Using a Network Physiology Approach to Prescribe Exercise for Exercise Oncology

Gwendolyn A. Thomas

Research output: Contribution to journalShort surveypeer-review

1 Scopus citations

Abstract

Current American College of Sports Medicine (ACSM) exercise guidelines for exercise oncology survivors are generic one-size fits all recommendations, which assume ideal or prototypic health and fitness state in order to prescribe. Individualization is based on the objective evaluation of the patient’s baseline physiological status based on a linear dose response relationship of endpoints. This is only a partial snapshot of both the acute and chronic responses exercise can provide. Each acute exercise session represents a unique challenge to whole-body homeostasis and complex acute and adaptive responses occur at the cellular and systemic levels. Additionally, external factors must be considered when prescribing exercise. Network physiology views the human organism in terms of physiological and organ systems, each with structural organization and functional complexity. This organizational approach leads to complex, transient, fluctuating and nonlinear output dynamics which should be utilized in exercise prescription across health states. Targeting health outcomes requires a multi-system approach as change doesn’t happen in only one system at a time or in one direction Utilizing a multi-system or person-centered approach, allows for targeting and personalization and understands and targets non-linear dynamics of change. Therefore, the aims of this review are to propose a paradigm shift towards a Network Physiology approach for exercise prescription for cancer survivors. Cancer treatment affects multiple systems that interact to create symptoms and disruptions across these and therefore, prescribing exercise utilizing both external daily factors and internal physiological networks is of the highest order.

Original languageEnglish (US)
Article number877676
JournalFrontiers in Network Physiology
Volume2
DOIs
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Physiology (medical)
  • Statistical and Nonlinear Physics

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