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The aims of this study were: (i) to measure the exercise intensity (EI) of the most common water-based exercises (WE) at different movement frequencies (f1 = 1.8–2.0 Hz; f2 = 2.0–2.2 Hz; f3 = 2.2–2.4 Hz) and at a standardize movement’s amplitude; (ii) to measure EI during a combination (MIX) of these WE. Five WE were selected: “running raising the knees high” (S); “jumping moving the legs sideways” (SJ); “jumping moving the legs backward and forward” (FJ); “alternate forward kicks” (FK); “alternate sideways kicks” (SK). Twelve physically active women were asked to perform these WE at the three frequencies, as well as a combination (MIX) of the WE. EI increased significantly (p

The aim of this study was to investigate the interplay between the arm stroke efficiency (an index of propelling efficiency, ηP) and the static and dynamic position in water (indexes of hydrodynamic resistance, Wd) in determining the energy cost of front crawl (C) during a swimmer’s growth. These three parameters are indeed related by the following equation: \( C = W_{{\text{d}}} /(\eta _{{\text{P}}} \cdot\eta _{{\text{o}}} ) \) where ηo is the overall efficiency of swimming. The experiments were carried out on 72 swimmers (38 M and 34 F; 8–19 years) who were asked to swim at 1 m s−1. The static position in water was assessed by measuring the underwater torque (T′); the dynamic position in water by measuring the projected frontal area (Aeff). The ratio between the average values of the eldest to youngest class of age was 3.84 and 2.27 for T′, 2.13 and 1.68 for Aeff, and 1.13 and 1.24 for ηP (in M and F, respectively). The increase in T′ and in Aeff was larger than the increase in efficiency suggesting that, in this age range, C should increase, the more so in M than F. Indeed, C increased by 1.58 in male and 1.17 in female swimmers. Based on the values of C and ηP (and assuming a constant value of ηo) it is possible to estimate that, in this age range, Wd increases by about 1.97 in male and 1.32 in female swimmers, an increase which is proportional to the observed increase in Aeff.

Two forces act on a human body motionless in water: weight (W) and buoyancy (B). They are applied to the center of mass (CM) and to the center of volume (CV) of the subject, respectively. CM and CV do not coincide; this generates a torque that is a measure of the tendency of the upper part of the body to rise, rotating around its center of mass. To quantify this tendency, Pendergast & Craig defined ‘underwater torque’ (T1) as the product of the net force with which the feet of a subject lying horizontally in water tend to sink, times the distance between the feet and the center of volume of the lungs. In this paper we have investigated: (a) the relationships between T1 and body weight (BW), height (H), body surface area (BS), body density (BD) and leg density (LD) in a group of 30 subjects (group A, 14 females and 16 males, age range 16-50 years); and (b) the effect of gender and growth on T1 in a group of 110 subjects (group B, 67 girls and 43 boys, age range 12-17 years). In group A, T1 was found to be linearly related with BW (r= 0.833, P < 0.001), H (r= 0.803, P < 0.001), BS (r= 0.866, P < 0.001), BD (r= 0.617, P < 0.001) and LD (r= 0.549, P < 0.005). A multiple linear regression analysis showed that BS and BD explained about 85% of the variability of T1 (r2= 0.85). In group B, T1 was found to increase linearly with age (r= 0.47, P < 0.01), the increasing rate being three times higher in boys compared with girls. As a consequence, the T1 ratio between boys and girls increased with age, from 1.69 at 13 years to 2.04 at 16 years.

At constant average speed (v), a balance between thrust force (Ft) and drag force (Fd) should occur: Ft−Fd = 0; hence the power generated by thrust forces (Pt = Ft·v) should be equal to the power needed to overcome drag forces at that speed (Pd = Fd·v); the aim of this study was to measure Pt (tethered swims), to estimate Pd in active conditions (at sprint speed) and to compare these values. 10 front crawl male elite swimmers (expertise: 93.1 ± 2.4% of 50 m world record) participated to the study; their sprint speed was measured during a 30 m maximal trial. Ft was assessed during a 15 s tethered effort; passive towing measurement were performed to determine speed specific drag in passive conditions (kP = passive drag force/v2); drag force in active conditions (Fd = kA·v2) was calculated assuming that kA = 1.5·kP. Average sprint speed was 2.20 ± 0.07 m·s-1; kA, at this speed, was 37.2 ± 2.7 N·s2·m-2. No significant differences (paired t-test: p > 0.8) were observed between Pt (399 ± 56 W) and Pd (400 ± 57 W) and a strong correlation (R = 0.95, p < 0.001) was observed between these two parameters. The Bland-Altman plot indicated a good agreement and a small, acceptable, error (bias: -0.89 W, limits of agreement: -25.5 and 23.7 W). Power thrust experiments can thus be suggested as a valid tool for estimating a swimmer’s power propulsion.

The aim of this study was to compare movement kinematics, cocontraction times, and metabolic data in expert and nonexpert Tai Chi practitioners. Significant differences were observed for all kinematic parameters: experts moved smoothly (lower jerk) and with a lower frequency. No differences in metabolic and electromyography data were observed but for the breathing pattern (experts breathed slowly and deeply). Movement frequency and breathing pattern are thus the main features that distinguish expert and nonexpert practitioners.

The aim of this study was to compare experts to naive practitioners in rating the beauty and the technical quality of a Tai Chi sequence observed in video-clips (of high and middle level performances). Our hypothesis are: i) movement evaluation will correlate with the level of skill expressed in the kinematics of the observed action but ii) only experts will be able to unravel the technical component from the aesthetic component of the observed action. The judgments delivered indicate that both expert and non-expert observers are able to discern a good from a mediocre performance; however, as expected, only experts discriminate the technical from the aesthetic component of the action evaluated and do this independently of the level of skill shown by the model (high or middle level performances). Furthermore, the judgments delivered were strongly related to the kinematic variables measured in the observed model, indicating that observers rely on specific movement kinematics (e.g. movement amplitude, jerk and duration) for action evaluation. These results provide evidence of the complementary functional role of visual and motor action representation in movement evaluation and underline the role of expertise in judging the aesthetic quality of movements.

The aim of this study was: i) to compute an energy balance of a slalom kayak competition by measuring the percentage contributions of the aerobic and anaerobic energy sources to total metabolic power (E(tot)); and ii) to compare these data with those obtained, on the same subjects, over a flat-water course covered at maximal speed in a comparable time. Experiments were performed on eight middle- to high-class slalom kayakers (24.8 +/- 8.1 years of age, 1.75 +/- 0.04 m of stature, and 69.8 +/- 4.7 kg of body mass) who completed the slalom race in 85.8 +/- 5.3 s and covered the flat water course in 88.1 +/- 7.7 s. E(tot) was calculated from measures of oxygen consumption and of blood lactate concentration: it was about 30 % larger during the flat water all-out test (1.72 +/- 0.18 kW) than during the slalom race (1.35 +/- 0.12 kW). However, in both cases, about 50 % of E(tot) derives from aerobic and about 50 % from anaerobic energy sources. These data suggest that, besides training for skill acquisition and for improving anaerobic power, some high intensity, cardiovascular conditioning should be inserted in the training programs of the athletes specialised in this sport.

