Interpretability and Validity of a Bifactor Model of ADHD in Young Adults: Assessing the General “g” and Specific IA and HI Factors

Goh, Patrick K.; Lee, Christine A.; Bansal, Pevitr S.; Aguerrevere, Luis E.; Rucker, Abby T.; Martel, Michelle M.

doi:10.1007/s10862-019-09774-7

Cited by 8 publications

(5 citation statements)

References 51 publications

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“…Findings are also consistent with prior work suggesting that inattentive symptoms capture information pertaining to both inattentive and hyperactive/impulsive symptom domains (Burns et al, 2020;Goh, Lee, Bansal, et al, 2020;Junghänel et al, 2020). Hence, inattentive symptoms, as a set, may ultimately be more useful in providing general information about ADHD phenotypes (and, by extension, diagnosis and impairment).…”

Section: Confirming the Utility Of An Abbreviated Set Of Symptoms In ...supporting

confidence: 86%

Application and expansion of an algorithm predicting attention-deficit/hyperactivity disorder and impairment in a predominantly White sample.

Goh,

Eng,

Bansal

et al. 2024

Journal of Psychopathology and Clinical Science

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Current assessment protocols for attention-deficit/hyperactivity disorder (ADHD) focus heavily on a set of highly overlapping symptoms, with well-validated factors like cognitive disengagement syndrome (CDS), executive function (EF), age, sex, and race and ethnicity generally being ignored. Using machine learning techniques, the current study aimed to validate recent findings proposing a subset of ADHD symptoms that, together, predict ADHD diagnosis, severity, and impairment level better than the full symptom list, while also testing whether the inclusion of the factors listed above could further increase accuracy. Parents of 1,922 children (50.1% male) aged 6–17 years completed rating scales of ADHD, CDS, EF, and impairment. Results suggested nine symptoms as most important in predicting outcomes: (a) has difficulty sustaining attention in tasks or play activities; (b) does not follow through on instructions and fails to finish work; (c) avoids tasks (e.g., schoolwork, homework) that require sustained mental effort; (d) is often easily distracted; (e) has difficulty organizing tasks and activities; (f) is often forgetful in daily activities; (g) fidgets with hands or feet or squirms in seat; (h) interrupts/intrudes on others; and (i) shifts around excessively or feels restless or hemmed in. The abbreviated algorithm achieved accuracy rates that did not significantly differ compared to an algorithm comprising all 18 symptoms in predicting impairment, while also demonstrating excellent discriminative ability in predicting ADHD diagnosis. Adding CDS and EF to the abbreviated algorithm further improved the prediction of global impairment. Continued refinement of screening tools will be key to ensuring access to clinical services for youth at risk for ADHD.

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Section: Confirming the Utility Of An Abbreviated Set Of Symptoms In ...supporting

confidence: 86%

Application and expansion of an algorithm predicting attention-deficit/hyperactivity disorder and impairment in a predominantly White sample.

Goh,

Eng,

Bansal

et al. 2024

Journal of Psychopathology and Clinical Science

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“…Based on the findings from the bifactor studies, the use of the summed total score has been recommended [16, 20, 31, 36] as well as the summed Inattention score in clinical samples [16]. However, summed scores, such as the ADHD-RS-IV total score and especially the subscale scores, are unweighted and not synonymous with the orthogonal general or the specific factor(s) in a bifactor model, whereas weighted factor scores are [26, 69, 72]. Nonetheless, Willoughby et al [31] found that a general factor score derived from a bifactor model in 1st graders was strongly associated with both the summed total, inattention, and hyperactivity/impulsivity scores and with symptom counts using the Disruptive Behavior Disorders Rating Scale [73].…”

Section: Discussionmentioning

confidence: 99%

“…Although we did provide norms for the ADHD-RS-IV summed subscale scores proposed by the scale developers, we suggest attaching more importance to the ADHD-RS-IV total score in clinical practice to approximate the general factor indicative of an overall liability for ADHD [26]. In addition to using the total score or alternatively an overall symptom count, Goh et al [72] proposed the implementation of specific factor scores in clinical practice. Future research should investigate the clinical usefulness and relevance of bifactor scores [69], and whether weighted bifactor scores could be used to establish norms enabling the clinical assessment of a child’s placement on the general and specific (Inattention) factor(s).…”

Section: Discussionmentioning

confidence: 99%

“…Future research should investigate the clinical usefulness and relevance of bifactor scores [69], and whether weighted bifactor scores could be used to establish norms enabling the clinical assessment of a child’s placement on the general and specific (Inattention) factor(s). If so, then computerized algorithms are needed [16, 72] to ease the practical and clinical interpretation of the factor score profiles once investigated throughout.…”

