Predi????o do desempenho em 10 km por meio de vari??veis metab??licas e mec??nicas: influ??ncia do n??vel de desempenho e da potencializa????o p??s-ativa????o

Submitted by Sara Ribeiro (sara.ribeiro@ucb.br) on 2018-08-08T17:35:32Z No. of bitstreams: 1 SebastianDelRossoDissertacao2018.pdf: 1818866 bytes, checksum: 3274a8646557f8e415e970e9bbe7a015 (MD5) Approved for entry into archive by Sara Ribeiro (sara.ribeiro@ucb.br) on 2018-08-08T17:35:47Z (GMT) No. of bitstreams: 1 SebastianDelRossoDissertacao2018.pdf: 1818866 bytes, checksum: 3274a8646557f8e415e970e9bbe7a015 (MD5) Made available in DSpace on 2018-08-08T17:35:47Z (GMT). No. of bitstreams: 1 SebastianDelRossoDissertacao2018.pdf: 1818866 bytes, checksum: 3274a8646557f8e415e970e9bbe7a015 (MD5) Previous issue date: 2018-02-28 The main goal of the present study was to identify the main determinants influencing and thus explaining pacing and performance during self-paced 10 km running time trial and develop prediction equations including metabolic/respiratory and neuromuscular variables. Twenty-seven well-trained runners (age = 26,4 ?? 6,5 years, training experience = 7,4 ?? 5,9 years, training volume = 89,1 ?? 39,1 km??week-1, VO2max = 62,3 ?? 4,5 mL??kg-1??min-1) completed three testing sessions: During the first session, body composition and mechanical variables (concentric peak velocity, PV; time to peak velocity, TPV; peak force, PF; and peak power, PP) in the half-squat (AG) and loaded squat jump (SSC) were measured. The second testing session was dedicated to assessing metabolic variables [VO2max, ventilatory thresholds (VT1 and VT2), cost of running (CR) and maximal speed (SMAX)] and vertical jump (CMJ) potentiation; while during the third session a 10 km self-paced time trial was carried out. Also, before and after (0, 3, 6, and 9 min) the 10 km, athletes completed 2 CMJ for measuring mechanical variables [eccentric displacement (DE), mean eccentric and concentric velocity (VME, VMC), eccentric and concentric peak velocity (PVE, PVC)]. Pacing was defined as the time (T10km) or speed (S10km) every 1000 m, and analysis of those factors influencing the 10 km performance was carried by means of hierarchic multiple regression, whit the inclusion of all available variables. In addition, regression analyses were performed to develop prediction equation for T10km. Cluster analyses were carried out to evaluate the effects of performance levels [high performance group, GAD; low performance group (GBD)] and jumping potentiation (potentiation group, GP; non-potentiation group, GNP). For the whole sample, the final model including SMAX, CR, o a AGVP, ??3-Pre CMJPVE (m??s-1), HRmax (bpm) and SSCPF (N) was statistically significant; r2 = 0,91, F(6-26) = 35,64, P < 0,001, EES = 0,76, r2ADJUSTED = 0,89; while the prediction model included the following variables: SMAX, CR and AGVP [r2 = 0,75; F(3-26) = 22,52; P < 0,001; EES = 1,23]. For the performance groups, there were significant main simple effects for time [F(2-52) = 12,20, P