Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that is characterized by a pattern of behavior, present in multiple settings (e.g., school and home), that can result in difficulties in social, educational, or work settings. Symptoms of the disorder are divided into two categories: inattention and hyperactivity with impulsivity (Diagnostic and Statistical Manual of Mental Disorders, 5th ed., or DSM–5; American Psychiatric Association [APA], 2013). Long-term controlled follow-up studies have shown that the disorder persists in a sizable number of adults who were diagnosed as having ADHD in childhood, and the estimated prevalence of adult ADHD is between 2.5% and 4% of adults worldwide (APA, 2013; Fayyad et al., 2007; Kessler et al., 2006; Wilens, Faraone, & Biederman, 2004). Moreover, the definition of ADHD has been updated in DSM–5 (APA, 2013) to more accurately characterize the experience of affected adults. This revision is based on nearly 2 decades of research showing that ADHD, although a disorder that begins in childhood, continues through adulthood (APA, 2013).

When ADHD persists into adulthood, it is often a disruptive syndrome that causes conflict in all major occupational role areas—work and school, home and family life, and social relationships (Gutman & Szczepanski, 2005). The functional and occupational implications of living with ADHD have become more evident as research on adult ADHD has increased. Adults with ADHD have been shown to be at greater risk for lower socioeconomic status; fewer years of education; lower academic achievement; lower rates of professional employment; more frequent job changes; more work difficulties; and increased rates of antisocial behavior and arrests, driving violations, parenting difficulties, and relationship difficulties manifested in interpersonal conflicts and a higher rate of spousal separation and divorce (Adler et al., 2008; Barkley, 2002; Barkley & Murphy, 2010; Ek & Isaksson, 2013; Johnston, Mash, Miller, & Ninowski, 2012; Solanto, Marks, Mitchell, Wasserstein, & Kofman, 2008; Wilens et al., 2004).

Therefore, it is not surprising that adults with ADHD face serious impediments to their quality of life (QoL) in multiple domains of well-being (Barkley, 2002; Barkley, Murphy, & Fisher, 2008; Matza, Van Brunt, Cates, & Murray, 2011; Wehmeier, Schacht, & Barkley, 2010; Wilens et al., 2004). Health-related quality of life (HRQoL) has become an increasingly important outcome measure in ADHD research and practice to assess the impact of the disorder in everyday terms that are meaningful to adults (Brod, Johnston, Able, & Swindle, 2006; Brod, Perwien, Adler, Spencer, & Johnston, 2005; Landgraf, 2007; Matza, Johnston, Faries, Malley, & Brod, 2007; Wehmeier et al., 2010).

ADHD is recognized as a developmental impairment that involves deficient executive functions (EFs; Brown, 2008, 2013). The term executive functions refers to a set of regulatory processes necessary for selecting, initiating, implementing, and overseeing thought, emotion, behavior, and certain facets of motor and sensory functions (Roth, Isquith, & Gioia, 2005). The broad and pervasive functional ramifications of ADHD have been shown to be uniquely affected by the cognitive executive symptoms of ADHD. EFs enable goal-directed behavior and play a critical role for all people as they manage multiple tasks of daily life. EFs consist of inhibition, initiation, sustaining effort, shifting cognitive set, working memory, emotional regulation, planning, organizing, and monitoring (Barkley, 2012; Brown, 2008, 2013; Castellanos, Sonuga-Barke, Milham, & Tannock, 2006; Lezak, Howieson, Loring, Hannay, & Fischer, 2004; Nigg et al., 2005; Roth & Saykin, 2004).

Converging evidence has pointed to prominent disturbances in a wide range of EFs in children and adults with ADHD that impede their daily functioning and QoL (Biederman et al., 2006, 2007; Brown, 2013; Ek & Isaksson, 2013; Nigg et al., 2005; Roth & Saykin, 2004). In addition, deficits in EFs have been found to have a negative impact on the functional outcomes of adults with ADHD beyond that conferred by the diagnosis of ADHD alone (Biederman et al., 2006; Solanto et al., 2008; Stern, Pollack, Bonne, Malik, & Maeir, in press). Taken together, these findings suggest that an intervention that focuses on EFs as well as on the occupational issues of ADHD may be a positive avenue to improving the daily functioning and QoL of adults with ADHD. Moreover, recognition is growing that treatment options for adult ADHD should include, in addition to pharmacological treatment, cognitive and behavioral interventions that take into consideration the comprehensive implications of the disorder, its functional outcomes, and overall QoL (Adler et al., 2008; Brod et al., 2006; Dodson, 2005; Gutman & Szczepanski, 2005; National Institute for Health and Clinical Excellence, 2013; Solanto et al., 2008). Therefore, occupational therapy can enhance the performance of adults with ADHD in several ways, targeting client factors (e.g., EFs), performance skills (e.g., time management, social interaction), and contexts (e.g., organization of the physical environment; American Occupational Therapy Association [AOTA], 2014). However, evidence-based occupational therapy interventions (assessment and treatment) for adults with ADHD are lacking in the occupational therapy literature and practice.

