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

Social learning-based health literacy promotion on the self efficacy and social anxiety of adolescents with type 1 diabetes

BACKGROUND AND OBJECTIVE

Type 1 diabetes mellitus one of the biggest health concerns around the world, is difficult to manage during adolescence. Among the non-medical methods of controlling this disease is empowerment through self-efficacy. Poor self-efficacy leads to social anxiety and ultimately deficiencies in diabetes. There is also a correlation among health literacy, self-efficacy, and social anxiety. Thus, the present study aimed to evaluate the impact of a literacy promotion training program based on social learning theory on the self-efficacy and social anxiety of adolescents with T1DM.

METHODS

The current research is a semi-experimental type that was carried out with the cooperation of 66 adolescents aged 15-18 years with type 1 diabetes in Iran (Tehran, 2022). It has control and intervention groups. The endocrinology and diabetes clinics of the intervention and control groups were randomly selected in a multi-stage manner (endocrine and diabetes clinic of children's medical center hospital for the control group and endocrine and diabetes clinic of Mofid hospital for the intervention group) and the participants were selected by Simple Random Sampling method (draw). The training program designed based on Bandura's social learning theory was used to teach adolescents during seven consecutive sessions of 30-45 min during one week. Questionnaires were completed before and one month after the intervention. Data were analysed in SPSS-25 software.

FINDINGS

The intervention for adolescents with T1DM in intervention group compared to the control group had a significant effect on improve health literacy (P<0.001), self-efficacy (P<0.001), and social anxiety (P<0.05).

CONCLUSIONS

The results can also be used to improve the capabilities of adolescents with T1DM, reduce and prevent disease complications, and develop operational-educational programs in the centers from which these adolescents receive various services.

TRIAL REGISTRATION

IRCT20210422051045N1.

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

Introduction

Attention-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.

Methods

Fifty-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.

Results

We 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).

Discussion

Our 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.

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

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.