Blood lactate and hormonal responses to prototype flywheel ergometer workouts.

The purpose of the study was to compare blood lactate and hormonal responses with flywheel ergometer (FERG) leg presses for preliminary assessment of workouts best suited for future in-flight resistance exercise. Comprised of 10 repetition sets, the workouts entailed 3 sets of concentric and eccentric (CE3) actions, or concentric-only actions done for 3 (CO3) or 6 (CO6) sets. Methods employed included assessment of blood lactate concentrations ([BLa-]) before and 5 minutes postexercise. Venous blood was also collected before and at 1 and 30 minutes postexercise to assess growth hormone, testosterone, cortisol concentrations ([GH], [T], [C]) and [T/C] ratios. [BLa-] were compared with 2 (time) x 3 (workout) analysis of variance. Hormones were assessed with 2 (gender) x 3 (time) x 3 (workout) analysis of covariances. Results showed [BLa-] had a time effect. Growth hormone concentration showed gender x workout, gender x time, and workout x time interactions, whereas [T] had a 3-way interaction. [C] had gender, time, and workout effects. [T/C] yielded a gender x time interaction. It was concluded that, because CO6 and CE3 yielded similar anabolic hormonal data but the latter had a lower [C] 30 minutes postexercise, CE3 served as the best workout. Although the FERG was originally designed for microgravity, the effort put forth by current subjects was like that for workouts aimed at greater athletic performance and conditioning. Practical applications suggest that eccentric actions should be used for FERG workouts geared toward muscle mass and strength improvement.

Impact of acceleration on blood lactate values derived from high-speed resistance exercise.

Acceleration, or an increase in the rate of movement, is integral to success in many sports. Improvements in acceleration often entail workouts done at intensities that elicit higher blood lactate concentrations (BLa). The purpose of the study is to assess the impact of acceleration on BLa. Methods required subjects (n = 45) to perform 4 workouts that each involved two 1-minute sets of hip- and knee-extension repetitions on an inertial exercise trainer (Impulse Training Systems, Newnan, Georgia). Subjects performed 2 workouts comprised solely of phasic or tonic repetitions; their sequence was randomized to prevent an order effect. Before and 5 minutes after exercise, subjects' BLa were assessed with a calibrated analyzer (Sports Resource Group, Hawthorne, New York). Post and delta (post-pre) BLa both served as criterion measures for multivariate analysis. Average and peak acceleration values, derived from both phasic and tonic workouts, served as predictor variables. Results showed statistical significance (p < 0.05; R = 0.2534) and yielded the following prediction equation from phasic workouts: delta BLa = 1.40 + 1.116 (average acceleration set 1)--0.011 (peak acceleration set 1)--0.634 (average acceleration set 2) + 0.005 (peak acceleration set 2). Conclusions suggest delta BLa variance, which represents the increase of the metabolite incurred from workouts, is most easily explained by average acceleration values, which describes the mean increase in the rate of movement from phasic workouts. To improve an athlete's tolerance for acceleration-induced BLa increases, workouts should be tailored with respect to the muscles involved and the duration of exercise bouts of their chosen sport.

Growth hormone levels after flywheel ergometer exercise: correlation with independent variables.

INTRODUCTION: In-flight muscle mass and strength losses are likely exacerbated by low growth hormone (GH) concentrations. Factors associated with exercise may foretell resultant GH levels and thereby help blunt future mass and strength losses. METHODS: To assess the ability of variables to predict GH variance from resistive exercise done on a flywheel ergometer (FE) designed for in-flight exercise, subjects (N=17) performed three types of workouts on the device. With a randomized design, subjects performed the workouts with the intent to determine if changes in post-exercise GH concentrations are impacted by contractile mode and workload. Body mass, blood lactate (BLa-) concentrations, and peak angular velocity (PAV), average power (AP), and total work (TW) from workouts attempted to predict GH variance. Pre-exercise blood draws, and at 1 and 30 min after workouts, were used to determine GH concentrations. BLa- levels were measured before workouts and at 5 min post-exercise. Delta (8, post-pre) and 30-min post-workout GH levels served as criterion variables. RESULTS: Multivariate regression with an alpha < or = 0.05 yielded the following significant prediction equation: deltaGH = 13.64 - 0.014 (body mass) - 0.607 (post-exercise BLa-) + 0.659 (deltaBLa-) - 0.624(PAV) + 0.653(TW) + 0.147(AP). DISCUSSION: Univariate correlations show body mass, deltaBLa-, and TW were the best predictors of deltaGH variance. Future research should also attempt to identify additional variables that account for the unexplained GH variance from FE workouts.