The initial step in the intervention process of occupational therapy that constitutes the basis for treatment is assessment. The purpose of the assessment process in occupational therapy for people with suspected cognitive disabilities, such as ADHD, is to better understand the occupational implications of the disability in daily life. Cognitive–functional evaluation (CFE) yields a comprehensive profile of the client’s cognitive strengths and weaknesses in occupational performance (Hartman-Maeir, Katz, & Baum, 2009). Different assessment tools are used to evaluate the cognitive–functional status of a person with suspected cognitive disabilities: interviews, rating scales, cognitive tests, and performance-based assessments.

In ADHD, EF impairments have been found to be more adequately identified by means of self-report ratings and clinical interviews that relate to impairments of self-management in day-to-day adaptive functioning rather than by cognitive tests (Barkley & Murphy, 2010; Brown, Reichel, & Quinlan 2009; Stavro, Ettenhofer, & Nigg, 2007; Stern et al., in press). A comprehensive assessment that takes the cognitive and functional influences of the disorder into account will help occupational therapy practitioners understand the needs of individuals with ADHD to guide further interventions (Hartman-Maeir et al., 2009). The purpose of this study was twofold: (1) to better understand the cognitive deficits of adults with ADHD in an occupational context and the deficits’ influence on QoL and (2) to examine the construct, convergent, and ecological validity of a CFE for adults with ADHD.

We used a two-group comparison, cross-sectional research design. The study was approved by the institutional review board ethics committee and consistent with the Helsinki Declaration. All participants signed informed consent forms.

Participants in the study were adults ages 18–60 yr. The ADHD group consisted of adults recruited by an advertisement offering computerized cognitive training for adults with ADHD at a university research center. All participants had a previous medical diagnosis of ADHD. The diagnosis was confirmed by a structured clinical interview implementing criteria specified by the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM–IV–TR; APA, 2000). Exclusion criteria were acute psychiatric disorders as defined by the Structured Clinical Interview for DSM–IV Axis I Disorders (SCID–CV; First, Spitzer, Gibbon, & Williams, 1996). Clinical interviews were administered by an experienced psychiatrist. A non-ADHD control group was composed of participants without an ADHD diagnosis recruited from a convenience sample. The exclusion of ADHD in the control group was verified by means of the Adult ADHD Self-Report Scale (ASRS) Symptom Checklist (Kessler et al., 2005; fewer than four of six symptoms in Part A).

The ASRS (Version 1.1) Symptom Checklist (Kessler et al., 2005), Hebrew version (Zohar, Gonen, & Yemini, 2007), is a widely used instrument designed to measure current ADHD symptoms. It consists of 18 items based on the DSM–IV–TR (APA, 2000) criteria for ADHD that are measured on a 5-point scale (0 = never, 4 = very often). The first 6 items (Part A) were found to be the most predictive of symptoms consistent with ADHD. If impairments are noted on 4 or more items in Part A, then the patient has symptoms highly consistent with ADHD in adults. A screener score consisting of these 6 items can be computed, yielding scores that may range from 0 to 24, with a four-stratum classification (0–9 = low negative range, 10–13 = high negative range, 14–17 = low positive range, 18–24 = high positive range) and a cutoff score of 14 (Kessler et al., 2007). The frequency scores on Part B (the remaining 12 questions) serve to further describe the patient’s symptoms. The ASRS Hebrew version has high internal consistency (αs = .82–.89), high test–retest reliability (rs = .60–.90), and significant discriminant validity. The 6-item screen (Part A) has a sensitivity of 40% and a specificity of 78% (Zohar & Konfortes, 2010).

