The Relationship Between Executive Functions and Quality of Life in Adults With ADHD

Abstract

Objective: Adult ADHD is associated with impaired quality of life (QoL) and deficient executive function (EF). Given the absence of studies examining the relationship between EF and health-related quality of life (HRQL) in this population, the purpose of the present study was to do so, by the use of rating scales and tests. Method: Adults with ADHD (n = 81) completed ADHD and EF questionnaires and a neuropsychological battery. Results: Small to large significant correlations were found between EF ratings and HRQL for most of the variables. No significant correlations were found between all but one EF test and HRQL. Both ADHD symptoms and EF rating, but not the EF test, were found to have a unique contribution to the HRQL. Conclusion: These results strengthen the ecological validity of the EF rating scales and their utility in identifying EF deficits with real-world implications for adults with ADHD.

Keywords

ADHD executive function (EF)EF ratings EF tests health-related quality of life (HRQL)

Introduction

ADHD is a chronic mental health childhood disorder characterized by inattention, impulsiveness, and hyperactivity (Diagnostic and Statistical Manual of Mental Disorders [4th ed., text rev.; DSM-IV-TR; American Psychiatric Association [APA], 2000). Long-term controlled follow-up studies have shown that ADHD in adults is a valid disorder that persists in a sizable number of adults and often results in functional impairment (Faraone & Antshel, 2008). The estimated prevalence of adult ADHD is approximately 4% of adults worldwide (Kessler et al., 2006; Wilens, Faraone, & Biederman, 2004).

Persons with ADHD may be significantly impaired in their capacity to manage and utilize their abilities for many important tasks of daily life (Brown, Reichel, & Quinlan, 2009). The functional implications of living with ADHD are becoming more evident as the research on adult ADHD increases. Adult ADHD causes considerable hardship, often characterized by academic, occupational, and/or emotional impairment and dysfunction within the family and society (Adler et al., 2006; Barkley & Murphy, 2010; Barkley, Murphy, & Fisher, 2008; Solanto, Marks, Mitchell, Wasserstein, & Kofman, 2008). Studies have revealed that adults with ADHD are at greater risk of lower socioeconomic status, and decreased academic achievements, levels of education, and professional employment. Moreover, they are often subject to more frequent job changes, difficulties at work, increased rates of antisocial behavior and arrests, driving violations, parenting difficulties, interpersonal conflicts, and higher rates of spousal separation and divorce (Adler et al., 2008; Barkley, 2002; Barkley et al., 2008; Brod, Johnston, Able, & Swindle, 2006; Johnston, Mash, Miller, & Ninowski, 2012; Kessler et al., 2006; Solanto et al., 2008; Wilens et al., 2004).

These impairments in all domains of major life activities affect the quality of life (QoL) of people with ADHD. Health-related quality of life (HRQL) can be defined as a multidimensional concept that reflects a number of subjective, physical, social, and psychological aspects of health. HRQL describes individuals’ subjective perception of their situation in life as evidenced by their physical, psychological, and social functioning (Wehmeier, Schacht, & Barkley, 2010). People with ADHD experience serious compromises in QoL in multiple domains of well-being (Barkley, 2002; Barkley et al., 2008; Matza, Van Brunt, Cates, & Murray, 2011; Wehmeier et al., 2010; Wilens et al., 2004). Thus, HRQL 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 et al., 2006; Brod, Perwien, Adler, Spencer, & Johnston, 2005; Landgraf, 2007; Matza, Johnston, Faries, Malley, & Brod, 2007; Mick, Faraone, Spencer, Zhang, & Biederman, 2008; Wehmeier et al., 2010). Wehmeier and colleagues (2010) suggest that the reduced QoL in ADHD cannot be explained by the core symptoms of the disorder alone (inattention, impulsiveness, and hyperactivity). They suggest that both deficits in executive functions (EFs) central to the disorder and emotional difficulties secondary to coping with ADHD contribute to the compromised HRQL in ADHD.

