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Associations between Sleep Characteristics, Seasonal Depressive Symptoms, Lifestyle, and ADHD Symptoms in Adults

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Title: Associations between Sleep Characteristics, Seasonal Depressive Symptoms, Lifestyle, and ADHD Symptoms in Adults
Language: English
Authors: Bijlenga, Denise, van der Heijden, Kristiaan B., Breuk, Minda, van Someren, Eus J. W., Lie, Maria E. H., Boonstra, A. Marije, Swaab, Hanna J. T., Kooij, J. J. Sandra
Source: Journal of Attention Disorders. Apr 2013 17(3):261-275.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Peer Reviewed: Y
Page Count: 15
Publication Date: 2013
Document Type: Journal Articles
Reports - Research
Descriptors: Attention Deficit Hyperactivity Disorder, Sleep, Depression (Psychology), Life Style, Adults, Symptoms (Individual Disorders), Gender Differences, Age Differences, Hyperactivity, Unemployment, Body Composition, Comorbidity, Physical Health, Mental Health, Eating Habits, Regression (Statistics), Incidence, Risk, Foreign Countries, Questionnaires, Multivariate Analysis
Geographic Terms: Netherlands
DOI: 10.1177/1087054711428965
ISSN: 1087-0547
Abstract: Objective: The authors explored associations between ADHD symptoms, seasonal depressive symptoms, lifestyle, and health. Method: Adult ADHD patients ("n" = 202) and controls ("n" = 189) completed the ASESA questionnaire involving lifestyle, eating pattern, and physical and psychological health, and validated measures on ADHD and sleep. ASESA is the Dutch acronym for Inattention, Sleep, Eating pattern, Mood, and General health questionnaire. Results: Indication for delayed sleep phase syndrome (DSPS) was 26% in patients and 2% in controls (p less than 0.001). Patients reported shorter sleep, longer sleep-onset latency, and later midsleep. Shorter ("R"[superscript 2] = 0.21) and later ("R"[superscript 2] = 0.27) sleep were associated with hyperactivity, male gender, younger age, and seasonal depressive symptoms. Seasonal depressive symptoms were related to hyperactivity, female gender, unemployment, and late sleep (pseudo "R"[superscript 2] = 0.28). Higher body mass index (BMI) was associated with shorter sleep in patients (rho = -0.16; "p" = 0.04) and controls (rho = -0.17; "p" = 0.02). Longer sleep showed lower odds for indication of metabolic syndrome (OR = -0.17; "p" = 0.053). Conclusion: DSPS is more prevalent in ADHD and needs further investigation to establish treatment to prevent chronic health issues. (Contains 5 tables.)
Abstractor: As Provided
Number of References: 66
Entry Date: 2014
Accession Number: EJ1012763
Database: ERIC
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  Value: <anid>AN0086217394;gs001apr.13;2013Mar22.13:10;v2.2.460</anid> <title id="AN0086217394-1">Associations Between Sleep Characteristics, Seasonal Depressive Symptoms, Lifestyle, and ADHD Symptoms in Adults </title> <p>JADspjadJ Atten DisordJournal of Attention Disorders1087-05471557-1246SAGE PublicationsSage CA: Los Angeles, CA10.1177/108705471142896510.1177_1087054711428965ArticlesAssociations Between Sleep Characteristics, Seasonal Depressive Symptoms, Lifestyle, and ADHD Symptoms in AdultsBijlengaDenise1van der HeijdenKristiaan B.2BreukMinda1van SomerenEus J. W.3LieMaria E. H.4BoonstraA. Marije1SwaabHanna J. T.2KooijJ. J. Sandra11PsyQ, Expertise Center Adult ADHD, The Hague, Netherlands2Leiden University, Center for the Study of Developmental Disorders, Leiden, Netherlands3Netherlands Institute for Neuroscience, Amsterdam, Netherlands4Academic Hospital Maastricht, MedPsych Center, NetherlandsDenise Bijlenga, PsyQ Program Adult ADHD, Carel Reinierszkade 197, 2593 HR, The Hague, Netherlands. Email: d.bijlenga@psyq.nl42013173261275© 2011 SAGE Publications2011SAGE PublicationsObjective: The authors explored associations between ADHD symptoms, seasonal depressive symptoms, lifestyle, and health. Method: Adult ADHD patients (n = 202) and controls (n = 189) completed the ASESA questionnaire involving lifestyle, eating pattern, and physical and psychological health, and validated measures on ADHD and sleep. ASESA is the Dutch acronym for Inattention, Sleep, Eating pattern, Mood, and General health questionnaire. Results: Indication for delayed sleep phase syndrome (DSPS) was 26% in patients and 2% in controls (p < .001). Patients reported shorter sleep, longer sleep-onset latency, and later midsleep. Shorter (R2 = .21) and later (R2 = .27) sleep were associated with hyperactivity, male gender, younger age, and seasonal depressive symptoms. Seasonal depressive symptoms were related to hyperactivity, female gender, unemployment, and late sleep (pseudo R2 = .28). Higher body mass index (BMI) was associated with shorter sleep in patients (ρ = −.16; p = .04) and controls (ρ = −.17; p = .02). Longer sleep showed lower odds for indication of metabolic syndrome (OR = −0.17; p = .053). Conclusion: DSPS is more prevalent in ADHD and needs further investigation to establish treatment to prevent chronic health issues.ADHDsleepcircadian rhythmseasonal depressive symptomssleep-onset latencysleep-onset insomniadelayed sleep phase syndromeIntroductionThe lifetime neuropsychiatric disorder ADHD is characterized by inattention and/or hyperactive/impulsive symptoms and presents with three clinical subtypes: the inattentive, the hyperactive/impulsive, and the combined subtypes (American Psychiatric Association [APA], 1994). ADHD commences in childhood and often persists into adulthood (Biederman, 1998). International prevalence estimates of adult ADHD range from 1% to 7% (M = 3.4%), with a majority of the combined subtype (Faraone, Sergeant, Gillberg, & Biederman, 2003; Fayyad et al., 2007). Around three quarters of adults with ADHD has at least one psychiatric comorbid disorder (Biederman et al., 1993; Kooij, Aeckerlin, & Buitelaar, 2001).Circadian rhythm disturbances are caused by a misalignment between the endogenous timing system in the brain and the external 24-hr environment (Bjorvatn & Pallesen, 2009). The behavioral domains that are affected in ADHD (hyperactivity, inattention, and impulsivity) seem closely related to disturbances in the circadian rhythm (Lecendreux & Cortese, 2007). Sleep disorders such as sleep-related movement disorders, parasomnias, hypersomnias, and circadian rhythm disorders are prevalent in most ADHD patients (Gau & Chiang, 2009; Gau et al., 2007; Gruber et al., 2009; Middelkoop, Gils, & Kooij, 1997; Owens, 2009; Philipsen et al., 2005; Philipsen, Hornyak, & Riemann, 2006; Stein et al., 2002; Surman et al., 2009; Van der Heijden, Smits, Van Someren, & Gunning, 2005; Walters, Silvestri, Zucconi, Chandrashekariah, & Konofal, 2008). Around 80% of ADHD patients may have a delayed sleep, which is prevalent in children and adolescents as well as in adults with ADHD (Van der Heijden, Smits, & Gunning, 2005; Van Veen, Kooij, Boonstra, Gordijn, & Van Someren, 2010). The delayed sleep phase syndrome (DSPS) is characterized by a delay of the sleep/wake cycle, with high activity level in the late evening and night, sleep-onset insomnia (SOI) when trying to get asleep early, and a preference for late sleep and late rise, which causes impairment in daily functioning. These people are often referred to as “night owls.” In our previous study by Van Veen et al. (2010), we measured salivary melatonin level in