ArticleInternational journal of preventive medicine2016
Physical Activity Level Improves the Predictive Accuracy of Cardiovascular Disease Risk Score: The ATTICA Study (2002-2012).
Article in International journal of preventive medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 15 citations in OpenAlex.
- Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology.American journal of preventive cardiology · 2022Review
- Cardiovascular mortality risk prediction using objectively measured physical activity phenotypes in NHANES 2003-2006.Preventive medicine · 2022Article
- Physical Activity and Cardiorespiratory Fitness: Vital Signs for Cardiovascular Risk Assessment.Current cardiology reports · 2021Review
- Observational
- Digital Health in Diabetes and Cardiovascular Disease.Endocrine researchReview
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough physical activity (PA) has long been associated with cardiovascular disease (CVD), assessment of PA status has never been used as a part of CVD risk prediction tools. The aim of the present work was to examine whether the inclusion of PA status in a CVD risk model improves its predictive accuracy.
methodsData from the 10-year follow-up (2002-2012) of the n = 2020 participants (aged 18-89 years) of the ATTICA prospective study were used to test the research hypothesis. The HellenicSCORE (that incorporates age, sex, smoking, total cholesterol, and systolic blood pressure levels) was calculated to estimate the baseline 10-year CVD risk; assessment of PA status was based on the International Physical Activity Questionnaire. The estimated CVD risk was tested against the observed 10-year incidence (i.e., development of acute coronary syndromes, stroke, or other CVD according to the World Health Organization [WHO]-International Classification of Diseases [ICD]-10 criteria). Changes in the predictive ability of the nested CVD risk model that contained the HellenicSCORE plus PA assessment were evaluated using Harrell's C and net reclassification index.
resultsBoth HellenicSCORE and PA status were predictors of future CVD events (P < 0.05). However, the estimating classification bias of the model that included only the HellenicSCORE was significantly reduced when PA assessment was included (Harrel's C = 0.012, P = 0.032); this reduction remained significant even when adjusted for diabetes mellitus and dietary habits (P < 0.05).
conclusionsCVD risk scores seem to be more accurate by incorporating individuals' PA status; thus, may be more effective tools in primary prevention by efficiently allocating CVD candidates.
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