was used as a
laboratory measure of disease activity. A blood sample
was obtained from the child in CRC and ESR analysis was
completed in the CHMRC clinical laboratory.
Data were analyzed using SPSS for Windows version 14.0
(SPSS Inc, Chicago, IL, USA). The first set of analyses
was conducted to address group differences in study
variables between children with active and inactive
disease (aim#1). Given the different types of variables
measured in this study, data analyses were blocked into
conceptual categories and then analyzed (e.g., demo-
graphics, disease related variables, symptoms (pain and
fatigue), anxiety, self-report habitual and laboratory sleep,
and PSG sleep) for group differences. Each category was
considered a separate analysis with significance set at
p<.05 (2-sided). Paired t-test or chi-squared test was used
to test for group differences.
Second, we examined relations among sleep and
symptoms (aim #2) with a series of stepwise regression
models. We explored how much of the variance in
disturbed sleep (arousals) was explained by baseline
anxiety and evening pain, and how much of the variance
in fatigue intensity was explained by anxiety, evening
pain, and disturbed sleep (TST and arousals). Age,
medications, and disease status were used as control
Finally, we explored developmental changes in sleep
with age. General linear model (GLM) analyses
(i.e., repeated measures analyses) were used to evaluate
main effects and interactions with sleep stages (i.e. stage
1, stage 2, stage 3, stage 4, REM, and wake) and
sleep disturbance variables (TST, SE, sleep latency) as
4Ward et al.
within-subjects factors, and age and disease condition as
the between-subjects factors.
The clinical characteristics of the children are presented
in Table I. The sample was 84% White, which is
representative of the Seattle area. There were similar
numbers of children in the active and inactive disease
groups and no differences between groups in age or sex.
As might be expected, compared to children with inactive
disease, children with active disease had higher mean
physician global rating, were taking more NSAIDS
¼10.6, p<.001) and other medications (w
p<.02). Pain and fatigue were higher in children with
active compared to those with inactive disease, but there
were no differences in sedimentation rate, disease
duration, disability, number of painful joints, or anxiety.
PSG and Self-report Sleep by Disease Condition
Table II shows PSG and self-report sleep measures by
disease condition. There were no group differences in PSG
variables, e.g., TST, SE, sleep latency, WASO, arousals, or in
sleep-related events, e.g., apnea/hypopnea index (AHI) and
periodic limb movements. More children with active disease
reported usually having trouble sleeping and waking up at
night, but their sleep latency and how rested they felt in the
morning were similar to those of children with inactive
disease. In the laboratory, children with active disease
reported longer sleep latency and more WASO, but these
differences were not statistically significant.
Predictors of Sleep Disturbance and Daytime
In the first regression model testing predictors of the
dependent variable disturbed sleep (arousals), age and
medications, anxiety, and evening pain explained 18% of
variance, but neither anxiety or pain had a significant
effect (both p>.05) (Table III). In the second regression
model testing predictors of the dependent variable
fatigue, age, medications and disease status, anxiety,
evening pain, TST, and arousals explained 36% of the
variance, and only age, disease status and evening pain
had a significant effect (all p<.04).
