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Inter-individual differences in stride frequencies during running obtained from wearable data.

B T Van Oeveren1, C J De Ruiter1, M J M Hoozemans1

  • 1a Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences , Vrije Universiteit Amsterdam, Amsterdam Movement Sciences , Amsterdam , The Netherlands.

Journal of Sports Sciences
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Stride frequency in runners is influenced by leg length, body mass, and age, with older runners adopting higher stride frequencies at faster speeds. Running frequency and duration also correlate with stride frequency.

Keywords:
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Area of Science:

  • Biomechanics
  • Exercise Physiology
  • Running Science

Background:

  • Stride frequency (SF) varies among runners, influenced by numerous physiological and biomechanical factors.
  • Understanding these factors is crucial for optimizing running performance and injury prevention.

Purpose of the Study:

  • To identify factors influencing individual differences in stride frequency (SF) in relation to running speed.
  • To analyze the relationship between SF and various anthropometric and training variables in a large cohort of runners.

Main Methods:

  • Utilized wearable device data (Garmin Inc.) from 256 runners, totaling over 16,000 hours of running data.
  • Employed a generalized linear model with random effects to analyze associations between SF and predictor variables, including interactions with speed.
  • Applied a stepwise forward procedure to identify significant influencing factors.

Main Results:

  • On a group level, SF is linearly related to running speed (SF = 75.01 + 3.006 * V).
  • Individually, the SF-speed relationship is best described by a second-order polynomial.
  • SF was negatively associated with leg length and body mass. Older runners exhibited higher SF at higher speeds. Run frequency and duration were positively related to SF.

Conclusions:

  • Leg length, body mass, age, run frequency, and duration are significant predictors of stride frequency at specific running speeds.
  • Running experience, performance metrics, and injury history did not show a significant association with stride frequency.
  • Individualized models are necessary to accurately capture the SF-speed relationship, which deviates from a simple linear group trend.