Early Intervention for Young Children with ADHD - Springer Link

School Mental Health (2011) 3:117–126 DOI 10.1007/s12310-011-9053-x

ORIGINAL PAPER

Early Intervention for Young Children with ADHD: Academic Outcomes for Responders to Behavioral Treatment George J. DuPaul • Lee Kern • Matthew J. Gormley Robert J. Volpe

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Published online: 30 March 2011 Springer Science + Business Media, LLC 2011

Abstract Symptoms of attention-deficit/hyperactivity disorder (ADHD) generally emerge in early childhood, and research has demonstrated that early intervention can effectively reduce those symptoms. Little attention, however, has addressed pre-academic skills in spite of data indicating older students with ADHD are at high risk for academic failure. In the current study, we examined whether behavioral response to psychosocial intervention was associated with improvement in early reading and math skills 12 and 24 months post-intervention. Participants were 41 children, 3–6 years old who received a comprehensive early intervention package across 6 months. Some differences in pre-academic skill performance between behavioral responders and non-responders (based on changes in oppositional behavior) were observed at 12 months; however, few differences maintained after 24 months. Behavioral responders (based on changes in ADHD behavior) showed improvements on only one measure (early numeracy). The findings underscore the need for specific pre-academic skill interventions in the context of ongoing behavioral interventions. Keywords ADHD Early intervention Academic outcome

G. J. DuPaul (&) L. Kern M. J. Gormley College of Education, Lehigh University, 111 Research Drive, Bethlehem, PA 18015, USA e-mail: [email protected] R. J. Volpe Northeastern University, Boston, MA, USA

Introduction Children and adolescents with attention-deficit/hyperactivity disorder (ADHD) frequently experience significant impairment in academic functioning throughout their education (American Psychiatric Association, 2000; Barkley, 2006). Specifically, students with ADHD are at higher than average risk for failing grades, grade retention, referral for special education services, and dropout from high school (e.g., Barkley, Murphy, & Fischer, 2008; Molina et al., 2009). In fact, the most ubiquitous long-term outcome associated with ADHD is academic underachievement and poorer than average educational outcomes (e.g., Barkley, Murphy, & Fischer, 2008). Given that significant symptoms of ADHD frequently appear during early childhood, there is emerging evidence that young children with this disorder enter elementary school already experiencing academic deficits (DuPaul et al., 2001; Lahey et al., 2004). Thus, it is important that early intervention efforts address or at the very least, assess emergent literacy and numeracy skills during the pre-school years. Various interventions have been studied for treating ADHD symptoms in young children including psychostimulant medication (e.g., Greenhill et al., 2006), parent education (e.g., Sonuga-Barke, Daley, Thompson, LaverBradbury, & Weeks, 2001), and classroom behavioral intervention (e.g., McGoey & DuPaul, 2000). These treatment approaches are effective in reducing the frequency and severity of ADHD symptomatic behaviors as well as improving child compliance and parent–child interactions (for review, see Ghuman, Arnold, & Anthony, 2008). The potential connection between intervention effects on behavioral symptoms and functional impairments is particularly important for children with ADHD given that it is a chronic disorder associated with impairment in multiple

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domains of functioning, academic functioning is a consistent predictor of long-term outcomes, and impairment (rather than symptoms) frequently is the impetus for referral for treatment (Owens, Johannes, & Karpenko 2009). Although one might assume that treatment-induced reduction in ADHD symptoms should lead to improvement in academic functioning, empirical findings with elementary-aged children appear to refute this assumption. Specifically, there is only moderate association between measures of ADHD symptoms and impairment (Gordon et al., 2006). Further, relatively small effect sizes were obtained for academic and social outcomes in the Multimodal Treatment of ADHD (MTA) study that employed state-of-the-art psychopharmacological and/or behavioral treatments for 14 months (MTA Cooperative Group, 1999) with similar findings extended to 8 years post-treatment (Molina et al., 2009). In similar fashion, academic performance and/or achievement effect sizes associated with behavioral, psychopharmacological, and psychosocial interventions for children with ADHD have been uniformly in the small range (between 0.11 and 0.33) in several metaanalyses (DuPaul & Eckert, 1997; Fabiano et al., 2009; Van der Oord, Prins, Oosterlan, & Emmelkamp, 2008). Very few studies have explicitly examined the correspondence between treatment effects on ADHD symptoms and concomitant changes in academic functioning. Owens, Johannes, and Karpenko (2009) conducted the most specific evaluation of this issue in a sample of 64 elementary school students with ADHD who received multiple school-based interventions (i.e., daily report card, behavioral consultation with teachers, and parent education in behavioral strategies) across one academic year. Reliable change indices, based on parent or teacher ratings, were used to designate children as symptom improvers or no-changers as well as improvers or no-changers in several domains of functioning, including academic performance. At a group level, symptom improvers showed significant reductions in overall impairment as a function of time whereas symptom no-changers evidenced no change in functioning over time. Alternatively, individual-level analyses indicated that a substantial percentage (26–50%) of symptom improvers did not show reliable change in overall functioning. In fact, only 25–43% of symptom improvers showed improvement in academic performance as a function of treatment. This discordance in treatment outcome between symptoms and academic functioning is of critical clinical importance and must be addressed more directly in ADHD intervention studies (Owens et al., 2009). The vast majority of intervention studies for young children with ADHD have ignored academic performance as a target for treatment as well as a monitored outcome. An exception is Kern et al. (2007) who examined a multicomponent early intervention protocol for young children with

