Data Driven Medical muscle Training
Description
Muscle loss is a concomitant of various diseases (cachexia) and the usual aging process (sarcopenia). Limiting this reduction and, ideally, even reversing it makes medical and economic sense. In medicine, the metabolic function (in addition to the pure movement) of the muscles is increasingly understood and recognized in their therapeutic relevance; for chronic diseases, e.g. HIV or cancer has been shown to help patients with higher muscle mass survive longer. From the point of view of most older people as well as from an economic perspective, it is desirable to make it possible to stay as long as possible in one's own living space. Well-developed muscles are a necessary prerequisite for this.Strength training is an established measure against cachexia / sarcopenia. At present, such training is characterized by a few non-dynamic parameters (type of exercise, number of repetitions, force to be applied). Recent findings (from molecular physiology and exercise science) suggest that the success of strength training is influenced not only by these static parameters, but also by the way in which movements are performed. The Institute of Systems Biology (Prof. Dr. Ernst Hafen) and the Laboratory of Sports Physiology (Prof. Dr. Christina Spengler) of the ETH Zurich are investigating ways to better parameterize training through additional mechano-biological descriptors, to characterize their relevance for muscle building and as a consequence to make the trainings accessible to further personalization and optimization. Specifically, not only the number of repetitions of a movement, but also the details of the execution (in particular the acceleration profiles) should be analyzed. The ultimate goal is to make patients optimally manage the movement during a workout with respect to stimulating muscle building.
Key Data
Projectlead
Deputy Projectlead
Project team
Dr. Philipp Ackermann, David Graf, Prof. Dr. Juan-Mario Gruber
Project partners
Eidgenössische Technische Hochschule Zürich ETH / Exercise Physiology Lab
Project status
completed, 02/2019 - 11/2019
Funding partner
Internal