Section: Discussionmentioning

confidence: 99%

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The factor structure of attention-deficit/hyperactivity disorder in schoolchildren

Arildskov

Virring

Lambek

et al. 2020

Preprint

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This study investigated the factor structure of attention-deficit/hyperactivity disorder (ADHD) by comparing the fit of a single-factor model, a correlated model with two or three factors, and a bifactor model with one general and two or three specific factors. Different three-factor solutions that varied with regard to the specification of the item "talks excessively" as impulsivity or hyperactivity were also tested. Parent ratings on the ADHD-Rating Scale (ADHD-RS-IV) were collected in a sample of 2044 schoolchildren (1st to 3rd grade) from the general population and in a clinical sample of 165 children and adolescents with ADHD referred to a public regional child and adolescent psychiatric hospital. Confirmatory factor analyses found a satisfactory fit for most models in both samples. However, a correlated three-factor model where "talks excessively" was included as an indicator of impulsivity and especially the bifactor version of this model with one general and three specific factors fit the data slightly better in the general population. In the clinical sample, a number of models performed equally well (the same version of the correlated three-factor model and all the bifactor models). Overall, the factor structure of ADHD seems to be better characterized by a bifactor model with a strong general factor and two or three weaker specific factors. Due to the strong general factor, we suggest emphasizing the ADHD-RS-IV total score rather than the subscale scores in clinical practice.

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“…Studies using total ADHD symptom severity composites have also identified both higher (e.g., van Ewijk et al., 2014 ) and lower FA (e.g., Albaugh et al., 2019 ). However, inattention and hyperactivity‐impulsivity symptoms, while correlated (e.g., Goh et al., 2020 ), are partially dissociable, particularly with regard to external correlates (e.g., Martel et al., 2011 ). Preliminary studies suggest that hyperactivity‐impulsivity is associated with higher FA (King et al., 2015 ; Wu et al., 2017 ), while inattention is associated with lower FA (Ashtari et al., 2005 ; Witt & Stevens, 2015 ), though in likely non‐overlapping brain regions, a finding that warrants further investigation.…”

Section: Introductionmentioning

confidence: 99%

Attention‐deficit/hyperactivity disorder and white matter microstructure: The importance of dimensional analyses and sex differences

et al. 2022

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Background Attention‐deficit/hyperactive disorder (ADHD) has substantial heterogeneity in clinical presentation. A potentially important clue may be variation in brain microstructure. Using fractional anisotropy (FA), previous studies have produced equivocal results in relation to ADHD. This may be due to insufficient consideration of possible sex differences and ADHD's multi‐componential nature. Methods Using whole‐brain analyses, we investigated the association between FA and both ADHD diagnosis and ADHD symptom domains in a well‐characterized, ADHD (n = 234; 32% female youth) and non‐ADHD (n = 177; 52% female youth), case‐control cohort (ages 7–12). Sex‐specific effects were tested. Results No ADHD group differences were found using categorical assessment of ADHD without consideration of moderators. However, dimensional analyses found total symptoms were associated with higher FA in the superior corona radiata. Further, inattention symptoms were associated with higher FA in the corpus callosum and ansa lenticularis, and lower FA in the superior longitudinal fasciculus (SLF), after controlling for overlap with hyperactivity‐impulsivity. Hyperactivity‐impulsivity symptoms were associated with higher FA in the SLF, and lower FA in the superior cerebellar peduncles, after control for overlap with inattention. Meanwhile, both categorical and dimensional analyses revealed ADHD‐by‐sex interactions (voxel‐wise p < .01). Girls with ADHD had higher FA, but boys with ADHD had lower FA (or no effect), compared to their same‐sex peers, in the bilateral anterior corona radiata. Further, higher ADHD symptom severity was associated with higher FA in girls, but lower FA in boys, in the anterior and posterior corona radiata and cerebral peduncles. Conclusions ADHD symptom domains appear to be differentially related to white matter microstructure, highlighting the multi‐componential nature of the disorder. Further, sex differences will be crucial to consider in future studies characterizing ADHD‐related differences in white matter microstructure.

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Interpretability and Validity of a Bifactor Model of ADHD in Young Adults: Assessing the General “g” and Specific IA and HI Factors

Cited by 8 publications

References 51 publications

Application and expansion of an algorithm predicting attention-deficit/hyperactivity disorder and impairment in a predominantly White sample.

Application and expansion of an algorithm predicting attention-deficit/hyperactivity disorder and impairment in a predominantly White sample.

The factor structure of attention-deficit/hyperactivity disorder in schoolchildren

Attention‐deficit/hyperactivity disorder and white matter microstructure: The importance of dimensional analyses and sex differences

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