Mental health problems in adolescents with delayed sleep phase: results from a large population-based study in Norway

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1 J Sleep Res. (2015) 24, Mental health and delayed sleep in adolescents Mental health problems in adolescents with delayed sleep phase: results from a large population-based study in Norway BØRGE SIVERTSEN 1,2,3, ALLISON G. HARVEY 4, ST ALE PALLESEN 5,6 and MARI HYSING 7 1 Division of Mental Health, Norwegian Institute of Public Health, Bergen, Norway; 2 Uni Research Helse, Bergen, Norway, 3 Department of Psychiatry, Helse Fonna HF, Haugesund, Norway, 4 Department of Psychology, University of California, Berkeley, CA, USA, 5 Department of Psychosocial Science, University of Bergen, Bergen, Norway, 6 Norwegian Competence Center for Sleep Disorders, Haukeland University Hospital, Bergen, Norway and 7 The Regional Centre for Child and Youth Mental Health and Child Welfare, Uni Research Helse, Bergen, Norway Keywords circadian rhythm sleep disorder, psychological correlates, epidemiology, adolesence Correspondence Børge Sivertsen, PhD, Division of Mental Health, Norwegian Institute of Public Health, Kalfarveien 31, 5018 Bergen, Norway. Tel.: ; fax: ; borge.sivertsen@fhi.no Accepted in revised form 28 September 2014; received 31 May 2014 DOI: /jsr SUMMARY The aim of the current study was to compare mental health problems, resilience and family characteristics in adolescents with and without delayed sleep phase (DSP) in a population-based sample. Data were taken from the youth@hordaland-survey, a large population-based study in Hordaland County in Norway conducted in In all, 9338 adolescents aged years (53.5% girls) provided self-reported data on a wide range of instruments assessing mental health symptoms, including depression, anxiety, obsessive compulsive behaviours, attention deficit hyperactive disorder (ADHD) symptoms, perfectionism, resilience and sleep. Measures of socioeconomic status were also included. Three hundred and six adolescents (prevalence 3.3%) were classified as having DSP [according to the International Classification of Sleep Disorders-2 (ICSD-2)] criteria. Adolescents with DSP reported higher levels of depression, anxiety and ADHD symptoms. Adolescents with DSP also exhibited significantly lower levels of resilience. The Cohen s d effect sizes ranged from small [obsessive compulsive disorder (OCD): d = 0.15] to moderate (inattention: d = 0.71). In the fully adjusted model, the significant predictors of DSP included inattention [odds ratio (OR): 2.11], lack of personal structure (OR: 2.07), low (OR: 1.85) and high (OR: 1.91) paternal education, parents not living together (OR: 1.81), hyperactivity/inattention (OR: 1.71) and poorer family economy (OR: 1.59). In conclusion, the high symptom load across a range of mental health measures suggests that a broad and thorough clinical approach is warranted when adolescents present with DSP. INTRODUCTION A typical characteristic of sleep in adolescence is a shift in the sleep/wake cycle, entailing later bedtimes and rebound sleep during weekends (Gradisar et al., 2011b). For a vast majority of adolescents this reflects a normal developmental pattern (Taylor et al., 2005). However, for some adolescents the mismatch between the sleep pattern and social obligations, accompanied by reduced daytime functioning, may reach the diagnostic threshold for Delayed Sleep Phase Syndrome (DSPS). To obtain a diagnosis of DSPS according to the newly published 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), there are three criteria which need to be met: (1) the characteristic misalignment of sleep, in conjunction with (2) excessive sleepiness or insomnia and (3) significant daytime impairment in social, occupational or other important areas of functioning (American Psychiatric Association, 2013). Functional impairment is one of the key aspects of delayed sleep phase (DSP), and is also included in the diagnostic criteria of DSPS (American Academy of Sleep Medicine, 2005). However, few studies have investigated the correlates of DSP, with some notable exceptions. DSP has been linked to poor academic performance (Pallesen et al., 2011; Saxvig et al., 2012), and is also associated with higher rates of school absence (Gradisar et al., 2011b; Sivertsen et al., 11

