The benefit of regular physical activity and exercise training for the prevention of cardiovascular and metabolic diseases is undisputed. Many molecular mechanisms mediating exercise effects have been deciphered. Personalised exercise prescription can help patients in achieving their individual greatest benefit from an exercise-based cardiovascular rehabilitation programme. Yet, we still struggle to provide truly personalised exercise prescriptions to our patients. In this position paper, we address novel basic and translational research concepts that can help us understand the principles underlying the inter-individual differences in the response to exercise, and identify early on who would most likely benefit from which exercise intervention. This includes hereditary, non-hereditary and sex-specific concepts. Recent insights have helped us to take on a more holistic view, integrating exercise-mediated molecular mechanisms with those influenced by metabolism and immunity. Unfortunately, while the outline is recognisable, many details are still lacking to turn the understanding of a concept into a roadmap ready to be used in clinical routine. This position paper therefore also investigates perspectives on how the advent of 'big data' and the use of animal models could help unravel inter-individual responses to exercise parameters and thus influence hypothesis-building for translational research in exercise-based cardiovascular rehabilitation.
Keywords: Cardiovascular rehabilitation; animal models; big data; exercise; immune system; machine learning; personalised medicine; responders/non-responders.