What Drives Risky Behavior in ADHD: Insensitivity to its Risk or Fascination with its Potential Benefits?

Abstract

Objective:

ADHD is linked to increased engagement in risky behavior (ERB). Recent work suggests that this link is mediated by the perceived benefits of the behaviors, but not by the perceived risks or the attitudes toward the risks. Here we examine this hypothesis, using the psychological risk-return and psychometric multidimensional measurement models.

Method:

Adults with or without ADHD completed questionnaires measuring the likelihood of different risky behaviors and the perceived risks and benefits ascribed to these behaviors. Participants’ ratings of 25 characteristics of various risky behaviors allowed us to derive two factors corresponding to perceived risk and perceived benefit of ERBs. Overall attitudes toward the perceived risks and benefits were extracted.

Results:

Perceived benefit mediated the link between ADHD and ERB, in both models. Attitudes toward the perceived risks mediated that link in the psychometric model only.

Conclusion:

Perceived benefit plays an important role in the link between ADHD and ERB.

Keywords

ADHD risky behavior perceptions psychometrics decision making

Introduction

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder defined by three clusters of symptoms: inattention, hyperactivity, and impulsivity (American Psychiatric Association, 2013). The estimated prevalence of the disorder among children is 5% to 7% (Polanczyk et al., 2014; Willcutt, 2012) and 2% to 4% among adults (Kessler et al., 2006; Simon et al., 2009). ADHD is associated with functional impairment reflected in long-term educational, social, and occupational disadvantage (Klein et al., 2012) and adverse health-related outcomes, such as physical injury (Nigg, 2013).

Compared with the general population, people with ADHD show increased engagement in risky behaviors, such as smoking, substance abuse, dangerous driving, and unprotected sex (Pollak et al., 2019). Thus individuals with ADHD have an increased tendency to engage in risky behaviors (ERB), that is, they choose courses of action that could produce losses or damages to self and possibly others. This general link between ADHD and ERB was found both in clinical and non-clinical samples (Shoham et al., 2016; Shoham et al., 2019).

Recent studies have investigated ERB among adults with ADHD, using the behavioral decision making framework. In this framework, risk taking involves choosing a course of action, for which there is a chance (but not full certainty) that some undesirable event will occur (Fox & Tannenbaum, 2011). Decision theory implies that the overall value of a risky alternative is the sum of the products of the values of the possible negative and positive outcomes, where these values are multiplied by their corresponding probability of occurrence (Hansson, 2007). According to classical decision theory, attitude toward risk is defined as a generic orientation toward taking or avoiding risk in situations having uncertain outcomes (MacCrimmon & Wehrung, 1990; Von Winterfeldt & Edwards, 1986). Furthermore, attitude toward risk is conceived as a personality trait on continuum, ranging from risk seeking (attraction to risk) to risk aversion (avoidance of risk) (Weber, 2010). In keeping with such a view, the finding of increased ERB among individuals with ADHD depicts them as risk-seekers.

In keeping with this view, classic ADHD research measured risk attitudes in terms of choices between gambles (Dekkers et al., 2016; Mowinckel et al., 2015). While recent meta-analysis found evidence for increased risk-taking among individuals with ADHD, contrary to expectation, this analysis found no evidence for a link between ADHD and risk-seeking per se. Rather, this work has suggested that risk taking in ADHD could be attributed to a general pattern of inefficient decision making (Dekkers et al., 2018). In the present study, we have set out to explore decision-making processes, aside from risk seeking, in order to understand better the link between ADHD and ERB.

Why do people engage in risky behavior? In attempt to understand people’s responses to risk, researcher in the area of behavioral decision making have suggested an important distinction between two theoretical concepts, risk perception and risk attitude (Weber, 2004). A person may engage in risky behavior because s/he is not aware of the risk or alternatively because she underestimates the risk associated with that activity. In this case, her ERB is attributed to risk perception. Alternatively, a person who is knowledgeable about the risk associated with a behavior, may nevertheless engage in it because he deems the risk acceptable. In the same vein he might avoid the risky activity because he deems the associated risk unacceptable. In this case, the ERB (of lack of) is attributed to the person’s risk attitude (Weber, 2004).

A person’s decision to engage in risky activity depends not only on the associated risk, but also on the benefits derived from the activity. The greater the benefits, the greater the chances that the person will engage in the risky behavior. Weber suggested a distinction between the perceived benefits and the attitude toward the benefits of an activity. The perceived benefit refers to the person’s anticipated enjoyment (or utility) from engaging in the activity, while the attitude toward (the perceived) benefits refers to importance the person places on attaining that benefit (Weber et al., 2002). Here too, the stronger a person’s attitude toward attaining the benefits of engaging in some risky activity, the more likely he or she is to engage in that activity.

In order to detect what drives risky behavior in ADHD, we compare two different ways of conceptualizing decision making under risk, one based on the psychological risk-return model, and the other, on a psychometric model.

Psychological risk-return model: The financial risk return model states that decisions are based on a trade-off between units of return or benefit (objective expected gains) and units of risk (objective variability of the expected gains). The psychological approach to quantifying risk-taking assumes a similar tradeoff between risk and benefit, with the understanding that risk and benefit are inherently subjective and multidimensional (Weber et al., 2002). Weber and her colleagues, therefore, measured the risk and benefit associated with risky behaviors using self-report questionnaires. Their respondents indicated their perceptions of risk and benefit based on their “gut feelings” rather than engaging in static assessment tasks of the sort used in classic decision research (Weber et al., 2002). Moreover, Weber asserts that it is mostly the perception of risk, rather than the attitude toward risk, that accounts for individual differences in risk-taking behavior (Cooper et al., 1988; Weber, 2004). The model embodies the distinction between perceptions and attitudes in a linear function of the form: [Preference(X) = a(Perceived Benefit(X)) + b(Perceived Risk(X)) + c] where a and b (the weights) designate the person’s attitudes toward the risk and benefit (respectively) that are associated with activity X (Weber et al., 2002).

Research has shown that children with ADHD tend to associate with risky behaviors less severe consequences (Farmer & Peterson, 1995). Similarly, adolescents with clinical levels of inattention were less likely to endorse negative expectations regarding cigarette smoking (Foster et al., 2012). Likewise, adolescents with ADHD associated alcohol abuse with less negative outcomes (Pedersen et al., 2014). Our research however, has not found evidence for ADHD-related altered attitudes toward perceived risk (Shoham et al., 2016). Our findings suggest that the perceived benefit mediates the link between ADHD and risky behavior (Shoham et al., 2016). This finding is in accord with Bruce et al. (2009) who provided qualitative evidence that children with ADHD tend to emphasize the positive aspects of decision outcomes (Bruce et al., 2009). Furthermore, levels of ADHD were found to be correlated with higher perceived benefit of sexual risky behavior among adults (Spiegel & Pollak, 2019), and adolescent hyperactivity/impulsivity symptoms were correlated with stronger endorsement of positive expectations regarding smoking (Foster et al., 2012)

The psychometric model: According to the psychometric model, people have a rich definition of risk, incorporating qualitative characteristics, such as the extent to which the behavior is voluntary, the extent to which its effects are immediate, and its catastrophic potential (Slovic, 1987; Slovic et al., 1985). These types of risk-related characteristics are underpinned by a small number of basic factors (Fischhoff et al., 1978; Slovic et al., 1985), including harms (e.g., severity of hazard), benefits (e.g., its magnitude), and other aspects of engagement in risky behavior. The literature suggests several psychosocial factors (e.g., parental monitoring, peer pressure) which affect ERB by lessening the negative perceptions and enhancing the positive perceptions surrounding the outcomes of risky behavior (Boyer, 2006; Slovic, 1992). If individuals with ADHD are more influenced by these psychosocial factors, then this might explain the higher prevalence of ERB among them (Gardner & Gerdes, 2015; Pollak et al., 2017). Other factors may affect ERB by decreasing or increasing the subjective probability of the undesirable result. These include previous experience and feedback (Jessup et al., 2008), emotional state of the decision maker (e.g., desire, anger, dread) (Ariely & Loewenstein, 2006; Lerner & Keltner, 2001) time until the realization of the outcome (Ariely & Zakay, 2001; Zohar & Erev, 2007), sensation-seeking as a personality trait (Zuckerman, 2007), and controllability of the risky activity (Benthin et al., 1993; Slovic, 1992). All of these factors are known to have significant impact upon judgment and choice and are also correlated with ADHD (Jackson & MacKillop, 2016; Maslowsky et al., 2011; Patros et al., 2016). Some were found to mediate the link between ADHD and specific risky behaviors (Bron et al., 2018; Graziano et al., 2015).

