Skip to main content
  • Study Protocol
  • Open access
  • Published:

Study protocol of a randomized trial of STRIPES: a schoolyear, peer-delivered high school intervention for students with ADHD



Youth with ADHD are at risk of academic impairments, dropping out of high school, and dysfunction in young adulthood. Interventions delivered early in high school could prevent these harmful outcomes, yet few high school students with ADHD receive treatment due to limited access to intervention providers. This study will test a peer-delivered intervention (STRIPES) for general education 9th grade students with impairing ADHD symptoms.


A type 1 hybrid effectiveness-implementation design will be used to evaluate the effectiveness of STRIPES and explore the intervention’s implementability. Analyses will test the impact of STRIPES vs. enhanced school services control on target mechanisms and determine whether differences in basic cognitive profiles moderate intervention response. The acceptability and feasibility of STRIPES and treatment moderators will also be examined.


This study will generate knowledge about the effectiveness and implementability of STRIPES, which will inform dissemination efforts in the future. A peer-delivered high school intervention for organization, time management, and planning skills can provide accessible and feasible treatment targeting declines in academic motivation, grades, and attendance during the ninth-grade year.

Trial registration

This study is registered on OSF Registries (10.17605/OSF.IO/Q8V6S).

Peer Review reports


Youth with ADHD experience critical academic impairment during high school (HS; grades 9–12, approximately 14–19 years old), including completing fewer assignments and earning lower grades and test scores [1,2,3,4,5] compared to peers without ADHD. Up to one third of youth with ADHD drop out of HS [6]. These academic problems predict severe dysfunction in young adulthood [7,8,9]. Students who do not finish HS risk further escalating problems (i.e., criminal behavior, unemployment, addiction, public assistance) [10,11,12], and young adults with ADHD are at elevated risk for each of these problems [13, 14].

Despite these difficulties, most HS students with ADHD receive no treatment (medication or psychosocial) and are placed in general education [2, 15, 16] despite academic difficulties. Though there are many evidence-based interventions for children with ADHD during elementary school years [17], almost no such interventions occur in HS settings due to structural and resource barriers. For example, elementary teachers typically work in one classroom and oversee behavioral interventions for students with ADHD [18]; however, HS teachers teach over 100 students and have little time for individual student support [19]. Additionally, HS counselor duties include graduation planning, parent liaising, and student scheduling rather than intervention implementation and support [20]. Ancillary intervention staff are needed to provide services [21]; however, funding for staff and services—particularly for students without special education entitlement—are declining [22].

In well-resourced HS settings, providers can offer services using elementary or middle school models [23]. The most promising may be interventions developed for middle schoolers [24] and adapted for HS youth in clinical [25], school [26], and intensive settings [27]. These interventions target two core ADHD deficits: executive functioning (EF) and academic motivation (AM) [28, 29]. They teach organization, time management, and planning (OTP) strategies and include motivational components such as goal setting, contingency management, and strength-based feedback [30]. Despite these approaches’ promise in clinical settings, intervention delivery by HS staff is often impeded [31, 32].

This challenge was demonstrated in a study that showed low success rate for an intervention that involved behavioral consultants identifying a school-staff interventionist for 218 adolescents with ADHD at 114 different schools [32]. Monthly contact between consultants and school-staff interventionists was challenging (38.5% success rate), and only 40.0% of interventionists completed in-person meetings with consultants. Lower success rates were associated with HS (vs. middle school) and general (vs. special) education settings. When surveyed about barriers, school staff highlighted resource problems. Thus, an ongoing challenge is identifying qualified and available interventionists to deliver interventions to general education HS students with ADHD.

Task shifting to peers as interventionalists

One group of interventionists who are available, qualified, and willing may be older HS student peers. Peers are numerous, free interventionists who possess more time than school staff to devote to service delivery. Additionally, students have ample opportunities to interact with peers during school, and may be highly motivated to deliver interventions to enhance college applications, earn community service hours, and serve as a learning experience. This lower-resource model may be fitting for general education students with ADHD who do not require intensive intervention.

HS students can deliver a range of interventions to peers with fidelity [33,34,35,36]. Meta analysis [37] suggests that peer- and adult-delivered disruptive behavior interventions targeting aggression produce similar effect sizes to professional delivered models. Peers play a central role in the lives of HS students [38] and can serve as salient reinforcers in behavioral therapy [39]. A peer-delivered intervention may provide social and mental health benefits to students with ADHD who are at risk for peer rejection and often have few friends [40].

Ninth grade as a strategic window for intervention

Ninth grade is an important time for students with ADHD to access services. Performance during this year is a robust predictor of HS dropout [41]. Further, declines in grade point average (GPA) [42], self-esteem [43], and adjustment to HS are prevalent among adolescents at this age, and this is prominent in students with ADHD, whose 9th grade year is the trough of their academic performance [4]. Interventions for ADHD may be particularly effective when delivered in 9th grade. In previous work [27], our team delivered a summer program to rising 6th and 9th graders with ADHD. The intervention had greater effects on symptoms and functioning in 9th (vs. 6th) graders. We hypothesize that greater cognitive maturity in 9th graders promoted skill uptake and generalization.

Development of a peer-delivered intervention

Students Taking Responsibility and Initiative through Peer Enhanced Support (STRIPES) is a peer-delivered intervention for general education 9th grade students with impairing ADHD symptoms that was developed over four years [44]. A stakeholder grounded approach was used to develop STRIPES from existing evidence-based treatments for ADHD [23]. Study 1 (N = 18) established initial acceptability (i.e., credibility, bond, satisfaction, perceived helpfulness) and feasibility of the service delivery model (i.e., attendance, fidelity). Qualitative methods were used for consumer perspectives on strengths, weaknesses, and strategies to improve STRIPES. Results indicated that an elective-pullout model best fit student’s needs. Study 2 (N = 72) was a randomized controlled trial that compared STRIPES to an active control in three HSs. Each school chose a tailored implementation model (i.e., STRIPES was delivered during lunch at one school, as elective pull-out at another, and after school at another). Peer-retrieval and elective pull-out model features possessed strong consumer fit, and when delivered with a population-fitting implementation model, the low-burden STRIPES intervention met acceptability and feasibility metrics and impacted AM, EF and student outcomes.

