Examining the Rate of Self-Reported ADHD-Related Traits and Endorsement of Depression,Anxiety,Stress,and Autistic-Like Traits in Australian University Students

Abstract

Objective: To investigate the rate of ADHD-related traits among young adults in an Australian university, and to examine whether higher endorsement of ADHD-related symptoms is associated with self-reported symptoms of depression, anxiety, distress, and autistic-like traits. Method: In total, 1,002 students aged 17 to 25 years completed the Conners’ Adult ADHD Rating Scale (CAARS), the Depression, Anxiety, and Stress Scale (DASS), and the Autism Quotient (AQ). Results: About 17.3% of students reported “at-risk” levels of ADHD-related symptoms. Regression analyses revealed that CAARS scores explained unique variance in self-reported levels of depression, anxiety, stress, and autism-related traits. Conclusion: The rate of self-reported ADHD symptoms is higher in Australian undergraduate students than that reported in previous studies using the CAARS to investigate rates of diagnosed students. Problems with self-concept accounted for the most unique variance in DASS subscale scores. Hyperactivity/restlessness and inattention/memory problems accounted for the most unique variance in AQ-Social and AQ-Attention-to-Detail scores, respectively.

Keywords

ADHD young adults undergraduate students depression anxiety stress autism-related traits

It was often assumed that symptoms of inattention, overactivity, and impulsivity denoting ADHD abated in adulthood; however, it is now clear that these symptoms persist in up to 70% of cases, and the proportion of adults in the general population (6%) reporting a diagnosis of ADHD is similar to that of school-aged children (7%; Faraone & Biederman, 2005; Green & Rabiner, 2012). The adult form of ADHD has been associated with poor functioning across life domains and compared with nonaffected counterparts, adults with ADHD have a history of poor academic achievement, often fail to have a stable professional career, experience fiscal problems, and struggle to maintain satisfying interpersonal relationships (Sibley et al., 2012). Moreover, individuals with ADHD tend to experience appreciable levels of internalizing and externalizing problems, and are more likely to engage in life-threatening behaviors such as dangerous driving, substance abuse, risky sexual behavior, criminality, and domestic violence (Lee, Oakland, Jackson, & Glutting, 2008).

Previous Estimates of ADHD in University Students

As some adolescents with ADHD often fail to achieve to the fullest of their academic potential due to associated learning difficulties, they may either not gain entry into university courses or may elect not to pursue tertiary studies (Atwoli, Owiti, Manguro, & Ndambuki, 2010). Thus, a lowered prevalence of 1% to 3% of ADHD in university settings has been expected (DuPaul et al., 2001). However, more recent recognition of the impact of ADHD on school-aged children’s psychological and academic functioning (Norwalk, Norvilitis, & MacLean, 2009) has led to schools becoming better at catering for the educational needs of children with ADHD (Mckee, 2008). Hence, increasing numbers of youth with ADHD are now opting to undertake university studies, and this is expected to continue into the future (Nugent & Smart, 2014).

Increasingly, research shows that the representation of university students with ADHD is indeed higher than once thought. Two U.S. studies asked students whether they had a current diagnosis of ADHD. In a sample of 3,400 undergraduate students from two universities in southeastern United States, one of which was public and the other private, 6.6% and 2.5% (respectively) of students reported a diagnosis (Glutting, Sheslow, & Adams, 2002). The different rates were attributed to differences in admission requirements (Glutting et al., 2002). In a survey of 201,818 first-year, full-time students, 6.4% of male students and 3.8% of female students (M = 5%) reported a diagnosis (Pryor, Hurtado, DeAngelo, Blake, & Tran, 2011).

Basing prevalence estimates on diagnostic status of ADHD in university students has limitations however.First, a diagnosis of ADHD was not confirmed via a clinical evaluation at the time the studies cited above were performed (Green & Rabiner, 2012). Second, diagnosis would have been based on past versions of the Diagnostic and Statistical Manual of Mental Disorders (DSM; that is, prior to the fifth edition published in 2013). Yet, these criteria were criticized for being developmentally inappropriate and restrictive, and therefore not adequately capturing manifestations of ADHD symptoms in adults (Mckee, 2008). Relying on students’ report of their diagnostic status may have also led to an underestimation of the number of students with pathological traits of ADHD. This is because adults with a childhood diagnosis and/or history of the disorder have been shown to dramatically downplay the severity of their problems (Sibley et al., 2012). Other research has instead relied on self-report measures of ADHD symptoms (Green & Rabiner, 2012). Rates of self-reported ADHD symptoms derived from the past research are provided in Table 1 below.

Table 1.

Rates of Self-Reported ADHD Symptoms From Past Research.

Reference	Self-reported scale	N	Country	Rate
Weyandt, Linterman, and Rice (1995)	Adult Rating Scale (ARS; Weyandt et al., 1995) Wender Utah Rating Scale (WURS; Ward, Wender, & Reimherr, 1993)	770	United States	7% (1.5 SD cutoff) 4% (2 SD cutoff)
DuPaul et al. (2001)	Young Adult Rating Scale (YARS; DuPaul et al., 2001)	1,209	United States, Italy, and New Zealand	2.9%-8.1% (males) 0%-3.9% (females)
Kwak, Jung, and Kim (2015)	Adult ADHD Self-Report Scale–Version 1.1 (ASRS v1.1; Adler et al., 2006)	2,172	Korea	7.6%
Atwoli, Owiti, Manguro, and Ndambuki (2010)	ASRS v1.1	458	Kenya	21.8%
Kavakci et al. (2012)	ASRS v1.1	980	Turkey	10.1%
Pope et al. (2007)	Conners’ Adults ADHD Rating Scales–Short Version (CAARS-S: S; Conners, Erhardt, & Sparrow, 1999)	1,182	United Kingdom	12%
Harrison, Alexander, and Armstrong (2013)	Conners’ Adults ADHD Rating Scales (CAARS; Conners, Erhardt, & Sparrow, 1999) Brown Attention Deficit Disorder Scale (BADDS; Brown, 1996)	98	Canada	6.7% (CAARS-S: L) 21%-35% (BADDS)

Overall, these studies show that between 0% and 21.8% of tertiary students report clinically significant symptoms associated with ADHD, and inattention has consistently been the most commonly endorsed ADHD-related symptom (DuPaul et al., 2001; Harrison, Alexander, & Armstrong, 2013; Kavakci et al., 2012). This variability in rates of self-reported symptoms is attributed to the different measures utilized, each with their own symptom threshold and criteria for clinical significance.

Hence, direct comparison of rates of self-reported symptoms across studies is not possible. Regardless of which self-report measure is used however, it appears that higher estimates are derived when the prevalence of self-reported ADHD-related symptoms is evaluated as compared with when prevalence of ADHD is based on diagnostic status. This is further supported by Garnier-Dykstra, Pinchevsky, Caldeira, Vincent, and Arria (2010) who in a sample of 1,080 university students found that 10.3% of the entire sample scored in the clinical range on the ADHD Self-Report Scale (ASRS), while only 2% of students reported a formal diagnosis of ADHD. In a study of 980 Turkish university students, 10.1% reported ADHD traits on the ASRS v1.1, while only 6.1% had a diagnosis (Kavakci et al., 2012).

While reporting of formal diagnoses may lead to an underestimation of the number of students who present with pathological traits of ADHD, self-report measures may yield a high rate of false positives and inflate the actual prevalence of ADHD (Gordon et al., 2006; Kavakci et al., 2012; Sibley et al., 2012). Indeed, when Murphy, Gordon, and Barkley (2002) surveyed 719 nonclinical individuals using a rating scale based on DSM- (4th ed.; DSM-IV; American Psychiatric Association [APA], 1994) criteria, 75% of respondents indicated experiencing six or more symptoms of ADHD. In some cases, high endorsement of symptoms may stem from a genuine misattribution of normal variations in cognition, such as in attention and working memory, to an underlying ADHD disorder (Murphy et al., 2002). This may be fueled by the increase in public awareness of ADHD which may influence individuals’ internal attributions of transient cognitive issues. Alternatively, students who fail to meet certain developmental demands and underperform at university may rate themselves highly on ADHD rating scales, and use ADHD to explain their underperformance (Molina & Sibley, 2014).

