Online and Intellectual Awareness of Executive Functioning in Daily Life among Adolescents with and without ADHD

Abstract

Objective

Executive function deficits (EFD) are a central mechanism underlying negative outcomes in ADHD. This study examined awareness of EFD manifested in “real-time” task performance (Online Awareness) and in general self-knowledge of daily activities, outside the context of a specific task (Intellectual Awareness) among adolescents with and without ADHD.

Methods

102 adolescents with (n = 52) and without (n = 50) ADHD were administered Weekly Calendar Planning Activity (WCPA) and Behavior Rating Inventory of Executive Function (BRIEF). Parents completed the BRIEF parent version. Awareness was defined using the discrepancy paradigm: performance versus estimation on WCPA for online awareness; self versus parent report on the BRIEF for intellectual awareness.

Results

Adolescents with ADHD overestimated their performance on the WCPA and underestimated their EFD on the BRIEF compared to parent’s ratings. The discrepancy scores in both types of awareness were significantly larger among ADHD than controls (p < .005).

Conclusions

Adolescents with ADHD demonstrate significantly lower rates of online and intellectual awareness of EFD compared to controls.

Keywords

self-awareness self-monitoring metacognition ecological assessment teenagers functional impairment overestimation Positive Illusory Bias (PIB)BRIEF

Introduction

ADHD is a highly prevalent neurodevelopmental disorder that persists through adolescence and adulthood (American Psychiatric Association (APA), 2013). The prevalence of ADHD in adolescence is estimated at 7% to 13% (Xu et al., 2018) and is associated with many functional impairments in everyday activities, challenges in social interactions, and academic underachievement (e.g., Biederman et al., 2011; Frazier et al., 2007). In the long term, adolescents diagnosed with ADHD are at risk for negative outcomes, including substance abuse (Molina et al., 2018), school dropout (Biederman et al., 2004), and driving violations (Barkley & Cox, 2007; Ratzon et al., 2017). Furthermore, adolescence has been described as a transitional developmental period, distinguished by a need for autonomy coupled with changes in physical, psychological, and social development (Esnaola et al., 2020; Weil et al., 2013), that may add more challenges to the day to day lives of this population.

Metacognition is defined as higher-level cognitive functions, including executive functions (EF) and awareness, which have been shown to significantly impact functional outcomes among individuals with developmental and acquired neurological health conditions (Katz & Maeir, 2011; O’Keeffe et al., 2007; Ownsworth & Clare, 2006; Toglia, 2018; Toglia & Maeir, 2018). Furthermore, metacognition is a foundation for ongoing self-management of a chronic neurocognitive health condition such as ADHD, which is the focus of the current study (Asherson et al., 2016; Turgay et al., 2012). The first component of metacognition is EF, defined as an interrelated set of higher-level neurocognitive abilities responsible for directing and coordinating goal-directed actions. EF comprises self-regulatory processes of cognition, emotions, and behaviors (Brown, 2013; Diamond, 2013; Miyake & Friedman, 2012; Zelazo, 2015). Executive Functions (EFs) help people mentally represent goals across time, anticipate future consequences of actions, and make behavioral adjustments when needed (Toglia & Katz, 2018). There is a strong relation between EF and ADHD, and many children, adolescents, and adults with ADHD exhibit EF deficits (EFD) that impact their functional performance and participation in life roles (Biederman et al., 2011; Brown, 2013; Silverstein et al., 2020). Adolescents with ADHD and EFD are the focus of the current study.

The second component of metacognition is awareness. This study is grounded in The Dynamic Comprehensive Model of Awareness (DCMA; Toglia & Kirk, 2000) which has been used to examine awareness among individuals with neurological health conditions such as acquired brain injury (Goverover et al., 2007), multiple sclerosis (Goverover et al., 2014), and recently also with ADHD (Kastner et al., in press; Levanon-Erez et al., 2019). The DCMA conceptualizes awareness as a dynamic interplay between the person and the context of the task. The individual is considered to develop two types of awareness as they interpret their occupational experiences over time: online awareness is situational and is influenced by the context of a specific task or situation. It includes both appraisal of task demands before a task and self-monitoring and self-regulation during and after the task. Thus, online awareness is usually assessed immediately before and/or after a specific task. Intellectual awareness (or general self-knowledge) exists outside the context of a specific task and encompasses the person’s perceptions, beliefs, and confidence regarding their everyday functioning, abilities, as well as challenges derived from a variety of health conditions. A dissociation between online awareness and intellectual awareness has been supported by research (Goverover et al., 2007; O’Keeffe et al., 2007; Robertson & Schmitter-Edgecombe, 2015). This distinction suggests that people may demonstrate good intellectual awareness in an interview yet poor online awareness within the context of an activity or vice versa.

The term metacognition is typically used in developmental literature (Flavell et al., 2001). However, other neuropsychology researchers conceptualized self-awareness as part of EF (Ardila, 2016; Lyzak, 1983; Stuss, 2011), suggesting that deficits in EF components may cause unawareness. Despite the different classifications of awareness, whether as a part of EF, or as a separate entity under the umbrella of metacognition, both intellectual and online awareness have been shown to significantly impact intervention outcomes and long-term self-management of chronic health conditions (Bodenheimer et al., 2002; Bussing et al., 2012; Toglia & Maeir, 2018). Individuals that understand their limitations, are more engaged in the intervention processes, and online awareness enables them to learn from their experiences and adapt their actions to support ongoing management of the challenges they face in daily life (Lyons & Zelazo, 2011).

Adolescents diagnosed with ADHD have been shown to have poor compliance with medications and psychosocial treatments (Khan & Aslani, 2019; Wehmeier et al., 2015). It is hypothesized that poor awareness of their challenges is an obstacle for engagement and persistence in the treatment process. There is some evidence for adolescents under-reporting of their ADHD symptoms and EFD (Steward et al., 2017; Volz-Sidiropoulou et al., 2016), a phenomenon known in the literature as a Positive Illusory Bias (PIB; Owens et al., 2007). Yet, others have shown that PIB is not universal and particularly only the social domain was stable (e.g., Bourchtein et al., 2017). Considering the centrality of EFD in the ADHD population, this study will focus on awareness of EFD from an ecological perspective, using measures that represent EF in “real-life.” The research on awareness of EFD among adolescents with ADHD is sparse. Only one study (n = 57) was found reporting significant differences between ADHD and controls regarding awareness of EFD, with a higher self-parent report discrepancy among adolescents with ADHD than controls (Steward et al., 2017).

