Physically active children with epilepsy have good objective sleep duration and efficiency despite subjective reports of fatigue and sleep problems

Epilepsy & Behavior 104 (2020) 106853

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Physically active children with epilepsy have good objective sleep duration and efficiency despite subjective reports of fatigue and sleep problems Jeffrey Do a,c, Richard J. Webster a, Patricia E. Longmuir a,c, Sara Ieradi a,b, Deepti Reddy a, Sharon Whiting a,b,c, Daniela Pohl a,b,c,⁎ a b c

Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada Division of Neurology, Children's Hospital of Eastern Ontario, University of Ottawa, Ottawa, ON, Canada Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada

a r t i c l e

i n f o

Article history: Received 31 October 2019 Revised 9 December 2019 Accepted 11 December 2019 Available online xxxx Keywords: Pediatrics Seizure Exercise Sleep efficiency Pedometer

a b s t r a c t Objective: The aim of this study was to longitudinally characterize in children with epilepsy the objective and subjective sleep quality and the relationship between increased physical activity and sleep as well as measures of psychosocial well-being. Methods: Baseline physical activity and sleep were established in children with epilepsy over four weeks, prior to a 12-week exercise intervention (weekly meeting with exercise counselor). Participants continuously wore a wrist pedometer (Fitbit Flex®) to capture daily number of steps, sleep efficiency, and total sleep time. The Early Childhood Epilepsy Severity Scale (E-Chess) assessed baseline epilepsy severity. Subjective sleep quality (Children's Sleep Habits Questionnaire, CSHQ), quality of life (KIDSCREEN-27; Pediatric Quality of Life Inventory, PedsQL™, 4.0 Core), fatigue (PedsQL™ Multidimensional Fatigue Scale), depression (Children's Depression Inventory-Short), and anxiety (Multidimensional Anxiety Scale for Children) were assessed pre- and post-interventions. Results: Our cohort of 22 children with epilepsy aged 8–14 years was similarly active to peers (11,271 ± 3189 mean steps per day) and displayed normal sleeping patterns (mean sleep efficiency: 87.4% ± 3.08 and mean total sleep time: 521 ± 30.4). Epilepsy severity assessed by E-Chess was low to moderate (median baseline EChess score of 6, interquartile range: 5–7). Study outcomes did not change with the intervention. Older children and those with lower baseline activity were more likely to increase their activity during the intervention. Changes in physical activity were not associated with changes in sleep outcomes when accounting for age, sex, and baseline E-Chess score. Subjective sleep quality marginally improved with the intervention (CSHQ total score: 44.5 ± 5.8 at baseline and 41.6 ± 7.2 at the end of study, p = 0.05). Quality of life, fatigue, depression, and anxiety did not change with the intervention (p = 0.55, 0.60, 0.12, and 0.69, respectively). Significance: Children with epilepsy who are as active as peers without epilepsy have good objective measures of sleep despite self-reported fatigue and parent-reported sleep problems. The physical activity of initially less active and older children with epilepsy may benefit from an exercise counseling intervention. © 2019 Elsevier Inc. All rights reserved.

1. Introduction Poor sleep quality is associated with adverse health outcomes and lower health-related quality of life in children [1,2]. Impaired sleep and poor sleep habits have been reported in children with epilepsy (CWE) using subjective questionnaires [3,4] and are associated with lower quality of life [5]. The relationship between epilepsy and sleep is

⁎ Corresponding author at: 401 Smyth Road, Ottawa, ON K1H 8L1, Canada. E-mail address: [email protected] (D. Pohl).

https://doi.org/10.1016/j.yebeh.2019.106853 1525-5050/© 2019 Elsevier Inc. All rights reserved.

bidirectional, whereby sleep deprivation can trigger epileptic events and seizures can disrupt sleep architecture [6,7]. Given the associations between sleep and health in CWE, it is important for clinicians to understand and address sleep in the overall management of these patients. A potential method for improving sleep in CWE is to increase their physical activity. In CWE, physical activity has been shown to improve quality of life and mental health outcomes, increase self-esteem, and benefit long-term general health [8,9]. Expert consensus also favors active participation in exercise and sports [9]. There is modest epidemiological and experimental evidence supporting an association between improved sleep quality and increased physical activity in healthy adults

