Exercise monitoring from a Network Physiology approach

Q: What is the clinical evidence from this study?

Study design: Review. Population: Exercise physiology and athletic performance monitoring. Intervention: Network Physiology approach to exercise monitoring vs. Traditional reductionist exercise monitoring.

Q: What does this research mean for the field?

Evaluating dynamic inter-organ interactions using Network Physiology frameworks enables the identification of novel network-based biomarkers for athletic fitness, fatigue, and recovery. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.ESTABLISHES_NEW_DIRECTION.

Lluc Montull; Sergi García‐Retortillo; Óscar Abenza; Yaopeng J. X. Ma; Robert Hristovski; Plamen Ch. Ivanov; Natàlia Balagué

doi:10.3389/fnetp.2026.1745246

October 29, 2026Frontiers in Network PhysiologyOpen Access

Exercise monitoring from a Network Physiology approach

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Key result

Network Physiology maps inter-organ dynamics during exercise to identify novel biomarkers for fitness and fatigue.

Why the study?

Current exercise monitoring strategies may not fully capture the physiological complexity of athletes health and performance. This article summarizes current advances, vision, and perspectives within the Network Physiology framework to investigate dynamic interactions among physiological systems during exercise.

Key Points

Summarize current advances and perspectives within the Network Physiology framework for evaluating dynamic physiological interactions and exercise-induced adaptations in athletes.
Synthesized conceptual and analytical frameworks evaluating dynamic inter-organ networks, including cardio-respiratory, cardio-muscular, cortico-cardiac, cortico-muscular, and inter-muscular coordination during exercise.
Assessed limitations of conventional exercise monitoring, focusing on complex adaptive systems principles, nonlinear dynamics, and signal non-stationarity.
Demonstrates that conventional exercise monitoring relies on discrete snapshots and reductionist models that fail to capture the dynamic, time-varying interactions between physiological systems.
Identifies dynamic inter-organ networks, such as cardio-muscular and inter-muscular coupling, as viable sources for a novel class of network-based biomarkers reflecting fitness, fatigue, and recovery.

Structured PICO

P

Population

Athletes and exercise participants at different levels of fitness and training

I

Intervention

Network Physiology analytical framework and dynamic network-based biomarkers (investigating inter-organ dynamic networks including cardio-respiratory, cardio-muscular, cortico-cardiac, cortico-muscular, and inter-muscular interactions)

C

Comparator

Traditional mechanistic and reductionist exercise monitoring models (evaluating physiological, psychological, biomechanical, and performance variables separately using maximal discrete values or averaged distributions)

This perspective article outlines how the Network Physiology framework can improve exercise monitoring by quantifying dynamic, time-varying interactions among organ systems (such as cardio-respiratory and inter-muscular networks) to better detect fatigue, fitness, and recovery compared to traditional static physiological markers.

Limitations

Low sampling rates and challenges in synchronizing certain physiological measures constrain dynamic network assessments.
Translation of physiological network analyses into practical monitoring tools for health or performance purposes remains limited.
Current applications primarily characterize dynamic inter-organ coordination and capture only selected levels of multiscale physiological organization.
Low sampling rates and challenges in synchronizing certain physiological measures constrain dynamic network assessments
Principal component analysis for cardio-respiratory coordination does not account for temporal directionality and nonlinear dynamic outcomes
Muscle synergy models inherently limit the dimensionality of neural control to be no greater than the number of recorded muscles and assume all motor neurons within a motor pool receive identical inputs
Synergy and coherence-based metrics mostly capture linear iso-frequency interactions and cannot measure nonlinear cross-frequency coupling or temporal directionality

Implication

Network biomarkers may refine athlete monitoring; leaves open prospective validation before clinical use.

Abstract

Advancements in big data, artificial intelligence and related technology are shaping the future of exercise monitoring, yet current monitoring strategies may not fully capture the physiological complexity of athletes’ health and performance. The multidisciplinary field of Network Physiology provides a conceptual and analytical framework to investigate the dynamic interactions, coordination and network integration among physiological systems in athletes during exercise, and thus, opens new directions in Exercise Physiology. This article aims to summarize the current advances, vision and perspectives within this framework, focusing on how investigations of the inter-organ dynamic networks (cardio-respiratory, cardio-muscular, cortico-cardiac, cortico-muscular, inter-muscular, etc.) can lead to valuable insights into athletes’ performance, exercise monitoring, the underlying regulatory mechanisms of systems adaptation and coordination in response to exercise-induced stress, and to identify a novel class of network-based biomarkers related to training, levels of fitness, fatigue, and recovery.

Discussion

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Cite This Study

Montull et al. (2026) conducted a review in Exercise physiology and athletic performance monitoring. Network Physiology approach to exercise monitoring vs. Traditional reductionist exercise monitoring was evaluated. Network Physiology frameworks evaluate dynamic inter-organ interactions during exercise to identify novel network-based biomarkers for fitness, fatigue, and recovery.

synapsesocial.com/papers/6ae584eb4c46c197bbab2d06 https://doi.org/10.3389/fnetp.2026.1745246

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Exercise monitoring from a Network Physiology approach

Key Points

Structured PICO

Limitations

Implication

Abstract

Discussion

Cite This Study

Exercise monitoring from a Network Physiology approach

Key Points

Structured PICO

Limitations

Implication

Abstract

Discussion

Cite This Study

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