Nevertheless, adults who display genuine symptoms of ADHD have been shown to display adequate insight into their difficulties, and may therefore reliably report their symptoms and seek treatment (Barkley, Knouse, & Murphy, 2011). Self-ratings of symptom severity can therefore capture a significant number of adults at risk of ADHD and/or who may have late symptom onset (Alexander & Harrison, 2013; Gray, Woltering, Mawjee, & Tannock, 2014). Thus, the true prevalence rate of ADHD is likely to be somewhere in between rates derived from studies using self-report measures and those from studies investigating prevalence based on diagnostic status.

This study’s first aim is to address a paucity of research studies examining the rate of self-reported ADHD symptoms in young adults in Australian universities. Such information is key to the conceptualization of ADHD among university students as it can be used to inform future prevalence studies in Australia (Lee et al., 2008). Importantly, in addressing this aim, the short version of Conners’ Adult ADHD Rating Scale (CAARS-S: S), a well-established measure of ADHD-related symptoms, was administered in an attempt to derive an estimate which closely approximates the true prevalence rate (Conners et al., 1999). The CAARS comprises 26 items that were derived directly from the longer 66-item version due to their high loadings on ADHD symptoms (Conners et al., 1999). The CAARS-S: S yields an ADHD index score which is currently the best screen to distinguish individuals who are highly likely to be diagnosed with ADHD from nonclinical adults (Dakwar et al., 2012). Arguably, measures that exclusively use the DSM criteria may restrict the assessment of ADHD symptoms to a limited list of symptoms, including inattention, hyperactivity, and impulsivity (Taylor, Deb, & Unwin, 2011). The CAARS has gained general acceptance as it incorporates items that assess not only symptoms directly linked to DSM criteria but also additional psychopathological symptoms that are inherent in ADHD such as emotional lability and self-concept (Kim, Lee, & Joung, 2013).

The CAARS-S: S has a positive predictive power of 85%, has high specificity, and was found to be less vulnerable to false-positive screening unlike the ASRS-V1.1 (Conners et al., 1999; Hines, King, & Curry, 2012). It also includes an inconsistency index, and a score higher than 8 indicates responses made in a random, careless, and unsystematic manner. Data from high scorers on this index can therefore be excluded from further analysis. Such an index is not included on the ASRS-V1.1, and it has been found that some participants who self-report elevated symptoms of ADHD on this measure also score highly on the CAARS-S: S inconsistency index (Hines et al., 2012). Finally, moderate to strong correlations have been found between observer ratings and self-report ratings on the CAARS-S: S, suggesting that individuals’ endorsement of ADHD symptoms can be adequately corroborated by informants (Conners et al., 1999; Taylor et al., 2011).

Depression, Anxiety, and Stress in University Students With ADHD-Related Traits

University life comprises several challenges and stressors for many students, particularly those with symptoms of ADHD (Harrison et al., 2013). Students with poor attention and high levels of impulsivity may be at particular risk of depression, anxiety, and distress due to a confluence of symptoms that can impede on their thinking processes, concentration, memory, organizational skills, inhibitory skills, and other skills necessary for adaptive functioning (Harrison et al., 2013). These issues may not always manifest during childhood but emerge as students step into the more demanding realms of tertiary education, and lose the support structures that parents and teachers had previously set up to assist them in managing their deficits in school (Gray et al., 2014). Thus, university students with these ADHD-related characteristics are prone to experiencing difficulties in adapting to increasing levels of academic, social, financial, and career-planning demands placed upon them due to inherent deficits in behavioral and emotional regulation (Gray et al., 2014). These may lead to an array of short- and long-term consequences, such as poor academic achievement (Hysenbegasi, Hass, & Rowland, 2005), interpersonal difficulties (Salzer, 2012), and suicidality (Garlow et al., 2008).

Cross-sectional and longitudinal research shows that when it comes to ADHD, comorbidity is the rule rather than the exception. Up to 84% of adult patients present with one comorbid diagnosis, and up to 61% present with two or more comorbidities (Amiri et al., 2013; Anastopoulos et al., 2016). Mood and anxiety disorders are the most frequently co-occurring disorders according to one of the largest epidemiological studies of adult ADHD to date (Kessler et al., 2006). Interestingly, Richards and colleagues (1999) examined differences in psychological functioning of college students with established diagnoses of ADHD, those with high levels of self-reported ADHD traits but with no diagnosis, and those without ADHD. Overall, both college students with confirmed ADHD and those with self-reported ADHD symptoms displayed similar psychological functioning, and rated themselves significantly higher on scales assessing somatization, interpersonal sensitivity, depression, anxiety, obsessive-compulsive symptoms, phobia, hostility, psychoticism, and psychological distress compared with those students with no ADHD. In a study of 98 Canadian university students, Harrison et al. (2013) also found significant correlations between students’ self-reported psychological functioning as measured by the Depression, Anxiety, and Stress Scale–21 (DASS-21; Lovibond & Lovibond, 1995) and ADHD symptoms on the CAARS (Conners, Erhardt, & Sparrow, 1999) and the Brown Attention Deficit Disorder Scale (BADDS; Brown, 1996; r = .67 and .56, respectively).

Conversely, Nelson and Gregg (2012) found no significant group differences in the rate of self-reported symptoms of depression and anxiety among a sample of 210 transitioning college students with ADHD compared with unaffected counterparts. Similar findings were made in a retrospective study of 54 university students who had presented for psychological assessment (Heilingenstein, Guenther, Levy, Savino, & Fulwiler, 1999). An explanation for this absence of group differences might be that when compared with young adults with ADHD who do not attend university, those who go on to study at postsecondary level may have higher levels of confidence in their ability to succeed, are more resilient, and perhaps even possess average or above average cognitive abilities (Heilingenstein et al., 1999). University students with ADHD may also present with less severe forms of the disorder relative to those who do not enroll into postsecondary courses (Rabiner, Anastopoulos, Costello, Hoyle, & Swartzwelder, 2008). The mixed results attained from a small number of studies highlight the need for more research into the association between symptoms of depression, anxiety, and stress in university students reporting ADHD symptoms.

There have also been suggestions that those adults who display primarily symptoms of inattentiveness or of the combined subtype are more likely to experience internalizing problems compared with individuals who display more hyperactive-impulsive symptoms (Wilens et al., 2009). Nevertheless, Nelson and Gregg (2012) did not find support for this association when they investigated a possible link between ADHD subtype (i.e., inattentive, hyperactivity/impulsive, or combined) and comorbidity in U.S. university students. The latter’s finding was based on a small sample size (N = 60) of tertiary students with ADHD who were specifically seeking a comprehensive psychological evaluation with the aim of gaining access to academic supports. These individuals may potentially differ from the wider population of students with high levels of ADHD-related traits, including those who do not seek academic accommodations (Nelson & Gregg, 2012). Hence, these findings may not be generalizable to the wider population of university students, including those in Australia.

