Developmental differences in structure of attention-deficit/hyperactivity disorder (ADHD) between childhood and adulthood

International Journal of Behavioral Development 36(4) 279–292 ª The Author(s) 2012 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0165025412444077 ijbd.sagepub.com

Michelle M. Martel,1 Alexander von Eye,2 and Joel Nigg3

Abstract The current paper utilizes a bifactor modeling approach to evaluate the structure of attention-deficit/hyperactivity disorder (ADHD) in adulthood and assess developmental continuity of ADHD structure between childhood and adulthood. The study compared traditional one-factor, two-factor, three-factor, and second-order factor models of ADHD with a bifactor model of ADHD. Developmental differences in ADHD structure were examined using an extension of the bifactor model: a two-group model comparing children and adults. Participants were 406 adults (49% male; 145 of 406 with ADHD), (18 to 37) years old, and 548 children (58% male; 302 of 548 with ADHD), 6 to 18 years old. A bifactor model of ADHD exhibited the best fit in adults and children compared to traditional models, suggesting continuity in the ADHD latent construct across development. However, significant differences in the factor loadings were evident between children and adults in the two-group bifactor model, suggesting changes in the relative importance of particular symptoms over time. Namely, hyperactivity symptoms appear to decline in importance relative to the ADHD phenotype between childhood and adulthood. Keywords attention problems, children, development, developmental psychopathology

Attention-deficit/hyperactivity disorder (ADHD) was defined by the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV-TR; American Psychiatric Association [APA], 2000) as a childhood disorder characterized by behavioral symptoms of inattention and hyperactivity-impulsivity with three subtypes: predominantly inattentive, predominantly hyperactiveimpulsive, and combined. Several modifications have been proposed in preparation for the next version of the DSM, DSM-5. Among considered changes is recognition of the need for developmentally appropriate criteria for adults with ADHD and possible revision of the ADHD subtyping approach (Kieling et al., 2010; Rohde, 2008). Such proposals, along with evidence that subtypes of ADHD are unstable over time (Lahey, Pelham, Loney, Lee, & Willcutt, 2005) and that there are developmental changes in normative levels of hyperactivity (Olson, 2002), beg the question of the continuity of the structure of ADHD from childhood to adulthood. Is there empirical evidence for the same construct/disorder across the life span? The current report undertakes an examination of the factor structure of adult ADHD using a bifactor structural model, as it compares to traditional factor structures of ADHD, with attention to developmental changes in this structure between childhood and adulthood. Efforts to validate ADHD’s symptom factor structure in adulthood remain somewhat limited. Work to date suggests that adult ADHD is a valid diagnosis with comparable psychiatric comorbidity, course and outcome, impairment, sex difference, family history, neuropsychological deficits, treatment response, molecular genetics, and neuroimaging findings as childhood ADHD (reviewed by Faraone et al., 2000; see also Biederman et al., 2006; Hervey,

Epstein, & Curry, 2004; Miller, Nigg, & Faraone, 2007). Prevalence rates of ADHD in adulthood are only slightly lower than in childhood at about 4.4% (Kessler et al., 2006), consistent with suggestions that only a minority of children with ADHD escape a chronic course. However, some normative developmental changes in symptoms are expected since there is an age-dependent decline in normative levels of hyperactivity, and symptom cut-offs are normed for children and adolescents (Lahey, Applegate, McBurnett, & Biederman, 1994). Only about 15% of those individuals who met diagnostic criteria in childhood continue to meet strictly-defined diagnostic criteria in adulthood, although 65% continue to experience at least some impairing symptoms (Faraone, Biederman, & Mick, 2006). Particularly problematic is that some symptoms are developmentally inappropriate when applied to adults (e.g., ‘‘runs about or climbs excessively’’); the hyperactivity symptom domain is most plagued by this issue. Further, there is some concern that, in adults, impulsivity may be a distinct source of impairment compared to hyperactivity (Barkley, Murphy, & Fischer, 2008).

1 2 3

University of New Orleans, USA Michigan State University, USA Oregon Health & Science University, USA

Corresponding author: Michelle M. Martel, Department of Psychology, University of New Orleans, 2005 Geology & Psychology Building, 2000 Lakeshore Drive, New Orleans, LA 70148, USA. Email: [email protected]

