Neuropsychological and Symptom Predictors of Diagnostic Persistence in ADHD: A 25-Year Follow-up Study

Abstract

Background:

This study extends long-term predictive research on ADHD by including both neuropsychological and symptom measures at baseline in adolescence as predictors of diagnostic persistence 25 years later.

Methods:

Nineteen males with ADHD and 26 healthy controls (HC; M/F = 13/13), were assessed in adolescence and 25 years later. Measurements at baseline included a comprehensive test battery measuring eight neuropsychological domains, an IQ estimate, the Child Behavior Checklist (CBCL), and the Global Assessment Scale of Symptoms. Differences between ADHD Retainers, Remitters, and HC were calculated with ANOVAs, and potential predictions of differences in the ADHD group by linear regression analyses.

Results:

Eleven (58%) participants retained their ADHD diagnoses at follow-up. Motor Coordination and Visual perception at baseline predicted diagnosis at follow-up. CBCL Attention problems at baseline in the ADHD group predicted variance in diagnostic status.

Conclusion:

Lower-order neuropsychological functions related to motor function and perception are important long-time predictors of persistence of ADHD.

Keywords

ADHD persistence predictors longitudinal neuropsychological symptoms

Introduction

For some individuals, ADHD is a persistent disorder with important social, vocational, and health-related ramifications (Agnew-Blais et al., 2016). Estimates of ADHD childhood cases persisting into adulthood vary significantly, ranging from 5% to 75% across studies (Caye, Rocha, et al., 2016). Being able to identify which cases of ADHD in childhood are at risk of a chronic course is of strong clinical relevance. Unfortunately, the currently available research attempting to answer this question is limited and largely inconsistent (Franke et al., 2018). A systematic review performed on predictive studies on ADHD persistence identified this field as an “overlooked question” constituting only 0.08% of the published literature on ADHD (Caye, Spadini, et al., 2016). Further, most of the predictive studies have investigated ADHD persistence into late childhood and early adolescence while few studies have followed children with ADHD into late adolescence and adulthood.

One of the most consistent predictors of ADHD persistence has been symptom severity (Eme, 2017; Kessler, Adler, Barkley, et al., 2005), in addition to comorbidities of conduct disorder and depression (Caye, Spadini, et al., 2016; Eme, 2017). These predictors were also found by Biederman et al. (2011) in their 11-year longitudinal study of ADHD, in addition to maternal mental health problems, a family history of ADHD, and psychosocial adversity. An international World Health Organization study found similar findings but did not find evidence of the predictive effects of psychosocial adversity (Lara et al., 2009). Both Cheung et al. (2015) and Biederman research groups (Biederman et al., 2011) discovered predictive effects of symptom severity on ADHD persistence, using both baseline parental rating measures and measures of movement intensity. Cheung et al. (2015) also found socio-economic status to be a predictor. However, the Multimodal Treatment Study of Children with ADHD (MTA) found no predictive effect of household income on diagnostic persistence of ADHD after 16 years. The study did confirm the predictive effects of childhood comorbidity and parental mental health (Roy et al., 2016).

Deficits in neuropsychological functions in ADHD have been widely documented (Frazier et al., 2004; van Lieshout et al., 2013), and ADHD is best regarded as a neuropsychological heterogeneous disorder (Coghill et al., 2014; Luo et al., 2019; Mostert et al., 2015). Both a review and a meta-analysis of Caye, Spandini, et al. (2016) and van Lieshout et al. (2013) reached the conclusion that there is little evidence of predictive effects of neuropsychological functions on ADHD persistence, apart from across time spans of only a few years within childhood. Only IQ may have a protective role (Cheung et al., 2015; Gao et al., 2015), but this was not supported by the MTA study (Roy et al., 2016). However, studies often include only few, or zero, neuropsychological measures apart from IQ. Two studies included more comprehensive test batteries (Sjöwall et al., 2015; van Lieshout et al., 2017). Sjöwall et al. (2015) had a follow-up period of 13 years (5–18 years of age at baseline) and found that only reduced working memory was a significant predictor of ADHD symptoms. This was replicated by van Lieshout et al. (2017) (5–19 years of age at baseline), although their study had only a 6-year follow-up interval. To our knowledge, no study has thoroughly investigated the predictive value of ADHD symptoms and neuropsychological functioning measured with a comprehensive test battery over more than two decades and into adulthood. Being able to predict the long-term persistence rates of the disorder is of strong clinical interest, as it could enable individually tailored long-term interventions and treatment planning.

