Currently, obesity in people with intellectual disabilities, whose daily lives are to a certain degree affec-ted by their impairment, is increasingly becoming the focal point of attention. However, there is a lack of scientific data for children with intellectual disabilities. The aim of this research is to determine whether children with intellectual disabilities are prone to obesity, whether obesity is gender- and age-specific, what is the distribution of intellectual disabilities in boys and girls and whether any level of intellectual disability shows a higher proclivity to obesity.The method employed was comparative and qualitative research approached deductively. For the collection of BMI indicators, InBody analyser was used to measure the data of 49 children attending two special needs primary schools.The findings of the research show that the BMI values of more than a half of children with intellectual disabilities in all assessed groups is within norm. Boys are estimated to have a lower BMI in middle adolescence than in early adolescence and school age. Girls are highly likely to have a higher BMI in late adolescence than in middle and early adolescence. In boys, obesity is associated with early ado-lescence and mild to moderate level of intellectual disability, whereas in girls with middle adolescence and mild level of intellectual disability.
- Keywords
- InBody,
- MeSH
- Child MeSH
- Body Mass Index * MeSH
- Humans MeSH
- Intellectual Disability * classification MeSH
- Adolescent MeSH
- Obesity MeSH
- Sex MeSH
- Statistics as Topic instrumentation MeSH
- Age Factors MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Adolescent MeSH
- Male MeSH
- Female MeSH
- Geographicals
- Czech Republic MeSH
- Keywords
- přípravek 10% Urea Krém, zvýšení hydratace kůže,
- MeSH
- Administration, Cutaneous * MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Urea * administration & dosage pharmacology therapeutic use MeSH
- Forearm MeSH
- Statistics as Topic methods organization & administration instrumentation trends MeSH
- Treatment Outcome * MeSH
- Wetting Agents * therapeutic use MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Female MeSH
- MeSH
- Mathematical Computing MeSH
- Numerical Analysis, Computer-Assisted MeSH
- Statistics as Topic instrumentation MeSH
- Conspectus
- Matematika
- NML Fields
- technika
- přírodní vědy
- NML Publication type
- elektronické časopisy
Sekvenční data jsou důležitým zdrojem lékařských znalostí. Tato specifická data mohou vznikat řadou různých způsobů. V tomto článku na příkladu konkrétní studie prezentujeme obecné postupy pro jejich dolování. Jde o preventivní dlouhodobou studii atherosklerózy – data jsou výsledkem dvě dekády trvajícího sledování vývoje rizikových faktorů a přidružených jevů. Hlavním cílem je identifikovat časté sekvenční vzory, tj. opakující se časové jevy, a studovat jejich možnou souvislost s objevením jedné ze sledovaných kardiovaskulárních nemocí. Z širší škály dostupných metod se soustředíme na induktivní logické programování, které potenciální vzory vyjadřuje ve formě rysů v predikátové logice prvního řádu. Rysy jsou nejprve automaticky extrahovány a následně sdružovány do pravidel, která představují výstupní formu získané znalosti. Navržený postup je porovnán s tradičnějšími metodami publikovanými dříve. Jde o metodu posuvných oken a epizodní pravidla.
Sequential data represent an important source of automatically mined and potentially new medical knowledge. They can originate in various ways. Within the presented domain they come from a longitudinal preventive study of atherosclerosis - the data consists of series of long-term observations recording the development of risk factors and associated conditions. The intention is to identify frequent sequential patterns having any relation to an onset of any of the observed cardiovascular diseases. This paper focuses on application of inductive logic programming. The prospective patterns are based on first-order features automatically extracted from the sequential data. The features are further grouped in order to reach final complex patterns expressed as rules. The presented approach is also compared with the approaches published earlier (windowing, episode rules).
- MeSH
- Algorithms MeSH
- Atherosclerosis diagnosis etiology MeSH
- Biomedical Research methods instrumentation trends MeSH
- Databases, Factual trends utilization MeSH
- Financing, Organized MeSH
- Cardiovascular Diseases diagnosis etiology MeSH
- Medical Informatics methods instrumentation trends MeSH
- Humans MeSH
- Longitudinal Studies MeSH
- Risk Factors MeSH
- Data Collection methods instrumentation trends MeSH
- Statistics as Topic methods instrumentation trends MeSH
- Models, Theoretical MeSH
- Check Tag
- Humans MeSH
sv.
- MeSH
- Statistics as Topic instrumentation MeSH
- Publication type
- Periodical MeSH
- Conspectus
- Statistika
- NML Fields
- statistika, zdravotnická statistika