patients with ADHD with and without SOI every hour between 09:00 p.m. and 01:00 a.m. We found that ADHD patients with SOI reached the threshold for dim-light melatonin onset (DLMO) at least 01:41 hr later than healthy controls, and 01:15 hr later than ADHD patients without SOI (Van Veen et al., 2010). The delayed sleep in ADHD thus may be characterized by a delayed DLMO, and possibly in some even a completely lacking nocturnal melatonin peak because in many patients no melatonin increase could be observed at 01:00 a.m., the time of the final measurements (Van der Heijden, Smits, Van Someren, et al., 2005; Van Veen et al., 2010). The results are comparable with those found in children with ADHD with and without SOI (Van der Heijden, Smits, Van Someren, et al., 2005). Also in other studies the relationship between ADHD, delayed sleep, and sleep-onset insomnia have been reported (Chiang et al., 2010; Lecendreux & Cortese, 2007).Delayed sleep can lead to sleep debt and daytime sleepiness because of the out-of-phase circadian rhythm of the patient in relation to the social environment that demands early wake-up time for school or work (Shirayama et al., 2003). Also, eating at socially demanded time, which may be out of phase with the patient’s biological clock for appetite and digestive hormone releases, is hypothesized to lead to eating at unpreferred times, skipping breakfast, and binge eating (Copinschi, 2005). On the long term, the eating and sleeping at undesirable times can contribute to the development of, among others, mood disorders, obesity, cardiovascular disease, and immune suppression (Copinschi, 2005; Fonken et al., 2010; Lewy, 2009; Shirayama et al., 2003). Studies show that hyperactivity as well as short sleep are related to obesity (Fuemmeler, Ostbye, Yang, McClernon, & Kollins, 2010; Lauderdale et al., 2009). We also know that a disrupted melatonin release leads to increased risk of cardiovascular disease and cancer (Blask, 2009; Puttonen, Harma, & Hublin, 2010; Viswanathan & Schernhammer, 2009). Overall, multiple studies have demonstrated that circadian sleep disturbances may be a hazard to public health, and therefore, research is needed to further develop prevention and treatment methods.A common comorbid disorder among individuals with ADHD is the seasonal affective disorder (SAD), which is characterized by episodes of major depression that recur during specific times of the year, usually in winter (Lurie, Gawinski, Pierce, & Rousseau, 2006). SAD is prevalent in 19% to 27% of adult ADHD patients (Amons, Kooij, Haffmans, Hoffman, & Hoencamp, 2006; Levitan, Jain, & Katzman, 1999). Patients with SAD have increased prevalence of high caloric intake, hypertension, obesity, diabetes, metabolic syndrome, and low physical exercise (Rintamaki et al., 2008). SAD and ADHD may share a common origin: In a relatively small sample, SAD and ADHD were more prevalent among women with a serotonin 2A-receptor gene (HTR2A) polymorphism (Levitan et al., 2002). Like ADHD, SAD is also linked to DSPS, seasonal bright light deficiency, and subjective sleep deficiency (Lewy, Lefler, Emens, & Bauer, 2006; Oyane, Ursin, Pallesen, Holsten, & Bjorvatn, 2008; Rintamaki et al., 2008). SAD can be effectively treated with bright light therapy in which the biological clock can be “reset” (Gruber, Grizenko, & Joober, 2007; Rastad, Ulfberg, & Lindberg, 2008). Bright light therapy is presumed to act on the biological clock of the brain localized in the hypothalamic suprachiasmatic nucleus (SCN). The SCN receives information about the time of day from the light intensity measured by the retina of the eye and controls, among others, the melatonin secretion in the pineal gland of the brain (Van Veen et al., 2010). In an open study, bright light therapy decreased the ADHD symptoms in patients with SAD and ADHD (Rybak, McNeely, Mackenzie, Jain, & Levitan, 2006). This notion, together with the extreme high prevalence of sleep disorders among patients with ADHD, led us to hypothesize an overlap in the neurobiological origin of ADHD, SAD, and DSPS, suggesting that delayed sleep, seasonal depressive symptoms, and severity of ADHD symptoms are associated, even among people without diagnosed ADHD.To understand associations between the circadian rhythm disorders and sleeping problems that are associated with ADHD and comorbid SAD, we obtained self-reported sleep/wake characteristics of ADHD patients and controls. We explored the association between general characteristics, lifestyle factors, physical and mental health, and sleep characteristics. We were especially interested in associations between sleep-onset latency (the time to fall asleep after lights out), bedtime, time of midsleep, seasonal depressive symptoms, BMI, and ADHD symptoms. We determined these associations among the patients and the control group to gain insight into its generalizability. This may help us to understand the impact of circadian rhythm disorders on health consequences on the long term and eventually to gain insight into appropriate prevention or treatment.MethodParticipantsA total of 202 patients with ADHD between 18 and 65 years of age, diagnosed by a trained psychologist or psychiatrist with childhood-onset and persistent ADHD between 2008 and 2009, were recruited from the PsyQ outpatient clinic, Program Adult ADHD, in The Hague, the Netherlands. The control group consisted of 75 adult participants who were recruited by the researchers in various locations such as public libraries and municipal buildings. The control group also included 114 students attending a 3rd-year bachelor course in Child Studies at Leiden University, The Netherlands, and one of their acquaintances above the age of 30 years to a maximum of 65 years of age. Participants were not informed about the objective of the study and participation in the study was voluntary.ProcedureThe patients were diagnostically assessed for ADHD using the semistructured diagnostic interview for ADHD in adults (DIVA; Kooij, 2010). A diagnosis of ADHD required participants to have experienced at least six out of nine Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; APA, 1994) symptoms of inattention and/or hyperactivity/impulsivity in childhood, a chronic persisting course of symptoms and impairment, and at least four out of nine DSM-IV symptoms of either or both domains in adulthood (Kooij, 2010). These diagnostic criteria were in accordance with the literature and with the proposed cutoff of the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-V; <ulink href="http://www.dsm5.org/;">http://www.dsm5.org/;</ulink> Barkley, 1997; Kooij et al., 2005). In addition, a standard checklist was used to systematically assess psychiatric comorbidity as well as physical diseases. During the initial consultation for diagnostic assessment of ADHD and comorbidity, the patients were invited to fill out the ASESA questionnaire by pen and paper. ASESA is the Dutch acronym for Inattention, Sleep, Eating pattern, Mood, and General health questionnaire.The controls were given oral and written information about the study by the researchers. Controls filled out the informed consent and gave their email addresses. The bachelor students in the control group were recruited by email invitation from their lecturers (K.B.H., H.J.S.B.) and were asked to forward the email to their acquaintance so he or she could enter