The propelling efficiency of the arm stroke (etaP) was estimated in a group of 63 male and female subjects (9-59 years of age) of good technical skill, swimming the front crawl at sub-maximal speeds. etaP was calculated on the basis of values of speed (v), stroke frequency (SF) and shoulder-to-hand distance (l, calculated from measures of arm length and elbow angle during the in-sweep) as proposed by Zamparo et al. (Eur J Appl Physiol 94:134-144, 2005). In both genders, the distance covered per stroke (Ds = v/SF) is similar before puberty, reaches its maximum at about 20 years of age and then steadily declines. l is significantly larger in males than in females and this difference tended to offset the differences in Ds so that etaP is almost the same in male and female swimmers of the same age group and swimming ability: about 0.31 before puberty, 0.38-0.40 at about 20 years of age and about 0.25 in swimmers older than 40 years of age. The development of etaP and Ds during the life span is similar to the changes in muscle strength and power reported in the literature suggesting that these parameters are related to the ability to exert forceful (and hence effective) strokes in water. Since the energy cost of swimming (C) depends essentially on etaP and the hydrodynamic resistance (Wd), these data further suggest that differences in C between genders are mainly to be attributed to differences in Wd, whereas differences across ages can be attributed also to changes in etaP.

The aim of this study was to investigate how fins with varying physical characteristics affect the energy cost and the efficiency of aquatic locomotion. Experiments were performed on ten college swimmers who were asked to swim the dolphin kick while using a monofin (MF) and to swim the front crawl kick with a small-flexible fin (SF), a large-stiff fin (LS) and without fins (BF, barefoot). The energy expended to cover one unit distance (C) was highest for BF (C=10.6+/-1.8 kJ m(-1) kg(-1) at 0.8 m s(-1)) and decreased by about 50% with LS, 55% with SF and 60% with MF, allowing for an increase in speed (for a given metabolic power) of about 0.4 m s(-1) for MF and of about 0.2 m s(-1) for SF and LS (compared with BF). At any given speed, the fins for which C was lower were those with the lowest kick frequency (KF): KF=1.6+/-0.22 Hz at 0.8 m s(-1) (for BF) and decreased by about 40% for SF, 50% for LS and 60% for MF. The decrease in KF from BF to SF-LS and MF was essentially due to the increasing surface area of the fin which, in turn, was associated with a higher Froude efficiency (eta(F)). eta(F) was calculated by computing the speed of the bending waves moving along the body in a caudal direction (as proposed for the undulating movements of slender fish): it increased from 0.62+/-0.01 in BF to 0.66+/-0.03 in SF and 0.67+/-0.04 in LS reaching the highest values (0.76+/-0.05) with MF. No single fin characteristic can predict a swimmer's performance, rather the better fin (i.e. MF) is the one that is able to reduce most KF at any given speed and hence to produce the greatest distance per kick (d=v/KF). The latter indeed increased from 0.50+/-0.01 m in BF to about 0.90+/-0.05 m in SF and LS and reached values of 1.22+/-0.01 m in MF.

The aim of this study was: (1) to assess the energy cost of swimming (C(s), kJ km(-1)) in a group of male (n = 5) and female (n = 5) elite swimmers specialised in long-distance competitions; (2) to evaluate the possible effect of a 2-km trial on the absolute value of C(s). C(s) was assessed during three consecutive 400-m trials covered in a 50-m pool at increasing speeds (v1, v2, v3). After these experiments the subjects swam a 2-km trial at the 10-km race speed (v2km) after which the three 400-m trials were repeated at the same speed as before (v5 = v1, v6 = v2, v7 = v3). C(s) was calculated by dividing the net oxygen uptake at steady state VO2ss by the corresponding average speed (v, m s(-1)). VO2ss was estimated by using back extrapolation technique from breath-to-breath VO2 recorded during the first 30 s of recovery after each test. C(s) increased (from 0.69 kJ m(-1) to 1.27 kJ m(-1)) as a function of v (from 1.29 m s(-1) to 1.50 m s(-1)), its values being comparable to those measured in elite short distance swimmers at similar speeds. In both groups of subjects the speed maintained during the 2-km trial (v2km) was on the average only 1.2% faster than of v2 and v6 (P>0.05), whereas C(s) assessed at the end of the 2-km trial (v2km) turned out to be 21 +/- 26% larger than that assessed at v2 and v6 (P

Water covers over 70% of the earth, has varying depths and temperatures and contains much of the earth's resources. Head-out water immersion (HOWI) or submersion at various depths (diving) in water of thermoneutral (TN) temperature elicits profound cardiorespiratory, endocrine, and renal responses. The translocation of blood into the thorax and elevation of plasma volume by autotransfusion of fluid from cells to the vascular compartment lead to increased cardiac stroke volume and output and there is a hyperperfusion of some tissues. Pulmonary artery and capillary hydrostatic pressures increase causing a decline in vital capacity with the potential for pulmonary edema. Atrial stretch and increased arterial pressure cause reflex autonomic responses which result in endocrine changes that return plasma volume and arterial pressure to preimmersion levels. Plasma volume is regulated via a reflex diuresis and natriuresis. Hydrostatic pressure also leads to elastic loading of the chest, increasing work of breathing, energy cost, and thus blood flow to respiratory muscles. Decreases in water temperature in HOWI do not affect the cardiac output compared to TN; however, they influence heart rate and the distribution of muscle and fat blood flow. The reduced muscle blood flow results in a reduced maximal oxygen consumption. The properties of water determine the mechanical load and the physiological responses during exercise in water (e.g. swimming and water based activities). Increased hydrostatic pressure caused by submersion does not affect stroke volume; however, progressive bradycardia decreases cardiac output. During submersion, compressed gas must be breathed which introduces the potential for oxygen toxicity, narcosis due to nitrogen, and tissue and vascular gas bubbles during decompression and after may cause pain in joints and the nervous system.

The energy cost of shuttle running (C netSR), over distances of 10–20 m, was reported to increase with the shuttle speed and to decrease with the shuttle distance. The aims of this study were to assess C netSR over a shorter distance (5 m), at different speeds, and to estimate the energy cost based on a simple kinematic analysis (C netK). Ten subjects (six basketball players, BP; four non-basketball players, NBP) performed ten shuttle runs (SR) with 30 s of passive recovery in-between, over a distance of 5 + 5 m (with a 180° change of direction); these experiments were repeated at different speeds (range 2–3.5 m s−1). The values of average (v mean) and maximal (v max) speed during each run were determined by means of kinematic analysis and C netK was calculated as: 0.96 $$v_{ \hbox{max} }^{ 2}$$ . C netSR was calculated based on data of oxygen uptake, blood lactate concentration and distance covered. The relationships between C (J m−1 kg−1) and v (m.s−1) are well described by C netK (all subjects) = 11.76v − 13.09, R 2 = 0.853; C netSR (BP) = 11.94v − 12.82, R 2 = 0.636; and C netSR (NBP) = 14.09v − 14.53, R 2 = 0.738. Hence C netSR ≈ C netK in BP, whereas C netSR > C netK in NBP (un-familiar with this specific motor task). The calculations proposed in this study allow to estimate C of short SR based on simple measures of v max and can be utilized to develop training protocols in basketball as well as in other team sports (characterized by repeated sprints over short distances).