The Behavior Rating Inventory of Executive Function–Adult Version (BRIEF–A; Roth et al., 2005) is a standardized self-report measure that captures adults’ views of their EFs in their everyday environment. It is designed for adults with a wide variety of developmental disorders and systemic, neurological, and psychiatric illnesses. The BRIEF–A is composed of 75 items rated on a 3-point scale that encompass nine theoretically and empirically derived clinical scales measuring various aspects of executive functioning (Inhibit, Shift, Emotional Control, Self-Monitor, Initiate, Working Memory, Plan/Organize, Task Monitor, Organization of Materials) that form two indices—the Behavioral Regulation Index (BRI) and the Metacognition Index (MI)—and an overall summary score, the Global Executive Composite (GEC). The BRIEF–A was standardized in the United States on 1,136 healthy adults ages 18–90, and normative data are provided according to age group (Roth et al., 2005). T scores are calculated for each scale, with higher scores indicating greater impairment. A score ≥65 signifies clinical impairment. The BRIEF–A has moderate to high internal consistency (αs = .73–.98), high test–retest stability (rs = .82–.94), and moderate interrater agreement between self- and informant reports (rs = .44–.68). Moreover, the BRIEF–A was found to significantly differentiate between adults with and without ADHD (Rotenberg-Shpigelman, Rapaport, Stern, & Hartman-Maeir, 2008; Shan et al., 2011).

The Canadian Occupational Performance Measure (COPM; Law et al., 2005) is an individualized measure designed for occupational therapists to identify individual occupational issues and to detect change in a client’s self-perception of occupational performance over time. The COPM is used to identify problem areas in occupational performance, provide a rating of the client’s priorities in occupational performance, evaluate performance and satisfaction relative to those problem areas, and measure changes in a client’s perception of her or his occupational performance. It is designed with a semistructured interview format and structured scoring method ranging from 1 to 10, on which higher ratings indicate greater importance, better performance, and more satisfaction. The measurement properties (reliability, validity, responsiveness) of the COPM have repeatedly been shown to be satisfactory to excellent (Carswell et al., 2004). The COPM has high test–retest reliability (rs = .84–.92) and good concurrent (criterion) and content validity and was found to be responsive to changes in occupational performance (Carswell et al., 2004; Law et al., 2005). Clinical utility, examined in several different studies, supports the use of the COPM with a wide variety of adult clients in many different settings (e.g., Atwal, Owen, & Davies, 2003; Chesworth, Duffy, Hodnett, & Knight, 2002; Edwards, Baptiste, Stratford, & Law, 2007; Kirsh & Cockburn, 2009; Lyons & Raghavendra, 2003).

The Adult ADHD Quality-of-Life Scale (AAQoL; Brod et al., 2005) is one of the most commonly used disease-specific instruments to measure HRQoL in adults with ADHD in both research and clinical practice (Marfatia, Shroff, Munshi, & Tiwari, 2011). The AAQoL consists of 29 items rated on a 5-point scale relating to frequency of occurrence that yields a total score (based on all 29 items) and four subscale scores: Productivity, Life Outlook, Relationships, and Psychological Health. Total and subscale raw scores are transformed to a scale ranging from 0 to 100, with higher scores indicating better assessment of HRQoL. The AAQoL has good internal consistency (α = .93) and test–retest reliability (intraclass correlation coefficient = .86) and discriminates between groups with and without ADHD (Brod et al., 2006; Matza et al., 2007, 2011).

Participants in the ADHD group were interviewed by an experienced psychiatrist for diagnostic purposes. The ASRS, BRIEF–A, and AAQoL were completed independently by participants, and the COPM was administered by an occupational therapist. Participants in the non-ADHD group completed the ASRS, BRIEF–A, and AAQoL via mail.

We performed statistical analysis with SPSS Version 20.0 (IBM Corp., Armonk, NY). We used descriptive statistics to present sample characteristics and BRIEF–A and COPM ratings. The frequencies of the occupational difficulties, as expressed by participants with ADHD on the COPM, were described. We performed t tests or χ² tests between groups when appropriate. Analysis of covariance (ANCOVA) and multiple analysis of covariance (MANCOVA) were used to compare the scores on the BRIEF–A and AAQoL between groups (controlling for age differences). Effect sizes were represented by partial η². Interpretation of effect sizes was based on Ferguson (2009), with a partial η² of .04 representing the minimum practically significant effect for social science data, a partial η² of .25 considered a moderate effect, and a partial η² of .64 considered a strong effect. We used Pearson correlation coefficient analyses to examine the relationships between variables. We based the interpretation of correlations on Cohen (1988), with a correlation of 0.1 considered small; 0.3, moderate; and 0.5, large. We computed hierarchical linear regression analysis to examine the unique and cumulative prediction of the measures on the AAQoL.