Current study

This project employs a type 1 hybrid effectiveness-implementation design [45], to evaluate the effectiveness of STRIPES while understanding the intervention’s implementability. Efforts to work collaboratively with schools to develop and test school-specific implementation plans will be supported by the Consolidated Framework for Implementation Research (CFIR) [46]. The CFIR identifies five major domains that interact to influence implementation and effectiveness: intervention, outer setting, inner setting, characteristics of individuals, and process. The CFIR will be used to guide STRIPES evaluation and implementation planning by supporting selection of implementation constructs to measure, methods of measurement, and design of implementation strategies.

Target mechanisms of intervention

We will target two inter-related mechanisms (see Fig. 1): (1) AM and (2) EF, which are linked to academic success in students with ADHD [47, 48]. Hallmarks of ADHD-related HS impairment are related to both EF and AM as reflected by poor work completion and inadequate test preparation [4].

Fig. 1
figure 1

STRIPES logic model

Dotted lines are hypothesized effects to be tested. Solid lines are previously established effects to be replicated

Academic motivation

AM mediates the relationship between ADHD-related rewards deficits and grades [49]. With respect to intrinsic motivation, students with ADHD report low academic interest and perceive schoolwork as highly aversive [50, 51]. Deficits in intrinsic motivation are tied to abnormal anticipatory dopamine response [52] — a core deficit in ADHD [53]. Thus, typical academic work feels less intrinsically rewarding to students with ADHD as they experience lower sense of novelty, curiosity, enjoyment associated with it. In HS, these deficits may be prominent due to the repetitive and complex nature of academic tasks [43]. Intrinsic motivation deficits are further hampered by ADHD-related learning problems that increase the aversiveness of schoolwork [54] and are compounded by ADHD-related delay aversion [28] — mental discomfort when tasks contain delayed rewards.

With respect to extrinsic, value-driven motivation, students with ADHD report valuing achievement and mastery less than peers [55,56,57,58,59]. For students with ADHD, a preference for immediate rewards [60] may prevent high valuation of long-term and symbolic reinforcers (i.e., grades). Students with ADHD show reduced sensitivity to future negative consequences [29], which reduces motivation to avoid negative outcomes (e.g., course failure). They also may exert lower academic effort because they perceive a reduced probability of earning high grades, due to years of school failure [61].

Executive functioning

Even with adequate AM, persistent difficulties with OTP likely reflect deficits in various domains of EF [62, 63] preventing ultimate academic success. EFs are top-down cognitive processes that help students implement actions in support of goals and suppress counterproductive motivational states [64]. EFs include working memory, response inhibition, and cognitive flexibility. Together, these EFs underlie OTP and goal-directed behaviors like planning, task-initiation, inhibiting unproductive behaviors, and task disengagement. EFs are notably impaired in students with ADHD [28, 65], who show consequent difficulties with OTP [66] and goal pursuit [67, 68].

Study aims

This study first aims to engage school stakeholders to establish school-specific variations of the STRIPES implementation strategy that attend to developmental, disorder, and context-specific considerations and to document interventionist implementation fidelity (Aim 1). The CFIR will be used to guide the selection of questions for stakeholder interviews about potential barriers and facilitators to the implementation of STRIPES in the school context [46]. A randomized trial will then evaluate the impact of STRIPES (vs. enhanced school services control; SSU +) on target mechanisms including EF, AM, and student outcomes (GPA and class attendance; Aim 2). We will also estimate the extent STRIPES remediates cognitive control and rewards processing deficits, facilitates the development of compensatory skills that overcome the impact of deficits, or both. Aim 3 will explore whether differences in basic cognitive profiles moderate intervention response. The acceptability and feasibility of STRIPES will be examined with consideration of student, peer interventionist, and school stakeholder perspectives, and cost to schools (Aim 4). Lastly, we will investigate ecologically valid treatment moderators (baseline GPA, history of ADHD, disruptive classroom behavior, peer characteristics; Aim 5).


Study timeline

Year 1 will focus on planning and collaborative work with two schools, in Seattle, WA and Miami, FL, to pilot school-specific implementation plans. In years 2 through 4, we will conduct a randomized controlled trial comparing (1) STRIPES to (2) SSU + . In each of the three annual cohorts, 24 9th grade students will be recruited at each of the two schools (12 per condition; total N = 144) and will be randomized within school and cohort using permuted block randomization. Teachers who nominate students will obtain written parental permission to nominate, a demographic form, and a DSM-5 ADHD checklist [32]. A core academic teacher completes the same ADHD checklist and impairment measures (IRS; [69, 70]). Ninth grade students will be eligible to participate if they display at least 4 symptoms of inattention or hyperactivity/ impulsivity and elevated academic impairment, defined as meeting two of the following: (1) D or F in an academic class, (2) at least 20% of assignments missing in a class, (3) “3” or higher on the teacher IRS academic impairment item (0-6 scale; [70]), (4) elevated score on the teacher Adolescent Academic Problems Checklist (AAPC; 4 items endorsed “pretty much/very much;” [69]). Students are excluded if placed in special education classes, as this study aims to test a low-cost intervention in general education settings. Students with comorbid learning disabilities will be eligible if placed in general education classes. Peer interventionists will be nominated by teachers and must have at least a 3.0 GPA and no suspensions during the past 12 months. Written parental consent and youth assent will be required [70].