In this study we used the psychological risk-return and psychometric models in measuring the perceptions of risk and benefit and also to extract the individual attitudes toward perceived risk and perceived benefit. Studies using the psychological risk-return model elicit from respondents their perceptions of risk and benefit. Studies relying on the psychometric model elicit from respondents assessments of characteristics of the risks and benefits associated with each behaviors, which could affect the tendency to engage in them. Attitudes toward risk and benefit are extracted by using the linear equation (Weber et al., 2002).

The Current Study

The goal of this study was to extend our investigation of the idea that perceptions of the benefit of risky behavior mediate the link between ADHD and ERB. We used different measures of perceived risk and benefit, on the basis of the two models discussed above. We addressed the following questions: First, do ADHD symptoms predict increased ERB? Second, do perceptions and attitudes toward the risk and benefit of risky decisions predict ERB? Finally, is the relationship between ADHD and ERB mediated by the perceptions and attitudes toward the risk and benefit of risky behavior? We tested the following hypotheses: (1) ADHD symptoms and ERB should be correlated, (2) perceptions of benefit, but not perceptions of risk, should mediate ERB among adults with ADHD symptoms, and (3) attitudes toward perceived risk should not corelate with ADHD symptoms.

Our study sample includes a high proportion of clinical cases. We employed a dimensional definition of ADHD, based on the Adult ADHD Self-Reporting Scale (ASRS: Kessler et al., 2005), rather than a categorical definition of ADHD (present vs. not present). Using the dimensional definition should capture better the diversity in the magnitude and severity of symptoms extant in the ADHD population (Marcus & Barry, 2011; Polderman et al., 2007). Furthermore, the dimensional definition should reduce the risk of overlooking hidden associations among symptoms, compared with a categorical definition.

Method

This study was conducted as a part of a larger research project (Shoham et al., 2019), which was approved by the Faculty of Social Sciences Ethics Committee at the Hebrew University of Jerusalem. All participants gave their informed consent after receiving a complete description of the study.

Participants

Two hundred adults, ages 20 to 40 were recruited in diverse geographical locations in Israel through advertisements. Ninety-seven participants (48.5%) were diagnosed as having ADHD by a neurologist or psychiatrist. This diagnosis was further confirmed by the Diagnostic Interview for ADHD in Adults (DIVA 2.0; Kooij & Francken, 2007). The remaining 103 participants (51.5%) did not meet the diagnostic criteria of ADHD, as confirmed by the same interview. Inclusion criteria included command of the Hebrew language and being in the age range of 20 to 40. Exclusionary criteria included a history of serious neurological or psychiatric illness (i.e., epilepsy, cerebral palsy, intellectual disability, autistic spectrum disorder, and past psychotic or bipolar episodes). Additional exclusion criteria included current depressive episodes or current post-traumatic stress disorder (PTSD), since these conditions could potentially hamper the ability to complete the lengthy questionnaire. We controlled for other psychiatric disorders, given the high prevalence of comorbidities among our participants and their associations with abnormal levels of risk-taking behavior (Brevers et al., 2013; Brogan et al., 2010; Giorgetta et al., 2012; McDonald et al., 2018; Ramrakha et al., 2000; Sip et al., 2018; Smoski et al., 2008; Strom et al., 2012). In order to control for the presence of psychiatric disorders, all participants underwent the Hebrew version of the Structured Clinical Interview for DSM-IV Disorders (SCID; First et al., 1996). All participants were asked not to take methylphenidate medications during the 24 hr preceding the experiment. Power calculations for multiple regression analysis indicated that a sample of this size should enable detection of small-to-moderate effects of f² = 0.1 with a power of 90% and a level of statistical significance of 0.01.

Measures

Demographic questionnaire: The study gathered demographic and background information including age, gender, level of education, current socio-economic status (SES) as defined by people-per-room, religiosity, and family status (See Table 1). This was necessary since some of these variables are known to affect risk-taking behavior (Abbott-Chapman & Denholm, 2001; Adler et al., 1994; Byrnes et al., 1999; Deakin et al., 2004).

Table 1.

Factor Analysis.

CHARTI scales	Perceived benefits factor	Perceived risks factor
Controllability of the hazard	0.275	−0.146
Knowledge of the hazard	0.004	0.281
Dread of the hazard	−0.042	0.660
Seriousness of the hazard	−0.075	0.779
Onset of the hazard	0.086	−0.003
Duration of the hazard	−0.102	0.211
General probability associated with the hazard	−0.079	0.745
Personal probability associated with the hazard	−0.092	0.774
Duration of benefit	0.457	−0.016
Onset of benefit	0.368	−0.110
Desire for benefit	0.742	−0.004
Benefit—anger management	0.612	0.030
General probability of benefit	0.538	0.203
Personal probability of benefit	0.766	−0.103
Magnitude of benefit	0.691	0.037
Perceived participation	0.217	0.196
Accessibility	0.518	−0.223
Sensation	0.773	0.043
Boredom	0.638	−0.068
Duration of decision making	0.391	−0.087
Concealment	−0.109	0.505
Conflict with family values	0.033	0.500
Peer influence	0.513	−0.127
Feedback	0.543	−0.164
Need for regulation	−0.022	0.393

Note. The factor analysis was based on extraction of principle components with varimax rotation yielding two factors: “Perceived Benefit” and “Perceived Risk.” Bolded values represent factor loadings above 0.4 for the variables.

Adult ADHD Self-Report Scale in Hebrew (ASRS-v1.1) (Kessler et al., 2005; Zohar & Konfortes, 2010). The severity of the participants’ ADHD symptoms was measured using the self-report ASRS-v1.1 scale. This scale contains 18 items, directly relating to the DSM–IV TR (American Psychiatric Association, 2000) diagnostic criteria. The Hebrew version (ASRS-v1.1; Zohar & Konfortes, 2010) has high reliability, α = .89 and acceptable levels of sensitivity and specificity, 62.7% and 68%, respectively. In a validation study of the Hebrew version, a sum score of 51 was indicated as a clinical cutoff (Zohar & Konfortes, 2010). A total score of the ASRS was created by summing the responses to all 18 items (score range 18–90). Two additional scores were created by calculating the sum of the 9 inattention items and the sum of the 9 hyperactivity/impulsivity items separately.

The Hebrew version of Diagnostic Interview for ADHD in Adults 2.0 (DIVA 2.0; Kooij & Franken, 2010): The DIVA 2.0 is a semi-structured diagnostic interview assessing ADHD in adults. It includes an evaluation of ADHD symptoms and an evaluation of impairment in functionality at present and in childhood. It is based on the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; American Psychiatric Association, 2000) criteria for ADHD and uses concrete, realistic examples for 18 different symptoms. All participants underwent assessment using the DIVA and other available which included reports, observations from parents, spouses, siblings, close friends, and teachers and, where necessary, consultation with a licensed neuropsychologist (Y.P.). For this study, the criteria were adapted to the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association, 2013). The DIVA has a sensitivity of 90.0% and a specificity of 72.9% (Pettersson et al., 2018).

The Hebrew version of the Structured Clinical Interview for DSM-IV Disorders (SCID; First et al., 1996): The SCID was used to determine the presence of current and lifetime psychiatric disorders. The SCID achieves a good-to-excellent inter-rater reliability (Lobbestael et al., 2011). It covers the diagnostic criteria for most axis-I appear in DSM-IV (APA, 1994) disorders.

As the SCID-5 has no official translation into Hebrew, participants were interviewed using the Hebrew version of the DSM-IV version of the SCID. The interviewers were therefore instructed to make detailed notes on all the symptoms for all disorders, focusing on information that deemed essential for making the diagnoses according to the DSM-5. Using the SCID protocol and the notes taken during the interview, the presence or absence of each disorder was determined for each participant (see Table 2) in consultation with a licensed neuropsychologist (Y.P.). We confirmed that all DSM-5 diagnostic criteria were met noting that the prevalence of Somatic Disorders might have been underestimated due to the significant revision of this category in converting the SCID-DSM-IV version to DSM-5. Following the exclusion of participants who met the exclusion criteria, disorders were combined into the remaining DSM-5 categories: obsessive-compulsive and related disorders, anxiety and related disorders, somatic symptom and related disorders, trauma and stress-related disorders, depressive disorders, feeding and eating disorders, substance use and addictive disorders. In addition, participants were asked to report of any history of a diagnosis of learning disabilities.

Table 2.

Demographic and Clinical Characteristics.

