Addressing the unresolved challenge of quantifying skiing exposure-A proof of concept using smartphone sensors
Open access
Date
2023-05-09Type
- Journal Article
ETH Bibliography
yes
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Abstract
In epidemiological studies related to winter sports, especially alpine skiing, an unresolved methodological challenge is the quantification of actual on-snow activity exposure. Such information would be relevant for reporting meaningful measures of injury incidence, which refers to the number of new injuries that occur in a given population and time period. Accordingly, accurate determination of the denominator, i.e., actual "activity exposure time", is critical for injury surveillance and reporting. In this perspective article, we explore the question of whether wearable sensors in combination with mHealth applications are suitable tools to accurately quantify the periods in a ski day when the skier is physically skiing and not resting or using a mechanical means of transport. As a first proof of concept, we present exemplary data from a youth competitive alpine skier who wore his smartphone with embedded sensors on his body on several ski days during one winter season. We compared these data to self-reported estimates of ski exposure, as used in athletes' training diaries. In summary, quantifying on-snow activity exposure in alpine skiing using sensor data from smartphones is technically feasible. For example, the sensors could be used to track ski training sessions, estimate the actual time spent skiing, and even quantify the number of runs and turns made as long as the smartphone is worn. Such data could be very useful in determining actual exposure time in the context of injury surveillance and could prove valuable for effective stress management and injury prevention in athletes. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000616016Publication status
publishedExternal links
Journal / series
Frontiers in Sports and Active LivingVolume
Pages / Article No.
Publisher
Frontiers MediaSubject
athlete; monitoring; mHealth; sensors; training diary; injury prevention; alpine skiingFunding
167302 - Personalized management of low back pain with mHealth: Big Data opportunities, challenges and solutions (SNF)
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ETH Bibliography
yes
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