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School Mental Health (2011) 3:117–126

ADHD that provides parent education in behavioral principles, behavioral consultation with pre-school teachers, assessment-based behavioral intervention in home and school settings, as well as parent education in injury prevention and promotion of early academic skills. After 12 months, significant reductions in oppositional and aggressive behaviors along with growth in early reading and mathematics skills were found with this intervention, although no differences in trajectories were found between children who received multicomponent intervention and parent education alone. Further, it was unclear whether the same children exhibited improvements in both behavior and academic skills (i.e., that behavior change was associated with concomitant growth in academic skills). Nevertheless, this was the first early intervention for ADHD study that monitored growth in early numeracy and literacy skills over time. Despite the promising findings by Kern et al. (2007), it remains unclear whether significant behavioral response (i.e., reduction in symptoms) is associated with improved functioning in important areas such as academic performance. It is possible that although concordance in treatment outcome has been absent or small in magnitude for older children, the impact of behavioral response could be greater in young children who are very early in their development of academic skills. It is possible that early behavioral response to treatment allows for greater learning of basic reading and math skills during the pre-school and initial school years. Further, the earlier an intervention occurs in the development of antisocial behavior (and concomitant academic impairment), the less complex the reinforcement system and history for those behaviors, and the more likely behaviors can be changed (Reid & Eddy, 2002). The purpose of the current study was to address a critical gap in the early intervention for ADHD literature by specifically examining the degree to which behavioral response to psychosocial intervention is associated with concomitant improvement in early reading and math skills. Children from the Kern et al. (2007) early intervention study were assessed across 24 months of multicomponent behavioral treatment. Behavioral response was determined in two ways. First, response was defined using the magnitude of change in teacher ratings of ADHD symptoms. Second, magnitude of change in teacher ratings of oppositional behavior (i.e., symptoms of oppositional defiant disorder) was used to define behavioral response. Oppositional behavior was examined in addition to ADHD symptom ratings because non-compliance and oppositional behaviors (rather than inattention, impulsivity, or activity level) were the primary intervention targets for most participants in this study. Developmentally appropriate, direct measures of academic performance were administered to evaluate preacademic skills. The primary aim was to determine whether behavioral responders to multicomponent early intervention


at 6 months differ from non-responders in terms of academic outcomes at 12 and 24 months. It was hypothesized that positive behavioral response (as defined by both ADHD and oppositional behavior) to initial stages of early intervention would be associated with positive academic response at least over the short term (12 and 24 months).

Method Participants and Setting All participants in the current study had previously participated in a study that compared the effectiveness of a multicomponent intervention to a parent education only intervention for pre-school children with ADHD (Kern et al., 2007). Briefly, Kern and colleagues recruited 135 participants across 4 years through periodic mailings to pediatricians’ offices, pre-schools, and day care centers within a 30-mile radius of Lehigh University. All participants underwent a two-stage screening process. During the first stage, a research assistant verified that children attended an early childhood center at least two times per week and that children met criteria for ADHD according to parent and teacher report on the Conners Rating Scales—Revised (Conners, 1997). During the second stage of screening, ADHD and oppositional defiant disorder symptoms were further assessed to determine children’s level of impairment. In addition, research assistants ensured that children did not meet criteria for autism, developmental disabilities, or conduct disorder. Clinicians conducted the Diagnostic Interview Schedule for Children (DISC; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone 2000) and the Children’s Global Assessment Scale (Shaffer et al., 1983) with the remaining parents. Children were excluded if they failed to meet diagnostic criteria for at least one ADHD subtype or met criteria for conduct disorder on the DISC. Additionally, children with a score above 80 on the Global Assessment Scale were excluded due to a lack of impairment. Finally, children with a score below 80 on the Differential Abilities Scale (Elliott, 1990) were excluded for the possibility of cognitive disabilities (see Kern et al., 2007 for a more through description). Participants who met all inclusion criteria were randomly assigned either parent education (PE) only or a multicomponent intervention (MCI) group. Participants in the current study consist of the MCI group (N = 71) from Kern et al. (2007). Those participants for whom all behavioral response data were available (n = 41) were included in analyses for the present study. There were no significant differences in age, gender, ethnicity, ADHD subtype, ODD diagnosis, receipt of medication, or SES between those MCI children included in these analyses relative to those without complete behavioral response data.

