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Background: The elderly need strength training more and more as they grow older to stay mobile for their everyday activities. The goal of training is to reduce the loss of muscle mass and the resulting loss of motor function. The dose-response relationship of training intensity to training effect has not yet been fully elucidated.
Methods: PubMed was selectively searched for articles that appeared in the past 5 years about the effects and dose-response relationship of strength training in the elderly.
Results: Strength training in the elderly (> 60 years) increases muscle strength by increasing muscle mass, and by improving the recruitment of motor units, and increasing their firing rate. Muscle mass can be increased through training at an intensity corresponding to 60% to 85% of the individual maximum voluntary strength. Improving the rate of force development requires training at a higher intensity (above 85%), in the elderly just as in younger persons. It is now recommended that healthy old people should train 3 or 4 times weekly for the best results; persons with poor performance at the outset can achieve improvement even with less frequent training. Side effects are rare.
Conclusion: Progressive strength training in the elderly is efficient, even with higher intensities, to reduce sarcopenia, and to retain motor function.
Adequate energy intake in adolescent athletes is considered important. Total energy expenditure (TEE) can be calculated from resting energy expenditure (REE) and physical activity level (PAL). However, validated PAL recommendations are available for adult athletes only. Purpose was to comprise physical activity data in adolescent athletes and to establish PAL recommendations for this population. In 64 competitive athletes (15.3 +/- 1.5yr, 20.5 +/- 2.0kg/m(2)) and 14 controls (15.1 +/- 1.1yr, 21 +/- 2.1kg/m(2)) TEE was calculated using 7-day activity protocols validated against doubly-labeled water. REE was estimated by Schofield-HW equation, and PAL was calculated as TEE:REE. Observed PAL in adolescent athletes (1.90 +/- 0.35) did not differ compared with controls (1.84 +/- 0.32, p = .582) and was lower than recommended for adult athletes by the WHO. In conclusion, applicability of PAL values recommended for adult athletes to estimate energy requirements in adolescent athletes must be questioned. Instead, a PAL range of 1.75-2.05 is suggested.
Changes in performance parameters over four consecutive maximal incremental cycling tests were investigated to determine how many tests can be performed within one single day without negatively affecting performance. Sixteen male and female subjects (eight trained (T): 25 +/- 3 yr, BMI 22.6 +/- 2.5 kg center dot m(-2), maximal power output (P-max) 4.6 +/- 0.5 W center dot kg(-1); eight untrained (UT): 27 +/- 3 yr, BMI 22.3 +/- 1.2 kg center dot m(-2), P-max 2.9 +/- 0.3 W center dot kg(-1)) performed four successive maximal incremental cycling tests separated by 1.5 h of passive rest. Individual energy requirements were covered by standardised meals between trials. Maximal oxygen uptake (VO2max) remained unchanged over the four tests in both groups (P = 0.20 and P = 0.33, respectively). P-max did not change in the T group (P = 0.32), but decreased from the third test in the UT group (P < 0.01). Heart rate responses to submaximal exercise were elevated from the third test in the T group and from the second test in the UT group (P < 0.05). The increase in blood lactate shifted rightward over the four tests in both groups (P < 0.001 and P < 0.01, respectively). Exercise-induced net increases in epinephrine and norepinephrine were not different between the tests in either group (P 0.15). If VO2max is the main parameter of interest, trained and untrained individuals can perform at least four maximal incremental cycling tests per day. However, because other parameters changed after the first and second test, respectively, no more than one test per day should be performed if parameters other than VO2max are the prime focus.
Background: Athletes may differ in their resting metabolic rate (RMR) from the general population. However, to estimate the RMR in athletes, prediction equations that have not been validated in athletes are often used. The purpose of this study was therefore to verify the applicability of commonly used RMR predictions for use in athletes. Methods: The RMR was measured by indirect calorimetry in 17 highly trained rowers and canoeists of the German national teams (BMI 24 +/- 2 kg/m(2), fat-free mass 69 +/- 15 kg). In addition, the RMR was predicted using Cunningham (CUN) and Harris-Benedict (HB) equations. A two-way repeated measures ANOVA was calculated to test for differences between predicted and measured RMR (alpha = 0.05). The root mean square percentage error (RMSPE) was calculated and the Bland-Altman procedure was used to quantify the bias for each prediction. Results: Prediction equations significantly underestimated the RMR in males (p < 0.001). The RMSPE was calculated to be 18.4% (CUN) and 20.9% (HB) in the entire group. The bias was 133 kcal/24 h for CUN and 202 kcal/24 h for HB. Conclusions: Predictions significantly underestimate the RMR in male heavyweight endurance athletes but not in females. In athletes with a high fat-free mass, prediction equations might therefore not be applicable to estimate energy requirements. Instead, measurement of the resting energy expenditure or specific prediction equations might be needed for the individual heavyweight athlete.