the online questionnaire as well. The researchers emailed the link to the online questionnaire to all recruited controls.The controls filled out the identical ASESA questionnaire, however implemented for internet assessment using a free open-source web-based survey tool (<ulink href="http://www.limesurvey.org">http://www.limesurvey.org</ulink>).MeasuresThe ASESA questionnaire was in Dutch and involved background characteristics and questions about current lifestyle, such as daily activities, working hours, number of hours of daylight, use of light at night, nicotine, caffeine, alcohol and drugs use, use of medication, eating pattern, and physical and psychological morbidities and complaints. An unstable eating pattern was defined as an eating pattern in which meals are often skipped and/or in case of regular binge meals. The ASESA also contained the following measures: the validated ADHD Rating Scale (ADHD-RS; DuPaul, Power, Anastopoulos, & Reid, 1998; Kooij et al., 2005), a Dutch questionnaire based on the validated Horne and Östberg Morningness–Eveningness Questionnaire (“Vragenlijst Ochtend/Avondmens”; VOA; Horne & Ostberg, 1976; Kerkhof, 1984; Natale, Esposito, Martoni, & Fabbri, 2006), the modestly validated Munich Chronotype Questionnaire (MCTQ; Zavada, Gordijn, Beersma, Daan, & Roenneberg, 2005), and the Seasonal Pattern Assessment Questionnaire–Global Seasonality Score (SPAQ-GSS; Mersch, Middendorp, Bouhuys, Beersma, & van den Hoofdakker, 1999; Mersch et al., 2004).The ADHD-RS is a self-evaluation of the DSM-IV ADHD symptoms hyperactivity and inattention that are scored for adulthood and childhood separately, each involving 23 items. The VOA is a 7-item survey that was used to determine the participants’ chronotype on a 5-point scale varying from extreme early chronotype to extreme late chronotype. The MCTQ involves 32 items on bedtime, rise time, sleep latency, daytime naps, and light exposure. It is scored for both workdays and free days, and is used to determine sleep duration, midsleep, and chronotype. The midsleep on free days, corrected for sleep debt on workdays (MSFsc) is a standardized measure which is predominantly used in chronobiology as a parameter for chronotype classification. The later one’s chronotype the later his midsleep, and vice versa. The MSFsc was calculated from the MCTQ following Roenneberg et al. (2004). From the MCTQ, we also calculated sleep length, defined as the difference between bedtime and wake up time subtracted by sleep-onset latency (Allebrandt et al., 2010). A variable length of sleep onset was defined when participants reported their sleep-onset latency to be different everyday. The SPAQ-GSS is a 6-item survey on seasonal changes in sleep length, social activities, mood, appetite, weight, and energy. Seasonal depressive mood was determined using the SPAQ-GSS score, which was based on the summed scores of the subscales that were each rated 0 (no change during the seasons) to 4 (extreme change during the seasons). A cutoff score of 11 out of 24 is needed for an indication of SAD (Mersch et al., 1999; Mersch et al., 2004). We defined a composed measure of an indication of DSPS, which consisted of a sleep-onset latency of 30 min or more in combination with a bedtime of 00:30 a.m. or later on workdays, or an “extreme evening type” score on the VOA. The completion time of the total ASESA questionnaire was 20 to 30 min.AnalysisWe compared sleep parameters between patients and controls. Because chronotype is age dependent (Roenneberg et al., 2007), we analyzed the sleep parameters within two age groups: ≤30 years and >30 years. Sleep parameters were checked for normality using the normal probability plot and compared between patients and controls using ANOVA for normal distributions and Mann–Whitney for non-normal distributions. To determine explained variances (β) of sleeping problems, we used linear regression with MSFsc as the dependent value, and background characteristics, ADHD characteristics, and lifestyle factors as independent variables. We applied two multinomial logistic regressions with SPAQ-GSS indication for SAD and short versus long sleep-onset latency as dependent values to determine associations between background characteristics, ADHD symptoms, lifestyle factors, and sleep/wake characteristics. We calculated relative risks (RRs) and odds ratios (ORs) of ADHD characteristics, lifestyle factors, and sleep/wake characteristics on SPAQ-GSS score within the patient and control groups. We calculated correlations between BMI and sleep duration and also between BMI and sleep-onset latency for both groups separately. Using binary logistic regressions we calculated the change in OR of various diseases per increased unit of BMI, with correction for age.We used SPSS 18.0 (Chicago, Illinois) for data analysis. An alpha level of ≤.05 was used for statistical significance.ResultsGeneral CharacteristicsWe included 391 adult participants, of which 202 were patients and 189 were controls. General characteristics are listed in Table 1. The patients (47% females) were between 18 and 64 years old (M = 34.9 years; SD = 10.6). The controls (65% female) were between 17 and 65 years old (M = 33.0 years; SD = 13.6). Clinical diagnoses according to the DSM-IV criteria of the patients were 83.2% ADHD–combined subtype, 16.3% ADHD–inattentive subtype, and 0.5% ADHD–hyperactive/impulsive subtype. As stated in Table 1, 76% of the patients had at least one comorbid disorder. The most frequent diagnosed comorbid disorders were depression (33%), anxiety disorder (21%), and sleep disorder (21%). In the group of controls, 4.3% met the criteria for ADHD based on their self-reported ADHD-RS scores. The ADHD patients reported more mental and physical problems than the controls (Table 1).Table 1.General Characteristics of the Group of Adults With ADHD (Patients) and Healthy Adults (Controls) With Tests for Statistical Differences Between Groups Using Chi-Square for Binary Data, Mann–Whitney for Ordinal Data, and t test for Interval and Ratio Data (N = 391).CharacteristicsPatients (n = 202)Controls (n = 189)pFemale, n (%)95 (47.0)123 (65.1)<.001Age in years; M (SD)34.9 (10.6)33.0 (13.6).121Diagnosed DSM-IV ADHD subtype Hyperactive/impulsive, n (%)1 (0.5)N/A Inattentive, n (%)33 (16.3)N/A Combined, n (%)168 (83.2)N/ADiagnosed DSM-IV current psychiatric comorbidity at time of assessment Depressive disorder, n (%)66 (32.8)N/A Seasonal affective disorder (SAD), n (%)16 (8.0)N/A Anxiety/panic disorder, n (%)42 (20.9)N/A Posttraumatic stress disorder (PTSD), n (%)12 (6.0)N/A Personality disorder, n (%)23 (11.4)N/A Sleep disorder, n (%)43 (21.4)N/A Eating disorder, n (%)10 (5.0)N/A Alcohol or drug use disorder,an (%)46 (22.9)N/ASelf-reported current psychiatric comorbid symptoms at time of assessment (Seasonal) depressive symptoms, n (%)37 (18.3)12 (6.3)<.001 Anxiety/panic symptoms, n (%)8 (4.0)3 (1.6).156 Stress/burnout/chronic fatigue, n (%)11 (5.4)1 (0.5)<.001Self-reported lifetime morbidity Respiratory disorder, n (%)63 (31.2)30 (15.9)<.001 Cardiovascular disorder, n (%)86 (42.6)34 (18.0)<.001 Nervous system disorder, n (%)88 (43.6)37 (37.6).228 Digestive system disorder, n (%)66 (32.7)35 (18.5).001 Urinary system disorder, n (%)26 (12.9)17 (9.0).221 Metabolic disorder, n (%)25 (12.4)12 (6.3).042 Immune system disorder, n (%)14 (6.9)5 (2.6).049 Skin disorder, n (%)90 (44.6)68 (36.0).084 Skeletal disorder, n (%)101 (50.0)68 (36.0).005 Cancer, n (%)5 (2.5)2 (1.1).291 Injury after accident, n (%)48 (23.8)36 (19.0).257BMI (n = 386); M (SD) range24.8 (4.9) 17-4523.2 (3.0) 17-33<.001 BMI < 18.5 (underweight), n (%)8 (4.1)6 (3.2).642 BMI ≥ 25 (overweight), n (%)93 (47.2)65 (34.4).010 BMI ≥ 30 (obese), n (%)33 (16.8)7 (3.7)<.001 BMI ≥ 40 (morbidly obese), n (%)2 (1.0)0 (0.0).165Vocational status (n = 388)<.001 Elementary school, n (%)24 (12.0)1 (0.5) Lower vocational training/lower secondary education, n (%)60 (30.0)16 (8.5) Higher secondary school/intermediate vocational training, n (%)56 (28.0)36 (19.1) Preuniversity education, n (%)13 (6.5)55 (29.3) Higher professional school, n (%)31 (15.5)58 (30.9) University, n (%)16 (8.0)22 (11.7)Main occupation (n = 387) Employed, n (%)104 (52.3)77 (41.0).033 Unemployed, n (%)54 (27.1)11 (5.9)<.001 Student, n (%)39 (19.6)97 (51.6)<.001 Retired, n (%)2 (1.0)3 (1.6).599Smoking behavior (n = 385) Smokes currently, n (%)102 (52.0)34 (18.0)<.001 Quit smoking, n (%)30 (15.3)31 (16.4).768Caffeinated drinks per day (n = 390); M (SD)1.3 (0.4)1.3 (0.5).291Alcoholic beverages >14 per week (n = 268), n (%)23 (16.5)9 (7.0).016Use of medication at time of assessment ADHD medication, n (%)36 (17.8)2 (1.1)<.001 Antidepressants, n (%)31 (15.3)7 (3.7)<.001 Melatonin, n (%)7 (3.5)0 (0).010 Other sleep medication, n (%)13 (6.4)1 (0.5).002Note: BMI = body mass index.Bold p-values are significant with α ≤ .05.Validated MeasuresIn Table 2, the ADHD-RS scores, SPAQ-GSS score, VOA chronotype, and sleep/wake characteristics are compared between the patients and controls and between the (≤30 and >30) age groups. The patients were more often evening chronotypes (both age groups p < .001) and more extreme evening chronotypes (≤30 years, p < .001; >30 years, p = .003). The age groups did not differ in respect to SPAQ-GSS indication for SAD.Table 2.Standardized Measures From Questionnaires (ADHD-RS Scores, SPAQ-GSS Indication of SAD, VOA Chronotype) and Sleep/Wake Characteristics of the Patients and the Controls With Tests for Statistical Differences Between Groups Using Chi-Square for Binary Data and t test for Interval and Ratio Data (N = 391).Age ≤ 30 years (n = 189)Age > 30 years (n = 202)Standardized measuresPatients (n = 83)Controls (n = 106)pPatients (n = 119)Controls (n = 83)pADHD-RS adulthood; M (SD)35.0 (10.3)14.1 (6.8)<.00132.4 (10.0)11.2 (7.2)<.001ADHD-RS childhood; M (SD)35.0 (12.8)11.7 (8.5)<.00132.9 (12.7)12.3 (10.2)<.001ADHD-RS score ≥23 (N = 391), n (%)74 (89.2)10 (9.4)<.001100 (84.0)5 (6.0)<.001SPAQ-GSS score for indication of SAD (n = 386), n (%)19 (22.9)31 (29.2).32630 (25.2)13 (15.7).103VOA: Evening chronotype (n = 389), n (%)37 (44.6)24 (22.6).00134 (29.1)7 (8.4)<.001VOA: Extreme evening chronotype (n = 389), n (%)16 (19.3)2 (1.9)<.00115 (12.8)1 (1.2).003Sleep/wake characteristics Bedtime at workdays (n = 320); M (SD)11:45 p.m. (1:19)11:10 p.m. (1:01).00211:33 p.m. (1:11)11:00 p.m. (0:47).001 Bedtime at free days (n = 367); M (SD)1:02 a.m. (1:28)0:13 a.m. (1:17)<.0010:20 a.m. (1:37)11:41 p.m. (1:05).002 Sleep duration workdays (n = 302); M (SD)7:25 hr (1:35)7:55 hr (1:11).0297:01 hr (1:22)7:42 hr (0:49)<.001 Sleep duration free days (n = 343); M (SD)7:44 hr (1:56)8:48 hr (1:17)<.0017:08 hr (1:45)7:55 hr (1:11).001 Sleep ≤ 7 hr on workdays (n = 315); n (%)22 (37.9)22 (21.8).02839 (47.0)19 (26.0).007 Sleep ≤ 7hr on free days (n = 361); n (%)27 (37.0)11 (10.5)<.00142 (41.6)21 (25.6).024 Sleep latency at workdays (n = 323); M (SD)0:39 hr (0:49)0:22 hr (0:19).0020:34 hr (0:37)0:12 hr (0:14)<.001 Sleep-onset latency at free days (n = 356); M (SD)0:35 hr (0:49)0:18 hr (0:16).0010:32 hr (0:34)0:14 hr (0:11)<.001 Sleep-onset latency ≥ 30 min at workdays (n = 348); n (%)33 (60.0)40 (37.9).00739 (48.1)7 (8.6)<.001 Sleep latency ≥ 30 min at free days (n = 356); n (%)33 (45.2)29 (27.6).01544 (45.8)13 (15.9)<.001 Variable length of sleep onset; n (%)22 (26.5)8 (7.5)<.00128 (23.5)5 (6.0).001 Wakes up > 2 times a night (n = 389); n (%)19 (22.9)8 (7.5).00331 (26.5)10 (12.0).013 Time of awake period at night ≥ 30 min (n = 251); n (%)8 (14.8)3 (6.4).17515 (16.9)3 (4.9).027 Midsleep on free days corrected for sleep debt (MSFsc; n = 355); M (SD)5:07 a.m. (1:26)4:30 a.m. (1:08).0014:10 a.m. (1:20)3:41 a.m. (0:55).005 Midsleep on free days after 5:00 a.m. (n = 355); n (%)38 (52.8)31 (29.8).00227 (27.8)4 (4.9)<.001 Indication of DSPS; n (%)a21 (32.8)3 (2.8)<.00118 (20.7)1 (1.2)<.001Note: ADHD-RS = ADHD Rating Scale; SPAQ-GSS = Seasonal Pattern Assessment Questionnaire–Global Seasonality Score; SAD = seasonal affective disorder; VOA = Vragenlijst Ochtend/Avondmens; DSPS = delayed sleep phase syndrome.aComposed measure of a sleep-onset latency ≥30 min with a bedtime of ≥00:30 a.m. on workdays, or an “extreme evening type” score on the VOA.Bold p-values are significant with α ≤ .05.Sleep/Wake CharacteristicsThere were differences between patients and controls on all sleep parameters, with less desirable values in the patient group (see Table 2). We also found that 25.8% of the patients and 2.1% of the controls (p < .001) met the criteria for the indication of DSPS. Within the patient group, the participants with the indication of DSPS, compared with the patients without indication of DSPS, more often skipped meals (89.7% vs. 64.9%; p = .003). Within the control group, the participants with indication of DSPS more often had a high score (≥23) on the ADHD-RS (50.0% vs. 7.1%; p = .002), more often three or more hyperactivity/impulsivity symptoms on the ADHD-RS (25.0% vs. 3.8%; p = .038). However, we did not find significant correlations between indication of DSPS and obesity, physical complaints, smoking, or indication for SAD in either group. Concerning light at night, we found a correlation between the duration of watching TV after 11.30 p.m. and the sleep-onset latency (ρ = 0.225; p = .002).Linear RegressionsTable 3 shows the multivariate linear regression for the impact of background variables, ADHD-RS score, seasonal depressive symptoms, use of medication, and lifestyle factors on midsleep and sleep length. Factors that had significant impact on midsleep were an ADHD diagnosis (β = 20.53; p = .048), having more than three hyperactive/impulsive symptoms on the ADHD-RS in adulthood (β = 0.94; p = .029), female gender (β = −37.61; p < .001), age (per year, β = −2.18; p < .001), and an indication for SAD (β = 21.73; p = .023). Unemployment and an unstable eating pattern had negative impact on midsleep but did not reach statistical significance. Factors that had significant impact on sleep length were ADHD diagnosis (β = 41.79; p = .002), age (per year, β = −2.04; p < .001), and unstable eating pattern (β = −22.66; p = .043). Gender had impact on sleep length but did not reach statistical significance.Table 3.Multivariate Linear Regression Model for the Impact of Background Characteristics, Measurement Scores (ADHD-RS and SPAQ-GSS), Medication, and Lifestyle Factors on Midsleep on Free Days Corrected for Sleep Debt (MSFsc) Expressed as Number of Minutes After Midnight, and Sleep Length on Free Days Expressed in Minutes.MSFsc (minutes after 0:00 a.m.) n = 351; R2 = .265Sleep length (min) n = 340; R2 = .210Factors (baseline/tested level)βSEpβSEp(Constant)381.7024.65<.001479.3031.85<.001Background characteristics: Group/ADHD diagnosis (controls/patients)20.5310.32.048−41.7913.39.002 