We examined age-related changes in endurance performance of marathon and half-marathon finishers. A total of 405 515 running times were separated into groups based on age, sex, and distance. After exclusion of repetitive running times, 300 757 runners were analyzed by ANOVA (factors: age, sex). For each age group (six decades, 20-79 years), mean running times for all finishers, as well as top-ten performers, were assessed. As expected, age and sex had significant influence on running times. Female running times were about 10 % (marathon) and 13 % (half-marathon) above the corresponding times of their age-matched peers. The main finding is that in our sample of trained subjects significant age-related losses in endurance performance did not occur before the age of 50 years. Mean marathon and half-marathon times were virtually identical for the age groups from 20-49 years. Moreover, age-related performance decreases (p < 0.01) of the 50 - 69-year-old subjects were only in the range of 2.6 - 4.4 % per decade. These results suggest that the majority of older athletes are able to maintain a high degree of physical plasticity. The hypothesis that lifestyle factors have considerably stronger influences on functional capacity than the factor age is also supported by these findings from physically active and fit elderly.

Hand-grip strength has been identified as one limiting factor for manual lifting and carrying loads. To obtain epidemiologically relevant hand-grip strength data for pre-employment screening, we determined maximal isometric hand-grip strength in 1,654 healthy men and 533 healthy women aged 20-25 years. Moreover, to assess the potential margins for improvement in hand-grip strength of women by training, we studied 60 highly trained elite female athletes from sports known to require high hand-grip forces (judo, handball). Maximal isometric hand-grip force was recorded over 15 s using a handheld hand-grip ergometer. Biometric parameters included lean body mass (LBM) and hand dimensions. Mean maximal hand-grip strength showed the expected clear difference between men (541 N) and women (329 N). Less expected was the gender related distribution of hand-grip strength: 90% of females produced less force than 95% of males. Though female athletes were significantly stronger (444 N) than their untrained female counterparts, this value corresponded to only the 25th percentile of the male subjects. Hand-grip strength was linearly correlated with LBM. Furthermore, both relative hand-grip strength parameters (F (max)/body weight and F (max)/LBM) did not show any correlation to hand dimensions. The present findings show that the differences in hand-grip strength of men and women are larger than previously reported. An appreciable difference still remains when using lean body mass as reference. The results of female national elite athletes even indicate that the strength level attainable by extremely high training will rarely surpass the 50th percentile of untrained or not specifically trained men.

Successful finishing of marathon requires regular endurance training and appropriate lifestyle. Thus, marathon running times and training data from large samples of physically active and fit elderly are ideal for the assessment of age-related performance. In the present study we analyzed 439 278 running times from result lists of 108 marathon competitions and data from a survey via internet questionnaire about training and behavioural factors of marathon finishers. Marathon times and 6 992 data sets from the internet questionnaire were separated into groups based on age and sex and analyzed by two-way ANOVA. Our main findings are that 1) there are virtually no relevant running time differences (p<0.01) in marathon finishers from 20 to 55 years and 2) the majority of middle-aged and elderly athletes have training histories of less than seven years of running. With the exception of marathon running times we did not encounter any significant gender related differences (p>0.01). The present findings strengthen the concept that considers aging as a biological process that can be considerably speeded up or slowed down by multiple lifestyle related factors.

All three interval modes resulted in physical response in an acceptable range of values, and thus can be recommended.

During non-steady-state exercise, dynamic changes in pulmonary oxygen uptake (VO₂pulm) are dissociated from skeletal muscle VO₂ (VO₂musc) by changes in lung and venous O₂ concentrations (CvO₂), and the dynamics and distribution of cardiac output (CO) between active muscle and remaining tissues (Qrem). Algorithms can compensate for fluctuations in lung O₂ stores, but the influences of CO and CvO₂ kinetics complicate estimation of VO₂musc from cardio-pulmonary measurements. We developed an algorithm to estimate VO₂musc kinetics from VO₂pulm and heart rate (HR) during exercise. 17 healthy volunteers (28 ± 7 years; 71 ± 12 kg; 7 females) performed incremental exercise using recumbent cycle ergometry (VO₂peak 52 ± 8 ml min(-1) kg(-1)). Participants completed a pseudo-random binary sequence (PRBS) test between 30 and 80 W. VO₂pulm and HR were measured, and CO was estimated from HR changes and steady-state stroke volume. VO₂musc was derived from a circulatory model and time series analyses, by solving for the unique combination of venous volume and the perfusion of non-exercising tissues that provided close to mono-exponential VO₂musc kinetics. Independent simulations showed that this approach recovered the VO₂musc time constant (τ) to within 7% (R(2) = 0.976). Estimates during PRBS were venous volume 2.96 ± 0.54 L; Qrem 3.63 ± 1.61 L min(-1); τHR 27 ± 11 s; τVO₂musc 33 ± 8 s; τVO₂pulm 43 ± 14 s; VO₂pulm time delay 19 ± 8 s. The combination of stochastic test signals, time series analyses, and a circulatory model permitted non-invasive estimates of VO₂musc kinetics. Large kinetic dissociations exist between muscular and pulmonary VO₂ during rapid exercise transients.