Author(s): WANG, L., HE Z, Institution: CHINA INSTITUTE OF SPORT SCIENCE, Country: CHINA, Abstract-ID: 843

INTRODUCTION:
Body composition is a key factor in athletic success. However, relying on total body mass (TBM) is problematic because it mixes force-generating muscle with inert non-muscular mass. Biomechanical principles suggest that this distinction is vital: extra mass helps in tasks using external resistance but hinders tasks requiring rapid body displacement. This study used a three-level meta-analysis to quantify how the body composition–performance link changes based on the tissue index used, the task demands, and the sport category.
METHODS:
A three-level random-effects meta-analysis was performed on 1,169 effect sizes from 201 studies involving 16,097 athletes. Body composition was classified as TBM, lean body mass (LBM), and muscle mass (MM). Performance domains included strength, speed/agility, power, aerobic, throwing, and sport-specific tasks. Moderator and interaction analyses examined the influence of composition type, performance domain, sport, sex, and age. Sensitivity analyses and multiple approaches to publication bias were applied to assess robustness.
RESULTS:
The meta-analysis included 1,169 effect sizes from 201 studies involving 16,097 trained athletes. The aggregate pooled correlation was moderate (r = 0.337, 95% CI [0.273, 0.398], p < 0.001). A significant hierarchy emerged among composition indices (QM[2] = 28.71, p < 0.001): muscle mass (MM) showed the strongest association (r = 0.607), nearly tripling the predictive utility of total body mass (TBM, r = 0.255). A critical BMT x performance domain interaction was identified (LRT = 19.07, p = 0.039). In absolute strength tasks, correlation strengths for all indices converged (r = 0.47–0.55). However, in speed and agility tasks, a striking divergence occurred: TBM showed no association with performance (r = -0.003, p > 0.05), whereas MM remained a potent correlate (r = 0.570). Sport category was a robust moderator (p < 0.001), with strength and power sports exhibiting the highest effect sizes (r = 0.737, PI [0.565, 0.848]), representing the only category where the prediction interval did not cross zero. Gender analysis revealed higher associations in men (r = 0.342) compared to women (r = 0.220, p = 0.027).
CONCLUSION:
Body composition is a context-dependent correlate of performance. Interaction analysis supports the force-to-mass ratio as the key biomechanical mechanism: in tasks requiring rapid movement, non-functional mass acts as a parasitic load that increases inertial costs without adding to propulsion. Practitioners should prioritize tissue-specific assessment (e.g., DXA-derived MM) over scale weight. Setting individual targets based on tissue quality can optimize output while reducing weight-related anxiety and the risk of RED-S. These data offer an evidence-based framework for aligning morphology with the idiosyncratic mechanical demands of specific sports.