ADHD has long been recognized as a developmental impairment in executive functions (Brown, 2008; Seidman, 2006). The term executive functions refers to a wide range of higher cognitive processes that regulate behavior, emotion, and cognition. These functions play a critical role in managing the multiple and complex tasks of daily life. EFs’ self-regulatory functions include inhibition, shifting, emotional regulation, initiation, working memory, planning, organizing, and monitoring (Biederman et al., 2006; Brown, 2008; Castellanos, Sonuga-Barke, Milham, & Tannock, 2006; Johnston et al., 2012; Lezak, Howieson, Loring, Hannay, & Fischer, 2004; Nigg et al., 2005; Roth & Saykin, 2004; Seidman, 2006). EFs are commonly evaluated with neuropsychological tests and ecological rating scales. There are many neuropsychological measures of EFs that have been used in ADHD and target individual aspects of executive functions. EF assessments commonly used for individuals with ADHD include the Stroop task (selective attention, inhibition), the Continuous Performance Test (CPT; sustained attention, processing speed), the Trail Making Test (psychomotor speed, visual search, working memory, set shifting), the Go/No-Go Test (response inhibition), the Wisconsin Card Sorting Test (abstract reasoning, concept formation, working memory, set shifting), the Controlled Word Association Test (verbal fluency), and the Tower of London (planning ability; Biederman et al., 2006; Biederman et al., 2007; Biederman et al., 2011; Castellanos et al., 2006; Seidman, 2006; Stavro, Ettenhofer, & Nigg, 2007). Some researchers have suggested that neuropsychological tests of executive abilities have implications for adaptive functioning outside of the laboratory (Stavro et al., 2007). In contrast, others claim that the link between laboratory measures and real-world functioning is weak, because isolated tests fractionate these integrative executive functions and are not sufficiently sensitive to their complexity. Furthermore, they found that EF impairments of ADHD are more adequately identified by self-report and clinical interviews that relate to impairments of self-management in day-to-day adaptive functioning rather than by neuropsychological testing (Barkley & Murphy, 2010; Brown et al., 2009; Stavro et al., 2007).

The issue of the prevalence of EF deficits in individuals with ADHD is controversial and largely dependent on the operational definition of EFs. A moderate percentage of individuals with ADHD (30%-50%) score in the impaired range in studies that examine specific neuropsychological components of EFs, such as inhibition, working memory, shifting sets, and verbal fluency (Biederman et al., 2006; Biederman et al., 2007; Biederman et al., 2011; Nigg et al., 2005; Seidman, 2006). However, studies that examined EF ratings in daily life activities found that the vast majority (89%-98%) of individuals with ADHD tested score within the impaired range (Barkley & Murphy, 2010; Biederman et al., 2011; Linder, Kroyzer, Maeir, Wertman-Elad, & Pollak, 2010).

The impact of EF impairments on HRQL has been demonstrated in adult populations with neurological and psychiatric health conditions (schizophrenia, brain injury). However, a thorough review of the literature did not reveal studies that document the relationship between EF and QoL in ADHD. The purpose of the present study is to examine the relationship between EF and HRQL in adults with ADHD. We first propose to examine the prevalence of EF impairment according to tests and rating scales in a sample of adults with ADHD. We will then examine the correlations of EF measures with HRQL, and then explore if and how much of the variance in HRQL is attributable to impaired EF beyond the scope of the core symptoms of ADHD.

Method

Participants

Eighty-one adults aged 18 to 58 years with ADHD participated in the study. The sample comprised individuals who responded to 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 DSM-IV-TR criteria (APA, 2000). Exclusion criteria were acute psychiatric disorders as defined by the Structured Clinical Interview for DSM-IV (APA, 1994) Axis I Disorders (SCID-I; First, Spitzer, Gibbon, & Williams, 1997). Clinical interviews were administrated by an experienced psychiatrist.

Measures

Adult ADHD Self-Report Scale (ASRS-v1.1) Symptom Checklist (Kessler et al., 2005) is a widely used instrument designed to measure current ADHD symptoms. It consists of 18 items based on the DSM-IV criteria for ADHD that are measured on a 5-point scale (0 = never, and 4 = very often), yielding scores that may range from 0 to 72. A screener score comprising the first 6 items of the ASRS can also be computed, yielding scores that may range from 0 to 24 (Kessler et al., 2007). To facilitate comparisons with other studies, we reported both ASRS total and screener scores.