Study conditions


STRIPES aims to deliver eight 30-min sessions per student over 16 weeks [44] via peer interventionists who are trained in aiding in goal setting and affirming positive steps. See Table 1 for a summary of intervention characteristics. Peer interventionists will receive four hours of initial training plus weekly supervision by a school staff sponsor. The school staff sponsor will receive two hours of initial training, weekly consultation with a school mental health liaison, and feedback on fidelity from research staff.

Table 1 STRIPES intervention characteristics


Students assigned to SSU + will be referred to their school counselor with the baseline (BL) assessment of symptoms and presenting problems, and their services received will be tracked.

Assessment procedures

Ecological school outcomes will be collected at five longitudinal time points (once per quarter in 9th grade and once in 10th grade). Target mechanisms will be assessed off medication at the five longitudinal time points and at BL, post-treatment at the end of 9th grade (FU1), and at the beginning of 10th grade (FU2). Compensatory strategies and cognitive processing measures will be collected at BL, FU1, and FU2.


Ecological school outcomes

GPA will be calculated by converting academic grades to a 5-point scale (i.e., 4.0 = A to 0.0 = F). Number of class absences will be calculated for each quarter.

Compensatory strategies

The academic skills subscale of the Adolescent Academic Problems Checklist [69] is a validated 24-item teacher-report measure of the application of OTP strategies in the classroom [69]. Factors include academic skills, disruptive behavior, and a total score, with strong internal reliability and concurrent validity [27, 69, 71]. The goal setting and planning section of the Self-Regulated Learning Interview Schedule [72] will be converted to self-report.

Target mechanisms

Functional indices of executive functions

Percentage of classes with recorded homework will be calculated for the last five school days [73]. Bookbag observations will be obtained by research assistants using the Organization Checklist [74]. Missing assignments will be gathered monthly through the online grade portal.

Academic motivation

A self-report change ruler measure will be used that rates various aspects of motivation using an 11-point Likert Scale (0 = not at all to 10 = extremely). The measure possesses established psychometric properties with adolescent populations [75] and is sensitive to change in treatment outcome studies for adolescents with ADHD [44, 76]. It also correlates strongly with longer motivational questionnaires but outperforms these measures at predicting behavioral intentions [77]. A validated 22-item adaptation for HS students [78] of the Basic Psychological Needs Scale will measure three conditions that promote intrinsic motivation [79].

Cognitive and rewards processing

Three indices of EF will be measured by cognitive tasks: working memory, response inhibition, and cognitive flexibility. Working memory performance will be assessed using the List Sorting Working Memory Test from the NIH Toolbox [80], which shows excellent test–retest reliability and convergent and discriminant validity with other working memory measures [81].

Response inhibition will be measured using a go/no-go task that uses both positively and negatively valenced emotional stimuli [82], shows good convergent validity [83], and is validated for adolescents [84]. Response inhibition will be evaluated using the Flanker Inhibitory Control and Attention Test from the National Institute of Health (NIH) Toolbox. Cognitive flexibility will be measured using the NIH Toolbox Dimensional Change Card Sort Test [80], which shows excellent developmental sensitivity and convergent validity [85]. Risky decision making will be assessed using a computerized Iowa gambling task (Hungry Donkey Task) [86]. This task shows good convergent validity in adolescents [87]. Delayed discounting will be measured using a computerized Choice-Delay Task [60], which shows developmental sensitivity [60] and correlates with symptoms of ADHD [88].

Engagement and fit


Treatment credibility will be measured using the Client Credibility Questionnaire [89]. Students will rate how logical they found treatment and how confident they were in the treatment using a 3-point scale (0 = not at all to 2 = very much). Participants will complete the Therapist Bond Scale [90], rating the degree to which they enjoyed working with peers during STRIPES using a 4-point scale (1 = not at all like you to 4 = very much like you). Lastly, participants will rate treatment satisfaction using a questionnaire developed for behavioral treatments [91] and adapted for adolescents with ADHD [27, 73]. Respondents indicate satisfaction using a 7-point Likert Scale (1 = Strongly Disagree, 7 = Strongly Agree).

Service utilization

A combination of district records collection and structured interviews with the student and the student services/special education team leaders at each school will track service utilization for both groups. Interviews will focus on school services accessed by students, with structured prompts ensuring coverage of multiple service modalities.

Potential covariates

Medication use will be monitored using the Services for Children and Adolescents-Parent Interview [92]. We will also measure the following potential covariates at BL: intelligence quotient, parent education level, race/ethnicity, age, gender, parent marital status, and free/reduced lunch.

Patient and public involvement

No patient involvement in the design of this study.

Data analysis plan

Analyses will be performed using Mplus version 7 or higher. Missing data will be handled with full information maximum likelihood estimation.

Aim 2

The direct effect of STRIPES on each variable (GPA, class attendance, AM, EF) will be evaluated using mixed models [93]. Each outcome will be analyzed with a separate model with time modeled as person-specific (months since BL). We will explore linear, non-linear (i.e., logarithmic, exponential, quadratic), and piece-wise growth models to determine whether there are unique influences of STRIPES on time frames, reflected by slope over time; the best model will be selected using likelihood ratio tests. Effects of time, group, and their interaction will serve as model predictors, and covariates will be included.

Aim 3

Aim 3 tests three sets of mediational effects. The mechanisms by which STRIPES affects improvement in student outcome will be evaluated using a structural equation modeling framework in Mplus [94]. Significance of indirect effects will be assessed by bootstrap confidence intervals [95, 96]. In each model, variables that are repeatedly measured (i.e., outcomes, AM, observed EF, rewards processing, motivational strategy, and cognitive control) will be modeled with a mixed model as described in Aim 2. Aim 3a examines the effect of STRIPES on outcomes via AM and observed EF. Aim 3b examines the effect of STRIPES on AM via rewards processing and/or motivational strategy use. Aim 3c examines the effect of STRIPES on observed EF via cognitive control and/or motivational strategy use. For each model, STRIPES predicts the intercept and slope of the mediator model (a paths) and the intercept and slope of the outcome model (c’ paths), while the intercept and slope of the mediator model predict the intercept and slope of the outcome model (b paths).