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J. Do et al. / Epilepsy & Behavior 104 (2020) 106853

and children [10,11]; however, the nature of this relationship has not yet been characterized in CWE. Methods to measure physical activity and sleep vary between studies. The use of pedometers to objectively measure physical activity in healthy children has been validated extensively [12,13] and is more accurate than subjective reports, which tend to overestimate activity [14]. Pedometers can be used to track physical activity longitudinally, including in CWE [15,16]. Modern pedometers also have the ability to objectively measure sleep quality and are likely similar to research-grade actigraphy but tend to overestimate total sleep time and sleep efficiency compared with gold-standard polysomnography [17]. Despite their lower accuracy for sleep quality [18], pedometers have the practical advantage of being able to track physical activity and sleep on a longitudinal basis at home, at a relatively low cost. As compared with polysomnography which is usually being conducted in an inpatient hospital setting, this may provide a more representative sampling of typical sleep quality of CWE in their home environment and offer useful information to clinicians for the management of these patients. To our knowledge, simultaneous and longitudinal objective measures have not yet examined daily sleep and physical activity in CWE. Therefore, this study aimed to determine the (1) feasibility of measuring sleep via pedometers and increasing physical activity through a threemonth motivational physical activity counseling program and (2) the relationship between increased physical activity and sleep in CWE. We hypothesized that increased physical activity would improve sleep quality, assessed using both objective (pedometer) and subjective (questionnaire) measures. Our goal was to provide novel insights into the objective and subjective sleep quality of CWE in their home environment and the potential interplay between physical activity and sleep. 2. Methods

3), and those meeting the criteria were approached by their neurologist during routinely scheduled appointments at the CHEO neurology clinic. If consenting to have their contact information released, eligible patients met with the research coordinator to ascertain interest and were given an information package and consent form. The research coordinator obtained consent (parent) and assent (child) from families agreeing to participate in the study. Enrolled patients were randomly assigned a unique study identifier. The patient list with associated study identifiers was stored in a locked filing cabinet in the neurology clinic. All study documents for each patient were identified only by the assigned participant identifier. 2.4. Study flow 2.4.1. Baseline (month 1) Enrolled participants were given a pedometer, and instructions/ technical support were provided. Pedometer data were uploaded wirelessly to the pedometer software which then automatically linked to the pedometer website in a password-protected location. The study team had access to each participant's website during the study period to view and retrieve step counts and sleep data. Participants were blinded to their step counts and sleep data during this baseline period. Reminders to sync data and charge the pedometer were given as required. At the end of the baseline period, families met with the research coordinator to complete questionnaires (demographics, quality of life, fatigue, depression, anxiety, and sleep habits). 2.4.2. Intervention (month 2–4) Families met in-person with an exercise counselor for a 1-h session to discuss strategies to increase physical activity. Topics discussed were self-efficacy, motivation, and potential barriers to increasing physical activity. Participants were shown how to see and track their daily

2.1. Study design We conducted a 4-month quasi-experimental study with measures pre- and postinterventions and each child acting as their own control. Daily activity and sleep were continuously monitored via a third-generation wrist-worn pedometer (Fitbit® Flex, Fitbit Inc., San Francisco, CA, USA) for one month to establish the baseline activity and sleep characteristics for each patient. Participants were then individually motivated once per week for three months to increase their physical activity while the pedometer monitoring continued. They received a 50-cent (Canadian Dollar [CAD]) incentive for each day of recorded steps and sleep and were allowed to keep their pedometer after the study ended to encourage continued physical activity. All participants received standard medical care by their treating neurologist throughout the study period. This study was approved by the Children's Hospital of Eastern Ontario (CHEO) Research Ethics Board. 2.2. Inclusion/exclusion criteria Study participants were included if they met the following criteria at the time of study enrollment: (1) 8–14 years of age and ambulatory, (2) a diagnosis of epilepsy with ≥ one seizure in the previous 12 months, (3) parents and participants were fluent in English or French (reading above grade two level), (4) had access to a Wi-Fi-enabled computer at least once per week, and (5) were willing to take action to increase their physical activity. Participants who were not permitted to participate in physical education at school or who had a family history of sudden cardiac arrest or sudden unexplained death in a family member (first degree) younger than 40 years old were excluded from the study. 2.3. Patient enrollment