There is a gap in the literature, regarding online and intellectual awareness of EFD among adolescents with and without ADHD. In order to evaluate online awareness, self-evaluation on performance-based cognitive-functional assessments that use simulated “real-life” tasks, have the potential to allow insights into individuals’ understanding of their daily performance (Wallisch et al., 2018). Furthering our understanding of awareness may be beneficial for designing effective cognitive-functional interventions for adolescents with ADHD.

The objectives of the present study are: (a) to examine online awareness of EFD on a performance-based cognitive-functional task, (b) to examine the intellectual awareness of EF in daily life using an ecological rating scale, and (c) to examine the reciprocal relations between the two types of awareness among adolescents with and without ADHD.

Method

Research Design

In this study, we used a cross-sectional design comparing two groups. Ethical approval for the study was obtained from the Helsinki ethics committee of the university medical center. Data were collected between the years 2016 and 2019. Both parents and adolescents signed informed consent. Participants in the ADHD group were recruited from neuropediatric clinics in the community and a tertiary care academic hospital. Participants in the control group were recruited through convenience sampling and snowballing techniques with age and gender matching to the research group.

Participants

The sample included 102 adolescents attending regular education schools, with (n = 52) and without ADHD (n = 50), between the ages of 12 and 18. Inclusion criteria for participants in the ADHD group were a valid ADHD diagnosis by neurologist or psychiatrist according to the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; APA, 2013) criteria confirmed by Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version 2.0 (Kiddie-SADS-PL; Kaufman et al., 1996) administered by an expert psychologist, as well as stable pharmacological status during last 3 months. Exclusion criteria for the ADHD group included an estimated IQ below 85: Lower than one standard score on Block Design and Vocabulary subtests of Wechsler Intelligence Scale for Children IV (WISC-IV; Wechsler et al., 2004) as well as the use of medications that do not allow for 24-hour pause (e.g., atomoxetine). Exclusion criteria for the control group included ADHD symptomatology according to Conners’ Parent Scales-Revised (CPRS-R; Conner, 2000) based on scores above the cutoff for clinical impairment in both hyperactivity-impulsivity and inattentiveness DSM-IV symptoms subscales (T > 65) as well as learning disabilities (based on parent report). The CPRS-R was chosen as a screening tool only for the control group to evaluate the occurrence of non-diagnosed ADHD and common comorbidities, unlike the KIDDIE-SADS-PL that was used to validate the ADHD diagnosis and common comorbidities in the ADHD group. Another exclusion criterion for both groups was additional primary neurological or psychiatric health conditions based on parent report (e.g., Autism Spectrum Disorder, Cerebral Palsy, Epilepsy, Intellectual Disability). In order to represent the adolescent ADHD population, common ADHD comorbidity was not an exclusion criterion but evaluated according to Kiddie-SADS-PL and presented in the description of the study group.

No significant age and gender differences were found between the ADHD (n = 52) and control (n = 50) groups. Significant group differences were found in mother’s years of education, t(99) = 3.68, p < .001, whereby mothers of the ADHD group had more formal education. No significant associations (all p > .1) were found between this variable and all other study measures. Descriptive statistics of the ADHD group revealed that 63.5% of the participants took medication, 57.7% were diagnosed with the combined type of ADHD, and 58% had at least one comorbidity in addition to the ADHD diagnosis. Regarding potential confounds on awareness scores, no significant differences were found between participants who took medication for ADHD compared to those who did not, nor between participants who demonstrated only an ADHD diagnosis (pure ADHD) compared to those who had ADHD and additional comorbidities (all p > .1) (see Table 1).

Table 1.

Participants Characteristics.

		ADHD group (n = 52)	Control group (n = 50)
Age
M (SD)		14.44 (1.58)	14.76 (1.75)
Range		12.0–17.5	12.08–17.92
Gender (%)
Male		69	64
Mother’s education, years M(SD)		18.29 (3.0)	16.16 (2.81)**
ADHD medication (%)	Yes	63.5%
ADHD presentation (%)
Inattentive		34
Hyperactive/impulsive		8.5
Combined		57.5
Comorbidities (%)
Depression		5.1
Anxiety		18
ODD		33.3
CD		25.6
LD		54
Pure ADHD (no comorbidities)		42%

Note. M = mean; SD = standard deviation; ODD = oppositional defiant disorder; CD = conduct disorder; LD = learning difficulties.

p < .01.

Procedure

After receiving ethical approval, physicians and therapists referred adolescents with ADHD to the study. In addition, families responded to ads on social media. Control participants were selected from a pool of typical subjects recruited for a study of typical Israeli adolescents (n = 88) on the Weekly Calendar Planning Activity (WCPA). Subjects with CPRS-R ADHD symptoms above cutoff scores (n = 5) and parent report of learning difficulties (n = 11) were excluded. Fifty subjects were chosen to match the age and gender of the ADHD group. A phone call was made for potential participants who contacted an e-mail. The study was described to the parents who provided principle consent and then were invited with their adolescent to the university research laboratory. The ADHD group was also tested by the WISC-IV, and parents underwent the KIDDIE-SADS-PL interview by an expert psychologist. The study was coordinated by an experienced occupational therapist, PhD student (first author O.F.).

Measures

Weekly Calendar Planning Activity (WCPA; Toglia, 2015) is a performance-based EF assessment comprising a multi-step activity. The participant is presented with 18 randomly ordered appointments that need to be scheduled in an empty 1-week calendar. Five rules need to be followed: (1) leave Tuesday free, (2) do not cross out appointments after they are entered in the calendar, (3) ignore distracting questions from the examiner, (4) inform the examiner when it is a specified time, and (5) inform the examiner when finished. Decision-making, recognition of conflicts and strategies required to place the appointments in the most accurate space, are needed to minimize errors. The examiner observes and records the strategies used by the clients during the task and also conducts a semi-structured interview immediately after the task, probing awareness of performance and strategy use. WCPA is scored for accuracy (number of accurate appointments scheduled out of 18), number of appointments entered, planning time (time from the beginning of the assessment until the first appointment is scheduled), total time and efficiency score (accuracy/total time), error types, number of rules followed, and number of strategies used. A high level of inter-rater reliability (.99) for total accuracy score was established (Weiner et al., 2012), as well as moderate to high (.60–.85) test-retest reliabilities for performance measures (Lahav et al., 2018). Furthermore, discriminant validity between typical adolescents and youth-at-risk (Toglia & Berg, 2013) and between adolescents with epilepsy and controls were found (Zlotnik et al., 2020). Finally, discriminant validity between college students with and without ADHD on various performance measures was found (Lahav et al., 2018). Since the WCPA is a valid measure, representing EF performance in “real-life” and was tested on adolescents with neuro-cognitive challenges, as well as college students with ADHD, this measure was chosen to examine online awareness in the current study.

