Occupational Status Is Compromised in Adults With ADHD and Psychometrically Defined Executive Function Deficits

Abstract

Objective: Problems related to executive function (EF) are frequently reported in adults with ADHD. However, only a subgroup of patients show deficits on common neuropsychological tests designed to measure EF. We investigated whether this subgroup also had higher levels of functional impairments, including unemployment, than the ADHD group without such deficits. Method: We defined executive function deficit (EFD) from selected tests from the Delis–Kaplan Executive Function System (D-KEFS) and assessed ADHD symptoms and psychiatric comorbidity with the use of questionnaires in 79 ADHD patients and 77 controls (IQ above 80 in both groups). Results: In the ADHD group, 24.3% had EFD. This subgroup showed significantly higher frequency of unemployment, more reading and writing problems, lower IQ scores, and more self-reported ADHD symptoms in childhood than the ADHD subgroup without EFD. Conclusion: These findings indicate that it may be possible to identify individuals at risk of functional impairments, and emphasizes the importance of effective treatment programs targeting EF.

Keywords

executive function D-KEFS ADHD executive function deficit occupational status

Introduction

ADHD is a diagnosis defined by developmentally inappropriate levels of hyperactivity, inattention, and impulsivity (American Psychiatric Association [APA], 1994). Approximately 40% to 60% of children with a diagnosis of ADHD are expected to have persisting symptoms into early adulthood (Faraone, Biederman, & Mick, 2006; Lara et al., 2009). Studies have shown that this group displays a range of functional impairments, from work-related problems to difficulties in social life (Biederman, Faraone, et al., 2006; Gjervan, Torgersen, Nordahl, & Rasmussen, 2012; Halmøy, Fasmer, Gillberg, & Haavik, 2009). Kessler, Adler, Ames, Barkley, et al., (2005) estimated that ADHD among workers was associated with an average of 35 days annual lost work performance. This represents 120 million days of annual lost work in the U.S. labor force, equivalent to US$19.5 billion lost human capital. However, individuals with ADHD constitute a heterogeneous group with high inter-individual variability (Wahlstedt, Thorell, & Bohlin, 2009), and identification of potential risk factors for functional impairment is therefore important to understand different developmental pathways within the group.

One such risk factor is deficits of executive functions (EFs), which is described as a core feature of ADHD (Barkley, 2010; Castellanos, Sonuga-Barke, Milham, & Tannock, 2006). EFs has been defined as “general-purpose control mechanisms that modulate the operation of various cognitive subprocesses and thereby regulate the dynamics of human cognition” (Miyake et al., 2000, p. 50). As such, EF is important for adaptation, self-regulation, social cognition, independence, and productivity, and deficits commonly lead to cognitive, emotional, and behavioral impairment (Eslinger, Flaherty-Craig, & Chakara, 2013). To prevent serious impairment of daily life function, it may thus be essential to identify and help individuals with ADHD and executive function deficits (EFD; Miller, Nevado-Montenegro, & Hinshaw, 2012).

However, there are several challenges concerning how to define EF, how to assess EF, and the relationship between EF and ADHD. Despite descriptions of EFD as a global feature of ADHD (Barkley & Fischer, 2011; Brown, 2006), neuropsychological assessments usually only identify deficits in a subgroup of individuals with ADHD. Some researchers argue that this is due to the structured test setting, which actually diminishes demands on some of the core problems in EFD, and that one should rather use rating scales measuring daily functioning when assessing EF (Barkley & Fischer, 2011; Toplak, West, & Stanovich, 2013). Accordingly, low correlations have been reported between self-reported and psychometric definitions of EFD (Biederman et al., 2008). Toplak et al. (2013) have suggested that the two methods assess different aspects of cognitive function. In line with this, Nigg, Willcutt, Doyle, and Sonuga-Barke (2005) have argued that the subgroup with executive deficits on neuropsychological tests has distinct features, and suggested inclusion of an executive deficit subtype of ADHD in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; APA, 2013). More knowledge about this cognitive heterogeneity in ADHD is important to obtain more targeted treatment and better screening procedures in the future. The group with psychometrically defined EFD from test performance may be a particularly vulnerable group with substantial problems in daily life activities (Miller et al., 2012; Nigg et al., 2005), underlining the importance of investigating functional impairment separately in this group.

