Pharmacological and psychosocial treatments for adolescents with ADHD: An updated systematic review of the literature

Clinical Psychology Review 34 (2014) 218–232

Contents lists available at ScienceDirect

Clinical Psychology Review

Pharmacological and psychosocial treatments for adolescents with ADHD: An updated systematic review of the literature Margaret H. Sibley a,⁎, Aparajita B. Kuriyan a, Steven W. Evans b, James G. Waxmonsky c, Bradley H. Smith d a

Florida International University, Miami, FL, USA Ohio University, Athens, OH, USA Pennsylvania State University Milton Hershey Medical Center, Hershey, PA, USA d University of Houston, Houston, TX, USA b c

H I G H L I G H T S • • • • •

Reviews past 13 years of research on treatment of ADHD in adolescence. A range of pharmacological and behavior therapies produced positive effects. Cognitive enhancement trainings were not effective treatments for ADHD in adolescence. Behavior therapy produced the greatest effects on impairment. Medication produced the greatest effects on symptoms.

a r t i c l e

i n f o

a b s t r a c t

Article history: Received 16 August 2013 Revised 8 January 2014 Accepted 13 February 2014 Available online 27 February 2014

Smith, Waschbusch, Willoughby, and Evans (2000) reviewed a small treatment literature on ADHD in adolescents and concluded that methylphenidate stimulant medication was a well-established treatment and behavior therapy (BT) demonstrated preliminary efficacy. This review extends and updates the findings of the prior one based on the previous 15 years of research. Studies published since 1999 were identified and coded using standard criteria and effect sizes were calculated where appropriate. Highlights of the last 15 years of research include an expansion of pharmacological treatment options and developmentally appropriate psychosocial treatment packages for adolescents with ADHD. Additionally, nonstimulant medications (e.g., atomoxetine) are now approved for the treatment of ADHD in adolescence. The review concludes that medication and BT produce a similar range of therapeutic effects on the symptoms of adolescents with ADHD. However, results suggest that BT may produce greater overall benefits on measures of impairment. There was no evidence that cognitive enhancement trainings, such as working memory training or neurofeedback improved the functioning of adolescents with ADHD. Whether to use medication, BT, or their combination to treat an adolescent with ADHD is complicated and we provide evidence-informed guidelines for treatment selection. The reviewed evidence does not support current American Academy of Pediatrics and American Academy of Child and Adolescent Psychiatry professional guidelines, which state that stimulant medication is the preferred treatment for adolescents with ADHD. Recommendations for assessment, practice guidelines, and future research are discussed. © 2014 Elsevier Ltd. All rights reserved.

Keywords: ADHD Adolescence Behavior therapy Stimulant medication

Contents 1. 2. 3.

ADHD in adolescence . . . . . . . . . Treatment in adolescence . . . . . . . Evaluating treatment efficacy . . . . . 3.1. Selection of dependent measures 3.2. Sample composition . . . . . . 3.3. Study design . . . . . . . . .

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⁎ Corresponding author at: Department of Psychiatry & Behavioral Health, Center for Children and Families, Florida International University, 11200 SW 8th Street, AHC-1 Room 146, Miami, FL 33199, USA. Tel.: +1 305 348 3005; fax: +1 305 348 3646. E-mail address: msibley@fiu.edu (M.H. Sibley).

http://dx.doi.org/10.1016/j.cpr.2014.02.001 0272-7358/© 2014 Elsevier Ltd. All rights reserved.

M.H. Sibley et al. / Clinical Psychology Review 34 (2014) 218–232

4. 5.

Method . . . . . . . . . . . . . . . . Results . . . . . . . . . . . . . . . . 5.1. Medication studies . . . . . . . 5.2. Behavior therapy . . . . . . . . 5.3. Cognitive enhancement training . 6. Discussion . . . . . . . . . . . . . . . 6.1. Medication . . . . . . . . . . . 6.1.1. Methylphenidate . . . . 6.1.2. Amphetamine . . . . . 6.1.3. Nonstimulants . . . . . 6.1.4. Additional domains . . . 6.2. Behavior Therapy . . . . . . . . 6.3. Cognitive enhancement training . 6.4. Overall efficacy . . . . . . . . . 6.5. Assessment of treatment response 6.6. Implications for practice . . . . . 6.7. Future directions . . . . . . . . 7. Conclusions . . . . . . . . . . . . . . Appendix A. Supplementary data . . . . . References . . . . . . . . . . . . . . . . .

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1. ADHD in adolescence The persistence of ADHD into adolescence was once debated by clinicians and researchers, but is now well established (Barkley, 2006). The impairments associated with ADHD in adolescence are described in a large body of research and reflected in influential documents such as the DSM-5 (American Psychiatric Association, 2013) and the American Academy of Pediatrics (AAP) and American Academy of Child and Adolescent Psychiatry practice guidelines (AACAP, 2007; AAP, 2011). For example, the most recent AAP clinical practice guidelines for ADHD include specific recommendations for the assessment, diagnosis and treatment of adolescents (AAP, 2011). These documents emphasize the importance of considering the symptoms, impairment, and treatment of ADHD within a developmental context. Puberty reflects a hallmark developmental change from childhood to adolescence. Along with physiological changes in the reproductive system, puberty is characterized by a period of rapid reorganization of neural circuitry, which is thought to influence regions of the brain associated with planning, drug sensitivity, response to reward, decisionmaking, and risk-taking (Sisk & Foster, 2004). Ensuing neuropsychological changes are likely to have specific consequences for the expression of ADHD, which is linked to deficits in each of these areas (Barkley, 1997; Drechsler, Rizzo, & Steinhausen, 2008; Iaboni, Douglas, & Ditto, 1997). Thus, it is no surprise that the expression of ADHD changes qualitatively in adolescence. More generally, previous research establishes that ADHD symptom severity decreases in adolescence, particularly for child-like hyperactive behaviors (Molina et al., 2009). However, adolescents with ADHD continue to experience elevated levels of inattention, hyperactivity, and impulsivity compared to typically developing peers (Sibley, Pelham, Molina, et al., 2012). Furthermore, these youth experience impairment in the same domains as children with ADHD (Wolraich et al., 2005), but the nature of their impairment may be more severe due to increased responsibility placed on adolescents and decreased supervision (Steinberg, Fletcher, & Darling, 1994). For example, like children, adolescents with ADHD display markedly poor school grades; however, they also experience difficulties with class attendance and long-term assignment completion, and are more likely to drop out of high school than peers (Kent et al., 2011). Furthermore, adolescents with ADHD also continue to display elevated rates of poor social skills, problems with peer relationships, and family conflict (Bagwell, Molina, Pelham, & Hoza, 2001; Edwards, Barkley, Laneri, Fletcher, & Metevia, 2001). However, adolescents with ADHD also are at a higher risk for developmentally-specific problems such as delinquency,

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substance abuse, and risky driving behavior (Charach, Yeung, Climans, & Lillie, 2011; Sibley, Pelham, Molina, et al., 2011; Thompson, Molina, Pelham, & Gnagy, 2007). For typically developing youth, adolescent maladjustment is generally short-lived and does not result in longterm impairment (Moffitt & Caspi, 2001). However, for adolescents with ADHD, problems often persist into adulthood (Barkley, Murphy, & Fischer, 2008).

2. Treatment in adolescence As discussed above, there is widespread agreement that developmental differences between children and adolescents necessitate qualitatively distinct approaches to treatment in these age groups. For school-aged children, Central Nervous System (CNS) stimulant medication, behavior therapy, and their combination are well-established treatments for ADHD (Greenhill et al., 2002; Pelham & Fabiano, 2008). Professional practice guidelines recommend combined treatment as a preferred approach for children (AACAP, 2007; AAP, 2011), with behavior therapy typically provided in the form of behavioral parent training, teacher-delivered behavioral interventions, and intensive peer interventions (e.g., Summer Treatment Program; Pelham & Fabiano, 2008). For example, in the largest ADHD treatment study to date (Multimodal Treatment of ADHD Study; MTA), participants were treated with a tri-component (parent, teacher, peer) behavior therapy package, stimulant medication, or their combination. Behavior therapy and stimulant medication offered unique benefits to the functioning of children with ADHD (Conners et al., 2001; MTA, 1999), yielding best results with combined treatment that included a low dose of medication. Early approaches to treating adolescents with ADHD applied childhood treatment packages to adolescents, with very modest considerations for developmental differences (Barkley, 2004; Smith, Waschbusch, Willoughby, & Evans, 2000). More recent research identifies clear problems with this strategy. Regarding stimulant medication, it is now clear that despite increases in the number of adolescents who are prescribed medication for ADHD (Visser et al., 2014), up to 90% of adolescents with ADHD refuse and subsequently desist stimulant medication by the end of high school (McCarthy et al., 2009; Molina et al., 2009). In a recent study (Biswas, Gnagy, Molina & Pelham, 2009), the primary reason for desistance cited by parents was that the teen was not motivated to take medication. By contrast desistant teens were most likely to report that they “got better and didn't need medication,” despite clear evidence to the contrary (Kent et al., 2011). Furthermore, among desistant teens, two-thirds of parents disagreed with the teen's decision to stop medication (Biswas et al., 2009). These data suggest that in adolescence, there

