Methodology for the assessment of the individual risk for the health of the children aged 12-15 years
DOI:
https://doi.org/10.32402/dovkil2019.04.044Keywords:
health, adolescents, individual risk, motor activity, body mass index, sensitivity of diagnostic testsAbstract
A search for the new methods for the assessment of the impact of a complex of internal and external factors on health in order to develop primary prevention tools is one of the topical tasks in the sphere of public health. Objective: We created a model of health risk for urban adolescents, engaged in sports/dance in the organized groups, based on the study of endo- and exogenous factors using Bayes theorem. Materials and methods: The model was developed taking into account anthropometric, behavioural, social, and demographic determinants identified in urban children aged 12-15 years in 2017 (n = 54) and 2018 (n = 60) with a help of information theory methods. Results: A model for the assessment of the health risk for urban adolescents, going in for sports, was developed on the basis of Bayes theorem. Negative factors, affecting adolescent health, are as follows: excess and insufficient body weight (p <0.05), the presence of chronic diseases (p <0.05), sleep less than 9 hours a day (p <0.05), age over 14.5 years (p <0, 05), children, going in for sports more and less 3-4 times a week (p <0.01) and with a duration less than 270 minutes a week for boys and 230 minutes a week for girls (p <0.05), low family income (p <0.05),absence of joined motor activity of parents with children (p <0.05), absence of motor activity in parents (p <0.05). Conclusions: A screening tool determines the risk of children’s health deterioration taking into account the anthropometric, behavioural, social, and demographic determinants with sensitivity of 92.9%, specificity of 85.7%, positive predictive value of 86.7%, negative predictive wave of 92.3 %. The test analytical accuracy is 89.3%.Downloads
References
1. WHO. Physical activity strategy for the WHO European Region 2016-2025. 2016. 32 p.
2. Booth F.W., Roberts C.K., Laye M.J. Lack of exercise is a major cause of chronic diseases. Compr Physiol. 2012. Vol. 2(2). P.1143-211. DOI : http://doi.org/10.1002/cphy.c110025
3. Антомонов М.Ю. Математическая обработка и анализ медико-биологических данных. К. : Мединформ, 2018. 579 с.
4. Strugnell C., Turner K., Malakellis M., Hayward J., Foster C., Millar L., Allender S. Composition of objectively measured physical activity and sedentary behaviour participation across the school-day, influence of gender and weight status: cross-sectional analyses among disadvantaged Victorian school children. BMJ Open.2016. Vol. 6(9). e011478. DOI : http://doi.org/10.1136/bmjopen-2016-011478
5. Gupta N., Mathiassen S.E., Mateu-Figueras G., Heiden M., Hallman D.M., Jørgensen M.B., Holtermann A. A comparison of standard and compositional data analysis in studies addressing group differences in sedentary behavior and physical activity. Int J Behav Nutr Phys Act. 2018. Vol.15(1). P. 53-67. DOI : http://doi.org/10.1186/s12966-018-0685-1
6. Foley L., Dumuid D., Atkin A.J., Olds T., Ogilvie D. Patterns of health behaviour associated with active travel: a compositional data analysis. Int J Behav Nutr Phys Act. 2018. Vol.15(1). P.26-38. DOI : https://doi.org/10.1186/s12966-018-0662-8
7. Пересипкіна Т.В., Редька І.В., Сидоренко Т.П., Пересипкіна А.М. Інформаційна значущість медико-соціальних факторів, які впливають на здоров’яорієнтовану поведінку школярів. Здоровье ребенка. 2019.Vol. 14. №. 3. Р. 165-170. DOI : https://doi.org/10.22141/2224-0551.14.3.2019.168768
8. Petzschner F.H., Weber L.A.E., Gard T., Stephan K.E. Computational Psychosomatics and Computational Psychiatry: Toward a Joint Framework for Differential Diagnosis. Biol Psychiatry. 2017. Vol. 82(6). P. 421-430. DOI : https://doi.org/10.1016/j.biopsych.2017.05.012
9. Pillai P.S., Leong T.Y. Alzheimer’s Disease Neuroimaging Initiative. Modeling Multi-View Dependence in Bayesian Networks for Alzheimer's Disease Detection. Stud Health Technol Inform. 2019. Vol. 21. P. 358-362.
10. Labelle C., Marinier A., Lemieux S. Enhancing the drug discovery process: Bayesian inference for the analysis and comparison of dose-response experiments. Bioinformatics. 2019. Vol. 35(14). P.i464-i473. DOI : https://doi.org/10.1093/bioinformatics/btz335
11. Barbosa N., Sanchez C.E., Vera J.A. et al. A physical activity questionnaire: Reproducibility and validity. Journal of Sports Science and Medicine. 2007. Vol. 6. P. 505–518.
12. Полька Н.С., Гозак С.В., Єлізарова О.Т., Станкевич Т.В., Парац А.М. Новітній підхід до оцінювання здоров’я підлітків у гігієнічних дослідженнях. Журнал НАМН України. 2019. T. 25. № 3. С. 227–231.
13. Москаленко В.Ф., Булах І.Є., Пузанова О.Г. Методологія доказової медицини. Київ : Медицина, 2014. 200 с.
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