Identification of individuals with EFD is dependent not only on the assessment method but also on the operationalization of EF. Miyake et al. (2000) have suggested that the concept of EF comprises several subfunctions, such as inhibition, working memory, and set-shifting, and that these subfunctions are involved in more complex and higher level functions, such as planning. They also found that EF is characterized by both unity and diversity. In line with this, there may be individual differences in functioning across different components of EF, and a test battery must therefore be rather extensive. The Delis–Kaplan Executive Function System (D-KEFS; Delis, Kaplan, & Kramer, 2001) is one of the few standardized test batteries specifically designed to capture such a wide range of EF components. Other assets of D-KEFS include its sensitivity to even the mild EFD characterizing individuals with ADHD (Swanson, 2005; Wiers, Gunning, & Sergeant, 1998). Inclusion of tests from D-KEFS is therefore expected to give a more coherent identification of different aspects of EF than a selection of neuropsychological tests from different test batteries and test traditions.

In addition to the challenge of assessing and defining EF, there is no consensus on how to distinguish a “deficit” from a “normal” function. Doyle, Biederman, Seidman, Weber, and Faraone (2000) defined EFD as a score of ≥1.5 standard deviations below the mean of matched controls (or what is within the poorest seventh percentile for non-normal distributed variables) on two or more tests of EF. This definition led to better discrimination between ADHD and controls than a definition based on results from a single test (Doyle et al., 2000). Using Doyle’s definition, Biederman, Petty, et al. (2006) showed that although EFD seemed to be independent of psychiatric comorbidity or severity of ADHD symptoms, EFD in ADHD was associated with low IQ and poor reading achievements, in addition to low academic and occupational functioning. This supports the use of results from more than one test when characterizing EFD in ADHD.

Several studies have investigated differences in EF between diagnostic subgroups of ADHD. Fewer studies have examined characteristics of the subgroup with neuropsychologically defined EFD, although this may represent a distinct subgroup of ADHD (Nigg et al., 2005). The study of Biederman, Petty, et al. (2006) is an exception. They included a set of standard neuropsychological tests selected from different test traditions to define a score of EFD, and called for other studies with the aim of replicating their findings. In the present study, we applied Doyle et al.’s definition of EFD on selected subtests from D-KEFS in a Norwegian sample of ADHD patients and controls. There are at least two arguments in favor of using the D-KEFS: (a) D-KEFS is widely used in the clinic, making the results more relevant for clinicians; and (b) use of a standardized test battery, developed within the same test tradition probably reduces error variance due to methodological differences between test measures. These advantages, in addition to the need for confirming the results from Biederman, Petty, et al. (2006) with other measures and in samples outside the United States, motivated the present study. Subtests from D-KEFS were selected to cover a wide range of EF, including the subfunctions described by Miyake et al. (2000). From the results presented by Biederman, Petty, et al. (2006), we hypothesized that a higher proportion of the ADHD group than the control group would show EFD, and that the combination of ADHD and EFD would be associated with low IQ, low education, problems with reading and writing, and unemployment. As EFD and ADHD combined is likely to be associated with a more severe impairment in daily functioning than ADHD alone, we also expected a high percentage of adults with ADHD and EFD to have been diagnosed in childhood.

Method

Participants in the Main Study: ADHD in Norwegian Adults

The present study is a substudy of the project, “ADHD in Norwegian Adults,” that currently includes 800 adult ADHD patients diagnosed according to Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; APA, 1994) criteria and 909 control persons. In the ADHD group, 340 participants were recruited from a national registry of adults diagnosed in Norway from 1997 to May 2005, who had undergone a diagnostic evaluation performed by expert committees with competence in diagnosing ADHD in children and adults. This registry was established during a period when adults with ADHD/hyperkinetic disorder were only allowed to receive stimulants after systematic and mandatory diagnostic evaluation by one of the three regional diagnostic committees. This ensured a standardized diagnostic practice across the country and adherence to ADHD diagnostic criteria. The committees consisted of three to five clinicians with special expertise and experience in diagnosing ADHD. Patients were referred to the expert teams by their hospital doctors, general practitioners, or psychiatrists. The referral procedure required records with descriptions of current functioning and symptoms, collateral information about behavior in childhood as well as results from physical and psychiatric examinations. Based on these records, the committees confirmed or disproved the diagnosis of ADHD. Although International Classification of Diseases–Tenth Revision (ICD-10) was the official diagnostic system in Norway, allowance was made for the diagnosis of the inattentive subtype in DSM-IV. Between 2005 and 2007, 1,700 invitation letters were sent to adults with ADHD included in this registry. The rest of the ADHD group was recruited from clinicians (psychiatrists, general practitioners, and psychologists) from all over the country. They were invited to recruit adults with ADHD who were formally diagnosed according to the national guidelines based on the criteria used by the expert teams, but without the mandatory evaluation of the committees as this arrangement ceased in 2005. The inclusion criteria in this project were a diagnosis of ADHD or hyperkinetic disorder according to the DSM-IV or the ICD-10 criteria and an age of 18 years or above. There were no formal exclusion criteria.

