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Introduction The prevalence of weight status disorders and body dissatisfaction is continually increasing in adolescents from European countries (Sanchez Cruz et al, 2013) (Eisenberg, Neumark-Sztainer, & Paxton, 2006). Physical capacities are related to body size. In particular, the weight status affects some motor and strength performances (Graf et al, 2004). Little is known, however, on how one’s physical fitness affects the perception of his/her own body image. The objective of the present study was to determine the interrelationships among body mass index, body image perception and motor performance in Italian second grade school students. Methods The sample included 61 adolescents (35 boys and 26 girls) from Bologna, Emilia-Romagna, Italy, aged 16-17 years. Height and weight were measured and BMI was calculated. Body image perception was assessed using Body Silhouette Charts (Collins, 1991). Physical assessments included a standing broad jump, hand grip dynamometer test, sit and reach flexibility test, gross motor skill test, and shuttle run endurance test. Results The BMI was similar in girls (21.4 kg/m2, SD: 2.0) and boys (22.4, kg/m2; SD: 2.9). However, girls overestimated their actual figure, and showed a higher body dissatisfaction , as revealed by a larger difference between their desired and perceived body image. Boys performed better than girls in the standing broad jump, dynamometer, and shuttle run tests, while girls had a higher flexibility. When controlling for gender, BMI was negatively related to performance in the standing broad jump and gross motor skill tests. When controlling for both gender and BMI, performing better in the standing broad jump and shuttle run involved a thinner body perception. Discussion Self-perception of body image is affected, apart from the actual body size, even by one’s aerobic and explosive strength capacity. Therefore, improving physical fitness may contribute to reduce body dissatisfaction in adolescents, by means of both loss of weight and change of perceived body image. References Sanchez Cruz et al. (2013). Rev Esp Cardiol. Doi: 10.1016/j.recesp.2012.10.016. Eisenberg, M. E., Neumark-Sztainer, D., & Paxton, S. J. (2006). J Psychosomatic Res, 61, 521–527. Graf et al. (2004). Int J Obes Relat Metab Disord, 28, 22-6. Collins M.E. (1991). International Journal of Eating Disorders, 10, 199-208.

Introduction Enjoyment of physical education (PE) is important to develop a positive attitude towards physical activity and sports practice. A variety of physical, psychological, and educational factors have been reported to affect PE class enjoyment, including body mass index (BMI), self-efficacy, perceived competence, and curriculum content (Fairclough, 2003; Barr-Anderson et al., 2008). This study aimed to analyse, in Italian adolescents, the effect of two different physical education programmes on PE enjoyment while considering the moderating effect of BMI, physical performance and selected self-perception variables. Methods 37 Italian high-school students (M:18, F:19), aged 15 to 17 years, participated to two-month PE interventions. A group (M:8, F:10) attended a non-traditional team sports program oriented towards fun and motivation, while another group (M:10, F:9) attended a program focused on individual fitness activities. Before intervention, the BMI, self-efficacy, perceived competence, explosive strength, and aerobic fitness were assessed. A physical activity enjoyment scale (PACES) (Kendzierski and De Carlo, 1991) was administered pre- and post-intervention. Results The mean preintervention PACES score was not significantly different between the team sports (61.6, SD:8.1), and the fitness (63.4, SD:7.8) groups. A significant effect PE programs was observed, with a mean score increase of 2.4 (5.9) in the team sports program, and a mean decrease of 2.9 (4.4) in the fitness group. This outcome was similar in boys and girls, and no moderating effect was noticed for physical performance, self-efficacy, and perceived competence. BMI only showed an effect, though limited, with a 1-unit higher BMI associated to a 0.5 lower difference in the PACES score change, for each given group. Discussion The present results show that a highly motivating PE program may increase PE enjoyment in adolescents independently from their perceived competence, self-efficacy and performance level. This emphasizes the usefulness to include, in high-school PE curricula, activities focusing on fun and entertainment rather than on performance enhancement. References Barr-Anderson DJ, Neumark-Sztainer D, Schmitz KH, Ward DS, Conway TL, Pratt C, Baggett CD, Lytle L, Pate RR (2008). Res Q Exerc Sport, 79(1), 18-27. Fairclough S (2003). Eur J Phys Educ, 8(1), 5-18. Kendzierski D, De Carlo KJ (1991). J Sport Exerc Psychol,13(1), 50-64.