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Thus while there is substantial support for the validity of the diagnosis of ADHD in adulthood, there is simultaneously substantial disagreement about how to best capture adulthood expression of ADHD symptomatology. For example, it is unclear whether all symptoms are still important, and whether impulsivity should receive distinct emphasis. A new, sophisticated structural model of ADHD has the potential to provide traction on this issue. The bifactor model1 of ADHD allows for a general, or ‘‘g,’’ ADHD factor, capturing the common variance across all ADHD symptoms, with partially distinct specific, or ‘‘s,’’ inattentive and hyperactiveimpulsive components, capturing the unique variance in inattentive and hyperactive-impulsive symptom domains. This conception critically differs from the DSM-IV diagnostic approach by suggesting that more pure ‘‘s’’ inattention and ‘‘s’’ hyperactivity-impulsivity may not be best classified as subsumed under a general ADHD category, but rather may be best understood as distinct or somewhat distinct pathways to the disorder, consistent with recent theory of dual- or multiple-pathways to ADHD (Nigg, Goldsmith, & Sachek, 2004; Sonuga-Barke, 2005). Further, support for this model might allow for a more nuanced understanding of developmental change in ADHD. Differences in the factor loadings of symptoms on the ‘‘g’’ and/or ‘‘s’’ factors of the model in children and adults may be able to account for developmental changes in the disorder’s symptom expression within and across individuals, and clarify the age appropriateness of individual ADHD symptoms. A second-order factor model is a logical alternative to a bifactor model. It allows inattentive, hyperactive, and impulsive symptom domains to be modeled separately with symptom domains being entirely encompassed by a higher-order ADHD diagnostic category. Thus this model suggests that shared variance between inattention and hyperactivity-impulsivity should be entirely captured by the ADHD diagnostic category, an idea most in line with the conceptualization in DSM-IV (APA, 2000). Structural models of ADHD in children have received a great deal of attention, but less work has addressed this same issue in adults. Confirmatory factor analyses in clinical and population samples of children have tended to support a two- (i.e., inattention vs. hyperactivity-impulsivity) or occasionally three- (i.e., inattention, hyperactivity, impulsivity) factor structure of ADHD (AmadorCampos, Forns-Santacana, Martorell-Balanzo, Guardia-Olmos, & Pero-Cebollero, 2005, 2006; Bauermeister, Alegria, Bird, RubioStipec, & Canino, 1992; Bauermeister et al., 1995; Burns, Walsh, Owen, & Snell, 1997; Burns, Walsh, Patterson, et al., 1997; Burns, Boe, Walsh, Sommers-Flanagan, & Teegarden, 2001; DuPaul et al., 1997; Gomez, Burns, Walsh, & Hafetz, 2005; Lahey et al., 1988; Pillow, Pelham, Hoza, Molina, & Stultz, 1998; Wolraich et al., 2003). Recent work that has comprehensively tested a series of currently available structural models (i.e., one-factor, two-factor, three-factor, second-order factor, and bifactor models) in children has found that a bifactor model appears superior to other existing models in describing the structure of ADHD (Martel, von Eye, & Nigg, 2010; Toplak et al., 2009). These results generally hold across maternal report, teacher report, and ‘‘or’’ algorithm (parent þ teacher report) symptom ratings (Martel et al., 2010; Toplak et al., 2009). To our knowledge, only one study to date has examined the factor structure of DSM-IV ADHD symptoms in adults. This study found most support for a three-factor model of inattention, hyperactivity, and impulsivity in an adult sample of 262 university students (Span, Earleywine, & Strybel, 2002). However, this study was conducted in a nonclinical sample that was not fully evaluated for presence of

DSM-IV ADHD criteria, and second-order factor and bifactor models were not examined. In order to comprehensively address the factorial structure of ADHD in adulthood, the current study provides a test of a set of factor models: one-factor ADHD; two-factor inattention and hyperactivity-impulsivity; three-factor inattention, hyperactivity, and impulsivity; second-order factor ADHD subsuming inattention, hyperactivity, and impulsivity; and bifactor with ‘‘g’’ ADHD and distinct ‘‘s’’ inattention and ‘‘s’’ hyperactivity-impulsivity components. In line with previous work in children (Martel et al., 2010; Toplak et al., 2009), it was hypothesized that a bifactor model would provide the best fit to ADHD symptom ratings in adults. In order to empirically assess developmental differences in the models, an extension of the bifactor model was utilized: a twogroup bifactor model comparing adults and children (for a description of this model, see von Eye, Martel, Lerner, Lerner, & Bowers, 2011). We tested the hypothesis that significant differences in factor loadings between the two groups would be apparent, particularly for the specific hyperactivity-impulsivity factor, mapping onto clinical observations that the relative prominence of hyperactivity symptoms declines between childhood and adulthood.

Method Participants Overview. Both

adults and children participated in the current study, and all participants provided written informed consent for study participation consistent with APA, National Institute of Health (NIH), and institutional review board guidelines. Adult participants were 406 adults (197 men), 18 to 37 years old, recruited from the community via media advertisements and general mailings/flyers for a study of the development of attention abilities and mailings to clinics targeting individuals with possible attention problems. Recruited adults were then evaluated for study eligibility and diagnostic status. They were initially included in one of two groups: ADHD (n ¼ 145) and non-ADHD comparison (‘‘controls,’’ n ¼ 201). Sixty additional adults who were classified as having situational or subthreshold ADHD (did not meet criteria for either ADHD or control group as explained later) were included to provide more complete coverage of the dimensional trait space of ADHD (Levy, Hay, McStephen, Wood, & Waldman, 1997; Sherman, Iancono, & McGue, 1997). The ADHD group included 64 adults with ADHD-predominantly inattentive type (ADHD-PI; i.e., met criteria for six or more inattentive symptoms, plus impairment, onset, and duration, and never in the past met criteria for combined type on the basis of self- and parent/other interview as detailed later), 63 adults with ADHD-combined type (ADHD-C; i.e., met criteria for six or more inattentive symptoms and six or more hyperactive-impulsive symptoms, plus impairment, onset, and duration), and 18 adults with ADHD-hyperactive-impulsive type (ADHD-PHI; i.e., met criteria for six or more hyperactiveimpulsive symptoms, plus impairment, onset, and duration). Descriptive statistics on the adult sample are shown in Table 1; the sample was broadly representative of the community from which they were recruited. Approximately 10% of adults with ADHD were prescribed psychostimulant medication. None of the adults in the current sample were siblings. Child participants were 548 children (321 boys), 6 to 18 years old, recruited from the community and then evaluated for study