In the control group, 715 individuals in the same age range as the patients were randomly recruited from the general population in Norway through the Medical Birth Registry of Norway (MBRN). This registry includes all persons born in Norway after January 1, 1967. A total of 2,963 letters of invitation were sent to the randomly selected nationwide sample. In addition, a subsample of controls was recruited by means of local advertisements. All participants donated blood or saliva samples and completed questionnaires. For all patients, the referring clinicians provided details concerning diagnoses and treatment history. The project was approved by the Regional Committee for Medical and Health Research Ethics of Western Norway (Institutional Review Board [IRB] 00001872). More details concerning the main study are described in previous publications (Halleland, Haavik, & Lundervold, 2012; Halmøy et al., 2009, 2010).

Participants in the Present Study

Participants living in and around Bergen were randomly selected from the main study and invited to take part in an extended assessment, including a psychiatric interview (to assess psychiatric comorbidity) performed by a psychiatrist, and neuropsychological testing performed by a trained test technician. The ADHD group included 79 individuals (M age = 34 years, range = 19-59), of whom 22 were recruited through the expert teams and the rest by clinicians as described above. The control group (n = 77, M age = 28.9 years, range = 19-45) included individuals originally recruited from the MBRN (n = 60) and by advertisement (n = 17).

Participants with an IQ score below 80 (n = 2, both from the ADHD group) and participants with autism spectrum disorder, tics, Tourette syndrome, or epilepsy (n = 4; 3 from the ADHD group) were excluded. Two of the participants in the control group had cutoff scores above the threshold for probable ADHD on the Adult ADHD Self-Report Scale (ASRS; Kessler, Adler, Ames, Demler, et al., 2005) in addition to fulfilling the criteria for both ADHD in childhood and adulthood on the psychiatric interview and were therefore also excluded. In the ADHD group, 71% presently used central stimulants or other ADHD medication. As medication may influence test results, the participants were instructed not to take medication on the day of testing.

Questionnaires/Tests Used in the Present Study

Self-report questionnaires

The Wender Utah Rating Scale (WURS; Ward, Wender, & Reimherr, 1993) was used to measure ADHD symptoms in childhood, and the ASRS to assess current ADHD symptoms. Cronbach’s alpha coefficient for WURS was .960 and .954 for ASRS.

Screening questions

The participants answered 31 questions concerning socio-demographic and clinical factors. Information about lifetime comorbidities was collected by asking the participants about other disorders, including bipolar disorder, significant depression/anxiety disorders, reading/writing difficulties, and so on. The validity of these self-reported diagnoses has been established in a previous study by Halmøy and colleagues (2010).

Wechsler Abbreviated Scale of Intelligence (WASI)

Two subtests (Matrix Reasoning and Vocabulary) from the WASI were used to estimate IQ according to the norms presented in the test manual (Wechsler, 1999).

Tests from D-KEFS

D-KEFS, developed by Delis, Kaplan, and Kramer (Delis et al., 2001), was designed to measure several subfunctions of EF. Subtests from four of the nine D-KEFS tests were included in the present study, all measuring functions that have been reported to be affected in ADHD (Boonstra, Oosterlaan, Sergeant, & Buitelaar, 2005; Halleland et al., 2012; Wodka et al., 2008). They were selected to cover a wide range of EF, including the three suggested by Miyake et al. (2000). With these subtests, we computed an overall variable to define whether the participants showed deficits on two or more EF subtests. The D-KEFS subtests are all commonly used, traditional tests that have been developed and modified by the authors of D-KEFS.

Trail Making Test (TMT)

The fourth condition was included as a measure of visual set-shifting and working memory. The participant is asked to alternate (drawing a line) between letter and number sequences, and the outcome measure is number of seconds used to complete the test.

Word Fluency Test

Three subscores were included. The first condition, where the participants are asked to search for words beginning with specific letters, was selected as a measure of word-generation. The fourth and fifth conditions, where the task is to alternate between two categories of words, were selected to measure different aspects of verbal category switching (number of correct responses and switching accuracy). The outcome measures are number of correct words and number of correct switches/switching accuracy for the first and the fourth/fifth conditions, respectively.

Color–Word Interference Test (CWIT)

Two EF measures were included. The third condition (CWIT inhibition) was used as a measure of inhibition, whereas the fourth condition requires both inhibition and set-shifting abilities. The task in the third condition (CWIT inhibition) is to inhibit reading words denoting colors while naming the incongruent color of the word. In the fourth condition (CWIT inhibition/set-shifting), the test person is asked to alternate between naming the color of the word (inhibiting the automatic response of reading) and reading the word whenever the word is framed. The outcome measure is seconds used to complete each condition.

Tower Test

The total achievement score was included as a measure of working memory function, inhibition, and the ability to plan ahead. In this test, the participant is instructed to build a tower while following certain rules. The achievement score is based on time, number of moves, and whether the tower is correct.

