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
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da UCB |
Texto Completo: | https://bdtd.ucb.br:8443/jspui/handle/tede/2441 |
Resumo: | 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<0,001), ??2 = 0,32] and group [F(1-25) = 49,91; P<0,001, ??2 = 0,66] and also differences in the explaining variables for T10km: GAD [SMAX; SSCPF, HRMEAN, CV10km e Post-0min CMJPVE, F(5-9) = 266,06; P <0,001; SSE = 0,09 min; r2ADJUSTED = 0,99]; GBD [VT2-%VO2max, ??6-Pre CMJEPV, CR; F(4-18) = 33,16; P <0,001, EES = 0,045 min; r2ADJUSTED = 0,88]. Furthermore, different prediction equations were found for each group: GAD ??? [T10km (min) = 68,65 ??? (1,084 ?? SMAX) ??? (0,008 ?? SSCPF) + (0,083 ?? AGCARGA); r2 = 0,98]; GBD - T10km (min) = 44,75 ??? (1,05?? SMAX) + (0,17??VT2-%VO2max) + (1,89 ?? CMJVME) ??? (0,061 ?? Age); r2 = 0,89]. For jump potentiation groups there were significant differences only in the last 400 m and RPE (GNP = 8,36 ?? 1,6 vs. GP = 6,8 ?? 1,7; P = 0,03). Also, jump potentiation correlated with the final 400 m time in the whole sample (r = -0.42; P = 0,031) and with RPE for the GAD group (r = -0,75; P = 0,032). In conclusion, the results of the present study suggest that mechanical factors are significant for endurance runners given that explain part of the variance in the T10km while allowed for performance prediction. Moreover, performance level appears to be related to neuromuscular differences influencing pacing whereas jump potentiation likely affects effort perception. |
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Biblioteca Digital de Teses e Dissertações da UCB |
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Boullosa, Daniel A.http://lattes.cnpq.br/9059576682332603http://lattes.cnpq.br/3239120088596927Del Rosso, Sebasti??n2018-08-08T17:35:47Z2018-02-28DEL ROSSO, Sebasti??n. 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. 2018. 73 f. Disserta????o (Programa Stricto Sensu em Educa????o F??sica) - Universidade Cat??lica de Bras??lia, Bras??lia, 2018.https://bdtd.ucb.br:8443/jspui/handle/tede/2441The 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<0,001), ??2 = 0,32] and group [F(1-25) = 49,91; P<0,001, ??2 = 0,66] and also differences in the explaining variables for T10km: GAD [SMAX; SSCPF, HRMEAN, CV10km e Post-0min CMJPVE, F(5-9) = 266,06; P <0,001; SSE = 0,09 min; r2ADJUSTED = 0,99]; GBD [VT2-%VO2max, ??6-Pre CMJEPV, CR; F(4-18) = 33,16; P <0,001, EES = 0,045 min; r2ADJUSTED = 0,88]. Furthermore, different prediction equations were found for each group: GAD ??? [T10km (min) = 68,65 ??? (1,084 ?? SMAX) ??? (0,008 ?? SSCPF) + (0,083 ?? AGCARGA); r2 = 0,98]; GBD - T10km (min) = 44,75 ??? (1,05?? SMAX) + (0,17??VT2-%VO2max) + (1,89 ?? CMJVME) ??? (0,061 ?? Age); r2 = 0,89]. For jump potentiation groups there were significant differences only in the last 400 m and RPE (GNP = 8,36 ?? 