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may be important limits to the feasibility of standard stimulant medication regimens. Traditional behavior therapy packages also face practical problems when delivered to adolescents. Behavioral parent training is a cornerstone psychosocial treatment for children (Chronis, Chacko, Fabiano, Wymbs, & Pelham, 2004), but parents and adolescents with ADHD may disengage from treatment due to high levels of parent–teen conflict (Edwards et al., 2001) and mounting parenting stress (Barkley, Edwards, Laneri, Fletcher, & Metevia, 2001; Evans, Sibley, & Serpell, 2009). In elementary schools, teachers typically orchestrate behavioral intervention delivery; however, secondary school teachers often expect students to function independently and regularly refuse to implement treatment (DuPaul & Weyandt, 2006; Fabiano et al., 2002). This teacher nonparticipation is a repeatedly cited implementation barrier in secondary schools (Evans, Serpell, Schultz, & Pastor, 2007; Fabiano et al., 2002). As a result, alternative models for secondary school-based treatment may be particularly necessary. Finally, unlike children, adolescents with ADHD may possess the cognitive capacity to play an active role in coordinating their own treatment, preparing them for an adult-care model (Weiss et al., 2008). However, for teens with ADHD, youth involvement must be integrated carefully, given the established tendency of these youth to underestimate or deny impairment (Fischer, Barkley, Fletcher, & Smallish, 1993) and resist some treatments (Barkley et al., 2001). Thus, in some cases, adolescent-directed interventions such as those delivered in peer settings (Summer Treatment Program— Adolescent; STP-A; Sibley, Pelham, Evans, et al., 2011) may be particularly appropriate in adolescence because they are more palatable to teens than outpatient care. The most recent review of adolescent-specific treatments for ADHD (Smith et al., 2000) spanned fifty years of research (1944–1998) and included fewer than twenty medication studies and only five psychosocial treatment studies. With few exceptions, these studies possessed very small sample sizes and were largely uncontrolled evaluations. Consistent with Smith and colleagues' recommendation for treatments that are customized to adolescence, recent research is characterized by specific efforts to: (1) expand the research base on pharmacological treatments and (2) develop and evaluate well-tailored behavioral intervention packages for adolescents with ADHD. Subsequently, the last fifteen years witnessed important advances in the treatment of adolescents with ADHD, necessitating an updated systematic review of this literature. 3. Evaluating treatment efficacy There are numerous methodological factors that should be considered to accurately evaluate treatment efficacy (Flay et al., 2005). Discussing all of these considerations is beyond the scope of this review; however, below we describe several important study characteristics that are unique or particularly relevant to studies of adolescents with ADHD. 3.1. Selection of dependent measures Assessing changes in functional impairment is a minimum standard for establishing that a treatment is efficacious (Flay et al., 2005). Although change in ADHD symptoms and functioning are related, symptom reduction does not always lead to reduced impairment (Owens, Johannes, & Karpenko, 2009). For example, early interpretations of the MTA reported the superiority of medication compared to behavior therapy by interpreting only symptoms indices (MTA, 1999). These conclusions were reframed after subsequent publications clarified that the long-term performance of treatment groups varied as a function of outcome measure (Conners et al., 2001). Similarly, review studies that solely consider symptom outcomes (Sonuga-Barke et al., 2013) report differing results from reviews that consider both symptoms and

impairment (Evans, Owens, & Bunford, in press). The distinction between symptoms and impairment are particularly relevant in adolescence given the documented discrepancy between symptom severity and functional impairment during these years (Evans et al., 2013; Molina et al., 2009). Beyond domain of outcome, the measures of outcome must be reliable and valid. The vast majority of treatment outcome measures are parent and teacher ratings. However, there is evidence that informants who are unblinded or involved in treatment may possess perceptual biases (e.g., parents and teachers; Jadad et al., 1996). Furthermore, self-report ratings may be particularly problematic for adolescents with ADHD, who characteristically underreport their impairments compared to adults (Fischer et al., 1993; Sibley, Pelham, Molina, et al., 2012). Informant perceptual biases may be particularly compounded in adolescence, when parent and teacher monitoring steeply diminishes (Steinberg et al., 1994). Subsequently, adult informants may possess limited opportunities to observe an adolescent's true impairment, obscuring changes in functioning over time (Wolraich et al., 2005). For example, teacher observations of adolescents are limited to an hour of daily classroom time. Not surprisingly, there is poor agreement between secondary school teachers (Evans, Allen, Moore, & Strauss, 2005) and between parents and teachers of adolescents with ADHD (Fischer et al., 1993). Direct observations and product measures may be particularly sensitive to treatment effects for adolescents (Evans et al., 2001; Pelham, Smith, et al., 2013); however, these data can be difficult to collect in naturalistic settings and are typically specific to academics. As a result, it appears necessary to adopt a multi-source and multi-method approach to assessing treatment response in adolescents with ADHD. However, this approach may be confusing to interpret when there is variability in outcome across measures or significant results for only a few measures in a large battery (De Los Reyes & Kazdin, 2006). 3.2. Sample composition Sample characteristics, such as age, socioeconomic status, race and ethnicity, cognitive abilities, and sample size often vary as a function of recruitment and diagnostic procedures. For example, referral-source (school vs. clinic vs. community) may relate to symptom severity, socioeconomic status, or impairment profile (Evans et al., in press). Furthermore, variability in informant, symptom-endorsement decision rules, and impairment criteria may influence the diagnostic process and subsequently, sample composition. For example, there are various methods of integrating reports from multiple diagnostic informants (Bird, Gould, & Staghezza, 1992)—an “and” rule requires all raters to endorse a symptom, while an “or” rule permits either informant to designate a symptom as endorsed. Seemingly subtle decisions in diagnostic procedure can move children above or below the diagnostic threshold (Valo & Tannock, 2010). As a result, samples that contain adolescents with differing symptom profiles may possess differential treatment effects. Participant comorbidity and treatment history may also influence results. For example, adolescents with comorbid conduct disorder (CD) may display a reduced response to behavioral treatment (Sibley, Smith, Evans, Pelham & Gnagy, 2012). On the other hand, elevated levels of anxiety may improve treatment response (MTA, 1999). Furthermore, some pharmacological treatments for ADHD may be counterindicated for adolescents with comorbid symptoms (Pliszka & AACAP Work Group on Quality Issues, 2007) and there is evidence that the optimal dose of medication may be moderated by previous stimulant experience (Greenhill et al., 2001). Thus, understanding the clinical profile of participants is integral to interpreting overall treatment response. 3.3. Study design Inevitably, systematic treatment reviews vary in their criteria for inclusion and efficacy. Some reviews adopt restrictive inclusion criteria,

M.H. Sibley et al. / Clinical Psychology Review 34 (2014) 218–232

Initial studies identified by electronic database search (N=4,035)

Additional studies identified by table of contents search (N=13)

Additional studies identified by reference list search (N=1)

221

Additional studies identified by personal correspondence (N=7)

Total studies identified for eligibility screening (N=4,056) Excluded: title and abstract indicated ineligibility based on aims, study design, sample composition, and/or outcome measures (N=3,985)

Studies considered for inclusion based on title and abstract (N=71)

Excluded: not treatment efficacy study (N=5)

Excluded: same sample as another study (N=8)

Studies included in review based on full text (N=53)

Excluded: invalid age range (N=2)

Excluded: invalid outcome measure (N=2)

Excluded: participants not diagnosed with ADHD (N=1)

Fig. 1. Overview of study inclusion procedure.