Results

Relation Between Conditions Within EF Tests

Although the conditions/subtests within each test have been designed to measure various functions, they may still target the same underlying theoretical construct. The two last conditions of the Word Fluency Test are used to measure set-shifting, while the inhibition and inhibition/set-shifting conditions from the CWIT are used as measures of inhibition. Pearson bivariate correlation analyses performed in both groups to investigate relations between the set-shifting conditions within each test, revealed high correlations within both groups of participants (CWIT inhibition and inhibition/set-shifting conditions: ADHD group, r = .616, p < .001; control group, r = .561, p < .001; Word Fluency Test, the two set-shifting conditions: ADHD group, r = 843, p < .001; control group, r = .917, p < .001). Deficit on two conditions within one test was therefore counted as a deficit on one test.

Calculation of Deficit

In accordance with Biederman et al. (2004), EFD was defined as raw scores on two or more tests that were at least 1.5 standard deviations from the mean of the control group if the variable was normally distributed. If the variable was non-normally distributed, the threshold was defined as within the poorest seventh percentile (TMT, inhibition condition from CWIT and Tower Test). See Table 1 for means and standard deviations on the D-KEFS subtests.

Table 1.

Means and SD on D-KEFS Tests in the ADHD and Control Group.

	ADHD (n = 74)		Control (n = 74)
	M	SD	M	SD	p
Trail Making Test, number–letter switching, time	76.7	23.5	66.4	20.4	.005
Word Fluency Test, letter fluency, total correct	40.8	12.6	43.1	11.2	.240
Word Fluency Test, switching, total correct	14.5	3.1	14.5	2.5	.977
Word Fluency Test, switching accuracy	13.2	3.1	13.2	2.7	.978
Color–Word Interference Test, inhibition, time	55.4	12.9	50.4	10.1	.010
Color–Word Interference Test, inhibition/switching, time	68.7	23.6	55.4	10.3	.000
Tower Test, total achievement score	17.3	3.3	18.4	3.8	.045

Note. D-KEFS = Delis–Kaplan Executive Function System.

Socio-Demographic and Clinical Data in the ADHD and Control Group

Statistically significant differences were observed between the ADHD group and the control group in age, mean IQ, and years of education, but not in sex distribution. A significantly higher proportion of individuals in the ADHD group were unemployed, had or have had different comorbid disorders/problems (alcohol problems, problems with other drugs, depression/anxiety, bipolar disorder, problems with reading and writing), or had parents or siblings with ADHD. Although all patients had symptoms and impairments consistent with ADHD before 7 years of age, only 9.6% of the patients received a formal diagnosis of ADHD in childhood. There were no significant differences (p > .05) in IQ, ASRS, or WURS scores between controls recruited from the MBRN or through advertisement. See Table 2 for socio-demographic and clinical information and data on comorbidity. Different sample sizes in different conditions are due to missing data. There were several reasons for missing data. For some of the variables, the participants had probably overlooked questions or forgotten to include the clinical data sheets provided by their physicians. For the variable “in work,” some are not included because they did not fit the categories “in work” or “out of work,” for example, students.

Table 2.

Socio-Demographical and Clinical Data.

	ADHD		Control
	M (SD)/%	n ^a	M (SD)/%	n ^a	p
Age, years	34.0 (9.6)	74	28.9 (6.8)	74	<.001
Males	45.9%	74	41.9%	74	.619
Education, years	12.6 (2.4)	74	15.2 (2.6)	74	<.001
Total IQ	110.4 (12.7)	71	115.7 (9.3)	74	.005
In work	40.0%	63	86.7%	45	<.001
WURS, mean score	56.3 (17.6)	65	17.0 (11.2)	70	<.001
ASRS, mean score	49.3 (9.0)	71	21.9 (8.4)	72	<.001
Prescribed medication^b (central stimulantia or other medication for ADHD)	71.0%	62
ADHD diagnosis received in childhood	9.6%	73	0.0%	74	.006
Lifetime difficulties with reading and writing	41.9%	74	9.5%	74	<.001
Lifetime problems with alcohol	18.9%	74	0.0%	74	<.001
Lifetime problems with other drugs	23.3%	73	1.4%	74	<.001
Lifetime depression/anxiety	59.5%	74	12.2%	74	<.001
Lifetime bipolar disorder	4.1%	74	0.0%	74	.078
Executive impairment	24.3%	74	10.8%	74	.031

Note. WURS = Wender Utah Rating Scale; ASRS = ADHD Self-Report Scale.

n varies due to missing data and reflects number of participants investigated for each variable.

All participants were asked not to take medication for ADHD the day of testing.