	Mdn	(25%–75%)
Age	24.0	(23.0–27.0)
SES ^PPR	1.00	(1.00–1.50)
ASRS
Total sum	46.08	(36.00–57.96)
Inattention	48.06	(36.00–61.92)
Hyperactivity	43.92	(34.02–55.98)
	n	%
Gender
Females	108	54%
Males	92	46%
Religiosity
Secular	107	53.5%
Traditional	28	14.0%
Religious	64	32.0%
Ultraorthodox	1	0.5%
Family status
Single	151	75.5%
Married	49	24.5%
Education
Less than high-school	1	0.5%
High-school	10	5.0%
Continuing education	10	5.0%
B.A.	160	80.0%
M.A.	17	8.5%
Ph.D.	2	1.0%
Learning disabilities ^SR	53	26.5%
Depressive disorders (past)	55	27.5%
Past (control/ADHD)	14/41	13.6/42.3%
Anxiety and related disorders	62	31.0%
Current (control/ADHD)	25/30	24.3%/30.9%
Past (Control/ADHD)	3/4	2.9%/4.1%
Obsessive Compulsive and related disorders	14	7%
Current (control/ADHD)	6/8	5.8%/6.2%
Past (control/ADHD)	0/0	0%/0%
*Substance use and addictive disorders	27	13.5%
Current (control/ADHD)	0/4	0%/4.1%
Past (control/ADHD)	4/22	3.9%/22.7%
Somatic symptoms disorders	5	2.5%
Current (control/ADHD)	3/2	2.9%/2.1%
Past (control/ADHD)	0/0	0%/0%
Feeding and eating disorders	16	8%
Current (control/ADHD)	2/7	1.9%/7.2%
Past (control/ADHD)	1/6	1%/6.2%
Trauma and stress-related disorders (past)	16	8%
Past (control/ADHD)	3/13	2.9%/13.4%
**Medication History for ADHD	82	41.0%
Current use (ADHD)	66	33.0%
Occasionally	47	23.5%
Daily	19	9.5%

Note. Categorical demographic data were reported in Shoham et al. (2019). ADHD = attention deficit/hyperactive disorder; Learning disabilities^SR = history of a diagnosis of learning disabilities was based on self-reports; SES^PPR = socioeconomic status was defined as people per room; RBs = risk-taking behaviors.

In the Substance use and addictive disorders category 3 participants appear both in current and past for different disorders (substance use disorder and alcohol use disorder), **Medication history relates to the ADHD group only.

Adult Risk-Taking Inventory (ARTI): We developed a self-report questionnaire in Hebrew (see English translation in Appendix A) for this study in order to measure adult ERBs. The questionnaire also probed the respondents’ perceptions of the risks and benefits of ERBs. The ARTI questionnaire consists of 40 items representing a wide range of real-life activities that carry the risk of producing harmful physical and nonphysical outcomes. About half of the items were adapted from the Domain-Specific Risk-Taking scale (DOSPERT: Blais, & Weber, 2006; Weber et al., 2002). Preliminary studies reveal that (1) the ARTI has good internal consistency (Cronbach’s α = .89), (2) the correlation between the ARTI and the DOSPERT score is high (r = .80), (3) the correlation between the ARTI and the ASRS scores is of medium size (r = .39), (4) the results of factor analysis are in accord with a unidimensional construct, and (5) test–retest reliability is high (r = .88).

The questionnaire asks respondents to rate (1) the likelihood that they would engage in each of a series of risky behaviors, using a 7-point rating scale (1 = “Extremely unlikely,” 7 = “Extremely likely”), (2) their perceptions of the risk of each behavior (1 = “Not at all risky,” 7 = “Extremely risky”), and (3) the benefit they expected to derive from engaging in each risky behavior (1 = “No benefit at all,” 7 = “Great benefit”). Indices of the ERB, the perceived risk, and perceived benefit were computed by averaging the corresponding scale scores.

A regression equation based on Weber et al. (2002) was used to predict the likelihoods ratings of the 40 risky behaviors included in ARTI made by each participant. The predictors were the perceived benefit and perceived risk of the behaviors as judged by each participant. The two regression coefficients (a, b) obtained in these analyses extracted, indexed the participant’s attitudes toward the perceived benefit and the perceived risk, respectively.

Characteristics of Adult Risk-Taking Inventory (CHARTI): A self-report questionnaire consisting of 25 characteristics of risky behaviors (see Appendix B) was developed in order to assess the psychometric model (Fischhoff et al., 1978). With this questionnaire, the respondents rated each risky behavior, with respect to all 25 characteristics. The goal of using the CHARTI was to detect the main factors and mediators of ERBs. The 25 characteristics were based on previous studies of risk-taking behavior in adults and adolescents. They included for example, dread from the risk, knowledge, probability, immediacy of benefit and hazard related to the risk, controllability, and thrill/boredom. Both extrinsic and intrinsic characteristics (which refer to outcomes and processes respectively) were included. The CHARTI questionnaire was administered after completing the ARTI questionnaire.

The CHARTI questionnaire required respondents to rate of each risky behavior on 25 characteristics. In order not to overload the respondents, we included in the CHARTI only 15 (out of the 40) risky behaviors that appeared in ARTI (see items marked with * in Appendix A). These 15 risky behaviors (RBs) were selected based on their prevalence in the ADHD population, as documented in the literature (i.e., substance use, gambling, injuries, financial investment, social, driving, sex, occupational, health, and well-being), and also on the basis of their psychometric properties (e.g., correlation with the ASRS in the validation sample of the ARTI). Each risky behavior involved more than one potential positive and one negative consequences. In order to control for individual views, only one consequence of each behavior was described (e.g., “To what extent are you afraid of contacting a sexually transmitted disease (STD) from engaging in unprotected sex?”). The consequences were derived from a pilot study, wherein people were asked to indicate the positive and negative consequences associated with each of the 15 RBs. We selected the ones that were mentioned most often in this pilot. The likelihood of engagement score was obtained by averaging the fifteen relevant risky behaviors (ARTI-15 RBs).

Validation of CHARTI

The internal consistency of all CHARTI-25 scales was good (Cronbach’s α = .66–.87). Exploratory factor analysis (see Table 1) was conducted using principal-axis factoring and varimax rotation on the CHARTI set of 25 characteristics. The Cattell scree test (Cattel, 1966) accorded with a 2-factor solution that accounts for 32.9% of the total variance in the items.

The factor analysis yielded two factors: The first factor, labeled “Perceived benefit,” included the following characteristics: desire for benefit, benefit—anger management, general probability of benefit, personal probability of benefit, magnitude of benefit, duration of benefit, accessibility, sensation, boredom, peer influence, and feedback. The second factor, labeled “Perceived risk,” included the following characteristics: dread of the hazard, seriousness of the hazard, general probability associated with the hazard, personal probability associated with the hazard, concealment, and conflict with family values.

Construct validity was tested by correlating the factor scores for the CHARTI with the ARTI variables. The correlational analysis revealed significant positive correlations between the perceived benefit factor of the CHARTI and the likelihood 40 RBs (r = .533, p < .01) and the perceived benefit of the ARTI (r = .636, p < .01) scales. The perceived risk factor of the CHARTI negatively correlated with the likelihoods 40 RBs (r = −.318, p < .01) scale and positively correlated with the perceived risk of the ARTI scale (r = .525, p < .01).

In order to calculate the individual factor scores for each participant, each score on the 15 items in all 25 scales was multiplied by each of the two factor’s varimax loadings. The individual data on each activity across the 25 scales were summed up and standardized. This provided two scales of standardized perceived risk and benefit of the CHARTI for each participant. To calculate the variables of attitude to the “Perceived Benefit” and to “Perceived Risk” factors, the ARTI likelihood-15RBs scores were regressed on the factors of the CHARTI, for each participant, using the Weber regression equation (Weber et al., 2002).

Procedure

After signing the consent form, all participants completed the ARTI-likelihood scale and also probed the participants’ perceptions of risk and benefit of the ARTI list of behaviors. The perceptions of risk and benefit questionnaires were presented in a counterbalanced order. Following the completion of these scales, participants completed the demographic questionnaire and the ASRS. Next, participants were presented with the CHARTI questionnaire. They rated each of the 15 risky behaviors with respect to each the 25 characteristics described above. The scales were presented in a randomized order to each participant (using the website http://www.endmemo.com/math/randomorder.php). Finally, participants were interviewed using the DIVA and SCID protocols. The interviews took place in places that were quiet, private and where the participants felt comfortable.

Participants could choose their preferred format of completing the questionnaires, using paper and pencil, in an interview format, or a combination of both. The full procedure required anywhere from 1.5 to 8 hr. The number and durations of the meetings were determined by the participants. The experimenter held up to four meetings per participant, with each one lasting anywhere between 1.5 and 6 hr for the control group and 2.5 to 8 hr for the ADHD group. Beverages and refreshments were available during the sessions. The participants received monetary compensation of approximately $40 for their participation. Participants who were students could also receive course credit.

Statistical Analysis

Skewness and kurtosis tests were used to assess the normality of the continuous variables. Since many of the variables were not normally distributed, non-parametric tests were used, including Spearman rho correlations and bootstrapping. A Spearman’s rho correlation was calculated to evaluate the relationship between the total ASRS scores and the demographic variables, other psychiatric disorders, and between all the ARTI and CHARTI variables. Correlations between the ADHD and all ARTI and CHARTI variables were also examined. All tests of significance were two-sided.

