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research-article2014

JADXXX10.1177/1087054714529818Journal of Attention DisordersLangberg et al.

Article

School Maladjustment and External Locus of Control Predict the Daytime Sleepiness of College Students With ADHD

Journal of Attention Disorders 1­–10 © 2014 SAGE Publications Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1087054714529818 jad.sagepub.com

Joshua M. Langberg1, Melissa R. Dvorsky1, Stephen P. Becker2, and Stephen J. Molitor1

Abstract Objective: The primary aim of this study was to evaluate whether school maladjustment longitudinally predicts the daytime sleepiness of college students with ADHD above and beyond symptoms of ADHD and to determine whether internalizing dimensions mediate the relationship between maladjustment and sleepiness. Method: A prospective longitudinal study of 59 college students comprehensively diagnosed with ADHD who completed ratings at the beginning, middle, and end of the school year. Results: School maladjustment at the beginning of the year significantly predicted daytime sleepiness at the end of the year above and beyond symptoms of ADHD. Locus of control mediated the relationship between maladjustment and daytime sleepiness. Conclusion: The significant school maladjustment difficulties that students with ADHD experience following the transition to college may lead to the development of problems with daytime sleepiness, particularly for those students with high external locus of control. This pattern is likely reciprocal, whereby sleep problems in turn result in greater school impairment, reinforcing the idea that life events are outside of one’s control. (J. of Att. Dis. XXXX; XX(X) XX-XX) Keywords sleep, ADD/ADHD, academic performance, adjustment, internalizing comorbidity

Introduction Sleep disturbances are common in individuals with ADHD, with prevalence rates ranging from 25% to 60% depending on the age of the sample and measure of sleep disturbance utilized (Yoon, Jain, & Shapiro, 2012). The high prevalence of sleep problems in individuals with ADHD is concerning because sleep plays a pivotal role in cognitive functioning, learning, executive function, and overall adjustment (see Astill, Van der Heijden, Van IJzendoorn, & Van Someren, 2012; Beebe, 2011, for reviews). Daytime sleepiness in particular is highly prevalent in individuals with ADHD and is associated with exacerbated symptoms of inattention (Weiss & Salpekar, 2010) and increased functional impairment (Langberg, Dvorsky, Marshall, & Evans, 2013). This is important because meta-analytic data suggest that in comparison with other measures of sleep functioning (e.g., short-term deprivation, sleep quality, and sleep duration), daytime sleepiness exhibits the strongest association with academic achievement (see Dewald, Meijer, Oort, Kerkhof, & Bögels, 2010). Despite the likely importance of daytime sleepiness in the functioning of individuals with ADHD, the association between sleepiness and ADHD is not wellunderstood. In particular, it is not clear why individuals

with ADHD exhibit high rates of daytime sleepiness (Yoon et al., 2012) or how psychosocial functioning and daytime sleepiness affect each other over time. Although the transactional nature of adjustment and sleep problems has not been studied in clinical samples of individuals with ADHD, extensive research has shown that sleep problems predict poorer academic adjustment and higher rates of externalizing and internalizing symptoms in nonclinical and community samples (see Astill et al., 2012, for a review). Only recently have researchers begun to explore the possibility that the relationship is reciprocal, and that poor adjustment may in fact lead to, or exacerbate, sleep problems. In what is likely the most comprehensive longitudinal study of this issue completed to date, Kelly and El-Sheikh (2013) found evidence for a reciprocal relationship between adjustment and sleep in a sample of 176 children followed for a period of 5 years and noted that this 1

Virginia Commonwealth University, Richmond, USA Miami University, Oxford, OH, USA

2

Corresponding Author: Joshua M. Langberg, Virginia Commonwealth University, 806 W. Franklin Street, P.O. Box 842018, Richmond, VA 23284, USA. Email: [email protected]

