The Development of Normative Fitness Data and Analyzing the Relationships Between 20MSFT and 2.4-km Run Performance in Australian Police Recruits.

Campbell, P, Maupin, D, Lockie, RG, Dawes, JJ, Simas, V, Canetti, E, Schram, B, and Orr, R. The development of normative fitness data and analyzing the relationships between 20MSFT and 2.4-km run performance in Australian police recruits. J Strength Cond Res 38(11): 1951-1958, 2024-Aerobic fitness testing plays a significant role in the academy training of law enforcement recruits. There is a lack of normative data for law enforcement recruits and substantial interchangeability for choice of field-based aerobic tests. Therefore, this study aimed to develop normative percentile rankings for the 20-m multistage fitness tests (20MSFT) and 2.4-km run specific to law enforcement recruits; and to analyze the relationship between the 2 tests by linear regression. Data from 5 cohorts of law enforcement recruits completing 20MSFT ( n = 1,536) and 2.4-km run ( n = 1,499) testing during basic training at an Australian Police Academy were retrospectively analyzed. Percentile ranks were calculated (20th, 40th, 50th, 60th, 80th, and 99th) based on the number of shuttles completed and time (seconds) to complete the 2.4-km run. Results were split by sex and age (i.e., <20 years, 20-29 years, 30-39 years, and 40-49 years). Linear regression analyzed the relationship between the 20MSFT to the 2.4-km run (using both seconds and km·h -1 ), producing predictive equations to translate scores between the 2 assessments. These data presented the first detailed normative values by age and sex for Australian law enforcement recruits. Furthermore, the results demonstrated regression models could explain 65-74% of the variance observed ( r2 = 0.649-0.741) between 20MSFT performance and 2.4-km run performance. This study's findings allow for normative performance for the purpose of benchmarking fitness, setting performance goal, and rehabilitation from injury. In addition, a valid conversion through a linear regression affords cross-agency comparisons and temporal changes in test selection/recruit cohorts.