The primary analysis examined whether the total scores of the perceived benefits, perceived risks, attitudes toward perceived risks, and attitudes toward perceived benefits of the risky behaviors (included in ARTI) mediated the relation between ADHD severity level (total ASRS scores) and the total score of the likelihoods of risk-taking. Demographic variables that were correlated with ADHD (measured in terms of the ASRS scores) and the various psychiatric comorbidities were co-variated.

The direct and indirect effects of ADHD symptoms on the likelihood-40RBs in the psychological risk-return model and on the likelihood-15RBs in the psychometric model were calculated using the multiple mediation approach and the SPSS macro (PROCESS, Model 6) provided by Hayes (2017). The multiple mediation model (Preacher & Hayes, 2008) involves analyses of the total indirect effect (the aggregate of all the mediators examined) and of the indirect effects of the specific mediators. The significance of the indirect effects was tested via a commonly performed bootstrap analysis, which allows for greater statistical power in multiple mediator analyses. The bootstrap analysis does not assume multivariate normality in the sampling distribution, only that the sample is representative of the population (Mallinckrodt et al., 2006; Preacher & Hayes, 2008; Williams & MacKinnon, 2008). Mediation is demonstrated when the indirect effect is significant (i.e., when the 95% bias-corrected confidence interval for the estimated parameter does not contain zero). All analyses were conducted using SPSS 25.0, including an SPSS macro designed for assessing multiple mediation models (Preacher & Hayes, 2008). The two-tailed significance level was set at 0.05. We ran a Monte Carlo power analysis for indirect effects (Schoemann et al., 2017) using the standardized coefficients of the mediation analysis that were found in Shoham et al. (2016). It was indicated that 200 participants should enable the detection of a similar effect with a power of 97% and a level of statistical significance of 0.01.

Results

Demographic and Clinical Data

The demographic and clinical characteristics of the sample are presented in Table 2.

Correlations between ASRS and demographic and clinical variables

Spearman’s rho correlation coefficients were computed between the ASRS scores and the demographic and clinical variables. Higher levels of ADHD symptoms were negatively correlated with education (r = −.189, p < .01) and positively correlated with learning disabilities (r = .394, p <. 001), past depressive disorders (r = .302, p < .0001), and the following lifetime Obsessive/Compulsive and related Disorder (OCD) (r = .202, p < .01), substance use and addictive disorders (r = .258, p < .0001), feeding and eating disorders (r = .197, p < .01) categories in DSM-5. By contrast, ASRS score did not correlate with gender, age, religiosity, family status, current SES, past trauma and stress-related disorders, lifetime anxiety and related disorders, or the somatic symptoms disorders categories in the DSM-5. For the sake of simplicity, education, learning disabilities, and all comorbid psychiatric disorders were co-variated in the mediation analysis. In total nine factors were covariate.

Descriptive statistics

Table 3 presents the descriptive statistics for the ARTI’s and the CHARTI’s likelihood, perception and attitude score scales. The variables conveying attitude scores were not normally distributed and, hence, were described in terms of non-parametric statistics.

Table 3.

Descriptive Statistics of the ARTI and CHARTI.

ARTI	Mean	SD	Skewness	Kurtosis
Likelihood-40 RBs	3.30	0.79	0.134	−0.394
Perception of risk	4.32	0.62	−0.221	−0.181
Perception of benefit	2.85	0.76	0.459	0.499
	Mdn	(25% to 75%)	Skewness	Kurtosis
Attitude toward perceived risk	−0.30	(–0.47 to –0.16)	0.047	3.108
Attitude toward perceived benefit	0.49	(0.31 to 0.67)	−1.162	2.561
CHARTI	Mean	SD	Skewness	Kurtosis
Likelihood- 15 RBs	2.96	0.86	0.340	−0.336
Perception of risk (factor)*	0.0000	1.000	−0.004	0.297
Perception of benefit (factor)*	0.0000	1.000	−0.278	−0.416
	Mdn	(25% to 75%)	Skewness	Kurtosis
Attitude toward perceived risk	−0.10	(–0.16 to –0.05)	−1.574	7.555
Attitude toward perceived benefit	0.09	(0.04 to 0.14)	−0.248	1.616

Note. *Standardized (see Validation of CHARTI); N = 200 (108 females, 92 males); RBs = risk taking behaviors; ASRS = Adult ADHD Self Report Scale; ARTI = Adult risk taking inventory; CHARTI = Characteristics of Adult risk taking inventory; SD = standard deviation; Mdn = median.

Order effect: The order in which the ARTI scales of perceptions of risk and benefit were completed had no effect upon either the ratings of the perceived risk (t₍₁₉₈₎ = −1.466, p = .144) or the perceived benefit (t₍₁₉₈₎ = 0.994, p = .322).

Correlations between ERB and clinical variables

Spearman’s rho correlation coefficients were computed between the ERB score and the clinical variables. Higher ERB scores were positively correlated with lifetime obsessive compulsive and related disorders (r = .143, p <. 05), and substance use and addictive disorders (r = .298, p < .0001). By contrast, ERB score did not correlate with past depressive disorders, trauma and stress-related disorders, lifetime anxiety and related disorders, somatic symptoms disorders, and, feeding and eating disorders categories in the DSM-5 nor with learning disabilities.

Hypotheses testing: Mediation analysis

The mediation analyses for both the psychological risk-return model and the psychometric model included psychiatric disorders, learning disabilities, and education as covariates. The path analysis depicts the direct effects and indirect effects of ADHD symptoms on risk-taking, through their effects on the perceived benefits and risks and the attitudes toward these perceptions, as they were operationalized in these models (see Figure 1). Together the model accounted for 59.6% and 55.7% (p < .0001) of the variability in risky behavior for the psychological risk-return model and for the psychometric model, respectively. The standardized regression coefficients of ADHD symptoms in predicting ERBs (not considering any mediators) were statistically significant (p < .001) in both models. The bootstrapped standardized indirect effect of the pathway mediated by perceived benefit in both models were significant. The indirect effects of the pathway mediated by attitude toward perceived benefit was not significant in both models. The indirect effects of the pathway mediated by perceived risk was not significant in both models. The indirect effect of the pathway mediated by the attitude toward perceived risk was not significant in the psychological risk-return model and was statistically significant in the psychometric model (p < .001). ADHD symptoms predicted risk-taking, even after accounting for the indirect effects in both models. Table 4 shows the coefficients and confidence intervals (CIs).

Figure 1a.

Path analyses predicting engagement in risky behaviors (ERB, using the likelihood scale) from ADHD, using the psychological risk-return model in mediation.

Figure 1b.

Path analyses predicting engagement in risky behaviors (ERB, using the likelihood scale) from ADHD, using psychometric model in mediation.

Table 4.

Mediation Coefficients and Confidence Intervals (CIs).

	Model R²	Indirect effect:				Direct effect
	Model R²	Perceived benefit	Perceived risk	Attitude to perceived benefit	Attitude to perceived risk	Direct effect
ARTI	59.6	0.143 95% CI [0.064, 0.249]	0.008 95% CI [−0.026, 0.049]	−0.011 95% CI [−0.038, −0.008]	0.002 95% CI [−0.006, 0.017]	0.255 95% CI [0.144, 0.366]
CHARTI	55.7	0.132 95% CI [0.079, 0.199]	−0.004 95% CI [−0.050, 0.044]	−0.022 95% CI [−0.064, −0.015]	0.065 95% CI [0.022, 0.127]	0.214 95% CI [0.092, 0.335]

Note. Final mediation path analysis predicting risky behavior. The values shown are the standardized regression coefficients of the indirect and direct effects (taking into account other mediators) of ADHD upon risky behavior. In bold are the variables that were significant (the respective 95% bias-corrected confidence intervals did not contain zero in bootstrap analyses). The covariates of other psychiatric disorders, education and learning disabilities are not shown in the table for the sake of brevity. N = 200 (108 females, 92 males).

Further analysis tested the role of ADHD dimensions in the mediation models. A model including the hyperactivity symptoms as a predictor, and the inattention symptoms as a covariate, yielded similar results for both models. On the other hand, a model including the inattention symptoms as the predictor, and the hyperactivity symptoms as a covariate, revealed no indirect effects, but showed a significant direct effect (the relevant data are reported in Appendix C).

Discussion

This study examined the links between engagement in risky behaviors (ERBs), perceptions of risk and benefit, and attitudes toward the perceived risks and benefits. We analyzed these links based on two different behavioral decision models.

Our first hypothesis that ADHD symptoms predict ERB was confirmed. The finding of this link accords with a previous study using the DOSPERT questionnaire in the general population (Shoham et al., 2016) and complements other research that documented an increase in engagement in specific risky behaviors among individuals with ADHD (for review, Pollak et al., 2019).

