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Dietary records are often biased, especially those of overweight individuals. The purpose of the study was to investigate underreporting among persons of normal weight with a very high energy intake (El).
The total energy expenditure (TEE) of 16 elite athletes (BMI 24 +/- 2 kg/m(2)) and 17 controls (BMI 23 3 kg/m2) was measured using the doubly-labeled water technique (DLW, 14d). El was estimated using 2 x 3-day dietary records. Underreporters were identified by BLACK'S cut-off (El:TEE < 0.76). 44% of athletes (El: 3584 824 kcal/d; TEE: 4621 1460 kcal/d) and 29% of controls (El: 2552 680 kcal/d; TEE: 3151 822 kcal/d) were identified as underreporters. TEE explains 52% of underreporting. In summary, a high energy intake seems to strongly predict underreporting. Prevalence and magnitude of underreporting increase with increasing energy intake.
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.
Test-retest-reliability of metabolic and cardiovascular load during isokinetic strength testing
(2012)
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/m2, 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 (α = 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.
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.
Background: Exercising at intensities where fat oxidation rates are high has been shown to induce metabolic benefits in recreational and health-oriented sportsmen. The exercise intensity (Fat(peak)) eliciting peak fat oxidation rates is therefore of particular interest when aiming to prescribe exercise for the purpose of fat oxidation and related metabolic effects. Although running and walking are feasible and popular among the target population, no reliable protocols are available to assess Fat(peak) as well as its actual velocity (VPFO) during treadmill ergometry. Our purpose was therefore, to assess the reliability and day-to-day variability of VPFO and Fat(peak) during treadmill ergometry running. Conclusion: In summary, relative and absolute reliability indicators for V-PFO and Fat(peak) were found to be excellent. The observed LoA may now serve as a basis for future training prescriptions, although fat oxidation rates at prolonged exercise bouts at this intensity still need to be investigated.
Background:
Exercising at intensities where fat oxidation rates are high has been shown to induce metabolic benefits in recreational and health-oriented sportsmen. The exercise intensity (Fat peak ) eliciting peak fat oxidation rates is therefore of particular interest when aiming to prescribe exercise for the purpose of fat oxidation and related metabolic effects. Although running and walking are feasible and popular among the target population, no reliable protocols are available to assess Fat peak as well as its actual velocity (V PFO ) during treadmill ergometry. Our purpose was therefore, to assess the reliability and day-to-day variability of V PFO and Fat peak during treadmill ergometry running.
Methods:
Sixteen recreational athletes (f = 7, m = 9; 25 ± 3 y; 1.76 ± 0.09 m; 68.3 ± 13.7 kg; 23.1 ± 2.9 kg/m 2 ) performed 2 different running protocols on 3 different days with standardized nutrition the day before testing. At day 1, peak oxygen uptake (VO 2peak ) and the velocities at the aerobic threshold (V LT ) and respiratory exchange ratio (RER) of 1.00 (V RER ) were assessed. At days 2 and 3, subjects ran an identical submaximal incremental test (Fat-peak test) composed of a 10 min warm-up (70 % V LT ) followed by 5 stages of 6 min with equal increments (stage 1 = V LT , stage 5 = V RER ). Breath-by-breath gas exchange data was measured continuously and used to determine fat oxidation rates. A third order polynomial function was used to identify V PFO and subsequently Fat peak . The reproducibility and variability of variables was verified with an int raclass correlation coef ficient (ICC), Pearson ’ s correlation coefficient, coefficient of variation (CV) an d the mean differences (bias) ± 95 % limits of agreement (LoA).
Results:
ICC, Pearson ’ s correlation and CV for V PFO and Fat peak were 0.98, 0.97, 5.0 %; and 0.90, 0.81, 7.0 %, respectively. Bias ± 95 % LoA was − 0.3 ± 0.9 km/h for V PFO and − 2±8%ofVO 2peak for Fat peak.
Conclusion:
In summary, relative and absolute reliability indicators for V PFO and Fat peak were found to be excellent. The observed LoA may now serve as a basis for future training prescriptions, although fat oxidation rates at prolonged exercise bouts at this intensity still need to be investigated.
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.