Predicting the Adult Clinical and Academic Outcomes in Boys With ADHD: A 7- to 10-Year Follow-Up Study in China

Ren, Yi; Fang, Xinyu; Fang, Hui; Pang, Gaofeng; Cai, Jing; Wang, Suhong; Ke, Xiaoyan

doi:10.3389/fped.2021.634633

Cited by 7 publications

(7 citation statements)

References 50 publications

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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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Section: Predictor Variablementioning

confidence: 99%

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

2022

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“…This may be related to the characteristics of ADHD symptoms. With increasing age of children with ADHD, functional impairments become more pronounced due to the persistence of ADHD symptoms (Agnew-Blais et al, 2018;Ren et al, 2021). Therefore, parents are more likely to be aware of ADHD.…”

Section: Discussionmentioning

confidence: 99%

“…ADHD not only has an obvious impact on children and adolescence, such as poor interpersonal relationship, poor academic performance, low self-esteem, and negative emotions, but also makes patients more prone to anxiety, depression, and other mental health disorders (Klassen et al, 2004;Barkley, 2014;Nourredine et al, 2021). Moreover, for 40-60% of children with ADHD, the disorder will persist into adulthood, resulting in reduced mental health, and social wellbeing (Faraone et al, 2006;Agnew-Blais et al, 2018;Posner et al, 2020;Ren et al, 2021). Children with ADHD are more likely than their peers to develop conduct disorders and antisocial personality disorders in adulthood, consequently increasing the risk of substance use disorders and incarceration (American Psychiatric Association, 2013).…”

Section: Introductionmentioning

confidence: 99%

Screening of ADHD symptoms in primary school students and investigation of parental awareness of ADHD and its influencing factors: A cross-sectional study

Wang

Dong

et al. 2022

Front. Psychol.

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ObjectiveThe present study aimed to compare the differences in positive screening rates of attention deficit hyperactivity disorder (ADHD) symptoms between parents and teachers in the same sample of primary school students. Concurrently, parental awareness and information sources of ADHD were investigated, and possible relevant factors affecting parental awareness and their influence on positive screening rate of ADHD were analyzed.MethodsA cross-sectional study was conducted in Changchun, China, between September 2020 and January 2021. Parents of 1,118 primary school students and 24 head teachers were recruited in the survey. Data were collected through a structured self-administered questionnaire. It consisted of socio-demographic characteristics, ADHD symptom screening questionnaire, parental awareness, and information sources of ADHD.ResultsAmong the 1,118 primary school students, 30 (2.7%) and 60 (5.4%) students were positive for Swanson, Nolan, and Pelham Rating Scale (SNAP-IV) screening in the parent version and teacher version, respectively. Parents had lower positive screening rates for ADHD symptoms than teachers. Relationship with children (mother, OR = 1.552, 95% CI = 1.104–2.180), bachelor degree or above of parents (OR = 1.526, 95% CI = 1.054–2.210), children’s sex (girl, OR = 1.442, 95% CI = 1.093–1.904), and age (OR = 1.344, 95% CI = 1.030–1.754), children’s grade (grade 2, OR = 0.522, 95% CI = 0.310–0.878; grade 3, OR = 0.388, 95% CI = 0.185–0.782), information sources of ADHD (medical staff, OR = 1.494, 95% CI = 1.108–2.015; family/relative/friend, OR = 1.547, 95% CI = 1.148–2.083; TV/Internet, OR = 3.200, 95% CI = 2.270–4.510) were the factors related to the parental awareness of ADHD.ConclusionParents and teachers of primary school students recognize ADHD symptoms differently. The positive screening rate of ADHD among teachers was significantly higher than that of parents. Relationship with children, educational level of parents, children’s sex, age, and grade, and information sources of ADHD are the relevant factors affecting parental awareness of ADHD. More efforts should be made to disseminate ADHD knowledge through mass media, and medical staff. Fathers, parents with low educational level, and parents of grade 2 and 3 pupils should be encouraged to acquire more knowledge on ADHD to improve the early recognition rate of ADHD symptoms.Clinical trial registration[http://www.chictr.org.cn/showproj.aspx?proj=54 072], identifier [ChiCTR2000033388].

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“…Higher cohesion/contradiction subscale scores indicated higher intimacy/conflict levels. The cohesion and contradiction subscales of the FES-CV had acceptable reliability and validity (for cohesion: Cronbach α = 0.75; for contradiction: Cronbach α = 0.67) ( 19 ), and both worked well in assessing different kinds of families and measuring family relationships/environment in China ( 27 , 28 ).…”

Section: Methodsmentioning

confidence: 99%

The Association of Maternal Emotional Status With Child Over-Use of Electronic Devices During the COVID-19 Pandemic

Guo

Zhou

et al. 2021

Front. Pediatr.

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The quarantine during the COVID-19 pandemic may generate high levels of maternal depression/anxiety, and maternal emotional status may affect child behavioral development. Online education during the pandemic may induce child over-use of electronic-devices. However, child electronic-device over-use (especially among children under 12 who are immature in physical and mental development) during the pandemic has not attracted sufficient attention, and the association of child over-use with maternal emotional status remains unknown. Therefore, this study aims to assess the characteristics of child electronic-device over-use and the association between maternal emotional status and child over-use among 1,300 children from nurseries (<3 years), kindergartens (3–6 years), and primary schools (6–12 years) in Shanghai and Wuhan during COVID-19. Mothers completed an online questionnaire (including the Self-Rating-Depression/Anxiety-Scales and Family-Environment-Scale). The use of electronic devices (mobile-phones, iPads, computers, and televisions) and online courses taken by the children were investigated. Associations of maternal emotional status with electronic-device-use by child age were analyzed. The proportions of children in nurseries, kindergartens and primary schools were 8.5, 44.5, and 47.0%, their percentages following online-courses were 24.5, 48.4, and 99.0%, and their rates of electronic-device over-use were 34.2, 62.2, and 93.4%, respectively. Significant associations were observed between higher maternal anxiety/depression levels and higher risks of mobile-phone/iPad over-use among preschoolers and primary-school students. Lower family intimacy and higher conflict levels were associated with higher maternal depression/anxiety levels and higher risks of electronic-device over-use. Our findings suggested that over-use of electronic-devices among children under 12 was common during COVID-19, especially among children ≥6 years, and online-teaching may exacerbate over-use. Maternal anxiety/depression levels were associated with over-use of portable internet-devices (mobile-phone/iPad), especially among preschoolers and school-aged students, and family environment may mediate the association. These findings may contribute to a better understanding of factors leading to over-use of electronic-device and developing strategies to decrease over-use during COVID-19.

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Predicting the Adult Clinical and Academic Outcomes in Boys With ADHD: A 7- to 10-Year Follow-Up Study in China

Cited by 7 publications

References 50 publications

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

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

Screening of ADHD symptoms in primary school students and investigation of parental awareness of ADHD and its influencing factors: A cross-sectional study

The Association of Maternal Emotional Status With Child Over-Use of Electronic Devices During the COVID-19 Pandemic

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