Table 1 Demographic data (continuous) at baseline (N = 22, unless otherwise indicated). Data are presented as median (interquartile range, IQR). Variable

Median (IQR)

Anthropomorphic measurements Age (years) Height (m) Weight (kg) BMI Waist circumference (cm)a Hip circumference (cm)a Waist-to-hip ratio (waist/hip)a Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg)

11 (10–13) 1.53 (1.42–1.60) 45.4 (33.6–54.9) 18.1 (15.7–20.6) 64.5 (60.0–70.1) 82.5 (74.5–90.1) 0.80 (0.80–0.80) 106 (102–112) 66.6 (65.4–68.2)

Home and school Number of siblings Current grade at school

2 (1–2) 6 (5–8)

Epilepsy characteristics Age of first seizure (years)b Number of epilepsy-related emergency room visits, previous yearb Number of visits to physician for epilepsy-related problems, previous yearb Early Childhood Epilepsy Severity Scale (E-Chess)a Activity parameters Average daily steps Average daily sleep efficiency (%) Average daily total sleep time (min) Total CSHQ scorec Screen-time weekday (hours) Screen-time weekend (hours) a

Recruitment occurred between May 2015 and November 2015. Eligible patients were prescreened for the inclusion criteria (criteria 1–

b c

N = 21. N = 20. N = 18, CHSQ: Children's Sleep Habits Questionnaire.

8 (8–12) 0 (0–1) 4 (3–6) 6 (5–7)

11,290 (8592–14,132) 88.3 (84.5–89.3) 517 (505–529) 46.0 (42.0–49.8) 1.0 (1.0–2.0) 3.0 (2.0–4.0)

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step counts on a computer using the pedometer software. An individualized weekly physical activity plan was developed with the goal of increasing daily steps by at least 3% each week, with a target to increase daily steps by 25% over the intervention period. At the end of each week, families met with the counselor via phone or Skype (30–60 min) to establish a new weekly physical activity plan if the previous weekly target was met. If weekly targets had not been achieved, barriers to physical activity were identified, and activity plans were adjusted to overcome these barriers. After the intervention period, families met with the research coordinator to receive their study incentive and complete questionnaires again (see Section 2.6.2 Secondary outcomes; demographics and epilepsy characteristics were not repeated). 2.5. Demographics and epilepsy characteristics Participant's parents/caregivers completed a demographic questionnaire and epilepsy report. A healthcare professional also completed the Early Childhood Epilepsy Severity Scale (E-Chess). Early Childhood Epilepsy Severity Scale is a six-item inventory measuring epilepsy severity with items (seizure frequency, time period over which seizures occurred, number of seizure types, number of anticonvulsants, and response to treatment) ranked on a scale of 0 to 3 and higher scores (range 0 to 18) reflecting increased severity [3,19]. Anthropometric measures were obtained by a healthcare professional (height, weight, waist and hip circumference as measured by the superior border of iliac crest and widest portion of the buttocks, respectively, and blood pressure). The mean of two (height and weight) or three (waist circumference, hip circumference, blood pressure) measurements was reported. 2.6. Study outcomes 2.6.1. Primary outcomes Physical activity (assessed by step counts), sleep efficiency (percent of time asleep relative to time spent in bed), and total time asleep were measured daily via the wrist-worn pedometer. Participants were instructed to manually turn on sleep mode when going to bed and turn off sleep mode when waking up in the morning.