Introduction Knowing the biomechanical key factors of each jump is fundamental for figure skating coaches in order to more effectively teach triple jumps to their athletes. Notwithstanding the number of studies on ice skating (King et al., 2004), no biomechanical jump analysis, to the knowledge of the present authors, performed on roller skating. The purpose of the present study was to compare some kinematic characteristics of double and triple Lutz jumps in roller skating. Methods 5 elite male skaters performing a series of double and triple Lutz were acquired using 10 infrared cameras (Smart-D, BTS, Milan, Italy, 250 Hz). For the trunk, the upper limbs and the foot segments, markers were attached directly on specific anatomical landmarks. For the thigh and the shank segments, the calibrated anatomical system technique was exploited (Cappozzo et al., 1995). The resultant velocity angle was also considered. Five key events were identified for each jump: toe-pick (instant in which the toe-pick impacted the ground), end of gliding (last contact of the left foot), take-off (last contact of the right foot), maximum height (top of the flight phase), and landing (instant of contact with the ground). Results The hip and the knee were flexed at the toe-pick, whereas the flexion was clearly lower at the take-off. On the contrary, the ankle at toe-pick was in the neutral position, and plantarflexed at the take-off, but differently depending on the skater. The skaters’ horizontal velocities at take-off for the triple Lutz (mean: 2.69 m/s) were lower than those for the double one (mean: 3.17 m/s). Vertical velocities at take-off showed slightly higher values for the triple (mean: 2.90 m/s) compared to the double (mean: 2.72 m/s) Lutz. The average resultant velocity angle in the 5 athletes was 40° in the double and 47° in the triple Lutz, at the take-off. The greatest difference between the double and triple jumps was the rotational velocity during the flight estimated from the rotation of the pelvis segment. The pelvis rotation velocity was higher in the triple Lutz already during the toe pick (means: 0.84 rev/s for double and 1.75 rev/s for triple one). Jumping height and flight time were higher for the triple jump for three skaters. Discussion For the first time a biomechanical comparison between the double and the triple Lutz in roller skating was performed. Some of these findings were already roughly known to coaches and skaters, but only in the present study some specific descriptors of the jump were characterized and quantified. References King D, Smith S, Higginson B, Muncasy B, Scheirman G. (2004). Sports Biomechanics, 3, 109-123. Cappozzo A, Catani F, Della Croce U, Leardini A. (1995). Clinical Biomechanics, 10, 171-178.

Introduction Running economy (RE) is a key determinant of performance in distance running, and it is of interest to understand how RE is associated to biomechanical characteristics of running gait. Previous studies showed that RE is proportional to the rate of muscle force production during running (Kram & Tailor, 1990; Pontzer 2007). In steady-speed running, the highest portion of muscle force is applied to support one’s own body weight (Kram & Tailor, 1990; Chang & Kram, 1999), but a significant amount of force is used to accelerate the center of mass (Chang, 1999) and to swing the oscillating limb (Modica & Kram, 2005). Therefore, the purpose of this study was to analyze the association between RE and the overall rate of force production, as estimated using a mathematical model. Methods RE was measured in ten high-level athletes while running on a track at the steady speed of 14 km/h. Furthermore, ground reaction force (GRF) data were collected with a force plate while running at the same speed. A mathematical model, adapted from Pontzer (2007), was used to estimate the rate of force production during running as a combination of i) the rate of the vertical component of GRF, ii) the rate of the horizontal component of GRF, and iii) the average force required to swing the limb in a stride, determined using a specific equation. A linear regression was conducted to assess the proportion of variance in RE explained by the modeled force values. Results The mean rate of the vertical component of GRF, when considered alone, explained a large portion of variability in RE (R2 = 0.71). The explained variance increased when the horizontal component of GRF and the force required to swing the limb were included in the model (R2 = 0.79 and 0.88, respectively). Discussion The present results confirm the previous conclusions that the force needed for supporting the runner’s body weight is a major determinant of RE (Kram & Taylor, 1990). Furthermore, including the horizontal component of GRF and the force needed for swinging the limb allows to obtain an index of overall force production that is highly associated with RE in trained distance runners. This finding provides further support to the observation that RE is proportional to the rate of force produced during running. From an applied perspective, the model used here can provide a reasonably simple evaluation tool when analyzing an athlete’s running mechanics with the goal to improve the economy of his/her running style. References Chang YM, Kram R (1999). J Appl Physiol, 86, 1657-1662. Kram R, Taylor CR (1990). Nature, 346, 265-267. Modica JR, Kram R (2005). J Appl Physiol, 98, 2126-2131. Pontzer H (2007). J Exp Biol, 210, 484-494.