First Night Effect
As shown in Table III, all children had reduced mean
TST, lower SE, and increased WASO on the adaptation
compared to the study night despite a similar time in bed
on both nights. Because SE of 85% is often used as PSG
indicator of poor sleep (Coates et al., 1982; Frankel,
Coursey, Buchbinder, & Snyder, 1976), we grouped all
the children into those above and below 85% and
compared mean SE and sleep latency for the adaptation
and the study night. SE was lower on the adaptation
Table I. Demographic and Clinical Characteristics
Age, years 8.1 1.8 8.92.0 1.6, 0.2
White 27 (84) 34 (89.4)
Other 5 (15.6) 4 (10.5)
Girls 8 (87.5) 31 (81.6)
Boys 4 (12.5) 7 (18.4)
Disease Type, n(%)
Oligoarticular 5 (46.8) 11 (28.9)
Polyarticular 16 (50) 24 (63.2)
Systemic 1 (.03) 3 (.08)
Disease duration, years 3.8 2.7 3.4 3.9, 1.8
0.06 0.2 2.8 23.4, 2.1**
6.2 6 8.2 85.9, 1.9
0.4 0.4 0.40.45 0.16, 0.25
9 (28) 26 (71)
Corticosteroids 7 (22) 12 (34)
Folic acid pathway 16 (50) 21 (55)
TNF alpha inhibitors 3 (9.3) 9 (13)
7 (22) 19 (50)
None 8 (25) 2 (5.3)
Pain intensity, (0–10)
A.M. 0.3 0.9 1.2 2.1 1.7, 0.14*
P.M. 1.1 1.9 1.4 2.3 1.3, 0.74
A.M. 0.2 0.8 0.9 2.5 1.6, 0.13
P.M. 0.4 0.9 1.0 2.4 1.4, 0.34
Frequency (yes/no) 3.1 1.9 3.4 2.5 1.4, 0.80
(not at all, to a lot)
6.4 3.8 9.4 5.8 5.6, 0.46*
Anxiety (x þSD)
Total anxiety 42.0 10.8 44.0 10.5 7.5, 3.6
Data are mean SD or n (%).
*Both p<.02, **p<.001
Sleep and Symptoms in Children with JRA 5
night compared to the study night; 36% of the children
had a mean SE of 77% and a mean sleep latency of
44 min. In the children with active disease and a SE of
85%, significantly (w
¼6.8, p<.009) more were in the
older (64%) compared to the younger (17%) age group.
Developmental Aspects of PSG Sleep
Based on previous published reports of age-related effects
on sleep in healthy children (Bes et al., 1991; Gaudreau
et al., 2001; Montgomery-Downs et al., 2006) and our
findings of a first night effect in children with JRA, we
examined the amount of sleep stages and wake as well as
indicators of sleep disturbance (TST, sleep latency, SE,
WASO, and arousals) as a function of age, night, and
disease condition. Table IV shows the PSG data by age
group (6–8 years and 9–11 years), night, and disease
condition. Although differences were not found for the
percentage of time children spent in each sleep stage and
wake for both nights, marginal residuals showed an age-
related effect in that the percentage of NREM stage 2 was
increased, while those of NREM stages 3 and 4 (slow
wave sleep) were decreased in the 9- to-11-year-old
children during both nights. As regards sleep disturbance
variables, TST was affected only by age; however, an age
by disease condition interaction was found both for sleep
latency and SE. We conducted these analyses controlling
for medications with similar results.
In this study of children with JRA, although there were
few differences in PSG and self-reported sleep between
children with active versus inactive disease, we found a
prominent ‘‘first-night’’ effect and evidence that age,
disease status and pain, but not sleep, contribute to
fatigue. Further, we found an age-related decrease in
sleep duration (TST), and interactions between age and
Table III. Predictors of Sleep Disturbance and Fatigue
Variable Unstandardized bStandardized b
Sleep Disturbance (arousal index)
Age .49 .34 .20
Medication 1.4 1.7 .11
Anxiety .13 .06 .30
Evening pain .56 .06 .23
Age .68 .37 .26
Medication .43 1.9 .03
Disease status 3.8 1.4 .38
Anxiety .05 .06 .12
Evening pain .83 .36 .32
TST .02 .02 .12
Arousal index .24 .15 .24
¼.05 for step 1; R
¼.13 for step 2, R
¼.18 for step 3, p<.04.
¼.20 for step 1, p<.02; R
¼.21 for Step 2, p¼.40, R
step 3, p<.03, R
¼.36 for step 4, p¼.19.
