Emotional Dysregulation in Emerging Adult ADHD: A Key Consideration in Explaining and Classifying Impairment and
Patrick K Goh1, Ashlyn W W A Wong1, Da Eun Suh1
1University of Hawai'i at Mānoa, Honolulu, USA.
Journal of Attention Disorders
|September 29, 2024
Summary
Emotional dysregulation and unawareness (EDU) significantly improve ADHD classification for impairment and internalizing problems. Machine learning models incorporating EDU enhance prediction of co-occurring issues in emerging adults.
Area of Science:
- Psychology
- Neuroscience
- Data Science
Background:
- Attention-Deficit/Hyperactivity Disorder (ADHD) is frequently associated with emotional dysregulation and unawareness (EDU).
- The incremental validity of EDU beyond ADHD symptoms in emerging adulthood requires further investigation.
- Understanding these relationships is crucial for classifying impairment and co-occurring problems.
Purpose of the Study:
- To determine the incremental validity of EDU in emerging adults with ADHD.
- To assess the utility of EDU in classifying impairment and co-occurring problems.
- To apply machine learning techniques for enhanced classification accuracy.
Main Methods:
- Utilized machine learning, specifically random forest analyses, on data from 1,539 college students with self-reported ADHD.
- Assessed ADHD symptoms, EDU, and co-occurring problems through questionnaires.
- Evaluated model performance in classifying impairment and internalizing problems.
Main Results:
- EDU dimensions significantly improved classification models for impairment and internalizing problems (p < .001).
- The ADHD + EDU classification model showed acceptable to excellent performance in a separate sample.
- Inattention and the Limited Access to Emotional Regulation Strategies EDU dimension were key predictive features.
Conclusions:
- EDU is a significant deficit in ADHD that helps explain co-occurrence with impairment and internalizing problems.
- Machine learning facilitates actuarial classification of ADHD-related outcomes.
- Incorporating multiple measures, including EDU, enhances classification accuracy.
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