Blood lactate kinetics is an important physiological determinant of endurance exercise performance. Recently, some studies reported that the blood glucose transition point can also be observed (blood glucose threshold; GT) and the GT is consistent with the lactate threshold (LT). However, we have recently reported that blood glucose kinetics and blood lactate kinetics were different during two sets of incremental running tests in the same day. This result suggested that influence of low glycogen storage on GT and LT are different. This study was intended to clarify the effect of low glycogen storage on the blood glucose and the blood lactate kinetics during incremental running test performed two successive days. Eight male endurance runners participated in incremental running test performed two successive days. The main finding was that the blood glucose was significantly lower in the second day than the first day during incremental test, although blood glucose was not different at rest in both days. However, blood lactate was not different form rest to fifth stages in both days, significantly lower only at the final stage in the second day than the first day. Respiratory exchange ration were lower in the second day compared to the first day. GT was significantly higher in the second day than the first day, but LT was not different in both days. We concluded that low glycogen storage effected blood glucose kinetics more than blood lactate kinetics, and resulted in only the change of GT.

A whole body indirect calorimeter provides accurate measurement of energy expenditure and the respiration quotient over long periods of time, but has limitations to assess dynamic changes in energy metabolism due to the small amplitude of the signal in relation to the size of the room. The present study aimed to improve algorithm for the whole body indirect calorimetry by adopting a deconvolution with a regularization parameter. Performance of the new algorithm was compared with trends identification (Med. & Biol. Eng. & Comput 34 : 212, 1996) against four validation tests. In a simulated problem, in which metabolic rate cycled with a period of 20 min, mean square errors (MSEs) computed at every 1 min by the deconvolution (0.0036 for O₂ consumption and 0.0017 for CO₂ production) were smaller than those for trends identification algorithms (0.0198 and 0.0142). Deconvolution algorithm clearly separated individual CO₂ infusion of 8 min intervals, while trends identification could no longer separate them. During the validation by ethanol combustion, which produced a near-steady state condition, the deconvolution (MSEs were 0.0022 for O₂ consumption and 0.0010 for CO₂ production) performed better than trends identification algorithms (MSEs were 0.0086 and 0.0041). When validated against direct measurement of gas production rate during non-steady state condition, produced by a human subject intermittently exercising in the calorimeter, deconvolution (MSEs were 0.0032 for O₂ consumption and 0.0038 for CO₂ production) performed better than trends identification algorithms (0.0182 and 0.0167). The new algorithm significantly improved transient response of the whole body indirect calorimeter.

This study was intended to clarify 1) the difference of the exercise intensity at blood lactate threshold (LT) and blood glucose threshold (GT), 2) the effect of exercise duration on the LT and GT during two sets of incremental running test. Ten male runners (age 25.0±3.2 yr, height 171.2±5.5 cm, body mass 57.9±4.0 kg, VO_2max 64.6±3.0 ml/kg/min) completed two sets of incremental running test (each set was set to run ten stages at 60-90% VO_2max). Second set was repeated after 8 min recovery. LT and GT speed were investigated at the first set. Lactate minimum (LM) and glucose minimum (GM) speed were selected where the blood lactate and glucose concentration were at the lowest during the second set. Using the indirect calorimetry (VO₂, VCO₂), fat and carbohydrate oxidation rates were calculated. GT was observed in all runners. VO₂ and energy expenditure were similar between the two incremental running tests, however, fat oxidation was significantly higher and carbohydrate oxidation was significantly lower during the first half of the second set. This change was regarded as the influence of the exercise duration in the first set. Furthermore, GM speed was significantly lower than GT speed, but LM speed and LT speed were not different. It was considered that the shift of GT was affected by the substrate utilization change during prolonged exercise.

The present study was conducted to obtain basic information about blood glucose fluctuation and relation with race performance during 100 km marathon. Subcutaneous glucose of one well-trained runner was measured by continuous glucose monitoring system (CGMS) at 5 min interval and blood samples for biochemical analysis were drawn at pre, middle and post of the race. Energy balance during one week prior to the 100 km race was recorded, and the whole energy and fluid intake during the race was analyzed. Blood glucose fluctuated reflecting duration of exercise and energy supply during the race. During the latter part of the race (65–70 km), abrupt declines in blood glucose level, which reflected insufficient carbohydrate intake before the race (119 g), were accompanied by decrease in running speed. The present report suggests that continuous glucose monitoring supplemented with standard nutritional and physiological measurement provides precise and valuable information on runner’s energy state during the ultra-endurance race, and that athletes need to reassess their preparation for the race and planning of energy intake during the race.

The present study was conducted to obtain basic information about blood glucose fluctuation and relation with race performance during 100 km marathon. Subcutaneous glucose of one well-trained runner was measured by continuous glucose monitoring system (CGMS) at 5 min interval and blood samples for biochemical analysis were drawn at pre, middle and post of the race. Energy balance during one week prior to the 100 km race was recorded, and the whole energy and fluid intake during the race was analyzed. Blood glucose fluctuated reflecting duration of exercise and energy supply during the race. During the latter part of the race (65–70 km), abrupt declines in blood glucose level, which reflected insufficient carbohydrate intake before the race (119 g), were accompanied by decrease in running speed. The present report suggests that continuous glucose monitoring supplemented with standard nutritional and physiological measurement provides precise and valuable information on runner’s energy state during the ultra-endurance race, and that athletes need to reassess their preparation for the race and planning of energy intake during the race.