2 12 B. Sivertsen et al. 2013). In terms of mental health, adolescents with DSP have elevated symptoms of anxiety and depression (Crowley et al., 2007; Saxvig et al., 2012), and DSP is prevalent in help-seeking adolescents with depression (Glozier et al., 2014). Moreover, DSP frequently co-occurs with symptoms of attention deficit hyperactive disorder (ADHD) in clinical adult samples (Bijlenga et al., 2013). However, this association has not been examined among adolescents. While DSP has not been studied in this context, there are familial characteristics and resilience factors that may be related to DSP. For example, various meaures of socioeconomic status (SES) are related to sleep problems in both children (Boe et al., 2012) and adults (Sivertsen et al., 2009), and parental involvement, e.g. structured routines and parent-set bedtimes have also been linked to better sleep and improved daytime functioning (Short et al., 2011). In line with this, Pallesen et al. (2011) found that the educational level of mothers was related inversely to behaviourally induced insufficient sleep syndrome in adolescents. However, none of these individual resilience and familial factors have been explored in relation to DSP. Increased knowledge about demographic and clinical factors that are associated with DSP is important, given the known link between DSP and a broad range of adverse consequences (such as increased school absenteeism; Sivertsen et al., 2013). Identification of possible mental health and familial correlates may both help to identify high-risk groups as well as possible targets for interventions for adolescents with DSP. Based on these considerations, the aim of the current study was to compare mental health functioning and family characteristics in adolescents with and without DSP. We expected adolescents with DSP to have more mental health problems and lower levels of resilience than their peers. We also expected lower SES to be associated with DSP. Data for this study were drawn from a large population-based survey conducted in 2012 of 9883 Norwegian adolescents aged years, the youth@hordaland study. MATERIALS AND METHODS Procedure In this population-based study from 2012, we used data from the youth@hordaland-survey of adolescents in the county of Hordaland in Western Norway. The general aim of the youth@hordaland-survey was to assess mental health, lifestyle, school performance and health service use in adolescents. All adolescents born between 1993 and 1995 and all students attending secondary education were, through collaboration with the Hordaland County, invited to participate. The adolescents received information about the study via their official school , and one school class (about 45 min) during regular school hours was allocated for them to complete the internet-based questionnaire. A teacher was present to organize the data collection and to ensure confidentiality. Those not in school received information by postal mail to their home addresses. Survey staff were available by telephone for both the adolescents and school personnel to answer queries related to the research. The adolescents parents were informed about the study while the adolescents themselves consented to participating in the study, as Norwegian regulations state that individuals aged 16 years and older are required to consent themselves. The study was approved by the Regional Committee for Medical and Health Research Ethics in Western Norway. Participants A total of adolescents were invited to participate in the survey, of whom agreed, yielding a participation rate of 53%. Sleep variables were checked for validity of answers based on preliminary data analysis, resulting in data from 374 adolescents being omitted due to obvious invalid responses (e.g. negative sleep duration and sleep efficiency). In all, 508 adolescents did not complete all items needed to define DSP. Thus, the total sample size in the current study was There were no significant age differences between participants being excluded due to invalid/missing response on the sleep items, but there were more boys (61.8%) than girls (38.2%) among the excluded participants. Measures Sociodemographic characteristics Gender and date of birth were identified through the personal identity number in the Norwegian National Population Register. All participants indicated their vocational status, with response options being high school student, vocational training and not in school. SES was assessed both by parental education and perceived family economy. Maternal and paternal education were reported separately, with three response options; primary school, secondary school and college or university. Perceived family economy (i.e. how well off the adolescent perceived their family to be) was assessed by asking the adolescents how their family economy is compared to most others. Response alternatives were (1) better family economy, (2) approximately like most others and (3) poorer family economy. The adolescents also indicated if they lived with both their parents (yes/no). DSP The following questions were used to assess DSP: At what time do you usually go to bed, How much time does it take before you fall asleep (hours and minutes), When do you usually get out of bed in the morning, How many nights per week do you have difficulties falling asleep (0 7), How many nights per week do you have problems with nightly awakenings (0 7) and How often do you oversleep ( never, seldom, sometimes, mostly, always )?. The participants