Gender (male/female)−37.618.69<.00120.9011.10.061 Age (interval scale)−2.180.41<.001−2.040.53<.001 BMI ≥ 30 (no/yes)4.3613.58.749−10.3417.78.561 Vocational status (lower/higher)0.410.73.5731.190.92.198 Paid employment (no/yes)−10.296.61.1203.718.38.658 Self-employed (no/yes)10.346.57.116−3.518.33.674 Studies (no/yes)−1.8310.76.865−2.7313.82.844 Unemployed (no/yes)−22.3512.53.075−23.1116.35.158 Retired (no/yes)24.2832.58.45724.0941.17.559Measurement scores: Adulthood H/I symptoms > 3 (no/yes)a0.940.43.0290.090.67.891 Adulthood I/A symptoms > 3 (no/yes)b0.370.38.332−0.450.48.343 SPAQ-GSS score for indication of SAD (no/yes)21.739.49.023−0.7212.11.953Use of medication: ADHD medication (no/yes)−22.7414.46.11725.0018.56.180 Antidepressants (no/yes)18.0414.60.21825.0119.53.201 Melatonin (no/yes)2.3131.54.94221.5640.04.591 Sleep medication (no/yes)−25.5422.10.249−17.1128.18.544Lifestyle factors: Currently smokes (no/yes)0.350.38.3640.340.48.478 Caffeine ≥ 5 units per day (no/yes)−2.149.32.8186.6012.02.583 Alcohol ≥ 16 units per week (no/yes)−0.270.30.9300.320.38.404 Unstable eating pattern (no/yes)15.378.60.075−22.6611.15.043 Physical exercise ≤ 3 hr per week (no/yes)0.030.35.943−0.600.44.168 High blood pressure (no/yes)0.160.10.121−0.830.13.534 Physical complaints (no/yes)−0.430.56.4391.010.70.153 Daylight per day ≤ 1 hr (no/yes)0.290.19.128−0.140.26.587Note: ADHD-RS = ADHD Rating Scale; SPAQ-GSS = Seasonal Pattern Assessment Questionnaire–Global Seasonality Score; BMI = body mass index; SAD = seasonal affective disorder.aHyperactivity/Impulsivity symptoms measured by ADHD-RS for adulthood.bInattention symptoms measured by ADHD-RS for adulthood.Bold p-values are significant with α ≤ .05.Logistic RegressionsTable 4 shows two multinomial logistic regressions for indication for SAD and sleep-onset latency. Although many variables were entered into the models, there were clear effects on seasonal depressive symptoms of having more than three hyperactive/impulsive symptoms on the ADHD-RS in adulthood (β = 1.35; p = .018), female gender (β = 1.77; p < .001), unemployment (β = 1.70; p = .016), and having a midsleep after 05:00 a.m. (β = 1.02; p = .004). There were significant effects on sleep-onset latency more than 30 min of having more than three hyperactive/impulsive symptoms on the ADHD-RS for adulthood (β = 1.49; p = .002), studying (β = 1.59; p < .001), retirement (β = 2.48; p = .035), short (≤7 hr) sleep on free days (β = 1.36; p < .001), and variable length of sleep onset (β = 1.60; p = .002); see Table 4. Short daylight exposure did not affect seasonal depressive symptoms or sleep-onset latency. Also, when tested linearly, the length of daylight exposure was not correlated with SPAQ-GSS indication for SAD (r = −.024; p = .648; data not shown).Table 4.Multinomial Logistic Regressions of Impact of Background Characteristics, Measurement Scores, Medication, Lifestyle, and Sleep/Wake Characteristics on SPAQ-GSS Indication of SAD and a Sleep-Onset Latency ≥ 30 min As Dependent Values. SPAQ-GSS Indication of SAD Nagelkerke Pseudo R2 = .278; SOI Nagelkerke Pseudo R2 = .404; N = 303.SPAQ-GSS indication of SADSleep-onset latencyFactors (baseline/tested level)βSEpβSEpIntercept0.562.34.8125.422.31.019Background characteristics: Group/ADHD diagnosis (controls/patients)−0.900.58.1220.850.57.132 Gender (male/female)1.770.41<.0010.050.33.872 Age > 30 years (no/yes)−0.230.45.609−0.480.41.241 BMI ≥ 30 (no/yes)−0.780.68.2490.340.58.554 Vocational status (lower/higher)0.770.51.1340.030.46.941 Paid employment (no/yes)0.030.42.9370.110.39.782 Self-employed (no/yes)1.000.62.110−0.750.59.202 Studies (no/yes)0.540.47.2481.590.43<.001 Unemployed (no/yes)1.700.70.0160.370.64.567 Retired (no/yes)1.051.35.4392.481.17.035Measurement scores: Adulthood H/I symptoms > 3 (no/yes)a1.350.57.0181.490.49.002 Adulthood I/A symptoms > 3 (no/yes)b−0.19.55.724−0.550.50.270 SPAQ-GSS score for indication of SAD (no/yes)———−0.200.36.574Use of medication: ADHD medication (no/yes)0.600.62.3390.020.55.973 Antidepressants (no/yes)0.610.65.3430.790.63.209 Melatonin (no/yes)1.611.33.227−0.481.21.694 Sleep medication (no/yes)−0.281.09.7991.731.11.120Lifestyle factors: Currently smokes (no/yes)0.470.42.269−0.300.39.445 Caffeine ≥ 5 units per day (no/yes)0.400.41.331−0.350.38.364 Alcohol ≥ 16 units per week (no/yes)0.800.75.2810.610.59.303 Unstable eating pattern (no/yes)0.110.37.7690.210.34.544 Physical exercise ≤ 3 hr per week (no/yes)−0.320.35.349−0.290.31.350 Physical complaints (no/yes)0.170.42.694−0.080.38.827 Daylight per day ≤ 1 hr (no/yes)−0.790.48.101−0.020.40.958Sleep/wake characteristics: Awake ≥ 3 times per night (no/yes)0.100.45.8330.690.43.106 Sleep ≤ 7 hr on free days (no/yes)−0.380.42.3591.360.37<.001 Variable length of sleep onset (no/yes)−0.190.50.7101.600.52.002 Sleep-onset latency ≥ 30 min (no/yes)−0.170.37.637——— MSFsc after 5:00 a.m. (no/yes)1.020.35.0040.510.32.112Note: SPAQ-GSS = Seasonal Pattern Assessment Questionnaire–Global Seasonality Score; SAD = seasonal affective disorder; SOI = sleep-onset insomnia; ADHD-RS = ADHD Rating Scale.aHyperactivity/Impulsivity symptoms measured by ADHD-RS for adulthood.bInattention symptoms measured by ADHD-RS for adulthood.Bold p-values are significant with α ≤ .05.We used binary logistic regressions with numbers of hours asleep on free days, corrected for age, as independent variables for an indication of metabolic syndrome within the total group. While the study did not include physical measures, we defined the indication of metabolic syndrome as at least one of the following: diabetes, elevated blood pressure, elevated cholesterol, and/or BMI ≥ 30. We found near-significant reduction of the OR for the indication of metabolic syndrome per hour more asleep, exp(B)= −0.173; p = .053.Risk and Odds for SADRR and OR within the patient and control groups of background characteristics, ADHD characteristics, lifestyle factors, and sleep/wake characteristics on SPAQ-GSS indication for SAD are shown in Table 5. From this table, it is evident that even in the control group, female gender (RR = 1.34; OR = 2.12; p = .019), high current ADHD-RS score (RR = 1.68; OR = 3.04; p = .042), and MSFsc after 5:00 a.m. (RR = 1.58; OR = 2.20; p = .009) result in higher risk for indication of SAD. Within the patient group, females had significantly higher risk of an indication of SAD as compared with men (RR = 1.42; OR = 2.19; p = .007). Interestingly, obesity gave higher risk of indication of SAD within the group of patients (RR = 1.02; OR = 1.04; p = .016) but had a protective effect for indication of SAD within the group of controls (RR = 0.98; OR = 0.97; p = .012), however small effects in both groups.Table 5.Relative Risks (RRs) and Odds Ratios (ORs) of Background Characteristics, ADHD Characteristics, Lifestyle Factors, and Sleep/Wake Characteristics on the SPAQ-GSS Indication of SAD Within the Patient (n = 202) and Control Groups (n = 189).Patients (n = 202)Controls (n = 189)RRORχ2pRRORχ2pBackground characteristics: Sex: Female vs. male1.422.19.0071.342.12.019 BMI: ≥25 (obese) vs. 18-25 (normal)1.021.04.0160.980.97.012ADHD characteristics: ADHD-RS score: >23 vs. ≤231.253.52.0841.683.04.042 H/I symptoms: >3 vs. ≤31.212.15.0641.533.27.220 H/I symptoms: >4 vs. ≤41.030.85.781N/AaN/Aa H/I symptoms: >5 vs. ≤50.990.96.922N/AaN/Aa I/A symptoms: >3 vs. ≤31.172.07.1651.553.42.079 I/A symptoms: >4 