Sleep Disturbance, Daytime Sleepiness, and Neurocognitive ...

sleep and cognition in children with juvenile arthritis

Sleep Disturbance, Daytime Sleepiness, and Neurocognitive Performance in Children with Juvenile Idiopathic Arthritis Teresa M. Ward, PhD, RN1; Kristen Archbold, PhD, RN2; Martha Lentz, PhD, RN1; Sarah Ringold, MD3; Carol A. Wallace, MD3; Carol A. Landis, DNSc, RN1 Biobehavioral Nursing and Health Systems, School of Nursing, University of Washington, Seattle, WA; 2Practice Division, College of Nursing, University of Arizona, Tucson, AZ; 3Seattle Children’s Hospital, Department of Pediatrics, University of Washington School of Medicine, Seattle, WA 1

Study Objectives: To compare daytime sleepiness and neurobehavioral performance in children with active and inactive juvenile idiopathic arthritis (JIA), and explore relations among measures of sleep disturbance, daytime sleepiness, and neurobehavioral performance. Design: Cross-sectional, comparison. Setting: A university-based research sleep laboratory. Participants: Seventy (70) children 6-11 years of age with active or inactive JIA. Measurements and Results: Self-reported daytime sleepiness, multiple sleep latency tests (MSLTs), and computerized neurobehavioral performance test scores were obtained after 2 nights of polysomnography. Children with active disease (mean physician global rating score = 2.9 ± 1.9 SD) showed shorter mean MSLT latency (15 ± 6.0 min) than those with inactive disease (16.5 ± 5.5 min, P < 0.03). Scores on neurobehavioral performance tests showed no group differences. However, number of wake bouts predicted sustained visual attention (rapid visual processing, P < 0.05) and apnea hypopnea index (AHI) predicted reaction time (P < 0.0001), after controlling for age, IQ, medication, and disease status. Conclusion: Indices of sleep disturbance were associated with validated tests of neurobehavioral performance in JIA, regardless of disease activity. Additional research is needed about the extent of sleep disturbances in relation to neurocognitive performance in JIA and compared to healthy children. Keywords: Juvenile idiopathic arthritis, polysomnography, neurobehavioral performance, daytime sleepiness Citation: Ward TM; Archbold K; Lentz M; Ringold S; Wallace CA; Landis CA. Sleep disturbance, daytime sleepiness, and neurocognitive performance in children with juvenile idiopathic arthritis. SLEEP 2010;33(2):252-259.

Juvenile idiopathic arthritis (JIA) (formerly known as juvenile rheumatoid arthritis [JRA]) is one of the most common inflammatory rheumatologic conditions in children and is estimated to affect approximately 300,000 children in the United States.1 JIA is a chronic disease with recurring episodes of acute inflammation. The course of the disease is highly variable. Some children experience infrequent episodes of inflammation and recover quickly from symptoms; others experience frequent disease episodes and significant disability.2 Many children show remarkable resilience in adapting to life with JIA, while others appear to be susceptible to the intensity of the symptoms and show physical, emotional and behavioral manifestations of marked distress. Children with JIA fatigue easily, experience joint inflammation, pain, limited mobility and report poor sleep quality and daytime sleepiness.3-8 Longer mean self-reported nap duration (~ one h) and shorter mean sleep latencies (10.4 ± 2.6 min)6 in multiple sleep latency tests (MSLTs) have been reported in JIA children compared to mean sleep latencies from previous studies of children (16.5 ± 3.3 min to 26.4 ± 2.8 min).7,8 Daytime sleepiness in JIA could be linked to reduced sleep continuity associated with some aspect of (e.g., inflammation) or response