3 DSP and mental health 13 provided sleep data separately for weekdays and weekends. No information regarding the time-frame of these symptom was available. To establish a proxy for assessing DSP (as closely as possible, given the available sleep items) in line with the International Classification of Sleep Disorders 2 (American Academy of Sleep Medicine, 2005), we employed the following criteria, as specified in Johnson et al. and published in Pediatrics (Johnson et al., 2006): (1) minimum 1-h shift in sleep-onset and wake times from the weekdays to the weekend, (2) complaint of frequent ( 3 days per week) difficulty falling asleep, (3) report of little or no ( 1 day per week) difficulty maintaining sleep and (4) frequent difficulty awakening (oversleep sometimes or more often). This operationalization has been applied previously in a recent publication from the youth@hordaland-survey (Sivertsen et al., 2013). No information was available concerning the adolescents desired bedtime or their inability to fall asleep at the desired time which, according to ICSD-2, would be required to fulfil the criteria for a clinical diagnosis of DSPS. The youth@hordaland-survey was not designed primarily to be a study of DSPS, and the fact that some adolescents were classified as having DSP is not equivalent to a clinical diagnosis. Depression Depression was assessed using the short version of the Mood and Feelings Questionnaire (SMFQ; Thapar and McGuffin, 1998). The SMFQ comprises 13 items assessing depressive symptoms rated on a three-point Likert scale. High internal consistency between the items and a strong unidimensionality have been shown in population-based studies (Sharp et al., 2006) and has also been confirmed based on data from the youth@hordaland-survey (Lundervold et al., 2013). The Chronbach s alpha of the SMFQ in the current study was Anxiety Symptoms of anxiety were assessed using the five-item inventory Screen for Child Anxiety Related Disorders (SCARED), which is the short form of the 41-item version screening inventory for anxiety disorders (Birmaher et al., 1999). The short form of the SCARED has showed similar psychometric properties to the full version. The Chronbach s alpha of the SCARED in the current study was nine items on an inattention subscale. The Chronbach s alpha of the ASRS in the current study was Obsessive compulsive behaviours Obsessive compulsive behaviours were assessed by five questions covering key aspects of obsessive compulsive disorder, as outlined by Thomsen (1998). The Chronbach s alpha in the current study was Resilience Resilience was asssessed by the Resilience Scale for Adolescents (READ; von Soest et al., 2010), which consists of 28 items rated on a five-point Likert scale, comprising: personal competence, social competence, structured style, family cohesion and social resources. The READ has shown adequate psychometric properties (Windle et al., 2011). The Chronbach s alpha of the READ in the current study was Perfectionism Perfectionism was asssessed by the short version of the perfectionism subscale from the Eating Disorder Inventory (EDI; Garner et al., 1985). The scale was adapted to a threepoint scale from the original six-point scale for this study. The Chronbach s alpha of the EDI in the current study was Statistics IBM SPSS statistics version 22 for Mac (SPSS Inc., Chicago, IL, USA) was used for all analyses. Chi-square tests were used to examine differences in demographic characteristics between adolescents with and without DSP. Independentsample t-tests were used to examine mean differences in mental health instruments (total scores converted to standardized T-scores), and between-group effect sizes [pooled standard deviation (SD)] were calculated using Cohen s d formula. Binary logistic regression analyses were used to assess the crude (unadjusted) associations between each individual predictor and DSP. Finally, fully adjusted logistic regression analyses were conducted (adjusting for all other significant variables from the univariate analyses) to test which of the variables were associated independently with DSP. For these analyses, all predictors were dichotomized (at the 90th percentile for sum score). ADHD symptoms Symptoms of inattention and hyperactivity were measured using subscales from the official Norwegian translation of the adult ADHD Self-Report Scale (ASRS; Kessler et al., 2007). The questionnaire was constructed originally for use in adults, but has been used recently in adolescents (Adler et al., 2012). ASRS is an 18-item self-report scale, comprising nine items on a hyperactivity impulsivity subscale and RESULTS Sociodemographic characteristics The mean age of the 9338 adolescent participants was 17.0 years (range 16 19), and the sample included more girls (53.5%) than boys (46.5%). The overall prevalence of DSP was 3.3%, which was significantly higher among girls (3.7%) than boys (2.7%).

