Predicting impairments of major life activities in the clinical assessment of adults with ADHD: A Pre-clinical study

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Running head: PREDICTING IMPAIRMENTS OF MAJOR LIFE ACTIVITIES

Predicting Impairments of Major Life Activities in

the Clinical Assessment of Adults with ADHD – A

Pre-clinical Study

J.C.Konen

S1125796

External supervisor: Dr. Anselm Fürmaier

Internal supervisor: Dr. Kim de Jong

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Abstract

Attention deficit hyperactivity disorder (ADHD) is a disorder that is heterogeneous and includes many different symptoms, impairments and complex etiologies. The aim of the present study was to explore presumable predictors (i.e. ADHD symptoms, basic cognitive functions, financial decision making) for impairments of major life activities in the categories family, risk, and finance, which were self-rated. Sixty-three German healthy participants (33 male, mean age = 34.65) were assessed with regard to the different functioning domains. Logistic regression analyses were carried out in order to explore the predictive validity. ADHD symptoms were the most valid predictor for impairments, whereas cognition and financial decision making could not generate significant results, stressing the need for reliable measures of impairments in major life activities.

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Predicting Impairments of Major Life Activities in the Clinical Assessment of Adults with ADHD

Attention deficit hyperactivity disorder [ADHD] is known to be one of the most commonly diagnosed childhood disorders for over the last decade (American Psychiatric Association [APA], 2013; Barkley, 2003). Previously it was assumed children would

“outgrow" the disorder when reaching adulthood and the brain is matured (Klein & Manuzza, 1991), but evidence against that has emerged (Goldstein, 2002). In more than half of the cases, the disorder may persist into adulthood (Mannuzza, Klein, Bessler, & Maloy, 1993; Weiss, Hechtman, Milroy, & Perlman, 1985). Nevertheless, the symptoms may change with the development of the individual, namely that the cognitive dysfunctions are more dominant and the hyperactive symptoms are less pronounced (Tucha, Sontag, Walitza, & Lange, 2009). The diagnosis of ADHD in adults is based on the criteria defined by the Diagnostic and Statistical Manual of Mental Disorders-DSM-5 (American Psychiatric Association, 2013) or the ICD-10 classification of mental and behavioral disorders (World Health Organisation, 1996) and requires the assessment of ADHD symptoms currently and retrospectively for childhood. ADHD is composed of two dimensions of age-inappropriate behavior, i.e. symptoms of inattention and symptoms of hyperactivity/impulsivity (American Psychiatric Association, 2013). Symptoms of inattention include difficulties of sustaining attention, reluctance to engage in tasks that require sustained mental effort, distraction of extraneous stimuli, etcetera. Hyperactive/impulsive symptoms comprise difficulty engaging in leisure activities quietly, excessive talk, impulsive concerning money, relationships, work, etcetera. 5 out of 9 symptoms in each category should be met in older adolescents and adults (age 17 and older; American Psychiatric Association, 2013). The etiological roots of ADHD can be attributed to neurobiological, neuroanatomical, neuropsychological, and environmental factors (Busch, 2010; Stubbe, 2000). Etiological models, however, do not distinguish between

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various forms of impairments in daily life activities - especially impairments of occupational functioning - when presenting symptoms of ADHD (Barkley et al., 2008).

While overt signs of hyperactivity and impulsivity decrease, inattention usually persists unchanged (Biederman, Mick, & Faraone, 2000), leaving many adults with ADHD with continued negative consequences of the disorder (Barkley et al., 2008; Wasserstein, Wolf, Solanto, Marks, & Simkowitz, 2008). Prevalence studies have estimated that about 2-8% of the adult population world-wide are diagnosed with ADHD and suggest that the

number of people suffering from symptoms is increasing (Weyandt & DuPaul, 2006; Hagar & Goldstein, 2005). One reason for the increasing number of people diagnosed with ADHD could be the diagnostic inflation or overdiagnosis (Batstra, Nieweg, Pijl, Van Tol, & Hadders-Algra, 2014), as clinicians may suggest an ADHD diagnosis without having performed an elaborate diagnostic assessment (Sciutto, & Eisenberg, 2007). Due to the lack of gold standard diagnostic tests, a multifaceted approach to diagnose ADHD is necessary (Booksh, Pella, Singh, & Gouvier, 2009).The diagnosis of ADHD in children is based on accumulating evidence from multiple sources and multiple types of assessment measures like behavior rating scales, diagnostic interviews with parents and teachers, direct observation of behavior, and collection of data that will establish symptom-related impairment in functioning (Fisher & Watkins, 2008). In children, multiple sources of data are available to support the diagnosis of ADHD. In adults the ADHD diagnosis is more complicated because it is difficult to find evidence for the disorder outside of self-report (Murphy & Schahar, 2000). Typically the diagnosis in adults is therefore largely based on self-report measures and on retrospectively establishing that a diagnosis of ADHD in childhood has been met (DSM–IV; American Psychiatric Association, 1994), which often is done again by using self-report instruments, such as the Homburg ADHD-scale for adults ([HASE]; Wardt et al., 1993; Gualtieri & Johnson, 2005). The major difficulty here is to establish that ADHD had been met in childhood, because few reliable sources can be found retrospectively (Wender, 1997).

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Unfortunately, an objective and valid diagnostic test procedure, like a biomarker for assessing ADHD, is not available up until now (Mittenberg, Patton, Canyock, & Condit, 2002;

Wasserstein, 2005).