(e.g., pain, anxiety) to the disease. Children with greater disease severity (e.g., greater number of joints affected) had higher scores on the Pediatric Daytime Sleepiness Scale, and daytime sleepiness was significantly correlated with parental reports of pain and degree of interference of JIA in the child’s life.3 Numerous factors (age, medications, disease status, anxiety, pain, total sleep time, and arousals) have been associated with fatigue in JIA,5,9 but less in known about daytime sleepiness. Daytime sleepiness could be related to conditions that disrupt sleep continuity and negatively affect daytime performance. Along with daytime sleepiness, parents often report on questionnaires symptoms suggestive of sleep disorders, such as parasomnias (sleep terrors, walking) and sleep disordered breathing (SDB).4,5 More arousals and awakenings/hour and a greater proportion of arousal-associated periodic limb movements have been reported in JIA compared to healthy children.6,7 In a previous report, we found a higher apnea/hypopnea index (AHI > 1)9 than had been reported in previous studies of JIA.6,7 Parental reports of snoring and sleepiness have been associated with poor cognitive performance.10,11 Daytime sleepiness that has been associated with arousals, snoring, and AHI 12-15 is thought to underlie poor school performance, negative mood, changes in behavior, and neurobehavioral performance in school aged children.16-19 In particular, decrements in verbal fluency, visual-spatial ability, sustained attention, mental flexibility, and vigilance have been noted.14,18,20-22 Most of the research on sleepiness and neurobehavioral performance has been studied in children with SDB. Daytime sleepiness has the potential to negatively impact neurobehavioral performance in JIA, but to our knowledge there have been no prior reports. As part of our ongoing research of sleep disturbance in children with JIA, we compared self-report and physiological

Submitted for publication February, 2009 Submitted in final revised form October, 2009 Accepted for publication November, 2009 Address correspondence to: Carol A. Landis, DNSc, RN, FAAN, Professor, Box 357266, Biobehavioral Nursing and Health Systems, University of Washington, Seattle, WA, 98195-7266; Tel: (206) 616-1908; Fax: (206) 543-4771; E-mail: [email protected] SLEEP, Vol. 33, No. 2, 2010

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Sleepiness and Neurocognitive Performance—Ward et al

Polysomnography The first night in the laboratory served as adaptation; the second night was the study night. A fixed 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. For all children, mean lights out on the study night was 21:14 and mean lights on was 07:15. Electrodes to record the electroencephalogram (EEG), electro-oculogram (EOG), electrocardiogram (ECG), electromyelogram (EMG), leg movements, and devices for respiratory monitoring were placed according to standard laboratory protocols approximately 2 hours prior to the scheduled bedtime. EEG electrodes were positioned at 2 frontal (F7, F8), 2 central (C3, C4), and 2 occipital (O1, O2) locations (International 1020 system of measurement). Linked EEG reference electrodes were placed over each mastoid bone (A1, A2). Chin EMG electrodes, right and left EOG (right and left outer canthi) electrodes, ECG electrodes (modified Lead II chest configuration), and bilateral anterior tibialis electrodes were placed. Nasal airflow was monitored with a pressure cannula placed in the nose (Pro-Tech Services, Inc., Mukilteo, WA). Chest and abdominal respiratory effort was measured by piezo respiratory effort bands placed around the chest and abdomen (Pro-Tech Services Inc., Mukkiteo, WA). Oxygen saturation was measured from the left or right index finger by a pulse oximeter (Nonin XPod, Nonin Med, Plymouth, MN). Snoring was measured by a small microphone sensor (Pro-Tech Services, Inc., Mukilteo, WA) placed on the throat, just lateral to the trachea. Electrophysiological signals were recorded and digitized by Somnologica data acquisition recording system (A10 recorders, Embla, Broomfield, CO) and displayed and stored on a desktop (Dell Pentium III) computer. The sampling rate for the EEG was set at 200 Hz for EMG, periodic leg movements, and ECG data; 100 Hz for the EOG signal and snoring sensor; 20 Hz for the nasal airflow and respiratory effort; and 1 Hz for the oximeter. Before each recording session, a standard 50 microvolt, 10 Hz calibration signal was recorded for 5 minutes. All digitized data were acquired and stored unfiltered and continuously displayed in 30-sec intervals during each recording.