Differences Between EFD Subgroups Across the ADHD and Control Group

Differences between the ADHD and control group in frequency of EFD (according to Doyle’s definition) and between the four groups (ADHD with EFD, ADHD without EFD, control with EFD, control without EFD) in occupational and educational status, psychiatric comorbidity, ADHD symptoms, medication, and diagnosis in childhood were investigated using χ² (Pearson) analyses for categorical dependent variables and ANOVA with correction for multiple comparisons (Tukey’s Honest Significant Differences [HSD]) for continuous dependent variables. Welch statistics were used whenever there was violation of the assumptions of Equality of Covariance or Equality of Error Variance. Only results from parametric statistical analyses are reported, as the significance levels were consistent with the levels generated by the non-parametric analyses. For significant results, we controlled for age by using binary logistic regression analyses for categorical dependent variables and one-way ANCOVA for continuous dependent variables. For significant differences between the groups stratified by EF, we also controlled for reading and writing problems. Significance level was set to 0.05 for all analyses.

EFD in the ADHD and Control Group

According to the definition of EFD used in the present study, 24.3% of the participants with ADHD and 10.8% of the controls were classified as having EFD, a statistically significant difference, χ²(1, n = 148) = 4.666, p = .031, that was not retained when age was included as a covariate in the binary logistic regression analysis (p = .098).

Frequency of Deficits on the EF Tests

The ADHD group had a higher frequency of deficits than the controls on all selected D-KEFS measures. However, the group differences were statistically significant only for the CWIT, χ²(1, n = 148) = 10.207, p = .002, with 32.4% of the participants being impaired on one or two of the CWIT subtests in the ADHD group, and 10.8% in the control group. Separate analyses of the two CWIT subtests showed a significant group difference for the inhibition/set-shifting condition (i.e., condition 4), with 28.4% showing deficit in the ADHD group and 6.8% in the control group (p < .001). The group differences were still significant after including age as a covariate (see Table 3 for details). The groups were not different with regard to sex on any subtest.

Table 3.

Frequency of Impairment on D-KEFS Tests.

	ADHD (n = 74)	Control (n = 74)
	%	%	p
Trail Making Test, number–letter switching, time	17.6	8.1	.085
Word Fluency Test, letter fluency, total correct	13.5	5.4	.092
Word Fluency Test, switching, total correct	8.1	4.1	.302
Word Fluency Test, switching accuracy	12.2	9.5	.597
Word Fluency Test, switching accuracy and switching total correct combined	13.5	9.5	.439
Color–Word Interference Test, inhibition, time	14.9	6.8	.112
Color–Word Interference Test, inhibition/switching, time	28.4	6.8	.001
Color–Word Interference Test, inhibition and inhibition/switching combined	32.4	10.8	.001
Tower Test, total achievement score	18.9	13.5	.372

Note. D-KEFS = Delis–Kaplan Executive Function System.

Differences Between the Groups With and Without EFD

The ADHD group with EFD was significantly older, had lower IQ score, more frequent reading and writing problems, and higher WURS scores than the ADHD group without EFD. Furthermore, the work status in the ADHD group with EFD was significantly different from the work status in the other groups. While 52.1% were employed in the ADHD group without EFD, the percentage of employed participants was only 6.7 in the ADHD group with EFD. In contrast, all controls with EFD were employed. The differences between the ADHD subgroups remained statistically significant after including age and reading and writing problems as covariates. There was no significant difference between the ADHD subgroups regarding use of ADHD medication. None in the ADHD group with EFD compared with 12.7% in the ADHD group without EFD had been diagnosed in childhood, a difference that was statistically non-significant. No significant differences were found between the ADHD subgroups regarding psychiatric comorbidity and education. For more information about group differences, see Tables 4 and 5.

Table 4.

Demographic and Clinical Functioning, Stratified by Impairment in Executive Functioning.

	ADHD with EFD (n = 18)	ADHD without EFD (n = 56)	Control with EFD (n = 8)	Control without EFD (n = 66)
	M (SD)/%	M (SD)/%	M (SD)/%	M (SD)/%
Age, years	38.2 (10.1)	32.7 (9.2)	26.3 (5.6)	29.2 (6.9)
Males	44.4%	46.4%	25.0%	43.9%
Education, years	11.3 (1.5)	13.0 (2.5)	13.6 (2.3)	15.3 (2.6)
In work	6.7%	52.1%	100%	85.4%
Total IQ	99.0 (11.8)	113.3 (11.5)	109.6 (10.0)	116.5 (9.0)
WURS, mean score	66.1 (19.7)	53.3 (16.0)	20.9 (14.9)	16.5 (10.8)
ASRS, mean score	51.7 (7.1)	48.4 (9.4)	23.5 (5.7)	21.7 (8.6)
Prescribed medication^a (central stimulantia or other medication for ADHD)	61.5%	73.5%
ADHD diagnosis received in childhood	0.0%	12.7%	0%	0%
Lifetime difficulties with reading and writing	66.7%	33.9%	50.0%	4.5%
Lifetime problems with alcohol	22.2%	17.9%	0%	0%
Lifetime problems with other drugs	22.2%	23.6%	0%	1.5%
Lifetime depression/anxiety	61.1%	58.9%	12.5%	12.1%
Lifetime bipolar disorder	5.6%	3.6%	0%	0%

Note. EFD = executive function deficit; WURS = Wender Utah Rating Scale; ASRS = ADHD Self-Report Scale.