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Further, children in the PE only group from the Kern et al. (2007) study were not included because they did not receive school-based behavioral services. Participant’s 6-month ratings on the Conners’ Teacher Rating Scales—Revised Long Form (CTRS-R:L) were used to determine behavioral responder status (defined below). For analyses of behavioral response using teacher rating of ADHD symptoms, 41 participants were classified as small responders (n = 26), moderate responders (n = 17), or non-responders (n = 15 for small responder analyses; n = 24 for moderate responder analyses). For analyses of behavioral response using teacher rating of ODD symptoms, all 41 participants were classified as small responders (n = 19), moderate responders (n = 17), or non-responders (n = 22 for small responder analyses; n = 24 for moderate responder analyses). Participant Description Results are reported on 41 participants aged 3–6 years (M = 4.38; SD = 0.89). Nine (22.0%) participants were 3 years old, 15 (36.6%) were 4 years old, 13 (31.7% were 5 years old, and 4 (9.8%) were 6 years old at the start of the study. Thirty-two (78.0%) participants were men and 9 (22%) were women. Twenty-nine (70.7%) were Caucasian, 4 (9.8%) were Hispanic, 1 (2.4%) was African American, and 7 (17.1%) identified as other. Finally, 3 (7.3%) participants were receiving psychotropic medication, 37 (90.2%) were not receiving psychotropic medication, and data were unavailable for the remaining participant (2.4%). Thirty-one (75.6%) of children had parents who were married, 3 (7.3%) had parents who were cohabitating, 3 (57.3%) had separated parents, and 3 (7.3%) had single parents. Data on parental marital status were not available for 1 (2.4%) of participants. Regarding highest parent education level, 9 (22.0%) of parents graduated high school or held a GED, 13 (31.7%) of parents had some college or post-high school training, 15 (36.6%) had graduated college, and 3 (7.3%) held an advanced graduate or professional degree. Highest education-level data were not available for 1 (2.4%) of participants’ parents. Highest household working status was full time for parents of 39 (95.1%) participants, unemployed but looking for employment for 1 (2.4%) participants’ parents, and household working status data were not available for 1 (2.4%) of participants’ parents. The Hollingshead scale (Hollingshead & Redlich, 1958) was utilized to generate employment status of participants’ parents. The most common occupation was skilled manual employee (n = 15; 36.6%). Administrative personnel (n = 8; 19.5%) and Clerical or sales worker (n = 5; 12.2%) followed. Finally, 4 (9.8%) were machine operators, 4 (9.8%) were business managers, 2 (4.9%) were higher executives, and 1 (2.4%) was an unskilled employee. Data for 2 (4.9%) participants’ parents were not available.

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A series of t tests or v2 (for categorical data) analyses were conducted to assess possible group differences in demographic variables. Results indicated that there were no significant differences between either responder group (small or moderate) relative to non-responders on any demographic category (See Tables 1 and 2 for demographic data for small and moderate ADHD behavioral responders and small and moderate ODD behavioral responders, respectively). Setting Interventions took place at home and in the child’s preschool or day care center. Pre-school or day care centers were both public and private (i.e., Head Start, school district early intervention programs, home-based day care centers, university-based programs, and early childhood centers). Parent education sessions were conducted at a location within the community that was convenient for the participants (e.g., church). Overview of Measures

remaining subtests are more appropriate for older children who have already begun to read. During the initial sound fluency task, the clinician reads the names of four pictures presented to the child. The clinician then verbally produces a single phoneme, and the child is asked to identify the picture that corresponds to that specific phoneme. Next, the clinician verbally produces a word that matches one of the four pictures. The child is asked to produce the beginning sound of the presented word. Administration of this task takes about 3 min and is scored as the number of sounds identified or produced correctly per min. The letter naming fluency task consists of children viewing a page that contains upper- and lowercase letters, naming as many as they can within 1 min. During the phoneme segmentation fluency task, children are instructed to verbally produce individual phonemes for words (consisting of three or four phonemes) after they are verbally presented by the clinician (e.g., ‘‘dog’’ would be ‘‘/d/ /o/ /g/’’). Administration continues until 2 min have elapsed. Students are given 1 point for each correct phoneme, and scores are reported as the total number of correct phonemes. The psychometric properties for the DIBELS have been demonstrated as adequate (Kaminski & Good, 1996), but are less appropriate for older students who have begun to read.