Our second hypothesis was confirmed. We found that perceptions of benefit, but not perceptions of risk accounted for the link between ADHD symptoms and ERB among adults. Both measures of perceived benefit were positively correlated with ERB, such that the greater the perceived benefit of the activity, the higher the likelihood of engaging in it. These findings were replicated with different measurements and are also in accord with studies reporting that individuals with ADHD tend to endorse the positive aspects of risky behaviors (Bruce et al, 2009; Foster et al., 2012; Spiegel & Pollak, 2019). Both measures of perceived risk were negatively correlated with ERB, such that the greater the perceived risk of the activity, the lower the likelihood of engaging in it. However, these measures of perceived risk did not mediate the linkage between ADHD and ERB. This accords with our previous study of the general adult population (Shoham et al., 2016), but not with several other studies reporting that ERB was correlated with lower probabilities and less severe consequences among individuals with ADHD (Farmer & Peterson, 1995; Foster et al., 2012; Pedersen et al., 2014).

Our third hypothesis on role of attitudes toward perceived risks and attitudes toward perceived benefits was only partially confirmed. The findings on the attitudes toward the perceived risk (ARTI) accord with the results of our previous study of the general population which used Weber’s model and the DOSPERT questionnaire (Shoham et al., 2016). In contrast with our previous findings, weaker attitudes toward the perceived benefits (in ARTI) did not significantly correlate with ADHD symptoms nor did they mediate ERB. In the psychometric model wherein respondents rated 25 characteristics of risky behaviors, the correlation between ERB and ADHD symptom level was mediated by the negative attitude toward perceived risk, but not by the attitudes toward the perceived benefit (in CHARTI). Notably, post hoc analysis suggested that attitudes toward perceived risks mediated the link between hyperactivity, rather than inattention, and ERB

These findings on attitudes toward perceived risks reveal a complex picture due to differences between the psychological risk-return and the psychometric models. The correlation between ADHD symptom-levels and attitudes toward the perceived risks (in CHARTI) is interpreted as risk-seeking. This does not accord with our hypothesis, and is not supported by a recent meta-analysis and previous empirical studies (Dekkers et al., 2018). The discrepancy in the results obtained with the psychological risk-return model and the psychometric model was apparently due to differences in the way the attitude variables were determined. The attitude variable in ARTI toward perceived risk was defined as the weight the participants assigned to their general “gut feeling” about the risk. Likely, participants, regardless of their ADHD level, automatically focused on the characteristics of the behavior that were subjectively most important to them. In contrast, with the psychometric model the attitudes were derived from the factor analysis conducted on the full series of items—the participants’ ratings of all the characteristics of the various kinds of risky behaviors (CHARTI). It is conceivable that in judging these characteristics, the participants’ attention was drawn to factors other than ones that receive their attention when making gut-level judgments of risk in response to the ARTI questionnaire.

To conclude, the mediation analysis supports a model in which more ADHD symptoms are associated with stronger perceptions of the anticipated benefits of engaging in risky behaviors, which in turn predict greater engagement in such behaviors. Attitudes toward the perceived risk (CHARTI) also play a role, meaning that adults with ADHD are less averse to risk.

Salience of the Benefits of Risky Behaviors

Why might adults with ADHD rate highly the benefits? This tendency might stem from personality traits that are common among people with ADHD, including delay aversion (Sonuga-Barke, 2005), sensation-seeking (Graziano et al., 2015), and the deficit shown in atypical sensitivity to reinforcement (Luman et al., 2010). The latter was described as one of the core deficits in ADHD (Luman et al., 2010). All of these traits have been related to dopamine deficiency which is known to cause continuous searches for reward (Cortese et al., 2007; Tripp & Wickens, 2008).

Delay aversion: One theoretical account postulates that impulsive choice of smaller immediate rewards over larger, but delayed rewards in ADHD, is the result of a two-component process (Sonuga-Barke, 2005). People with ADHD may have a steep delay of reinforcement gradient, leading to larger discounting of the value of late rewards. In addition, reward delay produces negative affect, which in turn, motivates individuals with ADHD to avoid settings where patience is needed (Marco et al., 2009; Sonuga-Barke et al., 1992; Van Dessel et al., 2018; Wilbertz et al., 2013). In our study, the 15 risky behaviors included in CHARTI were rated as having immediate benefits and delayed consequences. The desire to attain immediate satisfaction thus guided the respondents’ perceptions of the benefits of the risky behaviors.

Sensation seeking: Sensation seeking is described as a tendency to pursue intense novel and complex experience, despite the risk of suffering physical and nonphysical damages (Zuckerman, 2008). Sensation seeking induces continuous engagement in a variety of risky behaviors that would bring about satisfying rewards (Zuckerman, 2007). Sensation seeking was found to mediate ADHD-associated risk-taking (Graziano et al., 2015) and to predict weighing the benefits of risk as higher than their costs (Maslowsky et al., 2011). Sensation seeking is a prominent motive in ADHD (Graziano et al., 2014) and could thus impact the perceptions of benefits.

The display of greater sensation-seeking in ADHD populations has been attributed to a state of continuous under-arousal or disproportional boredom-evasion. In both instances, activities that induce positive arousal could be perceived as more rewarding. Notably, the preference for immediate rewards and sensation seeking are more highly correlated with the hyperactivity dimension than with the inattention dimension of ADHD (Lopez et al., 2015; Scheres et al., 2010). These observations are in accord with our findings that hyperactivity symptoms is correlated with the perceived benefit of risky behavior.

Atypical sensitivity to reinforcement: Reinforcement (e.g., reward, cost, feedback) is necessary for acquiring the executive skills and knowledge that are necessary for shaping and tuning judgment and behavior. Individuals with ADHD show abnormal behavior change followed reinforcement (Alsop et al., 2016; Johansen, et al, 2009). Their high sensitivity to rewards increases their motivation to engage to the activity.

While the explanations offered above (delay aversion, sensation seeking and atypical sensitivity to reinforcement) highlight neurobiological aspects, other explanations relates to the unique life experiences of people with ADHD. Their functional impairments in daily life leave them ill-equipped to cope various real-life challenges. Therefore, the benefits of risky behaviors thus become attractive to them. For instance, people with ADHD tend to show up late at work more than people without ADHD and thus bear the negative consequences of creating poor reputation, poor relationships with their superiors, and eventually risking job loss. Hence for them it may well worth the risk of taking risky actions, such as speeding up in driving to work. For people without ADHD, late arrival to work should carry less severe consequences. In general, exaggerated perceptions of benefits serve to increase the frequency of risky behaviors in daily life. This suggests a vicious circle, whereby risky behaviors make their benefits more salient, thereby encouraging their repetition (which could go on, at least as long the person is lucky enough not to suffer potential negative consequences).

In sum, for decades the leading paradigm for investigating the association between ADHD and risky behaviors involved choices between gambles. Much of the ADHD research showed no evidence for a link between ADHD and risk-seeking per se (Dekkers et al., 2018). Our shift to risk perception paradigms has yielded new types of clinical data that allow us to understand better the increased ERB among adults with ADHD. This new approach offers a new perspective on the link between ADHD and ERB. It implies that we should switch from a clinical perspective to a decision-making perspective, focusing on the decision-maker rather than on decision outcomes, and on risk perceptions rather than on risk attitudes. Most importantly, we might want to shift our attention from the risks associated with behavior to the study of the benefits associated with the behavior.

Clinical Implications

The documented risk-taking behavior among people with ADHD is a matter of concern, since it leads them to under-achieve and to suffer more health problems than normal. This study offers clinicians new insights into the tendency of individuals with ADHD to engage in risky behaviors. The finding that perception of benefit mediates the link between ADHD and engagement in risky behavior has implications for clinicians. In treating risk-taking, clinicians should attend to the ways their patients view the positive outcomes, rather than how they assess the potential risks. Therapeutic discourse could thus focus on the benefits attained from risky behaviors, in attempt to help patients develop more fine-tuned decision making skills. Individuals with ADHD should thereby gain better understanding their own and others’ actions.

In addition, the tools developed here could provide clinicians with means for intervention, including scales for measuring the perceptions of (and attitudes toward) the benefits and risks associated with risky behaviors. Finally, researchers could also use the tools developed here to investigate the effects of medical, psychological, and educational programs designed to treat increased risk-taking in individuals with ADHD, keeping in mind that risk-taking should not always be avoided, but rather needs to be managed.

Limitations

There are several limitations to this study. First, we relied on convenience sampling, so the age distribution was limited and individuals with higher education were over-represented, limiting the generalization to the entire ADHD population. Second, the tendency to engage in risky behaviors was assessed on the basis of self-reports, which were not validated by collateral reports. Third, attitudes toward risk were assessed on the basis of the 15 behaviors included the CHARTI, limiting the generalization to these activities. Fourth, we used a clinical sample in which nearly half were diagnosed with ADHD, most of whom had a history of medical treatment for ADHD. This is not surprising since individuals with such a diagnosis are often referred to treatment, with medical treatment being common for those individuals who are referred to neurologists. Importantly individuals who took medications for ADHD during the 24-hr period prior to participation were excluded. Clearly, testing the association between medical treatment and the ADHD-ERB linkage was beyond the scope of this study.

