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Alarachi A, Merrifield C, Rowa K, McCabe RE. Are We Measuring ADHD or Anxiety? Examining the Factor Structure and Discriminant Validity of the Adult ADHD Self-Report Scale in an Adult Anxiety Disorder Population. Assessment 2024; 31:1508-1524. [PMID: 38288573 PMCID: PMC11409565 DOI: 10.1177/10731911231225190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/18/2024]
Abstract
Adults with clinical anxiety have significant symptom overlap and above average rates of attention-deficit/hyperactivity disorder (ADHD). Despite this, ADHD remains a vastly under-detected disorder among this population, indicating the need for a screener with well-understood symptom dimensions and good discriminant validity. The current study compared competing models of ADHD as well as discriminant properties of self-reported ADHD symptoms as measured by the Adult ADHD Self-Report Scale (ASRS-v1.1) in 618 adults with clinical anxiety. A three-factor correlated model of Inattention, Impulsivity, and Hyperactivity, with the movement of one item, talks excessively, to a factor of Impulsivity from Hyperactivity fit better than the one-factor, two-factor, and traditional three-factor models of ADHD. Discriminant properties of the screener were fair to good against measures of clinical anxiety and distress; however, some items within the Hyperactivity factor (e.g., difficulty relaxing; feeling driven by a motor) loaded more strongly onto factors of clinical anxiety than ADHD when measures were pooled together. These results suggest that clinicians making differential diagnoses between adult ADHD and anxiety or related disorders should look for evidence of ADHD beyond the overlapping symptoms, particularly for those within the Hyperactivity factor.
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Affiliation(s)
- Arij Alarachi
- Anxiety Treatment and Research Clinic, St. Joseph's Healthcare Hamilton, Ontario, Canada
- Department of Psychology, Neuroscience, & Behaviour, McMaster University, Hamilton, Ontario, Canada
| | - Colleen Merrifield
- Anxiety Treatment and Research Clinic, St. Joseph's Healthcare Hamilton, Ontario, Canada
- Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Karen Rowa
- Anxiety Treatment and Research Clinic, St. Joseph's Healthcare Hamilton, Ontario, Canada
- Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Randi E McCabe
- Anxiety Treatment and Research Clinic, St. Joseph's Healthcare Hamilton, Ontario, Canada
- Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
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Jacobsson P, Hopwood CJ, Krueger RF, Söderpalm B, Nilsson T. Conceptualizing adult ADHD with the DSM alternative model of personality disorder. Personal Ment Health 2024. [PMID: 39239863 DOI: 10.1002/pmh.1632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 07/11/2024] [Accepted: 07/22/2024] [Indexed: 09/07/2024]
Abstract
Personality traits and personality disorders are related to ADHD and indicate dysfunction in clinical populations. The goals of this study were to examine how the DSM-5 Alternative Model of Personality Disorder (AMPD) a) indicates the presence of ADHD and b) communicates information about dysfunction over and above ADHD diagnosis. A sample of 330 adult psychiatric patients with and without ADHD (60% female; mean age 33 years) were assessed for ADHD symptoms, personality impairment, maladaptive personality traits, and functional life impairment domains. The maladaptive personality domain Disinhibition and particularly the lower order facet of Distractibility distinguished between individuals with psychiatric difficulties with and without ADHD. Distractibility is strongly related to the ADHD symptom dimension Inattentiveness, and Antagonism to Hyperactivity/impulsivity. General personality impairment augmented ADHD diagnosis in predicting life impairments. The AMPD has utility in ADHD assessments for diagnosis and prognosis.