Aims and Hypotheses

The goal of the current study is to expand the research literature examining the predictive abilities of neuropsychological and symptom measures by investigating a longer time period (25 years), including a larger neuropsychological test battery, and examining the long-term effects of these predictors on ADHD diagnosis. Due to the discrepancy between the thoroughness in earlier predictive studies and the present study when it comes to the baseline neuropsychological assessment, no hypotheses are stated on these predictors. We predict that baseline symptom severity will predict diagnostic persistence in adulthood.

Participants

Thorough descriptions of the procedures and demographic information of the research sample at baseline (T1) and at 25 years follow-up (T3) can be found in earlier publications (Øie & Rund, 1999; Øie et al., 2021; Torgalsbøen et al., 2021). The sample was also reassessed after 13 years (T2), but this data is not included in the current study (see Øie et al., 2010, 2011). The ADHD sample was mostly recruited from the National Centre for Child and Adolescent Psychiatry (NCCAP) in Oslo, Norway, while the rest were recruited from other outpatient clinics in Oslo. Diagnoses were made based on fulfilling the required eight diagnostic criteria of the DSM-III-R (American Psychiatric Association, 1987), as evaluated by mental health professionals using semi-structured clinical interviews and standardized rating scales. Their ADHD symptoms occurred both at home and at school and had occurred between the ages of 6 and 10 as assessed by the Parent’s Rating Scale (Wender et al., 1985). Diagnoses of ADHD subtypes were not made at T1, as they were first introduced in the DSM-IV. Comorbidities included oppositional defiant disorder (N = 9), developmental reading disorder (N = 2), and concurrent oppositional defiant disorder and developmental reading disorder (N = 3), corresponding with frequent comorbid diagnoses in the patient population (Franke et al., 2018). The mean age of the ADHD group at first assessment was 14.1 years (SD = 1.5). The ADHD group was exclusively male, which reflects the fact that the gender disparity seen in clinical and research practice was greater at this time than it is today (Biederman & Faraone, 2004). Twelve of the participants with ADHD received stimulant medication (11 used methylphenidate and one used dextroamphetamine) which was discontinued at least 24 hr ahead of testing. One of the subjects with ADHD received a small dose of haloperidol (1 mg/day) due to tics.

The participants in the healthy control group (HC) were recruited from schools in the local area and attended regular schooling at normal grade level. All research participants underwent the Child Behavior Checklist (CBCL; Achenbach & Edelbrock, 1991) to screen for mental health problems, with mothers acting as informants. Healthy controls with a raw score over 45 were excluded from the study (Øie & Rund, 1999), which was a cut-off at the 90th percentile set according to American norms and corrected for sex and age Øie et al., 1998). The mean age of the HC group at T1 was 15.8 years (SD 1.7). The HC group was significantly older than the ADHD group (p < .05). Both the ADHD and HC groups were screened at T1 by use of questionnaires and medical records to exclude participants with substance abuse, head injury with neurological complications, neurological disorder, and IQ < 70.

Nineteen of the original 20 subjects in the ADHD sample were available for assessment after 25 years (T3). The mean age of the ADHD group at T3 was 36.6 years (SD 1.6). One subject was deceased before T2 (information obtained from the Norwegian Cause of Death Registry). While 19 of the original 20 participants were retained in the study, only 11 of them retained their ADHD diagnosis at T3, five inattentive and six combined subtypes. Four of these 11 individuals fulfilled DSM-IV diagnostic criteria for only ADHD, while seven of them also fulfilled criteria for other mental disorders: five for depression or anxiety, one for bipolar disorder, and one for Tourette’s. At T3, one member of the ADHD group used prescription stimulants (Ritalin), three used a small dose of atypical antipsychotics (Seroquel), and one used antidepressant (Venlafaxine).

At T3, all the 26 individuals in the HC group available for reassessment at T3 still fulfilled criteria to serve as HC after being screened with the Mini-international neuropsychiatric interview (M.I.N.I.; Sheehan et al., 1994). Of the four who were not available, one was deceased due to medical issues (information obtained from the Norwegian Cause of Death Registry), two no longer wished to participate, and one had developed an illness incompatible with participation as a healthy control. The mean age of the HC group at T3 was 38.0 years (SD 1.6). The gender distribution of the HC group was evenly male and female (13/13).

