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

Abstract

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.