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Overtraining Syndrome as a Complex Systems Phenomenon.

Lawrence E Armstrong1, Michael F Bergeron2, Elaine C Lee1

  • 1Human Performance Laboratory, University of Connecticut, Storrs, CT, United States.

Frontiers in Network Physiology
|March 17, 2023
PubMed
Summary
This summary is machine-generated.

Overtraining Syndrome (OTS) in athletes is complex and lacks clear diagnosis or treatment. Viewing OTS as a complex biological system using advanced analyses can improve prevention and management.

Keywords:
exercisegenomehypothalamic-pituitary-adrenal axismetabolismnetworkoverreachingstress

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Area of Science:

  • Sports Medicine
  • Exercise Physiology
  • Systems Biology

Background:

  • Overtraining Syndrome (OTS) has been recognized for over 90 years, yet definitive diagnosis, reliable biomarkers, and effective treatments remain elusive.
  • Existing research often simplifies OTS, overlooking the individualized and sport-specific nature of symptoms and predisposing factors.

Purpose of the Study:

  • To propose a paradigm shift in understanding Overtraining Syndrome (OTS) by viewing it through the lens of complex biological systems and network physiology.
  • To advocate for the adoption of advanced analytical techniques for a more comprehensive evaluation of OTS.
  • To identify key areas for future research to enhance the prevention and management of OTS.

Main Methods:

  • Review of existing models and literature on Overtraining Syndrome (OTS).
  • Application of complex systems theory and network physiology principles to understand OTS.
  • Recommendation of trans-omic analyses and machine learning for data integration and pattern recognition.

Main Results:

  • Overtraining Syndrome (OTS) is characterized by myriad, non-linear interactions within complex biological systems, defying traditional cause-and-effect analysis.
  • A complex systems approach is necessary to accurately characterize and evaluate OTS.
  • Integration of multi-domain data (neural networks, HPA axis, microbiota, immune factors, energy availability) is crucial.

Conclusions:

  • Overtraining Syndrome (OTS) should be conceptualized as a complex system, moving beyond reductionist approaches.
  • Advanced methodologies like trans-omics and machine learning are essential for dissecting the complexity of OTS.
  • Future research should focus on multi-domain analyses to facilitate effective prevention and management strategies for OTS.