Current recommendations advise evaluating mediators individually before combining them into a model with simultaneous mediators [97]. For example, Aim 3a will examine AM as a mediator of the STRIPES to outcome relationship in one model, EF as a mediator of the STRIPES to outcome relationship in a second model, and examine both variables as simultaneous mediators in a final model.

Aim 4

Analyses will adhere to Consolidated Health Economic Evaluation Reporting Standard recommendations [98]. Costs will be compiled for each trial arm at the student, school, and district levels. Cost-effectiveness analysis is the preferred evaluation method for interventions involving well-defined, non-monetary outcome measures [99]. This method does not require that the effectiveness measure be translated into dollars, and it allows for comparisons across multiple interventions if they have common outcomes of interest. The primary learner outcomes for cost effectiveness analyses will be class attendance and GPA. In cases where one treatment arm is both more costly and more efficacious, incremental cost-effectiveness ratios (ICERs) will be computed. The ICER reports the additional cost of achieving a one-unit improvement in the outcome measures relative to the next least expensive tier. When there are statistically significant differences in outcomes, cost-effectiveness acceptability curves will be constructed to account for variability in the cost and outcome data. These curves yield the probability of one treatment being preferred over another assuming different threshold levels for the value of a unit of outcome improvement [100]. Additional one-way and probabilistic sensitivity analyses will be conducted as needed.

Statistical power

We estimate 5% attrition based on previous studies. Statistical models are outlined in Fig. 2. Aim 2. Based on simulations by Fan [101], we assessed statistical power for mixed models, given a fixed sample size of 144. Based on previous research [44], we expect group differences at later time points to range from medium to very large (d = 0.47, 0.85, 1.11, 1.47, 1.69, 2.05 for primary outcomes); Fan [101] found that statistical power exceeded 0.80 with samples of about 100 with medium or larger effects. Aim 3. For the indirect effects proposed in Aims 3a-3c, statistical power estimates are based on sample size calculations by Fritz and MacKinnon [102]. Statistical power for the indirect effect depends entirely on the values of the “a path” (X to M effect) and “b path” (M to Y effect) effect sizes. With a sample size of 144 and using preferred bootstrap methods, we have sufficient power to detect the indirect effect provided both the “a path” and the “b path” are greater than d = 0.35. Prior research [44] shows “a paths” for Aims 3a and 3b that range from d = 0.85 to 2.05. Aim 4. We will assess statistical power of the cost-effectiveness analysis using methods outlined by Boyd and colleagues [103]. Aim 5. Due to the complexity of estimating statistical power for the mixed models in Aim 5, we base our estimates of power on effects from the previous STRIPES trial [44], which used similar models for analysis with a substantially smaller sample. We used F statistics to estimate the non-centrality parameter, estimated a denominator degrees of freedom based on more observations (144 students each measured 5 times versus 72 students each measured 4 times), and estimated statistical power for a generic F test based on these values. Of the 35 effects examined, the mean power value was 0.61. Seven effects had estimated power > 0.80; based on the non-centrality parameters for these effects, we have adequate power to detect effects as small as Cohen’s d = 0.245, which is a small effect size.

Fig. 2
figure 2

Proposed mediational models in analytic plan


Given the increased difficulties experienced by 9th grade students with impairing ADHD symptoms, it is important to identify accessible and feasible interventions that target needed skills. This study will generate knowledge about the effectiveness and implementability of STRIPES, which will inform dissemination efforts in the future.

Availability of data and materials

Anonymized data will be available on reasonable request (contact corresponding author Stephanie K. Brewer, Ph.D.).



Attention-Deficit/Hyperactivity Disorder


Academic motivation




Consolidated Framework for Implementation Research


Executive functioning


End of 9th grade


Beginning of 10th grade


Grade point average


High school


Incremental cost-effectiveness ratio


National Institute of Health


Organization, time management, and planning

SSU + :

Enhanced school services control


Students Taking Responsibility and Initiative through Peer Enhanced Support


  1. Barkley R, Anastopoulos A, Guevremont D, Fletcher K. Adolescents with ADHD: patterns of behavioral adjustment, academic functioning, and treatment utilization. J Am Acad Child Adolesc Psychiatry. 1991;30:752–61.

    PubMed  Google Scholar 

  2. Barkley R, Fischer M, Smallish L, Fletcher K. Young adult outcome of hyperactive children: adaptive functioning in major life activities. J Am Acad Child Adolesc Psychiatry. 2006;45:192–202.

    Article  PubMed  Google Scholar 

  3. Fischer M, Barkley RA, Edelbrock CS, Smallish L. The adolescent outcome of hyperactive children diagnosed by research criteria: II. Academic, attentional, and neuropsychological status. J Consult Clin Psychol. 1990;58(5):580.

    Article  PubMed  Google Scholar 

  4. Kent KM, Pelham WE, Molina BSG, Sibley MH, Waschbusch DA, Yu J, et al. The academic experience of male high school students with ADHD. J Abnorm Child Psychol. 2011;39:451–62.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Weiss G, Hechtman L. Hyperactive Children Grown Up. 2nd ed. New York: Guilford; 1993.

    Google Scholar 

  6. Barkley R, Fischer M, Smallish L, Fletcher K. The persistence of attention deficit hyperactivity disorder into young adulthood as a function of reporting source and definition of disorder. J Abnorm Psychol. 2002;111:279–89.

    Article  PubMed  Google Scholar 

  7. Masten A, Roisman G, Long J, Burt K, Obradović J, Riley J, et al. Developmental cascades: linking academic achievement and externalizing and internalizing symptoms over 20 years. Dev Psychol. 2005;41:733–46.