The purpose of this study was to use the planimetric method to determine frontal area (Ap) throughout the stroke cycle in the four swimming strokes as well as during “streamlined leg kicking”. The minimum Ap values in all strokes are similar to those assessed during “streamlined leg kicking” (about 0.13 m2). Active drag (Da = 1/2 ρ Cd Ap v2) was then calculated/estimated based on the average Ap values, as calculated for a full cycle in each condition. Da is the lowest in the “streamlined leg kicking” condition (Da = 19.5v2, e.g., similar to the values of passive drag reported in the literature), is similar in front crawl (Da = 30.0v2), backstroke (Da = 26.9v2) and butterfly (Da = 28.5v2) and is the largest in the breaststroke (Da = 37.5v2). Based on the C vs. v relationships reported in the literature for the four strokes it is then possible to estimate drag efficiency: for a speed of 1.5 m s−1, it ranges from 0.035–0.038 (breaststroke and backstroke, respectively) to 0.052–0.058 (butterfly and front crawl, respectively). This study is the first to establish Ap values throughout the swimming cycle for all swimming strokes and these findings have implications for active drag estimates, for the energetics of swimming and for swimming speed.

In the last decade, great attention has been given to the improvements in swimming performance that can be obtained by wearing �technical swimsuits�; the technological evolution of these materials only marginally involved swim caps production, even if several studies have pointed out the important role of the head (as main impact point with the fluid) on hydrodynamics. The aim of this study was to compare the effects on passive drag (Dp) of 3 swim cap models: a smooth silicon helmet cap (usually used during swimming competitions), a silicon helmet cap with �dimples,� and a silicon helmet cap with �wrinkles.� Experiments were performed on 10 swimmers who were towed underwater (at a depth of 60 cm) at 3 speeds (1.5, 1.7, and 1.9 m·s-1) and in 2 body positions: LA (arms above the swimmer's head) and SA (arms alongside the body). The Dp values obtained in each trial were divided by the square of the corresponding speed to obtain the speed-specific drag (the k coefficient = Dp/v2). No differences in k were observed among swim caps in the LA position. No differences in k were observed between the smooth and dimpled helmets also in the SA position; however, the wrinkled swim cap helmet showed a significant larger k (4.4%) in comparison with the model with dimples, when the swimmers kept their arms alongside the body (in the SA position). These data suggest that wearing a wrinkled swim cap helmet can be detrimental to performance at least in this specific position.

Fluctuations in mechanical energy of the body center of mass (COM) have been widely analyzed when investigating different gaits in human and animal locomotion. We applied this approach to estimate the mechanical work in cross-country skiing and to identify the fundamental mechanisms of this particular form of locomotion. We acquired movements of body segments, skis, poles and plantar pressures for eight skiers while they roller skied on a treadmill at 14 km h−1 and a 2 deg slope using three different techniques (diagonal stride, DS; double poling, DP; double poling with kick, DK). The work associated with kinetic energy (KE) changes of COM was not different between techniques; the work against gravity associated with potential energy (PE) changes was higher for DP than for DK and was lowest for DS. Mechanical work against the external environment was 0.87 J m−1 kg−1 for DS, 0.70 J m−1 kg−1 for DP and 0.79 J m−1 kg−1 for DK. The work done to overcome frictional forces, which is negligible in walking and running, was 17.8%, 32.3% and 24.8% of external mechanical work for DS, DP and DK, respectively. The pendulum-like recovery (R%) between PE and KE was ~45%, ~26% and ~9% for DP, DK and DS, respectively, but energy losses by friction are not accounted for in this computation. The pattern of fluctuations of PE and KE indicates that DS can be described as a ‘grounded running’, where aerial phases are substituted by ski gliding phases, DP can be described as a pendular gait, whereas DK is a combination of both.

Shuttle runs can be used to study the physiological responses in sports (such as basketball) characterized by sprints (accelerations/decelerations) and changes of direction.Purpose:To determine the energy cost (C) of shuttle runs with different turning angles and over different distances (with different acceleration/deceleration patterns).Methods:Nine basketball players were asked to complete 6 intermittent tests over different distances (5, 10, 25 m) and with different changes of direction (180° at 5 and 25 m; 0°, 45°, 90°, and 180° at 10 m) at maximal speed (v ≍ 4.5 m/s), each composed by 10 shuttle runs of 10-s duration and 30-s recovery; during these runs oxygen uptake (VO2), blood lactate (Lab), and C were determined.Results:For a given shuttle distance (10 m) no major differences where observed in VO2 (~33 mL · min−1 · kg−1), Lab (~3.75 mM), and C (~21.2 J · m−1 · kg−1) when the shuttle runs were performed with different turning angles. For a given turning angle (180°), VO2 and Lab were found to increase with the distance covered (VO2 from 26 to 35 mL · min−1 · kg−1; Lab from 0.7 to 7.6 mM) while C was found to decrease with it (from 29.9 to 10.6 J · m−1 · kg−1); the relationship between C and d (m) is well described by C = 92.99 × d0.656, R2 = .971.Conclusions:The metabolic demands of shuttle tests run at maximal speeds can be estimated based on the running distance, while the turning angle plays a minor role in determining C.

The aim of this study was to test the hypothesis that the self selected speed in running (vss) is dependent upon the same factors that determine maximal speed in endurance events (e. g. the anaerobic threshold). Experiments were carried out on 8 recreational long distance runners (42.1 +/- 8.6 years of age, 70.1 +/- 10.6 kg of body mass, 1.74 +/- 0.06 m of body height) while they were participating in a 14 day relay race. During the "race" the subjects were not requested to perform maximally but only to cover their running turn (1 hour per day) at their preferred pace. The relationships between heart rate (HR), perceived exertion (RPE), blood lactate concentration ([La]b) and speed (v) were determined in each subject, before the race, during an incremental running test. From these relationships the speed corresponding to a 4 mM concentration of lactate in blood (v4mM) was calculated and found to be 14.3 +/- 1.8 km x h(-1) (n = 8). At this speed the RPE and HR values were 13.6 +/- 1.4 and 156.4 +/- 12.8 bpm, respectively. The average values of speed (vss, 13.4 +/- 0.6 km x h(-1)), RPE (13.5 +/- 1.4) and HR (154.4 +/- 7.6 bpm) measured during the race (n = 47) were not significantly different from those measured at the lactate threshold (v4mM, RPE4mM and v4mM). However, vss and the average HR during the race showed significantly lower variances than v4mM and HR4mM suggesting that, besides the need of avoiding lactate accumulation in blood, other factors must be involved in the choice of speed in running.

We measured the metabolic cost (C) and mechanical work of riding historic bicycles at different speeds: these bicycles included the Hobby Horse (1820s), the Boneshaker (1860s), the High Wheeler (1870s), the Rover (1880s), the Safety (1890s) and a modern bicycle (1980s) as a mean of comparison. The rolling resistance and air resistance of each vehicle were assessed. The mechanical internal work (W(INT)) was measured from three-dimensional motion analysis of the Hobby Horse and modern bicycle moving on a treadmill at different speeds. The equation obtained from the modern bicycle data was applied to the other vehicles. We found the following results. (i) Apart from the Rover, which was introduced for safety reasons, every newly invented bicycle improved metabolic economy. (ii) The rolling resistance decreased with subsequent designs while the frontal area and, hence, aerodynamic drag was fairly constant (except for the High Wheeler). (iii) The saddle-assisted body weight relief (which was inaugurated by the Hobby Horse) was responsible for most of the reduction in metabolic cost compared with walking or running. Further reductions in C were due to decreases in stride/pedalling frequency and, hence, W(INT) at the same speeds. (iv) The introduction of gear ratios allowed the use of pedalling frequencies that optimize the power/contraction velocity properties of the propulsive muscles. As a consequence, net mechanical efficiency (the ratio between the total mechanical work and C) was almost constant (0.273 +/- 0.015s.d.) for all bicycle designs, despite the increase in cruising speed. In the period from 1820 to 1890, improved design of bicycles increased the metabolically equivalent speed by threefold compared with walking at an average pace of ca. + 0.5 ms(-1) per decade [corrected]. The speed gain was the result of concurrent technological advancements in wheeled, human-powered vehicles and of 'smart' adaptation of the same actuator (the muscle) to different operational conditions.