The Behavior Rating Inventory of Executive Function–Adult Version (BRIEF-A; Roth, Isquith, & Gioia, 2005) is a standardized self-report measure that captures adults’ views of their executive functions 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 aged 18 to 90 years, and normative data are provided according to age groups (Roth et al., 2005). T-scores are calculated for each scale with higher scores indicating greater impairment. A score above 65 signifies clinical impairment. The BRIEF-A has moderate to high internal consistency (α = .73-.98), high test–retest stability (r = .82-.94) and moderate interrater agreement between self and informant report (r = .44-.68). Furthermore, the BRIEF-A was found to significantly differentiate between adults with and without ADHD (Shan et al., 2011; Rotenberg-Shpigelman, Rapaport, Stern, & Hartman-Maeir, 2008).

Adult ADHD Quality-of-Life Scale (AAQoL; Brod et al., 2005) is one of the most commonly used disease-specific instruments to measure HRQL in adults with ADHD both in research and clinical practice (Marfatia, Shroff, Munshi, & Tiwari, 2011). The AAQoL consists of 29 items rated on a 5-point scale relating to the 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 0- to 100-point scale with higher scores indicating better assessment of QoL. The AAQoL has good internal consistency (α = .93), test–retest reliability (intraclass correlation coefficient [ICC] = .86) and discriminates between groups with and without ADHD (Brod et al., 2006; Matza et al., 2007; Matza et al., 2011).

“IntegNeuro” assessment (Brain Resource Company, Ltd, Sydney, Australia) is a neuropsychological test battery consisting of 12 tasks that assess five cognitive domains: sensory-motor, learning and memory, language, attention and working memory, and executive function/planning. It is administered on a computer using a touchscreen interface and voice recording, and takes approximately 50 min to complete (Clark et al., 2006). The measure has established norms based on normative cohorts from the Brain Resource International Database, comprising healthy individuals from several different countries, including the United States, the United Kingdom, and Australia (n = 2,623) aged 6 to more than 89 years (Clark et al., 2006; Paul et al., 2007; Paul, Haque et al., 2005). The battery has adequate test–retest reliability (r = .35-.81) and cross-site reliability, indicating a high degree of similarity in cognitive function among individuals in developed countries (Europe, Australia, and the United States; Paul et al., 2007). The convergent validity of the tests was established in relation to commonly used paper-and-pencil cognitive assessments (r = .53-.77; Paul, Lawrence, et al., 2005). In this study, we analyzed the IntegNeuro measures of working memory, sustained attention, intrusions, inhibition, response variability, and fluency that are used in ADHD research (Williams et al., 2005; Williams et al., 2010).

Procedure

The study was approved by the institutional review board ethics committee and the Helsinki Declaration. All participants signed informed consent forms. Participants were interviewed by an experienced psychiatrist for diagnostic purposes. The IntegNeuro™ battery and the BRIEF-A, ASRS, and AAQoL were completed in a quiet laboratory.

Statistical Analysis

Statistical analysis was performed with the Statistical Package for Social Sciences (SPSS; Version 9.0) on standardized scores. Descriptive statistics were used to present the prevalence of EF deficits in the sample. Pearson correlation analyses were used to examine the relationships between variables. The magnitude of the correlation was interpreted as follows: r = ±0.1, small; r = ±0.3, medium; r ≥ ±0.5 large (Cohen, 1988). A hierarchical linear regression analysis was computed to examine the unique prediction of EF measures on the AAQoL beyond the ASRS measure of symptomology. The BRIEF-A scale and the “IntegNeuro” test, with the largest correlations with the AAQoL total score, were entered in a regression model together with the ASRS total score.

Results

Eighty-three adults applied to the study. Two applicants were excluded due to an acute psychiatric episode. Eighty-one adults with ADHD (40 men, 41 women) with a mean age of 36.20 years (SD = 10.18, range = 18-58) were enrolled in the study. All participants met the DSM-IV criteria for the diagnosis of ADHD; the majority was diagnosed with predominately inattentive type and no participant was diagnosed with predominantly hyperactive-impulsive type. The mean total score of the ASRS was 45.38 (SD = 8.13) and of the ASRS Screener was 15.85 (SD = 3.03). The characteristics of the sample are reported in Table 1.

Table 1.

Demographics and ADHD Characteristics.

Characteristic	%	No. of participants
Sex
Female	50.6	41
Male	49.4	40
Age, M ± SD	36.20 ± 10.18
Years of education, M ± SD	15.58 ± 2.95
Family status
Married	45.7	37
Single	49.4	40
Divorced	4.9	4
ADHD subtype
Combined type	35.8	29
Predominantly inattentive type	64.2	52
Predominantly hyperactive-impulsive type	0.0	0
DSM-IV Axis I comorbidity
Yes	53	43
No	47	38
Pharmacological treatment for ADHD
Yes	45.7	37
No	54.3	44

Note. DSM-IV = Diagnostic and Statistical Manual of Mental Disorders (4th ed.; text rev.; American Psychiatric Association, 1994).