requiring randomized controlled trials (RCTs) with experimentercompleted diagnoses (e.g., Division 53 Journal of Clinical Child and Adolescent Psychology: Special Issue on Evidence-Based Psychosocial Treatments for Children and Adolescents; Evans et al., in press; Pelham & Fabiano, 2008) or require blinded informants of outcome (e.g., Sonuga-Barke et al., 2013). Other treatment reviews employ less conservative criteria that allow treatment efficacy to be evaluated along a continuum. For example, the foremost lists of evidence-based programming for children and adolescents, the What Works Clearinghouse (U.S. Department of Education, 2013) and the National Registry for Evidenced-Based Programs and Practices (SAMHSA, 2013) consider an array of study designs (e.g., RCT, quasi-experimental, single-subject) when evaluating efficacy. In reviewing the state of research on adolescent-specific interventions for ADHD, applying conservative standards would include very few studies. Thus, when the literature is small, including all studies with adequate internal validity (e.g., diagnosed sample, quantitative data) may be most informative. However, under this approach it is particularly important to consider a treatment's level of evidence relative to the rigor of the study's design characteristics, and give greater weight to studies with stronger designs. 4. Method Studies were identified through the following procedure. First, an electronic database search was conducted using PsycINFO, PubMed, and Google Scholar. Three categories of search terms were used in combination: (1) sample age (adolescent, adolescence, teenager,

teen), (2) disorder (ADHD, Attention-Deficit/Hyperactivity Disorder, Attention Deficit Disorder, ADD, Attention Problems, hyperkinetic), and (3) treatment (treatment, intervention, program, medication, training). Following the online search, we listed the associated journal for each identified article and conducted an electronic search of the table of contents of these journals using the same search terms. We also assembled a list of additional journals in the field and searched these tables of contents as well. Next, the reference list for all identified articles was searched by hand. Finally, we contacted identified experts on the topic to request additional articles published or in press during the designated review time period. The following inclusion and exclusion criteria were applied to potentially eligible articles: (1) published or in press between 1999 and 2012, (2) all participants in the sample must meet the World Health Organization definition of adolescence (10.0–19.9 years of age; WHO, 2013), (3) all participants in the sample identified as meeting diagnostic criteria for ADHD, (4) quantitative data reported for at least one ecologically valid outcome measure (i.e., ADHD symptom severity or directly related impairment), (5) in the studies where individuals not meeting age or diagnostic criteria are also included, data for adolescents with ADHD must be presented separately, and (6) one of the oprimary aims of the study must be to evaluate treatment efficacy. We included all study designs that met the above criteria with the aim of conducting a thorough review of all systematic efforts to evaluate treatments for adolescents with ADHD. When a study represented secondary data analysis of an included study, the secondary study was retained when data analyses presented novel efficacy data (i.e., additional outcome

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Table 1 Treatment effects reported in controlled trials. Comorbid

Impairment

Source: measure

Source: measure

Source: measure

P: ADHD-RS (d = .98) P: ADHD-RS (5 mg d = .07; 10 mg d = .41) T: ADHD-RS (5 mg d = .07; 10 mg d = .33)

Barkley et al., 2000 (MAS; N = 35)

P: ADHD-RS (5 mg d = .05; 10 mg d = .20) T: ADHD-RS (5 mg d = .04; 10 mg d = .31)

Biederman et al., 2008 (GXR; N = 80) Bostic et al., 2000 (Pemoline; N = 21) Cox et al., 2006 (OROS MPH; N = 35) Cox et al., 2006 (MAS ER; N = 35) Evans et al., 2001 (IR MPH; N = 45)

P: ADHD-RS (nsig) P: ADHD-RS (d = 2.08)

T: IOWA-C (10 mg d = .49; 20 mg d = .77; 30 mg d = .91)

ODD P: ODD (5 mg d = −.04; 10 mg d = .17) T: ODD (5 mg d = −.01; 10 mg d = .01) A: ODD (5 mg d = −.10; 10 mg d = .02) ODD P: ODD (5 mg d = .09; 10 mg d = .17) T: ODD (5 mg d = .11; 10 mg d = .16) A: ODD (5 mg d = −.18; 10 mg d = .14) ODD P: ODD (sig)

ODD T: IOWA-C ODD (10 mg d = .35; 20 mg d = .47; 30 mg d = .56)

Findling et al., 2010 (MPH Transdermal N = 217) P: CRS (d = .94) C: ADHD-RS (d = 1.34) Findling et al., 2011 (LDX; N = 314) C: ADHD-RS (30 mg d = .80; 50 mg d = 1.23; 70 mg d = 1.10) Pelham, Meichenbaum, et al., 2013 (IR MPH; N = 25)

Affectivity A: PANAS Positive Affectivity (d = .27); PANAS Negative Affectivity (d = .09)

Pelham, Smith, et al., 2013 (IR MPH; N = 30)

T: IOWA-C (d = .53)

ODD T: IOWA-C (d = .35)

Pelham, Smith, et al., 2013 (Pemoline; N = 30)

T: IOWA-C (d = .21)

ODD T: IOWA-C (d = .00)

Riggs et al., 2004 (Pemoline; N = 69)

P: CPRS (d = .33)

Riggs et al., 2011 (OROS MPH; N = 303) Spencer, Wilens, et al., 2006; Spencer, Biederman, et al., 2006 (MAS XR; N = 280) Thurstone et al., 2010 (Atomoxetine; N = 70) Wilens, McBurnett, et al., 2006 (OROS MPH; N = 177)

A: ADHD-RS (d = −.22) NR: ADHD-RS (sig)

CD P: DISC (d = .19) Substance Use A: TLFB (d = .05); UDS (nsig) Substance Use A: TLFB (nsig); UDS (d = .22)

P: ADHD-RS (d = .53) A: ADHD-RS (d = −.10) P: ADHD-RS (d = .39) A: CWASS (d = .31) C: ADHD-RS (d = .57)

Driving A: Driving (sig) O: IDS (sig) Driving A: Driving (nsig) O: IDS (nsig) Academic O: work accuracy (10 mg d = .55; 20 mg d = .86; 30 mg d = .81); Quiz grades (10 mg d = .58; 20 mg d = .82; 30 mg d = .86) Lecture notes (10 mg d = .41; 20 mg d = .67; 30 mg d = .71) Essay length (10 mg d = .50; 20 mg d = .80; 30 mg d = .91); Essay accuracy (10 mg d = .32; 20 mg d = .43; 30 mg d = .57); Classroom behavior (10 mg d = .13; 20 mg d = .25; 30 mg d = .27) Overall A: quality of life (d = −.02) Family P: CBQ (d = .43) A: CBQ (d = .32) O: PAIRS Problem-Solving (d = .09); PAIRS Positive Behavior (d = .21); PAIRS Negative Behavior (d = .19) Academic T: IRS Academic (d = .04); IRS Teacher Relationship (d = .22); Class Preparation (d = .63); Note-taking (d = .95); Assignment Completion (d = .80) O: GPA (d = .40) Social T: IRS (d = .28) Academic T: IRS Academic (d = .30); IRS Teacher Relationship (d = .15); Prepared for Class (d = .79); Note-taking (d = .48); Assignment Completion (d = .73) O: GPA (d = .40) Social T: IRS (d = .13)

Substance use A: TLFB (d = .35); UDS (nsig) Family P: CCI (d = .46)

M.H. Sibley et al. / Clinical Psychology Review 34 (2014) 218–232

Pharmacological Bangs et al., 2007 (Atomoxetine; N = 142) Barkley et al., 2000 (IR MPH; N = 35)

ADHD

ADHD

Psychosociala Evans et al., 2011 (BT + MI; N = 49) Langberg et al., 2012 (BT; N = 47)

Meyer & Kelley, 2007 (SM; N = 42) Meyer & Kelley, 2007 (BT; N = 42) Molina et al. (2008) (BT; N = 20) Sibley et al. (2013) (BT + MI; N = 36)

Steiner et al. (2011) (AT; N = 41)

Steiner et al. (2011) (NF; N = 41)

Impairment

Source: measure

Source: measure

Source: measure

P: DBD-Inattention (d = .41); DBD-H/I (d = .90) T: DBD-Inattention (d = .17); DBD-H/I (d = .20) P: VARS-Inattention (d = .69); VARS-H/I (d = .22)

Academic T: IRS (d = .25); CPS (d = .26) O: GPA (ns) Social P: IRS (d = .26) T: IRS (d = .36) Academic P: COSS Task Planning (d = .88); COSS Organized Actions (d = 1.14); COSS Memory and Materials Management (d = .44); COSS Life Interference (d = 1.00); HPC Homework Completion (d = .66); HPC Materials Management (d = .40) T: Math COSS Task Planning (d = −.32); Math COSS Organized Actions (d = .02); Math Memory and Materials Management (d = −.26); Language Arts COSS Task Planning (d = .40); Language Arts COSS Organized Actions (d = −.11); Language Arts COSS Memory and Materials Management (d = .11) Family P: COSS Family Conflict (d = .60) Academic P: HPC (d = 3.89) T: CPS (d = 1.36); HW% (d = 1.36) Academic P: HPC (d = 4.44) T: CPS (d = 1.30); HW% (d = 1.51) Academic A: BASC- School (d = .76) O: GPA (d = .50) Overall P: Externalizing P: BASC (d = .20) A: ACPS (d = .56) Internalizing P: BASC (d = .47) A: BASC (d = .72) IRS (d = −.35) P: DBD-Inattention (d = 1.42); DBD-H/I (d = 1.20) T: ODD P: DBD ODD (d = .83) T: DBD ODD (d = −.23) Academic P: AAPC (d = 1.30); IRS (d = .75) T: AAPC (d = .00); DBD-Inattention (d = .23); DBD-H/I (d = −.54) IRS (d = −.49) O: Planner (d = 5.15); OC (d = .64); GPA (d = .25) Family P: CBQ (d = .82); CSQ (d = .39) A: CBQ (d = .65)

