What is it about?

Resistance exercise is medicine. Tracking crucial descriptors of resistance exercise, such as single repetition, contraction-phase specific and total time-under-tension, was cumbersome. Here, we showed that off-the-shelf smartphones can be used to extract these descriptors from user-exerted accelerations on a weight stack during the time a participant worked out on a resistance exercise machine.

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Why is it important?

Using this simple tool, healthcare professionals could monitor patient’s resistance exercise training as well as decreasing patient-self reporting burden. As such, rehabilitation protocols could then be individually adjusted. Due to the general ageing trends of the population, standardized reporting has been found to be important for personal resistance exercise interventions combating and/or reversing sarcopenia.


Big data approaches have the potential to solve scientific questions. Here we showed that smartphones can be vectors for reliably and validly collecting and reporting machine-based resistance exercise data. Identifying and reporting postulated descriptors of resistance exercise and/or methods both contribute to solving the dilemma of underreporting resistance exercise determinants. Therefore, distinct morphological, molecular and metabolic adaptations on the muscular level can be elucidated by off-the-shelf smartphone-based big data approaches.

Claudio Viecelli
ETH Zurich

Read the Original

This page is a summary of: Using smartphone accelerometer data to obtain scientific mechanical-biological descriptors of resistance exercise training, PLoS ONE, July 2020, PLOS,
DOI: 10.1371/journal.pone.0235156.
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