ADHD in adults is associated with multiple impairments in major life activities (Barkley & Fischer, 2010). Adults were shown to have a lower level of occupational

functioning than control groups (Barkley & Fischer, 2011). Their salaries are lower, their job performance was worse, they have higher rates of layoffs and job changes, and are less likely to work independently. In addition, they have trouble fulfilling work demands, have poorer performance at job interviews and complain more often about work difficulty (Barkley et al., 2008). Problems in emotion regulation and impulse control are another important

characteristic of patients with ADHD. ADHD patients are impatient and have low frustration tolerance, are angry and irritable quickly, and are easily emotionally excitable (Barkley, 2006; Martel, 2009; Skirrow, McLoughlin, Kuntsi, & Asherson, 2009). Moreover, adults with ADHD show higher rates of delinquency (Barkley et al., 2006; Mannuzza, Klein, & Moulton, 2002), addictive behaviors (Hallowell, 1995), and show a proneness to experience social problems (Manuzza, Klein, Bessler, Malloy, & LaPadula, 1993). ADHD is characterized by risk-taking behavior that may lead to a greater tendency for risky driving (Barkley,

Guevremont, Anastopoulos, DuPaul, & Shelton, 1993), sexual behavior on reaching sexual maturity, and a greater risk of early parenthood (Barkley, Fischer, Smallish, & Fletcher, 2006). The risk taking behavior is also reflected in money management. People with ADHD not only typically disregard future consequences; they also prefer immediate, small rewards to large, delayed rewards (Crone, Vendel, & van der Molen, 2003; Ernst, Grant, London,

Contoreggi, Kimes, & Spurgeon, 2003). Clark, Nower, and Walker (2013) even found hyperactive-impulsive type symptoms as statistically significant predictors of gambling behavior.

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Given these potential problems, it is important to assess clinical symptoms and /or impairments. Furthermore it is also crucial to plan individual treatment interventions, because ADHD is a heterogenic disorder with many different manifestations. Clinical assessment of ADHD, and thus, impairments in major life activities, is difficult due to several reasons. One major difficulty is to assess information from ultiple sources, such as parents, teachers, clinicians, and friends, in order to identify the individual characteristics of problems,

strengths, and functioning. Second, the assessments take place in the clinical setting and not in real life context, which does often correspond poorly to behaviour in daily routines.

Furthermore, assessments need to be performed in different settings to observe the

characteristics of problematic behavior (Kazdin, 2005). A third issue is the time-restriction many clinicians have to deal with when assessing patients in the clinical context. Multiple measures are included in the assessment of diagnosing ADHD, like an initial broad-spectrum assessment to identify symptoms (e.g. hyperactivity) and a narrower spectrum assessment of targets for intervention (Achenbach, 2005).

As a consequence of these multiple difficulties, the question remains what measurements in clinical research can be efficient to predict impairments of major life

activities in patients with ADHD. Therefore, the aim of the present study is to find significant predictors for impairments of major life activities to streamline clinical assessment, and to improve interpretation of test results and their impact on daily life. Self-report symptom questionnaires for ADHD symptoms, which are rated to be a valid means of assessing client’s specific symptoms, as well as to measure symptom severity, are one way to assess the

functioning of ADHD patients (Laban, 2014). Multiple researchers investigated the good internal consistency of measures like the Wender Utah Rating Scale [WURS] (Rossini & O’Connor, 1995). For instance, Ward et al. (1993) found the scales’ good internal consistency measured by split-half reliability coefficients. Also regarding test-retest reliability, the WURS fell in a good to excellent range (Rossini & O’Connor, 1995; Wierzbicki, 2005). Another way

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to assess the functioning of ADHD patients is a structured clinical ADHD interview, which contains DSM-IV criteria for ADHD (Barkley & Murphey, 2006). Moreover, adults suffering from ADHD often need to conduct neuropsychological tests which measure impairments in different areas of life and therefore having a good predictive value for real life impairments of adults with ADHD. Those tests are frequently used in clinical practice in order to support and to objectify subjectively experienced cognitive complaints. A large body of research found cognitive impairments in adults with ADHD in functions such as selective attention, divided attention, sustained attention, working memory, inhibition, problem solving, fluency,

concentration, vigilance, short-term memory, and learning abilities (Barkley, 1998). High levels of symptoms of ADHD tend to predict many problematic behaviors later in

development (Wåhlstedt, Thorell, & Bohlin, 2008). Previous research has shown that high levels of impulsive and inattentive behavior during the preschool age tend to show continuing problems in this domain (Campbell, 2002). Beyond that, high levels of these behaviours also show problems with other aspects socioemotional development (e.g. poor social competence), conduct problems, internalizing problems and dysfunctional emotional regulation (Spira & Fischel, 2005). Poor higher-order cognitive functions (e.g. executive functions [EF]) are another predictive factor for impairments in major life activities and of importance for the development of ADHD symptoms and of socioemotional problems (Wåhlstedt, et. Al, 2008). These cognitive functions are neurocognitive processes like response inhibition, emotional and motivational self-regulation, nonverbal and verbal working memory, planning and problem solving or strategy development, etc. (Barkley, 1997; Frazier, Demaree &

Youngstrom, 2004). Previous studies found longitudinal relations between early EFs and later behavior problems (Brophy, Taylor, & Hughes, 2002; Nigg, Quamma, Greenberg, & Kusche, 1999), where inhibitory control in preschool was related to both symptoms of hyperactivity and inattention at school age (Berlin, Bohlin, & Rydell, 2003). Several studies also found a relationship between poor executive functioning [EF] and ADHD symptoms leading to

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impairments in daily life situations (Barkley, 1998; Brocki & Bohlin, 2006; Martinussen, Hayden, Hogg-Johnson & Tannock, 2005).