Table II. Self-report and Polysomnographic Sleep Variables
Trouble sleeping (% yes) 54.1% 28.10%
Sleep latency >20 min
Wake up at night
Not rested in AM (%) 10.80% 9.40%
Laboratory AM Self-report
TST, min 517 180 507199 107, 89
Sleep latency, min 37 1.4 16 0.2 0.58, 0.16
Wake after sleep Onset, min 59 2.2 21 1.0 1.35, 0.19
Number of awakenings 0.8 1.3 1.3 2.2 0.35, 1.4
Time in bed, min 605.5 4 597 426, 9.6
SPT min 589 35 578 429, 6
TST min 547.5 4 548 419, 19
Sleep efficiency,% 90.5 59240.9, 3
Sleep latency Stage 2, min 19 15 20 26, 9
REM latency, min 1074 105 422, 17
Wake after sleep onset,% SPT 6.9 5 5.1 44, 0.2
NREM stage 1,%SPT 5.8 2 6.0 20.9, 1
NREM stage 2,%SPT 26.0 9 25.4 15, 4
NREM stage 3,%SPT 13.5 5 14.3 52, 3
NREM stage 4,%SPT 25.4 7 27.6 71, 6
REM,%SPT 22.4 3 21.5 53, 1
Fragmentation index/hr TST 4.8 1 5.2 20.4, 1
PSG Scored Events
Arousals,/h TST 8.3 3 9.0 51, 3
Apnea–hyponea Index, /h TST 1.83 1.3 0.8 1, 0.5
movements, /h TST
0.1 0.2 0.1 0.1 0.1, 0.1
SPT, sleep period time; TST, total sleep time. Data are percent of each group or
6Ward et al.
disease in time to fall asleep (sleep latency) and overall
SE. Younger children with active disease slept a bit more
and fell asleep more quickly than older children with
active disease. We discuss these findings relative to those
Sleep and Disease Condition
We were surprised that there were no differences between
children with active versus inactive disease both for PSG
and self-report sleep measures. Based on previous studies,
we anticipated that children with active disease would
show more disturbed sleep compared to those with
inactive disease. Compared to previous studies (Zamir
et al., 1998; Passarelli et al., 2006), children in our study
had a similar number of arousals, fewer periodic limb
movements, but a higher AHI (>1) that is indicative of
mild sleep disordered breathing (Redline et al., 2007).
These findings may reflect particular child characteristics,
distinct mechanisms, or a combination of medication use,
disease type, and severity. Children is our study had
longer mean TST (8.7 hr) and slightly higher mean
SE (90%) compared to previous laboratory studies
(6.3–7.9 hr; 86–88%) of children with JRA (Amos, 1997;
Coble, Kupfer, Taska, & Kane, 1984; Labyak et al., 2001;
Passarelli et al., 2006; Zamir et al., 1998). The differences
in TST and SE in our study may be attributed to the
addition of an adaptation night, and time in bed was
based on their usual sleep at home, not on a set laboratory
schedule. Alternatively, children in our study may have
had milder disease compared to children in other
published studies. Amounts of stage 1 and REM sleep
were similar to previous published reports (Passarelli
et al., 2006; Zamir et al., 1998), but the amount of NREM
stage 2 was lower and that of slow wave sleep (NREM
stage 3 and 4) was higher in our study (Passarelli et al.,
2006; Zamir et al., 1998). Age, medications, treatment
modalities, and more generous scoring of slow wave sleep
in our study may explain differences in the reported
amounts of NREM sleep stages. Further study is warranted
to compare PSG sleep stages in children with JRA and
healthy children of the same age.