Autism-Like Traits in Students With Symptoms of ADHD

Individuals with symptoms of ADHD may also exhibit autistic traits (i.e., social/communication difficulties, restricted and repetitive patterns of behavior and interests; Polderman, Hoekstra, Posthuma, & Larsson, 2014). Up to 44% of adults with autism spectrum disorders (ASDs) also qualify for a diagnosis of ADHD with deficits in attention and executive functioning being the most frequently reported shared pathophysiological impairments (Hofvander et al., 2009). These shared features point toward common causal factors, including genetic and nongenetic risk factors (Kern, Geier, Sykes, Geier, & Deth, 2015; Ronald, Simonoff, Kuntsi, Asherson, & Plomin, 2008). Importantly, genetic factors underlying core symptoms of ADHD appear to overlap with those underlying symptoms of ASD (Polderman et al., 2014), supporting assertions that poor attention and hyperactivity may exacerbate the severity of autism-related traits (Craig et al., 2015).

Empirical evidence supports associations between patterns of symptoms of both disorders. Polderman and colleagues (2014) found strong correlations between inattention and social/communication deficits. Modest correlations between hyperactivity/impulsivity, and restricted and repetitive behaviors and interests were also found. However, the measure of autistic traits administered in this study was based on DSM- (5th ed.; DSM-5; APA, 2013) criteria and had poor reliability in this nonclinical sample. Specifically, Cronbach’s alpha values of .53 and .49 were found for scales measuring social and communication difficulties, and the restricted and repetitive behaviors and interests, respectively (Polderman et al., 2013). A more reliable measure of autistic traits, the Autism Quotient (AQ; Cronbach’s alpha = .63 and .77; Baron-Cohen, Wheelwright, Skinner, Martin, & Clubey, 2001), has also been used to investigate associations between ASD and ADHD-related traits in a population-based study of 559 nonclinical adult twins and siblings from The Netherlands (Polderman et al., 2013). Results revealed that the cooccurrence of autistic traits and ADHD-related deficits is driven mainly by issues with attention and not by hyperactivity/impulsivity (Polderman et al., 2013).

Impairments associated with the co-occurrence of symptoms of ADHD and ASD may not be evident before adulthood (Sizoo et al., 2009). When those “late presentations” start to manifest in early adulthood, the distinction between ADHD and ASD may be blurred as motor hyperactivity ceases being a prominent feature of ADHD (Sizoo et al., 2009). Assessing inattention can also be problematic as individuals with traits of ASD and/or ADHD can focus for extensive periods of time when it comes to a particular area of interest but experience significant deficits in sustaining attention in most other areas (Leyfer et al., 2006). As such, impairments associated with a cooccurrence of both disorders may be missed in young adults attending university. Poor attention in both ADHD and ASD may nevertheless be the main source of difficulty in a university setting where increased academic demands may exceed the capacity for sustained attention (van Schalkwyk, Beyer, Martin, & Volkmar, 2016).

It is plausible that an increasing number of students with characteristics of both ADHD and ASD may be attending university because owing to disability rights legislations, they receive appropriate educational supports (Nugent & Smart, 2014; van Schalkwyk et al., 2016). In a study of 122 adults with ASD but with normal intelligence, 43% of participants presented with symptoms of the inattentive or combined subtypes of ADHD (Hofvander et al., 2009). That individuals with comorbid ASD and ADHD may in many cases have normal cognitive abilities suggests they may undertake studies at tertiary level, and counseling centers, including in university settings, can expect to encounter the consequences of this comorbidity on social and academic functioning (van Schalkwyk et al., 2016). Thus, it should be determined whether symptoms of ADHD are related with those of ASD in a university population.

The AQ, a widely used measure of autism-related traits in clinical and nonclinical samples, was used in this study (see Ruzic et al., 2015). It consists of 50 items assessing five theoretical aspects of autistic traits, namely social skills deficits, preference for routine, problems switching attention, poor imagination, and a strong interest for pattern and numbers (Baron-Cohen et al., 2001). Since the original study reporting on the psychometric properties of the AQ (Baron-Cohen et al., 2001), numerous factor analytical studies have endorsed between two and five AQ factors (Austin, 2005; Hoekstra, Bartels, Cath, & Boomsma, 2008; Kloosterman, Keefer, Kelley, Summerfeldt, & Parker, 2011; Stewart & Austin, 2009). Across all these studies, “Socialness” and “Attention to details and Patterns” were two key dimensions that were consistently found to underlie the AQ (Stewart & Austin, 2009). Moreover, both these factors map onto the two core DSM-5 diagnostic criteria for ASD, including deficits in social communication and reciprocity and, abnormally restricted and repetitive behavior, interests and activities (Davis et al., 2016). Hence, in the current study, the two-factor model was chosen as it has also been found to reliably and validly capture individual differences in autistic traits in a study of 961 university students (Hoekstra et al., 2008). The AQ-Attention-to-Detail factor has been shown to stand on its own, while the AQ-Social factor combines four other factors, namely social skills, communication, attentional switching, and imagination (Hoekstra et al., 2008).

Aims and Hypotheses

This study aimed to investigate the rate of self-reported ADHD-related symptoms in young adults attending university in Australia. It was expected that the rate of self-reported ADHD would be similar to that found in previous studies using the CAARS (Hypothesis 1; Harrison et al., 2013; Pope et al., 2007). Consistent with previous research using the same measure in a university population (Harrison et al., 2013), it was expected that symptoms of inattention would be the most commonly endorsed ADHD-related symptom (Hypothesis 2). Gender differences were also expected based on past cross-national research, showing that males are more likely than females to display ADHD symptoms (Hypothesis 3; DuPaul et al., 2001). second aim of this study was to clarify the mixed results pertaining to the coexistence of psychological problems (i.e., depression, anxiety, and distress) with ADHD-related symptoms in university students. Moreover, in light of previous research showing positive correlations between CAARS subscales and depression, anxiety, and stress (Alexander & Harrison, 2013; Harrison et al., 2013), and consistent with suggestions that autistic-like traits and ADHD-related symptoms may coexist in a number of individuals with high cognitive abilities and possibly pursuing university studies (van Schalkwyk et al., 2016), we anticipated that scores on the CAARS subscales would account for unique variance in self-reported depressive symptoms (Hypothesis 3a), anxiety (Hypothesis 4a) and stress (Hypothesis 5a), AQ Social (Hypothesis 6a) and AQ Attention to Detail (Hypothesis 7a) beyond the effects of gender. More specifically, we predicted that scores on self-reported inattention would account for the most variance in depression (Hypothesis 3b), anxiety (Hypothesis 4b), stress (Hypothesis 5b), AQ-Social (Hypothesis 6b), and AQ Attention to Detail (Hypothesis 7b).

Method

Participants

The study protocol was approved by the University of Western Australia Human Ethics Committee (RA/4/1/8003). A total of 1,381 students completed the measures administered as part of this study. Respondents were from a range of courses and at various levels of their undergraduate studies (i.e., from 1st-4th year). As this study’s aim was to investigate university students in the young adulthood stage of life, findings are based on those students aged between 17 and 25 years, inclusive, based on (a) the conceptual understanding that the age cohort between 18 and 25 years represents a distinct developmental period between adolescence and adulthood (Schwartz, Côté, & Arnett, 2005) and (b) to include those first-year university students (n = 127) who were not yet 18 years old at the time of study (in Australia, it is common for university students to be 17 years for part of their first year of study). Data from 371 students aged 26 years or above were deleted. Further exclusion criteria were applied to satisfy the requirements of the current study: First, three participants reported their gender as “Other,” and their data were removed from the dataset due to the absence of normative data in the CAARS manual for this group of individuals. Another five cases were deleted from the dataset due to high (>8) inconsistency index scores on the CAARS. The final dataset consisted of 1,002 cases. Of those, 34.4% were males and 65.6% were females with an age range of 17 to 25 years (M = 18.85, SD = 1.63).