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Affiliation(s)
- Peter Jacobsson
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sektionskansliet: Blå Stråket 15, vån 3, SU/Sahlgrenska University Hospital, Gothenburg, Sweden
- Region Halland, Varberg, Sweden
| | | | - Robert F Krueger
- Department of Psychology, N414 Elliott Hall, 75 East River, Parkway, University of Minnesota, Minneapolis, MN, USA
| | - Bo Söderpalm
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Sektionskansliet: Blå Stråket 15, vån 3, SU/Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Thomas Nilsson
- Centre for Ethics, Law and Mental Health (CELAM), Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
- Forensic Psychiatry, Sahlgrenska University Hospital, Gothenburg, Sweden
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Vajsz K, Paulina LR, Trejo S, Andaverde-Vega AA, Swanson JM, Miklósi M. Psychometric properties of the self-report version of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Scale in a sample of Hungarian adolescents and young adults. Front Psychiatry 2024; 15:1330716. [PMID: 39026526 PMCID: PMC11255780 DOI: 10.3389/fpsyt.2024.1330716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 06/10/2024] [Indexed: 07/20/2024] Open
Abstract
The Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour Scale (SWAN) measures the full spectrum of attention and activity symptoms, not just the negative end of the distribution. Previous studies revealed strong psychometric properties of the parent and teacher report versions; however, there is little research on the new self-report form of the SWAN. Therefore, our research aimed to explore the psychometric characteristics of the SWAN self-report. A non-clinical sample of young women (N = 664, mean age: 20.01 years, SD: 3.08 years) completed the SWAN self-report, the Strengths and Difficulties Questionnaire (SDQ) and the Mental Health Continuum Short Form (MHC-SF). We tested several models using confirmatory factor analyses to assess the factorial validity of the SWAN self-report. Distributional characteristics, convergent, and predictive validity were assessed. A bifactor model with a general factor and a specific inattention factor (bifactor-1) provided the best fit in our data (CFI = 0.977, TLI/NFI = 0.972, RMSEA = 0.053 [90% CI: 0.047 - 0.059], SRMR = 0.061, ω = 0.90). The reliability of the general ADHD factor was good (ωh = 0.87), and the specific inattention factor was acceptable (ωh = 0.73). The distribution of the SWAN self-report scores did not differ from the normal distribution. A strong correlation between the SWAN and the SDQ Hyperactivity subscale was found. The analyses revealed good predictive validity. Our results suggest that the SWAN self-report is a valuable tool for assessing symptoms of ADHD in adolescents and young adults.
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Affiliation(s)
- Kornél Vajsz
- Department of Clinical Psychology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- Department of Developmental and Clinical Child Psychology, Psychological Institute, Eötvös Loránd University, Budapest, Hungary
| | - Laura R. Paulina
- Department of Clinical Psychology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Salvador Trejo
- Facultad de Medicina y Psicología, Universidad Autónoma de Baja California, Tijuana, Mexico
| | - Adrián A. Andaverde-Vega
- Unidad Académica de Trabajo Social y Ciencias para el Desarrollo Humano, Universidad Autónoma de Tamaulipas, Ciudad Victoria, Mexico
| | - James M. Swanson
- Department of Pediatrics, University of California, Irvine, Irvine, CA, United States
| | - Mónika Miklósi
- Department of Clinical Psychology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- Department of Developmental and Clinical Child Psychology, Psychological Institute, Eötvös Loránd University, Budapest, Hungary
- Centre of Mental Health, Heim Pál National Pediatric Institute, Budapest, Hungary
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Hoffmann MS, Moore TM, Axelrud LK, Tottenham N, Rohde LA, Milham MP, Satterthwaite TD, Salum GA. Harmonizing bifactor models of psychopathology between distinct assessment instruments: Reliability, measurement invariance, and authenticity. Int J Methods Psychiatr Res 2023; 32:e1959. [PMID: 36655616 PMCID: PMC10485343 DOI: 10.1002/mpr.1959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/16/2022] [Accepted: 12/30/2022] [Indexed: 01/20/2023] Open
Abstract
OBJECTIVES Model configuration is important for mental health data harmonization. We provide a method to investigate the performance of different bifactor model configurations to harmonize different instruments. METHODS We used data from six samples from the Reproducible Brain Charts initiative (N = 8,606, ages 5-22 years, 41.0% females). We harmonized items from two psychopathology instruments, Child Behavior Checklist (CBCL) and GOASSESS, based on semantic content. We estimated bifactor models using confirmatory factor analysis, and calculated their model fit, factor reliability, between-instrument invariance, and authenticity (i.e., the correlation and factor score difference between the harmonized and original models). RESULTS Five out of 12 model configurations presented acceptable fit and were instrument-invariant. Correlations between the harmonized factor scores and the original full-item models were high for the p-factor (>0.89) and small to moderate (0.12-0.81) for the specific factors. 6.3%-50.9% of participants presented factor score differences between harmonized and original models higher than 0.5 z-score. CONCLUSIONS The CBCL-GOASSESS harmonization indicates that few models provide reliable specific factors and are instrument-invariant. Moreover, authenticity was high for the p-factor and moderate for specific factors. Future studies can use this framework to examine the impact of harmonizing instruments in psychiatric research.