1,6 vs. GP = 6,8 ?? 1,7; P = 0,03). Also, jump potentiation correlated with the final 400 m time in the whole sample (r = -0.42; P = 0,031) and with RPE for the GAD group (r = -0,75; P = 0,032). In conclusion, the results of the present study suggest that mechanical factors are significant for endurance runners given that explain part of the variance in the T10km while allowed for performance prediction. Moreover, performance level appears to be related to neuromuscular differences influencing pacing whereas jump potentiation likely affects effort perception.O objetivo do presente estudo foi analisar os diversos fatores que podem influenciar e por tanto explicar o desempenho em uma prova de corrida de 10 km assim como tamb??m em subsegmentos dos 10 km, e predizer desempenho a partir de vari??veis metab??licas/respirat??rias e neuromusculares. Para tal fim, 27 corredores bem treinados (idade = 26,4 ?? 6,5 anos, experi??ncia de treinamento = 7,4 ?? 5,9 anos, volume de treinamento = 89,1 ?? 39,1 km??semana-1, VO2max = 62,3 ?? 4,5 mL??kg-1??min-1) completaram tr??s sess??es de avalia????o: A primeira sess??o foi dedicada ?? determina????o das vari??veis mec??nicas (pico de velocidade conc??ntrica, PV; tempo at?? o pico de velocidade, TPV; pico de for??a, PF e pico de pot??ncia, PP) nos exerc??cios de m??dio agachamento (AG) e salto com sobrecarga (SSC) e das vari??veis associadas ?? composi????o corporal; durante a segunda sess??o se avaliaram vari??veis metab??licas [VO2max, limiares ventilat??rios (VT1, primer limiar ventilat??rio, VT2, segundo limiar ventilat??rio), custo energ??tico da corrida (CR) e velocidade m??xima (SMAX)] conjuntamente com a potencializa????o no salto vertical (CMJ); e durante a terceira sess??o se registrou o desempenho em uma prova simulada de 10 km (T10km) com monitoramento continuo da velocidade (GPS) e da frequ??ncia card??aca (FC). Antes e depois (0, 3, 6 e 9 min) dos 10 km os atletas completaram 2 saltos verticais (CMJ) para ?? avalia????o das vari??veis mec??nicas associadas ao salto [deslocamento exc??ntrico (DE), velocidade m??dia exc??ntrica e conc??ntrica (VME, VMC), pico de velocidade exc??ntrica e conc??ntrica (PVE, PVC)]. O ritmo de corrida foi definido como o tempo ou velocidade a cada 1000 m, e para as an??lises dos fatores implicados na vari??ncia do desempenho em 10 km foi realizada uma an??lise de regress??o m??ltipla hier??rquica utilizando todas as vari??veis dispon??veis. Al??m disso, an??lises de regress??o foram completadas para determinar equa????es de predi????o do T10km com vari??veis independentes das registradas durante a prova. Entanto que analises por conglomerados foram utilizados para analisar os efeitos do n??vel de desempenho (grupo de alto desempenho, GAD; grupo de baixo desempenho, GBD) e da potencializa????o do salto vertical (grupo