Finally, the completion of the set of questionnaires and the interview took relatively long time. We set as goal of making the procedure as comfortable as possible for the respondents. Therefore, at the start of the meeting detailed information was provided about the study. We let the respondents choose a convenient, private location for testing and also made available refreshments. Furthermore, we allowed the respondents to determine the number of meetings and the length of each. The researcher was present throughout the procedure. These means served to secure the participants’ genuine cooperation.

Conclusion

Only a handful of past studies have evaluated the role of perceptions of benefits and risks in ADHD. These studies tended to be descriptive and lacked a theoretical framework. To our knowledge, ours is the first study to address the link between ADHD and overall levels of risky behavior, using frameworks derived from behavioral decision theory. Applying two different frameworks, our study reveals how perceptions of benefit are linked to the decision to engage in increased risky behavior among adults with ADHD.

Supplemental Material

Supplementary_Material – Supplemental material for What Drives Risky Behavior in ADHD: Insensitivity to its Risk or Fascination with its Potential Benefits?

Supplemental material, Supplementary_Material for What Drives Risky Behavior in ADHD: Insensitivity to its Risk or Fascination with its Potential Benefits? by Rachel Shoham, Edmund Sonuga-Barke, Ilan Yaniv and Yehuda Pollak in Journal of Attention Disorders

Footnotes

Acknowledgements

The authors thank Sara Bar for her help in recruiting the participants for this study.

Author Contributions

Y.P., I.Y., E.S.B. and R.S. designed the study, R.S. collected and input the data. Y.P. and R.S. analyzed the data, R.S. wrote the paper. Y.P., E.S.B. and I.Y. reviewed and revised the paper.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Prof. Edmund Sonuga-Barke: Speaker fees, consultancy, research funding and conference support from Shire Pharma. Speaker fees from Janssen Cilag, Consultancy from Neurotech solutions, Aarhus University, Copenhagen University and Berhanderling, Skolerne, Copenhagen, KU Leuven. Book royalties from OUP and Jessica Kingsley. Grants awarded from MRC, ESRC, Wellcome Trust, Solent NHS Trust, European Union, Child Health Research Foundation New Zealand, NIHR, Nuffield Foundation, Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO), MQ-Transforming Mental Health.

Prof. Ilan Yaniv, Dr. Yehuda Pollak and Dr. Rachel Shoham report no competing interests.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was conducted with the financial support of an internal grant of the Authority for Research and Development, the Hebrew University of Jerusalem

Supplemental Material

Supplemental material for this article is available online.

ORCID iDs

Rachel Shoham

Yehuda Pollak

Author Biographies

Rachel Shoham, received her PhD from the Hebrew University of Jerusalem. She is a lecturer at the department of special education in the Talpiot College, Holon, Israel.

Edmund Sonuga-Barke, is the professor of Developmental Psychology, Psychiatry and Neuroscience at the Kings College London, UK. He was elected fellow of the British Academy (FBA—2018) and the Academy Of Medical Sciences (FMedSci—2016) for his research on the causes and treatment of ADHD—which has spanned thirty years.

Ilan Yaniv, is professor of psychology. He received his PhD at the University of Michigan (Ann Arbor) and is a faculty member in the Department of Psychology and the Federmann Center for the Study of Rationality at the Hebrew University of Jerusalem.

Yehuda Pollak, is an assistant professor at the Seymour Fox School of Education, the Hebrew University of Jerusalem, Israel.

References

Abbott-Chapman

Denholm

(2001). Adolescents’ risk activities, risk hierarchies and the influence of religiosity. Journal of Youth Studies, 4(3), 279–297. https://doi.org/10.1080/13676260120075428

Adler

N. E.

Boyce

Chesney

M. A.

Cohen

Folkman

Kahn

R. L.

Syme

S. L.

(1994). Socioeconomic status and health: the challenge of the gradient. American Psychologist, 49(1), 15–24. https://doi.org/10.1037/0003-066X.49.1.15

Alsop

Furukawa

Sowerby

Jensen

Moffat

Tripp

(2016). Behavioral sensitivity to changing reinforcement contingencies in attention-deficit hyperactivity disorder. Journal of Child Psychology and Psychiatry, 57(8), 947–956. https://doi.org/10.1111/jcpp.12561

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., Text Revised). Author.

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Association.

Ariely

Loewenstein

(2006). The heat of the moment: The effect of sexual arousal on sexual decision making. Journal of Behavioral Decision Making, 19(2), 87–98. https://doi.org/10.1002/bdm.501

Ariely

Zakay

(2001). A timely account of the role of duration in decision making. Acta Psychologica, 108(2), 187–207. https://doi.org/10.1016/S0001-6918(01)00034-8

Benthin

Slovic

Severson

(1993). A psychometric study of adolescent risk perception. Journal of Adolescence, 16(2), 153–168. https://doi.org/10.1006/jado.1993.1014

Blais

A. R.

Weber

E. U.

(2006). A Domain-Specific Risk-Taking (DOSPERT) scale for adult populations. Judgment and Decision Making, 1(1), 33–47.

10.

Boyer

T. W.

(2006). The development of risk-taking: A multi-perspective view. Developmental Review, 26(3), 291–345. https://doi.org/10.1016/j.dr.2006.05.002

11.

Brevers

Bechara

Cleeremans

Noël

(2013). Iowa Gambling Task (IGT): twenty years after–gambling disorder and IGT. Frontiers in Psychology, 4, 665. https://doi.org/10.3389/fpsyg.2013.00665

12.

Brogan

A. M. Y.

Hevey

Pignatti

(2010). Anorexia, bulimia, and obesity: Shared decision making deficits on the Iowa Gambling Task (IGT). Journal of the International Neuropsychological Society, 16(4), 711–715. https://doi.org/10.1017/S1355617710000354

13.

Bron

T. I.

Bijlenga

Breuk

Michielsen

Beekman

A. T.

Sandra

J. J.

(2018). Risk factors for adverse driving outcomes in Dutch adults with ADHD and controls. Accident Analysis & Prevention, 111, 338–344. https://doi.org/10.1016/j.aap.2017.12.011

14.

Bruce

B. S.

Ungar

Waschbusch

D. A.

(2009). Perceptions of risk among children with and without attention deficit/hyperactivity disorder. International Journal of Injury Control and Safety Promotion, 16(4), 189–196. https://doi.org/10.1080/17457300903306914

15.

Byrnes

J. P.

Miller

D. C.

Schafer

W. D.

(1999). Gender differences in risk taking: A meta-analysis. Psychological Bulletin, 125(3), 367–383. https://doi.org/10.1037/0033-2909.125.3.367

16.

Cattell

R. B.

(1966). The Scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276. https://doi.org/10.1207/s15327906mbr0102_10

17.

Cooper

A. C.

Woo

C. Y.

Dunkelberg

W. C.

(1988). Entrepreneurs’ perceived chances for success. Journal of Business Venturing, 3(2), 97–108. https://doi.org/10.1016/0883-9026(88)90020-1

18.

Cortese

Bernardina

B. D.

Mouren

M. C.

(2007). Attention-deficit/hyperactivity disorder (ADHD) and binge eating. Nutrition Reviews, 65(9), 404–411. https://doi.org/10.1301/nr.2007.sept.404 – 411

19.

Deakin

Aitken

Robbins

Sahakian

B. J.

(2004). Risk taking during decision-making in normal volunteers changes with age. Journal of the International Neuropsychological Society, 10(4), 590–598. https://doi.org/10.1017/S1355617704104104

20.

Dekkers

T. J.

Popma

van Rentergem

J. A. A.

Bexkens

Huizenga

H. M.

(2016). Risky decision making in attention-deficit/hyperactivity disorder: A meta-regression analysis. Clinical Psychology Review, 45, 1–16. https://doi.org/10.1016/j.cpr.2016.03.001

21.

Dekkers

T. J.

van Rentergem

J. A. A.

Huizenga

H. M.

Raber

Shoham

Popma

Pollak

(2018). Decision-making deficits in ADHD are not related to risk seeking but to suboptimal decision-making: Meta-analytical and novel experimental evidence. Journal of Attention Disorders, 1087054718815572.https://doi.org/10.1177/1087054718815572

22.

Farmer

J. E.

Peterson

(1995). Injury risk factors in children with attention deficit hyperactivity disorder. Health Psychology, 14(4), 325–332. https://doi.org/10.1037/0278-6133.14.4.325

23.

First

M. B.

Spitzer

R. L

Gibbon

Williams

J. B. W.

(1996). Structured Clinical Interview for DSM-IV Axis I Disorders, Clinician Version (SCID-CV). American Psychiatric Press, Inc.