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2.6.2. Secondary outcomes 2.6.2.1. Sleep habits. Sleep habits were assessed by the Children's Sleep Habits Questionnaire (CSHQ). This parent-reported 45-item questionnaire measures pediatric sleep problems. Typical weekly sleep behaviors were ranked on a 3-point scale indicating the frequency that the behavior occurs in a typical week (0 = up to once per week, 1 = two to four times per week, and 2 = five to seven times per week). Some items were reverse scored to ensure that a higher score reflected lower sleep quality. Items were subgrouped thematically into eight domains as reported in previous studies [20]. 2.6.2.2. Subjective fatigue. Fatigue was assessed using the Pediatric Quality of Life Inventory (PedsQL™) Multidimensional Fatigue Scale (MFS). This scale measures general fatigue, sleep/rest fatigue, and cognitive fatigue [21]. Scores are determined similarly to the PedsQL™ 4.0 Generic Core Scale. 2.6.2.3. Quality of life. Health-related quality of life was measured using KIDSCREEN-27 (child or adolescent version) and PedsQL™ 4.0 Generic Core Scale (child self-report, version for ages 8–12 or 13–18 as appropriate). The KIDSCREEN-27 survey is a validated assessment that includes 27 questions split into five subscales for physical well-being, psychological well-being, parents and autonomy, social support and peers, and school [22]. Each item is ranked on a 5-point scale ranging from 1 (never or not at all) to 5 (always or extremely). Greater quality of life is reflected by a higher score, with negatively oriented items reverse scored. A total score was obtained by adding the scores from all questions (range: 27 to 135). Based on a manufacturer provided scoring algorithm, raw scores were scored as Rasch scales and translated to tscores with a scale mean of 50 and standard deviation (SD) of 10 [23]. PedsQL™ 4.0 Generic Core Scale is a similar assessment with four subscales (physical, emotional, social, and school) [24]. Each question is ranked on a 5-point scale, ranging from 0 (never) to 4 (almost always). Negatively oriented items are reverse scored, and all scores are linearly transformed to a 100-point scale. Scores closer to 100 indicate a higher quality of life in the respective domain. A total score was obtained by calculating the mean score of all answered questions.

Table 2 Demographic data (categorical) and epilepsy characteristics at baseline (N = 22). Data are presented as frequency (N) and percent (%). Variable Sex Female School class placement Regular class Regular class with help Special class Family structure Biological two-parent family Reconstituted two-parent family Household income b$40,000 $40,000–$60,000 $60,000+ Subjective activity levela Not active Active once in a while, not regularly Active, but only in the last 6 months Regularly active Participation in extracurricular sports with adult coaching Frequency if yes Most days Few times a week Once a week a b c

N (%) 12 (54.5) 17 (77.3) 4 (18.2) 1 (4.55) 21 (95.5) 1 (4.55) 1 (4.55) 3 (13.6) 18 (81.8) 0 (0) 4 (18.2) 1 (4.55) 17 (77.3) 18 (81.8) 6 (33.3) 9 (50.0) 3 (16.7)

Variable Epilepsy type per patient Single type Two types Seizure type, past yearb Focal aware Focal impaired awareness Bilateral tonic–clonic Absence Myoclonic No. of current anticonvulsants None One Two Three or more Type of antiepileptic drug Carbamazepine Ethosuximide Lamotrigine Levetiracetam Otherc Comorbidities ADHD

Active defined as 60+-min moderate activity accumulated over the day, every day, OR vigorous activity done at least 3 times per week, for 20+ min each time. Because of dual classification of seizures, totals may add up to N100%. Clobazam (N = 1), clonazepam (N = 1), oxcarbazepine (N = 1), and lacosamide (N = 1).

N (%) 19 (86.4) 3 (13.6) 3 (13.6) 1 (4.55) 12 (54.5) 7 (31.8) 1 (4.55) 2 (9.09) 13 (59.1) 4 (18.2) 3 (13.6) 2 (9.09) 7 (31.8) 7 (31.8) 10 (45.4) 3 (13.6) 2 (9.09)