P: ADHD-RS (nsig) T: ADHD-RS (nsig) P: CRS-Inattention (d = .41); CRS-H/I (d = −.19); BASC-Attention (d = .10); BASC-H/I (d = .05) T: CRS-Inattention (d = .20); CRS-H/I (d = −.78) A: CRS-Inattention (d = −.33); CRS-H/I (d = −.45); BASC-Attention (d = .86); BASC-H/I (d = .18) P: CRS-Inattention (d = .56) CRS-H/I (d = .63); BASC-Attention (d = −.02); BASC-H/I (d = .39) T: CRS-Inattention (d = .13); CRS-H/I (d = −.44) A: CRS-Inattention (d = .08); CRS-H/I (d = −.18); BASC-Attention (d = .39); BASC-H/I (d = −.32)

ODD P: ODD (nsig) T: ODD (nsig) Academic O: WRAT (nsig) Executive functioning P: BRIEF (d = .45) T: BRIEF (d = −.43)

Executive functioning P: BRIEF (d = .20) T: BRIEF (d = −.02)

Note. Where effect sizes could not be computed from reported data, the reported statistical significance (sig = significant, nsig = non-significance) is reported instead; Treatments: IR = immediate release, XR = extended release, OROS = osmotic release, MPH = methylphenidate, MAS = mixed amphetamine salts, LDX = lisdexamfetamine, MI = Motivational Interviewing, BT = Behavior Therapy, SM = Self-Monitoring, WMT = Working Memory Training, AT = Attention Training, NF = Neurofeedback; Source: P = Parent, T = Teacher, A = Adolescent, C = Clinician, O = Observation; Measures: ADHD-RS = ADHD Rating Scale, ODD = Oppositional Defiant Disorder Rating Scale, IOWA-C = IOWA Conners, PANAS = Positive Affectivity Negative Affectivity Scale, CBQ = Conflict Behavior Questionnaire, PAIRS = Parent Adolescent Interaction Rating Scale, IRS = Impairment Rating Scale, GPA = Grade Point Average, TLFB = Time Line Follow Back (SU = Substance Use), CWASS = Conners–Wells Self-report of Symptoms Scale, CCI = Child Conflict Index, CPS = Classroom Performance Survey, VARS = Vanderbilt ADHD Rating Scale, COSS = Children's Organization Skills Scale, HPC = Homework Problems Checklist, HW = Homework, BASC = Behavioral Assessment Scale for Children, ACPS = Aggression and Conduct Problems Scale, AAPC = Adolescent Academic Problems Checklist, OC = Organization Checklist, CSQ = Caregiver Strain Questionnaire, WRAT = Wide Range Achievement Test, CRS = Conners Rating Scale, BRIEF = Behavioral Rating Inventory of Executive Functioning, IDS = Impaired Driving Score (simulator). a Barkley et al. (2001) was not included in this table because the data for the retained sample was not available separated by outcome measure.

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Cognitive Gray et al. (2012) (WMT; N = 60)

Comorbid

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measures). For example, open-label extension trials were not included as their purpose was to evaluate maintenance effects rather than acute efficacy (see Fig. 1). Two authors (MHS and ABK) cooperatively conducted the initial search and selected potentially eligible studies based on information provided in article titles and abstracts. Full text articles were obtained for all potential studies and subsequently evaluated for inclusion. Disagreements between the two authors were resolved through discussion. After selecting the final list for inclusion, 10% of the potentially eligible studies were randomly selected for an inter-rater reliability probe. An additional author (SWE) evaluated the reliability subsample for inclusion and agreement with the original coders (MHS and ABK) was 100%. Fig. 1 summarizes the outcome of the study inclusion process: 53 studies were included for review. All included studies were systematically coded for age range and mean, minority and gender distribution (% male and % white), diagnostic method (interview, rating scale, or a specified combination) study design (randomized controlled trial/crossover, quasi-experimental, open trial, case study, chart review, program evaluation) sample size, treatment dose (by weeks or hours), outcome measure, and effect size (where applicable). Though all included studies were coded and considered in the discussion of results, effect sizes (ES's) were only calculated for controlled trials to give greater weight to more rigorous studies,. ES's were calculated based on the mean baseline to post-treatment change in the treatment group or condition minus the mean baseline to posttreatment change in the control group or condition, divided by the pooled baseline standard deviation (Morris, 2008). One author (MHS) conducted the initial coding and 10% of coded articles were selected for reliability coding by an additional author (ABK). Agreement across categories was 95.0%. When aggregating ES's by treatment modality, 80% confidence intervals were computed following recommendations of Cohen (1990). 5. Results 5.1. Medication studies During the last 15 years, a variety of medications were evaluated for adolescents with ADHD (see Table A1). The current review identified 17 controlled trials that reported separate adolescent-specific outcomes (average N = 149.10), with the pharmaceutical industry funding a majority (58.8%) of these trials. With the exception of two trials of atomoxetine and one of extended release guanfacine, these studies primarily evaluated Central Nervous System (CNS) stimulants. In addition to the 17 controlled trials, eight open-label studies and two case reports were published since 1999. Although the sample sizes for controlled studies were larger than those in the previous review, most were insufficiently powered to compare relative doses. Similar to the previous review, typical participants were Caucasian males in middle adolescence and most ADHD diagnoses were made based solely on parent report (Smith et al., 2000). Similarly, indices of symptom change were primarily gathered from parents. Only six (35.3%) of the controlled trials included measures of impairment (see Table 1). Overall, ES's for medication (see Tables 1 & 2) ranged widely; however, mean effect sizes were small to medium for symptoms of ADHD and ODD (.22–.64) and for impairment domains (.21–.56). 5.2. Behavior therapy We identified 22 new studies of BT for adolescents with ADHD (see Table A1) —six controlled trials, two quasi-experimental studies, eight open trials, four case studies, and two program evaluations. These trials represented studies with younger and older adolescents, and in all but two cases (see Tables A1–A3), evaluated samples with a gold standard ADHD diagnosis. Like the previously reviewed studies (Smith et al., 2000), psychosocial treatment studies represented samples that were

majority White (86.4%) and male (90.9%). However, the average sample size (N = 30.4) of psychosocial treatment studies in this review was almost double that of the previous one (N = 17.5; Smith et al., 2000). Most studies employed methodologically robust multi-informant (63.6%) and multiple outcome (90.9%) assessment strategies, yielding valuable information about circumstances under which psychosocial interventions produced gains. Overall, ES's for psychosocial interventions (see Tables 1 & 2) ranged widely; however, mean effect sizes for symptoms of ADHD and ODD were small to medium (.34–.49) and for impairment domains were small to large (.31–1.20). 5.3. Cognitive enhancement training We identified three studies that evaluated two classes of cognitive enhancement training (CET): (1) neurofeedback (NF), or electroencephalograph (EEG) biofeedback and (2) working memory training (WMT). These studies consisted of two controlled trials and an open trial. CET studies were primarily conducted with younger adolescents who possessed community ADHD diagnoses and received treatment in school. All study samples were majority male and the racial and ethnic composition of the sample was reported for only one of the three studies. All studies employed methodologically sound multi-informant assessment strategies, but no studies examined intervention effects on impairment. Available symptom ES's for CET interventions suggested that overall, these treatments did not produce significant gains (see Tables 1 & 2). 6. Discussion 6.1. Medication The 2000 review by Smith and colleagues identified eight small methylphenidate (MPH) crossover trials (average N = 22.38), one controlled trial of a tricyclic antidepressant (N = 20), and five small open trials of nonstimulants (average N = 11.20; Smith et al., 2000). All stimulant trials used immediate-release methylphenidate (IR MPH) with doses ranging between .3 and .6 mg/kg. Parent and teacher ratings of ADHD and other externalizing symptoms typically served as the primary outcome measure, but direct observation and measures of academic productivity also were utilized. Among the evaluated treatments, only IR MPH was deemed well-established (Smith et al., 2000). During the review period, multiple stimulant and non-stimulants were newly approved for the treatment of ADHD in adolescents. When reviewing efficacy data for adolescents, it is important to note that current FDA standards grant approval across an entire sample age range, even if data do not demonstrate efficacy in a specific subpopulation (e.g., adolescents). Therefore, several medications were recently approved for the treatment of ADHD up to age 17 primarily on their effects in school-aged children (e.g., Kollins et al., 2011; Sallee et al., 2009). A majority of these studies were excluded from this review because they did not present separate data for adolescents (e.g., Kollins et al., 2011). The most common primary outcome measure in medication studies was baseline to endpoint change on the clinician-rated ADHD Rating Scale (ADHD-RS; DuPaul, Power, Anastopoulos, & Reid, 1998), with the parent as the primary informant. The mean effect size for reduction in ADHD symptom severity (see Table 2) was less than that reported for school aged children (AACAP, 2007; Greenhill et al., 2002). Only small effects were seen for reduction of ODD symptoms, but few studies measured this domain, limiting the ability to draw definitive conclusions. The percentage of participants rated as much or very much improved on the ADHD Clinical Global Impressions Improvement Subscale (CGII) by the prescribing clinician was a common secondary outcome. Six of the studies employed measures of impairment, although the impairment measures varied widely across studies. They included