The BRIEF-A mean T-scores on scales and indices as well as cutoff scores are presented in Table 2. Overall, within the study sample, higher scores (more deficient EFs) were obtained in the MI scales than in the BRI scales. The “working memory” mean scale score was the highest (M = 77.79, SD = 10.39) and the “self-monitor” mean scale score (M = 59.77, SD = 13.54) was the lowest, demonstrating a notable difference between the MI and BRI. When examining frequencies according to clinically significant cutoff scores (T-score ≥ 65), the vast majority of the sample scored within the clinically impaired range on the GEC and in the MI. Furthermore, all participants scored in the impaired range on at least one scale, and the mean number of scales in which scores were in the clinically impaired range was 5.99 (SD = 2.14, range = 1-9).

Table 2.

BRIEF-A Mean t Scores and Frequencies Above Cutoff (in the Clinically Significant Range).

	ADHD (n = 81)
BRIEF-A (T-scores)	M (SD)	Above cutoff % (n)
Inhibit	65.13 (10.88)	56.3 (45)
Shift	65.89 (11.58)	51.9 (42)
Emotional control	64.04 (12.01)	50.6 (41)
Self-monitor	59.77 (13.54)	37.0 (30)
BRI	66.95 (11.12)	57.5 (46)
Initiate	72.52 (10.71)	72.8 (59)
Working memory	77.79 (10.39)	88.9 (72)
Plan/organize	75.86 (9.97)	85.2 (69)
Task monitor	77.21 (9.47)	93.8 (76)
Organization of materials	67.78 (11.58)	63.0 (51)
MI	77.88 (9.62)	90.1 (73)
GEC	75.10 (9.07)	88.7 (71)

Note. BRIEF-A = Behavior Rating Inventory of Executive Function–Adult Version; BRI = Behavioral Regulation Index; MI = Metacognition Index; GEC = Global Executive Composite.

The mean IntegNeuro T-scores on the EF tests are presented in Table 3. The relative number of participants who scored below the cutoff (1.5 SD below the norm) ranged between 0% and 33.3% on the IntegNeuro™ measures. The highest percentages of deficits were found in the CPT variables. When examining the cumulative number of tests in which each participant scored below the cutoff, 22% of the sample scored in the normative range on all the “IntegNeuro” tests, 45% was impaired in one or two tests, and 33% was impaired in three or more tests.

Table 3.

Neuropsychological Test Battery (“IntegNeuro”) Results.

“IntegNeuro” tasks	T-scores M (SD)	% deficit (below 1.5 SD)
Working memory
Digit span forwards—Trials correct	46.53 (10.17)	8.8
Digit span backwards—Trials correct	49.22 (9.85)	3.8
Span of visual memory—Trials correct	44.63 (11.62)	23.4
Executive maze completion time (s)	41.97 (11.00)	25.0
Sustained attention
CPT total errors	43.51 (13.44)	28.2
CPT reaction time (s)	44.95 (16.24)	25.6
Inhibition
CPT: False-positive errors	43.76 (15.65)	25.6
Go/No-Go: False-positive errors^a	49.75 (10.14)	8.9
Verbal interference Part 1 reaction time (s)	37.93 (14.43)	31.6
Verbal interference Part 1 errors	50.24 (3.23)	0
Verbal interference Part 2 reaction time (s)	42.13 (14.17)	21.8
Verbal interference Part 2 errors	51.60 (10.04)	6.3
Intrusions
Switching of attention perseveration errors	50.18 (9.60)	8.8
Executive maze overrun errors	47.35 (15.01)	18.8
Verbal memory recall, errors of intrusion	47.05 (10.22)	15.2
Response variability
CPT: Variability in reaction time (s)	42.56 (14.83)	33.3
Go/No-Go: Variability in reaction time (s)	43.96 (9.84)	17.9
Fluency
Verbal fluency	44.33 (9.49)	17.7
Semantic fluency	47.55 (10.07)	9.0

Note. CPT = Continuous Performance Test; s = seconds.

