Objectives: To clarify the severity, specificity, and neurocognitive underpinnings of attention problems in very preterm children.
Study Design: A sample of 66 preterm (<32 weeks gestation), mean (SD) age 7.5 (0.4) years, and 66 age-matched term controls participated. Symptoms of inattention were assessed using parent and teacher-rated questionnaires, and neurocognitive measures included speed and consistency in speed of information processing, lapses of attention (tau), alerting, orienting, and executive attention, as well as verbal and visuospatial working memory. Group differences were investigated using ANOVA, and Sobel tests were used to clarify the mediating role of neurocognitive impairments on attention problems.
Results: There was a large decrease in visuospatial working memory abilities (P < .001, d = .87), and medium increases in tau (P = .002, d = 0.55) as well as parent and teacher ratings of inattention (range d = 0.40-0.56) in very preterm children compared with term peers. Tau and visuospatial working memory were significant predictors of parent (R(2) = .161, P < .001 and R(2) = .071, P = .001; respectively) and teacher (R(2) = .152, P < .001 and R(2) = .064, P = .002; respectively) ratings of inattention, and completely explained the effects of very preterm birth on attention problems.
Conclusions: Increased lapses of attention and poorer visuospatial working memory fully account for the attention problems in very premature children at school-age.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jpeds.2012.05.010 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Evaluating the effect of a non-linguistic cognitive intervention on functional communication in global aphasia: A case series study.
Int J Lang Commun Disord
January 2025
Department of Language and Cognition, University College London, London, UK.
Background: Global aphasia is a severe communication disorder affecting all language modalities, commonly caused by stroke. Evidence as to whether the functional communication of people with global aphasia (PwGA) can improve after speech and language therapy (SLT) is limited and conflicting. This is partly because cognition, which is relevant to participation in therapy and implicated in successful functional communication, can be severely impaired in global aphasia.
View Article and Find Full Text PDFDeep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation Research.
Sensors (Basel)
January 2025
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China.
With the digital transformation of the manufacturing industry, data monitoring and collecting in the manufacturing process become essential. Pointer meter reading recognition (PMRR) is a key element in data monitoring throughout the manufacturing process. However, existing PMRR methods have low accuracy and insufficient robustness due to issues such as blur, uneven illumination, tilt, and complex backgrounds in meter images.
View Article and Find Full Text PDFYOLOv8-LCNET: An Improved YOLOv8 Automatic Crater Detection Algorithm and Application in the Chang'e-6 Landing Area.
Sensors (Basel)
January 2025
Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China.
The Chang'e-6 (CE-6) landing area on the far side of the Moon is located in the southern part of the Apollo basin within the South Pole-Aitken (SPA) basin. The statistical analysis of impact craters in this region is crucial for ensuring a safe landing and supporting geological research. Aiming at existing impact crater identification problems such as complex background, low identification accuracy, and high computational costs, an efficient impact crater automatic detection model named YOLOv8-LCNET (YOLOv8-Lunar Crater Net) based on the YOLOv8 network is proposed.
View Article and Find Full Text PDFAn Attention-Based Multidimensional Fault Information Sharing Framework for Bearing Fault Diagnosis.
Sensors (Basel)
January 2025
School of Mechanical Engineering, Guizhou University, Guiyang 550028, China.
Deep learning has performed well in feature extraction and pattern recognition and has been widely studied in the field of fault diagnosis. However, in practical engineering applications, the lack of sample size limits the potential of deep learning in fault diagnosis. Moreover, in engineering practice, it is usually necessary to obtain multidimensional fault information (such as fault localization and quantification), while current methods mostly only provide single-dimensional information.
View Article and Find Full Text PDFVehicle Target Detection of Autonomous Driving Vehicles in Foggy Environments Based on an Improved YOLOX Network.
Sensors (Basel)
January 2025
College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China.
To address the problems that exist in the target detection of vehicle-mounted visual sensors in foggy environments, a vehicle target detection method based on an improved YOLOX network is proposed. Firstly, to address the issue of vehicle target feature loss in foggy traffic scene images, specific characteristics of fog-affected imagery are integrated into the network training process. This not only augments the training data but also improves the robustness of the network in foggy environments.
View Article and Find Full Text PDFWant 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!