vs. ≤41.121.63.2591.553.42.079 I/A symptoms: >5 vs. ≤51.131.63.211N/AaN/AaLifestyle factors: Eating pattern: unstable vs. stable1.091.42.4031.341.77.067 Daily daylight: ≤ 1 hr vs. >1 hr0.990.95.9510.970.95.882 Physical exercise: ≤3 hr per week vs. > 3 hr per week1.071.30.4941.121.21.551 Currently smokes: yes vs. no1.011.02.9580.900.84.642 Caffeine: ≥5 units per day vs. <5 units per day1.081.34.4510.860.77.488 Alcohol: ≥16 units per week vs. <16 units per week1.011.03.9590.260.16.056Sleep/wake characteristics: Sleep: ≤7 hr vs. >7 hr on free days1.151.64.2091.281.54.168 Sleep: ≤6 hr vs. > 6 hr on free days1.081.31.5740.880.50.520 Sleep rhythm: unstable vs. stable0.980.91.8311.532.37.135 MSFsc: ≥ 5:00 a.m. vs. < 5:00 a.m.1.051.20.6641.582.20.009 Sleep-onset latency: ≥30 min vs. <30 min1.151.73.1731.281.54.168Note: SPAQ-GSS = Seasonal Pattern Assessment Questionnaire–Global Seasonality Score; SAD = seasonal affective disorder; ADHD-RS = ADHD Rating Scale.aMore than two cells of the 2 × 2 table have observed value lower than 5.Bold p-values are significant with α ≤ .05.BMI and Sleep CharacteristicsThere were significant correlations between a higher BMI and shorter sleep duration on workdays (both variables continuous) in the patient (nonparametric correlation; ρ = −0.163; p = .042) and the control (ρ = −0.165; p = .024) groups. We did not find a correlation between BMI and sleep-onset latency on workdays for the patient group (ρ = −0.054; p = .531) but there was a relationship between higher BMI and shorter sleep-onset latency in the control group (ρ = −0.236; p < .001). Further analysis indicated that this effect was due to the group of older (>30 years) males within the control group (ρ = −0.357; p = .028); the other subgroups did not show significant relationship between BMI and sleep-onset latency. As expected, binge eating was associated with higher BMI in the younger (≤30 years) participants of the patient group (ρ = .483; p < .001), however nonsignificant in the other subgroups.DiscussionWe assessed the relationships between ADHD symptoms, sleeping characteristics, seasonal depressive symptoms, lifestyle factors, and physical and psychological health in a group of adult patients diagnosed with ADHD and a group of controls. In general, we found that hyperactivity had strong associations with a delayed sleep phase, physical morbidities, an unstable eating pattern, and seasonal affective symptoms. There was a relationship between watching TV late at night, hyperactivity, having variable sleep times, and problems falling asleep. Also, there was an indication of an association between shorter sleep and higher risk for an indication of metabolic syndrome.There were more sleeping problems and seasonal affective symptoms in the ADHD group than in the control group. The prevalence of our indication of DSPS within our ADHD group was much higher (26%) than in the control group (2%). The latter prevalence is comparable with findings of others (Hazama, Inoue, Kojima, Ueta, & Nakagome, 2008). We also found clear relationship between hyperactivity, female gender, unemployment, a late midsleep, and seasonal depressive symptoms. These associations between ADHD, SAD, and DSPS are also in line with other findings (Lecendreux & Cortese, 2007; Levitan et al., 1999). Even within the control group, there was evidence for associations between hyperactivity, late midsleep, and seasonal depressive symptoms. Females in general have earlier midsleep than men, but both genders have higher risk of SAD when they are hyperactive and have a late chronotype. This supports the hypothesis of a relationship between these factors, not only within our patient group but also in the general population.We think that our results can be generalized to the ADHD population because our patient group had similar prevalences of the ADHD subtypes as in other studies: 16% inattentive, 1% hyperactive/impulsive, 83% combined type (Kooij, 2010). Moreover, the prevalences of indications of SAD assessed by the SPAQ-GSS in our total group (23.8%) and in the group of patients with ADHD (24.9%) were comparable with findings of other studies (Amons et al., 2006; Levitan et al., 1999; Steinhausen, Gundelfinger, & Winkler Metzke, 2009). While this rate is far higher than the prevalence of SAD in the general population (0%-9.7%), we presume that this distinction may be caused by the high proportion of young females in our control group. Young females have higher prevalence of SAD than males in the general Dutch population (Mersch et al., 1999); in our control group females had 34% higher prevalence of an indication of SAD than men.Sleep characteristics were compared for two age groups separately to adjust for natural shifts of the circadian rhythms that come with age; young adults often have late chronotypes, but around the age of 30 their biological clock shifts forward to a more early chronotype (Roenneberg et al., 2004). We found that prevalences of DSPS were equal in the younger and the older age groups of the ADHD patients but that the prevalence of DSPS was lower in the older age group of the controls. This suggests that DSPS in ADHD is not age-related, whereas DSPS is age-specific to (late) adolescents in the general population and declines with age in healthy adults. We conclude that DSPS in ADHD is a chronic condition, which is in accordance to the genetic background of DSPS (Takahashi, Hohjoh, & Matsuura, 2000) and to the literature about the association of ADHD and certain clock genes (Kissling et al., 2008).We used the time of midsleep as a measure for sleep phase, which is very late in case of DSPS. The time of midsleep was later in younger males and in participants who reported high hyperactivity and seasonal depressive symptoms. Also, unemployment and having an unstable eating pattern were mildly associated with later midsleep. The factors that were associated with later midsleep were also associated with shorter sleep. However, other factors such as smoking, obesity, use of medication, caffeine, and alcohol did not additionally affect midsleep or sleep length. Moreover a longer sleep-onset latency was associated with hyperactivity and shorter sleep. We suggest that ADHD and its hyperactivity symptoms may be related to a late melatonin production, which results in a late sleep onset and a late midsleep, and possibly results in shorter sleep duration. The internal clock of these people is presumed to be out of phase with the external clock (i.e., “social time”), which may result in chronic sleep deprivation and an eating pattern that is guided by tiredness and loss of energy and must be compensated for by high caloric food intake, an association also found in numerous other studies (Copinschi, 2005; Knutson & Lauderdale, 2009; Lauderdale et al., 2009; Leproult & Van Cauter, 2010; Mota et al., 2008; Shea, Hilton, Orlova, Ayers, & Mantzoros, 2005; Spiegel, Knutson, Leproult, Tasali, & Van Cauter, 2005). This hypothesis is supported by our finding that with every sleeping hour, the OR for metabolic syndrome decreased by as much as 0.17. However, this needs further investigation in a prospective study.We should state some limitations of our study. First of all, although the found associations in this study do support our hypothesis, causality is not clear from our approach. For example, participants watching TV late at night had longer sleep-onset