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2.6.2.4. Depression and anxiety. Depressed mood was measured using the Children's Depression Inventory – Short (CDI-S) [25]. This 10-item survey measures depressive symptoms using a 3-point scale ranging from 0 (not true) to 2 (very true). Higher scores indicate more depressive symptoms. Raw scores were converted to t-values based on a manufacturer-supplied scoring algorithm. Anxiety was assessed using the Multidimensional Anxiety Scale for Children (short version, MASC-10) [26]. This 10-item survey uses a 4point scale ranging from 0 (never true about me) to 3 (often true about me). Higher scores indicate more symptoms of anxiety, and tscores were obtained similarly to the CDI-S. 2.7. Statistical analyses Baseline demographic information and epilepsy characteristics of the participants were summarized using median and interquartile range for continuous variables and frequency and percentage for categorical variables (Tables 1–2). Primary outcomes were summarized using box plots. Data from days during the baseline and intervention periods were included in the analysis if the following criteria were met: (1) step counts were between 1000 and 30,000 steps/day; and (2) there were sleep data the night after the matching day's activity. We successfully measured sleep in 56.0% of total nights of all participants (total nights measured for all children: 1526; mean ± SD nights per patient: 69.4 ± 25.5). Since sleep mode had to be manually turned on/off, some days (4.07% of total nights of all participants) were excluded because of inaccurate recording of sleep data. Nights with time spent in bed longer than 16 h were excluded as pedometers were unable to record sleep data beyond this time point (2.79% of total nights of all participants). Nights with time spent in bed shorter than 4 h were also excluded as these likely reflected erroneous turning on/off of sleep mode during the night (1.28% of total nights of all participants). Data meeting the aforementioned analysis criteria during the baseline period were averaged after visual inspection of the data that showed all baseline weeks were comparable and no washout period was required. The mean of all included days for the baseline and intervention periods, respectively, was used to assess the effect of the intervention using twotailed paired t-tests. Linear standard least squares models were used to test the relationship between objective physical activity and sleep quality. Secondary outcomes were scored as previously described, and the mean effect of intervention was assessed with two-tailed paired ttests or Wilcoxon signed-rank test as appropriate. All statistical analyses were performed using Microsoft® Excel version 16.26 or R statistical software version 3.6.1.

steps/day (physical activity), 87.4% ± 3.0 (sleep efficiency), and 8 h 41 min ± 30.4 min (time asleep). Baseline steps were negatively correlated with a change in steps over the study period (Fig. 2A, p = 0.03). There was no correlation between baseline sleep measures and a change in sleep quality over the study (Fig. 2B,C, p = 0.69 for sleep efficiency and p = 0.57 for time asleep), with the exception of two participants with low baseline sleep efficiency (Fig. 2B) who showed a large (N10%) increase in sleep efficiency over the study period. Lower baseline physical activity and older age were associated with a higher change (baseline to end of study) in physical activity (Table 3, p = 0.03 and 0.005, respectively; R2adj = 0.50). The interaction between age and baseline physical activity was p = 0.41; therefore, this interaction term was omitted from our reported model as it exceeds p b 0.4 [27]. Sex and baseline E-Chess total score were not associated with a change in physical activity (Table 3, p N 0.09). Sex, age, baseline EChess total score, and change in physical activity were not associated

3. Results There were 41 patients who met the inclusion criteria and who gave consent to be approached to participate in the study. Of these 41 patients, 26 participants consented to be enrolled, of which four were lost to follow-up. Many of the patients who declined to participate in the study said that they lacked the motivation to increase their physical activity. Baseline demographic data and epilepsy characteristics are summarized in Table 1 (continuous variables) and Table 2 (categorical variables). Our cohort was relatively healthy with low body mass index (BMI) and low parent-reported screen-time. Epilepsy severity was also low to moderate, with no children scoring greater than 11 on E-Chess. The majority of participants were in regular classes (N = 17/22), self-reported as regularly active (N = 18/22), had a single type of seizure (N = 19/22), and were on a single or no anticonvulsant (N = 15/22). There was no change in mean step count, mean sleep efficiency, or mean time asleep between baseline and intervention periods (Fig. 1; p = 0.99, d = 0.002; p = 0.42, d = 0.20; and p = 0.51; d = 0.14, respectively, paired t-test). Mean ± SD daily activity and sleep parameters (pooled baseline and intervention) were 11,271 ± 3189

Fig. 1. Baseline and intervention (A) mean daily steps, (B) mean daily sleep efficiency, and (C) mean daily total sleep time. Data are presented as box plots (N = 22), with lower and upper box boundaries = first and third quartiles, respectively, line inside box = median, X = mean, and lower and upper whiskers = minimum and maximum values, respectively. No statistically discernable difference between baseline and intervention (two-tailed paired t-test; p = 0.99, 0.42, 0.51 for A–C, respectively).