(MSLT) measures of daytime sleepiness and neurobehavioral performance between children with active and inactive JIA. Based on our prior findings of more pain and fatigue in children with active compared to inactive JIA,9 we hypothesized that daytime sleepiness would be increased and scores on neurobehavioral performance tests would be decreased in children with active compared to inactive JIA. We also explored relations among polysomnographic indices of sleep disturbance (number of wake bouts, snoring, arousals, apnea/ hypopnea index [AHI]), daytime sleepiness, and neurobehavioral performance. METHODS Participants Approval for this study was obtained from the Institutional Review Board at the Seattle Children’s Hospital (SCH) in Seattle, WA. From April 2004 through January 2007, a convenience sample of 70 children (64 girls) 6–11 years of age with active or inactive JIA and their parents were enrolled in this study. Children were excluded if they had a diagnosis of a psychiatric condition, diabetes, asthma, cancer; family history of narcolepsy in a first-degree relative, or a handicap that would interfere with neurobehavioral performance testing. Disease Related Variables Disease Duration was measured from the time the child was first diagnosed with JIA, obtained through a chart review, and confirmed by a pediatric rheumatologist. Disease Activity was measured by a pediatric rheumatologist who examined the child on the first day of the laboratory study 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 (≥ 1 on a 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).23 Pain location was measured by an investigator-developed skeletal figure, (Mr. Bones) included in a daily diary. Children circled the joints on Mr. Bones that corresponded to the location of their pain.5 The number of joints circled was summed to yield a total “joint hurt” score for each morning and each evening. Medications children received anytime during the study were scored as “yes” or “no” and classified into categories: (1) nonsteroidal anti-inflammatory drugs (NSAIDS); (2) corticosteroids; (3) disease modifying anti-rheumatic drugs (methotrexate, Arava); (4) tumor necrosis factor-α receptor inhibitors (etanercept, adalimumab, infliximab), other (e.g., vitamins), and none.

Sleep Stage Scoring and Variables For visual sleep and wake stage scoring, all channels of recorded data were displayed from 0.3 Hz to 40 Hz on a highresolution 21-inch color monitor. PSG recordings were scored manually into wake and sleep stages by a technologist according to standard criteria.24 Apneas (absence of airflow ≥ 2 breaths) and hypopneas (50% decrease in nasal airflow with a corresponding 3% decrease in oxygen saturation and/or associated arousal for at least 2 breaths) were scored according to published criteria for children,25 and expressed as an apnea/ hypopnea (AHI) index/hour of total sleep time. Periodic leg movements (PLM, ≥ 4 leg movements, of 0.5–5 sec duration with an interval of 5 to 90 sec),26 and arousals (shift to a fast EEG frequency lasting 3 to 15 sec) were scored manually and each expressed as an index/hour of total sleep time.27 Snoring during any sleep stage was scored as an increase in the amplitude of the snore signal that was 1.5 to 2 times a baseline (e.g., flat line in the recording) and reported as minutes.

General Procedures Details about study procedures were published previously.9 Each child and a parent came to University of Washington School of Nursing sleep laboratory for 2 consecutive nights of polysomnography (PSG). In the morning after the second night, children underwent 4 MSLTs at 2-h intervals beginning at 09:00 to assess daytime sleepiness and completed a battery of neurobehavioral performance tests at 10:00 and 14:00. All children were introduced to and completed the battery of neurobehavioral performance tests during the early evening of the first night in the laboratory. SLEEP, Vol. 33, No. 2, 2010

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Sleep variables were calculated for each night with a locally developed software program. Total sleep time (TST) was the amount of time in NREM stages 1- 4 and REM. The amount of wake after sleep onset (WASO) was expressed as a percentage of sleep period time (SPT) (time from sleep onset until final awakening). Sleep efficiency (SE) was expressed as a ratio of total sleep time/time in bed. Finally, wake bouts were defined as the total number of awakenings from any sleep stage that were ≥ 30 sec duration and reported as the total number that occurred throughout the sleep period.