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relationship was more robust in adolescence as compared with childhood. This important finding underscores the asof-yet untested possibility that maladjustment might result in daytime sleepiness in individuals with ADHD, particularly given the fact that individuals with ADHD frequently exhibit significant adjustment problems (Wehmeier, Schacht, & Barkley, 2010). Interestingly, severity of maladjustment often increases as youth with ADHD progress through adolescence (Wolraich et al., 2005) and this coincides with increases in the prevalence of sleep disturbances (Yoon et al., 2012). In particular, the transition to college is a developmental period when many individuals with ADHD experience clinically significant increases in school maladjustment. College students with ADHD often display low and failing grades, high rates of course withdrawals, poor time-management and organizational skills, and low self-efficacy about school performance (Weyandt et al., 2013). At least clinically, it makes sense that significant difficulties adjusting to the academic demands of college could result in daytime sleepiness. By definition, students experiencing school maladjustment are under considerable academic pressure and stress and may be experiencing symptoms of worry/ anxiety or feelings of inadequacy/failure related to their school performance. Accordingly, problems adjusting academically could lead to worry and distress (i.e., internalizing symptoms), which in turn could result in students feeling overwhelmed and tired during the day. Indeed, in non-ADHD samples, there is compelling evidence supporting the idea that stress and worry leads to the development of sleep problems. For example, in a large cross-sectional study of college students (n = 1,125), Lund, Reider, Whiting, and Prichard (2010) found that perceived stress was the single most important predictor of sleep, above and beyond sleep schedules and alcohol or drug use. When asked what factor most interferes with sleep, 68% of respondents listed stress, with academics being the most commonly cited reason for this stress (35%). Furthermore, in a longitudinal sample of 591 adults (ages 20-40), Hasler et al. (2005) found that anxiety/worry predicted daytime sleepiness at later timepoints but the reverse was not true (i.e., daytime sleepiness did not predict later anxiety). Approximately 20% to 30% of individuals with ADHD experience comorbid anxiety and/or mood problems (Wehmeier et al., 2010), and cross-sectional associations between comorbid internalizing and sleep disturbance and comorbid internalizing and maladjustment have been documented in individuals with ADHD (e.g., Accardo et al., 2012; Booster, DuPaul, Eiraldi, & Power, 2012; Mayes et al., 2009). However, the minimal longitudinal work in this area has not found evidence for internalizing predicting the development or persistence of sleep problems in individuals with ADHD, at least among children (Hansen, Skirbekk, Oerbeck, Wentzel-Larsen, & Kristensen, 2013).

Furthermore, there has been no longitudinal research that has evaluated the interplay between school maladjustment, internalizing symptoms, and daytime sleepiness. Accordingly, the purpose of this study is to longitudinally evaluate whether school maladjustment rated at the beginning of the school year predicts the daytime sleepiness of college students with ADHD at the end of the school year. Furthermore, this study will also evaluate the relationship between internalizing dimensions and daytime sleepiness and will explore whether specific aspects of internalizing mediate the relationship between school maladjustment and daytime sleepiness. Finally, given that not all college students with ADHD experience clinically significant school maladjustment, students will be grouped based on whether clinically significant problems are present to evaluate whether certain subgroups of students with ADHD exhibit higher rates of daytime sleepiness. The grouping analyses should be considered exploratory given the constraints associated with our moderate sample size. For the mediation analyses, the seven subscales that make up the Internalizing Composite Score on the Behavior Assessment System for Children, Second Edition, SelfReport of Personality–College Version (BASC-2: SRPCollege Version; Reynolds & Kamphaus, 2004) were entered simultaneously into the mediation model (Atypicality, Depression, Anxiety, Locus of Control, Social Stress, Sense of Inadequacy, and Somatization). Our hypothesis was that school maladjustment would predict daytime sleepiness above and beyond symptoms of ADHD and that college students with clinically significant school maladjustment (i.e., grouping analyses) would exhibit significantly higher rates of sleepiness as compared with those without significant maladjustment. For the mediation model, based on the work of Hasler et al. (2005) in adults, we predicted that symptoms of anxiety would mediate the relationship between school maladjustment and daytime sleepiness.

Method Participants Participants were undergraduate students enrolled in a large public university in Virginia. In total, 139 students called, expressed interest in the study, and completed a phone screen. Of these, 94 were eligible based on the phone screen (prior diagnosis of ADHD or at least four inattentive symptoms endorsed) and completed the inclusion/exclusion evaluation (described below). Sixty-eight participants met full study criteria and were enrolled. Given the focus on academics and school maladjustment, we limited the sample for the current study to those students taking >9 credit hours (n = 62), and 59 of these participants completed the primary study measures examined in this study. In comparing the

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Langberg et al. demographic characteristics of those participants for whom full data were available with those without complete data, no differences were found for age, gender, ethnicity, year in school, parent education level, family income, and ADHD medication status (ps > .05). Similarly, no differences were found for ADHD subtype, symptoms of ADHD, anxiety, depression, grade point average (GPA), or functional impairment ratings (ps > .05). The final sample of 59 participants included in this study ranged in age from 17 to 30 years (M = 19.90, SD = 2.75) and slightly over half (n = 32) were male. Forty-two participants (72%) self-identified as Caucasian; the remaining participants were either African American (n = 6), Hispanic (n = 6), or Multiracial (n = 5). Approximately half of the participants (n = 27) were in their first year of college, with remaining participants in their second (n = 13), third (n = 11), or fourth (n = 8) year. Based on procedures described below, 30 participants were diagnosed with Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) ADHD-inattentive type (ADHD-I) and 29 participants were diagnosed with ADHD-combined type (ADHD-C). Thirty-five participants were taking medication for ADHD and four were taking medications for other psychological disorders.