Measures

CAARS-S: S

Items on the CAARS are rated on a 4-point Likert-type (i.e., from 0-3) rating scale denoting the frequency at which individuals believe that they engage in each of the described behaviors (Conners et al., 1999). The CAARS was developed to inform the diagnosis of ADHD in individuals aged 18 years and above (Norwalk et al., 2009). It provides scores on five subscales. High scores on the Inattention/Memory Problems indicate problems with organization and completion of tasks, concentration, and learning new information fast. High scorers on the Hyperactivity/Restlessness subscale experience restlessness, feeling more “on-the-go” than others, and difficulty staying on task for extended periods of time. Respondents endorsing symptoms of Impulsivity/Emotional Lability experience swift changes in mood often, engage in more impulsive acts, and are prone to anger and irritability compared with others. High scorers on the Problems With Self-Concept subscale report having poor social relationships, low self-esteem, and low self-confidence compared with others. Raw scores on the scales are converted to T scores based on norms for age and gender. Higher scores indicate a higher likelihood of moderate to severe symptoms, and a T score of 65 or above on any of the subscales indicates clinical levels of ADHD symptoms.

Although the CAARS cannot be used exclusively to diagnose ADHD, its good psychometric properties make it a useful dimensional measurement of the frequency and intensity of adult ADHD-related symptoms in research (Conners et al., 1999). In this study, the internal consistency of the CAARS-S: S ranged from acceptable to good, and Cronbach’s alpha values for each subscale, respectively, for males and females were as follows: Inattention/Memory Problems = .82 and .81, Hyperactivity/Restlessness = .67 and .70, Impulsivity/Emotional Lability = .76 and .77, and Problems With Self-Concept = .87 and .85, and the ADHD Index = .82 and .82. Mean interitem correlations for each subscale ranged between .33 (ADHD Index) and .63 (Problems With Self-concept) for females. For males, mean interitem correlations for each subscale ranged between .33 (ADHD Index) and .65 (Problems With Self-concept). Hence, the Problems with Self-Concept was the most unidimensional scale. In the original normative study, the CAARS had high test–retest reliability (i.e., r = .80-0.91) over a month period. Confirmatory factor analysis in the original normative sample confirmed that the CAARS-S: S scales met criteria standards for good fit in both males and females (Conners et al., 1999). The title of the CAARS was omitted from the questionnaire so as not to give away the nature of the study to participants.

DASS-21

The DASS-21 assesses emotional and psychological distress along the axes of depression, anxiety, and stress/tension (Lovibond & Lovibond, 1995). The measure has good internal consistency. The Depression scale’s Cronbach’s alpha value was good for males at .85 and excellent for females at .90. Cronbach’s alpha values for the Anxiety subscales were .77 (acceptable) for males and .85 (good) for females. As for the Stress scale, Cronbach’s alpha values were .81 for males and .84 for females, and these are considered to be good. The DASS also boasts good construct validity and concurrent validity as shown in both nonclinical community and clinical samples (Henry & Crawford, 2005). Factor analyses using both clinical and nonclinical populations have supported a three-factor solution, and the measure was found to have good construct validity. The Depression subscale measures melancholy, hopelessness, devaluation of life, self-deprecating tendencies, lack and loss of interest and/or involvement, and apathy. The Anxiety subscale evaluates subjective experience of autonomic arousal, muscular tension, situational anxiety, and subjective experience of anxiety. The Stress subscale taps into the individual’s experience of chronic nonspecific arousal, nervous arousal, including proneness to agitation, irritability, overreactivity, and impatience and difficulty relaxing. Each subscale score was doubled to make them comparable with the full 42-item version of the DASS (Lovibond & Lovibond, 1995).

The AQ

The AQ is a 50-item self-report measure of autistic traits targeted at adults with an IQ score in the average or above average range (Baron-Cohen et al., 2001). Responses are made using a 4-point Likert-type system (1 = definitely agree, 2 = slightly agree, 3 = slightly disagree, and 4 = definitely disagree). Forty items were summed to obtain the AQ-Social score, whereas 10 items were summed to obtain the AQ-Attention-to-Detail score (see Hoekstra et al., 2008). In this study, reliability of the AQ-Social factor was good with Cronbach’s alpha values of .85 for males and females. For the AQ Attention to Detail, Cronbach’s alpha values were .68 for males and .76 for females, and these are considered to be acceptable. While higher mean ratings are typically found for males than for females (Ruzic et al., 2015), no gender differences in mean scores were found for either factor in this study (Table 2). The AQ has also been found to possess reasonable face validity, construct validity, and excellent test–retest reliability (Baron-Cohen et al., 2001).

Table 2.

Descriptive Statistics for CAARS, DASS, and AQ Subscales and Gender Differences in Scores.

Variables	Male		Female		p value	Observed range (total)
Variables	M	SD	M	SD	p value	Observed range (total)
CAARS subscales (n = 1,002)
Inattention/Memory Problems	54.42	10.39	56.25	11.10	.011	35-83
Hyperactivity/Restlessness	49.23	8.51	52.77	9.41	.000	29-78
Impulsivity/Emotional Lability	46.63	8.23	51.12	9.52	.000	34-83
Problems With Self-Concept	53.29	10.45	53.81	10.63	.459	34-76
ADHD Index	51.48	10.05	55.09	10.52	.000	30-87
DASS-21 subscales (n = 861)
Depression	10.54	8.27	10.83	9.25	.649	0-42
Anxiety	10.59	8.03	12.71	10.13	.001	0-42
Stress	12.96	8.67	15.37	9.77	.000	0-42
AQ (n = 861)
AQ Social	84.26	12.82	83.63	12.85	.150	51-134
AQ Attention to Detail	25.28	4.65	25.62	5.10	.337	11-40

Note. CAARS = Conners’ Adult ADHD Rating Scale; DASS = Depression Anxiety Stress Scales; AQ = Autism Quotient.

Procedure

Undergraduate psychology students enrolled in an introductory psychology laboratory class were invited to fill in the aforementioned measures. A participant information sheet providing relevant information about this study was included in the booklet. Students were advised that completion of the measures was entirely voluntary. They were also informed that filling in the measures may result in them being invited to participate in a subsequent study during the semester in exchange for unit credit points. Participants provided consent to filling in the measures and for their responses to be used in this study.

Data Analysis

Standardized T scores on each CAARS subscale were calculated as per the test manual. A cutoff T score of 65 or above on a subscale was used as an indication of significantly elevated ADHD-related symptoms (Conners et al., 1999) and in line with the first aim of the study, we examined how commonly symptoms of ADHD are reported among Australian university students. Four hierarchical multiple regression analyses (HMRAs) were also conducted to address the second aim of the study of investigating the amount of variability in depression, anxiety, stress, and autistic-like symptoms that could be predicted by levels of inattention, hyperactivity, impulsivity, problems with self-concept, and overall ADHD-related symptoms. As independent samples t tests revealed gender differences on the way participants scored themselves on the measures administered (Table 1), a dummy-coded gender variable was entered as predictor variable at Stage 1 of the regressions, and CAARS subscale scores were entered at Stage 2. Age was uncorrelated with all measures administered and was excluded from analyses. Scores on the DASS subscales and the AQ were entered as the outcome variables. SPSS student version 24 was used to conduct the analyses, and p values < .05 were statistically significant.

Results

Rate of Self-Reported Symptoms

Table 2 presents descriptive statistics pertaining to students’ overall scores on the CAARS, DASS, and the AQ, and highlights gender differences in symptoms. Table 3 presents the number and percentage of students who scored 65 or above on the CAARS: S-S subscales, and shows that 17.3% of students reported clinically significant levels of ADHD (as measured by the CAARS ADHD Index). In support of Hypothesis 2, symptoms tapping into the ADHD index (which represents overall symptom severity) were the most frequently endorsed followed by symptoms of inattention. Hypothesis 3 was not supported as although gender was significantly (p ≤ .001) related to ADHD symptoms, a greater proportion of females reported above-threshold levels of ADHD symptoms relative to males. Females also scored higher than males on the DASS subscales, with significant differences found on the Stress and Anxiety subscales. On the AQ, males scored higher than females, although this did not represent a significant difference.