All participants were asked not to take medication for ADHD the day of testing.

Table 5.

Demographic and Clinical Functioning, Stratified by Impairment in Executive Functioning: Comparison Between Different Groups.

	ADHD with EFD	ADHD with EFD	ADHD with EFD	ADHD without EFD	ADHD without EFD	Control with EFD
	ADHD without EFD	Control with EFD	Control without EFD	Control with EFD	Control without EFD	Control without EFD
Group comparisons	p	p	p	p	p	p
Age, years	ns	<.01	<.001	ns	ns	ns
Males	ns	ns	ns	ns	ns	ns
Education, years	ns	ns	<.001	ns	<.001	ns
In work	<.05	<.05	<.05	ns	<.05	ns
Total IQ	<.001	ns	<.001	ns	ns	ns
WURS, mean score	<.05	<.001	<.001	<.001	<.001	ns
ASRS, mean score	ns	<.001	<.001	<.001	<.001	ns
Prescribed medication^a (central stimulantia or other medication for ADHD)	ns	ns	ns	ns	ns	ns
ADHD diagnosis received in childhood	ns	ns	ns	ns	<.05	ns
Lifetime difficulties with reading and writing	<.05	ns	<.05	ns	<.05	<.05
Lifetime problems with alcohol	ns	ns	<.05	ns	<.05	ns
Lifetime problems with other drugs	ns	ns	<.05	ns	<.05	ns
Lifetime depression/anxiety	ns	<.05	<.05	<.05	<.05	ns
Lifetime bipolar disorder	ns	ns	ns	ns	ns	ns

Note. EFD = executive function deficit; ns = not significant; WURS = Wender Utah Rating Scale; ASRS = ADHD Self-Report Scale.

All participants were asked not to take medication for ADHD the day of testing.

Discussion

The present study confirmed and extended the findings from the study by Biederman, Petty, et al. (2006) showing a severe functional impairment in adults with combined ADHD and EFD. Both studies used the definition suggested by Doyle et al. (2000) to define EFD, but the present study included results on subtests within a more homogeneous test battery; the D-KEFS. As expected, EFD in the ADHD group was associated with significantly more functional deficits than in the control group. For example, all participants in the control group with EFD were employed, whereas this was true for only 6.7% of the ADHD group with EFD. Furthermore, the ADHD group with EFD had lower IQ scores, higher frequency of reading and writing problems, and higher scores on the WURS than the ADHD group without EFD. Surprisingly, none in the ADHD group with EFD, compared with 12.7% in the ADHD group without EFD, had been formally diagnosed with ADHD during childhood. In accordance with previous studies, no group differences were found with regard to psychiatric comorbidity.

Contrary to our expectations from Biederman, Petty, et al.’s (2006) study, the difference in frequency of EFD between the ADHD and control group was not statistically significant when age was included as a covariate. Frequency of EFD in the ADHD group was, however, in line with previous studies suggesting that approximately 30% of individuals with ADHD show deficits on neuropsychological tests measuring EF (Biederman et al., 2004; Biederman, Petty, et al., 2006). The cognitive heterogeneity confirmed by the present study emphasizes the importance of a neuropsychological examination, not primarily as a tool to diagnose ADHD, but to characterize strengths and weaknesses of importance for developing individualized intervention procedures (Haavik, Halmøy, Lundervold, & Fasmer, 2010; Pritchard, Nigro, Jacobson, & Mahone, 2012).

