Purpose: To assess the relationships between reactive strength measures and associated kinematic and kinetic performance variables achieved during drop jumps. A secondary aim was to highlight issues with the use of reactive strength measures as performance indicators. Methods: Twenty-eight national- and international-level sprinters, 14 men and 14 women, participated in this cross-sectional analysis. Athletes performed drop jumps from a 0.3-m box onto a force platform with dependent variables contact time (CT), landing time, push-off time, flight time, jump height (JH), reactive strength index (RSI, calculated as JH/CT), reactive strength ratio (RSR, calculated as flight time/CT), and vertical leg-spring stiffness recorded. Results: A Pearson correlation test found very high to near-perfect relationships between RSI and RSR (r = .91–.97), with mixed relationships between RSI, RSR, and the key performance variables (men: r = −.86 to −.71 between RSI/RSR and CT, r = .80–.92 between RSI/RSR and JH; women: r = −.85 to −.56 between RSR and CT, r = .71 between RSI and JH). Conclusions: The method of assessing reactive strength (RSI vs RSR) may be influenced by the performance strategies adopted, that is, whether athletes achieve their best reactive strength scores via low CTs, high JHs, or a combination. Coaches are advised to limit the variability in performance strategies by implementing upper and/or lower CT thresholds to accurately compare performances between individuals.
Robin Healy, Ian C. Kenny and Andrew J. Harrison
Derek Breen, Michelle Norris, Robin Healy and Ross Anderson
Purpose: Pacing strategies are key to overall performance outcome in distance-running events. Presently, no literature has examined pacing strategies used by masters athletes of all running levels during a competitive marathon. Therefore, this study aimed to examine masters athletes’ pacing strategies, categorized by gender, age, and performance level. Methods: Data were retrieved from the 2015 TSC New York City Marathon for 31,762 masters athletes (20,019 men and 11,743 women). Seven performance-classification (PC) groupings were identified via comparison of overall completion time compared with current world records, appropriate to age and gender. Data were categorized via, age, gender, and performance level. Mean 5-km speed for the initial 40 km was calculated, and the fastest and slowest 5-km-split speeds were identified and expressed as a percentage faster or slower than mean speed. Pace range, calculated as the absolute sum of the fastest and slowest split percentages, was then analyzed. Results: Significant main effects were identified for age, gender, and performance level (P < .001), with performance level the most determining factor. Athletes in PC1 displayed the lowest pace range (14.19% ± 6.66%), and as the performance levels of athletes decreased, pace range increased linearly (PC2–PC7, 17.52% ± 9.14% to 36.42% ± 18.32%). A significant interaction effect was found for gender × performance (P < .001), with women showing a smaller pace range (−3.81%). Conclusions: High-performing masters athletes use more-controlled pacing strategies than their lower-ranked counterparts during a competitive marathon, independent of age and gender.