    Article  PubMed  Google Scholar 

  8. Molina BS, Pelham WE Jr, Cheong J, Marshal MP, Gnagy EM, Curran PJ. Childhood attention-deficit/hyperactivity disorder (ADHD) and growth in adolescent alcohol use: The roles of functional impairments, ADHD symptom persistence, and parental knowledge. J Abnorm Psychol. 2012;121(4):922.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Rindfuss R, Cooksey E, Sutterlin R. Young adult occupational achievement: early expectations versus behavioral reality. Work Occup. 1999;26:220–63.

    Article  Google Scholar 

  10. Thornberry TP, Moore M, Christenson RL. The effect of dropping out of high school on subsequent criminal behavior. Criminology. 1985;23:3–18.

    Article  Google Scholar 

  11. Townsend L, Flisher AJ, King G. A systematic review of the relationship between high school dropout and substance use. Clin Child Fam Psychol Rev. 2007;10(4):295–317.

    Article  PubMed  Google Scholar 

  12. Stanard RP. High school graduation rates in the United States: Implications for the counseling profession. J Couns Dev. 2003;81(2):217–21.

    Article  Google Scholar 

  13. Barkley RA, Murphy KR, Fischer M. ADHD in Adults: What the Science Says. New York: Guilford; 2008.

    Google Scholar 

  14. Hechtman L, Swanson JM, Sibley MH, Stehli A, Owens EB, Mitchell JT, Abikoff HB. Functional adult outcomes 16 years after childhood diagnosis of attention-deficit/hyperactivity disorder: MTA results. J Am Acad Child Adolesc Psychiatry. 2016;55:945–52.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Bussing R, Zima BT, Mason DM, Porter PC, Garvan CW. Receiving treatment for attention-deficit hyperactivity disorder: Do the perspectives of adolescents matter? J Adolesc Health. 2011;49:7–14.

    Article  PubMed  Google Scholar 

  16. Sibley MH, Coxe SJ. The ADHD teen integrative data analysis longitudinal (TIDAL) dataset: background, methodology, and aims. BMC Psychiatry. 2020;20(1):1–12.

    Article  Google Scholar 

  17. Evans SW, Owens JS, Wymbs BT, Ray AR. Evidence-based psychosocial treatments for children and adolescents with attention-deficit/hyperactivity disorder. J Clin Child Adolesc Psychol. 2018;47:157–98.

    Article  PubMed  Google Scholar 

  18. Hart KC, Fabiano GA, Pelham WE, Evans SW, Manos MJ, Hannah JN, Vujnovic RK. Elementary and middle school teachers’ self-reported use of positive behavioral supports for children with ADHD: a national survey. J Emot Behav Disord. 2017;25:246–56.

    Article  Google Scholar 

  19. Benner AD, Graham S. The transition to high school as a developmental process among multiethnic urban youth. Child Dev. 2009;80:356–76.

    Article  PubMed  Google Scholar 

  20. American School Counselor Association (ASCA; 2013). Careers and Roles. In American School Counselor Association. Retrieved August 2013, from

  21. NCRI (2013). Multi-level prevention system. National Center on Response to Intervention. Retrieved August 2013 at

  22. American Association of School Administrators (AASA). (2012, March) Weathering the storm: How the economic recession continues to impact school districts. In American Association of School Administrators. Retrieved August 2013, from

  23. Evans SW, Owens JS, Wymbs BT, Ray AR. Evidence-based psychosocial treatments for children and adolescents with attention deficit/hyperactivity disorder. J Clin Child Adolesc Psychol. 2018;47(2):157–98.

    Article  PubMed  Google Scholar 

  24. Evans SW, Langberg JM, Schultz BK, Vaughn A, Altaye M, Marshall SA, Zoromski AK. Evaluation of a school-based treatment program for young adolescents with ADHD. J Consult Clin Psychol. 2016;84:15.

    Article  PubMed  Google Scholar 

  25. Sibley MH, Ortiz M, Graziano PA, Dick A, Estrada E. Metacognitive and motivation deficits, exposure to trauma, and high parental demands characterize adolescents with late-Onset ADHD. Eur Child Adolesc Psychiatry. 2019

  26. Evans SW, Schultz BK, DeMars CE. High school based treatment for adolescents with ADHD: Results from a pilot study examining outcomes and dosage. Sch Psychol Rev. 2014;43:185–202.

    Article  Google Scholar 

  27. Sibley MH, Coxe SJ, Campez M, Morley C, Olson S, Hidalgo-Gato N, et al. High vs Low Intensity Summer Treatment for ADHD Delivered at Secondary School Transitions. J Clin Child Adolesc Psychol. 2018;47:248–65.

    Article  PubMed  Google Scholar 

  28. Sonuga-Barke EJ. The dual pathway model of AD/HD: an elaboration of neuro-developmental characteristics. Neurosci Biobehav Rev. 2003;27:593–604.

    Article  PubMed  Google Scholar 

  29. Toplak ME, Jain U, Tannock R. Executive and motivational processes in adolescents with Attention-Deficit-Hyperactivity Disorder (ADHD). Behav Brain Funct. 2005;1:8.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Sibley MH. Motivational and Executive Functioning Considerations when Treating Adolescents with ADHD. In: Becker S, editor. ADHD in Adolescence: A Developmental Psychopathology Approach. New York: Guilford Press; 2017.

    Google Scholar 

  31. Kern L, Evans SW, Lewis TJ, State TM, Mehta PD, Weist MD, Willis HP, Gage NA. Evaluation of a comprehensive assessment-based intervention for secondary students with social, emotional, and behavioral problems. 2020.

  32. Sibley MH, Olson S, Morley C, Campez M, Pelham WE Jr. A school consultation intervention for adolescents with ADHD: barriers and implementation strategies. Child Adolesc Mental Health. 2016;21(4):183–91.

