A Clinical Evaluation Framework for Metabolic Equivalent (MET)-Guided Individualized Exercise Prescription in Optimizing Glycaemic Control in Type 2 Diabetes Mellitus
Keywords:
Type 2 Diabetes Mellitus, Metabolic Equivalent (MET), Individualized Exercise Prescription, Glycaemic ControlAbstract
Type 2 Diabetes Mellitus (T2DM) is a multifactorial metabolic disorder characterized by progressive insulin resistance and impaired glycaemic regulation, necessitating integrated pharmacological and lifestyle interventions. Among non-pharmacological strategies, structured physical activity remains a cornerstone; however, conventional exercise prescriptions often lack precision, personalization, and metabolic calibration. This study proposes a clinical evaluation framework based on Metabolic Equivalent of Task (MET)-guided individualized exercise prescription to optimize glycaemic control in T2DM patients. The framework integrates physiological intensity quantification, patient-specific metabolic profiling, and adaptive exercise dosing to enhance glycaemic outcomes and reduce HbA1c variability.
The proposed model synthesizes evidence from clinical trials, systematic reviews, and digital health innovations to construct a structured, scalable, and clinically implementable approach. Prior research highlights the superiority of resistance-based and structured exercise interventions over generalized aerobic activity in improving glycaemic outcomes (Kobayashi et al., 2023). Additionally, advancements in digital monitoring, reinforcement learning systems, and wearable biosensing technologies support the feasibility of real-time metabolic optimization (Forman et al., 2019; Saab et al., 2024). Emerging bio-integrated sensing frameworks further reinforce the role of continuous metabolic feedback systems in personalized healthcare delivery (Kaushik et al., 2020).
The study emphasizes the integration of MET-based stratification with clinical decision support systems, enabling precision-guided exercise dosing tailored to individual metabolic responses. Findings suggest that structured MET-guided interventions may significantly improve glycaemic stability, enhance insulin sensitivity, and reduce long-term diabetes-related complications. However, challenges such as adherence variability, infrastructural limitations, and inter-individual metabolic heterogeneity remain critical barriers to implementation.
This framework contributes to the advancement of precision exercise medicine and provides a scalable model for integrating metabolic analytics into routine diabetes management.
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Copyright (c) 2026 Julien Lefevre, Sophie Vermeulen
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