que exibiu potencializa????o, GP; grupo que n??o potencializou, GNP). Para o total de 27 atletas o modelo final que incluiu a SMAX (km??h-1), a CR (mL??kg-1??m-1), o a AGVP (m??s-1), o ??3-Pre CMJPVE (m??s-1), a FCmax (bpm) e a SSCPF (N) foi estatisticamente significativo; r2 = 0,91, F(6-26) = 35,64, P < 0,001, EES = 0,76, r2ajustado = 0,89. Por outra parte, o modelo para a predi????o do T10km, com vari??veis independentes da prova de 10 km, incluiu a SMAX, o CR e AGVP [r2 = 0,75; F(3-26) = 22,52; P < 0,001; EES = 1,23]. As analises por grupo de desempenho indicaram efeitos principais do tempo (Tempos parciais, Laps) [F(2-52) = 12,20, P<0,001), ??2 = 0,32] e do grupo [F(1-25) = 49,91; P<0,001, ??2 = 0,66] assim como diferencias nas vari??veis que explicaram a vari??ncia no T10km: para GAD [SMAX; SSCPF, FCM??DIA, CV10km e P??s-0min CMJPVE, F(5-9) = 266,06; P <0,001; SSE = 0,09 min; r2AJUSTADO = 0,99]; GBD [VT2-%VO2max, o ??6-Pre CMJEPV, CR; F(4-18) = 33,16; P <0,001, EES = 0,045 min; r2AJUSTADO = 0,88]. Adicionalmente, acharam-se equa????es diferentes para a predi????o do T10km em cada um dos grupos: GAD ??? [T10km (min) = 68,65 ??? (1,084 ?? SMAX) ??? (0,008 ?? SSCPF) + (0,083 ?? AGCARGA); r2 = 0,98]; GBD - T10km (min) = 44,75 ??? (1,05?? SMAX) + (0,17??VT2-%VO2max) + (1,89 ?? CMJVME) ??? (0,061 ?? Idade); r2 = 0,89]. Enquanto aos grupos de potencializa????o, se acharam diferen??as significativas entre os grupos na velocidade atingida s?? no segmento de 400 m finais e na PSE final (GNP = 8,36 ?? 1,6 vs. GP = 6,8 ?? 1,7; P = 0,03). Ademais, na amostra completa a potencializa????o correlacionou com o tempo nos 400 m finais (r = -0.42; P = 0,031) e no grupo GAD, correlacionou com a PSE (r = -0,75; P = 0,032). Em conclus??o, os resultados deste estudo sugerem que as vari??veis mec??nicas s??o importantes para corredores de 10 km j?? que permitem explicar a vari??ncia e predizer o desempenho. Al??m disso, o n??vel de desempenho parece estar associado com diferencias neuromusculares que influenciam o ritmo de corrida, entanto que a potencializa????o do salto vertical parece afeitar sobre tudo a percep????o do esfor??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: 2018-02-28application/pdfhttps://bdtd.ucb.br:8443/jspui/retrieve/5821/SebastianDelRossoDissertacao2018.pdf.jpgporUniversidade Cat??lica de Bras??liaPrograma Stricto Sensu em Educa????o F??sicaUCBBrasilEscola de Sa??de e MedicinaFor??aRitmo de corridaEconomia de corridaSalto com contramovimentoDesempenho de resist??nciaPotencializa????o p??s-ativa????oPost-activation potentiationCountermovement jumpEndurance performanceRunning economyStrengthPacingCNPQ::CIENCIAS DA SAUDE::EDUCACAO FISICAPredi????