latency. This finding can be explained by the fact that light at night leads to the decomposition of melatonin, which in turn leads to wakefulness at night. However, an alternative explanation might be that people with difficulties falling asleep tend to watch more TV at night. To gain insight into the causality of these associations, we would need to conduct a cohort study, including ADHD patients and healthy adults of various ages who are followed over time. Second, our control group consisted of an uneven distribution of genders and educational level in the younger ages, in which we mainly included young higher educated females. This was the result of our sampling method in the control group. Although this uneven distribution did cause some interpretation difficulties, we did find many differences between the groups that are not age or vocation specific. Third, some of the measures used in the ASESA questionnaire have not been extensively validated, such as the MCTQ, or have mixed reports on its validity, such as the SPAQ (Steinhausen et al., 2009). Fourth, we have currently no information about the consensus between self-reports and objective measures in patients with ADHD. Also, the undergraduate students’ sleep cycles may not be representative for the general population within that age range. Finally, some self-report measures were not valuable for analysis. For example, we did not find any association between amount of daylight exposure and seasonal depressive symptoms, which is incongruent to many other findings (e.g., Rybak et al., 2006). We therefore presume that a self-reported daylight exposure length is not a good measure of actual daylight exposure and may be subject to overestimation.Even in the light of the study’s limitations, the relationship between hyperactivity, late midsleep, shorter sleep, longer sleep-onset latency, unstable eating pattern, gender, BMI, and seasonal depressive symptoms is solid and consistent throughout the analyses, in the patient as well as in the control groups. Although associations between some of the entities have previously been investigated, the contribution of our study is the interrelatedness or circadian rhythm disturbances, weight, seasonal mood changes, and hyperactivity. There may be a common origin of the sleeping problems and the ADHD symptoms, which may lead to seasonal depressive symptoms in the long run. To explore this hypothesis, further investigation in a prospective cohort study is needed. In such a cohort study, sleeping and eating characteristics, genetics, hormonal, and immune responses of treated and untreated patients with ADHD and controls need to be observed and followed over several years. Such a cohort study will give insight into the interrelatedness of DSPS, ADHD, and the associated psychiatric and physical disease at the long term such as SAD, obesity, cardiovascular disease, diabetes, immune suppression, and cancer (Copinschi, 2005; Fonken et al., 2010; Knutson, 2010; Knutson & Van Cauter, 2008; Lewy, 2009; Shirayama et al., 2003).The population of patients with ADHD suffering from short sleep and resulting metabolic misalignment may serve as a model to explore the relationships between these factors for the Western population in general. In our society, sleep loss is common, as the 24-hr economy and the continued nighttime exposure to light by the computer may delay the sleep phase and shorten the sleep duration of many more people than those with ADHD alone. The current obesity epidemic, which is out of control and a major health concern, may in part be explained by these general changes in lifestyle since the last decades. Future research must result in new evidence whether advise on and treatment of the timing and the amount of hours of sleep can prevent such detrimental developments on weight and health in general.We thank all participants of the study and the staff of PsyQ Program Adult ADHD in The Hague for their time and effort to include patients to the study. This study was not funded. Research material was provided by PsyQ Program Adult ADHD in The Hague and by Leiden University.Authors’ NoteJ.S.K. and H.J.S.B. initiated the proposal. K.B.H., H.J.S.B., J.S.K., and A.M.B. wrote the study proposal and protocol. M.E.L. coordinated a pilot of the study. M.B., K.B.H., and A.M.B. collected and entered data. D.B. performed data analysis. D.B. and J.S.K. wrote the final version of the manuscript. D.B., E.J.S., and J.S.K. interpreted the data. All authors commented on the manuscript and approved the final version. J.S.K. has been a speaker for Janssen and Lilly B.V. and received unrestricted research grants from Janssen and Shire for other studies.Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.FundingThe author(s) received no financial support for the research, authorship, and/or publication of this article.Author BiographiesDenise Bijlenga, PhD, is psychologist and senior researcher at the PsyQ Expertise Center Adult ADHD in The Hague. She earned her PhD in medical decision-making in the area of obstetrics at the Academic Medical Center of the University of Amsterdam. Her expertise covers social research methodology and statistics, and research project management. Her current research interests include lifestyle characteristics and their effect on general health in adult ADHD, the measurement of bodily processes relating to biorhythm in adult ADHD with delayed sleep phase disorder, as well novel approaches to treatment of these sleep phase disorders.Kristiaan B. van der Heijden, PhD, is assistant professor at the Leiden Institute of Education and Child Studies and is affiliated to the Leiden Institute for Brain and Cognition at Leiden University. His research focuses on the association of sleep, circadian rhythm, and cognitive-behavioral functioning in normal and clinical child populations (ADHD in particular). Furthermore, he studies the pharmacological and behavioral treatment of sleep- and circadian rhythm disturbances in children. Another line of research explores several risk and protective factors of psychopathology (particularly antisocial behavior and aggression) in early childhood development, and the effectiveness of an early prevention program.Minda Breuk, MSc, has worked as psychologist and junior researcher at PsyQ Expertise Center Adult ADHD in The Hague. She gained two Master’s degrees at Leiden University in Child and Adolescent Psychology and in Clinical Psychology.Eus J. W. van Someren, PhD, was trained in physics, psychophysiology and neuropsychology and received a cum laude PhD in neurobiology from the faculty of Medicine. He is Head of the Department Sleep and Cognition at the Netherlands Institute for Neuroscience of the Royal Academy of Arts and Sciences and is professor of Neurophysiology at the VU University, Amsterdam. His expertise covers sleep, circadian rhythms, cognition, aging, thermoregulation, imaging and acquisition and analysis of physiological and behavioral time-series. His two main interests are brain mechanisms of insomnia and the effect of sleep disruption on brain function and cognition.Maria E. H. Lie, MD, has worked as physician at PsyQ Expertise Center Adult ADHD in The Hague and currently works as trainee psychiatrist at the Medical Psychiatry Center of the Maastricht Academic Hospital.A. Marije Boonstra, PhD, is neuropsychologist and currently working as