Quantifying the Impact of High-Intensity Interval Training on Chronic Disease Markers using Longitudinal Mixed-Effects Models

The rising prevalence of chronic non-communicable diseases, particularly type 2 diabetes mellitus and cardiovascular pathology, necessitates efficient therapeutic interventions. High-Intensity Interval Training has emerged as a potent modality for improving cardiometabolic health, yet the quantification of its efficacy is often hampered by statistical methodologies that fail to account for the complex, hierarchical nature of longitudinal clinical data. This study utilizes linear mixed-effects models to rigorously quantify the impact of a twelve-week High-Intensity Interval Training protocol compared to Moderate-Intensity Continuous Training on key chronic disease markers, specifically glycated hemoglobin and systolic blood pressure. By employing a random-intercept and random-slope framework, we analyzed data from 142 participants, accounting for intra-individual variability and missing data points inherent in longitudinal trials. Our analysis reveals that while both modalities induce favorable physiological adaptations, the interval training group demonstrated a significantly steeper trajectory of improvement in glycemic control and vascular function. Furthermore, the mixed-effects modeling approach highlighted substantial heterogeneity in individual responsiveness, suggesting that participant-specific factors play a critical role in intervention outcomes. These findings underscore the utility of advanced longitudinal modeling in exercise oncology and chronic disease management, providing a more robust estimate of treatment effects than traditional repeated-measures analysis of variance. The results advocate for the integration of interval training into standard care pathways and demonstrate the necessity of sophisticated statistical frameworks to fully capture the dynamic physiological responses to exercise stimuli.