and again at 14:00, in the same order, and took 35-45 min for each subject to complete. Testing took place in a separate room next to the sleep laboratory and each child’s performance was monitored by one of the sleep laboratory staff. Parents were not permitted in the room during testing sessions. CANTAB tests measuring reaction time, movement time, and sustained visual attention were used in this study. Movement was measured with the Motor Screening Test (MOT). Reaction time was measured with 1-choice and 5-choice Reaction Time (RTI) tests, and Match to Sample Visual Search (MTS) tests. Sustained visual attention was measured with the Rapid Visual Processing (RVP) test. Motor Screening Test was used to orient the subject to the use of the touch-screen and also measured movement time. A series of crosses is shown at different locations on the touch-screen and the subject must touch the center of the cross on each trial. Movement time (MOT) in milliseconds was averaged for the trials for each child and reported as the mean for each group. Reaction Time was measured with 1-choice and with 5-choice stimulus conditions. In these tests, a subject holds a press pad down, releases the press pad when a yellow circle is seen on the screen and must touch the yellow circle on the screen as quickly as possible. In the first stimulus condition the subject identifies a single object (e.g., yellow circle); in the second stimulus condition, the subject identifies a yellow circle from among 5 object choices. Simple reaction time (RTI) and 5-choice reaction time (RTI-5) were calculated as the time (milliseconds) from perception (release of press-pad) to touch of appropriate stimulus on screen and averaged for the trials for each child. Both RTI and RTI-5 were reported as the mean (milliseconds) for each group. Match to Sample Visual Search (MTS) is a test of reaction time that involves a visual search strategy to accurately identify a specific object. In this test, there is a speed versus accuracy trade-off, and the test results can be used as an indicator of impulsivity in reaction to the stimuli. An abstract pattern within a red square is displayed in the middle of the computer screen. After a brief delay, a varied number of similar patterns are shown in boxes surrounding the red square in the middle of the screen. The subject must determine which of these patterns matches the one in the middle of the screen and touch the appropriate box. The number of patterns displayed around the red square varies from 1, 2, 4, or 8 with each stimulus presentation. MTS percent correct was calculated based on the number of correctly identified targets out of a possible 48 total presented. The increase in time necessary to correctly identify a target presented from among 2 choices versus 8 choices was recorded in milliseconds and reported as reaction time (MTS latency change 2-8). MTS latency change 2-8 was averaged for each child and reported as mean for each group. Rapid visual processing (RVP) measures sustained visual attention. A white square appears in the middle of the computer screen and digits from 2 to 9 are singularly presented in a pseudorandom order at a rate of 100 digits per minute. Subjects must detect any of 3 possible target sequences (i.e., 2-4-6, 4-6-8, or 3-5-7) and push the press pad when the third number in the target sequence is presented. After a practice session, target sequences are presented 32 times. Signal detection theory was used as a theoretical basis to calculate the

Self-reported Daytime Sleepiness Self-reported daytime sleepiness was assessed prior to and after each MSLT test. Children completed a visual analog sleepiness scale that has been used to assess children’s perception of their daytime sleepiness.28,29 Anchors at either end of the 100 mm horizontal line were an “extremely awake” face (eyes wide open, humming a tune) and an “extremely sleepy” face (eyes closed, snoring). Children were instructed to place an “X” on line to express how s/he felt right now. Children < 8 years old were assisted by the laboratory technicians to complete this scale. Results were reported as the location of the X mark in millimeters from the “extremely awake” end of the scale. Multiple Sleep Latency Tests (MSLTs) Following the study night, 4 MSLTs were performed at 2-h intervals beginning at 09:00. Children were asked to lie down quietly in bed for a maximum of 20 minutes in a darkened room, to keep their eyes closed and to not resist falling asleep. Sleep latency was defined as the amount of time (minutes) from lights out to first epoch of NREM stage 1. If a child fell asleep, s/he was awakened after 2 to 3 sleep epochs. Latency to sleep in minutes over the 4 MSLT was averaged for each child. After each MSLT children were asked “did you fall asleep,” (yes or no) and “how many minutes did it take you to fall asleep.” Neurocognitive Testing Wechsler Abbreviated Scale of Intelligence The Wechsler Abbreviated Scale of Intelligence (WASI) was used to estimate intelligence quotient (IQ).30 The WASI was administered to each child by a trained pediatric neuropsychometrician on the first day of enrollment in the study. The WASI Full-Scale IQ score was calculated from scores of all 4 WASI subscales and used as the variable to estimate general intelligence. Cambridge Neuropsychological Test Automated Battery Tests from the Cambridge Neuropsychological Test Automated Battery (CANTAB) were used to measure neurobehavioral performance.31 CANTAB consists of a series of tasks that index 3 behavioral domains: (1) working memory/planning, (2) visual memory, and (3) visual attention. A series of computerized tests are presented visually on a color monitor with an attached touch-screen and a press pad to record responses to stimuli.32 For each test the child was seated in a chair with a foot rest directly opposite the computer monitor. A large ‘X’ in red tape was placed on the computer desk to designate the location of the press pad. The test battery was administered at 10:00 SLEEP, Vol. 33, No. 2, 2010

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variables of interest: probability of a hit (the proportion of correct responses given when a target sequence is presented) and probability of a false alarm (the proportion of responses when no target sequence is presented).31 A probability of a hit value close to 1.0 means the subject made nearly 100% correct responses. A probability of a miss value close to 1.0 means the subject made close to 100% inappropriate responses or false alarms. RVP probability of a hit and RVP probability of false alarm were averaged for trials for each child and reported as mean for each group.

mean MSLT sleep latency was significantly shorter in children with active disease compared to those with inactive disease (t = 2.2, P < 0.03). Twenty-eight percent of children with active disease and 24% of children with inactive disease reported that they fell asleep during at least one MSLT. Neurobehavioral Performance Scores on neurobehavioral performance tests for the morning and afternoon testing sessions are shown in Table 3. There were no significant main effects or interaction effects for disease status or time of day.