Even though cognitive tests are often and willingly used, they are also very general. A better contribution to predict impairments in major life activities could be financial decision making, due to its major importance in our daily lives. Studies about financial decision making, including neuropsychological tests measuring abilities required in financial decision making, determined the ability to measure specific impairments of major life activities

(Kershaw & Webber, 2008; Suto, Clare, Holland, & Watson, 2005). Webber & Lynne (2008) descried in their assessment of financial competence that the Financial Competence

Assessment Inventory [FCAI] is a helpful tool to ascertain people who are financially incompetent and thus can be used as a predictor for impairments of major life activities.

In summary, ADHD is a disorder that is heterogeneous and that includes many different symptoms, impairments and complex etiologies (Tarver, Daley, & Sayal, 2014). Predictors, and therefore the independent variables in this field, could be ADHD symptoms as assessed in self-report rating scales, basic cognitive functions as assessed in routine

neuropsychological assessments and/ or tests specifically designed for competences in financial decision making (e.g. money management, math skills, heuristics). The outcome measurements, and therefore the dependent variables, are self-rated impairments in several domains of daily functioning which are known to be often impaired in adults with ADHD, i.e. family, risk, and finance. Therefore it is crucial to investigate whether ADHD-like symptoms, cognitive functioning and financial decision making are predictors for impairments of these domains. Additionally, it is important to assess what is the most efficient measurement for a clinician, and what measurements are adequate for which impairments in life.

The present study is a pre-clinical study and includes a group of healthy individuals. With reference to the dimensional approach, ADHD symptoms, cognitive functions, and impairments, can be assessed on a continuous level and predictions can be made on basis of

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regressions. This serves as the basis for subsequent clinical studies, by preselecting sensitive measures for the prediction of major life impairments.

Using different assessment procedures and approaches, it is expected that ADHD-like symptoms, cognitive functioning and financial decision making are significant predictors for impairments in domains as family, risk taking and finance, whereas differences in predictive value are also expected. The ADHD symptoms and impairments will be assessed in a

dimensional approach with healthy subjects. The results can be used as a base for further clinical studies for adult patients suffering from ADHD.

Method Research Design

The design of this study was a pilot study with a non-experimental correlational

research design. This design is typically used to explore relationships among variables that are not manipulated (Fitzgerald, Rumrill, & Schenker, 2004). In this study, the predictive value of ADHD symptoms, cognitive functions, and financial decision making competences towards ‘impairments of major life activities’ within a sample of healthy participants was examined. Participants

The sample included data from 63 subjects who were recruited from a local community. All participants were German native speakers, and the number of males and females was approximately equal among the participants, 33 (52%) and thirty (48%), respectively. The age range was from 19 to 64 (M = 34.65, SD = 13.13). The assessment of marital status yielded that 23 participants (37%) were single, 9 (14%) were in a relationship, 5 (8%) were living together, 23 (37%) were married, and 3 (5%) participants were divorced. Additionally, the education level as well as years of education was considered. Four

participants had a lower secondary education, nine went to junior high school, and 49 participants went to high school. One participant did not give any information about level of education. The years of education ranged from 10 to 27 years (M = 16.75, SD = 3.72). The

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majority of the subjects had a paid job (Yes=46, No=17). Furthermore, the living environment situation was approximately equally weighted, where 29 participants lived in a city and 32 lived in a village. Two participants did not give any information (for detailed descriptives of the participants see Table 1).

Materials

Weiss Functional Impairment Rating Scale Self-report. The Weiss Functional

Impairment Rating Scale Self-Report [WFIRS-S] (Weiss, 2000) assesses to what degree an individual’s behaviour or emotional problems impact various clinically-relevant domains of functioning. It exists of 76 items in eight different categories (A. Family, B. Work, C. School, D. Life skills, E. Self-concept, F. Social, G. Risk, and H. Finance) rated on a four point Likert Scale (0 = never or never at all, 1 = sometimes or somewhat, 2 = often or much, 3 = very often or very much), and an additional answer category when the statement is not applicable (not applicable). The sum of all items in a category with a response value (0 through 3) divided by the sum of the total number of items that have been endorsed (excluding ‘not applicable’ items) indicates the mean rating of impairment in that category (range 0 to 3). Additionally, any item scored a ‘2’ or ‘3’ is two standard deviations outside the clinical norms for ADHD and would be considered impaired. The WFIRS-S has internal consistency

coefficient of greater than 0.9 with excellent sensitivity to change, and a great correlation between symptom change and improvement in ADHD symptoms (Weiss, 2000). In the current study only the domains family, risk and finance were administered. The scores are averaged across the items, the mean for family was 0.45 (SD = 0.35, range = 0 to 1.50), the mean for risk was 0.47 (SD = 0.33, range = 0 to 2.00) and the mean for finance was 0.82 (SD = 0.38, range = 0 to 1.57).

