Relationship between lower limb dynamics and knee joint pain

J Orthop Res. 1991 May;9(3):398-405. doi: 10.1002/jor.1100090312.


To test the hypothesis that appropriate and timely neuromuscular control of limb motions plays an important role in the preservation of joint health, we kinematically and kinetically examined the behavior of the legs of young adult subjects at heel strike during natural walking. We compared a group of 18 volunteers, who, we presumed, were preosteoarthrotic because of mild, intermittent, activity-related knee joint pain, with 14 age-matched asymptomatic normal subjects. The two groups of subjects exhibited similar gait patterns with equivalent cadences, walking speeds, terminal stance phase knee flexion, maximum (peak) swing angular velocity, and overall shape of the vertical ground reaction. However, our instrumentation detected statistically significant differences between the two groups within a few milliseconds of heel strike. In the knee pain group, the heel hit the floor with a stronger impact in this brief interval. Just before heel strike, there was a faster downward velocity of the ankle with a larger angular velocity of the shank. The follow-through of the leg immediately after heel strike was more violent with larger peak axial and angular accelerations of the leg echoed by a more rapid rise of the ground reaction force. This sequence of events represents repetitive impulsive loading, which consistently provoked osteoarthrosis in animal experiments. We refer to this micro-incoordination of neuromuscular control not visible to the naked eye as "microklutziness."

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Biomechanical Phenomena
  • Cumulative Trauma Disorders / etiology
  • Electromyography
  • Female
  • Gait / physiology*
  • Heel / physiology*
  • Humans
  • Kinetics
  • Knee Injuries / etiology*
  • Knee Injuries / physiopathology
  • Male
  • Movement Disorders / physiopathology
  • Pain / etiology*
  • Range of Motion, Articular
  • Regression Analysis
  • Time Factors
  • Videotape Recording
  • Walking / injuries*