The energy cost of front-crawl swimming (Cs, kJ · m−1) at maximal voluntary speeds over distances of 50, 100, 200 and 400 m, and the underwater torque (T′) were assessed in nine young swimmers (three males and six females; 12–17 years old). Cs was calculated from the ratio of the total metabolic energy (E s, kJ) spent to the distance covered. E s was estimated as the sum of the energy derived from alactic (AnAl), lactic (AnL) and aerobic (Aer) processes. In turn, AnL was obtained from the net increase of lactate concentration after exercise, AnAl was assumed to amount to 0.393 kJ · kg−1 of body mass, and Aer was estimated from the maximal aerobic power of the subject. Maximal oxygen consumption was calculated by means of the back-extrapolation technique from the oxygen consumption kinetics recorded during recovery after a 400-m maximal trial. Underwater torque (T′, N · m), defined as the product of the force with which the feet of a subject lying horizontally in water tends to sink times the distance from the feet to the center of volume of the lungs, was determined by means of an underwater balance. Cs (kJ · m−1) turned out to be a continuous function of the speed (v, m · s−1) in both males (Cs=0.603 · 100.228 v , r 2=0.991; n=12) and females (Cs=0.360 · 100.339 v , r 2=0.919; n=24). A significant relationship was found between T′ and Cs at 1.2 m · s−1; Cs=0.042T′ + 0.594, r=0.839, n=10, P

Hydrodynamics plays an important role in swimming because even small decreases in a swimmer’s drag can lead to performance improvements. During the gliding phases of a race, the head of a swimmer is an important point of impact with the fluid, and the swim cap, even if it covers only a small portion of the swimmer’s body, can have an influence on drag. The purpose of this study was to investigate the effects on passive drag (Dp) of wearing 3 different types of swim caps (LSC: a lycra cap; CSC: a silicone cap; HSC: a silicone helmet cap without seams). Sixteen swimmers were tested at 3 velocities (1.5, 1.7, 1.9 m/s), and the Dp measurements were repeated at each condition 5 times. A statistical analysis revealed significant differences in drag (p , 0.01) among caps: Dp is 5–6.5% lower for HSC than for CSC at all speeds and 6% lower in HSC than CSC at 1.9 m/s. No differences in Dp were observed between LSC and CSC at all speeds. Thus, the differences in Dp are based on the type of material (lycra vs. silicone) and on the presence/ lack of seams: the HSC swim cap is the most rigid, the most adherent to the swimmer’s head, and does not allow the formation of wrinkles compared with the other 2 investigated swim caps. Therefore, the following conclusions can be made: (a) swimmers should take care when selecting their swim cap if they want to improve the fluid dynamics at the “leading edge” of their body and (b) because Dp is affected by the swim cap model, care should be taken when comparing data from different studies, especially at faster investigated speeds.

For cyclic sports activities (on land and in water) the physical activity energy expenditure (PAEE) can be accurately measured when the energy cost (C) of that form of locomotion is known. C depends on the speed and the mode of locomotion and is calculated by the ratio E′/v where E′ is metabolic power (derived from aerobic and anaerobic energy sources) and v is the progression speed. C of a given form of locomotion, and at a given speed, depends also on the factors that affect mechanical work (W) and locomotion efficiency (ηL) In this chapter the determinants of C in land locomotion are presented and discussed with a particular focus on walking, running and cycling.

To estimate the anaerobic alactic contribution in a 200 m middle distance swimming trial by means of two different methods based: (1) on the fast component of the VO2 off-kinetics (Ana recovery) and (2) on the kinetics of maximal phosphocreatine splitting in the contracting muscle (Ana pcr). Ten elite male swimmers performed a 200 m front crawl trial at maximal velocity during which VO2 was directly measured using a telemetric portable gas analyser; during the recovery period VO2 data were collected until baseline values were reached. No significant differences between the two methods were observed; mean ± SD values were 31.7 ± 2.5 and 32.6 ± 2.8 kJ, for Ana pcr and Ana recovery, respectively. Despite the existence of some caveats regarding both methods for estimation of the anaerobic alactic contribution, data reported in this study indicate that both yield similar results and both allow to estimate this contribution in supra-maximal swimming trials. This has important implications on swimming energetics, since the non-inclusion of the anaerobic alactic contribution to total metabolic energy expenditure leads to an underestimation of the energy cost at supra-maximal speeds.

In this chapter the determinants of C (the energy cost of locomotion) in aquatic sport activities are presented and discussed with a particular focus on swimming (with different strokes) and on boat locomotion (e.g., rowing and kayaking). By contrast with locomotion on land, a large proportion of the power provided by the muscles is necessarily wasted to transfer water kinetic energy that is not useful for propulsion. Thus, the concept of propelling efficiency (the ratio of power output useful for propulsion to total power output) is fundamental to understanding these modes of locomotion. Furthermore, even if the literature provides all data needed to calculate C in this environment, it is rather difficult to estimate physical activity energy expenditure (PAEE) of aquatic sport activities, because of the multiplicity of factors that affect energy expenditure in water (e.g., mode of locomotion, speed, gender, age and technical skill).

Human performances in sports decline with age in all competitions/disciplines. Since the effects of age are often compounded by disuse, the study of master athletes provides the opportunity to investigate the effects of age per se on the metabolic/biomechanical determinants of performance. For all master age groups, swimming styles and distances, we calculated the metabolic power required to cover the distance (d) in the best performance time as: $$ E _{\text{maxR}}^{\prime } = C \times d/{\text{BTP}} = C \times v_{\max } , $$ where C is the energy cost of swimming in young elite swimmers, v max = d/BTP is the record speed over the distance d, and BTP was obtained form “cross-sectional data” ( http://www.fina.org ). To establish a record performance, $$ E_{\text{maxR}}^{\prime } $$ must be equal to the maximal available metabolic power $$ (E_{\text{maxA}}^{\prime } ) $$ . This was calculated assuming a decrease of 1% per year at 40–70 years, 2% at 70–80 years and 3% at 80–90 years (as indicated in the literature) and compared to the $$ E_{\text{maxR}}^{\prime } $$ values, whereas up to about 55 years of age $$ E_{\text{maxR}}^{\prime } = E_{\text{maxA}}^{\prime } ,$$ for older subjects $$ E_{\text{maxA}}^{\prime } > E_{\text{maxR}}^{\prime } ,$$ the difference increasing linearly by about 0.30% (backstroke), 1.93% (butterfly), 0.92% (front crawl) and 0.37% (breaststroke) per year (average over the 50, 100 and 200 m distances). These data suggest that the energy cost of swimming increases with age. Hence, the decrease in performance in master swimmers is due to both decrease in the metabolic power available $$ (E_{\text{maxA}}^{\prime } ) $$ and to an increase in C.