Cognitive Functions ADHD (CFADHD). The test set Cognitive Functions ADHD

[CFADHD] (Tucha, Fürmaier, Aschenbrenner, & Tucha, 2013) is a test battery for measuring the performance profile of adult patients with attention deficit hyperactivity disorder (ADHD)

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and for people seeking a clinical evaluation for ADHD in adulthood. It tests twelve cognitive functions of various domains, including attention, memory, executive functions, processing speed and subjective ability. The overall validity is given and the test- retest reliability coefficient of this measure (0.7 to 0.93) is good to excellent throughout all subtests (Tucha et al., 2013).

Vigilance.

One of the objective cognitive measures used was vigilance. The computerized test from the Vienna Test System (VTS, Vienna Test System, 2007) required visual mental effort to attend stimuli over a longer period of time (15 min), where monotony played a major role. Participants were presented with visual stimuli that occasionally diminished somewhat in intensity. The task was to respond to these occasional cases.

Selective attention.

Selective attention was another measurement from the Vienna Test System (VTS, Vienna Test System, 2007). Participants received relevant and irrelevant stimuli in one or both presentation modalities. The task was to react to changes in the relevant stimuli while ignoring irrelevant ones. The number of omissions and commissions were registered as a measure of vigilance and as a measure of selective attention, as well as the reaction times (RT) and the standard deviations (SD).

Verbal working memory.

Another application was the assessment of the capacity limits of verbal working memory (NBV Nbeck verbal, Vienna Test System, 2007). Participants were presented with a succession of 100 consonants with a presentation time of 1.5 seconds. If the currently

displayed consonant is identical to the consonant that appeared two places back the participant had to press the green button on the response panel. The number of correct hits was used as initial point for scoring.

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Inhibition was measured with the Stroop Color-Word Interference task (Bäumler, 1985; Stroop, 1935). This task consisted of three conditions, namely the Color-Word condition, the Color-Block condition, and the Color-Word Interference condition. In the Color-Word condition participants had to read color words (YELLOW, GREEN, BLUE, RED) that were printed in black ink on the screen as fast as possible and press the matching button on the response panel. In the Color-Block condition participants had to name colored rectangles (rectangles printed in yellow, green blue, red) and press the matching color button as fast as possible. In the Color-Word Interference condition, on the one hand, participants had to name the ink in the word that was presented and ignore the meaning of the printed word (e.g. yellow ink in BLUE). On the other hand, participants had to name the color word which was presented in a mismatching ink, so to ignore the ink (e.g. BLUE printed in yellow ink). Each trial consisted of the same number of stimuli. The time in seconds to complete each trial was registered. The variables used for analyzing were reading interference (the difference of the reaction time medians of the "reading interference condition" and the "reading

baseline") and the naming interference (the difference of the reaction time medians of the "naming interference condition" and the "naming baseline"; MacLeod, 1991).

Financial decision making (FDM).

Financial Competence Assessment Inventory.

Basic financial knowledge and competence was assessed with the financial

competence and assessment inventory [FCAI] (Kerschaw & Webber, 2008). This assessment uses a structured interview format and contains 38 items (questions and tasks) related to financial abilities. Observation techniques are also included in the FCAT, where participants are observed carrying out some financial tasks, such as counting money. Additionally, for the administration of the FCAI, a standard procedure is used in order to be able to objectively score the instrument according to the guidelines. The FCAI showed good reliability and validity (Kershaw et al., 2008). FCAI scores describe financial competence in two different

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ways. The first way includes six dimensions of financial competence (i.e. everyday financial abilities, financial judgement, cognitive ability, estate management, debt management, support resources) to be rated on a five point Likert scale (0 = little or no awareness, 1 = rudimentary awareness, 2 = partial understanding, 3 = adequate understanding, 4 = complete understanding; M = 3.70, SD = .50, range = 0 to 2). The second way transliterates the items into four subscales, designed to assess the main process involved in financial competence (i.e. understanding, appreciation, reasoning, expressing a choice). The subscale understanding was derived from the ability to identify and comprehend the concepts involved in the particular decision at hand (M = 3.62, SD = .61, range = 0 to 2). The subscale appreciation of the likely consequences of a decision for the individual was defined as the ability to think in an abstract manner about the situation and implications of a particular decision (M = 3.38, SD = .73, range = 0 to 3). The subscale reasoning is the ability to apply logic and weigh risks and benefits of a particular decision or course of action (M = 3.78, SD = .49, range = 0 to 2). Finally, the subscale expressing choice was defined as the ability to decide between two or more options and being able to convey that particular decision (M = 3.83, SD = .423, range = 0 to 2).

Iowa Gambling Task.

Performance on the Iowa Gambling Task (IGT; Bechara, 2007) has been shown to be a sensitive and well-validated computerized measure of impaired decision-making.

Participants are presented with four virtual decks of cards on a computer screen from which they have to make a series of selections in order to win as much, or lose as little, money as possible. Each deck contains winning and losing cards. Above the decks there are two visual bars (green and red) that informed the participant how much they had won or lost after every card selection. However, the decks are set up so that those with the highest immediate payoffs have the highest cumulative losses such that their repeated selection will result in an overall loss (Fernie & Tunney, 2005). Participants must learn to avoid selecting from these decks.

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The number of selections from each deck was recorded allowing a net score to be calculated by subtracting the number of disadvantageous choices (number of cards selected from decks A and B) from the number of advantageous choices (number of cards selected from decks C and D; M = 9.42, SD = 36.36, range = 0 to 146).