The Behavior Rating Inventory of Executive Function (BRIEF) Parent form (Gioia et al., 2000a) and BRIEF Self-Report (BRIEF-SR; Guy et al., 2004) is an ecological rating scale targeted to reflect the neuropsychological constructs of EF in daily life situations. Parent and SR forms are rated on a three-point Likert scale (“never,” “sometimes,” and “always”) based on child behavior over the past 6 months. The parent form is intended for parents of children and adolescents aged 5–18 years and comprises 86 items, whereas the self-report form encompasses 80 items and is intended for adolescents aged 12 to 18. Eight subscales are included: (a) inhibition, (b) shifting, (c) emotional control, (d) initiation, (e)working- memory, (f) planning, (g) organization of materials, and (h) monitoring. The BRIEF-SR includes one different subscale, “task completion,” that replaces the “initiation” scale. Scales are combined to form two indices—the Behavioral Regulation Index (BRI) and the Metacognition Index (MI), as well as an overall Global Executive Composite (GEC). The raw scores are transformed into T scores. The normative mean score is 50 (SD = 10). Atypical scores are indicated by T scores 65 or above, considered clinically impaired. Regarding the parent-report form, internal consistency (.80–.98), test-retest reliability (.79–.88), and discriminant validity have been established among children and adolescents individuals with ADHD (Gioia et al., 2000b; McCandless & O’Laughlin, 2007; Toplak et al., 2009). Regarding the self-report form, internal consistency (.72–.96), test-retest reliability (.59–.89), and inter-rater reliability (.36–.57) were established (Guy et al., 2004). Discriminant validity between adolescents with traumatic brain injury and healthy controls was found (Wilson et al., 2011). Recent studies compared BRIEF parent and self-report and levels of awareness abilities among healthy adolescents (Zlotnik & Toglia, 2018). In addition, unawareness was found among adolescents with ADHD using discrepancy scores (parent-self-report) compared to control group (Steward et al., 2017). For the purposes of the current study only the GEC score was chosen for statistical analysis.

Data Analysis

Awareness scores

Online awareness was operationally defined as the discrepancy between actual performance (accuracy score) and self-estimation after performance on the WCPA. Self-estimation was measured after finishing WCPA, while the researcher asked the participant: “eighteen appointments needed to be entered into the weekly calendar. Estimate the number of appointments that you entered accurately into the schedule” (Toglia, 2015, p. 142). These variables represented the same scale (0–18) and were derived from different times within the same subject, therefore, it is termed “Time effect.” Intellectual awareness was operationally defined as the discrepancy between adolescent self-report and parent report on the BRIEF-GEC, termed, “Reporter effect.” Mixed model ANOVA was computed whereby the within-subject factor was awareness (comparing performance versus estimation; “Time effect” or self and parent report; “Reporter effect”), and the between-subject factor was Group (ADHD vs. control).

Awareness categories

In addition to the continuous awareness scores, clinically meaningful categorical scores were calculated in order to evaluate the incidence in terms of unawareness in the study groups.

Awareness categories were calculated as follows: underestimation of performance (score −1), aware (score 0), and overestimation of performance (score 1) were computed based on 1.5 SD for each awareness score of the control group. Thus, for the online awareness, a discrepancy of 0–2.9 = “aware,” (−3) ≥ “underestimation,” and 3 ≥ “overestimation.” For the intellectual awareness, a discrepancy of 0–10.9 = ”aware,” (−11) ≥ “underestimation,” and 11 ≥ “overestimation.” Chi-square analysis was computed to compare awareness types (online and intellectual) between groups and to examine the association between online and intellectual awareness categories within groups. Significance was set at .05, and partial eta square (η²_p) is reported as the effect size for the ANOVA test, with η²_p of .01 considered a small effect, η²_p of .06 considered a medium effect, and η²_p of .14 considered a large effect size (Richardson, 2011).

Results

Online awareness

There was a statistically significant interaction of Time × Group on the WCPA accuracy score [F_(1,98) = 19.34, p < .001, η²_p = .17]. At time 1 (actual performance), the mean score of the ADHD group was lower than the control group, while at time 2 (estimation after performance), both groups estimated their performance as higher than time 1. The discrepancy between time 1 and 2 of ADHD group was significantly higher (Mean difference = −3.44, SD = 0.35, p < .001) than control group (mean difference = −1.26, SD = 0.35, p < .001) (see Table 2 and Figure 1).

Table 2.

Online Awareness: Interaction of Time (Performance vs. Estimation) × Group (ADHD vs. Controls) According to WCPA.

	ADHD group(n = 50) M (SD)	Control group(n = 50) M (SD)	Total(n = 100) M(SD)	Time effect F (p)	η²_p	Group effect F (p)	η²_p	Time × group F (p)	η²_p
Time 1: actual performance (accuracy score)	13.06 (3.19)	15.18 (1.79)	14.12 (2.78)
Time 2: estimation after performance	16.50 (1.63)	16.44 (1.63)	16.47 (1. 62)	89.91 (<.001)	.478	8.44 (.005)	.08	19.34 (<.001)	.16

Note. WCPA = Weekly Calendar Planning Activity; M = mean; SD = standard deviation.

Figure 1.

Online awareness: group × time on WCPA.

Intellectual awareness

The interaction effect between Reporter (adolescent vs. parent) and Group (ADHD vs. controls) was significant [F_(1,98) = 18.99, p < .001, η²_p = .17]. While in the ADHD group, parents reported higher BRIEF-GEC than their children, the parents in the control group reported lower BRIEF-GEC score as lower than their children. According to reporter 1 (parent), the mean score of the ADHD group was higher than the control group as well as reporter 2 (adolescent self-report). The discrepancy between reporter 1 and 2 of the ADHD group was significantly higher (mean difference = 7.08, SD = 10.03, p < .002) than in the control group (mean difference = −3.6, SD = 6.71, p = .004) that showed an opposite trend. In addition, differences between ADHD and controls were found on adolescent self-report (reporter 2), with the ADHD group reported significantly more EF impairment than controls [F_(1,96) = 8.94, p < .001] (see Table 3 and Figure 2).

