Data-driven resistance training against muscular atrophy

Researchers at ETH Zurich and ZHAW present a simple method to precisely map resistance exercise on machines and record missing comparative figures. This could help to develop optimized training strategies in the future, such as for age-associated muscular atrophy.

Muscles play a critical role in life. Skeletal muscle mass alone accounts for up to 40 percent of our body mass. Muscles turn chemical energy into mechanical energy and generate the power with which we breathe and move. Muscles also serve as a reservoir for carbohydrates, proteins, and fatty acids, and contribute significantly to our metabolism and energy balance.

However, muscle mass unfortunately declines continuously from the age of 40. This age-related muscular atrophy – sarcopenia – comprises about 6 percent over ten years. By the age of 80, a person will thus have lost about a third of their maximum muscle mass.  Physical performance reduces significantly and quality of life drops.

Resistance training is medicine

It is generally understood that physical activity can stimulate muscle growth. Resistance training is thus the key measure to counteract the negative effects of sarcopenia. However, what exactly targeted muscle training is and how it can optimally achieve its purpose is largely unknown.

Image credit: US Air Force via Wikimedia

“This is because resistance training is not mapped accurately enough in practice, so it is difficult to draw conclusions about muscle growth,” says Claudio Viecelli, PhD student at ETH's Institute of Molecular Systems Biology under Professor Ernst Hafen.

Viecelli aims to close this gap. For his dissertation, the molecular and muscle biologist worked with colleagues from Zurich University of Applied Sciences (ZHAW) and Kieser Training AG to develop an impressively simple method: it uses acceleration sensors in conventional smartphones to record resistance training variables on devices in a high temporal resolution. The researchers discussd their method in the specialist journal PLOS ONE.

Wanted: time under tension

Until now, the activity carried out with a weight during a resistance exercise session has usually been logged in terms of sets and repetitions. However, such training data is insufficiently comparable and thus sub-​optimal when it comes to examining the effects of training on muscle growth. The temporal patterns of resistance training are relevant to muscle plasticity.

The description figures required have been known theoretically for a long time. These include the “single repetition”, which consists of lifting and lowering the load; then the “contraction-​phase specific time”, which indicates the time the muscle is under tension during the lifting and lowering; and finally the “total time-​under-tension” – this quantifies how long the muscles were under tension during an exercise.

From acceleration to contraction

So far, there has been no suitable method of recording these values reliably in the gym. “It would require several stopwatches or even several assistants – but this is hardly practical. This is why this information is usually missing in training logs and scientific publications,” explains Viecelli, who himself is a resistance training enthusiast. While searching for a solution, he came up with the idea of using a smartphone as a digital analysis tool.