Homburg ADHD-scale for adults. The participants were assessed for ADHD

symptoms in present and childhood with the Homburg ADHD-scale for adults [HASE], which is an ADHD-specific self-report instrument (Rösler, Retz-Junginger, Retz, & Stieglitz, 2008). The HASE is composed of two scales: the Wender Utah Rating Scale [WURS-K] (Ward, et al., 1993) and the ADHD Self-Report Scale [ADHD-SR] (DuPaul et al., 1998). Both measuremts, the WURS-K and the ADHD-SR, demonstrated a good to excellent reliability and validity (Rösler, Retz, Thome, Schneider, Stieglitz, & Falkai, 2006). The WURS-K is to retrospectively diagnose ADHD in childhood through 25 items using a five point severity scale (0=does not apply, 1=mild manifestation, 2=medium manifestation, 3=clear

manifestation, 4= strong manifestation), where a score of 30 or higher in the German version indicates significant ADHD symptoms (Taylor, Deb, & Unwin, 2011). Mean for the total sample was 14.51 (SD = 10.35 and range = 0 to 46). The second scale, the ADHD-SR, identifies patients with current symptoms of ADHD through 18 items using a four point severity scale (0=does not apply, 1=mild manifestation, 2=medium manifestation 3=severe manifestation). The odd-numbered items (Items 1, 3, 5, 7, 9, 11, 13, 15, 17) compose the Inattention subscale and the even-numbered items (Items 2, 4, 6, 8, 10, 12, 14, 16, 18) compose the Hyperactivity-Impulsivity subscale (Pappas, 2006). The Total score is the total of the Inattention and Hyperactivity-Impulsivity subscale scores, M = 11.81, SD = 8.00, range = 0 to 33.

Procedure

Participants were first requested to fill in demographic information, and to complete forms assessing ADHD symptoms, and impairments (WURS-K; ADHD-SR; WFIRS-S).

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Second, the neuropsychological test battery was administered, with a duration of about 2.5 hours (CFADHD). Finally, financial decision making tests were performed, taking about 2 hours (FCAI; IGT). Clear formulations about the usage (e.g. reading out instructions) of these tests prevented slight differences during instructions and the researcher was always within reach for questions or remarks, so that the comprehension of instructions was ensured. The study was conducted under the guidelines of the Helsinki Declaration. Prior to the study, all participants signed statements of informed consent and were debriefed after the end of the assessment. The project was reviewed and approved by the Ethical Committee Psychology (ECP) of the psychology department of the University of Groningen, Netherlands.

Statistical Analysis

A multiple regression analysis was used to obtain an indication of the predictive validity of ADHD symptoms, cognitive functions and financial decision to predict for impairments of major life activities. Separate regression analyses for various domains of impairments were performed, one for ADHD symptoms, one for cognitive functions and one for financial decision making capacity. Afterwards, a hierarchical model combined these regressions.

Results

It was hypothesized that impairments in major life activities can be predicted through cognitive functions, financial decision making and/ or ADHD symptoms. There were three different categories in measuring impairments in major life activities, namely family, finance and risk. These categories were used as dependent variables in the statistical analysis of the data. Correlations within the three categories were all positive and within a moderate range. Significant correlations were found between the subscales family and risk, r(63) = .47, p = .00, between the subscales family and finance, r(58) = .35, p = .01, and between the subscales risk and finance, r(58) = .51, p = .00 (Table 2).

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Nine individual binary logistic regression models were computed in order to determine the validity of cognitive functioning, financial decision making and ADHD symptoms

(predictors) in predicting impairments in the categories ‘Family‘, ‘Risk’, and

‘Finance’(criterions). In order to account for multiple testing, a Bonferroni correction was used. For that purpose, the subtests of the neuropsychological tests, the financial tests, and the ADHD tests were entered as independent variables in the individual models and the category of impairment (i.e. family, risk, finance) was used as dependent variable.

Predicting Impairments in Family Functioning.

No significant model was found to predict an impairment in the category ‘Family’ in using neuropsychological tests, F(11,50) = 1.70, p = .10. The model explained 27.2% of the total variance in the category ‘Family’ (Cox & Snell R²). With regard to financial decision making, again no model was found to predict impairments in the category ‘Family’, F(8,53) = 1.80, p = .10, whereas 21.3% of the total variance was explained using this model. Finally, the model

to predict impairments in the category ‘Family’ by using ADHD symptom tests indicated a significant result, F(2,60) = 5.97, p = .00. R² indicated that 16.6% of the variance in the category ‘Family’ was explained by measures of ADHD symptom severity, even though the WURS-K nor the ADHD-SR obtained a valid and significant result (Table 3).

Predicting Impairments in Risk Taking.

No significant model was found to predict an impairment in the category ‘Risk’ in using neuropsychological tests, F(11,50) = 1.54, p = .15. The model explained 25.3% of the total variance (Cox & Snell R²). With regard to financial decision making, again no model was found to predict impairments in the category ‘Risk’, F(8,53) = 1.70, p = .12, whereas 20.4% of the total variance was explained using this model. Finally, the model to predict impairments in the category ‘Risk’ by using ADHD symptom tests indicated a significant result, F(2,60) = 11.26, p = .00. 27.3% of the variance in the category ‘Risk’ was explained by

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the ADHD symptom tests, whereas only the subtest ADHD-SR showed a significant result (Table 4).

Predicting Impairments in Finance Functioning.