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Table 1. Descriptive statistics on adult sample

Males n(%) Ethnic minority n(%) Age Income ($) IQ ADHD-C n(%) ADHD-PI n(%) ADHD-PHI n(%) ASPD n(%) Drug dependence n(%) MDD n(%) GAD n(%) Inattentive Sx Hyperactive Sx

Table 2. Descriptive statistics on child sample

ADHD n ¼ 145

Control n ¼ 201

Total N ¼ 406y

95(65.5) 16(11) 24(4.6) 11867.5(35484.3) 110.8(12.3) 63(43.5) 64(44.1) 18(12.4) 15(10.3) 8(5.5)

85(42.3) 37(18.5) 23.9(4.6) 49708(5628.5) 113.2(10.1) — — — 8(4) 4(2)

197(48.5)** 53(13)* 24(4.6) 7920.3(24120.6) 111.9(11) 63(15.5) 64(15.8) 18(4.4) 23(5.7)** 12(3)**

72(49.7) 28(19.3) 7(1.7) 5.6(2.5)

53(26.4) 10(5) 1.8(2.2) 1.7(2)

125(30.8)** 38(9.4)* 3.9(3.2)** 3.2(2.9)**

Note. *p < .05; **p < .01, via t-tests or chi-squares. ySixty adults were identified as having situational ADHD or were screened out of the study at a later point in time, but were included in study analyses because they had data on individual symptom items. ADHD-C ¼ ADHD combined subtype. ADHD-PI ¼ ADHD, predominantly inattentive subtype. ADHD-PHI ¼ ADHD, predominantly hyperactive-impulsive subtype. ASPD ¼ antisocial personality disorder, lifetime. MDD ¼ major depressive disorder, lifetime. GAD ¼ generalized anxiety disorder, lifetime. Sx ¼ symptoms.

eligibility and diagnostic status through a similar process as that used for the adults. Children were initially included in one of two groups: ADHD (n ¼ 302) and non-ADHD comparison youth (‘‘controls,’’ n ¼ 199). Forty-seven additional children who were classified as having situational or subthreshold ADHD (did not meet criteria for either ADHD or control group as explained later) were included to provide more complete coverage of the dimensional trait space of ADHD (Haslam et al., 2006; Levy et al., 1997; Sherman et al., 1997). The ADHD group included 110 children with ADHD-PI and 192 children with ADHD-C. The current sample included no children with ADHD-PHI, not atypical in this age range (e.g., Lahey et al., 2005; Shaw et al., 2007). Descriptive statistics for the child sample are shown in Table 2. Approximately 24% of children with ADHD were prescribed psychostimulant medication. Children came from 468 families; 80 families had two children in the study.

Identification and recruitment. All participants were recruited using a diverse set of recruitment strategies including radio, newspaper, and movie theater advertisements and general mailings/flyers targeting individuals who thought they or their children might have attention problems, and/or advertising a study of the development of attention, as well as mailings to local clinics (although less than 10% of the sample came from clinic advertisements), in order to recruit a representative sample of community volunteers. Prospective participants then underwent a standard multi-gate screening process to identify cases and non-cases eligible for the study. At stage 1, participants (or parents of participants) completed a telephone screen to assess eligibility. To be eligible to participate in the study, participants had to be a native English speaker and not have a sensorimotor disability, neurological illness, or be on a current prescription for antidepressant, antipsychotic, or anticonvulsant medication. These eligibility criteria were chosen to ensure study participants could adequately understand task

Males n(%) Ethnic minority n(%) Age Family income ($) IQ ADHD-C n(%) ADHD-PI n(%) ODD n(%) CD n(%) MDD n(%) GAD n(%) Inattentive Sx Hyperactive Sx

ADHD n ¼ 302

Control n ¼ 199

Total N ¼ 548y

204(67.5) 78(25.8) 11.3(2.9) 62643(67080) 110.3(14.9) 192(63.6) 110(36.4) 118(39.1) 18(6) 28(9.3) 29(9.6) 18(5) 11(7.2)

96(48.2) 54(27.1) 12.5(3.2) 75244(51109) 103.8(13.9) — — 26(13.1) 1(.5) 9(4.5) 11(5.5) 3.2(3.4) 2(2.2)

321(58.6)** 144(26.3) 11.7(3.1)** 66694(59532)* 106.2(14.7)** 192(35) 110(20.1) 161(29.4)** 19(3.5)** 41(7.5) 45(8.2) 11.3(8.4)** 7.9(7.5)**

