Emotional Dysregulation in Emerging Adult ADHD: A Key Consideration in Explaining and Classifying Impairment and Co-Occurring Internalizing Problems.

IF 2.7 3区 医学 Q2 PSYCHIATRY Journal of Attention Disorders Pub Date : 2024-11-01 Epub Date: 2024-09-28 DOI:10.1177/10870547241284829
Patrick K Goh, Ashlyn W W A Wong, Da Eun Suh, Elizabeth A Bodalski, Yvette Rother, Cynthia M Hartung, Elizabeth K Lefler
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Abstract

Objective: The current study sought to clarify and harness the incremental validity of emotional dysregulation and unawareness (EDU) in emerging adulthood, beyond ADHD symptoms and with respect to concurrent classification of impairment and co-occurring problems, using machine learning techniques.

Method: Participants were 1,539 college students (Mage = 19.5, 69% female) with self-reported ADHD diagnoses from a multisite study who completed questionnaires assessing ADHD symptoms, EDU, and co-occurring problems.

Results: Random forest analyses suggested EDU dimensions significantly improved model performance (ps < .001) in classifying participants with impairment and internalizing problems versus those without, with the resulting ADHD + EDU classification model demonstrating acceptable to excellent performance (except in classification of Work Impairment) in a distinct sample. Variable importance analyses suggested inattention sum scores and the Limited Access to Emotional Regulation Strategies EDU dimension as the most important features for facilitating model classification.

Conclusion: Results provided support for EDU as a key deficit in those with ADHD that, when present, helps explain ADHD's co-occurrence with impairment and internalizing problems. Continued application of machine learning techniques may facilitate actuarial classification of ADHD-related outcomes while also incorporating multiple measures.

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新成人多动症(ADHD)中的情绪失调:解释和分类障碍及并发内化问题的关键因素。
研究目的本研究试图利用机器学习技术,在多动症(ADHD)症状之外,澄清和利用新兴成人期情绪失调和不自知(EDU)的增量有效性,并对损伤和共存问题进行并发分类:方法:参与者为一项多站点研究中的 1539 名大学生(年龄 19.5 岁,69% 为女性),他们自我报告诊断为多动症,并填写了评估多动症症状、EDU 和共存问题的问卷:结果:随机森林分析表明,EDU维度显著提高了模型性能(ps 结论:结果支持EDU作为多动症症状和共存问题的评估指标:结果支持 EDU 是多动症患者的一个关键缺陷,当 EDU 存在时,有助于解释多动症与障碍和内化问题的共存。继续应用机器学习技术可能有助于对多动症相关结果进行精算分类,同时还能纳入多种测量指标。
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来源期刊
CiteScore
7.60
自引率
6.70%
发文量
71
审稿时长
6-12 weeks
期刊介绍: Journal of Attention Disorders (JAD) focuses on basic and applied science concerning attention and related functions in children, adolescents, and adults. JAD publishes articles on diagnosis, comorbidity, neuropsychological functioning, psychopharmacology, and psychosocial issues. The journal also addresses practice, policy, and theory, as well as review articles, commentaries, in-depth analyses, empirical research articles, and case presentations or program evaluations.
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