Generolo Jono Žemaičio Lietuvos karo akademija

Physical fitness is a fundamental component of military readiness, with aerobic capacity representing a key determinant of operational performance. In military education settings, training time is often limited, which increases the need for efficient and targeted conditioning strategies. High-intensity interval training (HIIT) has been widely discussed as a time-efficient method capable of eliciting meaningful physiological adaptations. However, evidence regarding its specific effects on aerobic capacity in military populations remains inconsistent. Therefore, further evaluation of structured HIIT interventions under real-world military conditions is warranted. First-year military students (n ≈ 80–90) were randomly assigned to either an intervention group completing a structured nine-week HIIT-based program or a control group following standard conventional physical training. The study was conducted across two consecutive measurement seasons using a randomized pre–post design. Aerobic capacity was assessed before and after the intervention using maximal oxygen uptake (VO₂max) determined via graded exercise testing. Both training programs were implemented within the regular physical training curriculum. The present study provides insight into the effects of a systematically designed HIIT intervention on aerobic capacity in a military student population. The findings contribute to a better understanding of how structured high-intensity training compares with conventional military physical training in terms of VO₂max adaptation. These results may help inform the development of efficient conditioning strategies within time-constrained military education environments. The study was conducted within a real-world military training setting, which may limit strict control over all external training influences. The duration of the intervention and the absence of intermediate physiological measurements may also have influenced the observed adaptations. Additionally, variability in sample size across measurement seasons should be considered when interpreting the findings.