Shoes and running injuries – where are we today? - Leaving the lab

Leaving the lab

"There are many factors for injuries and their interactions are a complicated puzzle that needs to be solved," Willem van Mechelen explained. Researchers are obviously ready to reconsider their present research approaches, measuring methods and statistical evaluations and to complement them with new approaches or to completely replace them.

For decades biomechanic scientists examined single parameters like impact forces, pronation, knee movement or pressure distribution, because they thought they might be the reason for certain injuries. They formulated hypotheses and examined them in experiments or in prospective studies. "We are entering a new era of data and knowledge collection," Matthew Nurse, once a student of Benno Nigg and today head of a research team at Nike, said. In the next decade research would leave the lab. "Research will go to the fields and measure how people train, when they rest and when they are in a contest."

"In the future you will have sensors all over the body that deliver more information on locomotion and bodily strain than the present techniques." Björn Eskofier from the University of Erlangen, Germany, provided an outlook for the possible future of biomechanics. Light, wearable systems, perhaps linked to smart phones or smart watches for data collection and storage, are supposed to collect data of the athlete. ECGs to record heart activity, EMGs to register muscular activities or acceleration recorders and gyroscopes to measure body and joint movements could soon be part of the basic equipment in biomechanic analyses.

This new approach, described as data-driven technologiy or simply "big data", collects data on motions, forces or muscular activities, as comprehensively as possible without formulating classical hypotheses. "We will have many data and therefore need an algorithm for the analysis," Björn Eskofier explained the challenge of this new technique. Traditional statistical methods could not handle so easily these amounts of data. This was similar to marketing, Matthew Nurse explained. "50% of the ad budget is squandered money. But we do not know which 50%. So 50 % of the data that we collect might not be relevant. But this is only found out after the evaluation.”

Björn Eskofier is convinced," Data-driven techniques can provide valuable means for analyses." Thus it was not only possible to process big amounts of data but also several dimensions could be examined at the same time. Instead of following only one hypothesis, especially in the case of the search for causes of injuries, the mathematic algorithm could find connections between individual parameters and thus give new insight how injuries occur. Eskofier countered the objection that this method could hardly distinguish between cause and effect, by explaining that "big data" naturally did not provide evidence, but valuable hints which areas would be worth looking into.