Note. *p < .05; **p < .01, via t-tests or chi-squares. yForty-seven children were identified as having situational ADHD or were screened out of the study at a later point in time, but were included in study analyses because they had diagnostic data. ADHD-C ¼ ADHD combined subtype. ADHD-PI ¼ ADHD, predominantly inattentive subtype. ODD ¼ oppositional defiant disorder. CD ¼ conduct disorder. MDD ¼ major depressive disorder, lifetime. GAD ¼ generalized anxiety disorder, lifetime. Sx ¼ symptoms.

instructions and to eliminate the confounds of comorbid conditions and medication use that could affect cognitive performance. Participants who passed this stage of screening went on to a second stage of screening. At stage 2, eligible participants and their parent or significant others (in the case of adults) completed semi-structured interviews and standardized normative rating scales, described later, to ascertain ADHD and comorbid psychopathology. Adult participants completed a retrospective Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS-E; Puig-Antich & Ryan, 1986) in order to assess current and past ADHD symptoms. Adult participants also completed the Barkley and Murphy (1998) current ADHD symptoms rating scale, the Conners, Erhardt, and Sparrow (1999) adult ADHD rating scale, the Achenbach (1991b) young adult self-report, and the Brown (1996) adult ADHD rating scale. Two other informants also reported on the adult participants’ ADHD symptoms. One informant (usually a parent) reported on childhood ADHD symptoms via a retrospective K-SADS ADHD module and the ADHD rating scale, and another informant (usually a partner or friend) completed a current K-SADS ADHD module and the Conners peer rating, the Barkley and Murphy peer ratings on adult symptoms, and a brief screen of antisocial behavior and drug and alcohol use. Parents of child participants completed either the Diagnostic Interview Schedule for Children (DISC-IV; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000) or the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS-E; Puig-Antich & Ryan, 1986). Pooling the data across families that received the K-SADS and the DISC was justified based on our analysis of agreement between the two methods in 430 youth for whom a parent completed both a K-SADS and a DISC-IV. The two interviews had adequate agreement for total number of symptoms (inattention, ICC ¼ .88; hyperactivity, ICC ¼ .86), presence of six or more symptoms of ADHD (k ¼ .79), presence of impairment (k ¼ .64), and presence of ADHD (defined as six or more symptoms þ crosssituational impairment in each interview for purposes of computing agreement; k ¼ .79). In addition, parents and teachers completed

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the following standardized rating scales: Child Behavior Checklist/ Teacher Report Form (CBCL/TRF; Achenbach, 1991a), Conners (1997) rating scales—revised, and the ADHD rating scale (ADHD-RS; DuPaul, Power, Anastopolous, & Reid, 1998). For adults and children, a clinical diagnostic team consisting of a board-certified psychiatrist and licensed clinical psychologist then used this information to arrive at a ‘‘best estimate’’ diagnosis (Faraone, 2000). Each member reviewed ADHD symptom counts from the semi-structured interviews, and raw scores and t-scores from the rating scales, to judge whether ADHD was present or absent, ADHD subtype (if applicable), and comorbid disorders. Each member reviewed information individually to reach a diagnostic decision based on aggregation of all aforementioned information, and then these decisions were compared. In the case of disagreement, consensus was reached by discussion. Interrater agreement was satisfactory on presence or absence of ADHD (k  .80) and ADHD subtype (k ranged from .74 to .89).

Measures Symptom counts.

For adults, current ADHD symptoms were obtained via self-report and peer report on the Barkley and Murphy (1998) ADHD rating scale. Individual symptoms were coded on a 0 to 3 rating scale. ‘‘Or’’ algorithm symptom ratings were also available (as discussed earlier and in Lahey et al., 1994) that integrated self- and peer report symptom ratings. The ‘‘or’’ algorithm was modified when assigning diagnoses, to avoid overinclusiveness, as follows: Adults had to have elevated scores above the eightieth percentile on at least one self- and peer-rating scale, and peer report could add no more than three symptoms to the self-rated symptom count (similar to the multimodal treatment of ADHD study [MTA] approach; Hinshaw et al., 1997). For two-group models involving children, current ADHD symptom ratings were obtained via maternal and teacher report on the ADHD rating scale, utilizing the same 0 to 3 rating scale. ‘‘Or’’ algorithm symptom ratings were also available, as discussed earlier, involving parent and teacher ratings. Self-ratings for adults and maternal ratings for children were emphasized in the presentation of study results for simplicity and to more closely mimic clinical procedures which typically stress self-ratings for adult diagnosis and maternal ratings for child diagnosis. However, secondary checks examined possible informant influence on the factorial models by examining the adult models using peer and ‘‘or’’ algorithm (i.e., self þ peer) symptom ratings in parallel to our prior examination of all informant models in our study of children (Martel et al., 2010).