Eighty-three adults with ADHD applied to the study. Two applicants were excluded because of an acute psychiatric episode. Eighty-one adults with ADHD (40 men, 41 women), with a mean age of 36.20 yr (standard deviation [SD] = 10.18), were enrolled in the study. All participants in this group met DSM–IV–TR (APA, 2000) criteria for the diagnosis of ADHD. The majority (64.2%) were diagnosed with predominately inattentive type, 35.8% were diagnosed with the combined type, and none were diagnosed with predominantly hyperactive–impulsive type.

Sixty-four adults without ADHD applied to the study. Six were excluded because their ASRS score was above the cutoff score. Fifty-eight adults without ADHD (22 men, 36 women), with a mean age of 29.29 yr (SD = 8.03), were enrolled in the study. The groups differed significantly in age (p = .00); therefore, we added age as a covariate in all subsequent analyses involving group comparisons. No significant differences were found between the groups on years of education (p = .41) or gender (p = .18). The mean 6-item screen score on the ASRS was 15.85 (SD = 3.03) for the ADHD group and 10.36 (SD = 3.41) for the non-ADHD group, with a significant difference (p = .00) between groups.

The BRIEF–A mean t scores on scales and indices for each group are presented in Table 1. Overall, in the ADHD group, higher scores (more deficient EFs) were obtained on the MI scales than on the BRI scales. The Working Memory mean scale score was the highest (77.79; SD = 10.39) and the Self-Monitor mean scale score (59.77; SD = 13.54) the lowest, demonstrating a notable difference between the MI and BRI. When examining frequencies according to clinically significant cutoff scores (t ≥ 65), the vast majority of the ADHD group scored within the clinically impaired range on the MI and the GEC (90.1% and 88.7%, respectively). Moreover, in the ADHD group all participants scored in the impaired range on at least one scale, and the mean number of scales on which scores were in the clinically impaired range was 5.99 (SD = 2.14; range = 1–9). In the non-ADHD group, no score >65 was obtained on the scales, indices, or global score.

Similarly, when examining frequencies according to clinically significant cutoff scores (t ≥ 65), we found very low percentages (1.7%–8.6%). Significant differences (p = .00) were found between the groups on all BRIEF–A scores, with mostly moderate to strong effect sizes (partial η²s = .241–.657).

The AAQoL mean total score for the ADHD group was 50.81 (SD = 13.66), with subscale means ranging from 43.94 (SD = 18.04; Life Productivity) to 60.27 (SD = 19.50; Relationship). The AAQoL mean total score for the non-ADHD group was 72.09 (SD = 17.95), with subscale means ranging from 66.23 (SD = 18.41; Life Outlook) to 76.37 (SD = 21.26; Life Productivity). We found significant differences (p = .00) between the groups on the AAQoL scales and total score. We found a moderate effect size (partial η² = .318) on the total score and small to moderate effect sizes (partial η²s = .124–.431) on the scales (see Table 2).

The COPM was administered to the ADHD group only. The mean COPM scores were 3.72 (SD = 1.46) for performance and 2.56 (SD = 1.51) for satisfaction. Table 3 describes the occupational difficulties reported by participants on the COPM. The participants’ reports were classified according to the type of cognitive difficulty they described along with its manifestations in their daily lives. The difficulties were listed by frequency, from the most frequent to the least frequent. A small number of occupational difficulties were also reported regarding self-esteem (7.41%) and restlessness (6.17%) that appeared in daily life. The most frequent difficulties reported by >50% of the sample were ignoring distractions during academic, work, social, leisure, and domestic occupations and getting started on written tasks and dealing with financial errands.

We found a medium significant correlation (r = −.331, p = .01) between the BRIEF–A GEC score and the COPM performance score. We found medium significant correlations between the AAQoL total score, the ASRS (r = −.445, p = .01), the BRIEF–A GEC (r = −.489, p = .01), and the COPM (r = .402, p = .01). Regression analysis revealed a significant unique contribution of each measure to the AAQoL, with the entire model explaining approximately 33% of the AAQoL variance (see Table 4).

The twofold purpose of this study was (1) to better understand the cognitive deficits of adults with ADHD in an occupational context and their influence on QoL and (2) to examine the construct, convergent, and ecological validity of a CFE for those adults. The participants in the ADHD group were asked to identify occupational difficulties in their daily life using the COPM. The findings demonstrate the broad implications of ADHD for participants’ engagement in their daily lives and highlight the involvement of EFs in these outcomes on the individual level. To the best of our knowledge, this is the first study in occupational therapy to provide empirical support for the cognitive–functional occupational concerns of adults with ADHD and emphasize the need for occupational therapy intervention in this population. Additionally, we found a significant medium correlation between the participants’ ratings of their occupational performance and their ratings of EF in daily life on the BRIEF–A. These findings further strengthen the premise concerning the impact of cognitive executive deficits on the occupational performance of adults with ADHD.