Table 3.

Intellectual Awareness: Interaction of Reporter (Adolescent Self-Report vs. Parent) × Group (ADHD vs. Controls) According to BRIEF-GEC.

	ADHD group (n = 50)	Control group (n = 50)	Total (n = 100)	Reporter effect		Group effect F (p)	η²_p	Reporter × group
	M(SD)	M(SD)	M(SD)	F(p)	η²_p	Group effect F (p)	η²_p	F (p)	η²_p
Reporter 1: parent report	61.19 (10.19)	41.44 (5.22)	51.11 (12.74)
Reporter 2: self-report	54.62 (9.74)	44.60 (8.21)	49.51 (10.26)	2.32 (.13)	.02	128.22 (<.001)	.57	18.99 (<.001)	.16

Note. M = mean; SD = standard deviation.

Figure 2.

Intellectual awareness: group × reporter (BRIEF-GEC, parent and adolescent self-report).

Associations/dissociations between online and intellectual awareness categories by group

Table 4 demonstrates a comparison of awareness categories (aware/underestimate performance/overestimate performance) between groups. Significant between-group differences were found for the online as well as intellectual awareness categories. Results show that in the ADHD group, 52% were aware of their online performance, while controls showed a higher rate of awareness, 78%. In the intellectual awareness categories, 54% were aware of their EF profile in daily life in the ADHD group, compared to 86% of controls.

Table 4.

Intellectual and Online Awareness Levels by Groups.

	ADHD (n = 48) n (%)			Control (n = 50) n (%)			χ ²	p
	Under	Aware	Over	Under	Aware	Over	χ ²	p
Online awareness*	0 (0)	25 (52)	23 (48)	2 (4)	39 (78)	9 (18)	12.59	.002
Intellectual awareness**	6 (12)	27 (54)	15 (30)	7 (14)	43 (86)	0 (0)	18.70	<.001

Note. Awareness categories based on 1.5 SD for each awareness score of the control group.

Online awareness categories: under≥ (−3), aware = 0–2.9, over ≥3.

Intellectual awareness categories: under≥ (−11), aware = 0–10.9, over ≥11.

Table 5 presents associations and dissociations between three categories of awareness in both measures (online and intellectual) within each group. In the ADHD group, results showed that while 31.3% were aware in both measures, 16.7% were unaware in both measures, 52% of the group demonstrate the dissociation between their online and intellectual awareness scores. The rates of association in the control group were higher, with 66% aware in both measures.

Table 5.

Online and Intellectual Awareness Categories within Groups.

			Intellectual awareness n (%)**			χ ²	p
		Awareness categories	Under	Aware	Over	χ ²	p
Online awareness, n (%)*	ADHD (n = 48)	Under	0 (0)	0 ())	0 (0)	.32	.85
		Aware	3 (6.3)	15 (31.3)	7 (14.6)
		Over	3 (6.3)	12 (25)	8 (16.7)
	Control (n = 50)	Under	0 (0)	2 (4)	0 (0)	.45	.78
		Aware	6 (12.5)	33 (66)	0 (0)
		Over	1 (2)	8 (12)	0 (0)

Note. Awareness categories based on 1.5 SD for each awareness score of the control group.

Online awareness categories: under ≥(−3), aware = 0–2.9, over ≥3.

Intellectual awareness categories: under ≥(−11), aware = 0–10.9, over ≥11.

Discussion

The current study aimed to examine awareness of EFD manifested in “real-time” task performance (online awareness) and in overall daily life (intellectual awareness) among adolescents with and without ADHD. Results support the significant occurrence of unawareness phenomenon among adolescents with ADHD compared to their non-ADHD peers and, more precisely, their overestimation of EF abilities. This study also demonstrates a contextual aspect of awareness among adolescents with ADHD, comparing intellectual and online awareness showing the considerable incidence of both types co-occurring, yet also significant dissociations between them.

Findings of online awareness will first be discussed in relation to the entire sample addressing the developmental stage of adolescence in general, and then the specificity of findings regarding the study group with ADHD. The significant trend of overestimation of performance in both groups is consistent with other studies that used a similar performance-estimation paradigm. Within a developmental framework, comparing typical adolescents to college students (Bakracevic & Licardo, 2010) and adults (Demetriou & Bakracevic, 2009), significantly poorer awareness of performance on academic and cognitive tasks was found among adolescents compared to the older groups. Another study investigated the performance-estimation discrepancy on the WCPA accuracy score, as used in the current study, among typical adolescents (Zlotnik & Toglia, 2018). Their results demonstrated a significant association between performance and self-estimation. This phenomenon is known as the “Dunning-Kruger effect” (Kruger & Dunnung, 1999), showing a negative association between competence and self-estimation in the general population. In other words, the poorer the competence in a certain capacity, the larger the overestimation. This effect can also be applied to the current study, whereby one could posit that adolescents with ADHD who demonstrated poorer competence than controls on the WCPA also showed a higher performance-estimation discrepancy, as evident in the significant Time × Group interaction effect. However, additional research is needed to explore alternative hypotheses to the “Dunning-Kruger effect” such as deficit in a specific monitoring mechanism (Groom et al., 2010; O’Conell et al., 2009) as well as motivational processes (Hoza et al., 2012), which may explain the larger performance-estimation gap in the ADHD group. Regarding our findings related specifically to the ADHD group, our results are in line with several similar studies. Fabiano et al. (2018) investigated teenage drivers with ADHD in a simulated driving task and found that they overestimated their driving competence. In addition, Weiner et al. (2012) studied youth at risk who also demonstrate EF challenges. Their preliminary data pointed to a lack of awareness among these youth about the quality of their own performance after WCPA task completion. Taken together, there is a small but growing body of evidence supporting the claim that adolescents with ADHD may overestimate their performance on complex tasks more so than typical controls. These findings may shed more light on the negative functional outcomes identified in this population, such as driving risks (e.g., Barkley & cox, 2007), school dropouts (e.g., Biederman et al., 2004), substance abuse (e.g., Molina et al., 2018), and social problems (e.g., Bunford et al., 2018). Poor awareness of task performance could block a vital step in adaptive self-regulation of behavior toward self-serving goal achievement. Self- regulation encompasses both detection of errors and problems as well as adaptive regulating responses to these challenges (Vohs & Baumeister, 2016). If poor performance on complex daily tasks is not detected, then it can be assumed, similarly to populations with neurological injuries, that the impetus to learn and apply effective coping strategies or to engage in therapeutic processes will be lacking (O’Keeffe et al., 2007; Ownsworth & Clare, 2006; Toglia, 2018). Further research is needed to explore the mediating role of online awareness of complex daily tasks in predicting regulatory responses and functional outcomes among adolescents with ADHD.