Table 3.

Number and Percentage of Students (by Gender) With a T Score of 65 or Above on the CAARS Subscale (n = 1,002).

CAARS subscale	Number with T ≥ 65		Percentage with T ≥ 65
CAARS subscale	Males	Females	Males	Females	Total
Inattention/Memory Problems	21	46	6.1	7.0	6.7
Hyperactivity/Restlessness	8	21	2.3	3.2	2.9
Impulsivity/Emotional Lability	2	7	0.6	1.1	0.9
Problems With Self-Concept	21	24	6.1	3.7	4.5
ADHD Index	40	133	11.6	20.2	17.3

Note. CAARS = Conners’ Adult ADHD Rating Scale.

Table 4 shows that, while the majority of students scored within the normal range on each of these measures, 17.4% of undergraduate university students reported experiencing mild to severe levels of symptoms of depression, 31.8% reported experiencing mild to severe levels of stress on the DASS-21. In addition, 11.3% reported borderline to high levels of autistic-like characteristics on the AQ. Table 5 shows that self-reported levels of depression, anxiety, and stress as well as autism-like traits were all positively correlated with the CAARS subscales.

Table 4.

Percentage of Symptom Endorsement on the DASS-21 and AQ.

DASS-21 (n = 861)	Normal (%)	Mild (%)	Moderate (%)	Severe (%)	Extremely severe (%)
Depression	82.58	11.15	6.16	0.12	0
Anxiety	66.78	11.73	15.33	4.76	0
Stress	92.22	5.92	1.86	0	0
AQ (n = 817)	Low range	Normal range	Slightly above average range	Borderline range	High range
	10.53	61.57	16.65	9.55	1.71

Note. DASS-21 = Depression Anxiety Stress Scales–21; AQ = Autism Quotient.

Table 5.

Summary of Intercorrelations for Scores on the CAARS-S: S, DASS-21, and AQ.

Measure	1	2	3	4	5	6	7	8	9	10
1. C—ADHD index	—
2. C—Inattention/memory	.74** [0.70, 0.77]	—
3. C—Hyper/restlessness	.68** [0.64, 0.71]	.48** [0.42, 0.53]	—
4. C—Impulsivity/emotional lability	.73** [0.70, 0.76]	.44** [0.38, 0.50]	.50** [0.45, 0.56]	—
5. C—Self-concept	.72** [0.68, 0.75]	.55** [0.50, 0.60]	.35** [0.29, 0.42]	.44** [0.38, 0.49]	—
6. DASS depression	.54** [0.49, 0.58]	.44** [0.38, 0.49]	.31** [0.25, 0.37]	.38** [0.32, 0.45]	.64** [0.60, 0.68]	—
7. DASS anxiety	.52** [0.47, 0.57]	.37** [0.30, 0.42]	.34** [0.27, 0.40]	.40** [0.34, 0.46]	.54** [0.49, 0.59]	.65** [0.60, 0.69]	—
8. DASS stress	.63** [0.59, 0.66]	.42** [0.35, 0.47]	.44** [0.39, 0.49]	.55** [0.50, 0.60]	.64** [0.60, 0.67]	.71** [0.67, 0.75]	.75** [0.71, 0.78]	—
9. AQ social	.32** [0.25, 0.39]	.26** [0.19, 0.33]	.04 [–0.03, 0.12]	.24** [0.17, 0.31]	.37** [0.30, 0.43]	.31** [0.24, 0.38]	.33** [0.26, 0.39]	.32** [0.26, 0.38]	—
10. AQ attention	.04 [–0.03, 0.12]	−12** [–0.20, −0.05]	.12** [0.04, 0.18]	.07 [0.00, 0.13]	.01 [–0.06, 0.08]	.01 [–0.06, 0.08]	.14** [0.07, 0.21]	.12** [0.04, 0.20]	−.04 [–0.11, 0.03]	—

Note. Bias-corrected and Accelerated 95% Confidence Intervals reported in square brackets. Listwise N = 861; C = CAARS; CAARS = Conners’ Adult ADHD Rating Scale–Short Version; DASS-21 = Depression Anxiety Stress Scale–21; AQ = Autism Quotient; CI = confidence interval.

p ≤ .01. **p ≤ .001 (one tailed).

Associations Among Measures

Bivariate correlations between all measures (Table 5) supported conducting the HMRAs to determine how much variance in depression, anxiety, stress, and autism-related traits can be accounted for by ADHD-related symptoms. Assumptions testing and checks were performed prior to the conduct of the HMRAs. Inspection of normal probability plots of standardized residuals and scatterplots of standardized residuals against predicted values indicated that the assumptions of normality, linearity, and homoskedasticity of residuals were satisfied (Field, 2013). Multicollinearity was not an issue in the current data as none of the independent variables were highly correlated (r ≥ .85; see Table 5). Tolerance values were all above 0.1, and variance inflation factor (VIF) statistics were below 10 and within acceptable limits (Field, 2013). Mahalanobis distance exceeded the critical chi-square value for df = 6(at α = .001) of 22.46, and two multivariate outliers were identified (Allen & Bennett, 2008). These were retained for further analyses, and robust regression using bootstrapped confidence intervals was derived for regression coefficients (Field, 2013).

Table 6 presents results for each HRMA with each DASS subscale as a predictor. At Stage 1 of the HRMA for depression, gender accounted for no significant variance in depression scores. With the addition of the CAARS subscale scores, Hypothesis 3a was supported as the predictors in combination explained an additional 43% of variance in self-reported feelings of depression adjusted R² = .42, F(6, 854) = 158.46, p < .001, thus representing a large effect size (f² = .75). Unlike predicted (Hypothesis 3b), the most important predictor was the Problems With Self-Concept score which uniquely accounted for almost 13% (ρ ≤ .001) of the variance in depression. The Inattention/Memory Problems and Impulsivity/Emotional Lability subscales each accounted for 0.4% and 0.5% of unique variance in depression, respectively. Hyperactivity/Restlessness and the ADHD Index accounted for no unique variance.

Table 6.

Unstandardized (B) and Standardized (β) Regression Coefficients, Standard Error (SE B), Squared Semipartial Correlations (sr2), and 95% Bias-Corrected and Accelerated CIs for Each Predictor Variable of Depression, Anxiety, and Stress.

Predictor variable	Depression			Anxiety			Stress
Predictor variable	B	SE B	β	B	SE B	β	B	SE B	β
Step 1
Gender	−0.15	0.31	−.02	−1.06*	0.31	−.11	−1.21**	0.32	−.12
Gender	[−0.74, 0.46]			[−1.69, −0.48]			[−1.87, −0.58]
R ²	.00			.01*			.20**
Step 2
Gender	0.32	0.23	.03	−0.43	0.26	.04	−0.24	0.23	−.02
Gender	[−0.15, 0.75]			[−0.99, 0.14]			[−0.71, 0.22]
Inattention/Memory	0.04*	0.02	.09	−0.01	0.02	−.03	−0.02	0.02	−.05
Inattention/Memory	[0.00, 0.08]			[−0.05, 0.02]			[−0.06, 0.01]
Hyperactivity/Restlessness	0.02	0.02	.04	0.03	0.02	.06	0.07*	0.02	.14
Hyperactivity/Restlessness	[−0.02, 0.06]			[−0.01, 0.07]			[0.04, 0.11]
Impulsivity/Emotional Lability	0.05*	0.02	.10	0.05*	0.02	.09	0.14*	0.00	.28
Impulsivity/Emotional Lability	[0.01, 0.09]			[0.00, 0.09]			[0.10, 0.18]
Problems With Self-Concept	0.22**	0.02	.54	0.17*	0.02	.39	0.21*	0.02	.48
Problems With Self-Concept	[0.19, 0.26]			[0.13, 0.21]			[0.18, 0.25]
CAARS ADHD Index	−0.00	0.03	−.10	0.07*	0.03	.15	0.01	0.03	.02
CAARS ADHD Index	[−0.07, 0.07]			[0.00, 0.14]			[−0.05, 0.70]
R ²	.43**			.34**			.51**
ΔR²	.43**			.33**			.50**

Note. Confidence intervals and standard errors are based on 1,000 bootstrap samples. CI = confidence interval; CAARS = Conners’ Adult ADHD Rating Scale.

p ≤ .05. **p ≤ .001.

