Children with Autism Spectrum Disorder (ASD) often struggle with dental care due to sensory sensitivities and behavioral issues, increasing their risk for oral health problems. Adaptation strategies such as visual aids, video modeling, and sensory-adapted environments aim to improve their dental experiences. A systematic review of randomized controlled trials (RCTs) was conducted according to PRISMA 2020 guidelines using the PubMed, Scopus, Embase, and Cochrane databases. Of the 1072 records screened, nine RCTs were included in the analysis. Studies included children with ASD under 18 years and compared dental adaptation techniques with traditional care. The risk of bias and study quality were assessed. The quality of evidence for the results was determined using the GRADE tool. Nine RCTs with sample sizes ranging from 25 to 138 participants showed significant improvements in oral hygiene, reduced anxiety, and increased cooperation. Video modeling and sensory-adapted environments were particularly effective in lowering distress during dental visits. Dental adaptation strategies, especially video modeling and sensory-adapted environments, effectively improve oral health outcomes and reduce anxiety in children with ASD. More research is needed to explore the long-term effects and include children with severe ASD.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11641856 | PMC |
http://dx.doi.org/10.3390/jcm13237144 | DOI Listing |
Community Dent Oral Epidemiol
January 2025
School of Clinical Dentistry, University of Sheffield, Sheffield, UK.
Aim: To explore the views of patients, caregivers, and dental professionals on the factors that influence implementation, processes, and effectiveness of a guided self-help cognitive behavioural therapy (CBT) intervention, 'Your teeth, you are in control' (YTYAIC), in the CALM trial.
Methods: Semi-structured interviews were conducted as part of this qualitative component of the process evaluation, and data were analysed using a framework approach based on the Consolidated Framework for Implementation Research (CFIR) and the Five Areas Model of CBT.
Results: Thirty-seven participants were recruited.
Science
January 2025
College of Science and Engineering, Flinders University, Bedford Park, South Australia, Australia.
Identifying what drove the late Pleistocene megafaunal extinctions on the continents remains one of the most contested topics in historical science. This is especially so in Australia, which lost 90% of its large species by 40,000 years ago, more than half of them kangaroos. Determining causation has been obstructed by a poor understanding of their ecology.
View Article and Find Full Text PDFInt J Clin Pediatr Dent
November 2024
Department of Pediatric and Preventive Dentistry, PDM Dental College and Research Institute, Bahadurgarh, Haryana, India.
Aim: The purpose of the study is to evaluate how well the Endovac system and conventional needle irrigation work to remove smear layers (SR) from primary teeth root canals.
Materials And Methods: Fifty extracted human primary teeth were divided into two equal sections vertically, then positioned within an acrylic model that was secured with screws. Group A (Endovac), = 25, and group B (traditional needle), = 25.
Adv Rheumatol
January 2025
Department of Ophthalmology, Otolaryngology, Head and Neck Surgery, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil.
Background: Endoplasmic reticulum stress (ERS) and the unfolded protein response (UPR) are adaptive mechanisms for conditions of high protein demand, marked by an accumulation of misfolded proteins in the endoplasmic reticulum (ER). Rheumatic autoimmune diseases (RAD) are known to be associated with chronic inflammation and an ERS state. However, the activation of UPR signaling pathways is not completely understood in Sjögren's disease (SD).
View Article and Find Full Text PDFBMC Oral Health
January 2025
Department of Clinical Dentistry, Faculty of Medicine, University of Bergen, Bergen, Norway.
Background: In the last years, artificial intelligence (AI) has contributed to improving healthcare including dentistry. The objective of this study was to develop a machine learning (ML) model for early childhood caries (ECC) prediction by identifying crucial health behaviours within mother-child pairs.
Methods: For the analysis, we utilized a representative sample of 724 mothers with children under six years in Bangladesh.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!