Procedure The inclusionary/exclusionary criteria were (a) consent for research staff to contact participants’ parent/guardian for a diagnostic interview, (b) meeting full diagnostic criteria for ADHD-I or ADHD-C, and (c) not meeting criteria for a pervasive developmental disorder, bipolar disorder, or psychosis. For our sample to be as representative as possible of the population of college students with ADHD, participants taking psychiatric medication or meeting criteria for other mental health disorders were not excluded. Diagnosis was determined through separate administration to both the student and their parent/guardian of both Parts I and II of the Conners’ Adult ADHD Diagnostic Interview for the DSM-IV (CAADID; Epstein, Johnson, & Conners, 2000; Epstein & Kollins, 2006). The CAADID interview assesses both current and childhood symptoms and impairment as well as age of onset and pervasiveness of symptoms across time. Part I of the interview provides a detailed patient history and Part II is the ADHD diagnostic interview. Strict diagnostic inclusion criteria were adhered to in this study because of questions/debates in the field regarding the validity of self-report in college students with ADHD and concerns about malingering. Specifically, parents/ guardians had to endorse at least six symptoms in an ADHD domain on the CAADID as present and impairing during childhood for a student to be included. Furthermore, the student and their parents/guardians had to endorse a total of six symptoms in a domain as currently present and impairing

on the CAADID. For documentation of current ADHD symptoms, we did allow parent interview data to be supplemented with student self-report and vice versa. However, both the parent and student had to endorse a minimum of four symptoms in a domain as currently present and impairing for supplementing to occur. Once an ADHD diagnosis was confirmed, self-report on the CAADID interview was prioritized in making ADHD subtype determinations. Flyers describing the study were included in the orientation packets of all incoming freshman, e-mailed to students currently receiving ADHD accommodations, and posted in the Disability Services Office, at Student Health, and in all university dorms. The flyers stated that students with difficulties with attention and concentration and/or students with a diagnosis of ADHD were eligible to receive a free diagnostic evaluation. Students completed baseline measures at the beginning of the school year (August), midyear ratings at the end of the first semester prior to the winter break (early December), and follow-up measures at the end of the school year (May; 9 months post-baseline). In this study, baseline is called T1, mid-year T2, and end of year T3. Predictor variables are from the T1 assessment, mediators from the T2 assessment, and the outcome variable (daytime sleepiness) is from the T3 assessment. Complete data are available at the T1 and T3 assessments but only 38 students returned ratings at T2 and were thus included in the mediation analysis. When compared with the full sample on the demographic variables presented in Table 1, participants who completed ratings at T2 were not significantly different from those who did not return ratings (ps > .05)

Measures ADHD symptoms. ADHD symptoms at T1 were assessed using the self-report version of the Barkley Adult ADHD Rating Scale–IV (BAARS-IV; Barkley, 2011). The BAARS-IV includes the 18 DSM symptoms of ADHD. Each item was rated using a 4-point scale (1 = never or rarely, 4 = very often). The BAARS-IV demonstrates satisfactory internal consistency and test–retest reliability (Barkley, 2011) and has been further validated in a large sample of college students specifically (Becker, Langberg, Luebbe, Dvorsky, & Flannery, 2013). To reduce the number of predictor variables included in the analyses (given sample size constraints), a total mean score of current ADHD symptoms was used in the current study (α = .84). School maladjustment and internalizing dimensions. Participants completed the BASC-2: SRP-College Version (Reynolds & Kamphaus, 2004) at T1 and T2. This measure, which was normed on a sample of 706 college students 18 to 25 years of age, is designed to assess the frequency or intensity with which an individual engages in a range of internal

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Table 1.  Correlations of Participant Characteristics With T3 Daytime Sleepiness and Sleep Duration. Variable Age (M = 19.90, SD = 2.75) Sex Race Year in school Housing status Employment status Employment hours (M = 14.32, SD = 4.65) ADHD subtype ADHD medication status Other psychotropic medication

Daytime sleepiness (26.22 ± 3.88)

Weeknight sleep duration (8.05 ± 0.97)

Weekend sleep duration (8.83 ± 1.23)

