| Literature DB >> 28420250 |
Dominique Hansen1,2, Paul Dendale1,2, Karin Coninx3, Luc Vanhees4, Massimo F Piepoli5, Josef Niebauer6, Veronique Cornelissen7, Roberto Pedretti8, Eva Geurts3, Gustavo R Ruiz3, Ugo Corrà9, Jean-Paul Schmid10,11, Eugenio Greco12, Constantinos H Davos13, Frank Edelmann14,15, Ana Abreu16, Bernhard Rauch17, Marco Ambrosetti18, Simona S Braga8, Olga Barna19, Paul Beckers20, Maurizio Bussotti21, Robert Fagard22, Pompilio Faggiano23, Esteban Garcia-Porrero24, Evangelia Kouidi25, Michel Lamotte26, Daniel Neunhäuserer6,27, Rona Reibis28, Martijn A Spruit2,29,30, Christoph Stettler31, Tim Takken32, Cajsa Tonoli33, Carlo Vigorito34, Heinz Völler35,36, Patrick Doherty37.
Abstract
Background Exercise rehabilitation is highly recommended by current guidelines on prevention of cardiovascular disease, but its implementation is still poor. Many clinicians experience difficulties in prescribing exercise in the presence of different concomitant cardiovascular diseases and risk factors within the same patient. It was aimed to develop a digital training and decision support system for exercise prescription in cardiovascular disease patients in clinical practice: the European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool. Methods EXPERT working group members were requested to define (a) diagnostic criteria for specific cardiovascular diseases, cardiovascular disease risk factors, and other chronic non-cardiovascular conditions, (b) primary goals of exercise intervention, (c) disease-specific prescription of exercise training (intensity, frequency, volume, type, session and programme duration), and (d) exercise training safety advices. The impact of exercise tolerance, common cardiovascular medications and adverse events during exercise testing were further taken into account for optimized exercise prescription. Results Exercise training recommendations and safety advices were formulated for 10 cardiovascular diseases, five cardiovascular disease risk factors (type 1 and 2 diabetes, obesity, hypertension, hypercholesterolaemia), and three common chronic non-cardiovascular conditions (lung and renal failure and sarcopaenia), but also accounted for baseline exercise tolerance, common cardiovascular medications and occurrence of adverse events during exercise testing. An algorithm, supported by an interactive tool, was constructed based on these data. This training and decision support system automatically provides an exercise prescription according to the variables provided. Conclusion This digital training and decision support system may contribute in overcoming barriers in exercise implementation in common cardiovascular diseases.Entities:
Keywords: Cardiovascular disease; exercise training; rehabilitation; training and decision support system
Mesh：
Year: 2017 PMID： 28420250 DOI： 10.1177/2047487317702042
Source DB: PubMed Journal: Eur J Prev Cardiol ISSN： 2047-4873 Impact factor: 7.804