Psychometric properties and factor structure of the Korean version of the screen for child anxiety related emotional disorders (SCARED)

Shin, Jiyoon; Kim, Kyoung Min; Lee, Kyung Hwa; Hong, Soon Man; Lee, Jung; Choi, Chong‐Ho; Han, Ji Youn; Kim, Seong Hae; Suh, Da Eun; Cho, Soo Churl; Kim, Jae Won

doi:10.1186/s12888-020-02505-3

Cited by 7 publications

(3 citation statements)

References 41 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Participants were asked to complete questionnaires including the Children’s Depression Inventory (CDI) ( 29 ) and the Beck Depression Inventory (BDI) ( 30 , 31 ). The Screen for Child Anxiety Related Disorders (SCARED) ( 32 , 33 ), and Family Adaptability and Cohesion Evaluation Scale IV (FACES-IV) ( 34 ) were further applied to assess subjective distress related to depression, anxiety, and family functioning, respectively. Finally, the Stroop task and the Auditory/Visual Continuous Performance Test (ATA) ( 35 ) were administered to each participant.…”

Section: Methodsmentioning

confidence: 99%

Development of an innovative approach using portable eye tracking to assist ADHD screening: a machine learning study

Yoo,

Kang,

Lim

et al. 2024

Front. Psychiatry

Self Cite

View full text Add to dashboard Cite

IntroductionAttention-deficit/hyperactivity disorder (ADHD) affects a significant proportion of the pediatric population, making early detection crucial for effective intervention. Eye movements are controlled by brain regions associated with neuropsychological functions, such as selective attention, response inhibition, and working memory, and their deficits are related to the core characteristics of ADHD. Herein, we aimed to develop a screening model for ADHD using machine learning (ML) and eye-tracking features from tasks that reflect neuropsychological deficits in ADHD.MethodsFifty-six children (mean age 8.38 ± 1.58, 45 males) diagnosed with ADHD based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition were recruited along with seventy-nine typically developing children (TDC) (mean age 8.80 ± 1.82, 33 males). Eye-tracking data were collected using a digital device during the performance of five behavioral tasks measuring selective attention, working memory, and response inhibition (pro-saccade task, anti-saccade task, memory-guided saccade task, change detection task, and Stroop task). ML was employed to select relevant eye-tracking features for ADHD, and to subsequently construct an optimal model classifying ADHD from TDC.ResultsWe identified 33 eye-tracking features in the five tasks with the potential to distinguish children with ADHD from TDC. Participants with ADHD showed increased saccade latency and degree, and shorter fixation time in eye-tracking tasks. A soft voting model integrating extra tree and random forest classifiers demonstrated high accuracy (76.3%) at identifying ADHD using eye-tracking features alone. A comparison of the model using only eye-tracking features with models using the Advanced Test of Attention or Stroop test showed no significant difference in the area under the curve (AUC) (p = 0.419 and p=0.235, respectively). Combining demographic, behavioral, and clinical data with eye-tracking features improved accuracy, but did not significantly alter the AUC (p=0.208).DiscussionOur study suggests that eye-tracking features hold promise as ADHD screening tools, even when obtained using a simple digital device. The current findings emphasize that eye-tracking features could be reliable indicators of impaired neurobiological functioning in individuals with ADHD. To enhance utility as a screening tool, future research should be conducted with a larger sample of participants with a more balanced gender ratio.

show abstract

Section: Methodsmentioning

confidence: 99%

Development of an innovative approach using portable eye tracking to assist ADHD screening: a machine learning study

Yoo,

Kang,

Lim

et al. 2024

Front. Psychiatry

Self Cite

View full text Add to dashboard Cite

show abstract

“…All study participants were assessed using the CDRS-R, Children’s Depression Inventory (CDI) [ 30 , 31 ], Beck Depression Inventory [ 32 , 33 ], Columbia Suicide Severity Rating Scale (C-SSRS) [ 34 , 35 ], Clinical Global Impressions-Severity, Children’s Global Assessment Scale [ 36 ], Screen for Child Anxiety Related Emotional Disorders (SCARED) [ 37 , 38 ], Family Adaptability and Cohesion Evaluation Scale-IV (FACES-IV) [ 39 ], and IQ at baseline evaluation. A 10-item questionnaire was also administered to assess the smartphone usage pattern of the study participants.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study

