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Heart rate variability–guided endurance training: evaluating strengths, weaknesses, opportunities, and threats for load prescription and adjustment

Aug 2026 · Frontiers in Sports and Active Living · Vol 8 · 1 citation · ⚡ 1 influential · 72 references
Medicine

TL;DR

The primary limitation of HRV-guided endurance training is no longer the availability of physiological data, but the lack of transparent and physiologically grounded decision frameworks that define how HRV-derived signals should be interpreted, weighted, and translated into valid and actionable training prescriptions.

Abstract

Heart rate variability (HRV) has been used to guide individualized endurance training prescription. By supporting training adjustments according to recovery status, HRV-guided approaches may improve the alignment between training load and physiological readiness. However, the practical value depends on the decision framework used to translate HRV measurements into training prescriptions. This paper applies a Strengths, Weaknesses, Opportunities, and Threats (SWOT) framework to critically evaluate the practical limitations of HRV-guided training regulation. Strengths include a well-established physiological rationale, evidence from controlled studies, expanding application across broader populations, and methodological advances in monitoring supported by wearable technologies. Weaknesses include the limited specificity of HRV as a global marker of cardiac autonomic modulation, the absence of standardized multimodal frameworks, and the persistent gap between research-based monitoring concepts and their implementation in practice. Opportunities lie in the development of transparent multivariate decision architectures, refinement of baseline and progression logic, and data-driven approaches to support individualized, context-aware training adjustments. Threats arise from rule-based decision frameworks that may inadequately capture the complexity and context-dependency of physiological adaptation, limited replication in real-world training environment, and growing dependence on proprietary metrics whose underlying algorithms lack transparency and may oversimplify training decisions. Overall, the primary limitation of HRV-guided endurance training is no longer the availability of physiological data, but the lack of transparent and physiologically grounded decision frameworks that define how HRV-derived signals should be interpreted, weighted, and translated into valid and actionable training prescriptions. Addressing this gap represents a critical step toward the development of adaptive training frameworks.

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