A marked ‘‘first-night’’ effect was evident in this
study. Compared to the study night, more than a third of
the children had longer sleep latency, less TST, and more
nocturnal wakefulness leading to reduced SE on the first
night in the laboratory. We attribute this to reactions of
the children to sleeping in a new environment. This
interpretation is supported by the observation that only
10% of the children exhibited reduced SE on the second
night. These findings are similar to those from Coble and
colleagues (1984) and Carskadon and colleagues
(Carskadon, Keenan, & Dement, 1987) who also
reported a ‘‘first-night’’ effect in healthy school-aged
children. Most of the reported PSG data in children are
based on a single night in a sleep laboratory, which may
overestimate the extent of sleep disturbance and not
Table IV. Polysomnography Sleep Variables for Adaptation and Study Nights by Disease Status and Age
Adaptation Night Study Night
Active disease Inactive disease Active disease Inactive disease
602 10 553 8 566 9 542 11 610 9 575 7 5918 559 9
548 13.6 481 11.2 501 12.1 494 14.6 563 9.6 537 8.0 561 8.6 528 10.4
89 2.1 82 1.8 84 1.9 88 2.3 91 1.2 90 .96 92 1.0 92 1.2
Sleep latency, min
16 5.4 42 4.4 27 4.8 21 5.8 13 4.0 23 3.3 22 3.6 18 4.3
WASO,% SPT 8.6 2.1 12.7 1.8 11.2 1.9 8.5 2.3 7.5 1.1 6.4 .91 4.8 .97 5.4 1.2
NREM stage 1,% SPT 7.0 .70 6.7 .60 6.2 .64 8.0 .78 5.8 .62 5.8 .51 5.7 .55 6.4 .66
NREM stage 2,% SPT 18.3 2.1 29.8 1.8 22.2 1.9 30.1 2.3 19.4 2.1 30.5 1.7 23.1 1.9 28.8 2.3
NREM stage 3,% SPT 15.9 .99 9.6 .84 14.5 .91 10.7 1.1 17.0 1.1 11.1 .89 16.5 .96 11.2 1.2
NREM stage 4,%SPT 29.8 1.6 21.1 1.3 28.9 1.4 24.3 1.7 29.9 1.7 22.3 1.4 29.2 1.5 25.3 1.8
20.3 1.1 20.0 .93 16.9 1.0 18.4 1.2 20.4 .95 23.7 .78 20.5 .84 22.9 1.0
Arousals, #/h 9.8 2.1 8.6 1.8 12.5 1.9 8.9 2.3 9.4 1.1 7.6 .90 9.6.96 8.2 1.2
SPT, sleep period time; TST, total sleep time. Data are mean SEM. Sleep efficiency % ¼TST/time in bed 100.
¼6.3, p<.02 main effect by disease condition and F
¼17.6, p<.001 by age group.
¼14.2, p<.001 main effect by age group.
¼5.1, p<.03 age group and disease condition interaction.
¼9.5, p<.003 age group and disease condition interaction.
¼5.3, p<.02 main effect by disease condition (adaptation night).
Sleep and Symptoms in Children with JRA 7
represent the most valid sleep assessment. A first night
effect suggests that some children may be vulnerable to
reduced sleep quality under conditions of ‘‘mild stress’’
(i.e., environmental change in sleep). It also is possible
that parental reports of usual bedtimes were inaccurate,
leading to artificially prolonged sleep latency and time in
bed. However, we were careful in establishing each child’s
laboratory schedule and used the same bedtime and rise
time such that time in bed was similar for both nights.
Relations Among Sleep, Pain, Anxiety,
Anxiety, pain, and fatigue are associated with disturbed
sleep but few studies have examined these variables
together. Previous studies (Bloom et al., 2002; Labyak
et al., 2001; Lewin & Dahl, 1999; Palermo & Kiska 2005;
Passarelli et al., 2006; Palermo 2000) report various
associations among sleep, pain, and anxiety and several
studies report higher levels of anxiety in children with JRA
and pain (Schanberg et al., 2003; Varni et al., 1996), and in
children with musculoskeletal pain syndromes (Meltzer,
Logan, & Mindell, 2005). Pain, even low levels of pain
intensity, has been linked to shorter TST, longer sleep
onset, and reduced sleep continuity. In this study,
compared to children with inactive disease, we found
that children with active disease had more pain and fatigue,
but no differences in anxiety. Anxiety and pain prior to
sleep onset, explained a small amount of the variance in
nocturnal arousals as an indicator of sleep disturbance on
the study night, but neither pain nor anxiety had an
important effect. When these variables were examined
together in one model, a modest amount of the variance in
fatigue was explained, but anxiety and disturbed sleep did
not have important effects. Rather age, disease status, and
pain were the best predictors of fatigue. The lack of a
negative impact of sleep on fatigue is probably related to
the observation that children slept fairly well on the second
night in the laboratory. Alternatively, we obtained measures
of fatigue in the evening. Effects of disturbed sleep on
fatigue might be more apparent if measures had been
obtained in the morning on awakening.