No significant model was found to predict an impairment in the category ‘Finance’ in using neuropsychological tests, F(11,45) = 1.14, p = .35. The model explained 21.9% of the total variance in the category ‘Finance’ (Cox & Snell R²). With regard to financial decision making, again no model was found to predict impairments in the category ‘Finance’, F(8,48) = .68, p = .71, whereas 10.2% of the total variance was explained using this model. Finally, the model to predict impairments in the category ‘Finance’ by using ADHD symptom tests indicated a significant result, F(2,55) = 7.24, p = .00. R² indicated that 20.8% of the variance in the category ‘Finance’ was explained by the ADHD measurements. Same as in the category ‘Risk’, only the subtest ADHD-SR showed a significant outcome (Table 5).

Discussion

The number of adults suffering from ADHD-like symptoms is increasing (Weyandt & DuPaul, 2006; Hagar & Goldstein, 2005), and the picture of the disorder is associated with various impairments in major life activities (e.g. problems with attention, working memory, problem solving, etc.; Barkley & Fischer, 2010). Therefore, it is crucial to not only have instruments to diagnose ADHD, like interviews and rating scales (Barkley & Murphey, 2006), but also instruments that focus on impairments in major life activities, in order to find an optimal therapeutic frame. Hence, the appraisal of impairment in major life activities is of great importance, especially in assessing ADHD in adults, in order to see which problems can occur to which extent.

The present study focused on three impairment categories, namely family, risk taking and finances. It was hypothesised that different tools like neuropsychological tests, financial tests and ADHD symptom tests would predict impairments in these three categories.

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CFADHD -Cognitive Tests. One aim of this study was to find a predictive value of

neuropsychological tests for impairments in major life activities. However, against our expectations, no significant results could be obtained. The logistic regression showed that the overall models were not significant, (Table 3-5), and the models only explained a small amount of variance of the impairment scales (Family R²= 27.2 %; Risk R² = 25.3 %; Finance R² = 21.9 %). The hypothesis was rejected. These results were inconsistent with Wåhlstedt’s ,

et. al (2008) outcomes. In their study, higher-order cognitive functions, like response

inhibition, nonverbal and verbal working memory (Barkley, 1997) and its impairments served as valid predictors for problems later in life. Also, other studies strengthen this statement by concluding that cognitive tests, next to rating scales and interviews, are the gold standard in predicting impairments in major life activities (Boonstra, Oosterlaan, Sergeant, & Buitelaar, 2005; Marchetta, Hurks, Krabbendam, & Jolles, 2008). Yet, others found only a subset of cognitive tests significantly valid in predicting impairments in major life activities (Nigg, Willcutt, Doyle, & Sonuga-Barke, 205).

Nevertheless, other studies also failed to find a relation between cognitive functioning and social competences (Biederman, Monuteaux, Doyle, Seidman, Wilens, Ferrero, &

Faraone, 2004) and thus confirm our results. Weak correlations between cognitive functioning and impairments in major life activities, found in Biederman’s et al. (2004) study, might be the reason why we could not detect a predictive value for neuropsychological tests.

Financial Tests. Furthermore, we asserted that financial decision making could predict

impairments in the three categories of major life activities (i.e. family, risk, finance). Similar to the outcome above, we also could not find any significant results by using financial tests to predict problems in life. Logistic regression models even failed to reveal occasionally

significant results within the subtests (Tables 3-5). Additionally, the proportions of explained variance in the impairment categories was low (Family R² = 21.3%; Risk R² = 20.4%: Finance R² = 10.2%). Regarding the background of using financial competence assessments to predict

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impairments in major life events and the fact that research about its contributions to these impairments is rare, we could not confirm evidence found in other studies (Kershaw & Webber, 2008; Suto, Clare, Holland, & Watson, 2005). Kershaw and Webber (2008) stated that the FCAI, which was the tool with the most diverse subtests for financial decision making in this study, was able to distinguish between groups with different types of cognitive

impairments (acquired brain injury, schizophrenia, dementia, intellectual disability) and identify specific areas of financial strengths and weaknesses experienced. Even though reliability and validity of this measurement was supported, regarding the above mentioned impairments (Kershaw & Webber, 2008), limitations as age differences or homogeneity in the nationality of participants in both studies could have led to different results. Also the fact that the FCAI was not tested on participants suffering from ADHD might have misled the current results. Important to mention in this context is also the incident that the majority of our participants did not have any financial problems nor was there a big variance among the sample.

ADHD Symptom Tests. Another predictive factor in this study for impairment in the

categories family, risk and finance was the ADHD measurement, which included the WURS-K and the ADHD-SR. In line with predictions, the results supported our expectation with regard to the three categories of functioning. The analysis revealed that the WURS-K

indicated a somewhat valid predictor in the category ‘Family’, whereas ADHD-SR showed a significant result in the categories ‘Finance’ and ‘Risk’ (Table 3-5). Even though the logistic regression models explained only a moderate fraction of the total variance (Family R² = 16.6 %, Risk R² = 27.3 %, Finance R² = 20.8 %), the significance and percentage of explained variance indicated a meaningful relationship. Here, it is important to distinguish between current symptoms and retrospective symptoms in childhood, as there are discrepancies in these two ADHD measurements and their results. For the category ‘Family’ the WURS-K nor the ADHD-SR achieved a significant result (Table 3), whereas the ADHD-SR showed

