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New insights into anterior cruciate ligament deficiency and reconstruction through the assessment of knee kinematic variability in terms of nonlinear dynamics

  • Knee
  • Published:
Knee Surgery, Sports Traumatology, Arthroscopy Aims and scope

Abstract

Purpose

Injuries to the anterior cruciate ligament (ACL) occur frequently, particularly in young adult athletes, and represent the majority of the lesions of knee ligaments. Recent investigations suggest that the assessment of kinematic variability using measures of nonlinear dynamics can provide with important insights with respect to physiological and pathological states. The purpose of the present article was to critically review and synthesize the literature addressing ACL deficiency and reconstruction from a nonlinear dynamics standpoint.

Methods

A literature search was carried out in the main medical databases for studies published between 1990 and 2010.

Results

Seven studies investigated knee kinematic variability in ACL patients. Results provided support for the theory of “optimal movement variability”. Practically, loss below optimal variability is associated with a more rigid and very repeatable movement pattern, as observed in the ACL-deficient knee. This is a state of low complexity and high predictability. On the other hand, increase beyond optimal variability is associated with a noisy and irregular movement pattern, as found in the ACL-reconstructed knee, regardless of which type of graft is used. This is a state of low complexity and low predictability. In both cases, the loss of optimal variability and the associated high complexity lead to an incapacity to respond appropriately to the environmental demands, thus providing an explanation for vulnerability to pathological changes following injury.

Conclusion

Subtle fluctuations that appear in knee kinematic patterns provide invaluable insight into the health of the neuromuscular function after ACL rupture and reconstruction. It is thus critical to explore them in longitudinal studies and utilize nonlinear measures as an important component of post-reconstruction medical assessment.

Level of evidence

II.

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Acknowledgments

This work is supported by the NIH (K25HD047194 and 1K99AG033684), the NIDRR (H133G040118 and H133G080023), and the Nebraska Research Initiative.

Author information

Authors and Affiliations

  1. Nebraska Biomechanics Core Facility, University of Nebraska at Omaha, 6001 Dodge Street, Omaha, NE, 68182-0216, USA

    Leslie M. Decker & Nicholas Stergiou

  2. Orthopaedic Sports Medicine Center, Department of Orthopaedic Surgery, School of Medicine, University of Ioannina, Ioannina, Greece

    Constantina Moraiti & Anastasios D. Georgoulis

  3. Department of Environmental, Agricultural and Occupational Health Sciences, College of Public Health, University of Nebraska Medical Center, 987850 Nebraska Medical Center, Omaha, NE, 68198-7850, USA

    Nicholas Stergiou

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  1. Leslie M. Decker
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  2. Constantina Moraiti
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  4. Anastasios D. Georgoulis
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Corresponding author

Correspondence to Nicholas Stergiou.

Appendix

Appendix

The behavior of a continuously changing system can be periodic, random or chaotic. Periodic systems are organized and are repeatable and predictable (Fig. 5). Random systems, on the other hand, contain no order and are unpredictable. Their behavior is never repeated (Fig. 6). Chaotic systems have characteristics of both. They seem to be random but they contain order and are deterministic in nature. They are unpredictable, very flexible and can operate under various conditions (Fig. 7).

Fig. 5
figure 5

Graphic representation of a periodic system [sin(1/10)] (a) and the corresponding phase plane plot (b), where the time series data are plotted versus the first derivative. The LyE and ApEn value for this system are 0 and 0, respectively

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Fig. 6
figure 6

Graphic representation of a random system (Gaussian noise centered on zero and a standard deviation of 1.0) (a) and the corresponding phase plane plot (b). The LyE and ApEn value for this system are 0.5 and 2, respectively

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Fig. 7
figure 7

Graphic representation of a chaotic system (the Lorentz attractor) (a) and the corresponding phase plane plot (b). The LyE and ApEn value for this system are 0.1 and 0.5, respectively

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Decker, L.M., Moraiti, C., Stergiou, N. et al. New insights into anterior cruciate ligament deficiency and reconstruction through the assessment of knee kinematic variability in terms of nonlinear dynamics. Knee Surg Sports Traumatol Arthrosc 19, 1620–1633 (2011). https://doi.org/10.1007/s00167-011-1484-2

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