Statistical Analyses Data were analyzed using SPSS for Windows version 14.0 (SPSS Inc, Chicago, IL). Data analyses were blocked into conceptual categories and then analyzed (e.g., demographics, disease related variables), PSG variables, including sleep disturbance variables (e.g., number of wake bouts, snoring, arousals, AHI), self-report and MSLT daytime sleepiness, and CANTAB performance tests (raw scores) for group differences. Each category was considered a separate analysis with significance set at P < 0.05 (2-sided).The first set of analyses was designed to address group differences in all study variables between children with active and inactive disease. Second, we examined effects of disease status (active or inactive) and time of day (morning and afternoon) on the CANTAB variables in a series of general linear model (GLM) repeated measures analyses. In these models, CANTAB test scores and MSLT sleep latency were the withinsubjects factors, and disease condition was the between-subjects factor. Third, we examined relations among sleep variables and CANTAB test variables with a series of simple correlations controlling for age and IQ. Finally, in stepwise regressions, we explored how much of the variance in these neurocognitive performance test scores was explained by wake bouts or AHI, controlling for age, IQ, medications, and disease status.

Sleep and Neurobehavioral Performance We conducted a series of partial correlations between sleep disturbance variables (number of wake bouts, snoring, arousals, AHI), MSLT sleep latency, and CANTAB test scores, controlling for age and IQ. We found no correlations between any of these variables and MSLT sleep latency. There was no correlation between snoring or arousals and any of the CANTAB test scores. However, mean wake bouts was inversely correlated with RVP probability of a hit both in the morning and afternoon (r = −0.36, P < 0.02; r = −0.30, P < 0.05, respectively), and with probability of false alarms in the afternoon (r = −0.32, P < 0.03). AHI was positively correlated with RTI 5-choice reaction time both in the morning and afternoon (r = 0.31, P < 0.04; r = 0.47, P < 0.001, respectively). Based on these empirical findings, we regressed wake bouts and AHI on measures of reaction time and sustained attention using age, IQ, medications and disease status as control variables. In a series of stepwise regressions, age, IQ, and medication were entered in the first step; disease status (active or inactive) was entered in the second step, and wake bouts or AHI was entered in the third step (Table 4). In the first model age, IQ, and number of wake bouts explained 40% and wake bouts accounted for 4% of the variance in RVP probability of a hit (P < 0.05). In the second model age, IQ, and AHI explained 61% of the total variance, and AHI accounted for 40% of the variance in RTI 5-choice reaction time (P < 0.0001). In a third model age, IQ, and number of wake bouts explained 14% of the variance, and wake bouts accounted for 4% of the variance in RVP of a false alarm, but was not significant (P = 0.10). Medication use and disease status were not significant (P > 0.05) in any of the models.

RESULTS Clinical Characteristics The clinical characteristics of the children are presented in Table 1. The sample was 84% white, which is representative of the greater 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 (χ2 = 11.4, P < 0.001) and other medications (χ2 = 5.9, P < 0.02). No differences for disease type, number of painful joints, WASI total score, and nocturnal PSG sleep variables were found between children with active and inactive disease. All children snored a considerable amount of total sleep time (active disease = 37.6%; inactive disease = 39.8%), but there were no differences between groups.

DISCUSSION The findings from this study are the first report of a laboratory assessment of daytime sleepiness in combination with validated tests of neurobehavioral performance in school-aged children with JIA. Children with active JIA had significantly shorter mean MSLT sleep latency compared to children with inactive disease but the actual group differences were small (< 2 min on average). These findings of sleep latency on MSLT tests are consistent with results from previous studies in healthy school-aged children,8,12,14,15,22,33 but not with a previous study in JIA that reported a mean MSLT sleep latency of 10.3 minutes.6,7 The most parsimonious explanation for these differences between studies is that children with active JIA in the current study had low disease activity. It is interesting to note that the mean estimate of sleep latency by child report was quite shorter than

Daytime Sleepiness Table 2 shows self-report and MSLT results for children with active and inactive JIA. There were no group differences in mean self-report of daytime sleepiness before or after each MSLT, and no differences in mean estimate of MSLT sleep latency in children with active and inactive disease. However, SLEEP, Vol. 33, No. 2, 2010