4 14 B. Sivertsen et al. Results from the chi-squared tests showed that there were no significant differences between the DSP group and the no DSP group in terms of maternal education. However, there was a significant difference in paternal education, with a higher percentage of the DSP group than the no DSP group having fathers with university/college education (51% versus 43%), as well as with only primary school (16% versus 10%). DSP was also associated with family economy. Specifically, more adolescents with DSP reported poorer family economy than the no DSP group (12% versus 7.0%, respectively). A higher proportion of the adolescents with than without DSP also had parents who were not living together (see Table 1 for details). DSP, mental health and recilience As detailed in Fig. 1 and Table 2, DSP was associated significantly with a range of different measures of mental health problems, with Cohen s d effect sizes ranging from small to moderate. Results from independent-sample t-tests showed that adolescents with DSP had significantly more symptoms of both inattention (d = 0.71), depression (d = 0.53), hyperactivity/impulsivity (d = 0.47) and anxiety (d = 0.29) compared to the no DSP group. No group differences were observed for the OCD and perfectionism measures. Compared to the no DSP group, the DSP group also scored significantly lower on all subscales on the resilience measure, including personal structure (d = 0.51), personal competence (d = 0.47), family cohesion (d = 0.46), social support (d = 0.31) and social comptetence (d = 0.21), all Ps < Table 1 Sociodemographic variables in adolescents with and without delayed sleep phase (DSP) in the youth@hordalandsurvey (n = 9338) No DSP (n = 9032) DSP (n = 306) P-level* Gender Girls, % Boys, % Maternal education, % University/college High school Primary school Paternal education, % University/college <0.001 High school Primary school Family economy Approx. like most others Better economy Poorer economy Parents not living together, % <0.001 *P-level derived from the overall chi-square tests. Binary logistic regression analyses using the 90th percentile as the cutoff for each of the mental health variables showed that most mental health problems increased the odds of being classified as having DSP. As detailed in Table 3, the largest odds ratios (ORs) were found for inattention (crude OR: 2.98) and hyperactivity/impulsivity (crude OR: 2.74), but personal structure, depression, personal competence, family cohesion and anxiety also increased the odds of DSP symptoms being present when analysed dichotomously (see Table 2 for details). No significant associations were found for symptoms of OCD, social support, social competence and perfectionism. Adjusted analyses To identify which predictors were associated independently with DSP, a series of fully adjusted logistic regression analyses was conducted. These analyses showed that inattention and lack of personal structure were associated most strongly with DSP (ORs: 2.1). Also, low and high paternal education, poorer family economy, parents not living together and hyperactivity/impulsivity remained associated significantly with DSP in the fully adjusted model (see Table 3 for details). Neither gender, depression, anxiety, personal competence nor family cohesion were associated significantly with DSP when adjusting for confounders. DISCUSSION The overall objective of this large population-based study was to compare mental health problems, resilience and family characteristics of adolescents with and without DSP in a population-based sample. A significant association was observed between DSP and mental health problems. This was evident across a wide range of instruments assessing psychological functioning. Specifically, DSP was associated significantly with symptoms of depression, anxiety and ADHD symptoms as well as lower levels of resilience. Adolescents with DSP also rated their family economy as worse than the no DSP group. After adjusting for comorbid symptoms, the remaining significant predictors of DSP were ADHD symptoms, lack of personal structure, paternal education, parents not living together and poorer family economy. Although DSP has been linked to a wide range of problems, including school absence (Saxvig et al., 2012; Sivertsen et al., 2013; Thorpy et al., 1988), only a few studies have examined the mental health of adolescents with DSP, and the majority have been small clinical studies. In a notable exception, Saxvig et al. (2012) found adolescents with DSP to have a significantly higher prevalence of both case-level anxiety (52%) and depression (35%) compared to the non- DSP group (29 and 9%, respectively). The current study replicates Saxwig et al. study s anxiety and depression findings and also extends it by showing that DSP is linked to several other domains of mental health functioning, including core symptoms of ADHD. Notably, the current