Neuropsychological Measures

A comprehensive neuropsychological test battery was used at T1. Various measures were used to construct composite scores to represent nine neuropsychological domains, as described in Øie et al. (2010); in short, z-scores were computed for all tests using the original HC group’s scores’ means and standard deviations. In cases where higher scores indicated dysfunction, their values were inverted to assure that high scores on the composite scores always indicated better function. The z-scores were based on the original HC group at T1, consisting of 30 people, meaning that the current HC group (N = 26) has slightly deviating scores on three domains.

All tests included in this neuropsychological test battery have shown good reliability (Bakker et al., 1978; Charter & Webster, 1997; Goldstein & Watson, 1989; Harper & Kraft, 1986; Kimura, 1980; Øie & Rund, 1999; Spreen & Strauss, 1991; Wechsler, 1974). The nine composite scores included the following tests: Auditory attention: The Seashore Rhythm Test (Lezak et al., 1995; Seashore et al., 1960), the Digit Span subtest from the WISC-R (Wechsler, 1974), and the Digit Span Distractibility Test (Oltmanns & Neale, 1975). Executive function: The number of perseverative responses of the Wisconsin Card Sorting Test (WCST, PC version; Heaton et al., 1981). Motor coordination: The Grooved Pegboard test of fine motor speed and agility (Matthews & Klove, 1964; Skogan et al., 2018). The measure used was the combined mean time in seconds it took to complete the task for the dominant and non-dominant hand. Selective attention: The Dichotic Listening Test (DL; Hugdahl & Andersson, 1986), and the mean number of correct right ear answers in the “forced right” condition and correct left ear answers in the “forced left” condition was the measure used. Verbal memory: The California Verbal Learning Test (Delis et al., 1987) where the measure used was the total amount of correct recollections in trial 1 to 5. Visual memory: The Kimura Recurring Figure test (Kimura, 1963). Visual perception: The Backwards Masking task (Green et al., 1994), where a traditional backward masking paradigm was used, originally developed by Sperling (1965; see Torgalsbøen et al., 2021, for a thorough description of the test). The mean number of correctly identified digits in both the 33 and 49 ms ISIs was computed. Visuomotor processing: The Trail Making Test A and B (Reitan & Wolfson, 1985), measured as seconds to complete, and the Digit Symbol-Coding subtest from the Wechsler Intelligence Scale for Children-Revised (WISC-R) (Wechsler, 1974), measured as number of symbols correctly coded in 120 s. IQ estimate: The Similarities and Block Design subtests from the WISC-R (Wechsler, 1974)—the IQ estimate was created by using Sattler and Dumont’s (2004) table chart for estimating full-scale IQ from combinations of subtests.

Symptom Measures

The Child Behavior Checklist (CBCL; Achenbach & Edelbrock, 1991) assesses various behavioral and emotional problems that have taken place in the last 6 months in children from 4 to 18 years of age, with parents acting as informants. In this study, the participants’ mothers were used as the informants, and the Attention problems and Externalizing subscales were selected for use as predictors due to their close association with ADHD symptoms, especially the Attention problems subscale (Lampert et al., 2004). The CBCL Total score was also included to account for the total load of potential problem behaviors. Previous findings have confirmed the CBCL as a valid screening instrument of ADHD, especially its Attention problems subscale (Hudziak et al., 2004), and its validity has been replicated in a Norwegian sample (Nøvik, 1999).

The Global Assessment Scale of symptoms (GAS) was also used to evaluate the symptoms of the ADHD group at T1 (Endicott et al., 1976). The treatment provider is asked to rate their patient’s symptoms on a scale of 0 to 100, divided into 10 even intervals, with higher scores implicating stronger symptoms and dysfunction. The clinician treating each respective research participant with ADHD completed the GAS in the present study.