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that of the mean MSLT in both groups, and shorter in the active versus the inactive disease group. Thus, children with Inactive Active 95% Confidence active disease were slightly sleepier than (n = 32) (n = 37) Interval children with inactive disease. 8.1 ± 1.8 8.9 ± 2.0 −1.7, 0.2 Age, years Daytime sleepiness is an often unWechsler Abbreviated Scale of 107.9 ± 13 104.1 ± 12 −2.3, 9.9 der recognized symptom in children Intelligence (Total Score) and may manifest as behavioral probEthnicity, n (%) lems, inattention, hyperactivity, or as White 25 (78.1%) 33 (89.2%) “sleepiness”(falling asleep in school, Other 7 (21.9%) 4 (10.8%) difficulty waking up in the morning).34-36 Sex, n (%) Several questionnaire surveys suggest Girls 28 (87.5%) 31 (83.8%) that 17% to 21% of school-aged children Boys 4 (12.5 %) 6 (16.2 %) and adolescents report daytime sleepiDisease Type, n (%) ness, although the precise frequency of Oligoarticular 15 (46.8%) 11 (29.7%) children affected by daytime sleepiness Polyarticular 16 (50.0%) 24 (64.9%) is unknown.34,35 What constitutes daySystemic 1 (0.03%) 2 (0.05%) time sleepiness varies developmentally 0.6 ± 0.25 2.9 ± 1.9 −3.4, −2.2* Physician Global Rating (0-10) as “how it feels to be sleepy” may mean different things to different children and Painful joints (Number) expressed in different ways. Parent and AM 0.2 ± 0.8 0.9 ± 2.5 1.6, 0.13 child questionnaires often describe dayPM 0.4 ± 0.9 1.0 ± 2.4 1.4, 0.34 time sleepiness as subjective and emoMedications, n (%) tional experiences including “tired,” NSAIDS 9 (28.1%) 26 (70.3%) “sleepy.” “wake up feeling unrefreshed,” Corticosteroids 7 (21.9%) 12 (32.4%) “cranky,” or behaviorally as “naps durDisease modifying anti-rheumatic drugs 16 (50.0%) 21 (56.8%) ing the day,” “inattentive,” and “falling TNF α inhibitors 3 (9.3%) 9 (24.3%) asleep in school.”37,38 Clinical symptoms Other 7 (21.9%) 19 (51.4%) of excessive daytime sleepiness can be None 8 (25.0%) 2 (5.4%) challenging to detect because of age difPolysomnography ferences, pubertal development stage, Time in bed, min 597.3 ± 38.3 605.5 ± 35.8 −26.0, 9.6 insufficient sleep (e.g., poor sleep hyTotal sleep time (TST), min 547.7 ± 41.4 547.5 ± 38.0 −18.9, 19.3 giene), or increased sleep drive (e.g., Sleep efficiency 91.7 ± 3.7 90.5 ± 5.0 −0.89, 3.3 narcolepsy).34-36 Wake after sleep onset, % SPT 5.1 ± 3.6 6.9 ± 4.7 −3.8, 0.18 The clinical cut-off value for excesWake bouts (Number) 30.3 ± 11.9 30.4 ± 9.9 −5.3, 5.2 sive daytime sleepiness as measured by Snoring, min 206.5 ± 131.4 218.0 ± 126.1 −73.0, 51.0 MSLT in children remains unclear and Arousals/h TST 9.0 ± 5.2 8.3 ± 3.1 −1.3, 2.8 criteria for “excessive daytime sleepiApnea hypopnea index/h TST 1.3 ± 0.8 1.8 ± 2.7 1.5, 0.5 ness” have not been quantified systemPeriodic limb movements /h TST 0.1 ± 0.2 0.1 ± 0.2 −0.1, 0.1 atically. Healthy school-aged children rarely nap during the day and often reSPT = sleep period time; Data are mean ± SD or n (%). *P < 0.001 (Physician global rating higher in children with active JIA compared to inactive JIA). main awake throughout a 20-min MSLT. Children typically have longer sleep latency than the cut-off criterion of 10 minutes used in the assessment of excessive daytime sleepiness Table 2—Self-reported sleepiness and multiple sleep latency test in adults. Results from several studies in school-age children report average sleep latencies from 16.0 to 27.5 min,8,13,14,39-42 and Active JIA Inactive JIA 95% Confidence some investigators use MSLT nap durations of 30 minutes.42 n = 38 n = 32 Interval Children with active and inactive JIA did not differ sigSelf-Report Sleepinessa nificantly on neurobehavioral performance tests. Compared Before nap 33.2 ± 30.0 36.5 ± 28.0 −3.9, 10.5 to children with inactive disease, we anticipated that children After nap 30.5 ± 28.8 32.6 ± 28.5 −4.8, 9.3 with active disease might show slower reaction times because Sleep Latency of symptoms or other disease-related factors, but this was not Self-report, min 4.3 ± 7.2 7.1 ± 15.0 −5.9, 0.49 observed. Total IQ scores for both groups were within the MSLT, minb 14.9 ± 5.9 16.5 ± 5.5 0.19, 3.0 normal range. We anticipated that all children might show slower reaction time in the afternoon relative to the morning a Data are mean of daytime sleepiness visual analogue scale measures testing session because of possible circadian rhythm effects before and after each mean sleep latency test (MSLT). on sleepiness and performance.9 Although GLM analyses did b Data are mean of 4 (MSLTs), t = 2.2, P < 0.03. not reveal an effect of disease status, children with inactive Table 1—Demographic and clinical characteristics

