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The research aimed to investigate back pain (BP) prevalence in a large cohort of young athletes with respect to age, gender, and sport discipline. BP (within the last 7days) was assessed with a face scale (face 1-2=no pain; face 3-5=pain) in 2116 athletes (m/f 61%/39%; 13.3 +/- 1.7years; 163.0 +/- 11.8cm; 52.6 +/- 13.9kg; 4.9 +/- 2.7 training years; 8.4 +/- 5.7 training h/week). Four different sports categories were devised (a: combat sports, b: game sports; c: explosive strength sport; d: endurance sport). Analysis was described descriptively, regarding age, gender, and sport. In addition, 95% confidence intervals (CI) were calculated. About 168 (8%) athletes were allocated into the BP group. About 9% of females and 7% of males reported BP. Athletes, 11-13years, showed a prevalence of 2-4%; while prevalence increased to 12-20% in 14- to 17-year olds. Considering sport discipline, prevalence ranged from 3% (soccer) to 14% (canoeing). Prevalences in weight lifting, judo, wrestling, rowing, and shooting were 10%; in boxing, soccer, handball, cycling, and horse riding, 6%. 95% CI ranged between 0.08-0.11. BP exists in adolescent athletes, but is uncommon and shows no gender differences. A prevalence increase after age 14 is obvious. Differentiated prevention programs in daily training routines might address sport discipline-specific BP prevalence.
Year-to-year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather-induced crop-yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here, we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state-of-the-art, process-based crop model simulations. We find that observed weather variations can explain more than 50% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50% in seven countries, including the United States. The explained variance exceeds 50% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop-yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process-based crop models not only account for weather influences on crop yields, but also provide options to represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.