5 DSP and mental health 15 T-score ADHD Inattention Depression ADHD Hyperactivity Anxiety OCD Perfectionism Social competence Social support Family cohesion Personal competence Personal structure ES = 0.71 ES = 0.53 ES = 0.47 ES = 0.29 ES = 0.15 ES = 0.01 ES = 0.21 ES = 0.31 ES = 0.46 ES = 0.47 ES = 0.51 DSP No DSP Figure 1. Mental health and resilience characteristics in adolescents with and without delayed sleep phase (DSP) in the youth@hordalandsurvey (n = 9338). Bars represent T-scores, error bars represent 95% confidence intervals. ES, Cohen s d between-group effect size. Table 2 Mental health charactersistics in adolescents with and without delayed sleep phase (DSP) in the youth@hordaland-survey (n = 9338)* Variables dichotomized at (90th percentile) No DSP DSP P-level Inattention (ASRS subscale) 8.1% (722) 20.7% (63) <0.001 Hyperacti/imp. (ASRS subscale) 7.4% (656) 17.9% (54) <0.001 Personal structure (READ subscale) 7.8% (679) 16.9% (50) <0.001 Depression (SMFQ total score) 8.9% (794) 17.0% (51) <0.001 Personal competence (READ subscale) 8.7% (750) 16.0% (47) <0.001 Family cohesion (READ subscale) 9.0% (781) 16.2% (48) <0.001 Anxiety (SCARED total score) 7.1% (640) 10.4%(32) OCD (OCD total score) 9.3% (838) 12.4% ( Social support (READ subscale) 7.5% (647) 9.4% (28) Social comptetence (READ subscale) 8.1% (702) 9.5% (28) Perfectionism (EDI total score) 9.3% (879) 10.4% (32) ASRS, Adult ADHD Self-Report Scale; SMFQ, Mood and Feelings Questionnaire, short version; SCARED, Screen for Child Anxiety Related Disorders; OCD, obsessive compulsive disorder; EDI, Eating Disorder Inventory. *P-level derived from Fisher s exact tests. study used a more stringent definition of DSP than the Saxwig study, resembling more the diagnostic criteria in the ICSD-2 (American Academy of Sleep Medicine, 2005), rather than relying upon delayed bedtimes in and difficulties waking up in the morning (Saxvig et al., 2012). As such, any comparison must be made with caution. Using a slightly different approach, Glozier et al. (2014) examined delayed sleep in 802 depressed young adolescents. In contrast to both the current and Saxwig et al. studies, Glozier et al. found that delayed sleepers showed no greater levels of depression symptom severity compared to adolescents without delayed sleep. Along the same lines, delayed sleep was not associated significantly with either mania, anxiety, psychological distress or quality of life in the already depressed adolescents (Glozier et al., 2014). This is in contrast to the findings in the current population-based study, where symptoms of both inattention and hyperactivity/impulsivity were significantly more prevalent in adolescents with DSP. To the best of our knowledge, this has not been examined previously in this age cohort, although in a clinical study of 202 adult ADHD patients the prevalence of DSPS was found to be 26%, compared to only 2% in the control group (Bijlenga et al., 2013). The high prevalence of ADHD symptoms in the current study of adolescents supports these findings. While symptoms of mental health problems were elevated in adolescents with DSP, family characteristics and resilience were also associated independently with DSP. The lower level of personal structure, as assessed by the resilience scale, may suggest that the adolescents are less able to structure and organize their lives in general, and not restricted to the sleep patterns. Thus, while a biological drive towards DSP makes the adolescent vulnerable, personal

6 16 B. Sivertsen et al. Table 3 Results from the adjusted regression analyses of which sociodemographical and mental health characteristics are associated independently with delayed sleep phase (DSP) in the youth@hordaland-survey (n = 9338)* Crude analyses Adjusted analyses Variables dichotomized at (90th percentile) OR 95% CI OR 95% CI Sociodemographic variables Gender (ref: boys) Girls Paternal education (ref: high school) Primary school University/college Family economy (ref: approx. like most others) Better economy Poorer economy Parents not living together Mental health variables (dichotomized) Inattention (ASRS subscale) Hyperacti/imp. (ASRS subscale) Personal structure (READ subscale) Depression (SMFQ total score) Personal competence (READ subscale) Family cohesion (READ subscale) Anxiety (SCARED total score) OR, odds ratio; CI, confidence interval; ASRS, Adult ADHD Self-Report Scale; SMFQ, Mood and Feelings Questionnaire, short version; SCARED, Screen for Child Anxiety Related Disorders. READ, Resilience Scale for Adolescents. *Note that only significant variables from Table 1 and 2 are included. Adjusted for all other variables. structure may exacerbate this susceptibility. In line with this hypothesis, a longitudinal study of adolescent sleep demonstrated that summer bedtime, a proxy of evening preference, was not related to bedtime during the school year, implying that there may be factors other than the biological drive towards later sleep that impacts sleep patterns (Asarnow et al., 2014). In the same study, late bedtime during the school year, not during the summer, was related to subsequent emotional and academic performance, supporting the importance of being able to adjust bedtimes to school obligations. In line with this, Wilhelmsen-Langeland et al. (2014) found that young DPSD patients scored significantly lower (Cohen s d = 1.45) than a control group on the conscientiousness personality trait. Parents may also contribute to the sleep of their adolescents, both through monitoring and parental inolvement. Parent-set bedtimes have been related consistently to earlier bedtimes and longer sleep duration among children and adolescents (Gangwisch et al., 2010; Randler and Bilger, 2009). The importance of family factors was also evident in the present study, as adolescents with DSP reported their families to be lower in terms of family cohesion, have lower SES and were more likely to live with a single parent. As specific parental behaviours regarding sleep were not assessed in the current study, the