High training intensities and volumes increase the risk of infection by impairing the immune function (Nieman, 2003; Nieman et al., 1989; 1990; Pedersen and Ullum, 1994; Peters and Bateman, 1983). Thus, elite athletes with high training intensities have a higher number of infectious episodes than recreational athletes and sedentary people (Gleeson and Walsh, 2012; Spence et al., 2007). However, in order to become an elite athlete absence of infections are important, and such findings may appear illogical (Malm, 2006). One issue in earlier studies has been that some conclusions regarding elite exercise training are based on non-elite athletes (Heath et al., 1991; Nieman et al., 1989; 1990) or on comparing athletes to non-athletes (Nieman et al., 2000). In addition, few studies have confirmed that pathogenic infections are present, but such studies have made statements that elite exercise training increases the odds ratio for an infection (Spence et al., 2007). Most athletes do not have the facilities required to carry out on-demand virus titers or establish bacterial cultures, and thus refrain from training based exclusively on self-diagnosis. The experience of these athletes thus makes the term Exercise Constrained Sick Days more appropriate. This can be defined as any day when the athlete chose not to train due to experienced symptoms of infections, self-reported or by a physician.
The relationship between the long term risk of URTI, immune function and exercise load is commonly modelled as a J-shaped curve with URTI on the y-axis and exercise load on the x-axis (Nieman, 1994a). This model suggests that sedentary individuals have a fixed risk of URTI, and that moderate exercise training decreases, while intense exercise training increases, the rate of URTI by a mechanism in which the immune system is modulated. Recently, support for this model was presented (Spence et al., 2007). The acute, repeated occurrence of impairment of immunity, the "open window" for infections to enter the body (Brines et al., 1996; Nieman, 1997; Pedersen and Bruunsgaard, 1995; Pedersen and Ullum, 1994) may result in a long-term increase in infection rates (the J-shaped curve).
We have previously shown that infection rates after a marathon increased only in subjects who had reported a pre-race infection (Ekblom et al., 2006). Consequently, we formed the hypothesis that the relationship between training and infection may form an S-curve when true elite athletes are included (Malm, 2006). Most studies have not differentiated between athletes with "high" and "elite" exercise loads, with the consequence that an incorrect definition of "elite" athletes has been used. We presented a pilot study data from one elite runner's training log that covered 16 years (Malm, 2006), and concluded that an increased rate of infection is incompatible with a high training volume. These findings are in line with the recently published review article by Dhabhar (Dhabhar, 2014), where short term physical stress such as exercise, which is perceived as a positive event by the athlete, belongs to the immunoprotective response, enhancing survival and promoting enhanced physical performance.
The aim of this study was to test the hypothesis that exercise training loads are negatively correlated to the self-reported number of Exercise Constrained Sick Days.
Male (N = 4) and female (N = 3) cross-country (XC) skiers, male biathletes (N = 2) and male long-distance runners (N = 2) completed the study. The inclusion criterion was that the athlete should have demonstrated "top national or international level performance in an endurance sports". This does introduce a certain amount of subjectivity into whether the criterion was met, but all subjects were accepted as elite athletes to the School of Sports Science at Umea University based on their competitive results from past years. Subjects were aged 17-24 years at the first year of reporting, and they reported a training period of 3-16 years. The number of observations of training years was 61 in all statistical calculations. A retrospective Power calculation was done (using the software G*power, version 3.1.3, wwwpsycho.uniduesseldorf.de/abteilungen/aap/gpower3/) for [Chi.sup.2] tests (goodness-of-fit for contingency tables) using an effect size of 0.3, p = 0.05, N = 61 and 2 degrees of freedom (between three levels of training volume) results in a Power of 0.54, while increasing the effect size to 0.5 gives a power of 0.95. Ethical permission (Ref. No. 2011236-31M) was granted by the Regional Ethics Committee for northern Sweden, located at Umea University. All subjects signed an informed consent form and the study was conducted in accordance with the WMA Declaration of Helsinki--Ethical Principles for Medical Research Involving Human Subjects 2008. The study met also the ethical standards of IJSM (Harriss and...