When it came to anxiety, gender explained only 1.1% of the variance in anxiety scores at Stage 1 of the HMRA. At Step 2, the CAARS subscales were added accounting for an additional 32.7% of variance in anxiety, thus supporting Hypothesis 4a. A combined effect of this magnitude represents a large effect size (f² = .51). Impulsivity/emotional lability, problems with self-concept, and the ADHD index individually accounted for significant unique variance in anxiety at 0.4%, 6.6%, and 0.3%, respectively. Unlike predicted by Hypothesis 4b, inattention accounted for no variance in anxiety score, and neither did hyperactivity/restlessness scores.

Stage 1 of the regression model for stress revealed that gender accounted for a significant 1.5% of variance in stress. The CAARS subscales were added and consistent with Hypothesis 5a, accounting for an additional 50% of total variance in students’ self-reported levels of stress in university students. This represented a large effect size (f ² = 1.06). Thus, students reporting higher levels of ADHD-related characteristics also experienced higher levels of stress. Hypothesis 5b was not supported as inattention/memory problems explained no significant variance in stress and neither did the ADHD index. Problems with self-concept accounted for the most unique variance (10.1%) ensued by impulsivity/emotional lability (3.2%) and hyperactivity/restlessness (1%).

Gender was a nonsignificant predictor of the AQ-Social score at Stage 1 of the HRMA (Table 7). At Stage 2, it became a significant predictor of AQ-Social scores, and CAARS scores accounted for an additional 18.4% of the variance in AQ-Social scores (thus supporting Hypothesis 6a that students who report higher levels of ADHD-related characteristics also report poor socialness). The combined effect is considered medium (f ² = .23). Hyperactivity/restlessness, impulsiveness and emotional lability, problems with self-concept, and the ADHD index all accounted for unique variance in AQ-Social scores (3.3%, 0.4%, 2.2%, and 0.7%, respectively).

Table 7.

Predictor variable	AQ social			AQ attention to detail
Predictor variable	B	SE B	β	B	SE B	β
Step 1
Gender	1.33	1.26	.05	−0.34	0.33	−.03
Gender	[−0.39, 3.06]			[−0.98, 0.36]
R ²	.02			.00
Step 2
Gender	2.10*	0.81	.08	−0.09	0.32	−.10
Gender	[0.50, 3.78]			[−0.79, 0.61]
Inattention/Memory	0.07	0.06	.06	−0.15**	0.02	−.32
Inattention/Memory	[−0.05, 0.18]			[−0.20, −0.10]
Hyperactivity/Restlessness	−0.36**	0.06	−.26	0.08*	0.03	.14
Hyperactivity/Restlessness	[−0.49, −0.24]			[0.02, 0.13]
Impulsivity/Emotional Lability	0.14*	0.07	.10	−0.00	0.03	−.00
Impulsivity/Emotional Lability	[0.10, 0.27]			[−0.06, 0.05]
Problems With Self-Concept	0.27**	0.06	.22	0.00	0.03	.01
Problems With Self-Concept	[0.16, 0.38]			[−0.04, 0.05]
CAARS ADHD Index	0.29*	0.10	.23	0.09*	0.04	.18
CAARS ADHD Index	[0.09, 0.48]			[0.00, 0.17]
R ²	.19**			.06**
ΔR²	.18**			.06**

Note. CIs and standard errors are based on 1,000 bootstrap samples. CI = confidence interval; AQ = Autism Quotient; CAARS = Conners’ Adult ADHD Rating Scale.

*p ≤ .05. **p ≤ .001.

Gender did not explain any variance in the AQ Attention to Detail at Step 1 of the regression. At Stage 2 of the model, CAARS scores accounted for an additional 6.1% of total variance in AQ Attention to Detail, thus supporting Hypothesis 6a that ADHD-related traits are associated with poorer ASD-related inattentiveness. A combined effect of this magnitude is considered small (f ² = .07). Supporting Hypothesis 6b, the CAARS’ Inattention/Memory Problems subscale explained the most (i.e., 4.5%) unique variance in AQ Attention to Detail. Hyperactivity/Restlessness and the ADHD Index accounted for small yet significant unique variance in AQ Attention to Detail at 1% and 0.4%, respectively.

Discussion

This examined how commonly students in an Australian university report ADHD-related symptoms. It also investigated whether reporting higher levels of ADHD-related symptom was related to higher reporting of depression, anxiety, stress symptoms, and autism-like traits. Of the 1,002 students who participated in this study, 17.3% individuals aged between 17 and 25 years reported experiencing disruption in attention, motor activation, and behavioral impulsivity at levels that are deemed to be characteristic of adult ADHD. These estimates were higher than those reported in previous studies using the CAARS (e.g., Harrison et al., 2013; Pope et al., 2007), and this result failed to support the first hypothesis. Harrison et al. (2013) however studied a small and restricted sample of 98 university students recruited from health and counseling services. Pope and colleagues (2007) obtained results based on a larger sample of university students from three U.K. universities and derived higher rates (i.e., 12%) than those reported by Harrison and colleagues (2013). However, all their participants were first- and second-year students undertaking a psychology degree. Conversely, current results were based on students at various stages of their undergraduate studies, enrolled in a wide range of courses (not only psychology). Hence, this study sample is more representative of the wider population of young adults in undergraduate courses. These results show that in Australia, a substantial proportion of young adults in undergraduate courses may experience significant ADHD-related difficulties and require support.

As a self-report measure was used to collect information about ADHD-related symptoms in this study, the estimate derived may be partly attributed to false positives (Atwoli et al., 2010). Some students identified as “at-risk” of ADHD may have subthreshold levels of ADHD-related impairments which would be insufficient for a diagnosis (Molina & Sibley, 2014). Temporary fluctuations in distractibility, mood, memory, concentration, self-belief, fatigue, and low frustration tolerance as well as lifestyle factors (i.e., poor sleep) and negative life events may have influenced students’ responses (Molina & Sibley, 2014). Psychological conditions such as depression, anxiety disorders, and substance abuse may also produce symptoms (i.e., inattention) that resemble those of ADHD, and could have also driven the high rates of self-reported ADHD symptoms (Fatseas, Debrabant, & Auriacombe, 2012). University students are indeed at higher risk of experiencing symptoms of poor mental health and psychological dysfunction relative to other adults (Stallman, 2010). However, the probability of false positives was reduced by (a) excluding data from students who provided invalid responses; (b) not offering incentives such as access to academic supports, accommodation, and/or medication for participating; and (c) removing the title of the CAARS from the measure, so that participants would not become cognizant of the fact that they were being assessed on a measure of ADHD symptoms.

