Dresden ist nicht nur ein Paradies für Kulturliebhaber, sondern auch für Sportler. Warum also nicht beides miteinander verbinden? Zahlreiche Grünflächen, das Terrassenufer an der Elbe und gut ausgebaute Wege inmitten der Natur führen entlang der schönsten Sehenswürdigkeiten. Mit diesem Wegweiser werden Sie die Stadt einmal anders kennenlernen. / Dresden is not only a paradise for culture lovers but also for athletes. So why not combine the two activities? Numerous parks, the Elbe embankment and well-surfaced paths away from the traffic take you past some of Dresden’s most interesting sights. Our guide has been written to help you see the city from a different perspective.

Anthropometric and physical fitness data can predict sport- specific performance (e.g., canoe sprint race time) in young athletes. Of note, inter-itemcorrelations (i.e.,multicollinearity)may exist between tests assessing similar physical qualities. However, multicollinearity among tests may change across age and/or sex due to age-/sex-specific non-linear development of test performances. Therefore, the present study aimed at analyzing inter- itemcorrelations between anthropometric, physical fitness, and sport-specific performance data as a function of age and sex in young canoe sprint athletes.

Background: During maximal cycling sprints, efficiency (η) is determined by the fiber composition of the muscles activated and cadence-dependent power output. To date, due to methodological limitations, it has only been possible to calculate gross efficiency (i.e., the ratio of total mechanical to total metabolic work) in vivo without assessing the impact of cadence and changes during exercise. Eliminating the impact of cadence provides optimal efficiency (ηopt), which can be modeled as a function of time. Here, we explain this concept, demonstrate its calculation, and compare the values obtained to actual data. Furthermore, we hypothesize that the time course of maximal power output (Pmax) reflects time-dependent changes in ηopt. Methods: Twelve elite track cyclists performed four maximal sprints (3, 8, 12, 60 s) and a maximal-pedaling test on a cycle ergometer. Crank force and cadence were monitored continuously to determine fatigue-free force-velocity profiles (F/v) and fatigue-induced changes in Pmax. Respiratory gases were measured during and for 30 min post-exercise. Prior to and following each sprint, lactate in capillary blood was determined to calculate net blood lactate accumulation (ΔBLC). Lactic and alactic energy production were estimated from ΔBLC and the fast component of excess post-exercise oxygen consumption. Aerobic energy production was determined from oxygen uptake during exercise. Metabolic power (MP) was derived from total metabolic energy (WTOT). ηopt was calculated as Pmax divided by MP. Temporal changes in Pmax, WTOT, and ηopt were analyzed by non-linear regression. Results: All models showed excellent quality (R2 > 0.982) and allowed accurate recalculation of time-specific power output and gross efficiency (R2 > 0.986). The time-constant for Pmax(t) (τP) was closely correlated with that of ηopt (τη; r = 0.998, p < 0.001). Estimating efficiency using τP for τη led to a 0.88 ± 0.35% error. Conclusions: Although efficiency depends on pedal force and cadence, the latter influence can be eliminated by ηopt(t) using a mono-exponential equation whose time constant can be estimated from Pmax(t).

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