Outcome Measures

Diagnostic Status

Diagnostic reassessment was performed at T2 (Øie et al., 2011) and at T3. The ADHD diagnoses at T3 were determined using the Mini-International Neuropsychiatric Interview (MINI; Sheehan et al., 1994) and information from patient case records. The original ADHD-group was also assessed at T3 with the self-report forms Adult ADHD Self-Report Scale (ASRS) (Kessler, Adler, Ames, et al., 2005) while all participants completed the ASEBA adult form (Achenbach & Rescorla, 2003) with its separate ADHD and Attention Subscales (ASR). The ASR is a self-report scale with items concerned with vocational, educational, and social function. Its total score, ASR Adaptive Functioning, can be divided into subscales based on the underlying themes of items. The Attention problems and ADHD subscales were used in the present study as ecological impairment-oriented measures of ADHD symptoms. The diagnostic criteria used were those of the DSM-IV. The DSM-IV corresponds well with the DSM-III-R, implicating good diagnostic continuity between the two sets of diagnostic criteria used in this study at T1 and T3 (Biederman et al., 1997). Based on the results of the diagnostic reassessment, the original ADHD group was divided into Retainers and Remitters.

All studies in this research project were approved by the Regional Committee for Medical Research Ethics in Eastern Norway (REK; REK Øst-Norge (REK 1) # 98-05-04113; 2015/180/ REK sør-øst C) and conducted in accordance with the Helsinki Declaration.

Data Analyses

All analyses were conducted using the statistical package SPSS, version 25.0. First, the ADHD group was divided into Retainers (diagnosis retainers) and Remitters. Preliminary ANOVAs were performed to investigate between-group relationships between all three groups including the HC group. This would address the research question regarding the impairment levels reported by the Retainers and Remitters compared with the HC group at T3. Thus, the between-group comparisons needed to be performed for three groups. Due to the limited sample size, three pairwise ANOVAs were chosen as a method rather than a three-way ANOVA. Group differences between ADHD Retainers and Remitters at T1 were then further investigated as candidate predictors of the outcome measures by using linear regression analyses. No regression analyses included the HC group, as the goal of this study was to predict in-group variation in only the ADHD sample at follow-up. When predictive effects between Retainers and Remitters were detected, hierarchical multiple regression analyses were performed. The largest variable was entered first, to see if the second one accounted for more variance. Due to the limited sample size, this was only done with up to two variables at a time. When testing for significance, the alpha-level threshold was set to 0.05, in accordance with conventional research practice. Predictions that reached a significance value between 0.1 and 0.05 are reported as a tendency despite being statistically insignificant due to the limited sample size and increased risk of Type II errors.

Results

All T1 and T3 variables included in the present study are included in Table 1. As expected from the diagnostic reclassification at T3, the Retainers scored above Remitters and HC on the ASR scales measuring ADHD-symptoms and Attention Problems specifically. The ASRS scales measuring total ADHD-symptom severity and Attention and Hyperactivity separately showed significant differences between Retainers and Remitters.

Table 1.

Characteristics and Predictors of the ADHD and HC Groups.

	Retainers	Remitters	HC	Significant effects
Sex (male/female)	11/0	8/0	13/13
Symptom ratings at T3 (mean T-scores and SD)
ASR ADHD	62.5 (10.8)	52.8 (3.3)	52.2 (5.1)	Ret > Rem & HC
ASR Attention	63.6 (9.0)	51.3 (2.9)	52.0 (4.5)	Ret > Rem & HC
ASRS Total	36.7 (9.3)	15.5 (7.8)	—	Ret > Rem
ASRS Attention	21.7 (4.8)	9.4 (4.8)	—	Ret > Rem
ASRS Hyperactivity	15.0 (6.0)	6.1 (3.8)	—	Ret > Rem
Predictor variables at T1
Auditory attention	−1.2 (1.1)	−0.7 (.9)	0.0 (0.7)	Ret & Rem < HC
Executive function	−0.5 (1.2)	−0.5 (1.1)	−0.1 (1.0)	No effect
Motor coordination	−1.4 (1.3)	0.7 (0.7)	0.1 (0.8)	Ret < Rem & HC
Selective attention	0.2 (1.0)	0.0 (1.0)	0.1 (0.9)	No effect
Verbal memory	−1.4 (1.1)	−0.9 (1.1)	0.0 (1.0)	Ret & Rem < HC
Visual memory	−0.7 (1.4)	−0.7 (2.0)	0.0 (1.0)	No effect
Visuomotor proc.	−1.2 (1.9)	−0.6 (0.8)	0.0 (0.8)	Ret < HC
Visual perception	−0.9 (0.6)	0.0 (0.9)	0.0 (0.9)	Ret < Rem & HC
FSIQ	105.5 (13.0)	107.1 (13.1)	115.9 (15.7)	No effect
GAS	51.9 (8.1)	50.9 (5.6)	—	No effect
CBCL Total	70.3 (4.6)	64.3 (6.5)	44.7 (8.5)	Ret > Rem > HC
CBCL Attention	71.3 (6.8)	60.9 (4.6)	51.8 (3.3)	Ret > Rem > HC
CBCL Externalizing	69.0 (8.8)	65.9 (4.5)	45.0 (7.7)	Ret & Rem > HC