The aim of this study was to compare different measurement techniques (indirect calorimetry, IC; heart rate monitoring, HR; an activity monitoring system, AH; rates of perceived exertion, RPE) to estimate physical activity intensity (light, moderate, vigorous) during water-based aerobic exercises (WE). Twelve healthy young women performed five common WE of 10-min duration at three frequencies in an indoor swimming pool. Data recorded from the 5th to 9th minute of exercise were averaged to obtain mean [Formula: see text] (IC), HR and AH values; RPE was recorded at the end of each WE. Oxygen uptake was also estimated from HR data using three different [Formula: see text] versus HR regression equation models. Significant correlations (p 0.001) were found for the indirect methods that used HR, RPE and AH data regressed as a function of [Formula: see text] (IC); the highest correlations were found between the measured values of [Formula: see text] (IC) and those estimated from the three [Formula: see text] versus HR equations (R 0.7 in all cases). An ANOVA test showed no significant differences between all predicted and measured [Formula: see text] values; however, when the BlandAltman analysis was considered, AH data showed the larger explained variances (95% CI) and the larger standard errors. These data indicate that the most accurate way to estimate physical activity intensity during WE is based on HR measurements.

The purpose of this study was to set up a protocol of intermittent exercise to train young basketball players. Twenty-one players were asked to complete (a) an incremental test to determine maximal oxygen uptake (VO2max), the speed at the ventilatory threshold (vthr) and the energy cost of "linear" running (Cr) and (b) an intermittent test composed of 10 shuttle runs of 10-second duration and 30-seconds of recovery (total duration: about 6 minutes). The exercise intensity (the running speed, vi) was set at 130% of vthr. During the intermittent tests, oxygen uptake (VO2) and blood lactate concentration (Lab) were measured. The average pretraining VO2 calculated for a single bout (131 ± 9 ml · min(-1) kg(-1)) was about 2.4 times greater than the subjects' measured VO2max (54.7 ± 4.6 ml · min(-1) · kg(-1)). The net energy cost of running (9.2 ± 0.9 J · m(-1) · kg(-1)) was about 2.4 times higher than that measured at constant "linear" speed (3.9 ± 0.3 J · m(-1) · kg(-1)). The intermittent test was repeated after 7 weeks of training: 9 subjects (control group [CG]) maintained their traditional training schedule, whereas for 12 subjects (experimental group [EG]) part of the training was replaced by intermittent exercise (the same shuttle test as described above). After training, the VO2 measured during the intermittent test was significantly reduced (p < 0.05) in both groups (-10.9% in EG and - 4.6 in CG %), whereas Lab decreased significantly only for EG (-31.5%). These data suggest that this training protocol is effective in reducing lactate accumulation in young basketball players.

The purpose of the present study was to determine the relative contribution of the aerobic (Aer), anaerobic lactic (AnL) and alactic (AnAl) energy sources during each of the four laps of a 200 m front crawl race. Additionally, energy cost (C) and arm stroke efficiency were also computed. Ten international swimmers performed a 200 m front crawl swim, as well as 50, 100, and 150 m at the 200 m pace. Oxygen consumption was measured during the 200 m swim and blood samples were collected before and after each swim; the C of swimming was calculated as the ratio of E tot to distance (where E tot = Aer + AnL + AnAl). Arm stroke efficiency was calculated by kinematic analysis as the speed of center of mass to the ratio of 3D hand speed. For the 200 m the contributions were 65.9% (Aer), 13.6% (AnL), and 20.4% (AnAl) whereas for each lap they were 44.6, 73.2, 83.3 and 66.6% (Aer), 14.1, 5.0, 4.4 and 28.1% (AnL) and 41.3, 21.8, 12.3 and 5.2% (AnAl) for the four laps, respectively. For the 200 m as a whole C was 1.60 kJ m−1 whereas C = 1.71, 1.56, 1.44 and 1.70 kJ m−1 for each consecutive lap, respectively. Arm stroke efficiency ranged from 0.40 to 0.43 and was significantly lower in the last lap as compared to the first (P = 0.002), suggesting the occurrence of fatigue. The decrease in arm stroke efficiency was mirrored by an increase in C as can be expected on theoretical grounds.

The mechanical power (Wtot, W·kg−1) developed during ten revolutions of all-out periods of cycle ergometer exercise (4–9 s) was measured every 5–6 min in six subjects from rest or from a baseline of constant aerobic exercise [50%–80% of maximal oxygen uptake (VO2max)] of 20–40 min duration. The oxygen uptake [VO2 (W·kg−1, 1 ml O2 = 20.9 J)] and venous blood lactate concentration ([la]b, mM) were also measured every 15 s and 2 min, respectively. During the first all-out period, Wtot decreased linearly with the intensity of the priming exercise (Wtot = 11.9−0.25·VO2). After the first all-out period (i greater than 5–6 min), and if the exercise intensity was less than 60% VO2max, Wtot, VO2 and [la]b remained constant until the end of the exercise. For exercise intensities greater than 60% VO2max, VO2 and [la]b showed continuous upward drifts and Wtot continued decreasing. Under these conditions, the rate of decrease of Wtot was linearly related to the rate of increase of V [(d Wtot/dt) (W·kg−1·s−1) = 5.0·10−5 −0.20·(d VO2/dt) (W·kg−1·s−1)] and this was linearly related to the rate of increase of [la]b [(d VO2/dt) (W·kg−1·s−1) = 2.310−4 + 5.910−5·(d [la]b/dt) (mM·s−1)]. These findings would suggest that the decrease of Wtot during the first all-out period was due to the decay of phosphocreatine concentration in the exercising muscles occurring at the onset of exercise and the slow drifts of VO2 (upwards) and of Wtot (downwards) during intense exercise at constant Wtot could be attributed to the continuous accumulation of lactate in the blood (and in the working muscles).

The energy cost to swim a unit distance (C sw) is given by the ratio $$ \dot{E}/v $$ where $$ \dot{E} $$ is the net metabolic power and v is the swimming speed. The contribution of the aerobic and anaerobic energy sources to $$ \dot{E} $$ in swimming competitions is independent of swimming style, gender or skill and depends essentially upon the duration of the exercise. C sw is essentially determined by the hydrodynamic resistance (W d): the higher W d the higher C sw; and by the propelling efficiency (η P): the higher η P the lower C sw. Hence, all factors influencing W d and/or η P result in proportional changes in C sw. Maximal metabolic power $$ (\dot{E}_{\max } ) $$ and C sw are the main determinants of swimming performance; an improvement in a subject’s best performance time can more easily be obtained by a reduction of C sw rather than by an (equal) increase in $$ \dot{E}_{\max } $$ (in either of its components, aerobic or anaerobic). These sentences, which constitute a significant contribution to today’s knowledge about swimming energetics, are based on the studies that Professor Pietro Enrico di Prampero and his co-workers carried out since the 1970s. This paper is devoted to examine how this body of work helped to improve our understanding of this fascinating mode of locomotion.