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assessments of quality of life, academic productivity, family relations, social functioning, and driving safety. The number of participants assessed in any one of these domains is relatively small, limiting the ability to draw strong conclusions about the impact of medication on actual real life functioning in adolescents with ADHD. The most robust effects were seen for academics with limited effects seen in other domains. While there are no reliable predictors of medication response in individual patients (Greenhill et al., 2002), the MTA found that parental ADHD, male gender, and receipt of public assistance predicted a reduced response to pharmacological and psychosocial treatment at 36 months when the mean participant age was 11.9 (Jensen et al., 2007). Studies in school-aged children may use analogue classrooms where direct observation is possible. However, almost all adolescent studies were conducted in outpatient settings using relatively brief weekly assessments. Unlike school aged children, parents may spend little time completing academic work with adolescents, limiting their ability to accurately rate treatment effects and thereby potentially contributing to the reduced treatment effects vs. younger children. For the sixteen controlled studies reporting results specifically for adolescents, 38% relied solely on parent report, with just 44% collecting ratings from multiple informants. Two (12%) studies collected outcome ratings only from teachers while a single study (6%) employed only laboratory measures of driving safety (Cox et al., 2006). Few studies examined the combination medication and behavioral treatment, even though medicated adolescents remain significantly impaired compared to non-ADHD peers (Molina et al., 2009). Two studies employed psychosocial interventions designed to reduce substance use, rather than ADHD (Riggs et al., 2011; Thurstone, Riggs, SalomonsenSautel, & Mikulich-Gilbertson, 2010). Three other studies were completed in a setting where concurrent psychosocial intervention was implemented (Evans et al., 2001; Pelham, Meichenbaum, et al., 2013; Pelham, Smith, et al., 2013); however, the selected methodology did not allow for direct comparison of the pharmacological and psychosocial treatments. Due to the diversity of medications and methodologies employed across the controlled trials, results from the representative studies of each medication class are discussed below.

Table 2 Mean group × time effect sizes for controlled medication, psychosocial, and cognitive enhancement training trials. N ADHD symptoms Medication Psychosocial Cognitive enhancement training

Mean ES

80% Confidence interval

11 3 1

.64 .49 .06

.47–.81 .45–.52 –

ODD/CD symptoms Medication Psychosocial

4 2

.22 .34

.16–.28 .32–.36

Academic impairment Medication Psychosocial

2 5

.56 1.20

.53–.58 .94–1.47

Social impairment Medication Psychosocial

1 1

.21 .31

– –

Family impairment Medication Psychosocial

1 2

.36 .77

– .72–.81

Note. Available effect sizes were aggregated by treatment type for 15 medication studies, five psychosocial studies, and three cognitive enhancement training studies. When multiple doses were evaluated in a study, the highest dose was included in aggregate ES calculations. When multiple measures were reported by a study within a single domain, ES's within each study were aggregated by domain. Table 1 presents detailed effect sizes for each included study.

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6.1.1. Methylphenidate Wilens, McBurnett, et al. (2006), Wilens, Newcorn, et al. (2006) completed an industry-funded trial of OROS MPH (Concerta), which was designed to mimic IR MPH given three times daily (see Table A1). This trial started with a four week open-label titration with dose fixation once ADHD symptoms improved by 30% (not necessarily the optimal dose), followed by a two week blinded comparison of placebo vs. optimal dose. All OROS doses outperformed placebo with a 47% mean decrease on the ADHD-RS vs. a 31% change for placebo (d = .57, see Table 1). Teens reported a smaller overall improvement than clinicians and parents, with a 35% decline in self-reported symptoms on active medication versus 20% on placebo (d = .31, see Table 1). Significant reductions with OROS vs. placebo were also seen in parent ratings of home conflict. On the CGI-I, clinicians rated 51% as much or very much improved on OROS vs. 33% on placebo. A similarly designed study in ages 6 to 12 found a comparable response rate for OROS but a placebo response rate of only 17% (Wolraich et al., 2001). The most common side effects in adolescents were headache, appetite loss, abdominal pain and insomnia. Findling et al. (2010) completed an industry-funded trial of transdermal MPH (Daytrana). The 216 participating teens were titrated to optimal dose under open label conditions prior to the double blind phase. The patch was worn for nine hours per day, which produces an effect for up to 12 h (Wilens et al., 2008). No adolescent or teacher report measures were incorporated. Medication produced a 19-point reduction in clinician ADHD-RS scores vs. a 9-point reduction for placebo (d = 1.34). Clinician CGI-I displayed a 66% response for medication and 31% for placebo. The larger medication response in this study versus OROS was likely due to titrating the dose to optimal response vs. only to a 30% reduction in the OROS trial. The most frequent adverse events were appetite loss, irritability and nausea. While industry studies primarily focused on symptom improvement, non-industry studies examined the impact of MPH on other outcomes. Pelham, Meichenbaum, et al. (2013) examined the effects of a .3 mg/kg dose of IR MPH on mother–child interactions in 25 adolescents with ADHD, compared to 14 non-ADHD teens. The interactions occurred during laboratory-based problem-solving discussions. As expected, adolescents with ADHD had greater conflict in their interactions compared to the non-ADHD peers. Even though all adolescents with ADHD had previously demonstrated a positive response to MPH, only mild effects (mean d = .19) were seen. The trial examined the immediate effects of medication, and sustained treatment may lead to differentially positive results as seen in a recent trial of parent–child ADHD dyads using lisdexamfetamine (Waxmonsky et al., 2013). Evans et al. (2001) examined the impact of IR MPH on academic performance and classroom behavior during a six-week therapeutic summer camp. Using a crossover design, participants were treated with three doses of MPH (10, 20 and 30 mg 3 times per day) and placebo under blinded conditions. Benefits were seen across measures of academic performance (quiz scores, lecture note accuracy), writing (detail, structure, and grammar), behavioral observation, and teacher symptom ratings. The majority of adolescents experienced a medium effect of medication at the 10 mg dose. For participants not responding to the 10 mg dose, over 50% improved on the 20 mg dose while an additional 20% responded to the 30 mg dose. These results document the efficacy of MPH on academic productivity in adolescents with ADHD. However, the classroom was a controlled setting with appreciable behavioral supports, and a similar protocol in naturalistic school settings produced smaller effects, possibly in part due to poor medication adherence (Pelham, Smith, et al., 2013). 6.1.2. Amphetamine There were no controlled trials of amphetamine-based products for adolescents with ADHD published before 1999 (Smith et al., 2000). Now, a substantial database exists, especially for ER amphetamine products, largely due to an influx of industry-sponsored trials. Spencer,