Falsely responding to a “No-Go” stimulus.

Small to medium significant (p < .05) correlations were found between the BRIEF-A scores and the ASRS total score (ASRS, with MI, r = .476; with BRI, r = .275; and with GEC, r = .467), whereas very small (r range = .007-.190) and nonsignificant correlations were found between all the IntegNeuro scores and the ASRS total score.

The AAQoL mean total score was 50.90 (SD = 13.85), with subscales means ranging from 43.55 (SD = 18.15, life productivity) to 60.46 (SD = 20.24, relationship). Small to large significant correlations were found between the AAQoL total score, the BRIEF-A indices, and five of the nine scales (see Table 4). No significant correlations were found between the AAQoL total score and the IntegNeuro™ scores, except for a small significant correlation with the Verbal Interference Stroop test error score (VI Err; r = .285, p = .01).

Table 4.

Correlations Between the AAQoL and BRIEF-A.

BRIEF-A	Correlation with AAQoL (total score)
Inhibit	−.263*
Shift	−.202
Emotional control	−.144
Self-monitor	−.213
BRI	−.256*
Initiate	−.418**
Working memory	−.204
Plan/organize	−.474**
Task monitor	−.473**
Organization of materials	−.448**
MI	−.507**
GEC	−.489**

Note. BRIEF-A = Behavior Rating Inventory of Executive Function–Adult Version; BRI = Behavioral Regulation Index; MI = Metacognition Index; GEC = Global Executive Composite; AAQoL = Adult ADHD Quality-of-Life scale.

Correlation is significant at the .05 level (two-tailed).

Correlation is significant at the .01 level (two-tailed).

To examine the unique prediction of EF measures on the AAQoL beyond that accounted for by ADHD symptomatology, a multiple regression analysis was computed. The variables that entered the model were the BRIEF-A plan/organize scale and the “IntegNeuro™” VI Err score, which had the highest correlation with the AAQoL, and the ASRS total score (see Table 5). The overall model was found to be significant (F = 13.293, p = .000), explaining approximately 34% of the variance of QoL. The ASRS and BRIEF-A scale were each found to uniquely contribute to the AAQoL, yet the “IntegNeuro” test did not contribute significantly beyond these variables.

Table 5.

Multiple Linear Regression Analyses of Executive Functions and ADHD Symptoms for HRQL.

Variable	B	β	T	p
ASRS total	−.524	−.311	−3.124	.003
BRIEF-A Plan/Organize Scale	−.448	−.326	−3.205	.002
“IntegNeuro™” (VI Err)	.718	.169	1.761	.082

Note. AAQoL = Adult ADHD Quality-of-Life Measure; BRIEF-A = Behavior Rating Inventory of Executive Function–Adult Version; MI = Metacognition Index; VI Err = Verbal Interference Stroop test errors.

Discussion

The purpose of the present study was to examine the relationship between EF neuropsychological and ecological measures commonly used in clinical practice, with a valid measure of HRQL in adults with ADHD. We also examined whether EF measures provide a unique prediction of the explained variance of HRQL, beyond that accounted for by ADHD symptomatology.

The participants in the study had a medical diagnosis of ADHD previous to their participation in the study. Nevertheless, all participants were interviewed by a psychiatrist to confirm diagnosis and control for comorbidity. The prevalence of ADHD DSM-IV subtypes found in this study (inattentive type, 64.2%; hyperactive-impulsive type, 0%; combined type, 35.8%) was similar to that described in other studies of adult ADHD (Matza et al., 2011). The present sample demonstrated clinically significant EF deficits in rating scales, with 96% of the sample demonstrating impairment in at least two scales. These percentages are almost identical to those revealed in previous studies that showed 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 et al., 2010). This finding of a high prevalence of EF deficits in adult ADHD does not imply that ADHD symptoms and EF deficits are identical. There may be some overlap between these variables; however, the effect sizes of the correlations were only low to moderate, suggesting a large percentage of unique variance for both symptoms and ecological EF deficits in adults with ADHD.

