<p>Running biomechanics has long questioned whether a universally optimal running pattern exists. Current evidence indicates that no single technique is optimal for all individuals. Instead, runners self-optimize their movement according to anatomical structure, neuromuscular capacity, experience, and environmental constraints. This narrative review first examines the diversity of running patterns and the concept of self-organization, emphasizing that multiple biomechanical configurations can yield comparable running economy when aligned with individual characteristics. We then analyze how biomechanical models represent human locomotion. The spring–mass model explains key features of high-speed, elastic-driven running but becomes progressively limited as speed decreases. Actuator-based models incorporating active work and dissipation provide a more comprehensive account of endurance locomotion. From this modeling perspective, running mechanics are interpreted as a speed-dependent continuum from elastic-driven strategies at sprinting speeds to more muscle-driven strategies at slower speeds. Running speed is identified as the primary independent variable structuring these trends, while individual responses introduce variability around group-level norms. Translating this framework into practice requires individualized assessment rather than prescriptive technique modification. We present the PIMP method (Preferred running form assessment, Identification of inefficiencies in vertical load management, and Movement Plan individualization) as a structured approach to intervention. Central to this framework is the “Mind to Move” principle, in which outcome-focused imagery promotes biomechanical coherence between intrinsic capacities and external demands. The bouncy spring metaphor reinforces short elastic actions, whereas the rolling wheel metaphor supports prolonged, muscle-driven efforts. Performance optimization thus depends on context-sensitive, individualized strategies rather than replication of idealized forms.</p>

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Mind to Move: A Narrative Review of Individualized Running Biomechanics Beyond the Spring-Mass Model

  • Cyrille Gindre,
  • Thibault Lussiana,
  • Bastiaan Breine,
  • Aurélien Patoz

摘要

Running biomechanics has long questioned whether a universally optimal running pattern exists. Current evidence indicates that no single technique is optimal for all individuals. Instead, runners self-optimize their movement according to anatomical structure, neuromuscular capacity, experience, and environmental constraints. This narrative review first examines the diversity of running patterns and the concept of self-organization, emphasizing that multiple biomechanical configurations can yield comparable running economy when aligned with individual characteristics. We then analyze how biomechanical models represent human locomotion. The spring–mass model explains key features of high-speed, elastic-driven running but becomes progressively limited as speed decreases. Actuator-based models incorporating active work and dissipation provide a more comprehensive account of endurance locomotion. From this modeling perspective, running mechanics are interpreted as a speed-dependent continuum from elastic-driven strategies at sprinting speeds to more muscle-driven strategies at slower speeds. Running speed is identified as the primary independent variable structuring these trends, while individual responses introduce variability around group-level norms. Translating this framework into practice requires individualized assessment rather than prescriptive technique modification. We present the PIMP method (Preferred running form assessment, Identification of inefficiencies in vertical load management, and Movement Plan individualization) as a structured approach to intervention. Central to this framework is the “Mind to Move” principle, in which outcome-focused imagery promotes biomechanical coherence between intrinsic capacities and external demands. The bouncy spring metaphor reinforces short elastic actions, whereas the rolling wheel metaphor supports prolonged, muscle-driven efforts. Performance optimization thus depends on context-sensitive, individualized strategies rather than replication of idealized forms.