Data analysis A series of confirmatory factor analyses was estimated using the Mplus software package, version 5 (Muthe´n & Muthe´n, 1998– 2008). Missingness was minimal in the current study, affecting less than 3% of the sample, and was addressed using pairwise present analysis. The presence of siblings in the child sample and the resulting nonindependence of data points were addressed using the clustering feature of Mplus. In order to address ordinal symptom ratings and resulting non-normality, weighted least squares means and variance (WLSMV) adjusted estimation was used. Model goodness of fit was evaluated using chi-square fit statistics, root mean square error of approximation (RMSEA), and

Table 3. Confirmatory factor analysis fit statistics for ADHD symptom ratings Chi-Square Self-rated symptoms One-factor model 399.37** Two-factor model 279.96** Three-factor model 238.83** Second-order factor model 319.76** Bifactor model 223.1** Peer-rated symptoms One-factor model 531.44** Two-factor model 361** Three-factor model 336.43** Second-order factor model would not converge Bifactor model 195.92** ‘‘Or’’ algorithm symptoms One-factor model 486.32** Two-factor model 326.35** Three-factor model 298.3** Second-order factor model would not converge Bifactor model 173.72**

df

CFI

RMSEA

59 66 67 132 117

.89 .93 .94 .93 .96

.12 .09 .08 .06 .05

135 134 132

.78 .87 .89

.10 .08 .07

117

.96

.05

135 134 132

.85 .92 .93

.10 .07 .07

117

.98

.04

Note. **p < .01.

comparative fit index (CFI), as recommended (Hu & Bentler, 1999; Kline, 2005). Smaller chi-square and RMSEA values and larger CFI values indicate better fit. Generally speaking, nonsignificant chi-square, RMSEA equal to or below .06, and CFI above .95 indicate good fit (Hu & Bentler, 1999; Kline, 2005). All fit indices were considered in evaluating model fit, and the best model was determined by the best overall fit indices.

Results Preliminary analyses: Simple models Simple models were first estimated, and model fit statistics are summarized in Table 3 and shown in Figures 1, 2, and 3. The one-, two-, and three-factor models all exhibited inadequate fit by the aforementioned criteria, as indicated by large, significant chi-square values and RMSEA values over .08 (one factor: w2[59] ¼ 399.37, p < .01; RMSEA ¼ .12; CFI ¼ .89; two-factor: w2[66] ¼ 279.96, p < .01; RMSEA ¼ .09; CFI ¼ .93; three factor: w2[67] ¼ 238.83, p < .01; RMSEA ¼ .08; CFI ¼ .94).

Primary hypothesis tests: Complex models Second-order factor model. In this model, inattentive, hyperactive, and impulsive symptoms loaded onto separate factors, but these three factors in turn defined a higher-order factor, termed ADHD. Thus this model assumes that ADHD as a diagnostic category satisfactorily accounts for the shared variance in inattentive, hyperactive, and impulsive symptoms. As shown in Table 3 and Figure 4, this model also exhibited unsatisfactory fit, with a large chi-square and an RMSEA of .06 (w2[132] ¼ 319.76, p < .01; CFI ¼ .93; RMSEA ¼ .06).

Bifactor model. In the bifactor model of ADHD, all symptoms were hypothesized to load onto a single general factor, termed ADHD. In addition, inattentive ADHD symptoms and hyperactive-impulsive ADHD symptoms were hypothesized to

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Close attention

Sustained attention .7 Listens .7 Follow through .84 Organization

Sustained mental effort

Loses things

.73

.75

.79

.8 Easily distracted .74 Forgetful .75 Fidgets

.54

Leaves seat

.71

Runs, climbs

.64

.8 Plays quietly .7 “Driven by motor” .84 Talks a lot .77 Blurts

.81

Waiting turn

.76

Interrupts, intrudes

Figure 1. One-factor ADHD model. Note. All path coefficients are standardized.

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ADHD

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Close attention

Sustained attention .72 .86

Listens

.78 Follow through .82 Inatt .77 .73

Organization

Sustained mental effort

.82 Loses things .87 .83

.85

Easily distracted

Forgetful

Fidgets

Leaves seat .73 .76

Runs, climbs

.83 Plays quietly Hyp

.78 .57

“Driven by motor”

.67 Talks a lot .73 .82

Blurts

.8 Waiting turn

Interrupts, intrudes

Figure 2. Two-factor inattention and hyperactivity-impulsivity model. Note. All path coefficients are standardized.

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Close attention

Sustained attention .72 Listens

.86 .78

Follow through .82 Inatt .77 .73

Organization

Sustained mental effort

.82 Loses things .87 .83

Easily distracted

.84

Forgetful

.77

Fidgets

Leaves seat .75 .78

Runs, climbs

.85 Plays quietly Hyper

.79 .58

“Driven by motor”

.68 Talks a lot

.83

Blurts .78

Impuls

.88

Waiting turn

.86

Interrupts, intrudes

Figure 3. Three-factor inattention, hyperactivity, and impulsivity model. Note. All path coefficients are standardized.