The findings of the current study concerning intellectual awareness are also noteworthy. The significant interaction effect between reporter (adolescent/parent) and group, demonstrated a higher discrepancy (poorer awareness) in the ADHD group compared to controls. The vast majority of controls demonstrated intact general awareness (86%) of their EF profile in daily life, while the rest of the control participants showed underestimation compared to parent report. Thus, a general overestimation tendency in intellectual awareness, such as the one found in online performance, was not found. Controls were either aware of their EF in daily life or underestimated their abilities compared to their parents. On the other hand, within the ADHD group, one-third of the group showed an overestimation of their abilities compared to their parent’s report. These findings are consistent with previous research demonstrating PIB of overall EFD in daily life among adolescents with ADHD (Steward et al., 2017). The current study reinforces these findings, using the same measure and applied to a larger sample. Both studies examined adolescent populations using the parent and self-report discrepancy method on BRIEF scores. However, the first study examined 57 adolescents aged 11 to 16 (mean age—13.27), while the current study tested 102 teenagers aged 12 to 18 (mean age—14.6). Moreover, the first study investigated only intellectual awareness, while the current study examined two types of awareness as well as applying categorical analyses of awareness levels, using cutoff scores (aware, underestimation, and overestimation of EF). These findings add to other studies of intellectual awareness for other domains that found overestimation of functioning among children and adolescents with ADHD. Several studies compared the Self-Perceptions Profile for Children (Harter, 2012) with a parent or teacher report, focusing on social acceptance (e.g., McQuade et al., 2017), scholastic competence (e.g., Owens & Hoza, 2003), and behavioral problems (e.g., Hoza et al., 2004). Similarly, underestimation of ADHD symptom severity was also found on to the Conners Rating Scales (CRS; Weiner et al., 2012). The categorical methodology for both awareness types enabled a closer examination of the frequencies of awareness categories, revealing PIB in 30% of the sample regarding intellectual awareness and 50% for online awareness. These findings are in line with Fefer et al.’s (2018) results that illustrated the same percentiles of overestimation regarding academic and social competence. In the same vein, albeit slightly higher, half of the ADHD group demonstrated online over-estimation of WCPA performance. Taken together, these studies provide strong evidence for the presence of PIB for a variety of domains in ADHD. Nevertheless, results also demonstrate that unawareness is not universal for all participants with ADHD. Thus, when analyzing mean group tendencies, the extent of the unawareness phenomenon may be overestimated. Considering that EF is a central impairment in ADHD with far-reaching functional implications (Barkley, 2012), intellectual and online awareness of EFD may be an important target for improving treatment compliance and functional outcomes. Metacognitive interventions supporting the discovery of adolescents’ challenges before, during and after complex cognitive functional experiences, may be beneficial and warrant further study (Levanon-Erez et al., 2019).

Crosstabs analysis of awareness categories enabled comparisons between awareness types. The results showed overlap between awareness types among approximately half of the ADHD group, while the rest of the sample showed dissociations between awareness types. This complex picture is accordant with the DCMA (Toglia & Kirk, 2000), which differentiates the two types of awareness, yet also claims that there is a dynamic interplay between them. Cognitive developmental theories posit that monitoring and reflecting on experiences is a prerequisite for self-awareness (Flavell et al., 2001; Lyons & Zelazo, 2011). Intellectual awareness may develop from interpreting experiences (online awareness) over time, which could explain overlap. However, results also suggest that even in the face of intellectual awareness, online awareness may be restricted due to contextual factors such as task difficulty, environmental factors, and personal characteristics (Toglia & Kirk, 2000). Thus, despite the common co-occurrence between awareness types, it is equally common to find dissociations, highlighting the need to address both types in research and clinical practice. Further knowledge regarding the underlying mechanisms of both awareness types will facilitate understanding the relation between them.

Limitation and Recommendations for Future Research

There are some methodological aspects that need to be considered for further research. This study was conducted on a referred sample of adolescents with ADHD and a convenience sample of controls. The mother’s education level was relatively high and may not represent the general ADHD or typical population. Data did not include information on participants’ socioeconomic status (SES) beyond mother’s education. This is a limitation of the current study since SES may impact the ADHD population and EFD (Russell et al., 2016). Furthermore, the different methods for the exclusion criteria in both groups may confine the comparison between them.

In addition, the literature has suggested that there may be different awareness profiles for boys and girls diagnosed with ADHD in relation to online awareness (Jiang & Johanston, 2017) as well as intellectual awareness (Hoza et al., 2002; Tu et al., 2019). Age, comorbidities for ADHD, and medical treatment can also mediate or moderate the ADHD risk for unawareness. Although preliminary analyses revealed no medication or comorbidity effect on awareness variables, the sample was not sufficiently powered to examine these issues in depth. Thus, further research is needed to investigate the effects of these intervening variables on awareness of EFD, using larger and more representative samples.

Conclusions

Adolescents with ADHD demonstrate significantly lower rates of online and intellectual awareness of EFD compared to their peers. Substantial rates of dissociation between awareness types support the theoretical distinction between online and intellectual awareness, according to DCMA (Toglia & Kirk, 2000). Since unawareness is conceptualized as a barrier to adolescents’ engagement in treatment, further research is needed to discover underlying mechanisms of awareness.

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.

ORCID iD

Orit Fisher

Author Biographies

Orit Fisher is a doctorate student and a lecturer in the school of occupational therapy at the Hebrew University of Jerusalem. She is a member and a researcher in the laboratory for neurocognitive rehabilitation at Hebrew University. Orit Fisher has 18 years of clinical experience working with children and adolescents diagosed with neurodevelopmental disorders. Areas of interest and research: ADHD in childhood and adolescence, cognitive interventions; executive functions; driving skills, and assistive technology.

Itai Berger is a professor at the Paul Baerwald School of Social Work and Social Welfare, The Hebrew University of Jerusalem, Jerusalem, Isreal, and the head of Pediatric Neurology Service, Assuta Ashdod University Medical Center, Ashdod, Israel.