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Wilens, et al. (2006), Spencer, Biederman, et al. (2006) published a multisite trial of ER mixed amphetamine salts (MAS XR—marketed as Adderall XR) in 297 adolescents. No teacher or youth symptoms reports were gathered. On the ADHD-RS, there was a 17.4 reduction in MAS XR treated participants vs. a 9.4 reduction for placebo. Between 50 and 65% of MAS XR treated participants were rated as much or very much improved on the CGI-I (varied by dose) vs. 26.9% of those receiving placebo. Comparatively, a similar percentage of MAS XR responders were seen in the trials of children ages 6–12 with a placebo response rate under 20% (Biederman, Lopez, Boellner, & Chandler, 2002). MAS XR was well tolerated with fewer than 2% attrition due to adverse events even though nearly 80% of participants were stimulant naive. Appetite loss, headache and insomnia were the most common side effects. Lisdexamfetamine (LDX—marketed as Vyvanse) is a prodrug version of dextroamphetamine, where the dextroamphetamine only becomes bioavailable after oral ingestion, making it harder to misuse by alternate routes (Jasinski & Krishnan, 2009). Although still classified as a controlled substance, its reduced abuse liability may be of clinical relevance for adolescents at risk to abuse or divert CNS stimulants. LDX was approved for adolescents based on a 4-week randomized, controlled study of 308 adolescents similar in design to the MAS XR study (Findling et al., 2011). All doses of LDX separated from placebo on the clinician rated ADHD-RS (47 to 55% reduction with LDX vs. 33% with placebo); however, parent and teacher reports were not collected. Based on CGI-I, 69% of the participants prescribed LDX were rated as much or very much improved compared to 39.5% with placebo. No differences were seen on youth reported Quality of Life; however, the short duration of the trial as well as the use of a QOL measure that was not specifically designed for individuals with ADHD. As with MAS XR, adolescent side effects and response rates were comparable to that seen in children, but the placebo response rate was nearly twice as large in teens (Biederman, Krishnan, Zhang, McGough, & Findling, 2007). 6.1.3. Nonstimulants In 2002, atomoxetine (ATX), which is marketed as Strattera®, became the first nonstimulant approved by the FDA for the treatment of children and adolescents with ADHD. ATX shares more in common with older tricyclic antidepressants (TCAs) than with CNS stimulants, but with a much improved safety profile compared to the TCAs (Waxmonsky, 2005). The initial trials that led to FDA approval included 176 adolescents (17% of the total sample; Wilens, Newcorn, et al., 2006). As ATX may take a week or more to achieve optimal effect, the trials lasted longer than most CNS stimulant trials but employed similar methodology. Mean ADHD-RS change was 14 points (40% reduction) for ATX versus seven points for placebo. While ATX typically produces a smaller reduction in ADHD symptoms than CNS stimulants, no differences in ATX efficacy were seen between children and adolescents (Wilens, Newcorn, et al., 2006). Compared to CNS stimulants, ATX has both advantages for adolescents (not a controlled substance, ability to cover the entire day) and disadvantages (need to take every day of week, delayed therapeutic onset and overall lower effect size) that make it a reasonable consideration in cases where CNS stimulants are inadvisable, ineffective, or intolerable. Alpha agonists (guanfacine and clonidine) have been used for decades in children with ADHD (Waxmonsky, 2005). However, only the ER versions are FDA approved (marketed as Intuniv and Kapvay). Like ATX, alpha agonists provide a non-controlled alternative to CNS stimulants with the tradeoff of lower effect sizes, the inability to use on an as needed basis, and a delayed therapeutic onset. Unlike ATX, alpha agonists are approved for adjunctive use with CNS stimulants, improving response rates by 25% over placebo (Kollins et al., 2011; Wilens et al., 2012). Only the monotherapy trial of ER guanfacine analyzed adolescents separately, and medication did not outperform placebo in this age range (Biederman et al., 2008; Sallee et al., 2009). The ER clonidine trials enrolled a lower percentage of teens and did not publish a

separate efficacy analyses for adolescents (Jain et al., 2011; Kollins et al., 2011). Hence, there is limited data to support their effectiveness in adolescents. There is only uncontrolled data to support the promise of additional non-stimulant agents such as bupropion (see Table A1). 6.1.4. Additional domains It is well established that ADHD is a risk factor for substance abuse (Charach et al., 2011). The use of CNS stimulants for the treatment of ADHD in substance abusing youth is controversial due to potential addiction risks in these medications (Waxmonsky & Wilens, 2005). In one of the first trials in this area, pemoline was somewhat effective for treating ADHD (d = .5) with no impact on substance use (Riggs, Hall, Mikulich-Gilbertson, Lohman, & Kayser, 2004). Recently, the same group examined the impact of OROS MPH in a 16-week randomized controlled trial of 303 adolescents with ADHD and a substance use disorder (Riggs et al., 2011). OROS was selected as its physical and pharmacological properties make it less likely to be abused than IR stimulants (Spencer, Biederman, et al., 2006). Participants were titrated under blinded conditions to the highest tolerable dose of OROS MPH or placebo during the first two weeks, with all participants receiving CBT for the duration of the trial. Unlike most prior ADHD trials, adolescent-reported ADHD-RS served as the primary outcome with parent-rated CGI and ADHD-RS as secondary outcomes. Self-report and urine tests were used to monitor substance use. There were nearly identical reductions in adolescent-reported ADHD-RS scores (19.2 vs. 21.2 point reduction), but OROS significantly outperformed placebo by parent report (14 vs. 8 point reduction). CGI response rates by adolescent report were similarly low for both groups (26% vs. 23%). There were no differences in selfreport of substance use although OROS produced more clean urine screens (3.8 vs. 2.8, d = .22). Despite active use of illicit substances, OROS was well tolerated. In a smaller (N = 70) but comparably designed study (Thurstone et al., 2010), twelve weeks of ATX treatment plus CBT produced similar reductions in teen-rated ADHD symptoms. However, ATX did not separate from placebo, despite mean difference in ADHD-RS scores that were comparable to those in the OROS trial. Unlike in the OROS trial, where a small difference was reported, there was no difference in substance use on any measure. The ATX was well tolerated. In both studies, authors speculated that the use of CBT may have impacted the degree of change observed in both arms. In summary, there is some limited evidence that OROS may be efficacious as an adjunct to psychosocial interventions for substance abusing youth with ADHD. ADHD also impairs driving performance in adolescents and adults (Thompson et al., 2007). In young adults, multiple CNS stimulants (Barkley, Murphy, O'Connell, & Connor, 2005; Cox et al., 2012) improve driving safety in naturalistic and laboratory settings compared to placebo. The evidence base in adolescents is strongest for OROS. Cox et al. (2006) reported that OROS produced benefits for up to 15 h, improving driving into the evening. Comparative studies produced less robust results for MAS XR than OROS especially in the evening, but it is unclear if observed differences extend from methodological issues or differences in medication properties (Cox et al., 2006, 2008). Pilot studies of nonstimulants in young adults do not demonstrate efficacy in driving (Barkley, Anderson, & Kruesi, 2007; Kay, Michaels, & Pakull, 2009) and there are no trials in adolescents. 6.2. Behavior Therapy Reviewing studies conducted through 1998, Smith et al. (2000) identified (1) a small crossover study of a note-taking intervention delivered directly to students, (2) two single-case studies of behavioral classroom interventions, (3) a small trial of teen-directed cognitive behavioral therapy (CBT; N = 12), and (4) an early evaluation of a parent–teen behavior therapy intervention (N = 61). With the exception of the teen-directed CBT study, these evaluations supported the initial promise of behavior therapy (BT) applied to adolescents.

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However, support for these interventions was preliminary and insufficient to deem BT as a recommended first-line treatment for adolescents in subsequent practice parameters (AACAP, 2007; AAP, 2011). The next wave of research represented efforts to develop and evaluate novel, adolescent-specific models for BT (see Table A2). Like traditional BT for ADHD in childhood, these treatments identify operationally defined target behaviors (e.g., turning in homework, speaking to parents respectfully, completing nightly study time) that are monitored daily and reinforced through age-appropriate contingency management (e.g., internet privileges, participation in extra-curricular activities, unmonitored time with friends). However, unlike BT for children, these new adolescent-specific BT programs promote teen autonomy and self-efficacy by establishing a collaborative relationship between parents and adolescents, often by holding joint parent–teen sessions. BT for teens aims to clarify behavioral expectations, elicit personal treatment goals from the adolescent, teach teens to voice ways in which they would like to increase personal independence, and provide behavioral contracts that are typically monitored with adult support. Our review identified BT programs delivered through after-school programs (e.g., Evans, Schultz, DeMars, & Davis, 2011; Molina et al., 2008), intensive summer programs (e.g., Sibley, Pelham, Evans et al, 2011), school counselors (Evans et al., 2007; Langberg, Epstein, Becker, Girio-Herrera, & Vaughn, 2012), and outpatient clinics (e.g., Antshel, Faraone, & Gordon, in press; Barkley et al., 2001; Fabiano et al., 2011; Sibley et al., 2013). Due to the unsupervised nature of adolescence, these treatments were infrequently delivered in the target environment. Rather, adolescents received training in skills to be applied to in classrooms, with peers, and at home (e.g., Langberg et al., 2012; Meyer & Kelley, 2007) that were sometimes monitored through behavioral contracts with adults (Barkley et al., 2001). Furthermore, these therapies indicated an emphasis on improving daily functioning, rather than symptom severity. Of the treatment programs reviewed, a majority designated academics (68.2%) or family conflict (22.7%) as the targeted domain of functioning, with additional applications of BT to driving (Fabiano et al., 2011) and peer group behavior (Evans et al., 2011; Sibley, Pelham, Mazur, et al., 2012). With respect to family conflict,effect sizes were medium to large (see Tables 1 & 2), which is promising given that parent–teen conflict is frequently noted as a barrier to both pharmacological and psychosocial treatments (e.g., Barkley et al., 2001). It is not surprising that BTs for teens primarily targeted academics because unlike in childhood, school failure during adolescence begets an immediate risk for very serious outcomes (e.g., high school dropout, delinquency, substance use). With a goal of preventing this academic failure, the last fifteen years witnessed particularly novel applications of BT to the secondary school setting (e.g., Evans et al., 2007, 2011; Langberg et al., 2012). Additionally, some recent treatments make notable use of modern technology such as web-based grade portals (Sibley et al., 2013), computerized driving monitoring devices (Fabiano et al., 2011), and video-feedback (Sibley, Pelham, Mazur, et al., 2012) to update and enhance delivery of BT to tech-savvy teens. The case studies reviewed reinforced existing evidence that BT delivered to teens in controlled and well-monitored settings produces generally large improvements in functioning (see Table A2). The majority of studies conducted represented uncontrolled open trials, program evaluations, and quasi-experimental investigations. These studies represented an important step between early pilot work on BT for teens (Smith et al., 2000) and more recent RCTs (see Table 1). All uncontrolled trials reported at least medium effects for BT on some outcome measures, justifying further evaluation of emerging BT models. Controlled evaluations of BT yielded significant effects, with a median between-group effect size of .411 (see Table 1), which is comparable to the reported 1 For each study, an average ES was obtained by computing the mean ES across dependent measures. The median ES across studies was then selected to represent central tendency. We mimicked the procedure used by Pelham and Fabiano (2008) to facilitate comparison of childhood and adolescent BT ESs.

