DATA-DRIVEN ANALYSIS OF HIIT EFFICIENCY IN WEIGHT LOSS AND BODY COMPOSITION IMPROVEMENT: EVIDENCE FROM COMPETITIVE INTELLIGENCE SYSTEMS

Abstract

Purpose:
The aim of the study is to evaluate the effectiveness of the High-Intensity Interval Training (HIIT) to enhance both the body composition and weight loss outcomes and to integrate a competitive intelligence method to help analyze performance data in the real-life fitness environment.
Methodology/Approach:
The observational data on the performance of fitness during 300 training records were collected in 12 weeks and quantitative and data-based research design was used. They used descriptive statistics, Pearson correlation analysis, and multiple linear regression to analyze the relationships between training variables (the duration of the workout, caloric expenditure) and physiological outcomes (weight loss, BMI, and body fat percentage). The analytical framework was also extended to cover the performance insights, which were in accordance with the tenets of competitive intelligence.
Findings:
The findings suggest that HIIT is effective in the process of weight loss and body composition. Caloric expenditure became the most significant predictor of weight loss (β= 0.35), and then the length of the workout (β= 0.28), and the centrality of energy balance was identified. There were large correlations between training variables and physiological outcomes, and therefore, longer and more intense sessions have a larger metabolic effect. Moreover, those who improved more had higher baseline BMI and body fat, which showed that variation in response varies according to physiological features.
Originality/Relevance:
The paper is relevant to the literature because it combines physiological, behavioral, and analytical levels into one empirical system. It presents the idea of competitive intelligence as a new approach to exercise science, which highlights the importance of data-driven decision-making to optimize training results. The results are useful in both developing individualized fitness programs and applying the adaptive and analytics training programs.