senior researcher at CED-Groep, a center for educational research. She also holds an assistant professorship at the Rotterdam Erasmus University, department of Developmental and Educational Psychology. She was formerly employed at the PsyQ Expertise Center Adult ADHD, after receiving her PhD on aspects of neurocognition in adult ADHD.Hanna J. T. Swaab, PhD, is clinical neuropsychologist, clinical psychologist and psychotherapist and full professor at the Leiden Institute of Education and Child Studies and affiliated tot the Leiden Institute of Brain and Cognition. She is head of the department of Clinical Child and Adolescent Studies at the University of Leiden and director of the outpatient department of the university. Her expertise is on neurodevelopmental disorders and her research focuses on risk and protective factors in the development of psychopathology, with a specific focus on neurobiological and neurocognitive factors.J. J. Sandra Kooij, MD, PhD, is psychiatrist and head of departments Program and Expertise Center Adult ADHD in The Hague, The Netherlands. She started as a psychiatrist in 1995 with the development of diagnostic assessment, treatment and research in the field of adult ADHD in the Netherlands. She is chair of the Dutch Network Adult ADHD and has initiated the coordination of the European Network Adult ADHD, in which now 20 countries take part. She is chair of the Guideline committee on Adult ADHD of the Dutch organisation of Psychiatry and initiator and chair of the international DIVA Foundation. In 2006 she received her PhD on clinical assessment and treatment studies in adult ADHD. Her current research areas include ADHD throughout the lifespan, general health, sleep, seasonal depressive symptoms, sexuality, genetics, and lifestyle in adult ADHD.ReferencesAllebrandtK. V.Teder-LavingM.AkyolM.PichlerI.Muller-MyhsokB.PramstallerP.. . .RoennebergT. (2010). CLOCK gene variants associate with sleep duration in two independent populations. Biological Psychiatry, 67, 1040-1047.American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders. Washington, DC: Author.AmonsP. J.KooijJ. J.HaffmansP. M.HoffmanT. O.HoencampE. (2006). 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Chronobiology International, 22, 267-278.</p> <aug> <p>By Denise Bijlenga; Kristiaan B. van der Heijden; Minda Breuk; Eus J. W. van Someren; Maria E. H. Lie; A. Marije Boonstra; Hanna J. T. Swaab and J. J. Sandra Kooij</p> </aug> <nolink nlid="nl1" bibid="bib1" firstref="ref1"></nolink>
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  Data: Associations between Sleep Characteristics, Seasonal Depressive Symptoms, Lifestyle, and ADHD Symptoms in Adults
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  Data: <searchLink fieldCode="AR" term="%22Bijlenga%2C+Denise%22">Bijlenga, Denise</searchLink><br /><searchLink fieldCode="AR" term="%22van+der+Heijden%2C+Kristiaan+B%2E%22">van der Heijden, Kristiaan B.</searchLink><br /><searchLink fieldCode="AR" term="%22Breuk%2C+Minda%22">Breuk, Minda</searchLink><br /><searchLink fieldCode="AR" term="%22van+Someren%2C+Eus+J%2E+W%2E%22">van Someren, Eus J. W.</searchLink><br /><searchLink fieldCode="AR" term="%22Lie%2C+Maria+E%2E+H%2E%22">Lie, Maria E. H.</searchLink><br /><searchLink fieldCode="AR" term="%22Boonstra%2C+A%2E+Marije%22">Boonstra, A. Marije</searchLink><br /><searchLink fieldCode="AR" term="%22Swaab%2C+Hanna+J%2E+T%2E%22">Swaab, Hanna J. T.</searchLink><br /><searchLink fieldCode="AR" term="%22Kooij%2C+J%2E+J%2E+Sandra%22">Kooij, J. J. Sandra</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Attention+Disorders%22"><i>Journal of Attention Disorders</i></searchLink>. Apr 2013 17(3):261-275.
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
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  Data: <searchLink fieldCode="DE" term="%22Attention+Deficit+Hyperactivity+Disorder%22">Attention Deficit Hyperactivity Disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Sleep%22">Sleep</searchLink><br /><searchLink fieldCode="DE" term="%22Depression+%28Psychology%29%22">Depression (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Life+Style%22">Life Style</searchLink><br /><searchLink fieldCode="DE" term="%22Adults%22">Adults</searchLink><br /><searchLink fieldCode="DE" term="%22Symptoms+%28Individual+Disorders%29%22">Symptoms (Individual Disorders)</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Age+Differences%22">Age Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Hyperactivity%22">Hyperactivity</searchLink><br /><searchLink fieldCode="DE" term="%22Unemployment%22">Unemployment</searchLink><br /><searchLink fieldCode="DE" term="%22Body+Composition%22">Body Composition</searchLink><br /><searchLink fieldCode="DE" term="%22Comorbidity%22">Comorbidity</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+Health%22">Physical Health</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+Health%22">Mental Health</searchLink><br /><searchLink fieldCode="DE" term="%22Eating+Habits%22">Eating Habits</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+%28Statistics%29%22">Regression (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Incidence%22">Incidence</searchLink><br /><searchLink fieldCode="DE" term="%22Risk%22">Risk</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Questionnaires%22">Questionnaires</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+Analysis%22">Multivariate Analysis</searchLink>
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  Data: 10.1177/1087054711428965
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  Data: 1087-0547
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  Data: Objective: The authors explored associations between ADHD symptoms, seasonal depressive symptoms, lifestyle, and health. Method: Adult ADHD patients ("n" = 202) and controls ("n" = 189) completed the ASESA questionnaire involving lifestyle, eating pattern, and physical and psychological health, and validated measures on ADHD and sleep. ASESA is the Dutch acronym for Inattention, Sleep, Eating pattern, Mood, and General health questionnaire. Results: Indication for delayed sleep phase syndrome (DSPS) was 26% in patients and 2% in controls (p less than 0.001). Patients reported shorter sleep, longer sleep-onset latency, and later midsleep. Shorter ("R"[superscript 2] = 0.21) and later ("R"[superscript 2] = 0.27) sleep were associated with hyperactivity, male gender, younger age, and seasonal depressive symptoms. Seasonal depressive symptoms were related to hyperactivity, female gender, unemployment, and late sleep (pseudo "R"[superscript 2] = 0.28). Higher body mass index (BMI) was associated with shorter sleep in patients (rho = -0.16; "p" = 0.04) and controls (rho = -0.17; "p" = 0.02). Longer sleep showed lower odds for indication of metabolic syndrome (OR = -0.17; "p" = 0.053). Conclusion: DSPS is more prevalent in ADHD and needs further investigation to establish treatment to prevent chronic health issues. (Contains 5 tables.)
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  Data: 2014
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  Data: EJ1012763
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          Numbering:
            – Type: volume
              Value: 17
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Journal of Attention Disorders
              Type: main
ResultId 1