System Maintenance Notice

Please note that maintenance work is scheduled between 4 AM and 5 AM - CET. During this time, you may experience reduced performance. Thanks for your patience.

Publications

Journal Article Ppublish

PubMed: 41525268 DOI: 10.1093/bmb/ldaf025

Determining the minimum number of steps required for reliable kinetic, kinematic, gait variability, and V̇O2 parameters in outdoor running.

Br Med Bull 157(1)

Authors: Johnny Padulo, Raffaele Scurati, Simone Baldo, Fabio Esposito, Leonardo Alexandre Peyré-Tartaruga

Affiliations

  • Department of Biomedical Sciences for Health, Università degli Studi di Milano, 20133 Milan, Italy.
  • Unidad Académica de Biofísica, Facultad de Medicina, Universidad de la República, 11800 Montevideo, Uruguay.
  • Human Locomotion Laboratory (LocoLab), Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy.

Abstract

INTRODUCTION

Outdoor running gait analysis using wearable sensors requires establishing minimum step requirements for reliable parameter measurement.

SOURCES OF DATA

Fourteen trained runners completed 10-minute trials at critical speed. RunScribe™ captured kinetic/kinematic data at 500 Hz. Convergence analysis determined minimum steps when cumulative means stabilized within ±2% for ≥10 consecutive steps.

AREAS OF AGREEMENT

Most parameters achieved excellent reliability (ICC > 0.900). Metabolic parameters (V̇O₂: 108 ± 32 steps) and step rate (124 ± 36 steps) converged fastest, while kinematic variables required moderate sampling (stride length: 152 ± 48 steps; contact time: 198 ± 52 steps).

AREAS OF CONTROVERSY

Complex biomechanical metrics showed substantial variability: leg stiffness (kleg: 342 ± 78 steps; kvert: 298 ± 71 steps) and gait variability (246 ± 12 steps) required substantially more data, challenging brief field assessment feasibility.

GROWING POINTS

This study establishes parameter-specific sampling protocols for outdoor gait analysis, demonstrating that different measurement domains require tailored data collection strategies to optimize ecological validity.

AREAS TIMELY FOR DEVELOPING RESEARCH

Future research should examine how speed, terrain, and environmental conditions affect sampling requirements. Adaptive protocols and machine learning approaches offer promising opportunities for optimizing data collection efficiency.

MeSH

Research Design, Gait Analysis, Data Collection, Running, Gait