• Dominique Hansen, Paul Dendale, Karin Coninx, Luc Vanhees, Massimo F Piepoli, Josef Niebauer, Veronique Cornelissen, Roberto Pedretti, Eva Geurts, Gustavo R Ruiz, Ugo Corrà, Jean-Paul Schmid, Eugenio Greco, Constantinos H Davos, Frank Edelmann, Ana Abreu, Bernhard Rauch, Marco Ambrosetti, Simona S Braga, Olga Barna, Paul Beckers, Maurizio Bussotti, Robert Fagard, Pompilio Faggiano, Esteban Garcia-Porrero, Evangelia Kouidi, Michel Lamotte, Daniel Neunhäuserer, Rona Reibis, Martijn A Spruit, Christoph Stettler, Tim Takken, Cajsa Tonoli, Carlo Vigorito, Heinz Völler, and Patrick Doherty.
• 1 Heart Centre Hasselt, Jessa Hospital, Belgium.
• Eur J Prev Cardiol. 2017 Jul 1; 24 (10): 1017-1031.

AbstractBackground 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.

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