The EF profile revealed higher deficit scores in the MI scales than in the BRI scales. This indicates a greater deficit in the metacognitive components of EF in adults with ADHD. Similar findings were reported in a study by Biederman and colleagues (2011), which showed higher scores on the BRIEF-A MI scales, in particular the “working memory” scale that revealed the highest degree of impairment, and relatively lower scores on the BRI scales. However, with respect to the neuropsychological tests scores, results demonstrated a much lower percentage of deficits. The findings indicated that 0% to 33.3% of the sample scored 1.5 SD below the norm on the “IntegNeuro” tests, with the highest percentage of impairment in the CPT variables. These results are in accordance with findings reported in other studies, revealing that approximately 30% to 50% of persons with ADHD score in the impaired range on EF neuropsychological measures (Biederman et al., 2006; Biederman et al., 2007; Biederman et al., 2011; Nigg et al., 2005; Seidman, 2006).

Participants’ AAQoL total mean score (M = 50.90) was somewhat lower than scores achieved in the study by Brod and colleagues (2006) in which a mean overall score of 60.0 was reported, and that of Matza and colleagues (2011) who reported total scores of 65.0 and 66.5 (in two periods of time). This can be explained by the severity of the disorder and/or by the extent of EF deficits and their effect on daily functioning. The participants in our study reported more ADHD symptoms (M ASRS Screener score = 15.85) in comparison with the symptom score reported in the study by Matza and colleagues (M ASRS Screener score = 11.5), which may explain the relatively low scores on the AAQoL. However, the previous studies did not examine EF; therefore, no comparison on this variable is possible at this point. Regarding the relationships between EF measures and HRQL, it is important to note that significant correlations were found with the BRIEF-A scores but not with the IntegNeuro tests (except for a low correlation with the Verbal Interference Stroop Test error score). These results strengthen the ecological validity of the EF rating scales and their utility in linking EF deficits with real-world implications in adults with ADHD. When examining the prediction of the EF measures that correlated with the AAQoL, in addition to ADHD symptomatology, it was found that the BRIEF-A scale uniquely contributed to the HRQL measure, beyond the ASRS. The “IntegNeuro” test score did not contribute significantly beyond these variables. The potential contribution of neuropsychological measures to HRQL warrants further study, as previous studies found that EF in neuropsychological batteries were found to predict a variety of functional outcomes in ADHD (Biederman et al., 2006; Biederman et al., 2007). The results of the regression analysis further showed that approximately 34% of the total variance in the AAQoL was explained by symptomatology and EF variables.

What additional factors may explain QoL in adults with ADHD? According to the International Classification of Functioning, Disability and Health (ICF; World Health Organization, 2001) model, a biopsychosocial approach is warranted when exploring the impact of health conditions on HRQL. In this vein, a secondary analysis was computed to explore the effects of comorbidity, pharmacological treatment, gender, education, and marital status on the AAQoL total score. No significant effects were found for any of the above variables. Further studies may explore the impact of additional biological, psychological, and social factors that may explain HRQL in adults with ADHD.

The strengths of this study include the well-established diagnosis of the sample, including clinical DSM-IV and SCID interviews. However, a methodological limitation regarding the sample should be considered. The sample included self-referred adults interested in participating in the study, who were aware of their ADHD and motivated to find ways to deal with it. Hence, we do not know if our results will generalize to adults with ADHD in the general population.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) declared receipt of the following financial support for the research, authorship, and/or publication of this article: The study was supported by the Rosenbaum Milton Endowment Fund for Research in the Psychiatric Science.

Author Biographies

Adi Stern, MSc, OTR, is a doctoral student in the Hadassah-Hebrew University School of Occupational Therapy, Jerusalem, Israel. She is also the head of occupational therapy service in the department of adult psychiatry at Hadassah University Medical Center.

Yehuda Pollak, PhD in Neuropsychology, is a Lecturer at the Department of Psychology, The Hebrew University of Jerusalem, Israel, and also a Clinical neuropsychologist at the Neuropediatric Unit, Shaare Zedek Medical Center, Jerusalem, Israel.

Omer Bonne, MD, is Associate Professor of Psychiatry, Chair, Department of Psychiatry, Hadassah-Hebrew University Medical School, Jerusalem, Israel.

Elad Malik, MD - Hadassah-Hebrew University Medical School, Jerusalem, Israel., is Psychiatry Resident, Department of psychiatry, Hadassah Medical Center, Jerusalem, Israel.

Adina Maeir, PhD, is School Chair and Director of Graduate Studies School of Occupational Therapy, Faculty of Medicine of Hadassah and Hebrew University of Jerusalem, Israel. Director of Neurocognitive Rehabilitation laboratory.

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