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significant outcomes for the categories ‘Risk’ and ‘Finance’ (Tables 4-5). These differences in measures to assess adult ADHD were already captured by Glöckner-Rist, Pedersen, and Rist (2013), where they organized the ADHD-SR into symptoms as inattention, hyperactivity, and impulsivity and the WURS-K into symptoms as inattention/hyperactivity, affect lability, depressivity, and conduct problems. However, only the first two WURS factors affected adult ADHD facets, which could be a reasonable point that only the measure for current symptoms resulted in significant outcomes. Furthermore, previous research affirms our hypothesis and its described results, where it is shown that high levels of symptoms of ADHD seem to serve as a precursor for a broad spectrum of problem behaviour later in life (Bagwell, Molina, Pelham, & Hoza, 2001; Biederman et al., 2000; Schatz & Rostain, 2006). These symptoms not only include the typical symptomatic picture of ADHD, such as impulsivity, inattention or hyperactivity, but an even more open facet of problematic behaviour, like problems with other aspects of socioemotional development (Angold, Costello, & Erkanli, 1999), which is especially important for our current study. However, using ADHD symptoms as a predictor of impairments in life also hides some pitfalls. Studies reported that not all problem categories can be predicted by using ADHD symptom checklists, nor can every age range be covered. Wåhlstedt, et al. (2008) could not cover domains such as internalizing problems or people in their younger ages. This might question which impairment categories can be included in being predicted by ADHD symptom tests.

Finally, with regard to the cognitive test batteries (CFADHD) more specifically designed measures seem to be necessary in order to gain better and clearer results. Here it might be beneficial to apply measurements that focus more on impairments that affect patients suffering from ADHD in daily life, such as self-organization or time- management (Barkley & Murphey, 2010). Anyway, even though no significant results could be found, does not indicate that cognitive deficits are not related to ADHD, as prior research has implied (Marchetta et al., 2008). In about 90 % of people with ADHD, cognitive deficits are present

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when ratings of cognitive functioning in daily life activities are used (Barkley & Murphy, 2010). Also the financial assessment needs more evaluation regarding its assessment for participants with ADHD, as mentioned above. Consequently, the present study does not fully integrate disorder specific issues and assessments to capture daily life experiences in adults with ADHD.

Limitations And Future Direction

The present results must be viewed in the context of some limitations. A primary deficiency is the nature of the sample. It is to be assumed that the samples may not be representative of the groups from which they were drawn. That is, participation in the study was voluntary without any incentive. That could have lead to less motivation in performing on a high level during the 4-5 hours test battery. However, the biggest limitation here is

probably the fact, that conclusions on ADHD patients are drawn using a healthy sample. Healthy adults cannot provide an insight into issues and problematic situations ADHD patients have to deal with on a daily basis, nor can valid conclusions be drawn which test batteries could be the most effective predictors in assessing impairments of major life

activities. There might also be a low variance in problems, due to the fact that healthy subjects may not experience significant problems in these three areas of life. Furthermore, even though this study can depict a higher diversity in the participant pool, the generalizability to other populations is still questionable and a greater variability would be desirable, especially in the context of cultural diversity. In addition, this assessment has not been performed in a clinical context, which could have misled the results.

Another aspect is the selection of impairment categories. The analysis by using only three categories (i.e. family, risk, finance) can lead to misinterpretations of the general usage of the tests. Other impairment areas, such as work, social or life concept, could result in different outcomes. This restricts the validity of the predictive value of the ADHD symptom tests we encountered in the current study. Furthermore the assessment type might have been a

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problem. The impairment measurement was solely based on self-reports, where a correlation to the other ADHD self-report scales is not surprising and easier to achieve than correlations between cognitive tests and self-reports of impairments. Also, the number of measurements used in this study could only be applicable for the categories applied in this context. Other criteria, like driving skills or drug abuse could lead to other results regarding the

measurements we used in this study. In general a broader neuropsychological assessment , including objective psychometric tests (Fuermaier, Tucha, Koerts, Aschenbrenner,

Kaunzinger, Hauser, Weisbrod, Lange, & Tucha, 2015), would allow a more precise and complete examination of its predictive value towards impairments in major life activities. A selection of tests and measures was necessary in order to keep the assessment within an acceptable time frame for participants. Nevertheless, the selection was based on clinical research, where the tests used in this study have been proven to be sensitive to impairments in major life activities (Boonstra et al., 2005, Tucha, Tucha, Laufkoetter, Walitza, Klein, & Lange, 2008). That means, for further investigation of this topic, it might be helpful to use more diverse impairment scales as well as a more comprehensive selection of test batteries in the domains neuropsychological tests (e.g. word recognition, flexibility, word fluency) and financial decision making tests (e.g Financial Decision-Making Interview [FDMI], Financial decision-making styles [FDS]). Due to the pre-clinical approach of this study, it is needed to find out whether these preliminary results would capture the same outcome as with people suffering from ADHD.

Furthermore, more insights into the predictive value of the tests mentioned above might be achieved by comparing adults with different subtypes of ADHD including a control group, hence conducting a quasi experimental research design. Previous research already discovered that subjective and objective measurements are crucial for clinical practice, but that both types of measurement reveal distinct types of information and cover different

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aspects of functioning (Fuermeier et al., 2015). However, further research on this issue needs to be conducted.

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Appendix Table 1

Characteristics of Participants (n = 63)

Mean (SD), or n (%)

Age (range, M, SD) 21-62, M=32.65, SD=11.65

Years of education (range, M, SD) 10-27, M=17.24, SD=4.02

Gender 13 female (21%), 13 male (21%)

Marital status

8 single (13%), 2 relationship (3%), 4 living together (6%), 12 married (19%), 1 divorced

(2%)

Educational level

1 lower secondary education (2%), 5 junior high school (8%), 21 high school (33%)

Paid job 21 Yes (33%), 6 No (10%)

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Table 2.