Dynamic Indicators of Basic Early Literacy Skills Bracken Basic Concepts Scale-Revised (BBCS-R) The DIBELS (Kaminski & Good, 1996) is a commonly used battery of standardized, individually administered measures to evaluate early literacy skills. For the current study, three of the seven subtests were utilized as dependent measures: initial sound fluency, letter naming fluency, and phoneme segmentation fluency. These subtests were chosen due to their measurement of early reading skills as is appropriate for our pre-school-aged sample. The

The BBCS-R (Bracken, 1998) is administered to children from 2 years, 6 months to 7 years, 11 months to assess children’s understanding of basic concepts. Concepts assessed include colors, numbers, letters, shapes, sizes, object comparisons, social awareness, direction/position, quantity, texture, and time. The assessment consists of children identifying one of the above concepts in stimulus

Table 1 Demographic and diagnostic characteristics of ADHD behavioral responders Measure

Small responder (n = 26)

Non-responder (n = 15)

t or v2

Mod responder (n = 17)

Non-responder (n = 24)a

t or v2

Age, months

53.89 (10.1)

51.75 (9.3)

0.75

56.63 (10.1)

50.62 (8.9)

2.17

Male, %

78.6

70.0

0.46

78.9

72.4

0.26

White, %

75.0

70.0

0.15

78.9

69.0

0.58

Parents’ highest occupation Parents’ highest education

4.24 (1.5) 4.24 (0.9)

3.60 (1.7) 4.40 (0.9)

4.6 (1.6) 4.2 (0.8)

3.6 (1.5) 4.4 (1.0)

ADHD combined, %

70.4

63.2

0.26

41.9

58.1

ADHD inattentive, %

3.7

15.8

2.05

0.0

14.8

3.08

ADHD hyperactive-impulsive, %

33.3

21.1

0.83

31.6

25.9

0.18

ODD, %

63.0

78.9

1.35

63.2

74.1

0.63

Receiving psychotropic medication, %

7.4

10.0

0.99

5.6

10.3

0.33

1.25 -0.53

2.13 -0.65 0.02

Data are presented as means and standard deviations or as percentages, as indicated ADHD attention-deficit hyperactivity disorder, ODD oppositional defiant disorder, Mod moderate a

The sample size for non-responders varies between small and moderate responder analyses as some of the children classified as small responders are re-classified as non-responders when using the more stringent standard for designating moderate response

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Table 2 Demographic and diagnostic characteristics of ODD behavioral responders Measure

Small responder (n = 19)

Non-responder (n = 22)

t or v2

Mod responder (n = 17)

Non-responder (n = 24)a

t or v2

Age, months

55.7 (8.8)

50.7 (9.3)

-2.0

55.7 (9.3)

51.1 (9.1)

0.00

Male, %

77.3

73.5

0.10

73.7

75.7

0.03

White, %

77.3

70.6

0.30

73.7

73.0

0.00

Parents’ highest occupation

3.86 (1.8)

3.82 (1.8)

0.02

3.7 (1.9)

3.9 (1.8)

0.61

Parents’ highest education

3.91 (1.5)

4.15 (1.2)

0.68

3.9 (1.6)

4.1 (1.2)

1.20

ADHD combined, %

68.8

68.2

0.00

68.4

68.6

0.00

ADHD inattentive, % ADHD hyperactive–impulsive, %

9.1 31.8

9.4 21.9

0.00 0.67

10.5 31.6

8.6 22.9

0.06 0.49

ODD, %

72.7

71.9

0.00

78.9

68.6

0.66

Receiving psychotropic medication, %

0.0

11.8

2.66

0.0

11.1

2.28

Data are presented as means and standard deviations or as percentages, as indicated ADHD attention-deficit hyperactivity disorder, ODD oppositional defiant disorder, Mod moderate a