Kim¹,

Wang²,

Kim³

et al. 2023

JMIR Form Res

Self Cite

View full text Add to dashboard Cite

Background Lack of quantifiable biomarkers is a major obstacle in diagnosing and treating depression. In adolescents, increasing suicidality during antidepressant treatment further complicates the problem. Objective We sought to evaluate digital biomarkers for the diagnosis and treatment response of depression in adolescents through a newly developed smartphone app. Methods We developed the Smart Healthcare System for Teens At Risk for Depression and Suicide app for Android-based smartphones. This app passively collected data reflecting the social and behavioral activities of adolescents, such as their smartphone usage time, physical movement distance, and the number of phone calls and text messages during the study period. Our study consisted of 24 adolescents (mean age 15.4 [SD 1.4] years, 17 girls) with major depressive disorder (MDD) diagnosed with Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version and 10 healthy controls (mean age 13.8 [SD 0.6] years, 5 girls). After 1 week’s baseline data collection, adolescents with MDD were treated with escitalopram in an 8-week, open-label trial. Participants were monitored for 5 weeks, including the baseline data collection period. Their psychiatric status was measured every week. Depression severity was measured using the Children’s Depression Rating Scale-Revised and Clinical Global Impressions-Severity. The Columbia Suicide Severity Rating Scale was administered in order to assess suicide severity. We applied the deep learning approach for the analysis of the data. Deep neural network was employed for diagnosis classification, and neural network with weighted fuzzy membership functions was used for feature selection. Results We could predict the diagnosis of depression with training accuracy of 96.3% and 3-fold validation accuracy of 77%. Of the 24 adolescents with MDD, 10 responded to antidepressant treatments. We predicted the treatment response of adolescents with MDD with training accuracy of 94.2% and 3-fold validation accuracy of 76%. Adolescents with MDD tended to move longer distances and use smartphones for longer periods of time compared to controls. The deep learning analysis showed that smartphone usage time was the most important feature in distinguishing adolescents with MDD from controls. Prominent differences were not observed in the pattern of each feature between the treatment responders and nonresponders. The deep learning analysis revealed that the total length of calls received as the most important feature predicting antidepressant response in adolescents with MDD. Conclusions Our smartphone app demonstrated preliminary evidence of predicting diagnosis and treatment response in depressed adolescents. This is the first study to predict the treatment response of adolescents with MDD by examining smartphone-based objective data with deep learning approaches.

show abstract

“…Our sample consisted of 147 participants aged 12 to 17 years who visited the child and adolescent psychiatric outpatient clinic at the Seoul National University Hospital during August 2015–June 2018. They were a part of a study that was investigating biomarkers of antidepressant response and suicidal behavior in depressed youth (NRF-2015R1A2A2A01004501) [ 33 , 34 ]. Initially, 152 subjects were recruited, but a total of 147 subjects were included in the analyses, since two depressed participants and three healthy controls withdrew their consent to participate.…”

Section: Methodsmentioning

confidence: 99%

The mediating role of internalizing and externalizing symptoms in the relationship between childhood trauma and suicidality among adolescents: a structural equation model

Kim

Shin

Kim

2021

Child Adolesc Psychiatry Ment Health

Self Cite

View full text Add to dashboard Cite

Background The objective of this study is to investigate the direct and indirect effects of childhood trauma, internalizing symptoms, and externalizing symptoms on suicidality among adolescents, thereby establishing a structural equation model. Methods The present study uses a cross-sectional descriptive design. Among 147 adolescents aged 12–17, 93 outpatients diagnosed with major depressive disorder and 54 controls were included in the study. They completed the Early Trauma Inventory Self-Report (Short Form) and Columbia Suicidality Severity Rating Scale. Their parents completed the Child Behavior Checklist. Analyses were performed using Pearson’s correlation and structural equation modelling. Results Childhood trauma had both direct and indirect effects, via internalizing symptoms and externalizing symptoms, on suicidality. Internalizing symptoms had a direct effect on suicidality. Meanwhile, externalizing symptoms were not directly associated with suicidality, but indirectly associated via internalizing symptoms. Conclusions Findings provide in-depth understanding of the mediating role of internalizing symptoms and externalizing symptoms in the relationship between childhood trauma and suicidality, suggesting that the therapeutic interventions for both internalizing symptoms and externalizing symptoms may be important to prevent suicide in adolescents with childhood trauma.

show abstract

Psychometric properties and factor structure of the Korean version of the screen for child anxiety related emotional disorders (SCARED)

Cited by 7 publications

References 41 publications

Development of an innovative approach using portable eye tracking to assist ADHD screening: a machine learning study

Development of an innovative approach using portable eye tracking to assist ADHD screening: a machine learning study

Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study

The mediating role of internalizing and externalizing symptoms in the relationship between childhood trauma and suicidality among adolescents: a structural equation model

Contact Info

Product

Resources

About