The BRIEF–A was used to study the executive profile of the participants. The results indicated a very high prevalence of clinically significant EF deficits, with 96% of the sample demonstrating impairment on at least two scales. This finding is similar to the findings of previous studies that reported very high rates of EF deficits in adult ADHD when using rating scales (89%–98%, Barkley & Murphy, 2010; 93%, Biederman et al., 2011; 90%, Linder, Kroyzer, Maeir, Wertman-Elad, & Pollak, 2010). The EF profile revealed higher deficit scores on the MI scales than on the BRI scales, indicating a greater deficit in the metacognitive components of EF in adults with ADHD. Similar findings were reported in a study by Biederman et al. (2011), in which participants had higher scores on the BRIEF–A MI scales, in particular on the Working Memory scale, that revealed the highest degree of impairment, and relatively lower scores on the BRI scales. This profile points to the specific executive challenges that adults with ADHD face, which may be different from the greater behavioral regulation challenges of children with ADHD.

Regarding the properties of the BRIEF–A as a valid measure of EFs for adults with ADHD, the findings of significant differences between the ADHD and non-ADHD groups support the construct validity of the measure. Moreover, we found significant correlations between the BRIEF–A scores and the AAQoL and the COPM, strengthening the ecological validity of the BRIEF–A and its utility in linking EF deficits with real-world implications in adults with ADHD.

The AAQoL mean scores of the participants in the ADHD group were significantly lower than those of the non-ADHD group. These findings add to the validation of use of the AAQoL to capture the specific impact of ADHD on QoL (Brod et al., 2006). The largest effect (partial η² = .431) was found on the Life Productivity domain score, highlighting the impact of ADHD on the functional aspects of QoL. The mean total score (50.81) of the ADHD group found in this study is lower than that reported in other studies (60.0, Brod et al., 2006; 65.0, Matza et al., 2011). This discrepancy may be explained by a difference in sample characteristics; the current sample consisted of people who were self-referred to treatment and therefore might have experienced more severe symptoms and cognitive deficits than the other samples. However, further comparisons between symptoms and cognitive deficits are not possible at this point because the other studies did not provide this information.

In this study, we used a CFE battery consisting of the BRIEF–A, COPM, and AAQoL. The significant moderate correlations that we found between the measures of symptoms, EFs, occupational performance, and QoL, as well as the unique contribution of each measure to the explained variance in QoL, support the validity of this CFE battery for this population. Taken together, these findings endorse the use of these measures to provide specific and ecological information that contributes to the understanding of QoL in people with ADHD.

The results of this study have the following implications for occupational therapy practice:

The study used a relatively small sample, which may limit generalizability to the general population of people with ADHD. Furthermore, the research and control groups were not matched on age. Despite statistical control for age in the data analyses, further studies are required with larger samples that are matched on age and other demographic variables that may have an impact on EF and QoL. Finally, we did not assess environmental factors in this study. To understand the factors that contribute to participation and QoL, it is important to include a measure of environmental factors (AOTA, 2014) in future studies. Future research in occupational therapy intervention for adults with ADHD will focus on the occupational concerns of ADHD, targeting these problems via valid change mechanisms in ADHD such as remediation, metacognitive learning, or environmental supports, to improve occupational performance and QoL.

This study contributes to the body of knowledge about the difficulties that adults with ADHD experience in their daily lives. The occupational performance of these adults has been shown to be affected by distinct EF deficits that are associated with ADHD. Both cognitive and functional difficulties led to a lessening of QoL in this population. To offer adults with ADHD the appropriate occupational therapy treatment, careful assessment of the unique cognitive and functional features of this population is needed. Occupational therapists have an important role in understanding cognitive deficits in an occupational context and in delivering interventions that will improve clients' skills, participation, and QoL. The evidence presented in this study suggests the use of a specific CFE battery to further guide occupational therapy intervention in this population. This battery includes measures that have been shown to be sensitive and specific for adults with ADHD.

We thank Meital Tsabari for her assistance in data collection. The study was supported by the Rosenbaum Milton Endowment Fund for Research in Psychiatric Science and The Fund in Memory of Naomi Kay.