Assessing sprint swim performance using load-velocity profiling

Abstract

A swim race consists of the start, free swimming, turn(s) and finish. In sprint events(50 and 100 m) free swimming accounts for approximately two-thirds of total race time, representing a principal determinant of performance. Biomechanically, improving free swimming speed requires maximising propulsive force while minimising resistive force, yet quantifying these interactions is challenging due to complex fluid dynamics. Fully-tethered swimming is commonly used to assess propulsive ability, but concerns regarding specificity and lack of velocity-related information have prompted the development of load-velocity (LV) profiling using motorised resistance devices. LV variables include maximum velocity (V0), theoretical maximum load (L0) expressed relative to body mass (rL0), and the LV slope.

This thesis comprises four studies with two primary aims: (1) develop and validate methods for quantifying the LV profile, and (2) examine longitudinal LV changes and evaluate an LV-based in-water resistance training intervention. Study 1 compared three profiling protocols and three data-processing methods, evaluating test-retest reliability, measurement error, and minimum detectable change thresholds. The Modified protocol offered a practical and time-efficient alternative without statistical differences from established methods. Study 2 examined relationships between the Modified protocol outputs and sprint performance, finding large to extremely large correlations (r = -0.618 to -0.955, p <0.05) supporting its diagnostic and training utility. Study 3 observed LV performance over 15 months identifying large to very large correlations between race performance and both L0 (r = 0.67, p <0.05) and V0 (r =0.73, p <0.05) with sex- and performance-level distinctions. Study 4 applied an LV based intervention to an elite sprinter, yielding measurable improvements in LV and swim performance.

Overall, LV profiling demonstrated reliability and practical utility for monitoring, talent identification, and individualised training prescription. Future research should explore stroke- and sex-specific differences, integrate kinematic and physiological data, and conduct controlled interventions to assess training responsiveness

Date of Award	Feb 2026
Original language	English
Sponsors	Department for the Economy
Supervisor	Carla McCabe (Supervisor) & Rodney Kennedy (Supervisor)