One-week arduous military exercise resulted in reductions in body mass and performance, as well as considerable hormonal disturbances. Our most important observation was that whereas the hormonal systems was normalized within 1 wk of rest and proper nutrition, lower body strength and jump performance were still depressed after 2 wk.

The aim of this study was to quantify peak age and improvements over the preceding years to peak age in elite athletic contestants according to athlete performance level, sex, and discipline. Individual season bests for world-ranked top 100 athletes from 2002 to 2016 (14,937 athletes and 57,049 individual results) were downloaded from the International Association of Athletics Federations' website. Individual performance trends were generated by fitting a quadratic curve separately to each athlete's performance and age data using a linear modeling procedure. Mean peak age was typically 25-27 y, but somewhat higher for marathon and male throwers (∼28-29 y). Women reached greater peak age than men in the hurdles and middle- and long-distance running events (mean difference, ±90% CL: 0.6, ±0.3 to 1.9, ±0.3 y: small to moderate). Male throwers had greater peak age than corresponding women (1.3, ±0.3 y: small). Throwers displayed the greatest performance improvements over the 5 y prior to peak age (mean [SD]: 7.0% [2.9%]), clearly ahead of jumpers, long-distance runners, hurdlers, middle-distance runners, and sprinters (3.4, ±0.2% to 5.2, ±0.2%; moderate to large). Similarly, top 10 athletes showed greater improvements than top 11-100 athletes in all events (1.0, ±0.9% to 1.8, ±1.1%; small) except throws. Women improved more than men in all events (0.4, ±0.2% to 2.9, ±0.4%) except sprints. This study provides novel insight on performance development in athletic contestants that are useful for practitioners when setting goals and evaluating strategies for achieving success.

Purpose: To quantify age of peak performance and performance improvements in the years preceding peak age in elite weightlifting and powerlifting athletes using results from powerlifting World Championships in 2003–2017 and weightlifting World Championships and Olympic Games in 1998–2017. Methods: Individual performance trends were derived by fitting a quadratic curve separately to each athlete’s performance and age data. Effects were evaluated using magnitude-based inferences. Results: Peak age (mean [SD]) was 35 (7) y for powerlifters and 26 (3) y for weightlifters, a large most likely substantial difference of 9, ±1 y (mean, 90% confidence limit). Men showed possibly higher peak age than women in weightlifting (0.8, ±0.7 y; small) and a possibly lower peak age in powerlifting (1.3, ±1.8 y; trivial). Peak age of athletes who ever won a medal was very likely less than that of nonmedalists in weightlifting (1.3, ±0.6 y; small), while the difference in powerlifters was trivial but unclear. Five-year improvements prior to peak age were 12% (10%) for powerlifters and 9% (7%) for weightlifters, a small possibly substantial difference (2.9, ±2.1%). Women exhibited possibly greater improvements than men in powerlifting (2.7, ±3.8%; small) and very likely greater in weightlifting (3.5, ±1.6%; small). Medalists possibly improved less than nonmedalists among powerlifters (−1.7, ±2.3%; small), while the difference was likely trivial for weightlifters (2.3, ±1.8%). Conclusion: These novel insights on performance development will be useful for practitioners evaluating strategies for achieving success.

Poor muscle strength and physical function have been associated with higher risk of hospitalisation and lower wellbeing among the elderly. Physical training increases muscle strength, endurance and physical function as well as psychological outcomes. Exercise among older adults is often used to improve these variables, but few have compared the effects of different types of training in parallel independent groups. Thus, the aim of the present study was to investigate the effect of three different types of training on body composition, muscle strength, physical function and well-being in the elderly. A total of 118 community-dwelling older adults (mean age 74.3, s04.6 years, 68% women) completed a 13-week randomised controlled exercise-trial involving four groups: traditional strength training (STG), functional strength training (FTG), endurance training (ETG) and control (CON). Stair climb with load improved more in FTG than CON (P B0.05), and the two strength groups performed better in the functional upper body test (P B0.05). STG increased strength more than CON in all exercises (P B0.001), while FTG increased strength more than CON in the chest press, shoulder press and knee extension (P B0.05). Lean body mass (LBM) increased in all training groups, which differed significantly from CON (P B0.05). ETG decreased their fat mass compared to CON (P B0.001). The only significant effect on well-being indices was improved life satisfaction in FTG at week 5 (P B0.05). We also observed significant correlations between change in some of the functional tests and change in positive affect.All three types of training can enhance physical capacity. Functional strength training can be a cost effective form of training in terms of less demand for instructors and equipment and could be an effective way to improve physical function, strength and indices of well-being in the elderly.

The aim of the study was to examine the test-retest reliability and agreement across methods for assessing individual force-velocity (FV) profiles of the lower limbs in athletes. Using a multicenter approach, 27 male athletes completed all measurements for the main analysis, with up to 82 male and female athletes on some measurements. The athletes were tested twice before and twice after a 2- to 6-month period of regular training and sport participation. The double testing sessions were separated by ~1 week. Individual FV-profiles were acquired from incremental loading protocols in squat jump (SJ), countermovement jump (CMJ) and leg press. A force plate, linear encoder and a flight time calculation method were used for measuring force and velocity during SJ and CMJ. A linear regression was fitted to the average force and velocity values for each individual test to extrapolate the FV-variables: theoretical maximal force (F0), velocity (V0), power (Pmax), and the slope of the FV-profile (SFV). Despite strong linearity (R2>0.95) for individual FV-profiles, the SFV was unreliable for all measurement methods assessed during vertical jumping (coefficient of variation (CV): 14–30%, interclass correlation coefficient (ICC): 0.36–0.79). Only the leg press exercise, of the four FV-variables, showed acceptable reliability (CV:3.7–8.3%, ICC:0.82–0.98). The agreement across methods for F0 and Pmax ranged from (Pearson r): 0.56–0.95, standard error of estimate (SEE%): 5.8–18.8, and for V0 and SFV r: -0.39–0.78, SEE%: 12.2–37.2. With a typical error of 1.5 cm (5–10% CV) in jump height, SFV and V0 cannot be accurately obtained, regardless of the measurement method, using a loading range corresponding to 40–70% of F0. Efforts should be made to either reduce the variation in jumping performance or to assess loads closer to the FV-intercepts. Coaches and researchers should be aware of the poor reliability of the FV-variables obtained from vertical jumping, and of the differences across measurement methods.