Empirical Evaluation of the Mass Balance Model in Human Bodyweight Regulation

Human bodyweight regulation is traditionally explained by the energy balance model (EBM), which attributes weight change to differences between caloric intake and expenditure. However, when applied to body composition analysis, this framework requires an inefficient two-step process: first converting ingested mass into energy units and then converting calculated energy imbalances back into mass changes to describe tissue dynamics. These conversions add complexity and propagate measurement uncertainty, limiting the ability to capture physiological mechanisms. This study proposes and evaluates a mass balance model (MBM) that expresses weight change directly in mass units, aligning the mathematical analysis with the modeled outcome. A detailed single-subject dataset consisting of 367 consecutive days of daily bodyweight and mass flux measurements collected under free-living conditions was used to evaluate the model’s predictive accuracy. The MBM accurately predicted weight and body composition dynamics without parameter fitting, with predictive power rooted in the model’s intrinsic structure. In contrast, the EBM required physiologically implausible parameter adjustments to reproduce the long-term pattern of weight change. A key finding is that net mass outflow is proportional to the square root of bodyweight, a relationship that parallels Torricelli’s principle of fluid dynamics and aligns with the quarter-power scaling law observed in biology. This relationship was confirmed analytically and validated against published fasting data, where the MBM captured both early rapid and later attenuated phases of weight loss, underscoring its robustness under extreme negative balance. Collectively, these results position the MBM as a conceptually simpler, mathematically consistent, and biologically faithful paradigm that may guide future research on obesity, dietary interventions, and metabolic health.