Running during sports and for physical activity often requires changes in velocity through acceleration and deceleration. While it is clear that lower extremity biomechanics vary during these accelerations and decelerations, the work requirements of the individual joints are not well understood. The purpose of this investigation was to measure the sagittal plane mechanical work of the individual lower extremity joints during acceleration, deceleration, and steady-state running. Ten runners were compared during acceleration, deceleration, and steady-state running using three-dimensional kinematics and kinetics measures. Total positive and negative joint work, and relative joint contributions to total work were compared between conditions. Total positive work progressively increased from deceleration to acceleration. This was due to greater ankle joint work during acceleration. While there was no significant change in total negative work during deceleration, there was a greater relative contribution of the knee to total negative work with a subsequent lower relative ankle negative work. Each lower extremity joint exhibits distinct functional roles in acceleration compared with deceleration during level running. Deceleration is dominated by greater contributions of the knee to negative work while acceleration is associated with a greater ankle contribution to positive work.
D.S. Blaise Williams III, Jonathan H. Cole and Douglas W. Powell
Mark H. Roltsch, Judith A. Flohr and Patricia B. Brevard
The purpose of this study was to examine the metabolic consequences of a moderate variation in dietary fat content of male endurance athletes during submaximal exercise. Six males (age, 29.8 ± 11 years; weight, 72.3 ± 10 kg) · with an average maximum oxygen uptake (V̇O2max) of 66 ± 10 ml/kg/min were tested on their normal diet and 3 experimental diets. The energy contributions from protein, carbohydrates, and fats were 16/59/22 (3% alcohol), 14/53/33, 13/72/15, and 16/61/23% for the normal diet (N), fat supplemented diet (F), high carbohydrate diet (C), and adjusted normal diet (AN), respectively. The F diet was designed to significantly increase fat content compared to the normal diet and be easily maintained by the athletes. Caloric content of the F, C, and AN diets were adjusted to meet estimated total daily energy expenditure. The difference between the N and AN diets is that the AN has been adjusted to meet estimated total daily energy expenditure. The diets were randomly assigned after substrate utilization testing on the N diet and were consumed for 7 days prior to testing. Substrate utilization was recorded at steady state (73 ± 1.4% of V̇O2max) while running on a treadmill for 40 min. There were no significant differences in respiratory exchange ratio between any of the dietary manipulations. No significant differences were observed for lactate, V̇O2, or HR during submaximal testing on the N, F, C, and AN diets. These data indicate that a fat supplemented diet did not affect substrate utilization during 40 min of steady-state submaximal exercise when compared to a high carbohydrate diet or the participant’s normal and adjusted normal diets.
Alan J. McCubbin, Anyi Zhu, Stephanie K. Gaskell and Ricardo J.S. Costa
; p = .080) (Figure 3 ). Figure 3 Mean ± SD (bars) and individual participant time to exhaustion at increasing exercise intensity, following 3-hr steady-state running at 60% V ˙ O 2 max . ▪ indicates CES-HGel, □ indicates CES-Std; CES-HGel = carbohydrate-electrolyte solution with sodium alginate
Toshimasa Yanai and James G. Hay
The purpose of this study was to test the hypothesis that, in human running at a given speed, runners select the combination of cycle rate (CR) and cycle length (CL) that minimizes the power generated by the muscles. A 2-D model of a runner consisting of a trunk and two legs was defined. A force actuator controlled the length of each leg, and a torque actuator controlled the amplitude and frequency of the backward and forward swing of each leg. The sum of the powers generated by the actuators was determined for a range of CRs at each of a series of speeds. The CR and CL vs. speed relationships selected for the model were derived from a series of CR and CL combinations that required the least power at each speed. Two constraints were imposed: the maximum amplitude of the forward and backward swing of the legs (±50°) and the minimum ground contact time needed to maintain steady-state running (0.12 sec). The CR vs. speed and CL vs. speed relationships derived on the basis of a minimum power strategy showed a pattern similar to those reported for longitudinal (within-subjects) analyses of human running. The anatomical constraint set a limit on the maximum CL attainable at a given speed, and the temporal constraint made CL decrease at high speeds. It was concluded that the process for selecting CL-CR combinations for human running has characteristics similar to the process for solving a constrained optimization problem.