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Close attention

Sustained attention .61 .82

Listens

.74 .71

Follow through

Inatt .66

Organization

.66

Sustained mental effort

.6 .77 .79

Loses things

.73 Easily distracted ADHD Forgetful .9 7 Fidgets

.67 Hyper

Leaves seat

.64 .75

Runs, climbs

.62

.8 7

Plays quietly .56 .53

“Driven by motor”

Talks a lot

Imp

.67

Blurts

.68 .72

Waiting turn

Interrupts, intrudes

Figure 4. Second-order factor model. Note. All path coefficients are standardized.

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load onto their own specific factors. While the bifactor model assumes that ADHD symptoms share some common variance (captured by the general ADHD factor), it differs from the second-order factor model in that the two symptom domains are also assumed to capture variance that is unique from the overarching diagnostic category via orthogonal factors. The bifactor model, with orthogonal general and specific factors, exhibited good fit (w2[117] ¼ 223.1, p < .01; CFI ¼ .96; RMSEA ¼ .05; Figure 5). Thus the bifactor model appeared to exhibit the best fit of the models tested.

Developmental differences Two-group bifactor model: Children versus adults.

In order to address questions of developmental differences in the factor structure of symptoms via a comparison of the model in adults and children, a constrained two-group bifactor model was estimated comparing adults and children. In this model, measurement parameters (i.e., factor structure, factor loadings, thresholds) were constrained to be equal across the groups. This constrained two-group model exhibited poor fit to the data (w2[101] ¼ 875.3; p < .01; CFI ¼ .97; RMSEA ¼ .1). Based on significant modification indices, most of the factor loadings were freed on the ‘‘s’’ hyperactivityimpulsivity factor: ‘‘fidgets,’’ ‘‘leaves seat,’’ ‘‘runs/climbs,’’ ‘‘talks a lot,’’ ‘‘blurts,’’ and ‘‘interrupts, intrudes.’’ In addition, the factor loading of ‘‘listens’’ was freed on the ‘‘s’’ inattention factor, and ‘‘runs/climbs,’’ ‘‘driven by a motor,’’ and ‘‘forgetful’’ were freed on the ‘‘g’’ ADHD factor. The variances of ‘‘s’’ inattention and ‘‘g’’ ADHD were also freed, based on significant modification indices. The model structure, however, remained unchanged. Once these factor loadings and variances were freed, fit of the model, shown in Figure 6, improved to reasonable (w2[138] ¼ 684.82, p < .01; CFI ¼ .98; RMSEA ¼ .069), suggesting that the factor loadings of the bifactor model, particularly ‘‘s’’ hyperactivityimpulsivity, change with age, but the structure of the model remains the same.

Data checks Informant effects.

As shown in Table 3, adult factorial model results generally held regardless of informant. The bifactor model was superior to one-factor, two-factor, and three-factor models, whether symptoms were rated by self, peers, or ‘‘or’’ algorithm. Further, although the bifactor model was superior to the secondorder factor model when using self-ratings, the second-order factor model would not converge using peer or ‘‘or’’ algorithm ratings.

Three-factor bifactor model. In principal, bifactor models can have any number greater than 1 of constituent factors. Therefore a three-factor model of inattention, hyperactivity, and impulsivity was also examined in the bifactor model framework with results similar to the two-factor model depicted earlier (w2[117] ¼ 256.59, p < .01; CFI ¼ .95; RMSEA ¼ .06), although the fit was not quite as good. Sex and diagnostic effects. Because the loadings of individual symptoms might be expected to vary based on sex or diagnostic status, these variables were entered in the bifactor model as covariates. Sex and diagnostic status were significant covariates in the model. Sex was a significant covariate for the ‘‘g’’ ADHD, ‘‘s’’ inattention,

and ‘‘s’’ hyperactivity-impulsivity factors in the adult model (all p < .01). Therefore a fully-constrained two-group model comparing males and females was estimated. In this model, all measurement parameters were constrained to be equal across the groups. This fully-constrained two-group model comparing males and females exhibited reasonable fit (w2[285] ¼ 462.7, p < .01; CFI ¼ .94; RMSEA ¼ .06). However, based on significant modification indices, correlations between ‘‘runs, climbs’’ and ‘‘leaves seat’’ and between ‘‘runs, climbs’’ and ‘‘driven by motor’’ were freed, leading to significant improvement in model fit (w2[283] ¼ 423.45, p < .01; CFI ¼ .95; RMSEA ¼ .05). The structure of the bifactor model remained the same between males and females. Diagnostic status was a significant covariate for ‘‘g’’ ADHD (p < .01), but not for ‘‘s’’ inattention or ‘‘s’’ hyperactivityimpulsivity (both p > .05). Therefore, a fully-constrained two-group model comparing those with and without ADHD was estimated, as described earlier. This model exhibited poor fit to the data (w2[285] ¼ 555.94, p < .01; CFI ¼ .88; RMSEA ¼ .07). However, based on significant modification indices, once the correlations between ‘‘sustained attention’’ and ‘‘loses things,’’ ‘‘loses things’’ and ‘‘forgetful,’’ ‘‘leaves seat’’ and ‘‘plays quietly,’’ and ‘‘driven by motor’’ and ‘‘talks a lot’’ were freed, model fit improved to reasonable (w2[279] ¼ 477.95, p < .01; CFI ¼ .91; RMSEA ¼ .06). Thus, again, it appears that the structure of the bifactor model remains the same in those with and without ADHD, consistent with a dimensional underlying structure of ADHD (Haslam et al., 2006; Sherman et al., 1997).