Note. The between-group differences as shown by the pairwise ANOVAs (p < .05). Neuropsychological domain composite scores given in z-scores based on the original 30 healthy controls. Due to four dropouts the current HC group does not average 0 on the Executive function, Motor coordination, and Selective attention domain composite scores.

CBCL scores given in the T-score format.

With regard to the predictors at TI, the ADHD Remitters and Retainers had increased behavioral and emotional impairment as measured by the CBCL at T1 compared to the HC group. However, the Retainers had significantly higher T-scores (more problems) than the Remitters on the CBCL Attention problems and CBCL Externalizing subscales (10 and 3 T-score points, respectively). The Remitters and Retainers did not significantly differ on symptom severity at T1 measured by GAS ratings. The between-group differences at T1 are displayed in Table 1. The Remitters and Retainers had significantly different scores on all outcome measures at T3 (See Table 1).

Predicting ADHD Diagnostic Persistence

Of the nine neuropsychological predictors at T1, only Motor coordination and Visual perception significantly predicted diagnostic persistence at T3. Motor coordination explained 31% of the variance in diagnosis (β = .559, F = 7.730, p = .013). Visual perception reached an explained variance value of 27% (β = .523, F = 6.417, p = .021). A hierarchical multiple regression analysis showed that when Visual perception was added onto Motor coordination, they explained 44% of diagnostic variance, but the R² change failed to reach significance (R² change = 12.5%, F change = 3.56, p = .078). When the order of the variables was reversed, the change reached significance (R² change = 16.4%, F change = 4.66, p = .047).

Looking at the CBCL measures, the Attention problems subscale at T1 significantly predicted diagnostic persistence at T3 with an explained variance of 45% (β = −.672, F = 13.976, p = .002). The Externalizing subscale did not significantly predict diagnostic persistence, while the CBCL Total explained 25% of the variance (β = −.500, F = 5.670, p = .029). When entered after the Attention problems subscale in a hierarchical multiple regression model, the Visual perception domain at T1 did not contribute with significant effects of its own. The Motor coordination domain significantly contributed with an additional 17% explained variance on top of the Attention problems subscale, ending up with a total explained variance of 62% (F change = 7.37, p = .015). The other symptom measure collected at T1, GAS, made no significant predictions on T3, on any outcome measure (Figure 1).

Figure 1.

Baseline attention symptoms in the ADHD sample.

Discussion

This 25-year follow-up study of a clinical sample of adolescents with ADHD examined the predictive effects of neuropsychological functioning and symptom severity on later diagnostic persistence. The roughly two-thirds of the ADHD sample who retained their diagnoses after 25 years had greater deficits in motor coordination and visual perception in adolescence than their remitted counterparts, as well as more attention problems as rated by their mothers. It was surprising that only the neuropsychological domains of Motor coordination and Visual perception predicted diagnostic persistence, and with a sizeable explained variance as well (31% and 27%, respectively). When hierarchically including both measures in the same regression model, their total explained variance increased by about 50% but left Visual perception as a non-significant contributor beyond the effect of Motor coordination. The results can be interpreted as indicating both shared and unique variance. Severity of attention problems at baseline predicted persisting ADHD diagnosis 25 years later, but Motor coordination still predicted significantly beyond the effect of the CBCL Attention problems subscale, to a total explained variance of 62%. When Visual perception was added to the CBCL Attention problems subscale, its added explained variance did not approach significance. The combination of these findings can be interpreted as indication that Motor coordination and Visual perception distinctly correspond to the two separate symptom factors known to exist in ADHD (Asherson et al., 2016). That Visual perception did not reach significance when added to the Attention problems subscale indicates shared variance with this observer rated scale. However, their respective places chronologically in the sequence of behavior are important to notice: the variance shown in Visual perception occurs in the span of milliseconds, whereas behavioral measures will by nature be an expression of the entire neural sequence of in- and outgoing information. In other words, this lower-order, pre-attentional sensory-perception function may be responsible for a segment of the expressed behavioral attention symptoms shown on the Attention problems subscale. This is in alignment with the findings that indicated that lower-order perception functions may create cumulative effects in higher-order systems (Lenz et al., 2010; Ríos et al., 2004). Impairments in motor control and visual perception have been reported previously in the literature. However, to the authors’ knowledge, this is the first study to document considerable predictive power of such deficits on long-term diagnostic persistence.