A particularly relevant test in such an examination seems to be the inhibition/set-shifting subtest from the CWIT, where the largest difference in frequency of deficit between the ADHD group and the control group was found. This may be explained by the complexity of the task, which probably renders it sensitive to subtle deficits of EF. This is supported by research showing that adding a set-shifting condition to a test of EF increases its sensitivity to dysfunctions associated with the frontal lobes (Bohnen, Twijnstra, & Jolles, 1992). The complexity of the inhibition/set-shifting condition from the CWIT probably reflects the interplay between set-shifting and response inhibition, and performance is thus likely to be dependent on more than just an additive effect (Halleland et al., 2012). Despite this, Lippa and Davis (2010) found that in a mixed group of patients referred to neuropsychological testing, many patients performed better on the inhibition/set-shifting than on the inhibition condition. This was later confirmed in a study of patients with schizophrenia (Savla et al., 2011) and suggests that the inhibition/set-shifting condition is not necessarily more difficult than the inhibition condition. The results in the present study may be explained by the ADHD group showing a reduction in learning effect from the former condition, or that the CWIT subtest measures an aspect of set-shifting abilities that represents a specific problem in individuals with ADHD (Halleland et al., 2012). In both cases, our findings indicate that this test is especially sensitive to problems related to ADHD, and that it should be included as part of a neuropsychological assessment of individuals with ADHD.

A high proportion within the ADHD group with EFD was currently unemployed, but this was also true for more than 50% of the ADHD group without EFD. Interestingly, all individuals in the control group with EFD were employed. This suggests that ADHD has a strong impact on work status (Kupper et al., 2012). However, the extremely low rate of employment in the ADHD group with EFD (6.7%) confirms that the combination of ADHD and EFD has a compound effect on function in adulthood. To rule out the possibility that the group with ADHD had a quantitatively lower executive deficit than the group with ADHD and EFD combined, we ran some additional analyses where we compared the summary scores on the executive tests for these groups. We found no significant differences between the groups, suggesting that the differences between the groups are not due to quantitative differences in EFD as measured in the present test battery.

Despite the significant difference in employment rate, no significant difference between the ADHD subgroups was found for level of education. The result may be due to not only small sample sizes but also characteristics of the educational system in Norway. Education is more accessible and affordable than, for example, in the United States (Usher & Medow, 2010), making it easier to complete an educational program in Norway. Length of education in Norway may therefore be a poor indicator of functional impairment. This is supported by the higher frequency of reading and writing problems in the ADHD group with EFD, which is in accordance with the study by Biederman, Petty, et al. (2006), showing that deficits of EF in ADHD are associated with poor reading achievement, and a recent study where the group with reading disability and ADHD scored lower on tests from the D-KEFS than those with ADHD without reading disability (Stern & Morris, 2013). The IQ level was also lower in the ADHD subgroup with EFD, consistent with studies showing that a deficit in EF is related to IQ (Sonuga-Barke, Lamparelli, Stevenson, Thompson, & Henry, 1994; Tillman, Bohlin, Sorensen, & Lundervold, 2009; Woods, Lovejoy, Stutts, Ball, & Fals-Stewart, 2000). Biederman, Petty, et al. (2006) pointed out that as ADHD itself takes a toll on the development of intelligence (Barkley, 1995; Faraone et al., 1993; Mahone et al., 2002; Sonuga-Barke, Daley, Thompson, & Swanson, 2003), the combination of ADHD and EFD have a considerable impact on intellectual function, and probably also on what is commonly referred to as the g-factor of cognitive function (Deary, Spinath, & Bates, 2006). In accordance with this, we argue that if the group with psychometrically defined EFD is a qualitatively distinct subgroup, this group may also be characterized by qualitatively different functioning in many domains. It is therefore possible that this subgroup has lower IQ because of a different etiology, without any causal link between IQ and EFD. This supports the view that although IQ accounts for much of the variance in another measure of cognitive function, this may be due to a common latent variable, such as ADHD itself, explaining both results (Dennis et al., 2009). This was supported by additional analyses where we correlated the different D-KEFS subtests with IQ, and found no correlations in the ADHD with EFD group, indicating that IQ is not directly associated with the deficit.

Despite the significant functional impairment found in the ADHD group with EFD, the higher scores on the WURS in this subgroup were not expected, as former studies have shown limited or no relationship between symptoms of ADHD and EF (Biederman et al., 2004; Biederman, Petty, et al., 2006). However, it has been shown that symptom remission is associated with improvement in neuropsychological functioning (Young & Gudjonsson, 2008). However, WURS has also been associated with a high level of false positives (Suhr, Zimak, Buelow, & Fox, 2009), and reports on the WURS items indicate that they may reflect present personality traits rather than symptoms specific to an ADHD diagnosis (Hill, Pella, Singh, Jones, & Gouvier, 2009). This was supported by the present study, showing a non-significant difference on the ASRS between the ADHD groups with and without EFD, indicating that EFD in ADHD is due to not just quantitative differences in the severity of the disorder but also qualitative differences between subgroups. It is also possible that lack of treatment in childhood may lead to both more severe symptoms in childhood and a more severe EFD in adulthood. In accordance with this, none in the ADHD group with EFD obtained an official diagnosis of ADHD or had been treated in childhood. As early treatment of ADHD is a predictor of employment in adults (Halmøy et al., 2009), it is essential to identify ADHD as early as possible and start interventions to improve everyday functioning in adulthood.