Discussion The current study extends previous work on adult ADHD by providing a comprehensive test of the factorial structure of adult ADHD with examination of how well a two-group bifactor model can account for developmental change in the symptom structure of ADHD between childhood and adulthood. To this end, more traditional one-factor, two-factor, three-factor, and second-order factor models were compared with a new bifactor model to describe the adult ADHD phenotype. Consistent with study hypotheses, a bifactor model of adult ADHD exhibited arguably the best fit with simultaneous estimation of a general ADHD factor and specific factors of inattention and hyperactivity-impulsivity. This bifactor model appeared sensitive to developmental differences in symptom structure between children and adults with ADHD since notable group differences were observed in factor loadings, particularly of the ‘‘s’’ hyperactivity-impulsivity factor, although the general bifactor structure held. A bifactor model of ADHD best approximated the structure of self-rated ADHD symptoms in adults as compared to the more traditional one-factor, two-factor, three-factor, and second-order factor models of ADHD. Thus the factor structure of ADHD in adults appears best represented by a general ‘‘g’’ ADHD factor and two specific ‘‘s’’ factors of inattention and hyperactivityimpulsivity, although examination of other alternative models might of course still be useful. This model suggests that both general ADHD and specific inattentive and hyperactive-impulsive components are important for fully elucidating an individual’s symptom presentation, possibly with implications for understanding interindividual differences in symptom presentation and for predicting intraindividual changes in symptom presentation over time. Support for a bifactor model suggests that individuals with ADHD

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Close attention

Sustained attention .41 .49 .33

Listens

.76

.4 Follow through

.62

.54 Inatt .65 .29

Organization

.51

Sustained mental effort

.38

.49 .58 Loses things .29 .41 .49

Easily distracted .75 Forgetful .55

Fidgets

.72

Leaves seat

.54

.02 .27

Runs, climbs

.19 Plays quietly Hyper

.7

.54

.33 .15

.53 “Driven by motor” .4

.45 Talks a lot .52

.45 .36

Blurts .53

.57 Waiting turn

.46 Interrupts, intrudes

Figure 5. Adult ADHD bifactor model. Note. All path coefficients are standardized. Italicized paths are nonsignificant (p > .05).

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Close attention

.62/.46 .46/.34

Sustained attention .6/.6 Listens

.35/.43 Follow through

.85/.73

.67/.51 Inatt

.61/.45

.77/.78 Organization .89/.74

.66/.47 .55/.43

Sustained mental effort

.74/.65

.46/.33 Loses things .59/.38

.85/.86 .65/.67

Easily distracted .82/.76 Forgetful

.63/.63

ADHD

.81/.57 Fidgets .62/.61 −.13/.09

Leaves seat .69/.65

−.11/.31 Runs, climbs −.05/−.22

Hyper

.17/.2

.64/.69 .75/.71

Plays quietly .8/.78

−.004/−.01

“Driven by motor”

.87/.79

Talks a lot

.66/.73

.36/.18 .43/.18 .18/.21

.39/.28

Blurts

.83/.77

Waiting turn

Interrupts, intrudes

Figure 6. Two-group (adult vs. child) ADHD bifactor model. Note. All path coefficients are standardized. Italicized paths are nonsignificant (p > .05). Child path coefficients are shown on left; adult coefficients are shown on right.

