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Direct linkMayara S. Bianchim; Melitta A. McNarry; Alan R. Barker; Craig A. Williams; Sarah Denford; Lena Thia; Rachel Evans; Kelly A. Mackintosh – Measurement in Physical Education and Exercise Science, 2024
This study aimed to develop and validate machine learning models to predict intensities in children and adolescents with cystic fibrosis (CF) across different accelerometry brands and placements. Thirty-five children and adolescents with CF (11.6 ± 2.8 yrs; 15 girls) and 28 healthy youth (12.2 ± 2.7 yrs; 16 girls) performed six activities whilst…
Descriptors: Models, Prediction, Children, Adolescents
Ronconi, Luca; Facoetti, Andrea; Bulf, Hermann; Franchin, Laura; Bettoni, Roberta; Valenza, Eloisa – Journal of Autism and Developmental Disorders, 2014
Since subthreshold autistic social impairments aggregate in family members, and since attentional dysfunctions appear to be one of the earliest cognitive markers of children with autism, we investigated in the general population the relationship between infants' attentional functioning and the autistic traits measured in their parents.…
Descriptors: Infants, Parents with Disabilities, Autism, Prediction
Mazer, Petra; Gischler, Saskia J.; van der Cammen-van Zijp, Monique H. M.; Tibboel, Dick; Bax, Nicolaas M. A.; Ijsselstijn, Hanneke; van Dijk, Monique; Duivenvoorden, Hugo J. – Developmental Medicine & Child Neurology, 2010
Aim: The aim of this study was to evaluate cognitive and motor development in children with major congenital anomalies and the predictability of development at age 5 years. Method: A prospective, longitudinal follow-up study was undertaken. The Dutch version of the Bayley Scales of Infant Development--Mental Developmental Index (MDI) and…
Descriptors: Scores, Intelligence Tests, Intelligence Quotient, Program Effectiveness