Unlike past research reporting on the prevalence of ADHD diagnosis in students, this study only collected information about current self-reported symptoms. As such, more stringent criteria for conceptualizing ADHD, such as age of onset and previous history of symptoms, were not gathered (Green & Rabiner, 2012). However, a strength of this approach is that it acknowledges that information about age of onset may be difficult to gather when trying to establish whether an adult should be diagnosed with ADHD (Hesslinger, Tebartz van Elst, Mochan, & Ebert, 2003). Wasserstein, Wolf, Solanto, Marks, and Simkowitz (2008) argue that some students experiencing late onset of ADHD-related symptoms may not have a childhood-onset developmental disorder, and therefore may have never received a diagnosis. For others, retrospective recall of childhood behavior may be difficult to obtain, and/or parents may not be able to corroborate their history. The effects of highly organized schools, home environments, or other support structures (i.e., sport clubs) can mitigate expressions of symptoms in childhood and adolescence, thus altering students’ perception of their behavior as well as that of their parents (Wasserstein et al., 2008). An unexpected but noteworthy finding in this study was the observed higher mean scores reported by females on all CAARS subscales (although not all statistically significant). This finding is discrepant with previous studies in children and adolescent groups which have consistently indicated that males are more likely than females to present with symptoms of ADHD and to reach the diagnostic thresholds in adulthood than in childhood (Lee et al., 2008). The CAARS’ internal reliability coefficients were found to be good and similar for both genders. Hence, the CAARS-S: S can be considered as having reliably measured the same underlying constructs (i.e., ADHD traits) in both genders. Gender differences observed in this study may have instead been a by-product of differences in response styles. Lee et al. (2008) argue that females with ADHD-related symptoms tend to perceive and rate themselves as experiencing more associated problems, including quick temperedness, low self-confidence, interpersonal difficulties. Males however may have more limited self-awareness of their issues and/or tend to underreport the extent to which they engage in ADHD-related behavior and symptoms. This observed gender difference may also be due to an underrepresentation of ADHD in females in past research, and this is likely a result of lower identification of ADHD symptoms in females in childhood. As Lee and colleagues also noted, a majority of referrals for ADHD diagnoses in children are based on manifestations of hyperactive, impulsive, and aggressive behavior, and these are more likely to be displayed by boys than girls. Conversely, females tend to display inattentiveness which may be missed and preclude them from being referred for a diagnosis at a young age. Finally, there were almost twice as many females as male participants, and this gender difference in participation rates could have also partly accounted for the higher means in self-ratings in females.

Our findings of high levels of ADHD-like traits in the current sample of university students being associated with increasing levels of depression, anxiety, and stress are consistent with previous research (Jarrett, 2016; Kwak, Jung, & Kim, 2015; Richards et al., 1999). This association may reflect underlying adjustment difficulties resulting from a cumulative effect of impairments related to students’ ADHD symptoms as well as stressful and negative environmental circumstances they experience (Kwak et al., 2015). High ADHD-related symptoms may also induce depressive tendencies and chronic unhappiness due to demoralization (Turgay & Ansari, 2006). In young adults who start experiencing the effects of ADHD symptoms when they reach university, this demoralization may arise from a sudden perceived inability to live up to their full potential (Turgay & Ansari, 2006). Struggling with ADHD symptoms can foster the development of negative self-schemas and increase the salience of personal failures, which in turn may contribute to symptoms of depression and stress in challenging situations (Knouse, Zvorksy, & Safren, 2013). There is also evidence that an association between ADHD traits and depression may stem from a function of family risk, whereby the existence of one disorder increases the risk for the other (Biederman et al., 1995).

Anxiety and stress in university students with clinical levels of ADHD-related symptoms may result from constant worry about productivity (i.e., on university tasks, work, etc.) and meeting concurrent demands from several aspects of life (Feifel, 2007). Moreover, in a sample of 421 university students aged 17 to 25 years, Jarrett (2016) noted that those reporting high levels of ADHD also experienced anxiety due to deficits in executive functioning. In this study, poor executive functioning was linked to difficulties in regulating emotions, self-organization, problem solving, and working memory. Furthermore, emotional dysregulation may influence the severity of ADHD-related traits and their associations with associated comorbidities such as depression (Seymour, Chronis-Tuscano, Halldorsdottir, Stupica, Owens, & Sacks, 2012), anxiety, and distress (Retz, Stieglitz, Corbisiero, Retz-Junginger, & Rosler, 2012). Evidence suggests that compared with typically functioning individuals, those with ADHD traits demonstrate less constructive patterns of emotional coping, poorer persistence on tasks in the wake of frustration, higher levels of negative affect, poor problem solving when faced with challenging and emotionally evocative situations, more focus on negative aspects of a situation, and difficulties in identifying and processing negative emotions (Retz et al., 2012; Seymour et al., 2012). Although these hallmarks of poor emotional regulation have been implicated in the development of depression, anxiety, and stress, research in this area is still in its infancy, and further research is still needed to elucidate these associations in individuals with “at-risk” levels of ADHD symptoms.

Contrary to expectations however, not every subscale of the CAARS-S: S emerged as a significant predictor of depression, anxiety, and stress in the sample studied. Inattention only accounted for a negligible proportion of variance in depression scores but failed to explain any unique variance in anxiety and stress scores. The ADHD index only accounted for unique variance in anxiety scores but not in depression or stress scales. These findings are surprising, given that past research associates poor mental health to symptoms of inattention and overall ADHD (Wilens et al., 2009). Conversely, both the Hyperactivity-Impulsivity and the Problems With Self-Concept subscales accounted for unique variance in depression, anxiety, and stress scores. Wilens and colleagues (2009) also observed that psychiatric comorbidity (including major depression and anxiety disorders) was associated with hyperactive-impulsive symptoms in adults with ADHD.

Different methodologies and conceptualizations of ADHD in previous studies compared with the current study preclude direct comparison of results. Moreover, in this study, students’ self-concept was assessed using a reliable and valid subscale on the CAARS which however includes a narrow range of characteristics that relate to poor self-belief, low self-confidence, and self-esteem issues. Nonetheless, some tentative explanations of the effects of poor self-concept and hyperactivity on depression, anxiety, and distress can be offered based on a study by Wilmshurst, Peele, and Wilmshurst (2011). Using the 82-item Tennessee Self-Concept Scale—second edition (Fitts & Warren, 1996), Wilmshurst et al. found that the self-concept of students with ADHD symptoms may be influenced by their perceived competence in coping with and managing environmental demands, and their ability to make optimal use of opportunities. Moreover, compared with nonaffected peers, they were particularly likely to struggle to maintain control and order when faced with the more dynamic, chaotic, and unsettled lifestyle that young adulthood and attending university bring. University also involves less direct contact with teaching staff, more complex and longer range projects and assignments as well as less frequent evaluative feedback compared with what students, particularly those with learning difficulties and ADHD, are used to from their secondary school years (Nelson & Gregg, 2012). At the end of secondary schooling, students with ADHD symptoms oftentimes feel ill equipped and unprepared to cope with the changes that university life brings, and to demonstrate higher personal responsibility and self-advocacy in their new environment (Nelson & Gregg, 2012). Hence, the experience of depression, anxiety, and stress among university students with ADHD symptoms may be elicited by their beliefs of how effective they are at navigating their new environments.

Added to the issue of poor self-concept, continuous inner mental distractions, incessant flow of irrelevant thoughts and feelings as well as poor emotional and behavioral regulation (indicative of hyperactivity and impulsivity) may bring about psychological issues in students with ADHD symptoms (Gray et al., 2014; Weyandt et al., 2003). Although these arguments were previously made with regard to students with established diagnoses of ADHD, they may also apply to those with high levels of ADHD-related symptoms considering that they exhibit similar difficulties and impairments (Richards et al., 1999). However, this proposition is conjectural, and future studies should more thoroughly investigate the effects of self-concept and elevated hyperactivity on university students, and to explore the specific mechanisms that may elicit feelings of depression, anxiety, and stress in these students.