The sample size for non-responders varies between small and moderate responder analyses as some of the children classified as small responders are re-classified as non-responders when using the more stringent standard for designating moderate response

pictures by pointing. For the current study, the school readiness composite score was used as an outcome variable. The school readiness composite consists of six subtests of the BBSC-R and measures the conceptual and receptive languages capabilities of the child. The BBSC-R has been found to have adequate internal consistency, test– retest reliability, and content validity (Bracken, 1998). Early Numeracy Skills Assessment (ENSA; Sokol, 2002) The ENSA was developed for the Kern et al. (2007) study to evaluate children’s early numeracy skills. It is an individually administered instrument that contains 11 subscales (e.g., rote counting, number identification, number naming fluency, number writing, and addition). ENSA quantity concepts (i.e., total) score was used as a dependent measure. Internal consistency for this measure is adequate (coefficient alpha = 0.76). Interscorer agreement (for 40% of ENSA administrations randomly selected across participants and phases) in the present study was 99%.

strong internal consistency, test–retest reliability, and concurrent validity (Conners, 1997). Procedures Assessment and Data Collection Assessment and data collection procedures are identical to Kern et al. (2007). Baseline assessments were completed at time of entry, and additional assessments were conducted every 6 months thereafter for 2.5 years. Both parents and teachers were compensated $50 for completing rating forms regarding the functioning of the participants. Graduate student research assistants, naı¨ve to the purpose of the study and blind to group status, administered the DIBELS, BBCS-R, and ENSA to children at school or home at each assessment point. Data from four data collection points (pre-treatment, 6 months, 12 months, and 24 months) were used in the current study. Overview of MCI

Conners’ Teacher Rating Scales-Revised Long Form (CTRS-R:L) The CTRS-R:L teacher form (Conners, 1997) is a 59 item measure used to evaluate teacher perceptions of ADHD symptomology and related behavior functioning. The measure consists of 11 scales including the DSM-IV Total T-Score (18 items tapping ADHD symptoms) and Oppositional Scale (6 items) that were used in the current study to determine behavioral responder status (see ‘‘Procedures’’ section). The CTRS-R:L was normed on over 8,000 male and female children aged 3–17. Further, the CTRS-R:L has

MCI focused on behavioral functioning, academic functioning (i.e., early numeracy and early literacy), and child safety. Specifically, the MCI consisted of parent education classes and functional assessment-based interventions at home and in the early childcare setting. Parent Education Parent education classes were delivered across 12 months and consisted of 20 two-hour sessions. Given the length of the enrollment period (4 years), parent education cohorts were formed approximately every 3 months and group size varied from 4 to 24 parents. An

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advanced graduate student served as a consultant for each cohort. Sessions were held at a mutually convenient time and place decided by the cohort. Transportation and childcare were available to participants to facilitate participation. On average, parents attended 37% of sessions (SD = 36.15; range = 0–100%). When a family missed a session, consultants attempted to individually deliver the information. If this was unsuccessful, the session materials were mailed to the family. Sessions consisted of didactic presentation of content supplemented with short video illustrations, modeling, group discussion, and role-play. Consultants were observed and received feedback from the project investigators (DuPaul or Kern) during their first session to ensure sessions covered the required material in an acceptable manner. Additionally, all sessions were audiotaped and 17.1% were randomly selected and evaluated for procedural integrity, measured by integrity checklists that contained 13–34 scorable items. Mean session integrity was 96.4%. The content of the sessions varied and included two sessions to provide an initial overview of the project and to ADHD in general. Three sessions described functional assessment; collecting antecedent, behavior, and consequence data; and training on summarizing data for intervention planning. One session was devoted to child safety and the prevention of accidental injury (e.g., car seat safety). Two sessions were dedicated to pre-academic skills focusing on early literacy and numeracy. Parents were provided with information from the Ladders to Literacy curriculum (Notari-Syverson, O’Conner, & Vadasy, 1998) and other resources about developmental expectations and activities to further develop early skills. The largest component (11 sessions) of the parent education facet of the MCI was the Community Parent Education (COPE; Cunningham, Bremnerm, & Secord, 1998) curriculum. The COPE curriculum is an empirically based program that details general behavior management strategies with a focus on increasing compliance through the utilization of evidence-based practices (e.g., time-out, when-then statements, and planned ignoring). A final session provided a summary of the main points of the previous sessions and reviewed future expectations.

to record actual antecedents, behaviors, and consequences. Cohort consultants also conducted direct observations for 2–4 h during this time period. Finally, a four-condition (i.e., control, task, low adult attention, and restricted access to a toy or activity) analog brief functional assessment was conducted. Following the assessments, data were summarized for each problem behavior. The cohort consultant and parent met and jointly reviewed the assessment data. The dyad then agreed upon an intervention strategy that contained both proactive and reactive strategies and included replacement behaviors for the targeted problem behavior. Finally, the cohort consultant provided instruction and support in the implementation of the chosen intervention in the form of modeling, practice, and feedback. In instances in which the parent reported an ineffective intervention, additional home visits were completed and the intervention was modified. Finally, cohort consultants conducted monthly home visits to ensure implementation and further hone the intervention.