    Article  Google Scholar 

  33. Stenhoff DM, Lignugaris/Kraft B. 2007 A review of the effects of peer tutoring on students with mild disabilities in secondary settings. Except Child. 2007;74(1):8–30.

    Article  Google Scholar 

  34. Fuchs D, Fuchs LS, Burish P. Peer-assisted learning strategies: an evidence-based practice to promote reading achievement. Learn Disabil Res Pract. 2000;15(2):85–91.

    Article  Google Scholar 

  35. Mastropieri MA, Scruggs TE, Spencer V, Fontana J. Promoting success in high school world history: peer tutoring versus guided notes. Learn Disabil Res Pract. 2003;18:52–65.

    Article  Google Scholar 

  36. Stephenson JM, Strange V, Forrest S, Oakley A, Copas A, Allen E, Johnson AM. Pupil-led sex education in England (RIPPLE study): cluster-randomised intervention trial. The Lancet. 2004;364:338–46.

    Article  Google Scholar 

  37. Wilson SJ, Lipsey MW, Derzon JH. The effects of school-based intervention programs on aggressive behavior: a meta-analysis. J Consult Clin Psychol. 2003;71(1):136–49.

    Article  PubMed  Google Scholar 

  38. Steinberg L, Morris AS. Adolescent development. Annu Rev Psychol. 2001;52(1):83–110.

    Article  PubMed  Google Scholar 

  39. Kalfus GR. Peer mediated intervention: a critical review. Child Fam Behav Ther. 1984;6:17–43.

    Article  Google Scholar 

  40. Bagwell C, Molina BSG, Pelham WE, Hoza B. Attention-deficit hyperactivity disorder and problems in peer relations: predictions from childhood to adolescence. J Am Acad Child Adolesc Psychiatry. 2001;40:1285–92.

    Article  PubMed  Google Scholar 

  41. Neild RC, Stoner-Eby S, Furstenberg F. Connecting entrance and departure the transition to ninth grade and high school dropout. Educ Urban Soc. 2008;40(5):543–69.

    Article  Google Scholar 

  42. Isakson K, Jarvis P. The adjustment of adolescents during the transition into high school: a short-term longitudinal study. J Youth Adolesc. 1999;28:1–26.

    Article  Google Scholar 

  43. Barber BK, Olsen JA. Assessing the transitions to middle school and high school. J Adolesc Res. 2004;19:3–30.

    Article  Google Scholar 

  44. Sibley MH, Morley C, Rodriguez L, Coxe SJ, Evans SW, Morsink S, Torres F. A Peer-delivered intervention for high school students with impairing ADHD symptoms. Sch Psychol Rev. 2020;49(3):275–90.

    Article  Google Scholar 

  45. Curran GM, Bauer M, Mittman B, Pyne JM, Stetler C. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012;50(3):217–26.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4(50):1–15.

    Google Scholar 

  47. Gropper RJ, Tannock R. A pilot study of working memory and academic achievement in college students with ADHD. J Atten Disord. 2009;12:574–81.

    Article  PubMed  Google Scholar 

  48. Langberg JM, Dvorsky MR, Evans SW. What specific facets of executive function are associated with academic functioning in youth with attention-deficit/hyperactivity disorder? J Abnorm Child Psychol. 2013;41:1145–59.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Lee NC, Krabbendam L, Dekker S, et al. Academic motivation mediates the influence of temporal discounting on academic achievement during adolescence. Trends in Neuroscience and Education. 2012;1:43–8.

    Article  Google Scholar 

  50. Carlson CL, Booth JE, Shin M, Canu WH. Parent-, teacher-, and self-rated motivational styles in ADHD subtypes. J Learn Disabil. 2002;35:104–13.

    Article  PubMed  Google Scholar 

  51. Morsink S, Sonuga-Barke E, Mies G, Glorie N, Lemiere J, Van der Oord S, Danckaerts M. What motivates individuals with ADHD? A qualitative analysis from the adolescent’s point of view. Eur Child Adolesc Psychiatry. 2017;1–10.

  52. Oudeyer PY, Kaplan F, Hafner VV. Intrinsic motivation systems for autonomous mental development. IEEE Trans Evol Comput. 2007;11(2):265–86.

    Article  Google Scholar 

  53. Volkow ND, Wang GJ, Newcorn JH, Kollins SH, Wigal TL, Telang F, Wong C. Motivation deficit in ADHD is associated with dysfunction of the dopamine reward pathway. Mol Psychiatry. 2011;16(11):1147.

    Article  PubMed  Google Scholar 

  54. Loe IM, Feldman HM. Academic and educational outcomes of children with ADHD. J Pediatr Psychol. 2007;32(6):643–54.

    Article  PubMed  Google Scholar 

  55. Barron KE, Evans SW, Baranik LE, Serpell ZN, Buvinger E. Achievement goals of students with ADHD. Learn Disabil Q. 2006;29:137–58.

    Article  Google Scholar 

  56. Colomer C, Berenguer C, Roselló B, Baixauli I, Miranda A. The impact of inattention, hyperactivity/impulsivity Symptoms, and executive functions on learning behaviors of children with ADHD. Front Psychol. 2017;8:540.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Gut J, Heckmann C, Meyer CS, Schmid M, Grob A. Language skills, mathematical thinking, and achievement motivation in children with ADHD, disruptive behavior disorders, and normal controls. Learn Individ Differ. 2012;22(3):375–9.

    Article  Google Scholar 

  58. Olivier MAJ, Steenkamp DS. Attention-deficit/hyperactivity disorder: underlying deficits in achievement motivation. Int J Adv Couns. 2004;26:47–63.

    Article  Google Scholar 

  59. Zentall SS, Beike SM. Achievement and social goals of younger and older elementary students: response to academic and social failure. Learn Disabil Q. 2012;35:39–53.