24.

Fischhoff

Slovic

Lichtenstein

Read

Combs

(1978). How safe is safe enough? A psychometric study of attitudes towards technological risks and benefits. Policy Sciences, 9(2), 127–152. https://doi.org/10.1007/BF00143739

25.

Foster

Racicot

McGrath

J. J.

(2012). Attention-deficit/hyperactivity disorder subtype differentially predicts smoking expectancies in adolescents. Journal of Adolescent Health, 51(4), 393–399. https://doi.org/10.1016/j.jadohealth.2012.01.014

26.

Fox

C. R.

Tannenbaum

(2011). The elusive search for stable risk preferences. Frontiers in Psychology, 2, 298. https://doi.org/10.3389/fpsyg.2011.00298

27.

Gardner

D. M.

Gerdes

A. C.

(2015). A review of peer relationships and friendships in youth with ADHD. Journal of Attention Disorders, 19(10), 844–855. https://doi.org/10.1177/1087054713501552

28.

Giorgetta

Grecucci

Zuanon

Perini

Balestrieri

Bonini

Sanfey

A.G.

Brambilla

(2012). Reduced risk-taking behavior as a trait feature of anxiety. Emotion, 12(6), 1373–1383. https://doi.org/10.1037/a0029119

29.

Graziano

P. A.

Reid

Slavec

Paneto

McNamara

J. P.

Geffken

G. R.

(2015). ADHD symptomatology and risky health, driving, and financial behaviors in college: The mediating role of sensation seeking and effortful control. Journal of Attention Disorders, 19(3), 179–190. https://doi.org/10.1177/1087054714527792

30.

Hansson

S. O.

(2007). “Risk”. The Stanford Encyclopedia of Philosophy (First published Tue Mar 13, 2007; substantive revision Thu Jul 19, 2018), Zalta

Edward N.

(Ed.). http://plato.stanford.edu/entries/risk/

31.

Hayes

A. F.

(2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Publications.

32.

Jackson

J. N.

MacKillop

(2016). Attention-deficit/hyperactivity disorder and monetary delay discounting: A meta-analysis of case-control studies. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1(4), 316–325. https://doi.org/10.1016/j.bpsc.2016.01.007

33.

Jessup

R. K.

Bishara

A. J.

Busemeyer

J. R.

(2008). Feedback produces divergence from prospect theory in descriptive choice. Psychological Science, 19(10), 1015–1022. https://doi.org/10.1111/j.1467-9280.2008.02193.x

34.

Johansen

E. B.

Killeen

P. R.

Russell

V. A.

Tripp

Wickens

J. R.

Tannock

Williams

Sagvolden

(2009). Origins of altered reinforcement effects in ADHD. Behavioral and Brain Functions, 5(1), 7. https://doi.org/10.1186/1744-9081-5-7

35.

Kessler

R. C.

Adler

Ames

Demler

Faraone

Hiripi

Howes

M. J.

Jin

Secnik

Spencer

Ustun

T. B.

Walters

E.E.

(2005). The World Health Organization adult ADHD self-report scale (ASRS): A short screening scale for use in the general population. Psychological Medicine, 35(2), 245–256. https://doi.org/10.1017/S0033291704002892

36.

Kessler

R. C.

Adler

Barkley

Biederman

Conners

C. K.

Demler

Faraone

S. V.

Greenhill

L. L.

Howes

M. J.

Secnik

Spencer

Ustun

T. B.

Walters

E. E.

Zaslavsky

A. M.

(2006). The prevalence and correlates of adult ADHD in the United States: Results from the National Comorbidity Survey Replication. American Journal of Psychiatry, 163(4), 716–723. https://doi.org/10.1176/ajp.2006.163.4.716

37.

Klein

R. G.

Mannuzza

Olazagasti

M. A. R.

Roizen

Hutchison

J. A.

Lashua

E. C.

Castellanos

F. X.

(2012). Clinical and functional outcome of childhood attention-deficit/hyperactivity disorder 33 years later. Archives of General Psychiatry, 69(12), 1295–1303. https://doi.org/10.1001/archgenpsychiatry.2012.271

38.

Kooij

Francken

(2007). Diagnostisch interview voor ADHD (DIVA) bij volwassenen. The Hague, the Netherlands, DIVA Foundation, 2007. https://www.van-mastrigt.info/wp-content/uploads/2015/01/Diag-nostischInterviewVoorADHDbijvolwassenen2007.pdf

39.

Kooij

Franken

(2010). DIVA 2.0. Diagnostisch interview voor ADHD bij volwassenen [DIVA 2.0. diagnostic interview for ADHD in adults]. Den Haag, DIVA Foundation 2010. http://www.divacenter.eu/

40.

Lerner

J. S.

Keltner

(2001). Fear, anger, and risk. Journal of Personality and Social Psychology, 81(1), 146. https://doi.org/10.1037/0022-3514.81.1.146

41.

Lobbestael

Leurgans

Arntz

(2011). Inter-rater reliability of the structured clinical interview for DSM-IV Axis I Disorders (SCID I) and Axis II Disorders (SCID II). Clinical Psychology and Psychotherapy, 18(1), 75–79. https://doi.org/10.1002/cpp.693

42.

Lopez

Dauvilliers

Jaussent

Billieux

Bayard

(2015). A multidimensional approach of impulsivity in adult attention deficit hyperactivity disorder. Psychiatry Research, 227(2–3), 290–295. https://doi.org/10.1016/j.psychres.2015.03.023

43.

Luman

Tripp

Scheres

(2010). Identifying the neurobiology of altered reinforcement sensitivity in ADHD: A review and research agenda. Neuroscience & Biobehavioral Reviews, 34(5), 744–754. https://doi.org/10.1016/j.neubiorev.2009.11.021

44.

MacCrimmon

K. R.

Wehrung

D. A.

(1990). Characteristics of risk taking executives. Management Science, 36(4), 422–435. https://doi.org/10.1287/mnsc.36.4.422

45.

Mallinckrodt

Abraham

W. T.

Wei

Russell

D. W.

(2006). Advances in testing the statistical significance of mediation effects. Journal of Counseling Psychology, 53(3), 372–378. https://doi.org/10.1037/0022-0167.53.3.372

46.

Marco

Miranda

Schlotz

Melia

Mulligan

Müller

Andreou

Butler

Christiansen

Gabriels

Medad

Albrecht

Uebel

Asherson

Banaschewski

Gill

Kuntsi

Mulas

Oades

. . . Sonuga-Barke

E. J. S.

(2009). Delay and reward choice in ADHD: An experimental test of the role of delay aversion. Neuropsychology, 23(3), 367–380. https://doi.org/10.1037/a0014914

47.

Marcus

D. K.

Barry

T. D.

(2011). Does attention-deficit/hyperactivity disorder have a dimensional latent structure? A taxometric analysis. Journal of Abnormal Psychology, 120(2), 427–442. https://doi.org/10.1037/a0021405

48.

Maslowsky

Buvinger

Keating

D. P.

Steinberg

Cauffman

(2011). Cost-benefit analysis mediation of the relationship between sensation seeking and risk behavior among adolescents. Personality and Individual Differences, 51(7), 802–806. https://doi.org/10.1016/j.paid.2011.06.028

49.

McDonald

C. C.

Sommers

M. S.

Fargo

J. D.

Seacrist

Power

(2018). Simulated driving performance, self-reported driving behaviors, and mental health symptoms in adolescent novice drivers. Nursing Research, 67(3), 202–211. https://doi.org/10.1097/NNR.0000000000000270

50.

Mowinckel

A. M.

Pedersen

M. L.

Eilertsen

Biele

(2015). A meta-analysis of decision-making and attention in adults with ADHD. Journal of Attention Disorders, 19(5), 355–367. https://doi.org/10.1177/1087054714558872

51.

Nigg

J. T.

(2013). Attention-deficit/hyperactivity disorder and adverse health outcomes. Clinical Psychology Review, 33(2), 215–228. https://doi.org/10.1016/j.cpr.2012.11.005

52.

Patros

C. H.

Alderson

R. M.

Kasper

L. J.

Tarle

S. J.

Lea

S. E.

Hudec

K. L.

(2016). Choice-impulsivity in children and adolescents with attention-deficit/hyperactivity disorder (ADHD): A meta-analytic review. Clinical Psychology Review, 43, 162–174. https://doi.org/10.1016/j.cpr.2015.11.001

53.

Pedersen

S. L.

Harty

S. C.

Pelham

W. E.

Gnagy

E. M.

Molina

B. S.

(2014). Differential associations between alcohol expectancies and adolescent alcohol use as a function of childhood ADHD. Journal of Studies on Alcohol and Drugs, 75(1), 145–152. https://doi.org/10.15288/jsad.2014.75.145

54.