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Table 3—Neurobehavioral performance tests in the morning (10:00) and afternoon (14:00) Inactive Disease (n=32)

Active Disease (n=37)

10:00

903.6 ± 199.8

921.1 ± 209.4

14:00

947.8 ± 261.1

903.7 ± 231.6

10:00

390.3 ± 69.4

454.8 ± 179.5

14:00

443.6 ± 153.2

442.0 ± 174.6

10:00

526.0 ± 125.2

524.1 ± 119.8

14:00

529.3 ± 138.4

523.6 ± 145.4

10:00

96.6 ± 4.0

95.7 ± 7.0

14:00

96.9 ± 3.4

94.5 ± 7.1

Table 4—Predictors of neurocognitive performance

RTI 5 Choice Reaction Time (ms)

2831.0 ± 1154.9

2153.3 ± 1544.6

10:00

0.27 ± 0.17

0.33 ± 0.22

14:00

0.33 ± 0.16

0.38 ± 0.22

10:00

0.03 ± 0.04

0.02 ± 0.03

14:00

0.03 ± 0.04

0.01 ± 0.04

0.01 0.002 0.06

0.49 0.26 0.17

Step 2 Disease Status

−0.014

0.05

−0.03

Step 3 Wake bouts

−0.004

0.002

−0.21

Step 1 Age IQ Medication

RVP Probability hit (p)

−32.5 −0.22 −87.7

9.6 1.4 54.4

−0.40 −0.02 −0.19


43.6

40.3

0.14

Step 3 AHI

50.8

6.4

0.69


−1.3 0.07 0.34

0.45 0.07 2.5

−0.36 0.12 0.02


−3.3

1.9

−0.23

Step 3 Wake bouts

−1.4

0.08

−0.21

Model 3: RVP probability of false alarm c

RVP Probability of false alarm (p)

MOT= motor screening test, RTI= reaction time test, MTS= match to sample test, RVP= rapid visual information processing test, ms=milliseconds, p = probability range 0–1.0.

disease had a small increase in RTI 5-choice reaction time in the afternoon relative to the morning. However, both simple and 5-choice response times were almost identical in the morning and afternoon sessions in the children with active disease (Table 3). As with our findings of daytime sleepiness, a lack of group differences in this study probably is attributable to children with low disease activity and few inflamed joints. These findings that disease activity was unrelated to neurobehavioral performance are consistent with findings from a previous study of cognitive function in a group of German children with systemic rheumatoid arthritis compared to age-matched healthy children.43 Most of the children with systemic JIA had active disease, yet showed no differences from healthy children in tests of memory, fine motor performance, and sustained attention. Further studies are needed on the association between effects of chronic disease, such as JIA, and time of day on neurobehavioral performance in comparison to healthy children. We found evidence that mild SDB and reduced sleep continuity were related to neurobehavioral performance in JIA. Children snored nearly 40% of total sleep time and had a mean apnea/hypopnea index > 1.0. The number of wake bouts and AHI were inversely associated with measures of sustained attention and reaction time. Further, more frequent wake bouts SLEEP, Vol. 33, No. 2, 2010