Ephraim S. Grossman trained and practiced as a school psychologist in the municipal psychological service of the Education Department of the city of Jerusalem. While serving as a psychologist at the Neuro-Cognitive center of the Pediatric Division in Hadassah Medical Center, Dr. Grossman gained additional experience in assessment and diagnosis of children with ADHD. As a senior lecturer at the Department of Education at Ariel University, Dr. Grossman teaches courses related to Psychology, School Consulting, Learning Disorders and Evaluation.

Adina Maeir is an academic member in the School of Occupational Therapy of the Faculty of Medicine at Hebrew University and served as the Chair of the school (2013-2019). She founded the laboratory for neurocognitive rehabilitation at Hebrew University which is dedicated to understanding cognitive deficits in the context of real-world outcomes within a bio-psycho-social model of health. Her research includes the development of assessment and treatment protocols for individuals with acquired and developmental neurocognitive deficits across the lifespan. Prof. Maeir has 30 years of clinical experience in cognitive rehabilitation.

References

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

Ardila

(2016). Is “self-consciousness” equivalent to “executive function”?. Psychology & Neuroscience, 9(2), 215. DOI: 10.1037/pne0000052

Asherson

Buitelaar

Faraone

S. V.

Rohde

L. A.

(2016). Adult attention-deficit hyperactivity disorder: Key conceptual issues. The Lancet Psychiatry, 3(6), 568–578.

Bakracevic

Licardo

(2010). How cognitive, metacognitive, motivational and emotional self-regulation influence school performance in adolescence and early adulthood. Educational Studies, 36(3), 259–268.

Barkley

R. A.

(2012) Executive functions: What they are, how they work, and why they evolved. Guilford Press.

Barkley

R. A.

Cox

(2007). A review of driving risks and impairments associated with attention-deficit/hyperactivity disorder and the effects of stimulant medication on driving performance. Journal of Safety Research, 38, 113–128.

Biederman

Monuteaux

M. C.

Doyle

A. E.

Seidman

L. J.

Wilens

T. E.

Ferrero

Morgan

C. L.

Faraone

S. V.

(2004). Impact of executive function deficits and attention-deficit/hyperactivity disorder (ADHD) on academic outcomes in children. Journal of Consulting and Clinical Psychology, 72(5), 757–766. https://doi.org/10.1016/j.psychres.2010.08.029

Biederman

Petty

C. R.

Clarke

Lomedico

Faraone

S. V.

(2011). Predictors of persistent ADHD: An 11-year follow-up study. Journal of Psychiatric Research, 45, 150–155.

Bodenheimer

Lorig

Holman

Grumbach

(2002). Patient self-management of chronic disease in primary care. JAMA, 288(19), 2469–2475.

10.

Bourchtein

Langberg

J. M.

Owens

J. S.

Evans

S. W.

Perera

R. A.

(2017). Is the positive illusory bias common in young adolescents with ADHD? A fresh look at prevalence and stability using latent profile and transition analyses. Journal of Abnormal Child Psychology, 45(6), 1063–1075. https://doi.org/10.1007/s10802-016-0248-3

11.

Brown

T. E.

(2013). A new understanding of ADHD in children and adults: Executive function impairments. Routledge.

12.

Bunford

Evans

S. W.

Langberg

J. M.

(2018). Emotion dysregulation is associated with social impairment among young adolescents with ADHD. Journal of Attention Disorders, 22(1), 66–82.

13.

Bussing

Zima

B. T.

Mason

D. M.

Meyer

J. M.

White

Garvan

C. W.

(2012). ADHD knowledge, perceptions, and information sources: Perspectives from a community sample of adolescents and their parents. Journal of Adolescent Health, 51(6), 593–600.

14.

Conner

K. C.

(2000). Conner’s rating scales-revised. Multi-Health Systems Inc.

15.

Demetriou

Bakracevic

(2009). Reasoning and self-awareness from adolescence to middle age: Organization and development as a function of education. Learning and Individual Differences, 19, 181–194.

16.

Diamond

(2013). Executive functions. Annual Review of Psychology 64, 135–168.

17.

Esnaola

Sesé

Antonio-Agirre

Azpiazu

(2020). The development of multiple self-concept dimensions during adolescence. Journal of Research in Adolescence, 30(S1), 100–114.

18.

Fabiano

G. A.

Schatz

N. K.

Hulme

K. F.

Morris

K. L.

Vujnovic

R. K.

Willoughby

M. T.

Hennessey

Lewis

K. E.

Owens

Pelham

W. E.

(2018). Positive bias in teenage drivers with ADHD within a simulated driving task. Journal of Attention Disorders, 22(12), 1150–1157. https://doi.org/10.1177/1087054715616186.

19.

Fefer

S. A.

Ogg

J. A.

Dedrick

R. F.

(2018). Use of polynomial regression to investigate biased self-perceptions and ADHD symptoms in young adolescents. Journal of Attention Disorders, 22(12), 1113-1122. DOI: 10.1177/1087054715573993

20.

Flavell

J. H.

Miller

P. H.

Miller

S. A.

(2001). Cognitive development (4th ed.). Prentice-Hall Inc.

21.

Frazier

T.W.

Youngstrom

E.A.

Glutting

J. J.

Watkins

M. W.

(2007). ADHD and Achievement: Meta-analysis of the child, adolescent, and adult literatures and a concomitant study with college students. Journal of Learning Disabilities, 40(1), 49–65. https://doi.org/10.1177/00222194070400010401

22.

Gioia

G. A.

Isquith

P. K.

Guy

S. C.

Kenworthy

(2000a). Behavior rating inventory of executive function (BRIEF). Psychological Assessment Resources.

23.

Gioia

G. A.

Isquith

P. K.

Guy

S. C.

Kenworthy

(2000b). Test review behavior rating inventory of executive function. Child Neuropsychology, 6(3), 235–238. https://doi.org/10.1076/chin.6.3.235.3152

24.

Goverover

Genova

Griswold

Chiaravalloti

DeLuca

(2014). Metacognitive knowledge and online awareness in persons with multiple sclerosis. NeuroRehabilitation, 35(2), 315–323.

25.

Goverover

Johnston

M. V.

Toglia

DeLuca

(2007). Treatment to improve self-awareness in persons with acquired brain injury. Brain Injury, 21(9), 913–923.