J. Do et al. / Epilepsy & Behavior 104 (2020) 106853

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Fig. 2. Correlation of baseline and change (intervention–baseline) in (A) mean daily steps, (B) mean daily sleep efficiency, and (C) mean daily total sleep time. Data are presented as scatterplots (N = 22) with white squares, gray circles, and black triangles indicating a baseline E-Chess total score of 0 to 5, 6 to 10, and 11+, respectively. A dashed line indicates the median change across all participants. A dotted line indicates a linear regression. Pearson's correlation coefficient (lower to upper 95% confidence interval) for all participants is presented in the upper right corner of each panel. Note that panel B excludes outlier points (72.7, 15.1 and 76.6, 12.8) from analysis.

with a change in sleep efficiency (Table 3, p N 0.11). Sex was associated with a change in time asleep (Table 3, p = 0.05; R2adj = 0.38), with boys having a change in time asleep of 24.7 min more than girls. Age, baseline E-Chess total score, and change in physical activity were not associated with a change in time asleep (Table 3, p N 0.17). A model including seasonality revealed no effect. We subsequently excluded seasonality to increase model stability in view of the low sample size of the study. Similar to the entire cohort, a subgroup analysis of children with baseline physical activity of less than 12,000 steps/day (N = 12 children) showed no statistically significant change in steps, sleep efficiency, or time asleep with the intervention. Over the study period, these initially less active children on average increased their daily steps by 735 ± 1900 steps and had a mean sleep efficiency and time asleep decrease of

0.44 ± 1.87% and 9.85 ± 29.6 min, respectively. In comparison, children with initially higher activity (baseline steps: ≥12,000 steps, N = 10) on average decreased their daily steps by 870 ± 1560 steps over the study period and had a mean sleep efficiency and time asleep increase of 2.33 ± 6.54% and 22.2 ± 29.3 min, respectively. Parent-reported estimated daily sleep duration was similar between baseline and end of study (Table 4, mean duration of 9.41 h ± 1.06, p = 0.71). Overall subjective sleep quality, as assessed by the total CSHQ score, marginally improved by the end of the study (p = 0.05, Wilcoxon signed-rank test). The sleep duration subscale score also improved with the intervention (Table 4, p = 0.03). Response rate for this 45-item survey was low, with only N = 11 parents completing the survey at baseline and end of study.

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Table 3 Parameter estimates from linear standard least squares models for delta (intervention– baseline) physical activity (PA) as a function of baseline PA, age, sex, and baseline E-Chess total score (E-Chess); delta sleep efficiency (SE) and delta total sleep time (TST) as a function of age, sex, baseline E-Chess total score, and delta PA. N = 22 for all estimates. Values are bolded where p ≤ 0.05.

Delta PA Intercept Sex (male) Age E-Chess Baseline PA Delta SE Intercept Sex (male) Age E-Chess Delta PA Delta TST Intercept Sex (male) Age E-Chess Delta PA

Estimate (standard error)

p-Value

−4676 (2522) 1149 (636.1) 527.9 (165.8) 148.0 (153.3) −0.25 (0.10)

0.08 0.09 0.005 0.35 0.03

−5.78 (8.68) 3.21 (2.00) 0.10 (0.68) 0.64 (0.52) −0.001 (0.0007)

0.51 0.13 0.89 0.24 0.11

57.4 (51.3) 24.7 (11.8) −5.74 (4.03) 0.22 (3.06) −0.004 (0.004)

0.28 0.05 0.17 0.94 0.30

Adjusted R2 0.50

0.13

Table 4 Subjective sleep measures assessed by the Children's Sleep Habits Questionnaire (CSHQ) at baseline and at the end of the study (N = 21 for sleep duration, N = 11 for scores. Note that numbers for CSHQ total score differ from Table 1 as paired data were required for analysis). Data are presented as mean (standard deviation, SD) and p-value for a twotailed paired t-test (continuous variables) or Wilcoxon signed-rank test (categorical variables). Values are bolded where p ≤ 0.05. Metric

Baseline

End of study

p-Value

Estimated daily sleep duration (hours) CSHQ total score Subscale scores Bedtime resistance Sleep onset delay Sleep duration Sleep anxiety Night wakings Parasomnias Sleep disordered breathing Daytime sleepiness