The aim of this study was to investigate the role of trunk incline (TI) and projected frontal area (A(eff)) in determining drag during active/passive measurements. Active drag (D(a)) was measured in competitive swimmers at speeds from 0.6 to 1.4 m s(-1); speed specific drag (D(a)/v(2)) was found to decrease as a function of v (P < 0.001) to indicate that the human body becomes more streamlined with increasing speed. Indeed, both A(eff) and TI were found to decrease with v (P < 0.001) whereas C(d) (the drag coefficient) was found to be unaffected by v. These data suggest that speed specific drag depend essentially on A(eff). Additional data indicate that A(eff) is larger during front crawl swimming than during passive towing (0.4 vs. 0.24 m(2)). This suggest that D(a)/v(2) is larger than D(p)/v(2) and, at a given speed, that D(a) is larger than D(p).

In this study, we examined the mechanics and energetics of locomotion with a paddle-wheel boat and a water bike. Power output (Wtot) was measured directly on the water bike by means of an instrumented chain-ring. The simultaneous assessment of oxygen uptake (VO2) allowed the computation of the "overall" efficiency of locomotion (etao = Wtot/VO2). Mean etao was 0.27 (s = 0.02), which was unaffected by the speed, and was assumed to be the same for the two boats as both are semi-recumbent bicycles. For the paddle-wheel boat, Wtot was then obtained from etao and measures of VO2. The power to overcome (passive) drag was calculated as Wd = D x v (where D is the force measured by means of a load cell when towing the boats at given speeds). Propelling efficiency was calculated as etap = Wd/Wtot, which was lower with the paddle-wheel boat (mean 0.35, s = 0.01) than with the water bike (mean 0.57, s = 0.01). The observed differences in etap and Wd explain why at the highest speed tested (approximately 3 m s(-1), the energy required to cover a unit distance with the water bike is similar to that required to move the paddle-wheel boat at 1.3 m s-1).

When dealing with human-powered vehicles, it is important to quantify the capability of converting metabolic energy in useful mechanical work by measuring mechanical efficiency. In this study, net mechanical efficiency (eta) of riding a recumbent bicycle on flat terrain and at constant speeds (v, 5.1-10.0 m/s) was calculated dividing mechanical work (w, J/m) by the corresponding energy cost (C(c), J/m). w and C(c) increased linearly with the speed squared: w = 9.41 + 0.156 . v(2); C(c) = 39.40 + 0.563 . v(2). eta was equal to 0.257 +/- 0.0245, i.e. identical to that of concentric muscular contraction. Hence, i) eta seems unaffected by the biomechanical arrangement of the human-vehicle system; ii) the efficiency of transmission seems to be close to 100%, suggesting that the particular biomechanical arrangement does not impair the transformation of metabolic energy in mechanical work. When dealing with human-powered vehicles, it is important to quantify mechanical efficiency (eta) of locomotion. eta of riding a recumbent bicycle was calculated dividing the mechanical work to the corresponding energy cost of locomotion; it was practically identical to that of concentric muscular contraction (0.257 +/- 0.0245), suggesting that the power transmission from muscles to pedals is unaffected by the biomechanical arrangement of the vehicle.

In supra-maximal exercise to exhaustion, the critical velocity (cv) is conventionally calculated from the slope of the distance (d) versus time (t) relationship: d = I + St. I is assumed to be the distance covered at the expense of the anaerobic capacity, S the speed maintained on the basis of the subject’s maximal O2 uptake $$(\dot{V}\hbox{O}_{\rm 2max}).$$ This approach is based on two assumptions: (1) the energy cost of locomotion per unit distance (C) is constant and (2) $$\dot{V}\hbox{O}_{2\rm{max}}$$ is attained at the onset of exercise. Here we show that cv and the anaerobic distance (d anaer) can be calculated also in swimming, where C increases with the velocity, provided that $$\dot{V}\hbox{O}_{2\rm{max}},$$ its on-response, and the C versus v relationship are known. d anaer and cv were calculated from published data on maximal swims for the four strokes over 45.7, 91.4 and 182.9 m, on 20 elite male swimmers (18.9 ± 0.9 years, 75.9 ± 6.4 kg), whose $${\dot{V}}\hbox{O}_{2\rm{max}}$$ and C versus speed relationship were determined, and compared to I and S obtained from the conventional approach. cv was lower than S (4, 16, 7 and 11% in butterfly, backstroke, breaststroke and front crawl) and I (=11.6 m on average in the four strokes) was lower than d anaer. The latter increased with the distance: average, for all strokes: 38.1, 60.6 and 81.3 m over 45.7, 91.4 and 182.9 m. It is concluded that the d versus t relationship should be utilised with some caution when evaluating performance in swimmers.

With the aim of computing a complete energy balance of front crawl, the energy cost per unit distance (C = Ev(-1), where E is the metabolic power and v is the speed) and the overall efficiency (eta(o) = W(tot)/C, where W(tot) is the mechanical work per unit distance) were calculated for subjects swimming with and without fins. In aquatic locomotion W(tot) is given by the sum of: (1) W(int), the internal work, which was calculated from video analysis, (2) W(d), the work to overcome hydrodynamic resistance, which was calculated from measures of active drag, and (3) W(k), calculated from measures of Froude efficiency (eta(F)). In turn, eta(F) = W(d)/(W(d) + W(k)) and was calculated by modelling the arm movement as that of a paddle wheel. When swimming at speeds from 1.0 to 1.4 m s(-1), eta(F) is about 0.5, power to overcome water resistance (active body drag x v) and power to give water kinetic energy increase from 50 to 100 W, and internal mechanical power from 10 to 30 W. In the same range of speeds E increases from 600 to 1,200 W and C from 600 to 800 J m(-1). The use of fins decreases total mechanical power and C by the same amount (10-15%) so that eta(o) (overall efficiency) is the same when swimming with or without fins [0.20 (0.03)]. The values of eta(o) are higher than previously reported for the front crawl, essentially because of the larger values of W(tot) calculated in this study. This is so because the contribution of W(int) to W(tot )was taken into account, and because eta(F) was computed by also taking into account the contribution of the legs to forward propulsion.

In this paper a complete energy balance for water locomotion is attempted with the aim of comparing different modes of transport in the aquatic environment (swimming underwater with SCUBA diving equipment, swimming at the surface: leg kicking and front crawl, kayaking and rowing). On the basis of the values of metabolic power (E), of the power needed to overcome water resistance (Wd) and of propelling efficiency (etaP=Wd/Wtot, where Wtot is the total mechanical power) as reported in the literature for each of these forms of locomotion, the energy cost per unit distance (C=E/v, where v is the velocity), the drag (performance) efficiency (etad=Wd/E) and the overall efficiency (etao=Wtot/E=etad/etaP) were calculated. As previously found for human locomotion on land, for a given metabolic power (e.g. 0.5 kW=1.43 l.min(-1) VO2) the decrease in C (from 0.88 kJ.m(-1) in SCUBA diving to 0.22 kJ.m(-1) in rowing) is associated with an increase in the speed of locomotion (from 0.6 m.s(-1) in SCUBA diving to 2.4 m.s(-1) in rowing). At variance with locomotion on land, however, the decrease in C is associated with an increase, rather than a decrease, of the total mechanical work per unit distance (Wtot, kJ.m(-1)). This is made possible by the increase of the overall efficiency of locomotion (etao=Wtot/E=Wtot/C) from the slow speeds (and loads) of swimming to the high speeds (and loads) attainable with hulls and boats (from 0.10 in SCUBA diving to 0.29 in rowing).