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Affiliation(s)
- Maurício Scopel Hoffmann
- Department of NeuropsychiatryUniversidade Federal de Santa MariaSanta MariaBrazil
- Graduate Program in Psychiatry and Behavioral SciencesUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
- Section on Negative Affect and Social ProcessesHospital de Clínicas de Porto AlegreUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
- Care Policy and Evaluation CentreLondon School of Economics and Political ScienceLondonUK
| | - Tyler Maxwell Moore
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Luiza Kvitko Axelrud
- Graduate Program in Psychiatry and Behavioral SciencesUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
- Section on Negative Affect and Social ProcessesHospital de Clínicas de Porto AlegreUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
| | - Nim Tottenham
- Department of PsychologyColumbia UniversityNew YorkNew YorkUSA
| | - Luis Augusto Rohde
- Graduate Program in Psychiatry and Behavioral SciencesUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT‐CNPq)São PauloBrazil
- Department of Psychiatry and Legal MedicineUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
| | - Michael Peter Milham
- Nathan S. Kline Institute for Psychiatric ResearchOrangeburgNew YorkUSA
- Center for the Developing BrainChild Mind InstituteNew YorkNew YorkUSA
| | - Theodore Daniel Satterthwaite
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Informatics and Neuroimaging CenterPhiladelphiaPennsylvaniaUSA
| | - Giovanni Abrahão Salum
- Graduate Program in Psychiatry and Behavioral SciencesUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
- Section on Negative Affect and Social ProcessesHospital de Clínicas de Porto AlegreUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT‐CNPq)São PauloBrazil
- Department of Psychiatry and Legal MedicineUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
- Center for the Developing BrainChild Mind InstituteNew YorkNew YorkUSA
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Gomez R, Chen W, Houghton S. Differences between DSM-5-TR and ICD-11 revisions of attention deficit/hyperactivity disorder: A commentary on implications and opportunities. World J Psychiatry 2023; 13:138-143. [PMID: 37303925 PMCID: PMC10251354 DOI: 10.5498/wjp.v13.i5.138] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/02/2023] [Accepted: 04/18/2023] [Indexed: 05/19/2023] Open
Abstract
Current ICD-11 descriptions for attention deficit hyperactivity disorder (ADHD) were recently published online, in the same year as the DSM-5-TR (text revised edition) was released. In this commentary, we compare and contrast the DSM-5/DSM-5-TR and ICD-11 diagnostic criteria, summarize important differences, and underscore their clinical and research implications. Overall, three major differences emerge: (1) The number of diagnostic criteria for inattention (IA), hyperactivity (HY) and impulsivity (IM) symptoms (i.e., DSM-5-TR has nine IA and nine HY/IM symptoms, whereas ICD-11 has 11 IA and 11 HY/IM sym-ptoms); (2) the clarity and standardization of diagnostic thresholds (i.e., the diagnostic thresholds for symptom count in IA and HY/IM domains are explicitly specified in DSM-5-TR, whereas in ICD-11 they are not); and (3) the partitioning of HY and IM symptoms into sub-dimensions (i.e., difference in partitioning HY and IM symptom domains relates to the differences between the current and previous editions of DSM and ICD, and this has important research implications). Currently, no ICD-11 based ADHD rating scales exist and while this absence represents an obstacle for respective research and clinical practice, it also presents opportunities for research development. This article highlights these challenges, possible remedies and novel research opportunities.
