Literature DB >> 32767716

Predicting cumulative load during running using field-based measures.

Anne Backes1, Sebastian Deisting Skejø2, Paul Gette3, Rasmus Østergaard Nielsen2,4, Henrik Sørensen2, Cédric Morio5, Laurent Malisoux1.   

Abstract

The main objective was to investigate whether the cumulative load of the lower limbs, defined as the product of external load and step rate, could be predicted using spatiotemporal variables gathered with a commercially available wearable device in running. Therefore, thirty-nine runners performed two running tests at 10 and 12 km/h, respectively. Spatiotemporal variables (step rate, ground contact time, and vertical oscillation) were collected using a commercially available wearable device. Kinetic variables, measured with gold standard equipment (motion capture system and instrumented treadmill) and used for the calculation of a set of variables representing cumulative load, were peak vertical ground reaction force (peak vGRF), vertical instantaneous loading rate (VILR), vertical impulse, braking impulse, as well as peak extension moments and angular impulses of the ankle, knee and hip joints. Separate linear mixed-effects models were built to investigate the prediction performance of the spatiotemporal variables for each measure of cumulative load. BMI, speed, and sex were included as covariates. Predictive precision of the models ranged from .11 to .66 (R2 m ) and .22 to .98 (R2 c ), respectively. Greatest predictive performance was obtained for the cumulative peak vGRF (R2 m  = .66, R2 c  = .97), VILR (R2 m  = .43, R2 c  = .97), braking impulse (R2 m  = .52, R2 c  = .98), and peak hip extension moment (R2 m  = .54, R2 c  = .90). In conclusion, certain variables representing cumulative load of the lower limbs in running can be predicted using spatiotemporal variables gathered with a commercially available wearable device.
© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  biomechanics; injury prevention; running; sports injury; wearables

Mesh:

Year:  2020        PMID: 32767716     DOI: 10.1111/sms.13796

Source DB:  PubMed          Journal:  Scand J Med Sci Sports        ISSN: 0905-7188            Impact factor:   4.221


  6 in total

1.  Wearables for Running Gait Analysis: A Systematic Review.

Authors:  Rachel Mason; Liam T Pearson; Gillian Barry; Fraser Young; Oisin Lennon; Alan Godfrey; Samuel Stuart
Journal:  Sports Med       Date:  2022-10-15       Impact factor: 11.928

Review 2.  Sacral stress fractures in athletes.

Authors:  Eran Beit Ner; Oded Rabau; Saad Dosani; Uri Hazan; Yoram Anekstein; Yossi Smorgick
Journal:  Eur Spine J       Date:  2021-11-02       Impact factor: 3.134

3.  Sacral acceleration can predict whole-body kinetics and stride kinematics across running speeds.

Authors:  Ryan S Alcantara; Evan M Day; Michael E Hahn; Alena M Grabowski
Journal:  PeerJ       Date:  2021-04-12       Impact factor: 2.984

4.  Principal Component Analysis of the Running Ground Reaction Forces With Different Speeds.

Authors:  Lin Yu; Qichang Mei; Liangliang Xiang; Wei Liu; Nur Ikhwan Mohamad; Bíró István; Justin Fernandez; Yaodong Gu
Journal:  Front Bioeng Biotechnol       Date:  2021-03-25

5.  Effect of Increased Flexor Hallucis Longus Muscle Activity on Ground Reaction Force during Landing.

Authors:  Kosuke Oku; Daisuke Kimura; Tomotaka Ito; Akiyoshi Matsugi; Tatsuya Sugioka; Yusuke Kobayashi; Hayato Satake; Tsukasa Kumai
Journal:  Life (Basel)       Date:  2021-06-29

Review 6.  Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis.

Authors:  Lauren C Benson; Anu M Räisänen; Christian A Clermont; Reed Ferber
Journal:  Sensors (Basel)       Date:  2022-02-23       Impact factor: 3.576

  6 in total

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