.05 −.11 .04 .03 −.06 .17 −.08 .17 −.02 .04

−.05 −.01 .09 .06 .28 .14 −.15 −.04 −.01 −.05

−.07 .14 −.67 .13 .08 −.02 .03 −.01 .04 −.21

Note. n = 59. Age is calculated in years. For sex, 0 = female, 1 = male. For race, 0 = non-Caucasian, 1 = Caucasian. For ADHD medication status, 0 = not taking medication for ADHD, 1 = taking medication for ADHD. For other psychotropic medication, 0 = not taking other psychotropic medication, 1 = taking psychotropic medication for reasons other than ADHD. For ADHD subtype, 0 = ADHD Predominately Inattentive Type, 1 = ADHD Combined Type. For housing status, 0 = participant not living at home, 1 = participant living at home. For employment status, 0 = participant not employed, 1 = participant employed. For employment hours, participants estimated the average number of hours worked per week. *p < .05. **p < .01. ***p < .001.

thoughts and external behaviors. The BASC-2: SRP-College consists of 185 total items, which are rated on either a 4-point rating scale (1 = never; 2 = sometimes; 3 = often; 4 = almost always) or as true/false. Completion of the measure results in the formulation of 16 individual scales for which T-scores are calculated. Thirteen of these are further grouped into three composite scores and three are not grouped into any composite. The composite scores all obtained test–retest reliability coefficients above α = .70 for the normed population. In the present study, the T1 School Maladjustment scale was used as the primary independent variable of interest. The School Maladjustment scale consists of six true/false items and five items rated along the 4-point scale. The items in the School maladjustment scale ask questions regarding a student’s attitudes and beliefs toward school and its associated demands. Example items on this scale include, “I never seem to feel like working on assignments” answered true/false, and “I feel like I belong at my school” answered using the 4-point rating scale. In addition, the BASC-2:SRP-College Version includes an Internalizing Problems composite that consists of seven subscales that focus on the endorsement of thoughts and behaviors often associated with internalizing difficulties, and these seven subscales were administered at T2 and considered as potential mediators in the present study. Specifically, the Atypicality scale probes the frequency of unusual behaviors or delusional thoughts. The Anxiety scale examines endorsements of frequent fears or worries, including specific (e.g., tests) and nonspecific (e.g., the future) events. The Depression scale measures endorsements of thoughts related to depressive symptoms, such as feelings of anhedonia and hopelessness. The Sense of Inadequacy scale examines the endorsement of attitudes or

beliefs reflecting a general difficulty or inability to succeed or to achieve a goal. The Somatization scale measures the endorsement of frequent physical ailments or discomforts. The Locus of Control scale examines how much an individual endorses that aspects of his or her life are dictated or determined by other people or forces (e.g., “I can never really do what I want to do,” “I am blamed for things I do”). Finally, the Social Stress scale measures perceptions and feelings about an individual’s social functioning, such as perceptions of peer acceptance or rejection (e.g., “My friends have more fun than I do,” “People act as if they don’t hear me”). Daytime sleepiness. The Pediatric Daytime Sleepiness Scale (PDSS; Drake et al., 2003) was specifically developed and validated as a self-report measure to examine the relationship between daytime sleepiness and academic functioning. The PDSS is one of six sleep measures to meet criteria as “well-established” according to the American Psychological Association (APA) Division 54 evidence-based assessment criteria (Lewandowski, Toliver-Sokol, & Palermo, 2011). Although the PDSS was originally designed to assess daytime sleepiness in middle school age adolescents (11-15 years of age), the measure has been used in multiple studies that included older adolescents/young adults (e.g., Maganti et al., 2006; Tan, Healey, Gray, & Galland, 2012). The PDSS consists of eight items loading onto a single factor. Participants rate each item on a scale from 4 (always) to 0 (never). In addition, an item was added that asked participants how many hours they slept per night. The PDSS total sum score at T3 was examined as the dependent variable in the present study (α = .96), with the T1 PDSS total sum score also used as a covariate in the mediation analyses.

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Analytic Plan

correlated with T3 daytime sleepiness and were therefore not considered further. Variable means, standard deviations, and intercorrelations are displayed in Table 2. As hypothesized, T1 school maladjustment was significantly and strongly associated with T3 daytime sleepiness (r = .47, p < .001). T1 school maladjustment was also significantly positively associated with T2 depression (r = .32, p = .03), locus of control (r = .42, p = .003), social stress (r = .36, p = .01), and sense of inadequacy (r = .39, p = .006). In turn, of the internalizing dimensions, only T2 locus of control was significantly positively associated with T3 daytime sleepiness (r = .32, p = .03) although T2 depressive symptoms and T2 somatization were both marginally positively associated with T3 daytime sleepiness (rs = .25 and .26, ps = .09 and .07, respectively). Of note, T3 daytime sleepiness was not significantly associated with either weeknight or weekend sleep duration (ps > .05). Although ADHD symptom severity was not significantly associated with school maladjustment, internalizing symptoms, or daytime sleepiness, ADHD symptoms were retained for inclusion in the regression and mediation analyses to ensure that results were not attributable to differences in ADHD symptom severity across participants.