A novel apparatus, composed by a controllable treadmill, a computer, and an ultrasonic range finder, is here proposed to help investigation of many aspects of spontaneous locomotion. The acceleration or deceleration of the subject, detected by the sensor and processed by the computer, is used to accelerate or decelerate the treadmill in real time. The system has been used to assess, in eight subjects, the self-selected speed of walking and running, the maximum "reasonable" speed of walking, and the minimum reasonable speed of running at different gradients (from level up to +25%). This evidenced the speed range at which humans neither walk nor run, from 7.2 +/- 0.6 to 8.4 +/- 1.1 km/h for level locomotion, slightly narrowing at steeper slopes. These data confirm previous results, obtained indirectly from stride frequency recordings. The self-selected speed of walking decreases with increasing gradient (from 5.0 +/- 0.8 km/h at 0% to 3.0 +/- 0.9 km/h at +25%) and seems to be approximately 30% higher than the speed that minimizes the metabolic energy cost of walking, obtained from the literature, at all the investigated gradients. The advantages, limitations, and potential applications of the newly proposed methodology in physiology, biomechanics, and pathology of locomotion are discussed in this paper.

SUMMARYThe aim of the present study was to quantify the improvements in the economy and efficiency of surface swimming brought about by the use of fins over a range of speeds (v) that could be sustained aerobically. At comparable speeds, the energy cost (C) when swimming with fins was about 40 %lower than when swimming without them; when compared at the same metabolic power, the decrease in C allowed an increase in v of about 0.2 ms-1. Fins only slightly decrease the amplitude of the kick (by about 10 %) but cause a large reduction (about 40 %) in the kick frequency. The decrease in kick frequency leads to a parallel decrease of the internal work rate (Ẇint, about 75 %at comparable speeds) and of the power wasted to impart kinetic energy to the water (Ẇk, about 40 %). These two components of total power expenditure were calculated from video analysis (Ẇint) and from measurements of Froude efficiency(Ẇk). Froude efficiency(ηF) was calculated by computing the speed of the bending waves moving along the body in a caudal direction (as proposed for the undulating movements of slender fish); ηF was found to be 0.70 when swimming with fins and 0.61 when swimming without them. No difference in the power to overcome frictional forces(Ẇd) was observed between the two conditions at comparable speeds. Mechanical efficiency[Ẇtot/(Cv), where Ẇtot=Ẇk+Ẇint+Ẇd]was found to be about 10 % larger when swimming with fins, i.e. 0.13±0.02 with and 0.11±0.02 without fins (average for all subjects at comparable speeds).

A model has recently been proposed to predict the changes of mechanical power (W) during a maximal explosive effort (such as a standing high jump off both feet) following an adaptation (e.g. training/de-training). The model is based on the assumption that, all other things being equal (ceteris paribus), the predicted changes in W depend on the measured changes of muscle force (F) or cross-sectional area (CSA) only. It follows that, if the measured changes in W are not equal to those predicted by the model, factors other than a change in F (or CSA) must be responsible for this difference. The model does not allow the determination of factors specifically involved in the adaptation process but it helps in discriminating whether an adaptation has taken place at a local level (when the observed changes in F would be attributed to factors other than the observed changes in CSA, e.g. co-contractions, fibre type modifications...), or at a central level (when the observed changes in W would be attributed to other factors than the observed changes in F, e.g. co-ordination of multiple joints and muscle groups...), or in both regions. In this paper the model has been applied to data reported in the literature on disuse (BR, bed rest), de-conditioning (SF, space flight), strength training (ST) and de-training (DT). The results of these calculations have confirmed previous observations on the determinants of the adaptation process and further suggest: (1) that training for one specific motor task (e.g. ST) could affect the performance of a second task (e.g. a maximal explosive jump) but that, as soon as the trained motor task is terminated (DT), this ability is re-gained; and (2) that neuromuscular impairment in disuse (BR) is closer to de-training than to the de-conditioning brought about by weightlessness (SF).

The mechanical efficiency of cycling with a new pedal-crank prototype (PP) was investigated during an incremental test on a stationary cycloergometer. The efficiency values were compared with those obtained, in the same experimental conditions and with the same subjects, by using a standard pedal-crank system (SP). The main feature of this prototype is that its pedal-crank length changes as a function of the crank angle being maximal during the pushing phase and minimal during the recovery one. This variability was expected to lead to a decrease in the energy requirement of cycling since, for any given thrust, the torque exerted by the pushing leg is increased while the counter-torque exerted by the contra-lateral one is decreased. Whereas no significant differences were found between the two pedal-cranks at low exercise intensities (w*=50-200 W), at 250-300 W the oxygen uptake (V*O2, W) was found to be significantly lower and the efficiency (eta=w*/V*O2) about 2% larger (p0.05, Wilcoxon test) in the case of PP. Even if the measured difference in efficiency was rather small, it can be calculated that an athlete riding a bicycle equipped with the patented pedal-crank could improve his 1h record by about 1 km.

Oxygen consumption at steady state ( VO2, l x min(-1)) and mechanical power (W, W) were measured in five subjects riding a human-powered vehicle (HPV, the Karbyk, a four-wheeled recumbent cycle) on a flat concrete road at constant sub-maximal speeds. The external mechanical work spent per unit of distance (W, J x m(-1)), as calculated from the ratio of W to the speed (v, m x s(-1)), was found to increase with the square of v: W = 8.12 + (0.262 x v2) (r = 0.986, n = 31), where the first term represents the mechanical energy wasted, over a unit of distance, against frictional forces (rolling resistance, Rr), and the second term (k x v2) is the work performed, per unit distance, to overcome the air drag. The rolling coefficient (Cr, obtained dividing Rr by m x g, where m is the overall mass and g is the acceleration of gravity) amounted to [mean (SD)] 0.0084 (0.0008), that is about 60% higher than that of a racing bicycle. The drag coefficient was calculated from the measured values of k, air density (p) and frontal area (A) [Cx = k x (0.5 x A x rho)-1], and amounted to 1.067 (0.029), that is about 20% higher than that of a racing bicycle. The energy cost of riding the HPV (Ck, J x m(-1)) was measured from the ratio of metabolic power above rest (net VO2, expressed in J x s(-1)) to the speed (v, m x s(-1)); the value of this parameter increased with the square of v, as described by: Ck = 61.45 + (0.675 x v(2)) (r = 0.711, n = 23). The net mechanical efficiency (eta) was calculated from the ratio of W to Ck: over the investigated speed range this turned out to be 0.22 (0.021). Best performance times (BPTs) of a "typical" elite athlete riding the Karbyk were calculated over the distances of 1, 5 and 10 km: these were about 8% longer than the BPTs calculated, on the same subjects, when riding a conventional racing bicycle.