Home Intervention Following the three parent education sessions regarding functional assessment, the cohort consultant administered a problem identification interview (Kratochwill & Bergan, 1990) to each participant. The interview identified behaviors of concern for each family, operational definitions of problem behaviors, antecedents and consequences for these behaviors, previous interventions implemented, and finally, the child’s interests and preferred rewards. Following the interview, parents were instructed to conduct 1–2 weeks of direct observation

The independent variable for the current study was behavioral responder status at the first assessment point (i.e., 6 months after baseline). Behavioral response was defined in two ways. First, CTRS-R:L DSM-IV Total T-score effect size was used to gauge response based on change in ADHD symptoms. Effect size was calculated as ES = (pre-treatment CTRS-R:L DSM-IV Total T-score - 6-month CTRS-R:L DSM-IV Total T-score)/pre-treatment CTRS-R:L SD, where the group SD was used in the denominator. Second, CTRS-R:L Oppositional T-score

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Pre-school Interventions Preliminary procedures were similar to those used to develop the home intervention. A problem identification interview (Kratochwill & Bergan, 1990) was completed with the lead pre-school teacher or the primary childcare provided. Additionally, the cohort consultant conducted 2–5 direct observations lasting several hours each. Again, the goal of the observations was to facilitate the creation of individualized interventions based on the perceived function of the problem behavior. The consultant subsequently recommended interventions to the school staff, and an intervention was selected collaboratively with school personnel. If necessary, pre-school personnel was instructed and trained to implement the intervention through modeling, practice, and feedback. In approximately 30% of cases, a class-wide intervention was implemented prior to an individual intervention due to developmentally inappropriate practices (e.g., excessive activity length), or a lack of structure or consistency (e.g., failure to follow schedule). Classroom interventions were implemented for approximately 12 to 18 months. Responder Status


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effect size was used to determine response to behavioral intervention based on change in ODD symptoms. Effect size was calculated as: ES = (pre-treatment Conners Oppositional T-score – 6-month Conners Oppositional T-Score)/pre-treatment Conners Oppositional SD, where the group SD was used in the denominator. For both sets of analyses, behavioral responders were operationally defined as children who received an effect size greater than or equal to 0.25. Two responder thresholds were created: small (ES C 0.25) and moderate (ES C 0.50) to assess for the possibility of greater academic gains for individuals who showed greater behavioral gains. Children with an effect size smaller than 0.25 were categorized as non-responders.

Results Means and standard deviations for all dependent measures across two time points (12 and 24 months) for small and moderate behavioral response groups are presented in Tables 3 and 4 for ADHD behavioral response and Tables 5 and 6 for oppositional behavioral response. To answer the primary research question, a series of one-way analyses of covariance across responder groups was conducted with pre-treatment scores on each dependent variable serving as covariate (i.e., to control for possible pretreatment group differences). Given the exploratory nature of this study, an alpha level of 0.05 was used for each analysis. Effect size was estimated by calculating partial g2 values to represent the percentage of variance in academic performance accounted for by behavioral responder status. ADHD Small Behavioral Response Group There were no statistically significant differences between responder groups on any of the academic measures for 12-month outcomes and for all but one measure for 24-month outcomes. Behavioral responders obtained significantly higher ENSA quantity concepts scores than non-responders at 24 months (F (1, 28) = 6.60, p \ 0.05, partial g2 = 0.19).

ADHD Moderate Behavioral Response Group There were no statistically significant differences between responder groups on any of the academic measures for either 12- or 24-month outcomes. Oppositional Small Behavioral Response Group For 12-month outcomes, statistically significant differences between responder groups were obtained for DIBELS letter naming fluency (F (1, 35) = 6.89, p = 0.01, partial g2 = 0.16), DIBELS phoneme segmentation fluency (F (1, 35) = 6.18, p = 0.02, partial g2 = 0.15), and ENSA quantity concepts (F (1, 38) = 4.48, p = 0.04, partial g2 = 0.10). In all three cases, behavioral responders obtained significantly higher scores than non-responders, after controlling for pre-treatment scores. Groups did not differ significantly for DIBELS initial sound fluency and Bracken school readiness composite score. At 24 months, performance on only one variable, DIBELS letter naming fluency, differed significantly between responder groups (F (1, 24) = 5.86, p = 0.02, partial g2 = 0.20). Statistically significant group differences were not found for DIBELS initial sound fluency, DIBELS phoneme segmentation fluency, Bracken school readiness composite, and ENSA quantity concepts. Oppositional Moderate Behavioral Response Group At 12 months, statistically significant differences between responder groups were found only for DIBELS letter naming fluency (F (1, 35) = 4.92, p = 0.03, partial g2 = 0.12). Group differences were not statistically significant for any of the remaining measures. Identical results were obtained for 24-month outcomes. The only variable to differ significantly across groups was DIBELS letter naming fluency (F (1, 24) = 5.78, p = 0.02, partial g2 = 0.19). Group differences were not statistically significant for any of the remaining measures.