    Article  Google Scholar 

  60. Scheres A, Dijkstra M, Ainslie E, Balkan J, Reynolds B, Sonuga-Barke E, Castellanos FX. Temporal and probabilistic discounting of rewards in children and adolescents: effects of age and ADHD symptoms. Neuropsychologia. 2006;44(11):2092–103.

    Article  PubMed  Google Scholar 

  61. Newark PE, Elsässer M, Stieglitz RD. Self-esteem, self-efficacy, and resources in adults with ADHD. J Atten Disord. 2016;20(3):279–90.

    Article  PubMed  Google Scholar 

  62. Gollwitzer PM, Brandstätter V. Implementation intentions and effective goal pursuit. J Pers Soc Psychol. 1997;73:186.

    Article  Google Scholar 

  63. Zimmerman BJ. Becoming a self-regulated learner: An overview. Theory into practice. 2002;41:64–70.

    Article  Google Scholar 

  64. Kim SI. Neuroscientific model of motivational process. Front Psychol. 2013;4:98.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Castellanos FX, Sonuga-Barke EJ, Milham MP, Tannock R. Characterizing cognition in ADHD: beyond executive dysfunction. Trends Cogn Sci. 2006;10:117–23.

    Article  PubMed  Google Scholar 

  66. Kofler MJ, Sarver DE, Harmon SL, Moltisanti A, Aduen PA, Soto EF, Ferretti N. Working memory and organizational skills problems in ADHD. J Child Psychol Psychiatry. 2018;59(1):57–67.

    Article  PubMed  Google Scholar 

  67. Hoza B, Waschbusch DA, Owens JS, Pelham WE, Kipp H. Academic task persistence of normally achieving ADHD and control boys: self-evaluations, and attributions. J Consult Clin Psychol. 2001;69:271.

    Article  PubMed  Google Scholar 

  68. Nyman A, Taskinen T, Grönroos M, Haataja L, Lähdetie J, Korhonen T. Elements of working memory as predictors of goal-setting skills in children with attention-deficit/hyperactivity disorder. J Learn Disabil. 2010;43:553–62.

    Article  PubMed  Google Scholar 

  69. Sibley MH, Altszuler AR, Morrow AS, Merrill BM. Mapping the academic problem behaviors of adolescents with ADHD. Sch Psychol Q. 2014;29:422–37.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Fabiano GA, Pelham WE, Waschbusch DA, Gnagy EM, Lahey BB, Chronis AM, et al. A practical measure of impairment: Psychometric properties of the Impairment Rating Scale in samples of children with attention deficit hyperactivity disorder and two school-based samples. J Clin Child Adolesc Psychol. 2006;35:369–85.

  71. Sibley MH, Rodriguez LM, Coxe SJ, Page T, & Espinal K. What Works for Whom and Why?: comparing psychosocial treatment modalities for adolescents with ADHD. J Clin Child Adolesc Psychol. 2019.

  72. Zimmerman BJ, Pons MM. Development of a structured interview for assessing student use of self-regulated learning strategies. Am Educ Res J. 1986;23:614–28.

    Article  Google Scholar 

  73. Sibley MH, Pelham WE, Derefinko KD, Kuriyan AB, Sanchez F, Graziano PA. A pilot trial of Supporting Teens’ Academic Needs Daily (STAND): a parent-adolescent collaborative intervention for ADHD. J Psychopathol Behav Assess. 2013;35:436–49.

    Article  Google Scholar 

  74. Evans SW, Schultz BK, White LC, Brady C, Sibley MH, Van Eck K. A school-based organization intervention for young adolescents with attention-deficit/hyperactivity disorder. Sch Ment Heal. 2009;1:78–88.

    Article  Google Scholar 

  75. Aliotta SL, Vlasnik JJ, DeLor B. Enhancing adherence to long-term medical therapy: a new approach to assessing and treating patients. Adv Ther. 2004;21:214–31.

    Article  PubMed  Google Scholar 

  76. Sibley MH, Comer JS, Gonzalez J. Delivering parent-teen therapy for ADHD through videoconferencing: a preliminary investigation. J Psychopathol Behav Assess. 2017;39:467–85.

    Article  PubMed  PubMed Central  Google Scholar 

  77. LaBrie JW, Quinlan T, Schiffman JE, Earleywine ME. Performance of alcohol and safer sex change rulers compared with readiness to change questionnaires. Psychol Addict Behav. 2005;19:112–5.

    Article  PubMed  Google Scholar 

  78. Tian L, Chen H, Huebner ES. The longitudinal relationships between basic psychological needs satisfaction at school and school-related subjective well-being in adolescents. Soc Indic Res. 2014;119(1):353–72.

    Article  Google Scholar 

  79. Ryan RM, Deci EL. Intrinsic and extrinsic motivations: classic definitions and new directions. Contemp Educ Psychol. 2000;25:54–67.

    Article  PubMed  Google Scholar 

  80. Weintraub S, Dikmen SS, Heaton RK, Tulsky DS, Zelazo PD, Bauer PJ, Fox NA. Cognition assessment using the NIH Toolbox. Neurology. 2013;80(11 Supplement 3):S54–64.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Tulsky DS, Carlozzi NE, Chevalier N, Espy KA, Beaumont JL, Mungas D. V. NIH toolbox cognition battery (CB): measuring working memory. Monogr Soc Res Child Dev. 2013;78:70–87.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Hare TA, Tottenham N, Davidson MC, Glover GH, Casey BJ. Contributions of amygdala and striatal activity in emotion regulation. Biol Psychiat. 2005;57:624–32.

    Article  PubMed  Google Scholar 

  83. Schultz KP, Fan J, Magidina O, Marks DJ, Hahn B, Halperin JM. Does the emotional go/no-go task really measure behavioral inhibition?: convergence with measures on a non-emotional analog. Arch Clin Neuropsychol. 2007;22(2):151–60.