Correlations within the WFIRS subscales

WFIRS subscale Family Risk Finance

Family - .47 .35

Risk .47 - .51

Finance .35 .51 -

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Table 3

Logistic Regression Model to Predict Impairments in the Category ‘Family’ as Measured With Neuropsychological, Financial, and ADHD Symptom Tests

Predictor variables B SE B p Cognitive functions WAFV- Oa 0.70 0.062 0.266 WAFV- Ca 0.022 0.035 0.522 WAFV- RTa 0.000 0.001 0.753 WAFV- SDa -0.001 0.002 0.552 WAFS- Ob 0.079 0.043 0.075 WAFS- Cb 0.025 0.011 0.029 WAFS- RTb 0.001 0.01 0.420 WAFS- SDb -0.004 0.003 0.129 NBVc 0.017 0.018 0.357 STROOP- Readingd 0.980 0.615 0.117 STROOP- Namingd -1.351 0.488 0.008 Total R² = 0.272i

Financial decision making

FCAI- Abilitiese 0.057 0.042 0.182 FCAI- Judgemente 0.085 0.041 0.044 FCAI- Estatee 0.050 0.046 0.280 FCAI- Cognitione 0.072 0.049 0.147 FCAI- Debte 0.020 0.034 0.549 FCAI- Supporte 0.036 0.045 0.428 FCAI- Totale -0.068 0.040 0.096

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IGTf -0.002 0.001 0.209 Total R² = 0.213i ADHD- Symptoms WURS-Kg 0.006 0.078 0.200 ADHD-SRh 0.012 0.006 0.051 Total R² = 0.166*i

Note. ADHD = attention deficit hyperactivity disorder a

Comprehensive assessment of vigilance (O = Omission; C = Comission; RT = Reaction time; SD = standard deviation). b Comprehensive assessment of selective attention (O = number of omission; C = number of comission; RT = Reaction time; SD = standard

deviation). c Nbeck verbal working memory. d Stroop Color-Word Interference task ( Reading = reading interference; Naming = Naming interference). e Financial Competence and Assessment Inventory (Abilities = every day financial abilities; Judgment = financial

judgment; Estate = estate management; Cognition = cognitive ability; Debt = debt

management; Support = Support resources; Total = total score). f Iowa Gamnling Task. g

Wender Utah Rating Scale – Short version. h ADHD Self-report Scale. i Cox and Snell R². * p < .05

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Table 4

Logistic Regression Model to Predict Impairments in the Category ‘Risk’ as Measured With Neuropsychological, Financial, and ADHD Tests

Predictor variables B SE B p Cognitive functions WAFV- Oa 0.057 0.060 0.343 WAFV- Ca 0.040 0.034 0.243 WAFV- RTa 0.000 0.001 0.726 WAFV- SDa -0.004 0.002 0.115 WAFS- Ob 0.083 0.042 0.052 WAFS- Cb 0.022 0.011 0.053 WAFS- RTb 0.000 0.001 0.916 WAFS- SDb -0.004 0.003 0.134 NBVc 0.029 0.017 0.102 STROOP- Readingd 0.844 0.594 0.162 STROOP- Namingd -0.007 0.427 0.988 Total R² = 0.253i

FCAI- Abilitiese 0.017 0.040 0.679 FCAI- Judgmente 0.014 0.039 0.731 FCAI- Estatee 0.014 0.044 0.743 FCAI- Cognitione 0.013 0.046 0.781 FCAI- Debte 0.001 0.032 0.967 FCAI- Supporte -0.040 0.043 0.350 FCAI- Totale -0.016 0.038 0.686

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IGTf 0.000 0.001 0.778 Total R² = 0.204i ADHD- Symptoms WURS-Kg 0.005 0.004 0.207 ADHD-SRh 0.017 0.005 0.003* Total R² = 0.273*i

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Table 5

Logistic Regression Model to Predict Impairments in the Category ‘Finance’ as Measured With Neuropsychological, Financial, and ADHD Tests

Predictor variables B SE B p Cognitive functions WAFV- Oa -0.043 0.073 0.560 WAFV- Ca -0.008 0.041 0.854 WAFV- RTa -0.000 0-001 0.992 WAFV- SDa 0.003 0.003 0.263 WAFS- Ob 0.106 0.053 0.051 WAFS- Cb 0.014 0.013 0.285 WAFS- RTb 0.000 0.002 0.696 WAFS- SDb -0.003 0.003 0.311 NBVc 0.016 0.021 0.462 STROOP- Readingd 0.846 0.734 0.255 STROOP- Namingd -0.664 0.576 0.255 Total R² = 0.219i

FCAI- Abilitiese 0.037 0.049 0.446 FCAI- Judgemente 0.029 0.047 0.542 FCAI- Estatee 0.033 0.054 0.540 FCAI- Cognitione 0.054 0.056 0.339 FCAI- Debte -0.019 0.038 0.613 FCAI- Supporte 0.043 0.052 0.414 FCAI- Totale -0.044 0.046 0.349

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IGTf 0.001 0.002 0.495 Total R² = 0.102i ADHD- Symptoms WURS-Kg -0.007 0.005 0.189 ADHD-SRh 0.024 0.007 0.000* Total R² = 0.208*i