Variations in heart rate during exercise correlate with changes of exercise intensity and may be measured directly by radiotelemetry and continuous ECG recording. The heart rate can also be recorded in the memory of a microcomputer, which can be carried on the wrist as easily as a watch. The device has a transmitter and a receiver. By recording the heart rate during a training session or a segment of training, and calculating the average of the heart rate and comparing this average to both the maximum heart rate of the individual and his heart rate at rest, the relative heart rate to the intensity of the work load (% maximum heart rate) can be calculated. These results are useful in planning optimal training intensities for both the healthy and rehabilitating athlete. The use of target heart rate as a tool for exercise prescription is common. It represents the percentage difference between resting and maximum heart rate added to the resting heart rate. For calculating target heart rate there are also 2 other methods. The first represents the percentage of the maximum heart rate (%HRmax) calculated from zero to peak heart rate. The second represents the heart rate at a specified percentage of maximum MET (VO2max). An appropriate individual heart rate for each level of an endurance performance is best determined in the laboratory. This is carried out by increasing the speed of the runner in stages on a treadmill and by measuring the oxygen uptake, the lactic acid concentration in the blood and corresponding variations in the heart rate.(ABSTRACT TRUNCATED AT 250 WORDS)

PurposeOur recent study of three accelerometer brands in various ambulatory activities showed that the mean amplitude deviation (MAD) of the resultant acceleration signal performed best in separating different intensity levels and provided excellent agreement between the three devices. The objective of this study was to derive a regression model that estimates oxygen consumption (VO2) from MAD values and validate the MAD-based cut-points for light, moderate and vigorous locomotion against VO2 within a wide range of speeds.Methods29 participants performed a pace-conducted non-stop test on a 200 m long indoor track. The initial speed was 0.6 m/s and it was increased by 0.4 m/s every 2.5 minutes until volitional exhaustion. The participants could freely decide whether they preferred to walk or run. During the test they carried a hip-mounted tri-axial accelerometer and mobile metabolic analyzer. The MAD was calculated from the raw acceleration data and compared to directly measured incident VO2. Cut-point between light and moderate activity was set to 3.0 metabolic equivalent (MET, 1 MET = 3.5 ml · kg-1 · min-1) and between moderate and vigorous activity to 6.0 MET as per standard use.ResultsThe MAD and VO2 showed a very strong association. Within individuals, the range of r values was from 0.927 to 0.991 providing the mean r = 0.969. The optimal MAD cut-point for 3.0 MET was 91 mg (milligravity) and 414 mg for 6.0 MET.ConclusionThe present study showed that the MAD is a valid method in terms of the VO2 within a wide range of ambulatory activities from slow walking to fast running. Being a device-independent trait, the MAD facilitates directly comparable, accurate results on the intensity of physical activity with all accelerometers providing tri-axial raw data.

Heavy exercise is associated with an increased risk of upper respiratory tract infections. Strenuous exercise also causes gastrointestinal (GI) symptoms. In previous studies probiotics have reduced respiratory tract infections and GI symptoms in general populations including children, adults, and the elderly. These questions have not been studied in athletes before. The purpose of this study was to investigate the effect of probiotics on the number of healthy days, respiratory infections, and GI-symptom episodes in marathon runners in the summer. Marathon runners (N = 141) were recruited for a randomized, double-blind intervention study during which they received Lactobacillus rhamnosus GG (LGG) or placebo for a 3-mo training period. At the end of the training period the subjects took part in a marathon race, after which they were followed up for 2 wk. The mean number of healthy days was 79.0 in the LGG group and 73.4 in the placebo group (P = 0.82). There were no differences in the number of respiratory infections or GI-symptom episodes. The duration of GI-symptom episodes in the LGG group was 2.9 vs. 4.3 d in the placebo group during the training period (P = 0.35) and 1.0 vs. 2.3 d, respectively, during the 2 wk after the marathon (P = 0.046). LGG had no effect on the incidence of respiratory infections or GI-symptom episodes in marathon runners, but it seemed to shorten the duration of GI-symptom episodes.

The neural activation (iEMG) and selected stride characteristics of six male sprinters were studied for 100-, 200-, 300- and 400-m experimental sprints, which were run according to the velocity in the 400 m. Blood lactate (BLa) was analysed and drop jumps were performed with EMG registration at rest and after each sprint. Running velocity (P less than 0.001) and stride length (P less than 0.05) decreased and contact time increased (P less than 0.01) during the 400-m sprint. The increase in contact time was greatest immediately after runs of 100 and 300 m. The peak BLa increased and the rate of BLa accumulation decreased with running distance (P less than 0.001). The height of rise of the centre of mass in the drop jumps was smaller immediately after the 300 m (P less than 0.05) and the 400 m (P less than 0.01) than at rest, and it correlated negatively with peak BLa (r = -0.77, P less than 0.001). The EMG and EMG:running velocity ratio increased with running distance. It was concluded that force generation of the leg muscles had already begun to decrease during the first quarter of the 400-m sprint. The deteriorating force production was compensated for until about 200-300 m. Thereafter, it was impossible to compensate for fatigue and the speed of running dropped. According to this study, fatigue in the 400-m sprint among trained athletes is mainly due to processes within skeletal muscle rather than the central nervous system.