Introduction During cycling and running, the locomotor rhythm can modulate the heart rate (HR), although the characteristics of cardiolocomotor coupling are different in the two exercise modes (Nomura et al., 2003). Furthermore, it was recently demonstrated that in cycling the interactions between the locomotor and heart rhythms result in a new heart rate variability (HRV) spectral component corresponding to pedalling frequency (Blain et al., 2009). This study aimed to analyse the spectrum of the RR signal collected during an incremental running test, in order to individuate the frequency and magnitude of spectral components originating from cardiolocomotor interactions. Methods On a grass field, nineteen male soccer players performed an exhaustive running test, with speed starting at 8.5 km/h and increasing by 0.5 km/h every minute. During the test, self-selected stride rate (SR) and RR periods were measured. A Short-time Fourier transform was applied to the RR series to estimate the power spectra. Results In all the subjects, the analysis of time-varying spectra allowed to detect two spectral frequency components related to SR. The first component (F1) was at a frequency of one half the SR (1.2 to 1.6 Hz), whereas the frequency of the second component (F2) equalled the absolute value of the SR-HR difference (0 to 0.8 Hz). At 80, 90, and 100% of maximum HR, the magnitude of F1 was 19.2 ± 10.2, 17.8 ± 9.3, and 15.0. ± 8.2 % of total high-frequency power (HF, >0.15 Hz). At the same intensities, F2 represented 19.6 ± 13.4, 16.6 ± 9.5, and 13.2 ± 7.4 % of HF. Discussion The presence of F1 confirmed what observed in cycling (Blain et al., 2009), i.e. a spectral component corresponding to the main locomotor rhythm that represents a significant portion of HF power and appears even when the cardiac and locomotor rates are not synchronous. The mechanism underlying F1 is probably the modulation of venous return by limb muscles contraction. The main finding of this study is however the appearance of the F2 component. F2 could have links with the relationship between the instantaneous RR variation and the phase of the cardiac cycle in which the vastus lateralis contraction occurs (Nomura et al., 2006). Finally, the presence of F2 in the same frequency ranges of the low frequency and the respiratory components could lead to overestimate the spectral power relative to those components, requiring caution when interpreting the frequency analysis of HRV collected during running. References Blain G, Meste O, Blain A, Bermon S (2009). Am J Physiol Heart Circ Physiol, 296(5), H1651-H1659. Nomura K, Takei Y, Yanagida Y (2003). Eur J Appl Physiol, 89,221-229. Nomura K, Takei Y, Yoshida M, Yanagida Y (2006). Eur J Appl Physiol, 97, 240-247.

INTRODUCTION HRV monitoring, economic and non-invasive, allows the detection of possible symptoms of autonomic imbalance and has been suggested as potential tool to prevent overtraining in competitive sport [2]. Previous studies showed that resting heart rate variability (HRV) measures, indices of autonomic function, are influenced by exercise, both after a single bout [1] and training cycles [2]. To date however specific short- and long-term effects of training on HRV parameters are not completely clear. Our aim is to check the predictability of daily HRV measures time series on the basis of training volume and intensity series. METHODS HRV was recorded in a middle-distance runner (VO2max = 65 ml*Kg-1*min-1; Age:23,2; height: 175 cm; bm:64,5 Kg), at the awakening, for 90 consecutive days, with a telemeter (Polar S810; Polar Electro Oy, Finland), for 5 min in supine and 5 min in upright position. Data were processed through the HRV Analysis Software 1.1 for windows (developed by The Biomedical Signal Analysis Group, Department of Applied Physics, University of Kuopio, Finland, and free available at the website http://venda.uku.fi/research/biosignal). All the commonly used time and frequency domain HRV measures [3] were computed. The athlete trained once a day, in the evening. Volume (min) and intensity (speed %AT) were recorded. For each of HRV indexes a forecasting model was computed treating training volume and intensity separately as independent variables (predictors). Stationary R-squared (R2) was considered to determine the goodness of the model. The bivariate time series analysis was performed through the software SPSS® 14.0 (SPSS Inc, USA) RESULTS Considering training volume as the predictor variable, the upright HR is the better predictable measure (R2=0,505), while for other parameters models are not good (R2