First, correlation analyses were conducted to whether any baseline participant demographics/characteristics (i.e., age, sex, race, medication status, employment status, housing status) or ADHD variables (i.e., ADHD subtype) were significantly associated with T3 daytime sleepiness and should therefore be included as covariates in the primary analyses. Second, correlation analyses were conducted to examine the interrelations of T1 (i.e., ADHD symptom severity, school maladjustment, daytime sleepiness), T2 (i.e., internalizing dimensions), and T3 (i.e., daytime sleepiness, weekday and weekend sleep quantity) variables. Next, a hierarchical regression analysis was conducted to examine whether T1 school maladjustment significantly predicted T3 daytime sleepiness after controlling for T1 ADHD symptom severity. Finally, the MEDIATE macro for SPSS (Hayes & Preacher, 2013) was used to test whether the association between T1 school maladjustment and T3 daytime sleepiness was mediated by any of the T2 internalizing dimensions. Specifically, all seven internalizing dimensions were entered simultaneously into a multiple mediation model. Bootstrapping tests of mediation (10,000 replications in the current study) are preferred over earlier recommendations for tests of mediation (Baron & Kenny, 1986), particularly in smaller samples, as bias-corrected bootstrapped estimates of the confidence intervals (CIs) for indirect effects (denoted as ab below) do not assume normality of the distribution of sampled indirect effects like the Sobel test does (Preacher, Rucker, & Hayes, 2007). For these analyses, 95% CIs are considered significant if they do not encapsulate zero. In addition to examining the relation between baseline maladjustment and sleepiness dimensionally, grouping analyses were also conducted. Participants were grouped based on their school maladjustment score at the beginning of the school year, T1. Participants were categorized as having high levels of school maladjustment if their ratings on the BASC-2 (Reynolds & Kamphaus, 2004) School Maladjustment subscale was T-score of ≥ 60 (84th percentile; at-risk for clinically significant problems). Using these thresholds, two groups were created: (a) ADHD present but not school maladjustment (n = 45) and (b) ADHD and school maladjustment present (n = 14). Next, an independent samples t test was run to compare the two groups on daytime sleepiness. Cohen’s d effect sizes were calculated to determine the magnitude of the difference between the groups on daytime sleepiness ratings (Group 1 M − Group 2 Mean/pooled SD).

Results Correlation Analyses As shown in Table 1, none of the participant demographic or characteristic variables (i.e., age, sex, race, medication status, housing status, employment status) were significantly

Regression Analysis A hierarchical regression analysis was conducted to examine whether T1 school maladjustment remained significantly associated with T3 daytime sleepiness after controlling for T1 ADHD symptom severity. As displayed in Table 3, T1 ADHD symptoms were not significantly associated with T3 daytime sleepiness in Step 1, and T1 school maladjustment significantly positively predicted T3 daytime sleepiness in Step 2. Thus, the regression model provided an initial step in showing a prospective association between school maladjustment and daytime sleepiness.

Mediation Analysis Next, a mediation model was conducted, which included T1 school maladjustment as the predictor variable, the seven T2 internalizing dimensions as possible mediators, and T3 daytime sleepiness as the outcome variable, with T1 ADHD symptoms and T1 daytime sleepiness entered as covariates. This ensured that the indirect effect from school maladjustment to daytime sleepiness via internalizing was not attributable to the stability of daytime sleepiness over time. Mediation results using the MEDIATE macro are summarized in Figure 1. A total effect from T1 school maladjustment to T3 daytime sleepiness was not present (c = .20, SE = .05, p < .001), primarily due to a strong and significant association between T1 and T3 daytime sleepiness (b = .51, SE = .14, p = .001), and current mediation guidelines are clear that an indirect effect may exist in the absence of a direct effect (see Preacher et al., 2007). In line with this possibility, and as

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Table 2.  Means, Standard Deviations, and Intercorrelations of Predictor, Mediator, and Outcome Variables. 1