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are a heterogeneous group that can arrive at ADHD diagnosis in different ways. That is, while some individuals with ADHD are characterized by ‘‘g’’ ADHD (or both inattentive and hyperactiveimpulsive symptoms), other individuals with ADHD exhibit only ‘‘s’’ inattention or ‘‘s’’ hyperactivity-impulsivity. Thus ADHD appears to be an overarching diagnostic category characterized by partially distinct pathways via inattention and hyperactivityimpulsivity, consistent with recent theory of the disorder (Nigg, 2006; Sonuga-Barke, 2005) and with implications for DSM-5 criteria for the disorder. Unsurprisingly, within adults several ADHD symptoms exhibited non-significant loadings on the ‘‘s’’ hyperactivity-impulsivity factor, but all symptoms on the ‘‘s’’ inattention and ‘‘g’’ ADHD factors exhibited significant loadings. This likely reflects the developmental decline in the importance of hyperactivity over time (Hart, Lahey, Loeber, Applegate, & Frick, 1995). Specifically, ‘‘fidgets,’’ ‘‘leaves seat,’’ ‘‘runs, climbs,’’ and ‘‘driven by motor’’ exhibited non-significant loadings on the ‘‘s’’ hyperactivity-impulsivity factor in adults, suggesting a potential developmental decline of the importance of these symptoms during adulthood. In contrast, the impulsive symptom items ‘‘blurts,’’ ‘‘waits turn,’’ and ‘‘interrupts, intrudes’’ exhibited relatively strong loadings on the ‘‘s’’ hyperactivity-impulsivity factor during adulthood, suggesting that impulsivity may be a more prominent manifestation of ‘‘s’’ hyperactivity-impulsivity during adulthood. Further, most loadings of symptoms on the ‘‘g’’ ADHD factor were higher than loadings of symptoms on the ‘‘s’’ hyperactivity-impulsivity factor, again suggesting decline in the importance of hyperactive symptoms in adulthood ADHD, particularly relative to ‘‘g’’ ADHD. A two-group model comparing the bifactor model, already supported in childhood (Martel et al., 2010), with the bifactor model supported here in adulthood suggested meaningful changes in the factor loadings between childhood and adulthood. Most loadings had to be freed on the ‘‘s’’ hyperactivity-impulsivity factor, suggesting broad changes in the developmental presentation of ‘‘s’’ hyperactivity-impulsivity, as hypothesized. Particularly the factor loadings of ‘‘fidgets,’’ ‘‘leaves seat,’’ ‘‘runs/climbs,’’ ‘‘talks a lot,’’ ‘‘blurts,’’ and ‘‘interrupts, intrudes’’ were freed on the ‘‘s’’ hyperactivity-impulsivity factor, consistent with the idea of important developmental change in the ‘‘s’’ hyperactivity-impulsivity construct between childhood and adulthood. Further, the fit of the bifactor model was similar regardless of symptom rater and number of ‘‘s’’ factors included, although a two-factor bifactor exhibited slightly better fit than a three-factor bifactor model in line with prior work (Amador-Campos et al., 2005, 2006; Bauermeister et al., 1992, 1995; Burns et al., 2001; Burns, Walsh, Owen, & Snell, 1997; Burns, Walsh, Patterson et al., 1997; DuPaul et al., 1997; Gomez et al., 2005; Lahey et al., 1988; Pillow et al., 1998; Wolraich et al., 2003). In addition, no striking structural differences in the bifactor model were noted based on adult sex or diagnostic status. Of course, however, the study has a number of limitations. A key limitation to these inferences is that the current study is cross-sectional and utilizes two-group models to make inferences about developmental changes in ADHD structure. Now that cross-sectional data suggest developmental change in ADHD, they justify investment in more stringent testing using a longitudinal sample. Although an important strength of the current study was the reliance primarily on community-based recruitment (so results cannot be very easily explained by sampling biases inherent in examining clinic-referred cases), the bifactor model still requires

evaluation in other samples, including general population samples (not enriched for ADHD) and clinic-referred samples of adults. While effects replicated across reporters and it has been argued that questionnaire ratings of ADHD are as valid as interviews (Pelham, Fabiano, & Massetti, 2005), it will be useful to validate these models using clinical interview data of ADHD symptoms. Lastly, although fit of the bifactor model exhibited arguably the best fit of the models tested here, it was not perfect and is a fairly complex model, suggesting that evaluation of other models of ADHD may also be useful. Overall, the current study provides support for a bifactor model of ADHD in adulthood, as compared to one-factor, two-factor, three-factor, and second-order factor models, across self, peer, and ‘‘or’’ algorithm ADHD symptom ratings. Although the same bifactor model of ADHD has now been supported in childhood and adulthood, illustrative differences in the factor loadings of the model between childhood and adulthood suggest striking developmental differences and possibly changes in the relative importance of symptoms over time. Namely, hyperactivity—but not impulsivity—symptoms seem to become a less essential component of the disorder in adulthood. Overall, use of a bifactor model of ADHD allows for a nuanced understanding of the ADHD phenotype, as well as provides a more complete picture of developmental change in this phenotype between childhood and adulthood than heretofore. Acknowledgments We are indebted to the study participants and staff who made this study possible. Funding This research was supported by NIH National Institute of Mental Health Grant R01-MH63146 and MH59105 to Joel Nigg. Note 1. Related to multi-trait-multi-method models (Campbell & Fiske, 1959), the bifactor model, also called hierarchical model, was introduced to methodologists decades ago (Holzinger & Swineford, 1937). However, it was not introduced to the psychopathology field until more recently (Gibbons & Hedeker, 1992). References Achenbach, T. (1991a). Manual for the child behavior checklist/4–18 and 1991 profile. Burlington, VT: University of Vermont, Department of Psychiatry. Achenbach, T. (1991b). Manual for the young adult self report and young adult behavior checklist. Burlington, VT: University of Vermont, Department of Psychiatry. Amador-Campos, J. A., Forns-Santacana, M., Martorell-Balanzo, B., Guardia-Olmos, J., & Pero-Cebollero, M. (2005). Confirmatory factor analysis of parents’ and teachers’ ratings of DSM-IV symptoms of attention deficit hyperactivity disorder in a Spanish sample. Psychological Reports, 97, 847–860. Amador-Campos, J. A., Forns-Santacana, M., Martorell-Balanzo, B., Guardia-Olmos, J., & Pero-Cebollero, M. (2006). DSM-IV attention deficit hyperactivity disorder symptoms: Agreement between informants in prevalence and factor structure at different ages. Journal of Psychopathology and Behavioral Assessment, 28, 23–32.

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