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median ES for childhood BT studies (.44–.46; Pelham & Fabiano, 2008). It is notable that across all controlled BT studies, robust treatment effects were present for parent-rated and directly observed outcomes, with very small effects reported for GPA, and almost no significant effects for teacher-rated outcomes. With occasional exceptions (e.g., Schultz, Evans, & Serpell, 2009), this pattern was also present in uncontrolled studies (see Table A2). Smaller ES's reported in the school setting may reflect a measurement issue because, unlike teacher ratings and GPA, observed academic indices tended to yield large effects (Meyer & Kelley, 2007; Sibley et al., 2013). Namely, secondary school teachers may not notice an adolescent's improvement because they spend very little time in the presence of their students (less than an hour a day) and typically teach 100 students daily (Eccles, 2004). However, small effect sizes for teacher ratings and GPA may also underscore noted challenges to effectively delivering BT in secondary school settings. Thus, improvements witnessed by parents at home may not generalize to the school setting. With evidence that BT can be effectively delivered in schools when resources are sufficient (Langberg et al., 2012), increasing the number of mental health professionals or behavioral support specialists in secondary schools may facilitate delivery of school-based BT. However, when school resources are scarce, it may be necessary to develop academic BT models that overcome an increasingly widespread shortage of secondary school intervention staff (AASA, 2012). Alternatively, relieving these professionals from administrative tasks (e.g., scheduling) could also provide additional time for intervention delivery. The studies reviewed herein also indicated the potential for parents to collaborate with teens to maximize effects of BT. These studies displayed an increased emphasis on parent–teen collaboration with a parent component included in 81.8% of reviewed treatments, compared to only 20.0% of previous studies (Smith et al., 2000). For example, a previously reviewed CBT protocol (Morris, 1993) did not include a parent component and reported no therapeutic benefit. However, a recent open trial (Antshel et al., in press) showed the promise of CBT when delivered to parents and teens as a collaborative team. Considering historic parental hesitance to engage in BT with resistant teens (Barkley et al., 2001), the new generation of parent–teen BTs made ready use of Motivational Interviewing (MI; Miller & Rollnick, 2013) to engage dyads (Evans et al., 2011; Fabiano et al., 2011; Sibley et al., 2013), with initially promising results. Many of the studies reviewed (50.0%) employed a multimodal psychosocial treatment package (e.g., school and home components). Adolescents spend time in multiple settings daily and subsequently, multimodal approaches may maximize results. Relatedly, the reviewed BTs represented a range of doses (2 to 360 h direct treatment). However, it does not appear (see Tables 1 and A2) that study effect sizes were directly associated with treatment dose. These data indicate that low intensity and inexpensive psychosocial treatments may significantly improve the functioning of many adolescents with ADHD. The primary therapeutic mechanism of these low-intensity interventions is teaching adults (e.g., parents, school counselors) who have daily contact with adolescents to regularly collaborate with teens in support of BT delivery (e.g., Fabiano et al., 2011; Langberg et al., 2012; Sibley et al., 2013). Therefore, although fewer hours of direct clinical contact are needed to deliver the intervention, treatment becomes infused in the adolescent's daily routine through checklists or contracts with an adult. Of course, intensive programs (e.g., Challenging Horizons Program, Evans et al., 2011; Summer Treatment Program—Adolescent, Sibley, Pelham, Evans, et al., 2011) may be necessary for adolescents with the severest impairments. Across studies, there was almost no follow-up of adolescents after active treatment terminated. It is possible that intensive treatments lead to increased maintenance of therapeutic effects and consequently separate from low intensity treatments in post-intervention periods. Further studies are needed to address which forms of adolescent BT work for whom under which conditions.

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6.3. Cognitive enhancement training

6.5. Assessment of treatment response

Since the previous review, a third treatment modality, CET, emerged to treat adolescents with ADHD. The aim of these programs is to improve ADHD-related cognitive deficits through computerized tasks that promote memory and attention through practice and immediate performance feedback. The difference between the two types of programs is that NF bases its feedback on brain waves measured by EEG, and WMT provides feedback based on task performance. Overall, the two RCTs found no effects for academic or classroom behavior measures. However, one study found effects on parent rating scales for a minority (25.0%–41.7%; see Table 1) of measured constructs (Gray et al., 2012; Steiner, Sheldrick, Gotthelf, & Perrin, 2011). It is possible that these few positive effects (Steiner et al., 2011) were due to interventionist attention; thus, future studies of WMTs should include a control group with the same level of interactions with the research team. The third reviewed study compared two types of WMT tasks and did not find significant effects on any measures. The results reviewed herein diverge from a handful of studies with school-aged children that report symptom effects for NF (e.g., Gevensleben et al., 2009), but are similar to two recent broad meta-analysis on WMTs (Melby-Lervåg & Hulme, 2013; Rapport, Orban, Kofler, & Friedman, 2013), which concluded that WMTs did not improve functioning. Although some studies reported gains on computerized working memory measures, improvements did not translate into changes in functioning. As a result, it is unlikely that CET is beneficial to adolescents with ADHD.

The reviewed studies revealed several important considerations for assessing treatment response in ADHD adolescents. Most studies adopted a multi-informant assessment battery, as is highly recommended in the measurement of functioning and treatment outcome in ADHD youth (Pelham, Fabiano, & Massetti, 2005). Across controlled evaluations of medication, BT, and CET, 75.0% of studies contained parent-reported effects that surpassed effects reported by teachers and adolescents (see Table 1). Furthermore, compared to studies with ADHD children (Wolraich et al., 2001), placebo response rates were higher for parent-reported outcomes in the adolescent medication studies. Parents display documented expectancies toward treatment that may influence their perceptions of therapeutic response (Waschbusch, Pelham, Waxmonsky, & Johnston, 2009). The inflated placebo response rates detected herein suggest that this bias may be especially pronounced for parents of adolescents with ADHD, who may have limited information about their son or daughter's daily functioning. These data highlight the need to improve the rigor of existing studies to combat expectancy effects in parents with active control conditions and improved efforts to blind parents to treatment group, particularly in BT studies. While parents may inflate an adolescent's response to treatment, it appeared that adolescents and teachers did the opposite. These informants traditionally produce temporally stable and sometimes deflated ratings of symptoms and impairment in treatment studies due to: (1) the tendency for adolescents to deny impairments (e.g., Sibley, Pelham, Molina, et al., 2012) and (2) the brevity of daily interactions between secondary school students and teachers (Evans et al., 2005). Thus, adolescents may not report a response to treatment when they do not perceive impairment and busy secondary school teachers may not notice improvements in adolescent behavior over time. For example, Evans et al. (2001) reported large medication effects for teacherrated functioning in an analogue classroom. However, using the same sample, measures, and medication doses, Pelham, Smith, et al. (2013) reported far smaller teacher-rated effects when the medication trial was extended to the naturalistic secondary school setting even though large improvements in direct product measures were still observed. Though evaluated in a minority of studies, direct academic product indices appeared most sensitive to pharmacological and BT effects (see Table 1). GPA provided an exception to this trend, with perhaps the smallest reported treatment effects overall (see Table 1). It should be noted that GPA may be a problematic index of treatment response in secondary schools due to the way in which it is aggregated across time, classes, and indices of academic functioning. Notably, a promising new direction for the measurement of academic functioning and treatment response in adolescents with ADHD may be the use of assignment level academic performance, now increasingly available through online grade portals (Lacina, 2006). These data can provide daily objective measurement of work completion, tests and quizzes performance, and work accuracy. This assignment-level data, supplemented with direct product indices such as observations of materials organization, accuracy of class notes, and preparation for class, are likely to be the most sensitive indices of treatment effects for adolescents with ADHD (see Tables 1, A1–A3).