Lara A. Carlson, Kaylee M. Pobocik, Michael A. Lawrence, Daniel A. Brazeau and Alexander J. Koch
Background: Sleep deprivation negatively affects cognition, pain, mood, metabolism, and immunity, which can reduce athletic performance. Melatonin facilitates sleepiness and may be affected by the proximity of exercise to sleep. Purpose: To evaluate the influence of exercise time of day on salivary melatonin (s-melatonin) responses. Methods: Twelve regularly exercising men (age 20.75 [0.62] y, height 1.75 [0.04] m, mass 73.63 [10.43] kg, and maximal oxygen consumption 57.72 [6.11] mL/kg/min) participated in a randomized, crossover design. Subjects completed 3 protocols—morning exercise (09:00 h), afternoon exercise (16:00 h), and no exercise (CON)—at least 5 d apart. Exercise sessions consisted of 30 min of steady-state running at 75% of maximal oxygen consumption. Saliva was collected via passive drool at 20:00, 22:00, and 03:00 h following all sessions. Results: Repeated-measures analysis of variance revealed significant time (P = .001) and condition (P = .026) effects for melatonin. Levels of s-melatonin were significantly increased at 03:00 h compared with 20:00 and 22:00 h for all conditions. Post hoc analyses revealed that s-melatonin at 22:00 h was significantly higher after morning exercise (16.5 [7.5] pg/mL) compared with afternoon exercise (13.7 [6.1] pg/mL) sessions (P = .03), whereas neither exercise condition significantly differed from the control (P > .05). Conclusions: It appears that exercising in the afternoon may blunt melatonin secretion compared with morning exercise. If sleep is an issue, morning exercise may be preferable to afternoon exercise.
Blaine E. Arney, Reese Glover, Andrea Fusco, Cristina Cortis, Jos J. de Koning, Teun van Erp, Salvador Jaime, Richard P. Mikat, John P. Porcari and Carl Foster
with the BORG-RPE and BORG-CR10, supporting the validity of the BORG-RPE for sRPE. The magnitude of the present correlations during interval training sessions is in agreement with the original data of Foster et al 4 ( r = .65) when comparing mean %HRR and BORG-CR10 during 30-minute steady-state
, 32% fat) to meet energy needs for daily training loads, performed an incremental exercise test to volitional exhaustion (IET). One-week later participants performed an endurance exercise test (EET), comprising 120 mins steady state running at 60% V ˙ O 2max consuming carbohydrates at 90 g/h (2
Andreas Apostolidis, Vassilis Mougios, Ilias Smilios, Johanna Rodosthenous and Marios Hadjicharalambous
). Between jumps, 1 minute of recovery was allowed. Following this first battery of CMJ, SJ, and RT tests, the participants started performing the simulating treadmill soccer-game protocol. 21 This consisted of three 22.5-minute periods, followed by a steady-state running to exhaustion. The treadmill
Stephanie K. Gaskell, Rhiannon M.J. Snipe and Ricardo J.S. Costa
challenge. A formulated carbohydrate supplement gel disk containing 30 g of carbohydrates (2∶1 glucose∶fructose) with accompanying temperate water (10% w/v; 316 mOsmol/kg; ∼20 °C water temperature) was ingested at 0 min and every 20 min, thereafter until the completion of the 2-hr steady-state running
Christian A. Clermont, Lauren C. Benson, W. Brent Edwards, Blayne A. Hettinga and Reed Ferber
data were aligned with the output of the watch via cadence so that the biomechanical output was known at each race distance. The Lumo Run® device only records steady-state running data. Nevertheless, if at any point, the runner’s speed was slower than 1.8 m/s, which is approximately the minimum speed