No muscles, no life. They store energy and generate the strength with which we move. As we age, muscle atrophy, or sarcopenia as it is scientifically known, sets in. ETH has just declared war on this problem, and the smartphone is playing an important role.
Our muscles develop through physical activity. Strength training is therefore the key to countering the negative effects of age-related muscle atrophy - which starts to appear from the age of 40 - known as sarcopenia. However, it is still largely unknown what targeted muscle training is and how it optimally fulfils its objective.
Muscle tension should dissipate: researchers at the Institute of Molecular Systems Biology at ETH Zurich want to fill the gaps in this area. Claudio Viecelli, a molecular and muscle biologist, developed a simple method for his thesis in collaboration with the Zurich University of Applied Sciences (ZHAW), whereby the acceleration sensors on conventional smartphones register variable training resistances. The study was recently published in the journal PLOS ONE.
What is the role of molecular systems?
**Claudio Viecelli:**the function of biological systems is generally studied at the level of individual biochemical mechanisms. The discipline of systems biology, on the other hand, is interested in the interactions between a large number of individual biological elements such as genes, proteins and metabolites. We try to use the resulting networks of interacting elements to understand and predict, for example, how metabolism, cell division or developmental processes proceed and how diseases develop.
For the past two years, you have been working on the study that has just been published. What is it about?
It's about describing bodybuilding as objectively and meaningfully as possible.
Isn't that already the case?
No, precisely. On the contrary, in fact. Admittedly, studies on the subject, as numerous as they are, generally compare what is not comparable. Inferences about muscle formation are hardly possible.
What's the problem? What is lacking in these studies?
Until now, in bodybuilding, we have only recorded the number of sets and repetitions performed with a load. However, these training data are insufficiently comparable and therefore not optimal for studying the possible effects of training on muscle formation. The temporal pattern of strength exercise is relevant to muscle physiology.
And ETH is now filling this gap with its latest study?
Yes, that's right. The necessary description parameters have, in theory, been known for some time: the "single repetition", i.e. the fact of lifting and lowering the load; the "specific contraction times" indicating the duration of the respective tension of the muscles for the "single repetition"; the "specific contraction times" indicating the duration of the respective tension of the muscles for the "single repetition".e of respective muscle tension for the ascent and descent; and finally the "total duration of tension", i.e. the length of time the muscles are tense during an exercise.
Wouldn't it be enough to give someone a stopwatch?
Unfortunately, it's not that simple. A supporting example: you lift the weight during the concentric phase, hold it and then release it during the eccentric phase. You perform 12 repetitions in 90 seconds. To determine the actual duration of the different phases and the total duration of the tension using a stopwatch, for all the repetitions, would require more than one stopwatch and one person to operate it. In practice, this is impossible. Until now, there has been no simple method for collecting and describing this data on bodybuilding equipment in an objective, valid and reliable way. What's more, the average hand-eye delay is 180 milliseconds. At 80 milliseconds, the smartphone delay is much lower and the measurement more accurate.
So the smartphone enters the equation.
We placed it on top of the weight column, as you can see in the video above. During movement, the phone's sensors measure accelerations as the weights are lifted and lowered. The accelerometer is nothing more than a three-dimensional construction that measures the acceleration of the smartphone (in all three dimensions). We can read the duration of the tension in the concentric and eccentric phases. For example, we can tell that a concentric contraction lasted three seconds or that the following eccentric contraction lasted five seconds. This also allows us to study the total duration of the exercise and the number of repetitions actually performed. We tested this method on 22 people.
Muscles - which store carbohydrates, proteins and lipids - make a significant contribution to our metabolism and energy balance and convert chemical energy into mechanical energy. Skeletal muscle alone accounts for up to 40% of our body weight.
However, according to Claudio Viecelli, muscle mass decreases steadily from around the age of 40. This age-related muscle atrophy reaches around 6% in ten years. Up to the age of 80, a person loses between a quarter and a third of their maximum muscle mass. The result is a reduction in performance and quality of life.
So bodybuilding isn't just about gaining mass, but protecting your health too?
That's right. Thanks to a specially programmed application, we can read a large amount of data from the speed profile derived. The potential for science to have such a scalable solution is immense. If a large number of people were to use such an application and anonymously make their personal training data available to science, we would be in a better position to solve musculoskeletal problems.musculoskeletal problems - i.e. inflammatory and degenerative diseases of the musculoskeletal system - and, at the same time, ease the burden on our healthcare system.
Your vision is of a digitised weight room?
In the future, you'll place your smartphone on the weight column, finish your workout and have recorded all the data that's relevant to you. From there, it will be possible to create precise, individualised training programmes.
The data collected in this way would be worth its weight in gold to you as a scientist, wouldn't it?
Of course it would. We can use these devices to collect data for science and the user. The overall aim is to create an effective and efficient data-based decision matrix for bodybuilding. With these calculations, the instructor - or trainer - simply obtains more data from research in order to treat the client or patient even more successfully. It's not about replacing people. It's about giving them the tools they need to do their jobs better.
What is the role of genetics? Wouldn't it be more effective to aim for a personalised strength training session
? In research, we assume that genetics account for around 40% of adaptation. But if we can't describe strength training exactly, how are we going to deal with genetics? Just analysing your microbiome - that is, your gut bacteria - would already have an effect on your diet and the type of training you do. We're not there yet.
You've already moved on to a follow-up study that I was lucky enough to take part in. What is it about?
In the first study, we asked people to train at their personal pace. And we were able to show that our algorithm apparently works very well in a dynamic environment if we don't set any constraints. Now we want to find out how our algorithm behaves in extreme situations. For example, when someone trains slowly or very quickly with high or low loads. We are immersed in data analysis.
The question remains as to when the digitised weight room will become a reality. According to Claudio Viecelli, this depends on the business model in question. Does a supplier equip the equipment with appropriate sensors, or are the measurements taken, as in his study, via users' smartphones? Claudio Viecelli is talking about seven-figure investments here and assumes that within two to five years, the digitised gym will be a reality.
Is this a sensible step towards personalised strength training or another way of accessing our data?
What do you think about data-driven strength training?
The competition has ended.
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