We sought to investigate the effects of wearing a mobile respiratory gas analysis system during a treadmill test on blood lactate (bLa) concentrations and commonly applied bLa thresholds. A total of 16 recreational athletes (31 +/- 3 years, V0205: 58 6 ml min(-1)-kg(-1)) performed one multistage treadmill test with and one without gas exchange measurements (GEM and noGEM). The whole bLa curve, the lactate threshold (LT), the individual anaerobic thresholds according to Stegmann(IAT(sr)) and Dickhuth (IAT(Di)), and a fixed bLa concentration of 4 mmob.l(-1) (OBLA) were evaluated. The bLa curve was shifted slightly leftward in GEM compared to noGEM (P<0.05), whereas the heart rate response was not different between conditions (P= 0.89). There was no difference between GEM and noGEM for LT (2.61 +/- 0.34 vs. 2.64 +/- 0.39 m(-1) s(-1) P=0.49) and IAT(st) (3.47 +/- 0.42 vs. 3.55 +/- 0.47m-s(-1), P=0.12). However, IATD(Di) (3.57 +/- 0.39 vs. 3.66 +/- 0.44m-s(-1), P<0.01) and OBLA (3.85 +/- 0.46 vs. 3.96 +/- 0.47m-s-1, P<0.01) occurred at slower running velocities in GEM. The bLa response to treadmill tests is mildly affected by wearing a mobile gas analysis system. This also applies to bLa thresholds located at higher exercise intensities. While the magnitude of the effects is of little importance for recreational athletes, it might be relevant for elite athletes and scientific studies.
Background Preparticipation examinations (PPE) are frequently used to evaluate eligibility for competitive sports in adolescent athletes. Nevertheless, the effectiveness of these examinations is under debate since costs are high and its validity is discussed controversial.
Purpose To analyse medical findings and consequences in adolescent athletes prior to admission to a sports school.
Methods In 733 adolescent athletes (318 girls, 415 boys, age 12.3+/-0.4, 16 sports disciplines), history and clinical examination (musculoskeletal, cardiovascular, general medicine) was performed to evaluate eligibility. PPE was completed by determination of blood parameters, ECG at rest and during ergometry, echocardiography and x-rays and ultrasonography if indicated. Eligibility was either approved or rated with restriction. Recommendations for therapy and/or prevention were given to the athletes and their parents.
Results Historical (h) and clinical (c) findings (eg, pain, verified pathologies) were more frequent regarding the musculoskeletal system (h: 120, 16.4%; c: 247, 33.7%) compared to cardiovascular (h: 9, 1.2%; c: 23, 3.1%) or general medicine findings (h: 116, 15.8%; c: 71, 9.7%). ECG at rest was moderately abnormal in 46 (6.3%) and severely abnormal in 25 athletes (3.4%). Exercise ECG was suspicious in 25 athletes (3.4%). Relevant echocardiographic abnormalities were found in 17 athletes (2.3%). In 52 of 358 cases (14.5%), x-rays led to diagnosis (eg, Spondylolisthesis). Eligibility was temporarily restricted in 41 athletes (5.6%). Three athletes (0.4%) had to be excluded from competitive sports. Therapy (eg, physiotherapy, medication) and/or prevention (sensorimotor training, vaccination) recommendations were deduced due to musculoskeletal (t:n = 76,10.3%; p:n = 71,9.8%) and general medicine findings (t:n = 80, 10.9%; p:n = 104, 14.1%).
Conclusion Eligibility for competitive sports is restricted in only 5.5% of adolescent athletes at age 12. Eligibility refusals are rare. However, recommendations for therapy and prevention are frequent, mainly regarding the musculoskeletal system. In spite of time and cost consumption, adolescent preparticipation before entering a career in high-performance sports is supported.