Table 3 Means and standard deviations of outcome measures for small ADHD behavioral responders Measure

Small responder 12 months

Non-responder 12 months



DIBELS initial sound fluency

13.9 (10.0)

14.1 (10.0)

21.4 (16.3)

28.8 (20.1)

DIBELS letter naming fluency

28.0 (24.5)

16.9 (19.9)

43.6 (25.8)

33.8 (30.5)

DIBELS phoneme segmentation fluency

13.3 (18.3)

10.8 (14.8)

29.0 (20.4)

22.8 (19.3)

Bracken school readiness composite

101.4 (23.7)

109.5 (9.7)

109.0 (10.5)

100.3 (27.3)

ENSA quantities concepts raw score

6.6 (2.0)

5.9 (2.0)

7.9 (0.5)

7.1 (1.3)

Standard deviations are in parentheses DIBELS Dynamic Indicators of Basic Early Literacy Skills, BBCS-R Bracken Basic Concepts Scale-Revised, ENSA Early Numeracy Skills Assessment

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Table 4 Means and standard deviations of outcome measures for moderate ADHD behavioral responders Measure

Moderate responder 12 months





15.1 (11.6)

13.2 (9.4)

25.4 (16.6)

24.0 (19.4)


30.1 (24.5)

19.9 (19.1)

48.5 (26.0)

33.5 (27.9)


15.5 (20.3)

10.2 (14.4)

37.6 (17.9)


107.4 (17.8)

102.2 (21.5)


7.0 (1.7)

5.9 (2.2)

110.9 (8.4) 7.8 (0.6)

19.6 (18.2) 101.4 (24.3) 7.4 (1.2)


Table 5 Means and standard deviations of outcome measures for small ODD behavioral responders Measure






15.5 (10.2)

12.1 (10.2)

26.2 (18.3)

24.4 (19.8)


35.7 (23.7)

13.7 (12.7)

51.9 (26.4)

24.3 (20.6)


22.7 (19.1)

4.4 (9.4)

34.9 (18.9)

16.3 (16.7)


103.0 (25.1)

105.6 (14.8)

105.2 (25.7)

104.9 (12.5)


7.2 (1.4)

5.6 (2.3)

7.9 (0.5)

7.2 (1.3)


Table 6 Means and standard deviations of outcome measures for moderate ODD behavioral responders Measure






15.6 (10.4)

12.4 (10.1)

26.6 (18.9)

24.1 (19.1)


36.3 (25.1)

15.2 (13.1)

53.0 (27.0)

25.0 (20.1)


22.2 (20.2)


102.4 (26.4


7.1 (1.4)

6.2 (11.0)

35.1 (19.6)

17.3 (16.6)

105.7 (14.4)

104.7 (26.5)

105.4 (12.2)

5.8 (2.3)

7.9 (0.5)

7.3 (1.2)


Discussion We hypothesized that positive child behavioral response following 6 months of intervention would be associated with positive academic outcomes at 12 and 24 months. This hypothesis was not confirmed for the responder groups defined by improvements in ADHD symptoms for all measures except ENSA quantities concept raw score at 24 months. When behavioral response was defined by improvements in oppositional behavior, the small responder group scored significantly higher than the nonresponder group at 12 months on DIBELS letter naming

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fluency, DIBELS phoneme segmentation fluency, and ENSA quantities concepts raw score. The moderate responder group scored significantly higher than the nonresponder group only on the DIBELS letter naming fluency. At 24 months, both the small and moderate responder groups scored significantly higher than non-responders only on DIBELS letter naming fluency. Thus, the findings suggest that in general, reductions in ADHD symptoms and oppositional behavior do not necessarily portend greater acquisition of academic readiness skills. The fact that behavioral response based on oppositional behavior showed a stronger association with academic improvement