    Article  Google Scholar 

  84. Hare TA, Tottenham N, Galvan A, Voss HU, Glover GH, Casey BJ. Biological substrates of emotional reactivity and regulation in adolescence during an emotional go-nogo task. Biol Psychiat. 2008;63(10):927–34.

    Article  PubMed  Google Scholar 

  85. Zelazo PD, Anderson JE, Richler J, Wallner-Allen K, Beaumont JL, Weintraub S. II. NIH toolbox cognition battery (CB): Measuring executive function and attention. Monogr Soc Res Child Dev. 2013;78:16–33.

    Article  PubMed  Google Scholar 

  86. Crone EA, van der Molen MW. Developmental changes in real life decision making: performance on a gambling task previously shown to depend on the ventromedial prefrontal cortex. Dev Neuropsychol. 2004;25:251–79.

    Article  PubMed  Google Scholar 

  87. Crone EA, Van Der Molen MW. Development of decision making in school-aged children and adolescents: Evidence from heart rate and skin conductance analysis. Child Dev. 2007;78:1288–301.

    Article  PubMed  Google Scholar 

  88. Scheres A, Lee A, Sumiya M. Temporal reward discounting and ADHD: task and symptom specific effects. J Neural Transm. 2008;115:221–6.

    Article  PubMed  Google Scholar 

  89. Silverman WK, Kurtines WM, Ginsburg GS, Weems CF, Lumpkin PW, Carmichael DH. Treating anxiety disorders in children with group cognitive-behavioral therapy: a randomized clinical trial. J Consult Clin Psychol. 1999;67:995–1003.

    Article  PubMed  Google Scholar 

  90. Shirk SR, Saiz CC. Clinical, empirical, and developmental perspectives on the therapeutic relationship in child psychotherapy. Dev Psychopathol. 1992;4(4):713–28.

    Article  Google Scholar 

  91. MTA Cooperative Group. Multimodal Treatment Study of Children with ADHD. A 14-month randomized clinical trial of treatment strategies for attention-deficit/hyperactivity disorder. Arch Gen Psychiatr. 1999;56(12):1073–86.

    Article  Google Scholar 

  92. Hoagwood K, Jensen PS, Petti T, Burns BJ. Outcomes of mental health care for children and adolescents: I. A comprehensive conceptual model. J Am Acad Child Adolesc Psychiatry. 1996;35:1055–63.

    Article  PubMed  Google Scholar 

  93. Preacher KJ, Hayes AF. Assessing mediation in communication research. London: The Sage sourcebook of advanced data analysis methods for communication research; 2008. p. 13–54.

    Google Scholar 

  94. Muthén LK, Muthén B. Mplus. The comprehensive modelling program for applied researchers: user’s guide. 2019;5.

  95. MacKinnon DP, Lockwood CM, Williams J. Confidence limits for the indirect effect: distribution of the product and resampling methods. Multivar Behav Res. 2004;39(1):99–128.

    Article  Google Scholar 

  96. Preacher KJ, Hayes AF. SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behav Res Methods Instrum Comput. 2004;36(4):717–31.

    Article  PubMed  Google Scholar 

  97. MacKinnon DP. Introduction to statistical mediation analysis. Routledge; 2012.

    Book  Google Scholar 

  98. Husereau D, Drummond M, Petrou S, et al. Consolidated health economic evaluation reporting standard statement. BMJ. 2013;2013(346): f1049.

    Article  Google Scholar 

  99. Gold MR, Siegel JE, Russel LB, Weinstein MC. Cost-effectiveness in health and medicine. New York: Oxford University Press; 1996.

    Book  Google Scholar 

  100. Van Hout BA, Al MJ, Gordon GS, Rutten FF. Costs, effects, and C/E ratios alongside a clinical trial. Health Econ. 1994;3(5):301–19.

    Google Scholar 

  101. Fan X. Power of latent growth modeling for detecting group differences in linear growth trajectory parameters. Struct Equ Model. 2003;10(3):380–400.

    Article  Google Scholar 

  102. Fritz MS, MacKinnon DP. Required sample size to detect the mediated effect. Psychol Sci. 2007;18(3):233–9.

    Article  PubMed  Google Scholar 

  103. Boyd KA, Briggs AH, Fenwick E, Norrie J, Stock S. Power and sample size for cost-effectiveness analysis: fFN neonatal screening. Contemp Clin Trials. 2011;32:893–901.

    Article  PubMed  Google Scholar 

Download references


The authors gratefully acknowledge the participants, their parents, and the school staff who participate in this study. They also thank the research staff who help with study administration, data collection, and data entry.


This study is funded by the Institute of Education Sciences National Center for Education Research (grant R305A210462). The opinions expressed are those of the authors and do not represent views of the Institute of Education Sciences or the U.S. Department of Education.

Author information

Authors and Affiliations



FM and SB drafted the manuscript. MS conceptualized the study and wrote the grant application for funding. SB, JR, AL, and TP are coinvestigators on the grant and contributed to the conceptualization of the project and writing of the grant application for funding. SC conceptualized, wrote, and provided feedback on the analytic plan. PM, SV, and MO are project coordinators at the study sites and organized the measurement battery. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Stephanie K. Brewer.

Ethics declarations

Ethics approval and consent to participate

All methods will be carried out in accordance with the Declaration of Helsinki principles for medical research involving human subjects. This study has been approved by institutional review boards at Seattle Children’s Research Institute and Florida International University. Adolescents in this study will be 9th graders (14–15 years old). Thus, informed consent/assent will be obtained from all participants and a parent/legal guardian. If a peer interventionist (11th or 12th grader) is over the age of 18, they will provide consent (in lieu of assent).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Macphee, F.L., Brewer, S.K., Sibley, M.H. et al. Study protocol of a randomized trial of STRIPES: a schoolyear, peer-delivered high school intervention for students with ADHD. BMC Psychol 11, 268 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: