Autism is characterized by differences in social communication and interaction, as well as restricted and repetitive patterns of behavior or interests. Autistic individuals may experience challenges in understanding social cues, expressing emotions, or adapting to changes in routine.
ADHD, on the other hand, is marked by persistent inattention, hyperactivity, and impulsivity, which can affect an individual’s ability to focus, organize tasks, and control their behavior.
Despite these distinctions, autistic people and those with ADHD may share some overlapping traits, such as difficulties with attention regulation, social interaction, and managing sensory input.
Understanding these shared and unique characteristics is essential for providing accurate diagnoses and appropriate support.
Waldren, L. H., Leung, F. Y. N., Hargitai, L. D., Burgoyne, A. P., Liceralde, V. R. T., Livingston, L. A., & Shah, P. (2024). Unpacking the overlap between autism and ADHD in adults: A multi-method approach. Cortex: A Journal Devoted to the Study of the Nervous System and Behavior, 173, 120–137. https://doi.org/10.1016/j.cortex.2023.12.016
Key Points
- The main findings of this study on the overlap between Autism and ADHD traits in adults include:
- Network analysis revealed greater distinction than overlap between Autism and ADHD traits, supporting the separability of the two conditions.
- Attention control emerged as a potential transdiagnostic process linking Autism and ADHD traits based on self-report data.
- Cognitive experiments did not find unique associations between Autism, ADHD, and attention control when accounting for socio-demographic factors.
- The disparity between self-report and cognitive data raises questions about the challenges of using self-report measures for making inferences about cognitive processes.
- The research, while informative, has certain limitations such as the potential lack of sensitivity in the cognitive attention control tasks and the challenges of accurately capturing behavioral and cognitive performance in neurodivergent populations.
- Understanding the overlap between Autism and ADHD is crucial for improving diagnostic precision, tailoring interventions, and supporting the growing population of adults seeking neurodevelopmental support later in life.
Rationale
Autism and ADHD are two of the most common neurodevelopmental conditions, collectively affecting approximately 6-14% of the population (Frances et al., 2022).
Despite their high co-occurrence in clinical settings, research has typically focused on each condition separately (e.g., Livingston et al., 2022; Riglin et al., 2022; Taylor et al., 2021), limiting our understanding of the transdiagnostic processes that may underpin their similarities and co-occurrence.
Furthermore, most previous research linking Autism and ADHD has focused on children and adolescents, despite them being lifelong conditions (Hargitai et al., 2023).
Addressing these gaps, the current study aimed to investigate the overlap between Autism and ADHD traits in large adult samples using a multi-method approach.
Method
The study employed a multi-method approach across three studies to investigate the overlap between Autism and ADHD traits in adulthood.
Procedure
- Studies 1 and 2: Participants completed self-report measures of Autism (AQ28) and ADHD (ASRS) traits in a counterbalanced order.
- Study 3: A sub-sample of Study 2 participants completed the Three-Minute Squared Tasks, a cognitive measure of attention control.
Sample
- Study 1: 504 UK adults, nationally representative by age and sex.
- Study 2: 5000 UK and US adults aged 18-89 years.
- Study 3: 500 UK adults, broadly representative of the UK population.
Measures
- AQ28: The 28-item Autism-Spectrum Quotient (AQ28) is a self-report measure of Autism traits. It was chosen for its suitability in capturing the Autism phenotype across both males and females and for open-science research.
- ASRS: The 18-item Adult ADHD Self-Report Scale (ASRS) is a self-report measure of ADHD traits. The ASRS was selected for its high classification accuracy (96.2%) and suitability for open-science research.
- Three-Minute Squared Tasks: This cognitive measure of attention control consists of three adapted versions of classical attention tasks: Stroop, Simon, and Flanker. Each task includes a 30-second practice followed by a 90-second experiment period. The Three-Minute Squared Tasks were chosen for their well-validated ability to capture attention control as a latent construct in both online and lab-based samples.
Statistical measures
- Network analysis: This technique identifies the unique relationships between Autism and ADHD traits while accounting for all other traits in the network. The analysis focuses on the links between specific traits, the connectivity across the whole network, and the links within and between each condition.
- Multiple regression: This statistical method is used to explore the predictors of Autism traits, ADHD traits, and attention control. The analysis examines the relationships between these variables and socio-demographic factors such as sex, age, education, and co-occurring mental health conditions.
- Structural equation modeling (SEM): SEM is a multivariate statistical technique that allows for the examination of bi-directional relationships between Autism, ADHD, and attention control. In this study, SEM is used to test the factor structures of the AQ28 and ASRS, explore the latent correlations between Autism, ADHD, and attention control, and investigate the potential role of attention control in predicting Autism and ADHD traits (and vice versa).