than did ADHD behavioral response is not surprising given that most teachers identified oppositional and non-compliant behaviors as primary targets for intervention. Although we speculated an advantage in pre-academic skills with reductions in behavior problems coupled with early exposure to academic instruction, it appears that intervention for early academic and literacy skills must be specifically and independently designed in the context of ongoing behavioral interventions. At the same time, it is interesting to note that the responder groups (regardless of defining response based on ADHD or oppositional behaviors) scored higher than the non-responder group on most measures at both time points. Although not all of the differences reached thresholds for statistical significance, the consistent direction of differences suggests perhaps a very slight advantage in preacademic skills for young children with fewer ADHD and/ or oppositional symptoms. Given that most differences were small, however, targeted early academic instruction is still needed. Also unexpected was that the small responder group (based on changes in oppositional behavior) showed a larger number of significant differences on pre-academic assessments than the moderate responder group when compared with the non-responders. At the same time, test scores were fairly equivalent across the two responder groups. Thus, it may be that small improvements to oppositional and noncompliant behaviors are sufficient to make improvements in other skills that contribute to early academic learning. Across the two responder groups (based on oppositional behavior) and at 12 and 24 months, significant differences of moderate to large magnitude consistently emerged for DIBELS letter naming fluency. In the sequence of the early literacy skills tested, this is one of the first to be taught. Specifically, letter naming precedes initial sound fluency, phoneme segmentation fluency, and school readiness, which comprise the other skills we assessed. Thus, the participants had the most exposure to this skill. It may be that repeated exposure and practice are needed for acquisition. In addition, this may be the only skill that the younger participants in our study were expected to master. Given the small sample size at each age level, it was not possible to parcel out age differences with respect to skill acquisition. Further research should examine the relationship between behavior, age, duration of practice, and early academic skill acquisition. The group differences at the two time points also pose interesting questions. Fewer significant differences (for responders based on oppositional behavior) were observed at 24 months following initial intervention. This suggests the possibility that children with ADHD become more challenged as academic demands increase. That is, after initial skills are acquired (e.g., letter naming), the number and difficulty of ensuing skills amplify. For example, the

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Bracken school readiness composite assesses a wide variety of skills, including knowledge of colors, numbers, letters, shapes, sizes, object comparisons, social awareness, direction/position, quantity, texture, and time. Children generally acquire these skills through both incidental learning and direct instruction. It is possible that more rote skills learned through repetition and ongoing exposure, such as letter naming, are acquired more readily whereas attention and behavioral difficulties interfere with acquisition of the more complex and higher-level academic skills. This is consistent with the research indicating a small association between behavior and academic performance with older school age students with ADHD, when more complex skills are required (e.g., Owens, Johannes, & Karpenko 2009). One limitation of our study procedures is that the extent to which participants received early intervention specifically focused on pre-academic skills is unclear. Parent education sessions included content to teach families strategies for working with their child on early literacy and numeracy skills. This instruction was presented during a single session, and no data were collected to evaluate implementation. Further, parent attendance was relatively low (31% of sessions on average) and that could limit implementation. Therefore, the extent to which parents actually worked with their children on these skills is not known. In general, it is very possible that parents do not persist when children experience skill difficulty, similar to their difficulties persisting with instruction when their child engages in problem behavior. Also, interventions implemented in the pre-school settings were assessment based and generally focused on behavioral difficulties. For example, interventions frequently addressed problems that occurred during pre-academic activities, such as inattention or overactivity during circle time. In this case, intervention could have involved early academic skills, such as providing a child with an individualized calendar with the daily date highlighted during calendar activities. The interventions, however, were not linked to a formal and comprehensive assessment of preacademic skills and did not explicitly teach early academic skills. Finally, there was a great deal of variability in the participating pre-schools/childcare settings. Although some were structured and had organized activities, many were very unstructured with few pre-academic demands or learning activities. Thus, many of the participants did not receive planned and systematic pre-academic instruction. Another limitation is that the ENSA was developed for the current project and is still an exploratory assessment. Items from the ENSA were derived from the Pennsylvania state standards for early academics and are aligned with expectations for pre-school age children in the state. Nonetheless, to date, we have not conducted research to assess the psychometric properties of this assessment.

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Although additional research is needed to further determine the relationship between behavior and early academic skills, the current study suggests that intervention in both areas is needed, particularly as children age and the number and difficulty of pre-academic expectations increase. An important message for practitioners is that reductions in behavior problems do not necessarily mean that academic skill improvements are forthcoming. For young children with or at-risk for ADHD, assessment must focus on both behavioral and pre-academic areas with intervention addressing each area independently, in order to assure they enter school with skills commensurate with their peers. Acknowledgment The preparation of this article was supported by the National Institute of Mental Health, Grant R01-MH61563. However, the opinions and positions are of the authors and no endorsement by NIMH should be inferred.

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