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Volker Scheer, Tanja I. Janssen, Solveig Vieluf and Hans-Christian Heitkamp

Purpose: Trail running is a complex sport, and performance prediction is challenging. The aim was to evaluate 3 standard laboratory exercise tests in trail runners and correlate measurements to the race time of a trail competition evaluating its predictive power. Methods: Nine competitive male trail runners (mean age: 31 [5.8] y) completed 3 different laboratory exercise tests (step, ramp, and trail tests) for determination of maximal oxygen uptake (VO2max), vVO2max, ventilatory (VT) and lactate thresholds (LT), mechanical power output, and running economy (RE), followed by a 31-km trail race. Runners had previously participated in the same race (previous year) and finished in the top 2%. Finishing times (dependent value) were tested in multiple-regression analysis with different independent value combinations. Results: Linear-regression analysis revealed that variables measured during step and ramp tests significantly predicted performance. Step-test variables (speed at individual anaerobic threshold 16.4 [1.7] km/h and RE 12 km/h in %VO2max 65.6% [5.4%]) showed the highest performance prediction (R 2 = .651, F 2,6 = 5.60, P = .043), followed by the ramp test (vVO2max 20.3 [1.3] km/h; R 2 = .477, F 1,7 = 6.39, P = .04) and trail test (maximal power 3.9 [0.5] W/kg, VO2max 63.0 [4.8] mL O2·kg−1·min−1, vVT1 11.9 [0.7] km/h; R 2 = .68, F 3,5 = 3.52, P = .11). Adding race time from the preceding year to the step test improved the predictive power of the model (R 2 = .988, F 3,5 = 66.51, P < .001). Conclusions: The graded exercise test (VO2max, individual anaerobic threshold, and RE) most accurately predicted a 31.1-km trail-running performance. Combining submaximal intensities (individual anaerobic threshold and RE) with the previous year’s race time of that specific event increased the predictive power of the model to 99%.

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Lena Hübner, Solveig Vieluf, Ben Godde and Claudia Voelcker-Rehage

It remains controversial whether aging influences motor learning and whether physiological factors, such as local strength or fitness, are associated with fine motor performance and learning in older adults (OA). OA (n = 51) and young adults (YA, n = 31) performed a short-term motor learning session using a precision grip force modulation task. The rate of improvement of OA compared with YA was steeper with respect to performance variability and temporal precision. Both age groups showed positive transfer during an unpracticed variant of the force modulation task. Local muscle strength (pinch and grip strength) and high cardiovascular fitness positively predicted fine motor performance, whereas initial performance, muscle strength, and motor fitness (heterogeneous motor test battery) negatively predicted rate of improvement. Analyses indicated potentials, but also limits of plasticity for OA.