9.38 (1.15) 44.5 (5.8)

9.47 (0.96) 41.6 (7.2)

0.71 0.05

8.1 (2.3) 1.4 (0.8) 5.0 (2.0) 5.6 (1.6) 4.0 (1.2) 8.4 (1.1) 3.0 (0.0) 11.9 (3.5)

7.5 (2.5) 1.3 (0.6) 3.9 (1.2) 5.2 (1.5) 4.1 (1.2) 8.3 (1.5) 3.0 (0.0) 11.0 (3.2)

0.58 1 0.03 0.41 0.83 0.92 – 0.31

0.38

Measures of psychosocial well-being and quality of life (KIDSCREEN27, PedsQL™ 4.0 Core, PedsQL™ MFS, CDI-S, and MASC-10) did not change between baseline and end of study (Table 5, p N 0.09). Average quality of life, number of depressive symptoms, and number of anxious symptoms, respectively, were normal and not different from the manufacturer-reported population mean (KIDSCREEN-27 pooled t-scores: 52.0 ± 10.5, mean CDI-S t-score: 46.3 ± 9.60, mean MASC-10 t-score: 49.1 ± 11.1). Self-reported fatigue was high, with worse fatigue for the sleep/rest subscale (mean PedsQL™ MFS subscore: 59.9 ± 20.8). 4. Discussion The novel aspect of our study is the longitudinal and objective measurements of both physical activity and sleep quality in CWE. We demonstrated the feasibility of continuously measuring physical activity and objective sleep quality in the home environment using wrist-worn pedometers. Although we did not find an association between physical activity and sleep quality, our cohort of relatively active CWE had good objective measures of sleep, contrasting with the common notion that CWE have impaired sleep and subjective patient or parent reports of poor sleep. It is not surprising that the physical activity of our cohort did not increase with the intervention as the baseline activity level of our cohort (11,271 steps/day) was comparable with healthy children (with an average activity level of 11,691 steps/day [12]) and approached the recommended daily activity level of 12,000 steps/day [28]. Compared with previously published cohorts of CWE, reporting activity ranging from 6865 [15] to 9035 [16] steps/day, our CWE were very active. Since children with lower baseline steps and older children were more likely to increase their activity with the intervention in our study, exercise counseling is likely more effective for lower activity children, who consequently have the most to gain from increasing their activity, and older children, who may be more able to engage in changing their activity. Since overall physical activity was not increased, we are unable to conclude whether an increase in physical activity would impact sleep quality. Sleep efficiency of our cohort (87%) was comparable (≤5% difference defined as clinically acceptable agreement for pedometers [29]) with healthy children using actigraphy (84.0% [30]) and polysomnography (83.4% [31]) and for polysomnography in CWE with good epilepsy control (83% [32]). Time asleep (8 h 41 min) was also comparable (≤ 30 min difference [29]) with healthy children with

actigraphy (8 h 23 min [30]) but was longer than polysomnography in healthy children (7 h [31]) and CWE (6 h 30 min [32]). These comparisons suggest that our cohort had sleep that was comparable with children without epilepsy (i.e., “good sleepers”). Since our CWE were already sleeping well, there may not have been any room for an effect of physical activity on sleep quality. While epilepsy severity of the participants was not associated with physical activity or sleep, our cohort had relatively well-controlled epilepsy. Sleep efficiency and time asleep are known to decrease with poor epilepsy control [32] so a broader sample of children with worse epilepsy may have been able to benefit more from the intervention in comparison with the children with milder epilepsy in our study who were generally “good sleepers”.

Table 5 Psychosocial profile at baseline and at the end of the study (N = 18 unless otherwise indicated, since paired data required for analysis). Data are presented as mean score (standard deviation, SD) and p-value for a two-tailed paired t-test, unless otherwise indicated. Metric

Baseline

End of study

p-Value

106 (25.8)

109 (18.0)

0.55b

56.0 (13.4) 53.2 (9.06) 47.6 (7.45) 51.8 (13.8) 50.9 (15.2)

55.9 (10.5) 52.4 (7.21) 48.5 (6.60) 53.5 (10.6) 50.3 (11.5)

0.97 0.76 0.59 0.65 0.81

77.6 (10.6) 83.6 (13.4) 72.6 (16.0) 80.3 (21.8) 70.3 (18.2)

75.9 (10.8) 78.7 (11.5) 73.2 (17.0) 81.5 (14.1) 68.5 (15.3)

0.50 0.30 0.90 0.71 0.58

PedsQL™ Multidimensional Fatigue Scale Total score 69.3 (16.4) General fatigue 78.9 (16.3) Sleep/rest fatigue 58.0 (18.1) Cognitive fatigue 70.4 (22.9)

67.5 (15.6) 73.8 (15.1) 61.8 (23.6) 66.0 (19.9)

0.60 0.28 0.43 0.31

KIDSCREEN-27 Total scorea t-Score Physical well-beingc Psychological well-beingc Parent relations and autonomyc Social support and peers School PedsQL™ 4.0 cored Total score Physical Emotional Social School

Children's Depression Inventory, Short version (CDI-S)c Total raw scorea 1 (2.0) 1 (1.5) t-Score 48.0 (12.9) 44.6 (6.31)

0.09b 0.12

Multidimensional Anxiety Scale for Children (MASC-10)e Total raw scorea 9.5 (6.5) 11 (5.5) t-Score 48.5 (10.8) 49.6 (11.3)

0.75b 0.69

a b c d e

Median (interquartile range). Wilcoxon signed-rank test. N = 19. N = 17. N = 16.

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Given that objective measures of sleep quality of the children did not change with the intervention, the minor improvement in subjective sleep quality over the course of the study likely represents a placebo effect of the intervention. The parent-reported CSHQ may reflect parental concerns that overestimate actual sleep problems of their children. The intervention, aiming to improve physical activity and consequently sleep, may have reassured concerned parents and resulted in the lower CSHQ score at the end of the study compared with baseline. A parent-reported sleep diary may have provided additional information and should be considered for future studies. Despite good objective measures of sleep, several children still had total CSHQ scores above the cutoff for clinically significant sleep problems, and self-reported fatigue of our cohort was high [33]. Estimated daily sleep duration was comparable with previous reports for CWE (9 h 26 min for our cohort compared with 9 h 5 min [34]) but exceeded objectively measured time asleep by 45 min. While this overestimation of sleep time was b10%, the discrepancies between objective and subjective sleep quality and duration should be considered in future research assessing sleep in children as subjective measures of sleep quality may result in overestimations of sleep problems and time asleep. Our study is limited as a single-center evaluation and a relatively small sample (N = 22) of CWE. Our relatively healthy, happy, active, and “good sleeper” group of CWE may not be representative of a broader group of CWE. Recruiting patients into a study to increase their physical activity may create a sampling bias for already active children as children with lower activity levels may be less inclined to participate. There are potential limitations to the accuracy of wrist-worn pedometers in the determination of sleep duration as they may overor underestimate sleep duration compared with polysomnography [31]. Pedometers have been shown to have similar sensitivity and specificity for sleep measurement as validated actigraphs in adults but require further validation in children [18]. Although there was no clear relationship between physical activity and sleep in our cohort, the effect may be different in CWE with lower baseline activity, more sleep problems, or children with worse epilepsy. Future studies of activity and sleep patterns in CWE would benefit from the lessons learnt in this study. Being more restrictive with inclusion and exclusion criteria and prescreening for inactive kids with poor sleep and suboptimal seizure control may help select CWE who may benefit from exercise counseling. 5. Conclusions Our study is the first to longitudinally and objectively measure physical activity and sleep using pedometers in CWE. We have shown that a group of relatively active and healthy CWE have good objective sleep quality despite reports of fatigue and sleep problems. The precise relationship between daily physical activity and sleep remains to be further characterized. Acknowledgments This study was supported by an internal grant from the Children's Hospital of Eastern Ontario Research Institute. The authors would like to thank all of the children and their families who participated in the study. Many thanks to Drs. Doja, McMillan, Sell, and Venkateswaran for their assistance with recruitment. Declaration of competing interest None. References [1] Magee CA, Robinson L, Keane C. Sleep quality subtypes predict health-related quality of life in children. Sleep Med 2017;35:67–73. https://doi.org/10.1016/j.sleep.2017. 04.007.

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