particular pathways are not evident. However, economic strains and single-parent householding may lead to parents having fewer oppportunities to monitor their adolescents and facilitate their adolescents regarding establishing a good sleep pattern. Study limitations There are some methodological limitations of the present study that should be noted. First, the cross-sectional nature of the study does not allow for causal inferences. Thus, longitudinal studies concerning the relationship between prepubertal mental health and later development of DSP are needed to shed light on the developmental patterns. Secondly, the definition of DSP represents another important limitation of the present study. The designation of DSP is based solely upon self-report, and consequently lacks clinical evaluation and measurement by actigraphy or sleep diary. Importantly, we had no information regarding the desired bedtime or adolescents inability to fall asleep at the desired time which, according to the ICSD-2, is required in order to meet the criteria for a clinical diagnosis of DSPS. Although self-reported sleep parameters, including sleep onset latency (SOL) and wake after sleep onset (WASO), typically differ from those obtained from objective assessments (Lauderdale et al., 2008), recent studies have shown that such self-report sleep assessments can be recommended for the characterization of sleep parameters in both clinical and populationbased research (Zinkhan et al., 2014). Also, the accuracy of self-reported SOL and WASO are generally better among adolescents than in older adults (Dillon et al., 2014), and a recent study of young adolescents in Hong Kong found good agreement between actigraphy-measured and questionnairereported sleep durations (Kong et al., 2011). Furthermore, while we assessed a wide range of mental health domains,

7 DSP and mental health 17 there may be other factors not addressed in the current study that could impact sleep in adolescents. For example, sleep phase misalignment may be a marker of more serious psychiatric disorders, such as a prodromal phase for more severe psychiatric disorders (e.g. bipolar disorder or schizophrenia). Thirdly, the assessment of mental health was based upon self-report instruments, without validated cutoff for Norwegian adolescents. Along the same lines, the many associations between self-report measures may result in over-inflation of these associations. In addition to using continuous sum scores, the 90th percentile was chosen as the cutoff for all mental health instruments. Although scoring above the 90th percentile should not be taken as an indication of a clinical diagnosis, we consider it a useful way of communicating the clinical aspect of the associations. Another limitation is the inclusion of a relatively low number of adolescents not in school. Although the few adolescents not attending school in the present study did not have a higher rate of DSP, a higher participation rate among those adolescents would be needed to draw conclusions regarding this group specifically. Finally, attrition from the study could affect generalizability, with a response rate of 53% and with adolescents in schools over-represented. Based upon previous research from the former waves of the Bergen Child Study, non-participants have been shown to have more psychological problems than participants (Stormark et al., 2008), and it is therefore likely that the prevalence of both DSP and mental health problems may be underestimated in the current study. Clinical implications The high degree of overlap between DSP and mental health problems warrants a thorough diagnostic evaluation and differential diagnosis when adolescents present with DSP. For example, the total score on the SMFQ in adolescents with DSP in the current study (8.8) was comparable to the pretreatment depression score in a randomized controlled trial (RCT), including adolescents with DSPS (total score SMFQ 7.5; Gradisar et al., 2011a). In that study, the treatment effect of combined cognitive behavioural therapy and bright light therapy showed a large reduction of depression symptoms, suggesting that both the sleep problems and the co-occurring depressive symptoms may be targeted through the same interventions (Gradisar et al., 2011a). Finally, the current results demonstrate the importance of familial characteristics in understanding DSP, and that families should be included in interventions. AUTHOR CONTRIBUTIONS BS & MH were involved in acquisition of data. BS & MH were responsible for conception and design of the study, conducted the statistical analysis and drafted the manuscript. SP & AH gave critical revision of the manuscript for important intellectual content. BS & MH had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript. CONFLICT OF INTEREST No conflicts of interest declared. REFERENCES Adler, L. A., Shaw, D. M., Spencer, T. J. et al. Preliminary examination of the reliability and concurrent validity of the attention-deficit/hyperactivity disorder self-report scale v1.1 symptom checklist to rate symptoms of attention-deficit/hyperactivity disorder in adolescents. J. Child. Adolesc. Psychopharmacol., 2012, 22: American Academy of Sleep Medicine. The International Classification of Sleep Disorders: Diagnostic and Coding Manual, ICSD-2. American Academy of Sleep Medicine, Westchester, IL, American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th edn. 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