2

3

  1. T1 ADHD total — .12 −.06 symptoms  2. T1 school — .47*** maladjustment   3.  T1 daytime sleepiness —  4. T2 atypicality  5. T2 depression  6. T2 anxiety   7.  T2 locus of control   8.  T2 social stress   9. T2 sense of inadequacy 10.  T2 somatization 11.  T3 daytime sleepiness 12. T3 weeknight sleep duration 13. T3 weekend sleep duration M 2.60 50.56 25.79 SD 0.48 9.03 4.35

4

5

6

7

8

−.04

−.06

.17

.03

−.09

.001 −.15 —

9 −.04

10

11

−.08

.06

12

13

.22 −.07

.32* −.003

.42**

.36*

.39** −.05

.47*** −.02

.32* .29 —

.10 .39** .52*** .22 —

.06 .38** .60*** .29* .46** —

.20 .19 .33* .33* .35* .34* —

.12 .48*** .38** .56*** .13 .34* .14

.60*** .11 .25 .18 .32* .03 .04

.16 −.11 .06 −.05 −.04 −.07 .08

.33* −.29 −.21 −.25 −.32* −.22 −.002

—

.26 —

.06 −.14 —

.10 .23 .11

.11 .43** .42** —

.08

— 47.87 47.62 52.00 7.40 5.57 11.75

50.49 9.49

47.06 8.51

52.81 10.01

50.51 11.76

26.22 3.88

8.05 8.83 0.97 1.23

Note. Sample size varies between 38 and 59 across measures/timepoints (see “Method” section for specific ns). *p < .05. **p < .01. ***p < .001.

Table 3.  Hierarchical Regression Model of T1 School Maladjustment Predicting T3 Daytime Sleepiness Above and Beyond T1 ADHD Symptoms. Step 1 model summary   T3 daytime sleepiness   T1 ADHD total   T1 school maladjustment

B

SE

Step 2 model summary

β

t

B

2

.47 —

F(1, 57) = 0.20, R = .003 1.07 .06 — —

SE

β

t 2

.44 —

.002 .20

F(1, 56) = 15.82, R = .22*** .96 .000 .002 .05 .47 3.98***

Note. n = 59. *p < .05. **p < .01. ***p < .001.

shown in Figure 1, there was a significant indirect effect from T1 school maladjustment to T3 daytime sleepiness via T2 locus of control specifically (T1 School Maladjustment → T2 Locus of Control → T3 Daytime Sleepiness ab = .08, SE = .05, 95% CI = [0.004, 0.20]) over and above T1 daytime sleepiness and ADHD symptom severity. In addition, the paths from T1 school maladjustment to both T2 social stress and T2 sense of inadequacy were also significant, but neither social stress nor sense of inadequacy in turn predicted T3 daytime sleepiness. Thus, the indirect effect from school maladjustment to daytime sleepiness was specifically and uniquely through locus of control.

Group Comparison Analyses As hypothesized, the two school maladjustment groups significantly differed in total daytime sleepiness problems,

t(57) = 3.12, p = .003. Specifically, the group with ADHD and elevated school maladjustment at T1 (M = 28.86, SD = 3.51) had significantly higher levels of daytime sleepiness at T3 in comparison with the ADHD and low school maladjustment group (M = 25.40, SD = 3.65, p = .003, d = .97).

Discussion Extensive research has examined the prevalence of sleep problems in individuals with ADHD and the role that sleep disturbances play in the development of externalizing and internalizing symptoms and maladjustment (see Astill et al., 2012; Yoon et al., 2012, for reviews). Only recently has research begun to focus on the possibility that the relationship between sleep and internalizing/externalizing symptoms and maladjustment is reciprocal (Kelly & El-Sheikh, 2013). To our knowledge, the present study is the first

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Langberg et al.

T2 Atypicality

- .0 01

(.1 .06 5

.1

(.1

)

.25

( .16

-

.20)*

.49 (

8)*

(.2

0)*

5

(.1

3)

T2 Anxiety

T2 Locus of Control

.45 (.1

-.3

.03

-.0

1(

.07

)

.17 (

.07)*

* Indirect Effect via Locus of Control: ab = .08, SE = .05, 95% CI = 0.004, 0.20

T1 School Maladjustment

.48

0)

7)

( .1

T2 Depression 2)

(.2

5)

T3 Daytime Sleepiness

9)

-.15 (.0

T2 Social Stress

)

(.06

-.08 T2 Sense of Inadequacy

)

.06

( .07

T2 Somatization

Figure 1.  Indirect effects model of T1 school maladjustment predicting T3 daytime sleepiness via T2 internalizing domains (n = 38).