Results
- Studies 1 and 2: Network analysis revealed greater distinction than overlap between Autism and ADHD traits, supporting their diagnostic separability. However, attention control emerged as a potential transdiagnostic process linking the two conditions.
- Study 3: Cognitive experiments did not find unique associations between Autism, ADHD, and attention control when accounting for socio-demographic factors, diverging from the self-report findings and raising questions about the challenges of using self-report measures to infer cognitive processes.
Insight
This study provides novel insights into the overlap between Autism and ADHD traits in adulthood.
While network analysis of self-report data suggested attention control as a potential transdiagnostic process, cognitive experiments did not support this finding when accounting for socio-demographic factors.
The disparity between self-report and cognitive data raises important questions about the challenges of accurately capturing behavioral and cognitive performance in neurodivergent populations.
Future research should focus on combining self-report, cognitive, multi-informant, and clinical assessments to better understand the links between Autism and ADHD in adults.
Strengths
This study had several methodological strengths, including:
- Large, representative adult samples
- Multi-method approach (self-report and cognitive measures)
- Robust statistical analyses (network analysis, SEM)
- Open science practices (pre-registration, open data and code)
Limitations
Despite strengths, this study also came with several limitations, including:
- Potential lack of sensitivity in the cognitive attention control tasks
- Challenges in accurately capturing behavioral and cognitive performance in neurodivergent populations
- Limited generalizability due to geographical restriction to UK and US samples
Implications
The findings of this study have important implications for clinical practice and future research.
The greater distinction than overlap between Autism and ADHD traits supports the need for condition-specific tailored interventions, while also highlighting the importance of considering co-occurrence during assessment and diagnosis.
The disparity between self-report and cognitive data emphasizes the need for a multi-method approach in neurodiversity research, combining self-report, cognitive, multi-informant, and clinical assessments.
Future research should focus on developing more sensitive cognitive measures and investigating the role of socio-demographic factors in the manifestation of Autism and ADHD traits.
References
Primary reference
Waldren, L. H., Leung, F. Y. N., Hargitai, L. D., Burgoyne, A. P., Liceralde, V. R. T., Livingston, L. A., & Shah, P. (2024). Unpacking the overlap between autism and ADHD in adults: A multi-method approach. Cortex: A Journal Devoted to the Study of the Nervous System and Behavior, 173, 120–137. https://doi.org/10.1016/j.cortex.2023.12.016
Other references
Frances, L., Quintero, J., Fernández, A., Ruiz, A., Caules, J., Fillon, G., Hervas, A., & Soler, C. V. (2022). Current state of knowledge on the prevalence of neurodevelopmental disorders in childhood according to the DSM-5: A systematic review in accordance with the PRISMA criteria. Child and Adolescent Psychiatry and Mental Health, 16(1), 27. https://doi.org/10.1186/s13034-022-00462-1
Hargitai, L. D., Livingston, L. A., Waldren, L. H., Robinson, R., Jarrold, C., & Shah, P. (2023). Attention-deficit hyperactivity disorder traits are a more important predictor of internalising problems than autistic traits. Scientific Reports, 13(1), 31. https://doi.org/10.1038/s41598-022-26350-4
Livingston, L. A., Waldren, L. H., Walton, E., & Shah, P. (2022). Emotion processing differences mediate the link between sex and autistic traits in young adulthood. JCPP Advances, 2(3), Article e12096. https://doi.org/10.1002/jcv2.12096
Riglin, L., Wootton, R. E., Livingston, L. A., Agnew-Blais, J., Arseneault, L., Blakey, R., Agha, S. S., Langley, K., Collishaw, S., O’Donovan, M. C., Davey Smith, G., Stergiakouli, E., Tilling, K., & Thapar, A. (2022). “Late-onset” ADHD symptoms in young adulthood: Is this ADHD? Journal of Attention Disorders, 26(10), 1271–1282. https://doi.org/10.1177/10870547211066486
Taylor, E. C., Livingston, L. A., Callan, M. J., Hanel, P. H. P., & Shah, P. (2021). Do autistic traits predict pro-environmental attitudes and behaviors, and climate change belief? Journal of Environmental Psychology, 76, Article 101648. https://doi.org/10.1016/j.jenvp.2021.101648
Keep Learning
Here are some reflective questions related to this study that could prompt further discussion:
- How might the disparity between self-report and cognitive data inform our understanding of the lived experiences of neurodivergent individuals?
- What are the potential implications of the greater distinction than overlap between Autism and ADHD traits for the current trend towards more general neurodevelopmental clinical support?
- How can future research address the challenges of accurately capturing behavioral and cognitive performance in neurodivergent populations?
- What role might socio-demographic factors play in the manifestation and co-occurrence of Autism and ADHD traits, and how can this be further investigated?
- How can the findings of this study inform the development of more targeted interventions and support for adults with Autism and ADHD?