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Affiliation(s)
- Rapson Gomez
- School of Science, Psychology, and Sport, Federation University, Melbourne 3806, Australia
| | - Wai Chen
- Curtin Medical School, Curtin University, Perth 6102, Australia
| | - Stephen Houghton
- Graduate School of Education, The University of Western Australia, Perth 6009, Australia
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Arildskov TW, Virring A, Lambek R, Carlsen AH, Sonuga-Barke EJS, Østergaard SD, Thomsen PH. The factor structure of attention-deficit/hyperactivity disorder in schoolchildren. RESEARCH IN DEVELOPMENTAL DISABILITIES 2022; 125:104220. [PMID: 35462238 DOI: 10.1016/j.ridd.2022.104220] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 02/21/2022] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Most studies support a bifactor model of childhood ADHD with two specific factors. However, several studies have not compared this model with a bifactor model with three specific factors, few have tested the actual strength of the factors, and none have examined whether "talks excessively" should be treated as a hyperactivity versus impulsivity symptom in children with ADHD. AIMS To examine the factor structure of ADHD symptoms and evaluate the relative strength of potential factors. METHODS Parent-reports on the ADHD-Rating Scale (ADHD-RS-IV) were collected for 2044 schoolchildren from the general population and 147 children with ADHD from a clinical sample. Single-, two- and three-(correlated and bi-)factor models were tested using confirmatory factor analysis. RESULTS Most models had a satisfactory fit. However, a correlated three-factor model where "talks excessively" was included as an indicator of impulsivity, and especially a bifactor model with one strong, well-defined general and two/three (ICD-10 defined) weak specific factors fit the data slightly better than the remaining models. CONCLUSIONS The factor structure is best characterized by a bifactor model with a strong general factor and two/three weaker specific factors. Therefore, we suggest emphasizing the ADHD-RS-IV total score rather than the subscale scores in clinical practice.
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Affiliation(s)
- Trine Wigh Arildskov
- Department of Child and Adolescent Psychiatry, Aarhus University Hospital, Psychiatry, Aarhus N, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
| | - Anne Virring
- Department of Child and Adolescent Psychiatry, Aarhus University Hospital, Psychiatry, Aarhus N, Denmark
| | - Rikke Lambek
- Department of Psychology and Behavioral Sciences, Aarhus University, Aarhus, Denmark
| | | | - Edmund J S Sonuga-Barke
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Søren D Østergaard
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Affective Disorders, Aarhus University Hospital, Psychiatry, Aarhus N, Denmark
| | - Per Hove Thomsen
- Department of Child and Adolescent Psychiatry, Aarhus University Hospital, Psychiatry, Aarhus N, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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7
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Liu L, Wang Y, Chen W, Gao Y, Li H, Wang Y, Chan RCK, Qian Q. Network analysis of 18 attention-deficit/hyperactivity disorder symptoms suggests the importance of " Distracted" and " Fidget" as central symptoms: Invariance across age, gender, and subtype presentations. Front Psychiatry 2022; 13:974283. [PMID: 36339870 PMCID: PMC9633674 DOI: 10.3389/fpsyt.2022.974283] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 10/06/2022] [Indexed: 11/13/2022] Open
Abstract
The network theory of mental disorders conceptualizes psychiatric symptoms as networks of symptoms that causally interact with each other. Our present study aimed to explore the symptomatic structure in children with attention-deficit/hyperactivity disorder (ADHD) using network analyses. Symptom network based on 18 items of ADHD Rating Scale-IV was evaluated in 4,033 children and adolescents with ADHD. The importance of nodes was evaluated quantitatively by examining centrality indices, including Strength, Betweenness and Closeness, as well as Predictability and Expected Influence (EI). In addition, we compared the network structure across different subgroups, as characterized by ADHD subtypes, gender and age groups to evaluate its invariance. A three-factor-community structure was identified including inattentive, hyperactive and impulsive clusters. For the centrality indices, the nodes of "Distracted" and "Fidget" showed high closeness and betweenness, and represented a bridge linking the inattentive and hyperactive/impulsive domains. "Details" and "Fidget" were the most common endorsed symptoms in inattentive and hyperactive/impulsive domains respectively. On the contrary, the "Listen" item formed a peripheral node showing weak links with all other items within the inattentive cluster, and the "Loss" item as the least central node by all measures of centrality and with low predictability value. The network structure was relatively invariant across gender, age and ADHD subtypes/presentations. The 18 items of ADHD core symptoms appear not equivalent and interchangeable. "Distracted" and "Fidget" should be considered as central, or core, symptoms for further evaluation and intervention. The network-informed differentiation of these symptoms has the potentials to refine the phenotype and reduce heterogeneity.
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Affiliation(s)
- Lu Liu
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Wai Chen
- Mental Health Service, Fiona Stanley Hospital, Perth, WA, Australia.,Curtin Medical School, Curtin University, Perth, WA, Australia.,Curtin enAble Institute, Curtin University, Bentley, WA, Australia.,Graduate School of Education, University of Western Australia, Perth, WA, Australia.,School of Medicine, University of Notre Dame Australia, Fremantle, WA, Australia.,School of Psychology, Murdoch University, Perth, WA, Australia
| | - Yuan Gao
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Haimei Li
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Yufeng Wang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Qiujin Qian
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
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