6.4. Overall efficacy Maximally successful treatment for an adolescent with ADHD must address both behavioral symptoms (i.e., inattention, executive functioning deficits, motor overactivity, impulsivity) and impairment (i.e., school grades, parent–teen relationship, social relations, driving). Considering only controlled studies (see Table 1), our review revealed that 35.3% of medication trials, 80% of BT studies, and no CET studies evaluated measurable change on both symptoms and impairment indices. Despite an under-emphasis on impairment outcomes in pharmacological studies, our results suggested that both pharmacological treatment and BT produced a similar range of effects on symptoms and impairment (when evaluated; see Table 1). Studies of CET did not appear to produce meaningful remediation of ADHD symptoms or impairment. Medication and BT demonstrated a similar range of reductions in ADHD symptoms, teen driving, and academic impairment (see Table 1). With respect to impairment indices, effects appeared slightly larger for BT than medication. However, we caution the interpretation of these results as very few medication studies evaluated impairment. Medium to large effects were present across many BT studies that evaluated impairment, while reported impairment effects for medication studies were typically small (see Table 1). It should be noted that the majority of medication studies that evaluated effects on impairment were conducted in the context of adjunctive BT (Evans et al., 2001; Pelham, Meichenbaum, et al, 2013; Pelham, Smith, et al., 2013), which may have limited observed effects. Finally, peer impairment appeared to be the most treatmentresistant domain for adolescents with ADHD. Almost no controlled studies evaluated effects in this domain, and those that did found small effects (Evans et al., 2011; Pelham, Smith, et al., 2013). These outcomes highlight the need to explore novel treatments that enhance social functioning in adolescents with ADHD. The quasi-experimental and case studies reviewed offered some promise in this domain, with a handful of studies reporting larger effects than controlled evaluations (e.g., Evans et al., 2007; Sibley, Pelham, Mazur, et al., 2012).

6.6. Implications for practice Prominent practice parameters recommend medication as a first line treatment for adolescents with ADHD, advocating for BT only when pharmacotherapy is insufficient (AACAP, 2007; AAP, 2011). The evidence reviewed herein demonstrates a similar range of effect sizes for medication and BT. Though controlled pharmacological trials were approximately three times as large in size (average N = 149.1 vs. 48.5) and number (N = 17 vs. 6) as controlled psychosocial treatment studies, most medication studies did not evaluate indices of impairment. Thus, we believe it is premature for practice parameters to

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include preferential statements about the efficacy of medication over psychosocial treatments—especially given the substantial psychosocial ES's for impairment measures (see Table 2). In upcoming years, we hope that revised practice parameters will reflect the following recommendations. First, treatment planning for an adolescent with ADHD requires a careful resource assessment. Inexpensive, low-intensity interventions (e.g., low doses of stimulant medication, school-based BT) may be an appropriate first line approach for most adolescents with ADHD, as these treatments can produce effect sizes that are equivalent to higher doses (see Table 1). Regarding medication, 80–90% of teens desist medication use in adolescence (Molina et al., 2009) and report poor satisfaction with medication in naturalistic trials (e.g., Pelham, Smith, et al., 2013). Thus, when considering medication treatment, the adolescent's potential for adherence must be evaluated. The accuracy of an adolescent's self-perception, his/her motivation to change, and family functioning are key predictors of adherence to an adolescent's medical regimen (Biswas et al., 2009; Bullington et al., 2007) and can be assessed easily with brief rating scales. Adolescents who possess sufficient insight into their impairments, seek to improve, and possess supportive families may be most likely to adhere to and subsequently benefit from pharmacological treatments. As noted, oppositional teens may show greater acute benefit from BT than medication (see Tables 1 & 2), possibly due to documented problems with medication adherence in these youth (Kennard et al., 2004). Additionally, when administering BT, it is imperative that an adult collaborator is identified who can consistently oversee treatment. As reviewed, adolescent-directed BT is successfully implemented by a range of adults in a variety of settings. Selecting the form of behavior therapy to implement is largely a function of: (1) what skills will be most beneficial to the adolescent in his/her current environment and (2) who can consistently monitor application of these skills. In some cases, an adult may not be regularly available to implement BT and poor resources may preclude clinical or school-based intervention. In these cases, medication may be a more appropriate first-line approach. Conversely, MI may be an adjunctive BT component that motivates hesitant adult stakeholders to dedicate time to working with an adolescent (e.g., Sibley et al., in press). In some cases a combined medication/BT approach may produce maximal therapeutic benefit to the adolescent. However, to date there are no systematic evaluations of combined treatment for adolescents with ADHD. The MTA reported optimal treatment effects when medication and BT were delivered in combination to elementary-aged children (Conners et al., 2001); however, the year 8-year MTA follow-up data, collected when participants were approximately 17 years of age, suggests that the superiority of combined treatment dissipates in adolescence (Molina et al., 2009). Documented adolescent medication desistance paired with stronger maintenance effects for BT were cited explanations for the convergence of childhood medication, BT, and combined treatment groups at 8-year follow-up (Molina et al., 2009). Therefore, it is unclear whether combined treatment is superior in adolescence. For example, two studies that combined medication with CBT (Riggs et al., 2011; Thurstone et al., 2010) reported limited benefits for improving ADHD symptoms compared to CBT + placebo. However, both studies targeted adolescents with active substance use disorders and relied on self-report of ADHD symptoms, limiting the ability to generalize findings. On the other hand, medication studies conducted in the context of behavioral treatment (e.g., Evans et al., 2001) appear to report more robust effects in academic domains (see Table 1), possibly indicating that BT amplifies medication effects in teens. Thus, the interaction between medication and BT in adolescence is still poorly understood, begging the need for studies of systematically delivered combined treatment for adolescents.

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6.7. Future directions There is great potential for novel applications of combined treatment to enhance clinical care for adolescents with ADHD. If BT stands to enhance medication effects (e.g., Evans et al., 2001), perhaps adjunctive BT (e.g., parent–teen contracting + MI) could improve medication adherence. In support of this possibility, there is evidence from childhood samples that stimulant medication adherence appears to be strongest when paired with behavioral treatment (Stein, Klein, Greenhouse, & Kogan, 2012). As another example, adolescents with ADHD perform particularly poorly on high-stakes standardized tests (Hannon & McNaughton‐Cassill, 2011), which mark important educational milestones. There is evidence that stimulant medication taken on the day of the test produces important acute effects on test-taking performance (Evans et al., 2001), while BT that addresses study skills enhances test preparation (e.g., Evans et al., 2011). Thus, combined therapy may offer important opportunities to: (1) increase stimulant medication utilization in adolescence and (2) maximize treatment outcome by targeting complementary deficits (e.g., test preparation and attention during testing). Despite promising applications of a combined treatment in adolescence, there are no studies of this approach. Beyond the basic efficacy questions covered in this review, there are important future directions for treatment research. Foremost, to clarify the extent to which psychoactive medication improves the daily life functioning of adolescents with ADHD, it is recommended that future pharmacological studies include impairment-based outcome measures. Furthermore, there is a need for larger clinical trials evaluating psychosocial treatments for ADHD, particularly for intensive peer-setting interventions such as the STP-A (Sibley, Pelham, Evans, et al., 2011), on which there is no controlled research. Larger controlled psychosocial treatment studies would allow for nuanced investigation of treatment mechanisms and dosing and sequencing issues. As reviewed, psychosocial treatments for adolescents with ADHD take many forms and intensities—yet, it is unclear how a multimodal approached might be optimized for efficacy and cost-effectiveness. It is also possible that better understanding of hypothesized psychosocial and medication treatment moderators, such as ethnicity, race, and gender, would lead to an improved ability to match adolescents to individualized treatment sequences. Finally, future research must evaluate treatment maintenance, long-term treatment effects, and transfer of gains. For example, it most cases, it appears necessary for an adult to supervise the treatment of an adolescent with ADHD; thus, future work should investigate treatment components that enhance adolescent self-sufficiency in preparation for adult care. In addition, public efforts must be made to increase access to effective treatments in everyday settings such as schools and primary care facilities.

7. Conclusions In sum, this review documents considerable growth in the number of treatment studies on adolescents with ADHD in the past 15 years and an increase in the range of effective therapies available to these youth. Despite historically poor treatment utilization (Biswas et al., 2009), a new generation of pharmacological and BT treatments, delivered separately or in combination, offer adolescents with ADHD a promise of increased access to care. Clinicians should carefully consider the fit between an adolescent and available empirically-supported treatments to maximize the likelihood of treatment adherence and efficacy. Ineffective or unpalatable interventions may further erode an adolescent's already limited motivation for treatment. A successful transition to adulthood requires mastery of organization, interpersonal, and planning skills (Steinberg et al., 1994) and abstinence from serious risk-behaviors. Thus, adolescence is a particularly critical period for sustained treatment of ADHD symptoms and impairments.

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Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.cpr.2014.02.001.

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