In this study, students with high levels of ADHD characteristics also reported higher levels of autism-like traits. Surprisingly, most ADHD symptoms, except inattention, contributed to social deficits associated with autistic traits. Social skills as measured by the AQ in this study also encompassed other dimensions pertaining to preference for routine, ability to switch attention and display imagination (Hoekstra et al., 2008). Hence, current results suggest that internal restlessness and proneness to boredom, impulsive behavioral tendencies (e.g., interrupting during conversation), and/or poor self-views can lead to social difficulties, make it hard to keep track of relevant social information, engage in mindreading, and adopt flexible behaviors (Hoekstra et al., 2008). As expected, ADHD-related inattention was related, albeit weakly, to lower levels of attention to detail. Hyperactivity was also significantly associated with poor attention to detail. These results show that attentional difficulties (e.g., distractibility, poor concentration, etc.) and hyperactivity/restlessness can reduce the individual’s ability to pay attention to, and remember details and patterns in information (Hoekstra et al., 2008).

These aforementioned findings are somewhat inconsistent with Polderman and colleagues’ (2013) study which found that the cooccurrence of ADHD symptoms and autism-related traits is not related to issues with behavioral hyperactivity but instead can be attributed to attentional switching. A main point of difference between the current study and that of Polderman and colleagues is that the latter examined attention switching as a separate factor in the five-factor model. Evidence suggests not only that this factor may be composed of items that do not reliably measure attention switching but also that attention switching itself is not specific to ASDs (Hoekstra et al., 2008; Kloosterman et al., 2010). That ADHD symptoms may influence autistic-like traits is an important finding as clinicians, particularly in university counseling centers, may encounter and treat individuals with these cooccurring symptoms (Leitner, 2014; van Schalkwyk et al., 2016). The cooccurrence of symptoms of ADHD and ASD is also associated with increased risk of impairments of daily living, poor psychosocial functioning as well as psychopathology (Saccani, Buzzi, & Paccione, 2013).

Caution should be exercised when interpreting this study’s findings. This study employed a cross-sectional design, and participants reported their symptoms either over a brief period (e.g., DASS-21) or over an undefined time period (e.g., CAARS and AQ). ADHD is a chronic condition, and the reporting of symptoms having been present for at least 6 months is a key requirement in informing a diagnosis (APA, 2013). Participants were not required to rate items on the CAARS based on their behaviors in the 6 months preceding the study. Assessing symptoms over a longer and better defined time period could control for the possibility of students reporting temporary symptoms and would be better aligned with DSM-5’s diagnostic criteria (APA, 2013).

Self-report measures of ADHD symptoms are typically symptom based (Gordon et al., 2006). Yet, level of functional impairment, not number of symptoms, is a more important determinant of pathology and treatment seeking (Gordon et al., 2006). Many of the students reporting clinical levels of ADHD symptoms in this study and in past studies (see Table 1) may not have in their daily lives exhibited signs of maladjustment or poor functioning that would have been commensurate with a diagnosis of ADHD. Future explorations of the rates of ADHD in Australian universities should investigate potential areas of impairment in major life domains, including secondary school graduation, amount of time spent in jobs, composition of friendship groups, number of traffic offenses and criminal prosecutions among others.

Information about students’ symptom was collected via self-report measures only. Shared method variance could have therefore driven the correlations between ADHD symptoms, and the DASS and AQ (Lindell & Whitney, 2001). Future studies may benefit from adopting a more objective and multimethod assessment approach with less reliance on self-reported behavioral ratings (DuPaul, Weyandt, O’Dell, & Varejao, 2009). This would also permit a more complete clinical picture of the individuals in line with internationally accepted diagnostic criteria and clinical guidelines (National Health and Medical Research Council, 2012).

In this study, all constructs were treated as dimensional rather than categorical. Hence, in assessing current experience of symptoms, no information about participants’ diagnostic statuses (i.e., ADHD, mood disorders, anxiety, and ASD) was collected, nor were they diagnosed as part of this study. Future research should determine how many students exhibit critical levels of ADHD symptoms after controlling for effects of other mental health problems and environmental circumstances that can influence their response on measures of ADHD. Information about previous diagnosis of ADHD, mood disorder, anxiety disorders, and ASD could also help determine the association between diagnostic statuses and self-report of symptoms, and how these are related.

Finally, although the current sample included students undertaking various undergraduate courses, coming from various ethnic backgrounds and socioeconomic statuses, and having varying mental health histories and family histories of ADHD, such key demographic information was not collected. Yet, this information could have provided further insight into participants’ response styles and patterns of self-reported symptoms across various social groups (Norvilitis, Ingersoll, Zhang, & Jia, 2008; Russell, Ford, Williams, & Russell, 2016).

Despite its shortcomings, this study is the first to report on the rate of self-reported ADHD-related symptoms among undergraduate students in an Australian university setting and, to investigate the relationship between ADHD symptoms and those of depression, anxiety, distress and autistic-like traits. Current findings suggest that it is imperative to identify students who are at high risk of ADHD, and who may benefit from further comprehensive clinical assessments and treatments when necessary to circumvent the occurrence of negative outcomes such as depression, anxiety, and stress. Moreover, high levels of autism-like traits were found in those with higher levels of ADHD-related characteristics, suggesting that clinicians need to assess for the potential cooccurrence of both disorders in individuals presenting for services, and that disability services should consider the types of supports these individuals may require to allow them progress academically and socially. As self-report measures may be conducive to a high number of false positives and because relying on diagnostic history can lead to underestimation of true ADHD prevalence rates, it is recommended that future studies in an Australian context include data on students’ level of functional impairment and symptom severity gathered from a wider range of objective sources.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Marie M. A. Nankoo has completed a Bachelor Psychology with Honours at Curtin University of Technology and has acquired experience working with and observing children and adolescents with neurodevelopmental disorders such as those on the autism spectrum and ADHD (including those with complex presentations). Marie is currently undertaking a Master of Clinical Neuropsychology and Doctor of Philosophy (combined degree) at the University of Western Australia. The aims of her PhD project are to (i) investigate the rate of ADHD-symptomology in young adults attending university and (ii) explore the cognitive mechanisms underlying emotional regulation deficits in adults with ADHD.

Romina Palermo is an associate professor in the School of Psychological Science at the University of Western Australia, leading the Person and Emotion Perception Laboratory. She is also a Chief Investigator and co-Program leader of the Person Perception program of the Australian Research Council Centre of Excellence in Cognition and its Disorders, an Associate Investigator of the Social Potential in Autism Unit at the Friedrich Schiller University Jena, Germany, and an Honorary Associate of the Department of Cognitive Science at Macquarie University, Australia. Her research aims to understand the perceptual, cognitive, and neural mechanisms underlying person perception, including how these processes interact with attention and executive functioning and, her lab also investigates ways emotion processes break-down in children and adults with atypical development, psychopathology, or brain injury.

Jason Alan Bell was awarded a PhD in Psychology, in 2008. He has over 40 peer reviewed papers in international research journals. His lab investigates the mechanisms of human cognition and perception, with a focus on understanding how differences in these processes contribute to the development and maintenance of clinical disorders such as ADHD, Eating Disorders, Obesity, Stroke, and Depression.

Carmela M. Pestell is an associate professor, a registered clinical neuropsychologist and clinical psychologist with over 20 years’ experience working actively as a clinician, researcher and supervisor. She is currently based at the School of Psychological Science, University of Western Australia and is the Director of the Robin Winkler Clinic, a professional training facility for postgraduate students. Dr Pestell has extensive experience working with children and adults with ADHD and, as an inaugural manager/team leader of the Complex Attention and Hyperactivity Disorders Service (CAHDS) in Western Australia, she was instrumental in setting up this state-wide ADHD service and establishing their research programme. Dr Pestell was also; a member of a health department committee reviewing mental health services for adults with ADHD (2010-2011) and, part of a state-wide psychiatry interest group on ADHD.

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