Pettersson

Söderström

Nilsson

K. W.

(2018). Diagnosing ADHD in adults: an examination of the discriminative validity of neuropsychological tests and diagnostic assessment instruments. Journal of Attention Disorders, 22(11), 1019–1031. https://doi.org/10.1177/1087054715618788

55.

Polderman

T. J.

Derks

E. M.

Hudziak

J. J.

Verhulst

F. C.

Posthuma

Boomsma

D. I.

(2007). Across the continuum of attention skills: a twin study of the SWAN ADHD rating scale. Journal of Child Psychology and Psychiatry, 48(11), 1080–1087. https://doi.org/10.1111/j.1469-7610.2007.01783.x

56.

Pollak

Dekkers

T. J.

Shoham

Huizenga

H. M.

(2019). Risk-taking behavior in Attention Deficit/Hyperactivity Disorder (ADHD): A review of potential underlying mechanisms and of interventions. Current Psychiatry Reports, 21(5), 33. https://doi.org/10.1007/s11920-019-1019-y

57.

Pollak

Poni

Gershy

Aran

(2017). The role of parental monitoring in mediating the link between adolescent ADHD symptoms and risk-taking behavior. Journal of Attention Disorders, 24(8), 1141–1147. https://doi.org/10.1177/1087054717725875

58.

Polanczyk

G. V.

Willcutt

E. G.

Salum

G. A.

Kieling

Rohde

L. A.

(2014). ADHD prevalence estimates across three decades: An updated systematic review and meta-regression analysis. International Journal of Epidemiology, 43(2), 434–442. https://doi.org/10.1093/ije/dyt261

59.

Preacher

K. J.

Hayes

A. F.

(2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.87

60.

Ramrakha

Caspi

Dickson

Moffitt

T. E.

Paul

(2000). Psychiatric disorders and risky sexual behaviour in young adulthood: Cross sectional study in birth cohort. BMJ, 321(7256), 263–266. https://doi.org/10.1136/bmj.321.7256.263

61.

Scheres

Tontsch

Thoeny

A. L.

Kaczkurkin

(2010). Temporal reward discounting in attention-deficit/hyperactivity disorder: The contribution of symptom domains, reward magnitude, and session length. Biological Psychiatry, 67(7), 641–648. https://doi.org/10.1016/j.biopsych.2009.10.033

62.

Schoemann

A. M.

Boulton

A. J.

Short

S. D.

(2017). Determining power and sample size for simple and complex mediation models. Social Psychological and Personality Science, 8(4), 379–386. https://doi.org/10.1177/1948550617715068

63.

Shoham

Sonuga-Barke

E. J.

Aloni

Yaniv

Pollak

(2016). ADHD-associated risk taking is linked to exaggerated views of the benefits of positive outcomes. Scientific Reports, 6(1), 34833. https://doi.org/10.1038/srep34833

64.

Shoham

Sonuga-Barke

E. J.

Yaniv

Pollak

(2019). Attention deficit/hyperactivity disorder is associated with a wide-spread pattern of risky behavior across activity domains. Journal of Attention Disorders, 1087054719875786. https://doi.org/10.1177/1087054719875786

65.

Simon

Czobor

Bálint

Mészáros

Bitter

(2009). Prevalence and correlates of adult attention-deficit hyperactivity disorder: Meta-analysis. The British Journal of Psychiatry, 194(3), 204–211. https://doi.org/10.1192/bjp.bp.107.048827

66.

Sip

K. E.

Gonzalez

Taylor

S. F.

Stern

E. R.

(2018). Increased loss aversion in Unmedicated Patients with Obsessive–compulsive Disorder. Frontiers in Psychiatry, 8, 309. https://doi.org/10.3389/fpsyt.2017.00309

67.

Slovic

(1987). Perception of risk. Science, 236(4799), 280–285. https://doi.org/10.1126/science.3563507

68.

Slovic

(1992). Perception of risk: Reflections on the psychometric paradigm. In Golding

Krimsky

(Eds.), Theories of risk (pp. 117–152). Praeger.

69.

Slovic

Fischhoff

Lichtenstein

(1985). Characterizing perceived risk (1985). In Kates

R. W.

Hohenemser

Kasperson

J. X.

(Eds.), Perilous progress: Managing the hazards of technology (pp. 91–125). Westview.

70.

Smoski

M. J.

Lynch

T. R.

Rosenthal

M. Z.

Cheavens

J. S.

Chapman

A. L.

Krishnan

R. R.

(2008). Decision-making and risk aversion among depressive adults. Journal of Behavior Therapy and Experimental Psychiatry, 39(4), 567–576. https://doi.org/10.1016/j.jbtep.2008.01.004

71.

Sonuga-Barke

E. J.

(2005). Causal models of attention-deficit/hyperactivity disorder: From common simple deficits to multiple developmental pathways. Biological Psychiatry, 57(11), 1231–1238. https://doi.org/10.1016/j.biopsych.2004.09.008

72.

Sonuga-Barke

E. J. S.

Taylor

Sembi

Smith

(1992). Hyperactivity and delay aversion—I. The effect of delay on choice. Journal of Child Psychology and Psychiatry, 33(2), 387–398. https://doi.org/10.1111/j.1469-7610.1992.tb00874.x

73.

Spiegel

Pollak

(2019). ADHD and increased engagement in sexual risk-taking behavior: The role of benefit perception. Frontiers in Psychology, 10, 1043. https://doi.org/10.3389/fpsyg.2019.01043

74.

Strom

T. Q.

Leskela

James

L. M.

Thuras

P. D.

Voller

Weigel

Yutsis

Khaylis

Lindberg

Holz

K. B.

(2012). An exploratory examination of risk-taking behavior and PTSD symptom severity in a veteran sample. Military Medicine, 177(4), 390–396. https://doi.org/10.7205/MILMED-D-11-00133

75.

Tripp

Wickens

J. R.

(2008). Research review: dopamine transfer deficit: A neurobiological theory of altered reinforcement mechanisms in ADHD. Journal of Child Psychology and Psychiatry, 49(7), 691–704. https://doi.org/10.1111/j.1469-7610.2007.01851.x

76.

Van Dessel

Sonuga-Barke

Mies

Lemiere

Van der Oord

Morsink

Danckaerts

. (2018). Delay aversion in attention deficit/hyperactivity disorder is mediated by amygdala and prefrontal cortex hyper-activation. Journal of Child Psychology and Psychiatry, 59(8), 888–899. https://doi.org/10.1111/jcpp.12868

77.

Von Winterfeldt

Edwards

. (1986). Decision analysis and behavioral research. Cambridge University Press.

78.

Weber

E. U.

(2004). Perception matters: Psychophysics for economists. In Carrillo

Brocas

(Eds.), Psychology and Economics (pp. 165–176). Oxford University Press.

79.

Weber

E. U.

(2010). Risk attitude and preference. Wiley Interdisciplinary Reviews: Cognitive Science, 1(1), 79–88. https://doi.org/10.1002/wcs.5

80.

Weber

E. U.

Blais

A. R.

Betz

N. E.

(2002). A domain-specific risk-attitude scale: measuring risk perceptions and risk behaviors. Journal of Behavioral Decision Making, 15(4), 263–290. https://doi.org/10.1002/bdm.414

81.

Wilbertz

Trueg

Sonuga-Barke

E. J.

Blechert

Philipsen

Tebartz van Elst

(2013). Neural and psychophysiological markers of delay aversion in attention-deficit hyperactivity disorder. Journal of Abnormal Psychology, 122(2), 566–572. https://doi.org/10.1037/a0031924

82.

Willcutt

E. G.

(2012). The prevalence of DSM-IV attention-deficit/hyperactivity disorder: A meta-analytic review. Neurotherapeutics, 9(3), 490–499. https://doi.org/10.1007/s13311-012-0135-8

83.

Williams

MacKinnon

D. P.

(2008). Resampling and distribution of the product methods for testing indirect effects in complex models. Structural Equation Modeling: A Multidisciplinary Journal, 15(1), 23–51. https://doi.org/10.1080/10705510701758166

84.

Zohar

A. H.

Konfortes

(2010). Diagnosing ADHD in Israeli adults: the psychometric properties of the adult ADHD Self Report Scale (ASRS) in Hebrew. The Israeli Journal of Psychiatry and related sciences, 47(4), 308–315.

85.

Zohar

Erev

(2007). On the difficulty of promoting workers’ safety behaviour: Overcoming the underweighting of routine risks. International Journal of Risk Assessment and Management, 7(2), 122. https://doi.org/10.1504/IJRAM.2007.011726

86.

Zuckerman

(2007). Sensation-seeking and risk. American Psychological Association.

87.

Zuckerman

(2008). Personality and sensation seeking. In Boyle

G. J.

Matthews

Saklofske

D. H.

(Eds.), The SAGE handbook of personality theory and assessment, Vol. 1. Personality theories and models (pp. 379–398). Sage Publications, Inc.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.27 MB