0.05 0.004 0.10

Model 2:b RTI 5-choice reaction time

MTS percent correct (%)

14:00

Standardized β


RTI Simple Reaction Time (ms)

2191.8 ± 1263.3

SE β

Model 1:a RVP probability hit

MOT Movement Time (ms)

MTS Mean Latency Change (2-8) (ms) 2694.2 ± 1507.8 10:00

Unstandardized β

Variable

n = 63, R2 = 0.40 for step 1; ∆ R2 = 0.001 for Step 2, ∆ R2 = 0.041 for Step 3, P < 0.05. b n = 62, R2 = 0.22 for step 1, P < 0.002; ∆ R2 = 0.02 for Step 2 P = 0.28, ∆ R2 = 0.40 for Step 3, P < 0.001. c n = 65, R2 = 0.13 for step 1, P = 0.04; ∆ R2 = 0.04 for Step 2 P = 0.09, ∆ R2 = 0.04 for Step 3, P = 0.10. a

was associated with measures of sustained attention; both with a decreased probability of making a correct response and with an increased probability of making a response in the absence of a stimulus (error of commission). In previous studies, fragmented sleep has been associated with reduced sustained attention20,44,46 and verbal fluency in school-aged children.17 Fragmented sleep also has been correlated significantly with errors of commission on a continuous processing task and with poor performance on a common test of working memory.47 Previous studies have found associations between mild SDB (number of arousals/hour, AHI) and tests of sustained attention.17,18,20,48 In the current study, AHI in combination with age and IQ was a significant predictor of reaction time and explained 40% of the variance. These findings suggest an adverse effect of mild SDB on neurobehavioral performance in school-aged children. However, investigators have 257


used different performance tests and tasks, measures of sleep disturbance (arousals, number of obstructive events, percent hypoxia), definitions of obstructive sleep apnea, samples (community versus clinic), and have not controlled for age and IQ. Most of the research on sleep and neurobehavioral performance has been conducted in healthy school-age children. Given observations in this study and a previous study,43 that rheumatoid disease activity may be unrelated to deficits in neurobehavioral performance, additional research is needed to ascertain the prevalence of sleep disorders and fragmentation and the impact on neurobehavioral performance in children with rheumatologic disorders. Such studies ought to compare children with active and inactive disease to age-matched healthy controls. There are several limitations of our study that require comment. We lack data from a healthy aged-matched control group for comparison with children with JIA, the cross-sectional design limits directionality and does not permit an assessment of how sleep and neurobehavioral performance is affected by changes in disease activity overtime. We used CANTAB measures of sustained attention and reaction time, and lack data on other neurocognitive tests of executive function. Disease severity did not emerge as a significant factor in predicting neurobehavioral performance, possibly because the participants of this study had low disease activity, and biomarkers of inflammation might be more sensitive indicators of disease activity. Elevated levels of C-reactive protein (CRP) have been associated with daytime sleepiness and with severity of SDB (AHI, arousals) in 3-18 year-old children.49 Children with JIA often have elevated levels of CRP, but little is known about this or other indicators of inflammation and sleep or neurobehavioral performance in children with inflammatory conditions. The treatment of JIA has changed considerably over the last 10 years. Our ability to recruit children with a greater number of inflamed joints and higher JIA disease activity was challenging as these children are treated immediately with local injections into inflamed joints or with aggressive medication treatment, sometimes requiring hospitalization. Future studies would benefit from additional measures of disease activity and need to include a control group of healthy children for comparison. In conclusion, there is a paucity of data on sleep and daytime sleepiness and no prior reports from studies of neurocognitive performance in children with JIA. Additional research is needed to gain a better understanding about the prevalence and extent of SDB in JIA, and its relation to neurobehavioral functioning in children with JIA to control children. Adequate and good-quality sleep is essential for health and normal growth and development in school aged children. Insufficient or disturbed sleep in children has been associated with, daytime sleepiness, poor neurobehavioral performance, and problematic behaviors (e.g. hyperactivity, decreased attention span, distractibility, impulsivity).8,14,15,21,48,50,51

ing, 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 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 (NCRR), M01-RR-00037. disclosure statement This was not an industry supported study. The authors have indicated no financial conflicts of interest. References

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Acknowledgments The authors thank the children and families who helped with this research. We thank Linda Peterson, Research Coordinator, Dr. Laurie Beitz, 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 recordSLEEP, Vol. 33, No. 2, 2010

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