These findings call into relevance the concept of DAMP—deficits of attention, motor function and perception (Gillberg, 1983) and ESSENCE- Early Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examinations (Gillberg, 2010). It is defined as the co-occurrence of ADHD with developmental coordination disorder (DCD), in the absence of a severe intellectual disability (IQ < 50) or cerebral palsy. Despite its focus on motor skills and development, the coding instructions for the DCD allow for the supplementary coding of sensory deficits such as seen in neurological soft signs (American Psychiatric Association, 2000; Gillberg, 2003b). ESSENCE is associated with substantial long-term impairments, proposed to be more severe than ADHD without motor and perception deficits (Gillberg, 2003a, 2010), as the present study confirms. Systematic literature reviews show that approximately half or more of all individuals with ADHD show impairments in motor control and motor skills, probably satisfying the conditions for concurrent DCD (Damme et al., 2015; Kaiser et al., 2015). Most research on visual processing in ADHD focuses on visual working memory, but some studies have found impairment also in low-level visual processing, such as Panagiotidi et al. (2017) finding an association between oculomotor disturbances and ADHD symptoms. A recent study deconstructing different visual sub-processes found visuo-spatial processing speed to be more impaired in ADHD than the often found and non-specific finding of a visual working memory impairment (Cardillo et al., 2020), thus also indicating that low level visual processes play a role in the disorder.

Given the high prevalence rates for motor dysfunctions and findings of visual impairments, it is surprising that these have not been given more attention in the theoretical literature on ADHD, particularly when attempting to predict its long-term outcomes. One reason for this discrepancy may be that motor difficulties in ADHD are often interpreted as expressions of hyperactivity-impulsivity rather than “true” motor difficulties in large parts of the literature (Egeland et al., 2012). Egeland et al. (2012) stipulated that there may be different causes of such motor difficulties depending on subtypes, with the combined subtype being caused by hyperactivity-impulsivity (i.e., higher-order regulation deficits) and the inattentive subtype being caused by genuine motor difficulties (i.e., lower-order motor deficits). This would not coincide completely with the present findings: although the ADHD Retainers, who had apparent substantial motor skills impairment, exhibited strong symptoms of inattention, they also had high Externalizing symptoms.

Our findings may have implications for future research. The potential predictive relationships of these DAMP/ESSENCE-like symptoms with diagnostic persistence would make strong candidates for therapeutic and preventive interventions. One example of the development of such interventions is the promising results of physical therapy on the motor impairments exhibited by children with concurrent ADHD and DCD (Watemberg et al., 2007). The theoretical implications of this research would need to be examined further.

Most neuropsychological predictors included in our study made no significant predictions. Negative findings are important in clinical research (Easterbrook et al., 1991), but in a small sample such as this, the lack of statistical power creates an increased likelihood of Type II error (Banerjee et al., 2009). This lends reduced credibility to the negative findings.

While the significant neuropsychological predictor variables were somewhat surprising, the most consistent and strong predictor across all outcome variables in this study was the Attention problems subscale of the CBCL. One perspective would be that it is hardly surprising that ratings of attention deficits in adolescence predict the outcome measures included here, as the ADHD diagnostic criteria includes content similar to the CBCL Attention problems subscale. In other words, the present findings have merely shown that attention deficit ratings in adolescence predict attention deficit ratings in adulthood. As past behavior is known as a possible predictor of future behavior (Harris et al., 2016), these would not be very noteworthy findings, but only reflect trait stability. The authors would contend with such a perspective, but would like to stress that the powerful association over 25 years serves as a useful replication of the predictive power of symptom measures in adolescence, even if it was in accordance with the existing literature. Additionally, that the effect is so strong is a robust finding in itself, where close to half the diagnostic variance at T3 is being explained by a single maternal rating measure in adolescence 25 years earlier. The literature is also clear that there is considerable complexity in the multifactorial causes of behavior, in the sense that trait stability may not always be expected over longer periods of time (Montano & Kasprzyk, 2015).