26.

Groom

M. J.

Cahill

J. D.

Bates

A. T.

Jackson

G. M.

Calton

T. G.

Liddle

P. F.

Hollis

(2010). Electrophysiological indices of abnormal error-processing in adolescents with attention deficit hyperactivity disorder (ADHD). Journal of Child Psychology and Psychiatry, 51(1), 66–76. https://doi.org/10.1111/j.1469-7610.2009.02128.x

27.

Guy

S. C.

Isquith

P. K.

Gioia

G. A.

(2004). Behavior rating inventory of executive function-self-report version. Psychological Assessment Resources.

28.

Harter (2012). Self-perception profile for children: Manual and questionnaires. University of Denver, Department of Psychology.

29.

Hoza

Gerdes

A. C.

Hinshaw

S. P.

Arnold

L. E.

Pelham

W. E.

Molina

B. S. G.

Abikoff

H. B.

Epstein

J. N.

Greenhill

L. L.

Hechtman

Odbert

Swanson

J. M.

Wigal

(2004). Self-perceptions of competence in children with ADHD and comparison children. Journal of Consulting and Clinical Psychology, 72(3), 382–391. https://doi.org/10.1037/0022-006X.72.3.382

30.

Hoza

Pelham

W. E.

Jr. Dobbs

Owens

J. S.

Pillow

D. R.

(2002). Do boys with attention deficit/hyperactivity disorder have positive illusory self-concepts? Journal of Abnormal Psychology, 111, 268–278.

31.

Hoza

Waschbusch

D. A.

Vaughn

Murray-Close

McCabe

(2012). Can children with ADHD be motivated to reduce bias in self-reports of competence? Journal of Consulting and Clinical Psychology, 80(2), 245–254.

32.

Jiang

Johnston

(2017). Controlled social interaction tasks to measure self- perceptions: No evidence of positive illusions in boys with ADHD. Journal of Abnormal Child Psychology, 45, 1051–1062. https://doi.org/10.1007/s10802-016-0232-y

33.

Khan

M. U.

Aslani

(2019). A review of factors influencing the three phases of medication adherence in people with attention-deficit/hyperactivity disorder. Journal of Child and Adolescent Psychopharmacology, 29(6), 398–418. https://doi.org/10.1089/cap.2018.0153

34.

Kastner

Shukrun-Velder

Traub Bar-Ilan

Bonne

Maeir

(2022). Pilot study of cog-fun A: A metacognitive-functional intervention for adults with ADHD. American Journal of Occupational Therapy. 76(2)

35.

Katz

Maeir

(2011). Higher-level cognitive functions enabling participation: Awareness and executive functions. In Katz

(Ed.), Cognition, occupation, and participation across the life span: Neuroscience, neurorehabilitation, and models of intervention in occupational therapy (3rd ed., pp. 13–40). AOTA Press.

36.

Kaufman

Birmaher

Brent

Rao

Ryan

N. D.

((1996. Kiddie-SADS PL. Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Centre.

37.

Kruger

Dunning

(1999). Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 1121–1134.

38.

Lahav

Ben-simon

Inbar-Weiss

Katz

(2018). Weekly Calendar Planning Activity for university students: Comparison of individuals with and without ADHD by gender. Journal of Attention Disorders, 22(4), 368–378.

39.

Levanon-Erez

Kampf-Sherf

Maeir

(2019). Occupational therapy metacognitive intervention for adolescents with ADHD: Teen Cognitive-Functional (Cog-Fun) feasibility study. British Journal of Occupational Therapy, 82(10), 618–629.

40.

Lyons

K. E.

Zelazo

P. D.

(2011). Monitoring, metacognition, and executive function: Elucidating the role of self-reflection in the development of self-regulation. Advances in Child Development and Behavior, 40, 379–412.

41.

Lezak

M. D.

(1983). Neuropsychological assessment (2nd ed.). New York, NY: Oxford University Press.

42.

McCandless

O’Laughlin

(2007). The clinical utility of the behavior rating inventory of executive function (BRIEF) in the diagnosis of ADHD. Journal of Attention Disorders, 10(4), 381–389. https://doi.org/10.1177/1087054706292115

43.

McQuade

J. D.

Mendoza

S. A.

Larsen

K. L.

Breaux

R. P.

(2017). The nature of social positive illusory bias: Reflection of social impairment, self-protective motivation, or poor executive functioning? Journal of Abnormal Child Psychology, 45, 289–300. https://doi.org/10.1007/s10802-016-0172-6

44.

Miyake

Friedman

N. P.

(2012). The nature and organization of individual differences in executive functions: Four general conclusions. Current Directions in Psychological Science, 21, 8–14. https://doi.org/10.1177/0963721411429458

45.

Molina

B. S. G.

Howard

A. L.

Swanson

J. M.

Stehli

Mitchell

J. T.

Kennedy

T. M.

Epstein

J. N.

Arnold

L. E.

Hechtman

Vitiello

Hoza

(2018). Substance use through adolescence into early adulthood after childhood-diagnosed ADHD: Findings from the MTA longitudinal study. Journal of Child Psychology and Psychiatry 59(6), 692–702. https://doi.org/10.1111/jcpp.12855

46.

O’Conell

R. G.

Bellgrove

M. A.

Dockree

P. M.

Lau

Hester

Garavan

Fitzgerald

Foxe

J. J.

Robertson

I. H.

(2009). The neural correlates of deficient error awareness in attention-deficit hyperactivity disorder (ADHD). Neuropsychologia, 47, 1149–1159.

47.

O’Keeffe

Dockree

Moloney

Carton

Robertson

I. H.

(2007). Awareness of deficits in traumatic brain injury: A multidimensional approach to assessing metacognitive knowledge and online-awareness. Journal of International Neuropsychological Society, 13(1), 38–49.

48.

Owens

J. S.

Goldfine

M. E.

Evangelista

N. M.

Hoza

Kaiser

N. M.

(2007). A critical review of self-perceptions and the positive illusory bias in children with ADHD. Clinical Child and Family Psychology Review, 10, 335–351. https://doi.org/10.1007/s10567-007-0027-3

49.

Owens

J. S.

Hoza

(2003). The role of inattention and hyperactivity/impulsivity in the positive illusory bias. Journal of Consulting and Clinical Psychology, 71(4), 680–691. https://doi.org/10.1037/0022-006X.71.4.680

50.

