School-based intervention for adolescents with attention-deficit/hyperactivity disorder: Effects on academic functioning

DuPaul, George J.; Evans, Steven W.; Cleminshaw, Courtney L.; Kipperman, Kristen L.; Qiong, FU; Benson, Kari

doi:10.1016/j.jsp.2021.07.001

Cited by 20 publications

(10 citation statements)

References 35 publications

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“…According to previous studies, which point to inattention as the strongest factor associated with academic underachievement 8 , 30 – 33 , students with increased levels of attention problems were the most likely to perform poorly on all academic subjects. These children are easily distracted, often appears not to be listening, and have difficulties concentrating on one task, holding attention for long, following instructions, or completing school work 2 , 28 , 34 . Thus, it is unsurprising that they experience problems in an academic environment, which may lead to lower levels of achievement.…”

Section: Discussionmentioning

confidence: 99%

The impact of psychopathology on academic performance in school-age children and adolescents

Pagerols

Prat

Rivas

et al. 2022

Sci Rep

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Psychiatric symptoms have consistently been associated with negative educational outcomes. However, possible confounding variables, such as comorbid mental and environmental conditions, have not been well addressed. This study examined whether mental health problems were significantly linked to academic performance in a Spanish school-based sample, after adjustment for co-occurring psychiatric symptoms and multiple contextual factors. Parents completed a questionnaire regarding child’s sociodemographic characteristics (i.e., gender, age, type of school, socioeconomic status, ethnicity), stressful events (i.e., adoption, parental divorce/separation, grade retention) and lifestyle (i.e., diet, sleep, screen time), along with the Child Behavior Checklist (CBCL). Academic performance was obtained from school records. The sample comprised 7036 students aged 5–17 with full data on the CBCL. Mixed-effects ordinal logistic regression analyses were conducted to investigate the association between psychopathology and academic achievement, controlling for potential confounders. When examined separately, higher scores on the CBCL scales were related to lower grades, regardless of sociodemographic factors. However, after controlling for the presence of other psychiatric symptoms, we found that students who reported more anxious/depressed and thought problems were less likely to perform poorly, while those with increased levels of attention problems and delinquent behavior had higher risk for academic underachievement. These associations remained mainly the same once stressful events and lifestyle were taken into account. This investigation demonstrates that anxious/depressed symptoms, thought problems, attention problems, and delinquent behavior are independently associated with academic performance, which emphasize the need for preventive and treatment interventions targeted at students’ mental health to improve their psychological well-being and functioning at school.

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Section: Discussionmentioning

confidence: 99%

The impact of psychopathology on academic performance in school-age children and adolescents

Pagerols

Prat

Rivas

et al. 2022

Sci Rep

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“…Based on an extensive literature review about ADHD, the predictor variables were used in this paper as: child's age [34][35][36][37][38][39][40], sex of the child [34][35][36][37][38][40][41][42], mother's age [35,41,42], allergies, arthritis, asthma, brain injury, headaches, anxiety [34,38,39,43], depression [34,36,38,43], health insurance [34,35], alcohol [36,41], race [28,34,35,37,38,40], family structure [34,35], mother's education [34,35,38,40,42], very low birth weight (LBW) [38], LBW [35,38], premature [35,36,38,...…”

Section: Predictor Variablementioning

confidence: 99%

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

2022

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Attention deficit hyperactivity disorder (ADHD) is one of childhood’s most frequent neurobehavioral disorders. The purpose of this study is to: (i) extract the most prominent risk factors for children with ADHD; and (ii) propose a machine learning (ML)-based approach to classify children as either having ADHD or healthy. We extracted the data of 45,779 children aged 3–17 years from the 2018–2019 National Survey of Children’s Health (NSCH, 2018–2019). About 5218 (11.4%) of children were ADHD, and the rest of the children were healthy. Since the class label is highly imbalanced, we adopted a combination of oversampling and undersampling approaches to make a balanced class label. We adopted logistic regression (LR) to extract the significant factors for children with ADHD based on p-values (<0.05). Eight ML-based classifiers such as random forest (RF), Naïve Bayes (NB), decision tree (DT), XGBoost, k-nearest neighborhood (KNN), multilayer perceptron (MLP), support vector machine (SVM), and 1-dimensional convolution neural network (1D CNN) were adopted for the prediction of children with ADHD. The average age of the children with ADHD was 12.4 ± 3.4 years. Our findings showed that RF-based classifier provided the highest classification accuracy of 85.5%, sensitivity of 84.4%, specificity of 86.4%, and an AUC of 0.94. This study illustrated that LR with RF-based system could provide excellent accuracy for classifying and predicting children with ADHD. This system will be helpful for early detection and diagnosis of ADHD.

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“…This suggests that additional supports both in the family and academic setting may be necessary to effectively remediate the academic difficulties faced by students with ADHD and SLD. Particularly, teachers may play an important role in the educational development of affected children as evidenced by studies showing that school-based interventions enhance the academic achievement of children and adolescents with ADHD mainly by optimizing their classroom behavior, self-regulation, organizational skills, and homework performance ( 72 , 73 ).…”

Section: Discussionmentioning

confidence: 99%

The impact of attention-deficit/hyperactivity disorder and specific learning disorders on academic performance in Spanish children from a low-middle- and a high-income population

et al. 2023

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IntroductionPast research has demonstrated that attention-deficit/hyperactivity disorder (ADHD), specific learning disorders (SLD), and socioeconomic status (SES) affect a host of educational outcomes. However, there are no studies examining whether SES moderates the association between these neurodevelopmental disorders (ND) and the academic achievement of children and adolescents. The present investigation examined the impact of ADHD and SLD on academic performance in 1,287 Spanish students aged 5–17 from a low-middle (LM)- and a high-income population, when adjusted for comorbidity and demographic factors that may influence educational functioning.MethodsParents completed a questionnaire regarding demographic data along with the Strengths and Difficulties Questionnaire. Additionally, teachers provided information on learning difficulties trough the Protocol for Detection and Management of Dyslexia. Teacher’s Version. Academic performance across multiple domains (i.e., first language, foreign language, mathematics) was obtained from school records. ND were determined using standardized diagnostic methods based on the Diagnostic and Statistical Manual of Mental Disorders criteria. To examine the effects of ADHD and SLD on academic achievement and the potential moderating role of SES, a series of ordinal logistic regressions were conducted.ResultsEmotional/behavioral problems, learning difficulties, and ND were more frequent among individuals from the LM-income population. After controlling for gender, age, parental divorce/separation, grade retention, frequency of screen use, and daily meals, both ADHD and SLD were associated with worse educational outcomes. Lower SES also increased the risk for academic impairment, although the interactions with ADHD or SLD were not significant.ConclusionThese findings indicate that ADHD and SLD exert a pervasive impact on academic performance across different socioeconomic backgrounds. Therefore, early detection and effective intervention strategies aimed at students with these ND are crucial to improve their educational functioning and mitigate the negative consequences related to academic problems.

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School-based intervention for adolescents with attention-deficit/hyperactivity disorder: Effects on academic functioning

Cited by 20 publications

References 35 publications

The impact of psychopathology on academic performance in school-age children and adolescents

The impact of psychopathology on academic performance in school-age children and adolescents

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

The impact of attention-deficit/hyperactivity disorder and specific learning disorders on academic performance in Spanish children from a low-middle- and a high-income population

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