The strong predictive power of the Attention problems subscale is also interesting considering the high rate of comorbidity. Indeed, half the ADHD sample had either a diagnosed developmental reading disorder, oppositional defiant disorder, or both of these commonly comorbid disorders in ADHD. Given the premises that (a) the CBCL Total detects a broader range of symptoms of mental illness than just the Attention problems subscale, and (b) that comorbidity predicts persistence of ADHD pathology (Caye, Spadini, et al., 2016), one could have expected the CBCL Total to make stronger predictions than just a single subscale.

Also, the present results from the Externalizing subscale are seemingly in conflict with several previous studies that have found that both inattentive and hyperactive-impulsive symptom ratings possess predictive power on diagnostic persistence (Biederman et al., 2011; Gao et al., 2015). There are several possible interpretations of this conflicting finding. First of all, the finding might be “true” in its own right; that there are no differences in the childhood levels of externalizing behavior in children with remittent or persistent ADHD, at least not when investigating a longer time span than previous studies. This would indicate that despite their disruptive qualities exasperating teachers and parents alike, externalizing behaviors are not what clinicians should look to when attempting to make prognostic evaluations of the diagnostic outcomes of their clients, but attention symptoms. It is also possible that the negative finding is a Type II error, either due to unsystematic error, that is, coincidence, or because of a lack of statistical power to yield significant effects when the group differences are small. Contrary to the large difference between Retainers and Remitters on the Attention problems subscale at baseline, which constitutes a standard deviation, there is only a 3-point difference on the Externalizing subscale. If this trend were replicated in a larger sample, it could yield a significant effect, even if the variance explained would be small.

Strengths and Limitations

Strengths of the study are its inclusion of a comprehensive neuropsychological test battery and its long-time frame of 25 years from adolescence into middle adulthood. Another strength of the study is its high retention rate, with only one of the original 20 participants in the ADHD sample not included at T3, and that was due to death. An additional strength of the study is the small age variability in the ADHD sample, with a standard deviation of only 1.4 years. This reduces the likelihood that developmental effects are obfuscated by a too wide age group. One limitation of this study is its small sample size, making it clearly underpowered and vulnerable to Type II errors. As this is a study reporting on a clinical, all-male, Norwegian sample of ADHD, there are limitations to the generalizability of its findings, for instance to ADHD in the general population, other cultures and/or ethnicities, and gender.

Clinical Implications

The findings of the present study are clinically relevant in several ways. Neuropsychological assessment is often employed in the process of diagnosing ADHD, and often includes the Grooved Pegboard test. Ninety-one percent of Norwegian clinical neuropsychologists already include this test in their standard testing procedure (Egeland et al., 2016), and the current findings adds value to their interpretations made when working with individuals with ADHD. This could also inform their treatment decisions. Further, knowledge of the strong predictive validity of maternal ratings of symptoms is useful to all clinicians, especially when complemented by awareness of the fact that in the present study, clinicians’ evaluations measured by the GAS made no significant predictions on outcomes. When it comes to psychopharmacological interventions, future research should investigate more closely how their effects on ADHD symptoms relate to motor- and perception symptoms, and whether this could mediate a mechanism of change on likelihood of diagnostic persistence. Due to the heterogeneity of ADHD and that the current findings implicate that individuals with DAMP/ESSENCE-like symptoms are a subgroup of the disorder with a higher likelihood of a persistent course, investigating these issues on samples consisting only of individuals with DAMP/ESSENCE-like symptoms could be worth consideration.

Footnotes

Acknowledgements

We are grateful to all the individuals who participated in the study.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The project was (partly) financed by the Extra Foundation through The Norwegian Council for Mental Health and from the Regional Network for Psychosis Research.

ORCID iD

Merete Glenne Øie

Author Biographies

Merete Glenne Øie, PhD, is a professor at the Department of Psychology at the University of Oslo, and research adviser at the Innlandet Hospital Trust, Brumunddal, Norway.

Tor Amund Voll Storaas, Cand. psychol., is a clinical psychologist, currently working at Oslo University Hospital, Oslo, Norway.

Jens Egeland, PhD, is a professor at the Department of Psychology at the University of Oslo, and research director at the Division of Mental Health and Addiction in Vestfold Hospital Trust, Norway.

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