Performance and hormones were determined in eight middle- and nine long-distance runners after an increase in training volume (ITV, February 1989) or intensity (ITI, February 1990). Seven runners participated in both studies. The objective was to cause an overtraining syndrome. The mean training volume of 85.9 km week-1 increased within 3 weeks to 176.6 km week-1 during ITV and 96-98% of traini...

Rapid adaptation schemes that employ the EM algorithm may suffer from overtraining problems when used with small amounts of adaptation data. An algorithm to alleviate this problem is derived within the information geometric framework of Csiszár and Tusnády, and is used to improve MLLR adaptation on NAB and Switchboard adaptation tasks. It is shown how this algorithm approximately optimizes a di...

Fasted or weight-category athletes manage their training under strict diet conditions that could impair the stress-recovery balance and result in acute or chronic fatigue. However, to date, no validated biomarker are available to quantify this phenomena. The aim of this study was to assess the validity of a specific index combining plasma albumin and weight change to detect nutrition-related ri...

Five men undertook two intensive interval training sessions per day for 10 days, followed by 5 days of active recovery. Subjects supplied a venous blood sample and completed a mood-state questionnaire on days 1, 6, 11 and 16 of the study. Performance capabilities were assessed on days 1, 11 and 16 using a timed treadmill test to exhaustion at 18 kmh-1 and 1% grade. These individuals became acut...

Background and Study Aim. Continuously increasing the volume intensity of training sessions often leads to overtraining. It has been demonstrated that glutathione supplementation might improve aerobic metabolism in skeletal muscle reduce exercise-induced fatigue. The aim study was assess effect on fatigue, recovery processes, competitive results elite swimmers during a six-week period.
 Ma...

| We elucidate essential diierences between feed-forward neural network models and conventional linear statistical models. When the target is overrealizable, the MLE of the former shows worse generalization, while experimental results reveals that iterative learning of a neural network shows eminent overtraining and better generalization in the middle.