Note. Unstandardized coefficients shown outside parentheses; standard errors are shown inside parentheses. Dashed paths are nonsignificant (ps > .05). Analyses controlled for T1 ADHD symptom severity and T1 daytime sleepiness, which in the final model were not significantly associated with any of the mediator or outcome variables with the exception of T1 daytime sleepiness being significantly positively associated with T3 daytime sleepiness (b = .45, SE = .16, **t = 2.86). *p < .05.

longitudinal study to evaluate whether the difficulties with academic functioning and adjustment common to individuals with ADHD predict problems with daytime sleepiness. In addition, this study also considered the role that comorbid internalizing symptoms play in the relationship between adjustment and sleepiness. Findings suggest a link from school maladjustment to daytime sleepiness, above and beyond the impact of ADHD symptoms. The mediation model revealed that locus of control mediated the relation between school maladjustment and sleepiness, even after controlling for baseline levels of daytime sleepiness. Self-reported school maladjustment rated at the beginning of the school year was a strong predictor of daytime sleepiness rated at the end of the school year (r = .47; see Table 2). This suggests that the relationship between sleep and adjustment is indeed reciprocal and may in fact be stronger than what has been found in community samples where clinically significant impairment is less prevalent (e.g., Kelly & El-Sheikh, 2013). However, the findings from this study also suggest that it is important to consider

the role that internalizing symptoms play in the relationship between maladjustment and sleep. Our hypothesis that the relationship between school maladjustment and sleepiness would be mediated by symptoms of anxiety was not supported. In fact, school maladjustment and anxiety were not significantly correlated nor were anxiety and daytime sleepiness (see Table 2). However, school maladjustment at T1 was significantly related to locus of control at T2, which in turn predicted daytime sleepiness at T3 (see Figure 1). Interestingly, there is an existing literature focusing on the importance of locus of control both for school performance and for health-related behaviors such as sleep. The construct of locus of control is typically separated into external and internal dimensions. Individuals with external locus of control believe their behavior (and that of other people) is not within their control and that positive events can largely be attributed to chance or luck. In contrast, individuals with internal locus of control feel that they have influence over the events in their lives and the power to shape their future. College students with an external locus

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of control are significantly more likely to smoke and consume alcohol and are less likely to engage in physical exercise and healthy nutrition patterns (Helmer, Kramer, & Mikolajczyk, 2012; Steptoe & Wardle, 2001). Indeed, Roddenberry and Renk (2010) found that locus of control partially mediated the relationship between stress and physical illness in a sample of 159 college students, with higher levels of stress and illness associated with higher levels of external locus of control. Importantly, there is also research suggesting that individuals with ADHD are significantly more likely to have an external locus of control orientation in comparison with peers (Lufi & Parish-Plass, 1995) and to attribute academic successes to luck and failure to factors outside of their control (e.g., Hoza et al., 2001; Pelham, Waschbusch, Hoza, Pillow, & Gnagy, 2001). Accordingly, the pattern of mediation results found in this study makes sense. Students with ADHD who have a difficult time adjusting to college academically likely end up feeling stressed and overwhelmed by school. As noted in the Introduction, there is an established, strong relationship between stress, and academic stress in particular, and sleep in college students (Lund et al., 2010). The results of the current study take the findings of the Lund et al. (2010) study further, and extend them to a clinical sample of college students diagnosed with ADHD, by suggesting that students with an external locus of control are most likely to have school maladjustment lead to sleep problems. Specifically, college students with ADHD who believe that events are not in their control may be more likely to worry and ruminate about their stressors, resulting in feelings of tiredness. Group-based analyses comparing college students with at-risk levels of maladjustment with those students with ADHD below the maladjustment threshold yielded a similar pattern of results. In terms of effect sizes, students with ADHD and maladjustment experienced markedly higher rates of daytime sleepiness (d = .97) in comparison with individuals with ADHD alone. Overall, these data suggest that daytime sleepiness may not be intrinsic to all individuals with ADHD; rather, it is likely that certain specific subgroups of individuals with ADHD have significant difficulties with daytime sleepiness.

prior levels of each factor impacting subsequent domains, and repeated measurements over extended developmental periods are needed to evaluate this possibility. Third, this study only evaluated the impact of adjustment on daytime sleepiness and a multi-method approach evaluating both nighttime and daytime sleep disturbances using objective and subjective measures would be a stronger. Finally, approximately half the sample was beginning their freshman year of college when they completed the T1 assessment battery. Accordingly, their ratings of school maladjustment likely reflected their feelings about school and their perceptions of their academic abilities as related to their performance in high school. Indeed, the fact that half of the samples were freshman may explain why only 14 (24%) of students endorsed clinically significant levels of maladjustment (i.e., they had not yet begun to experience the academic difficulties commonly associated with the transition to college).

