Factors affecting the behavior of children with ASD during the first outbreak of the COVID-19 pandemic.

Neurol Sci

Unit of Neurology, Division of Pediatrics, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.

Published: May 2021

Aim And Methods: Cross-sectional data from 118 Chilean children with ASD collected during the pandemic outbreak of COVID-19 in 2020 were evaluated to analyze predictors of behavioral problem impairment.

Results: Forty-five percent of parents stated that their children's behavioral difficulties increased in intensity or frequency. The adjusted predictors were having a family member hospitalized with COVID-19 (OR = 4.11; 95% CI = 1.53-11.1) and parents' mental health disorders during the pandemic (OR = 2.43; 95% CI = 1.01-5.83).

Conclusion: Potentially modifiable psychosocial factors affecting children's behavior should be considered in a possible second outbreak.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7914113PMC
http://dx.doi.org/10.1007/s10072-021-05147-9DOI Listing

Publication Analysis

Top Keywords

children asd
8
outbreak covid-19
8
factors behavior
4
behavior children
4
asd outbreak
4
covid-19 pandemic
4
pandemic aim
4
aim methods
4
methods cross-sectional
4
cross-sectional data
4

Similar Publications

The aim of this study is to investigate the impact of using probiotics with strains related to dopamine and gamma-aminobutyric acid production on clinical features of autism spectrum disorder (ASD) and/or attention deficit/hyperactivity disorder (ADHD). This randomized, controlled trial involved 38 children with ADHD and 42 children with ASD, aged 5-16 years, who received probiotics (Lactiplantibacillus plantarum and Levilactobacillus brevis 109/cfu/daily) or placebo for 12 weeks. Parent-reported symptoms were assessed using Conners' 3rd-Ed and the Social Responsiveness Scale Test, 2nd-Ed (SRS-2), and children completed the Conners Continuous Performance Test, 3rd-Ed (CPT 3) or Conners Kiddie CPT, 2nd-Ed (K-CPT 2).

View Article and Find Full Text PDF

Children and adolescents with neurodevelopmental disorders such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) may be more susceptible to early life stress compared to their neurotypical peers. This increased susceptibility may be linked to regionally-specific changes in the striatum and amygdala, brain regions sensitive to stress and critical for shaping maladaptive behavioural responses. This study examined early life stress and its impact on striatal and amygdala development in 62 children and adolescents (35 males, mean age = 10.

View Article and Find Full Text PDF

One of the key challenges in autism is early diagnosis. Early diagnosis leads to early interventions that improve the condition and not worsen autism in the future. Currently, autism diagnoses are based on monitoring by a doctor or specialist after the child reaches a certain age exceeding three years after the parents observe the child's abnormal behavior.

View Article and Find Full Text PDF

Improving Imitation Skills in Children with Autism Spectrum Disorder Using the NAO Robot and a Human Action Recognition.

Diagnostics (Basel)

December 2024

Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.

Autism spectrum disorder (ASD) is a group of developmental disorders characterized by poor social skills, low motivation in activities, and a lack of interaction with others. Traditional intervention approaches typically require support under the direct supervision of well-trained professionals. However, teaching and training programs for children with ASD can also be enhanced by assistive technologies, artificial intelligence, and robotics.

View Article and Find Full Text PDF

Developing a simplified measure to predict the risk of autism spectrum disorders: Abbreviating the M-CHAT-R using a machine learning approach in China.

Psychiatry Res

January 2025

Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510630, China. Electronic address:

Background: Early screening for autism spectrum disorder (ASD) is crucial, yet current assessment tools in Chinese primary child care are limited in efficacy.

Objective: This study aims to employ machine learning algorithms to identify key indicators from the 20-item Modified Checklist for Autism in Toddlers, revised (M-CHAT-R) combining with ASD-related sociodemographic and environmental factors, to distinguish ASD from typically developing children.

Methods: Data from our prior validation study of the Chinese M-CHAT-R (August 2016-March 2017, n = 6,049 toddlers) were reviewed.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!