Maximal explosive power during two-leg jumps was measured on four sedentary subjects [mean age 43.0 (SD 10.3) years, mean height 1.74 (SD 0.04) m, mean body mass 73.5 (SD 1.3) kg] using a sledge apparatus with which both force and speed could be directly measured. Different after-loads were obtained by positioning the sledge at five different angles (SA, alpha) in respect to the horizontal so that m x g x sin alpha (where m is the sum of body mass and the mass of the sledge seat, g the acceleration due to gravity) decreased (on average) from 78% body mass at 30 degrees to 27% body mass at 10 degrees, thus simulating conditions of low gravity. The subjects were asked to jump maximally, without counter movement, starting from 70 degrees, 90 degrees, 110 degrees, and 140 degrees of knee angle (KA); the protocol being repeated at 10 degrees, 15 degrees, 20 degrees, 25 degrees and 30 degrees SA. The average (W+(mean)) power output during concentric exercise (CE) was found to decrease when the starting KA was increased, but to be unaffected by SA (i.e. by the after-load, the simulated low g). The higher values of W+(mean) were recorded at 90 degrees KA [15.01 (SD 1.46) W x kg(-1), average for all subjects at all SA]. The subjects were also asked to perform counter movement (CMJ) and rebound jumps (RE) at the same SA as for CE. In CMJ and RE maximal power outputs were also found to be unaffected by the SA; W+(mean) amounted to 16.03 (SD 0.28) W x kg(-1) in CMJ and 16.88 (SD 0.36) W x kg(-1) in RE (average for all subjects at all SA). In CE, CMJ and RE, the instantaneous force at the onset of the positive speed phase (F(i)) was found to increase linearly with SA (i.e. with increasing m x g x sin alpha), and the difference between F(i) in CMJ or RE and F(i) in CE (F(i) in CMJ minus F(i) in CE and F(i) in RE minus F(i) in CE) was unaffected by SA. This indicated that both maximal power and the elastic recoil were unaffected by simulated low g ranging from 1.71 m x s(-2) (at 10 degrees SA) to 4.91 m x s(-2) (at 30 degrees SA).

The energy cost of kayaking per unit distance (C(k), kJ x m(-1)) was assessed in eight middle- to high-class athletes (three males and five females; 45-76 kg body mass; 1.50-1.88 m height; 15-32 years of age) at submaximal and maximal speeds. At submaximal speeds, C(k) was measured by dividing the steady-state oxygen consumption (VO(2), l x s(-1)) by the speed (v, m x s(-1)), assuming an energy equivalent of 20.9 kJ x l O(-1)(2). At maximal speeds, C(k) was calculated from the ratio of the total metabolic energy expenditure (E, kJ) to the distance (d, m). E was assumed to be the sum of three terms, as originally proposed by Wilkie (1980): E = AnS + alphaVO(2max) x t-alphaVO(2max) x tau(1-e(-t x tau(-1))), were alpha is the energy equivalent of O(2) (20.9 kJ x l O(2)(-1)), tau is the time constant with which VO(2max) is attained at the onset of exercise at the muscular level, AnS is the amount of energy derived from anaerobic energy utilization, t is the performance time, and VO(2max) is the net maximal VO(2). Individual VO(2max) was obtained from the VO(2) measured during the last minute of the 1000-m or 2000-m maximal run. The average metabolic power output (E, kW) amounted to 141% and 102% of the individual maximal aerobic power (VO(2max)) from the shortest (250 m) to the longest (2000 m) distance, respectively. The average (SD) power provided by oxidative processes increased with the distance covered [from 0.64 (0.14) kW at 250 m to 1.02 (0.31) kW at 2000 m], whereas that provided by anaerobic sources showed the opposite trend. The net C(k) was a continuous power function of the speed over the entire range of velocities from 2.88 to 4.45 m x s(-1): C(k) = 0.02 x v(2.26) (r = 0.937, n = 32).

The aim of this study was the quantitative evaluation of the myotatic reflex in a group of 26 patients affected by stationary spastic paresis (6: hemiparesis; 5: paraparesis; 8: tetraparesis; 7: multiple sclerosis) before and after a treatment of hydro-kinesy therapy. The treatment was carried out in an indoor pool containing warm (32 degrees C) sea water and consisted of active and passive motion exercises, coordination exercises and immersion walking. The measured parameters were: (i) the peak input force (F-pH) measured by means of an instrumented hammer with which the patellar tendon was hit; and (ii) the peak value of the corresponding reflex force of the quadriceps femoris (F-pQ) measured by means of a load cell connected to the subject's ankle. The peak values of the reflex response (F-pQ) were found to increase as a function of the intensity of the imposed stimulus and to reach a plateau between 15 and 30 N of F-pH, A Student's t test applied to the paired values of F-pQ (as measured at plateau conditions) on both the lower limbs, before and after therapy, showed no significant changes due to the treatment in the four groups of subjects. However, if all subjects were grouped regardless the type of illness: 1) the average reflex response of the affected limb (the one characterized before therapy by the higher F-pQ values) was found to decrease following the treatment (75.1 +/- 26.7 N pre therapy and 69.1 +/- 29.3 N post therapy, p = 0.07, n = 26); and 2) the effect of the treatment was found to be significantly larger (p = 0.04, n = 26) on the affected limb (Delta F-pQ = 6.07 +/- 16.5 N) as respect with the contra lateral one (Delta F-pQ = -0.16 +/- 12.1 N). (C) 1999 Elsevier Science Ltd. All rights reserved.

VO2max and best performance times (BPTs) obtained during maximal voluntary trials over 1, 2, 5, and 10 km from a stationary start were assessed in 10 elite cyclists. Steady-state VO2 and peak blood lactate concentration ([La]b) were also determined in the same subjects pedaling on a track at constant submaximal speeds. The energy cost of cycling (Cc, J.m-1) was calculated as the ratio of VO2, corrected for glycolytic energy production and expressed in W, to v (m.s-1). Individual relationships between Cc and v were described by: Cc = Ccrr + k1 v2 where Ccrr is the energy spent against friction and k1 v2 is that spent against drag. Overall energy cost of cycling (Cctot) was obtained, adding to Cc the energy spent to accelerate the total moving mass from a stationary start. Individual theoretical BPTs were then calculated and compared with the actual ones as follows. The maximal metabolic power sustained at a constant level by a given subject (Emax, W) is a known function of the exhaustion time (te). It depends on his VO2max and maximal anaerobic capacity; it was obtained from individual VO2max and [La]b values. The metabolic power (Ec, W) necessary to cover any given distance (d) is a known function of the performance time over d (td); it is given by Ec = Cctot v = Cctot d td. For all subjects and distances, the t values solving the equalities Emax F(te) = Ec F(td) were calculated and assumed to yield theoretical BPTs. Calculations showed a fairly good agreement between actual and calculated BPTs with an average ratio of 1.035 +/- 0.058.

The maximal explosive power during a two legs jump was measured on four competitive athletes [mean age 24 (SD 4.3) years; height 1.79 (SD 0.09) m; body mass 68.7 (SD 12.8) kg] at different starting knee angles (70, 90, 110, 130 and 150°). The experiments were performed on a newly developed instrument with which both force and speed could be measured using a force platform and a wire tachometer, respectively, and on a conventional force platform. At the smallest knee angle (70°) the mean power output ( $\bar\dot W$ in watts per kilogram) developed during the jump was found not to differ significantly between the two methods (P > 0.1). At the larger knee angles $\bar \dot W$ was 18.4% (90°), 34.5% (110°), 47.4% (130°) and 19.4% (150°) higher using the conventional force platform (P

The energy cost of level walking (Cw) was measured from the ratio of O2 consumption to speed (from 0.1 to 1.2 m.s-1) in hemiplegic patients (n = 20) and in a control group of healthy subjects (n = 17). Average age and body mass were 58, 54 years and 73, 78 kg, respectively. In hemiplegic patients Cw was higher than in control subjects (average value at 1.0 m.s-1 = 3.6 and 3.3 J.m-1.kg-1, respectively) and this difference increased at lower speeds (from 5.1% at 1.2 m.s-1 to 28.7% at 0.1 m.s-1).