Limitations

Conclusion

There are a number of important limitations that need to be considered. First, the sample size was modest and as such, the grouping analyses should be considered exploratory and need to be replicated. Second, to better understand the reciprocal relationship between adjustment and daytime sleepiness, studies with additional timepoints are needed (see Kelly & El-Sheikh, 2013, for an example). Specifically, the relationship between adjustment, internalizing, and sleep is likely cyclical and transactional in nature, with

Sleep disturbances are clearly linked with externalizing/ internalizing symptoms and maladjustment in individuals with and without ADHD. This study suggests that the relationships among these variables are likely reciprocal and cyclical, and this possibility merits further study. If future research confirms that school maladjustment or functional impairment in general is an important factor in the development of sleep disturbances, it will be important to evaluate whether currently available evidence-based ADHD

Future Directions The findings presented in this study are novel in that they suggest the potential for a relatively parsimonious explanation for the presence of sleep problems in individuals with ADHD. That is, the maladjustment/impairments common to individuals with ADHD may contribute to the high prevalence of sleep disturbances found in this population. Future research using larger samples is needed to confirm these findings. In addition, future research is needed to evaluate whether this reciprocal relationship exists in younger adolescents with ADHD (e.g., middle school age) and in adults with ADHD, or if this phenomenon is specific to college when many young adults experience adjustment difficulties. Research is also needed to explore other alternate pathways from maladjustment to sleepiness. For example, it may be that specific aspects of school maladjustment such as procrastination, poor time-management, and ineffective studying lead youth with ADHD to stay-up later and to get less sleep, causing them to feel tired during the day. Understanding more specifically which aspects of maladjustment are linked with sleepiness will aid in the development of targeted intervention.

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Langberg et al. interventions, psychosocial and pharmacological, lead to a reduction in sleep problems. To date, sleep disturbances have rarely been examined as outcome variables in intervention research for individuals with ADHD, and it is unknown whether traditional interventions such as behavioral parent training or academic skills training impact sleep. The efficacy of currently available ADHD interventions for sleep will likely depend on the specific aspects of maladjustment found to lead to daytime sleepiness. That is, if it is procrastination and inefficient studying that leads to sleep problems, interventions that target these factors specifically could lead to an improvement in daytime sleepiness. However, if as suggested in this study, it is the fact that maladjustment leads to internalizing symptoms that in turn results in daytime sleepiness, then likely only interventions that include a cognitive behavioral therapy (CBT) component to specifically address internalizing symptoms would be effective. Importantly, if a reciprocal relationship is confirmed in future research, this would suggest that solely intervening with sleep problems alone (e.g., targeting sleep hygiene) would probably not be sufficient for individuals with ADHD, as the underlying causes of the feelings of stress, worry, or inadequacy (i.e., maladjustment) would not have been addressed. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) received the following financial support for the research, authorship, and/or publication of this article: During the conduct of this research and preparation of this manuscript, the first author was supported in part by R01MH082865 from the National Institutre of Mental Health and by R305A130011 from the United States Department of Education, Institute of Education Sciences.

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Author Biographies Joshua M. Langberg, PhD, is an Assistant Professor in the Department of Psychology at Virginia Commonwealth University (VCU) where he serves as Co-Director for the Center for ADHD Research, Education, and Service. Dr. Langberg’s research and professional interests are focused on improving the academic performance of children and adolescents with Attention-Deficit/ Hyperactivity Disorder (ADHD). His school-based intervention research is supported by the National Institutes of Health (NIH) and U.S. Department of Education, Institute of Education Sciences (IES). Melissa R. Dvorsky, BA, is a doctoral student in child-clinical psychology in the Department of Psychology at Virginia Commonwealth University (VCU). Her research and professional interests are focused on evaluating evidence-based assessment and intervention strategies in school settings for youth with AttentionDeficit/Hyperactivity Disorder (ADHD) and examining factors related to successful outcomes, including the role of youth, family, and school characteristics. Stephen P. Becker, MA, is a clinical psychology doctoral candidate at Miami University and O’Grady Resident in Behavioral Medicine and Clinical Psychology at Cincinnati Children’s Hospital Medical Center. His research focuses on ADHD with specific interests in comorbidity, psychosocial adjustment, and sleep functioning. Stephen Molitor, BA, is currently a graduate student in the Department of Psychology at Virginia Commonwealth University (VCU). His research interests are focused on investigating the cognitive deficits associated with child and adolescent psychopathology, such as Attention-Deficit/Hyperactivity Disorder (ADHD).

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