Limitations

The findings of above average IQ levels in both the ADHD and the control group, combined with a high non-response rate, suggest a selection bias toward recruitment of well-functioning individuals in both groups. Still, the present study, as well as a previous study from our research group, found that the ADHD group was severely impaired (Halmøy et al., 2009), especially with regard to occupational success. Because of the high prevalence of co-existing psychiatric conditions in ADHD (Sobanski et al., 2007), a clinical control group would probably have provided insight into the specificity of the neuropsychological deficits observed in the present study. In addition, we cannot conclude that functional impairment is caused by the EFD. It is, for example, possible that being unemployed leads to EFD instead of the other way around, although this is not supported by the results in the control group.

It could be argued that the contrast measures (scores included to control for basic functioning) from D-KEFS should have been included in the present study. However, although D-KEFS consists of validated standardized tests, the reliability, or the estimate of how much of the test variance that is actually true variance, is low for most of the D-KEFS contrast scores. This indicates that most of the variance is measurement error variance. For this reason, it has been suggested that D-KEFS contrast measures should not be used in neuropsychological decision making (Crawford, Sutherland, & Garthwaite, 2008). In addition, we wanted to compare the results from the present study with the Biederman, Petty, et al. (2006) study, which did not include such measures. The inclusion of several EF tests may also have reduced some of the “noise” from basic conditions. When including an extensive number of tests, one has to be aware of the risk of fatigue/boredom when individuals with ADHD take part in a neuropsychological examination. By careful selection of tests and using a test procedure where the executive tests were administered either early in the assessment or early after a break, the test condition in the present study was considered to be optimal for the participants. This was supported by a very good collaboration from all participants.

Finally, it is important to highlight that we have only examined psychometrically defined EFD in this study. Other subgroups may also have EF problems, but at a level that is not detected by standard neuropsychological tests. In addition, the relatively small sample in the present study could lead to Type II errors not only for the calculation of EFD prevalence differences (80% power to show significant effect of the present difference would require >200 participants) but also to detect differences between the EFD groups (n = 26, 80% power to detect an effect size of 0.5 difference requires minimum 28 participants). Consequently, even larger studies may be needed to examine the full impact of EFD in ADHD (Field, 2009). Despite this, our results indicate that the group with neuropsychologically defined EFD is a specific subgroup associated with more negative functional impairments (Nigg et al., 2005). The findings of the present study are thus highly relevant for clinicians in their work when selecting screening procedures and developing interventions.

Conclusion

This study confirms and strengthens previous findings suggesting that ADHD in combination with EFD is associated with a more extensive functional impairment than either ADHD or EFD alone. The deficits were detected by tests from a widely used test battery (i.e., D-KEFS), which reinforces the clinical utility of the present study. The findings also underline that ADHD is a heterogeneous disorder and suggest that characterization of subgroups is important when developing targeted interventions.

Footnotes

Authors’ Note

H.B.H. has been enrolled in “The National Program for Integrated Clinical Specialist and PhD-training for Psychologists” in Norway. This program is a joint cooperation between the Universities of Bergen, Oslo, Tromsø, the Regional Heath Authorities, and the Norwegian Psychological Association.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: During the past 3 years, Dr. Haavik has received honoraria for lectures in continuing medical education programs sponsored by Novartis, Lilly, and Janssen-Cilag. None of the other authors have any other further disclosures.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The program is funded jointly by the Ministry of Education and Research and the Ministry of Health and Care Services. This work was also supported by the K.G. Jebsen Foundation for Medical Research, the Western Norwegian Health Authorities (Helse Vest), and European Community’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement 602805.

Author Biographies

Helene B. Halleland, PhD, is a psychologist with interest in ADHD and neuropsychology. She is currently working at Haukeland University Hospital in Bergen, Norway.

Lin Sørensen is an associate professor at the Department of Biological and Medical Psychology, University of Bergen. Her primary research interests are cognitive and emotional/motivational control in children with symptoms of ADHD.

Maj-Britt Posserud, MD, PhD, is a postdoctoral fellow at Haukeland University Hospital, Bergen. Her research interest is related to clinical and biological characteristics of individuals with ASD, ADHD, and neuropsychiatric disorders.

Jan Haavik, MD, PhD, is a professor of biomedicine at the University of Bergen and psychiatrist at Haukeland University Hospital. He is interested in molecular mechanisms of the nervous system and clinical aspects of brain disorders, including ADHD.

Astri J. Lundervold is a professor at the Department of Biological and Medical Psycholoty, University of Bergen. Her research interest includes characterization of cognitive and emotional behavior associated with normal function and neurodevelopmental disorders across the life span.

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