It is of note that in our sample, mean fatigue
frequency and intensity scores were lower than those
previously reported in children with cancer, the popula-
tion for whom the instrument was developed (Hinds &
Hockenberry 2001; Hinds et al., 1999). This instrument
has been widely used in 7 to 12-year-old children with
cancer; however, the symptoms of fatigue may vary in
children with different chronic illnesses (i.e., cancer vs.
JRA vs. sickle cell disease) (Davies, Whitsett, Bruce, &
McCarthy 2002; Hinds et al., 1999; Hockenberry-Eaton
et al., 1998; White, 2001). Additional research on fatigue
in children with JRA is needed to further our under-
standing of the interplay among fatigue, pain, and sleep.
Developmental Aspects and Sleep
Children with JRA showed age-related (developmental)
changes in sleep amount and NREM sleep stages.
Compared to younger children, older children with JRA
had less total sleep and showed increased stage 2 sleep
and decreased slow wave sleep (stages 3 and 4). These
findings provide support for the notion that age-related
changes occur in NREM sleep in preadolescent children
with JRA. Typically compared to preadolescent children,
adolescents show large reduced amounts of slow wave
sleep, and these changes are usually attributed to changes
in neuroendocrine function with puberty (Carskadon
et al., 1987; Dahl 1996; Dahl & Lewin 2002). There is a
paucity of research pertaining to sleep and age-related
changes in children 8–11 years. Few studies of healthy
children have been reported with which to compare our
results. Most of the research on sleep patterns stem from
parental report or one night of PSG that may not
accurately depict developmental changes in preadolescent
sleep patterns. Future studies comparing children with
JRA or other chronic illnesses to healthy children are
needed to verify our observations.
There are several limitations worth noting in this study.
First, the convenience sample was primarily Caucasian that
limits generalizability to all children with JRA, but our
sample is fairly representative of children with active and
inactive JRA and who live in the Pacific Northwest. Second,
a cross-sectional design limits directionality, as all the
associations are potentially bidirectional. Third, depression
was not measured in this study and could have influenced
sleep outcomes. Fourth, we did not have a healthy control
group for comparison to adequately address developmen-
tal changes in preadolescent sleep patterns.
Conclusion and Future Direction
In summary, few studies have examined both PSG and
self-reported sleep in children with JRA, and the impact
of disturbed sleep on symptoms of pain and fatigue,
anxiety, and daytime function. Poor sleep quality
associated with arousals or sleep-related respiratory
disturbance can lead to fatigue and decrements in
daytime functioning that may negatively impact a
8Ward et al.
child’s well being and quality of life. Further studies are
warranted to examine objective and self reports of sleep
in relation to daytime functioning in children with JRA.
The authors thank the children and families who helped
with this research. We thank Linda Peterson, Research
Coordinator, Dr Laurie Beitz, MD, and the staff in the
Rheumatology Clinic for recruiting the participants. We
thank Ernie Tolentino, Laboratory Manager, and the sleep
laboratory staff, James Rothermel, Taryn Jenkins, David
Krizan, and Paul Wilkinson for recording, processing, and
scoring of the sleep data. We also thank Hieke
Nuhsbaum for data entry and Salimah Man, Yuen Song,
Tuyet Nguyen, Sarah Shapro, and Whitney Jewell for
helping with data collection and processing. We also
thank Dr Susan Labyak who began this research when
she was a faculty member at the University of
Washington, School of Nursing. This research was
supported by grants from the National Institute of
Nursing Research, T32 NR0710, NR08136, the Center
for Women’s Health and Gender Research, NR04011,
and the National Center for Research Resources, M01-RR-
Conflicts of interest: None declared.
Received April 27, 2007; revisions received and accepted
November 9, 2007
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