Ownsworth

Clare

(2006). The association between awareness deficits and rehabilitation outcome following acquired brain injury. Clinical Psychology Review, 26, 783–795.

51.

Ratzon

Z. R.

Kadury-Lunievsky

Ashkenasi

Laks

Cohen

H. A.

(2017). Simulated driving skills evaluation of teenagers with attention deficit hyperactivity disorder before driving lessons. American Journal of Occupational Therapy, 71, 7103220010. https://doi.org/10.5014/ajot.2017.020164

52.

Richardson

(2011). Eta squared and partial eta squared as measures of effect size in educational research. Educational Research Review, 6, 135–147.

53.

Robertson

Schmitter-Edgecombe

(2015). Self-awareness and traumatic brain injury outcome. Brain Injury, 29(7–8), 848–858.

54.

Russell

A. E.

Ford

Williams

Russell

(2016). The association between socioeconomic disadvantage and attention deficit/hyperactivity disorder (ADHD): A systematic review. Child Psychiatry & Human Development, 47(3), 440–458.

55.

Silverstein

M. J.

Faraone

S. V.

Leon

T. L.

Biederman

Spencer

T. J.

Adler

L. A.

(2020). The relationship between executive function deficits and DSM-5-defined ADHD symptoms. Journal of Attention Disorders 24(1), 41–51.

56.

Stuss

D. T.

(2011). Traumatic brain injury: relation to executive dysfunction and the frontal lobes, Current Opinion in Neurology, 24 (6), 584-589. doi:10.1097/WCO.0b013e32834c7eb9

57.

Steward

K. A.

Tan

Delgaty

Gonzales

M. M.

Bunner

(2017). Self-awareness of executive functioning deficits in adolescents with ADHD. Journal of Attention Disorders, 21(4), 316–322. https://doi.org/10.1177/1087054714530782

58.

Toglia

(2015). Weekly Calendar Planning Activity (WCPA): A performance test of executive function. AOTA Press.

59.

Toglia

(2018). The dynamic interactional model and the multicontext approach. In Katz

Toglia

(Eds.), Cognition across the lifespan (4th ed., pp. 355–385). AOTA Press.

60.

Toglia

Berg

(2013). Performance-based measure of executive function: Comparison of Community and At-Risk Youth. American Journal of Occupational Therapy, 67, 515-523.

61.

Toglia

Katz

(2018). Executive Functioning: Prevention and health promotion for at-risk populations and those with chronic disease. In: Katz

Toglia

(Eds.) Cognition occupation, and participation across the lifespan: Neuroscience, neurorehabilitation, and models of intervention in occupational therapy (4th ed., pp. 129–142). AOTA Press.

62.

Toglia

Kirk

(2000). Understanding awareness deficits following brain injury. NeuroRehabilitation, 15, 57–70.

63.

Toglia

Maeir

(2018). Self-Awareness and metacognition: Effect on occupational performance and outcome across the lifespan. In Katz

Toglia

(Eds.), Cognition, occupation, and participation across the lifespan (4th ed., pp. 143–163). AOTA Press.

64.

J. W.

Owens

E. B.

Hinshaw

S. P.

(2019). Positive illusory bias still illusory? Investigating discrepant self-perceptions in girls with ADHD. Journal of Pediatric Psychology, 44(5), 576–588. https://doi.org/10.1093/jpepsy/jsy109

65.

Turgay

Goodman

D. W.

Asherson

Lasser

R. A.

Babcock

T. F.

Pucci

M. L.

Barkley

(2012). Lifespan persistence of ADHD. The Journal of Clinical Psychiatry, 73(2), 192–201.

66.

Vohs

K. D.

Baumeister

R. F.

(2016). Handbook of self-regulation: Research, theory, and applications (3rd ed.). The Guilford Press.

67.

Volz-Sidiropoulou

Boecker

Gauggel

(2013). The Positive Illusory Bias in children and adolescents with ADHD: Further evidence. Journal of Attention Disorders, 20(2), 178–186. https://doi.org/1087054713489849

68.

Wallisch

Little

L. M.

Dean

Dunn

(2018). Executive function measures for children: A scoping review of ecological validity. OTJR: Occupation, Participation and Health, 38, 6–14. https://doi.org/10.1177/1539449217727118

69.

Wechsler

Kaplan

Fein

Morris

Kramer

J. H.

Maerlender

Delis

D. C.

(2004). The Wechsler intelligence scale for children-fourth edition integrated. The Psychological Corporation.

70.

Wehmeier

P. M.

Dittmann

R. W.

Banaschewski

(2015). Treatment compliance or medication adherence in children and adolescents on ADHD medication in clinical practice: Results from the COMPLY observational study. Attention Deficit Hyperactivity Disorder, 7, 165–174. https://doi.org/10.1007/s12402-014-0156

71.

Weil

L. G.

Fleming

S. M.

Dumontheil

Kilford

E. J.

Weil

R. S.

Rees

Dolan

R. J.

Blakemore

S. J.

(2013). The development of metacognitive ability in adolescence. Consciousness and Cognition, 22, 264–271.

72.

Weiner

N. W.

Toglia

Berg

(2012). Weekly Calendar Planning Activity (WCPA): A performance-based assessment. American Journal of Occupational Therapy, 66, 699–708.

73.

Wilson

K. R.

Donders

Nguyen

(2011). Self and parent ratings of executive functioning after adolescent traumatic brain injury. Rehabilitation Psychology, 56(2), 100–106. https://doi.org/10.1037/a0023446

74.

Strathearn

Liu

Yang

Bao

(2018). Twenty-year trends in diagnosed attention-deficit/hyperactivity disorder among US children and adolescents, 1997–2016. JAMA Network Open, 1(4), e181471. https://doi.org/10.1001/jamanetworkopen.2018.1471

75.

Zelazo

P. D.

(2015). Executive function: Reflection, iterative reprocessing, complexity, and the developing brain. Developmental Review, 38, 55–68.

76.

Zlotnik

Schiff

Ravid

Shahar

Toglia

(2020). A new approach for assessing executive functions in everyday life, among adolescents with genetic generalised epilepsies. Neuropsychological Rehabilitation, 30(2), 333–345. https://doi.org/10.1080/09602011.2018.1468272

77.

Zlotnik

Toglia

(2018). Measuring adolescent self-awareness and accuracy using a performance-based assessment and parental report. Frontiers in Public Health 6, 15. https://doi.org/10.3389/fpubh.2018.00015