Physical activity (PA) is associated with benefit in both primary and secondary cardiovascular prevention. A balanced exercise program which incorporates both resistance and aerobic exercise is the foundation to developing a long-lasting wellness lifestyle. AI and machine learning can be performed for different usages in fitness such as training sessions monitoring and personalized training plan. The aim of the project is to evaluate the efficacy of Biocircuit in the functional capacity and in the prescription of physical activity in subjects with cardiovascular diseases. Methods. All subjects refer to an extra-hospital rehabilitation clinic (ABCardio, Bologna). The functional capacity of the subjects will be evaluated through the following tests: Maximal cardiopulmonary exercise test (CPET), 1-km Moderate Treadmill-Walking Test (1-k TWT), Six-minute walking test (6MWT), One-repetition maximum (1-RM) and exercise program to improve cardiovascular endurance and muscle strength. All exercises will be performed through Biocircuit (Technogym, Cesena) a circuit training fully automated and based on the "Biodrive" electromechanical motor built on aerospace technology. The collected data are transmitted to the private cloud-based for data storage and background processing. PA levels and lifestyle evaluation will take place through the administration of questionnaires: the 7-day physical activity recall questionnaire (PAR), SF-12 questionnaire and the evaluation of METhour/week. Expected Results. Increase and maintenance of usual weekly physical activity and validation of a new algorithm for the estimation of VO2peak on Biocircuit instruments are the two main results that the project intends to achieve. Conclusion. Biocircuit applied to secondary prevention can provide a contribution to the formulation and monitoring of personalized programs in cardiac patients.

Biocircuit®: An innovative adaptive circuit training for outpatients with cardiovascular disease. Methodological approach.

Masotti S.;Mandini S.;Mazzoni G.;Zerbini V.;Raisi A.;Piva T.;Grazzi G.
2023

Abstract

Physical activity (PA) is associated with benefit in both primary and secondary cardiovascular prevention. A balanced exercise program which incorporates both resistance and aerobic exercise is the foundation to developing a long-lasting wellness lifestyle. AI and machine learning can be performed for different usages in fitness such as training sessions monitoring and personalized training plan. The aim of the project is to evaluate the efficacy of Biocircuit in the functional capacity and in the prescription of physical activity in subjects with cardiovascular diseases. Methods. All subjects refer to an extra-hospital rehabilitation clinic (ABCardio, Bologna). The functional capacity of the subjects will be evaluated through the following tests: Maximal cardiopulmonary exercise test (CPET), 1-km Moderate Treadmill-Walking Test (1-k TWT), Six-minute walking test (6MWT), One-repetition maximum (1-RM) and exercise program to improve cardiovascular endurance and muscle strength. All exercises will be performed through Biocircuit (Technogym, Cesena) a circuit training fully automated and based on the "Biodrive" electromechanical motor built on aerospace technology. The collected data are transmitted to the private cloud-based for data storage and background processing. PA levels and lifestyle evaluation will take place through the administration of questionnaires: the 7-day physical activity recall questionnaire (PAR), SF-12 questionnaire and the evaluation of METhour/week. Expected Results. Increase and maintenance of usual weekly physical activity and validation of a new algorithm for the estimation of VO2peak on Biocircuit instruments are the two main results that the project intends to achieve. Conclusion. Biocircuit applied to secondary prevention can provide a contribution to the formulation and monitoring of personalized programs in cardiac patients.
2023
979-8-3503-1604-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2534220
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