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????oinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UCBinstname:Universidade Católica de Brasília (UCB)instacron:UCBLICENSElicense.txtlicense.txttext/plain; charset=utf-81905https://200.214.135.178:8443/jspui/bitstream/tede/2441/1/license.txt75558dcf859532757239878b42f1c2c7MD51ORIGINALSebastianDelRossoDissertacao2018.pdfSebastianDelRossoDissertacao2018.pdfapplication/pdf1818866https://200.214.135.178:8443/jspui/bitstream/tede/2441/2/SebastianDelRossoDissertacao2018.pdf3274a8646557f8e415e970e9bbe7a015MD52TEXTSebastianDelRossoDissertacao2018.pdf.txtSebastianDelRossoDissertacao2018.pdf.txttext/plain210596https://200.214.135.178:8443/jspui/bitstream/tede/2441/3/SebastianDelRossoDissertacao2018.pdf.txtb48ae85662e56c0d83c72d67f6621c15MD53THUMBNAILSebastianDelRossoDissertacao2018.pdf.jpgSebastianDelRossoDissertacao2018.pdf.jpgimage/jpeg5833https://200.214.135.178:8443/jspui/bitstream/tede/2441/4/SebastianDelRossoDissertacao2018.pdf.jpg4154808afaeb335aba30dfed9da73787MD54tede/24412018-08-09 01:09:59.976TElDRU4/P0EgREUgRElTVFJJQlVJPz8/P08gTj8/Ty1FWENMVVNJVkEKCkNvbSBhIGFwcmVzZW50YT8/Pz9vIGRlc3RhIGxpY2VuPz9hLCB2b2M/PyAoYXV0b3Igb3UgbyB0aXR1bGFyIGRvcyBkaXJlaXRvcyBkZSBhdXRvcikgY29uY2VkZSA/PyBVbml2ZXJzaWRhZGUgQ2F0Pz9saWNhIGRlIEJyYXM/P2xpYSAoVUNCKSBvIGRpcmVpdG8gbj8/by1leGNsdXNpdm8gZGUgcmVwcm9kdXppciwgdHJhZHV6aXIgKGNvbmZvcm1lIGRlZmluaWRvIGFiYWl4byksIGUvb3UgZGlzdHJpYnVpciBhIHN1YSB0ZXNlIG91IGRpc3NlcnRhPz8/P28gKGluY2x1aW5kbyBvIHJlc3VtbykgcG9yIHRvZG8gbyBtdW5kbyBubyBmb3JtYXRvIGltcHJlc3NvIGUgZWxldHI/P25pY28gZSBlbSBxdWFscXVlciBtZWlvLCBpbmNsdWluZG8gb3MgZm9ybWF0b3MgPz91ZGlvIG91IHY/P2Rlby4KClZvYz8/IGNvbmNvcmRhIHF1ZSBhIFVDQiBwb2RlLCBzZW0gYWx0ZXJhciBvIGNvbnRlPz9kbywgdHJhbnNwb3IgYSBzdWEgdGVzZSBvdSBkaXNzZXJ0YT8/Pz9vIHBhcmEgcXVhbHF1ZXIgbWVpbyBvdSBmb3JtYXRvIHBhcmEgZmlucyBkZSBwcmVzZXJ2YT8/Pz9vLgoKVm9jPz8gdGFtYj8/bSBjb25jb3JkYSBxdWUgYSBVQ0IgcG9kZSBtYW50ZXIgbWFpcyBkZSB1bWEgYz8/cGlhIGEgc3VhIHRlc2Ugb3UgZGlzc2VydGE/Pz8/byBwYXJhIGZpbnMgZGUgc2VndXJhbj8/YSwgYmFjay11cCBlIHByZXNlcnZhPz8/P28uCgpWb2M/PyBkZWNsYXJhIHF1ZSBhIHN1YSB0ZXNlIG91IGRpc3NlcnRhPz8/P28gPz8gb3JpZ2luYWwgZSBxdWUgdm9jPz8gdGVtIG8gcG9kZXIgZGUgY29uY2VkZXIgb3MgZGlyZWl0b3MgY29udGlkb3MgbmVzdGEgbGljZW4/P2EuIFZvYz8/IHRhbWI/P20gZGVjbGFyYSBxdWUgbyBkZXA/P3NpdG8gZGEgc3VhIHRlc2Ugb3UgZGlzc2VydGE/Pz8/byBuPz9vIGluZnJpbmdlIGRpcmVpdG9zIGF1dG9yYWlzIGRlIG5pbmd1Pz9tLgoKQ2FzbyBhIHN1YSB0ZXNlIG91IGRpc3NlcnRhPz8/P28gY29udGVuaGEgbWF0ZXJpYWwgcXVlIHZvYz8/IG4/P28gcG9zc3VpIGEgdGl0dWxhcmlkYWRlIGRvcyBkaXJlaXRvcyBhdXRvcmFpcywgdm9jPz8gZGVjbGFyYSBxdWUgb2J0ZXZlIGEgcGVybWlzcz8/byBpcnJlc3RyaXRhIGRvIGRldGVudG9yIGRvcyBkaXJlaXRvcyBhdXRvcmFpcyBwYXJhIGNvbmNlZGVyID8/IFVDQiBvcyBkaXJlaXRvcyBhcHJlc2VudGFkb3MgbmVzdGEgbGljZW4/P2EsIGUgcXVlIGVzc2UgbWF0ZXJpYWwgZGUgcHJvcHJpZWRhZGUgZGUgdGVyY2Vpcm9zIGVzdD8/IGNsYXJhbWVudGUgaWRlbnRpZmljYWRvIGUgcmVjb25oZWNpZG8gbm8gdGV4dG8gb3Ugbm8gY29udGU/P2RvIGRhIHRlc2Ugb3UgZGlzc2VydGE/Pz8/byBvcmEgZGVwb3NpdGFkYS4KCkNhc28gYSB0ZXNlIG91IGRpc3NlcnRhPz8/P28gZGVwb3NpdGFkYSB0ZW5oYSBzaWRvIHJlc3VsdGFkbyBkZSB1bSBwYXRyb2M/P25pbyBvdSBhcG9pbyBkZSB1bWEgYWc/P25jaWEgZGUgZm9tZW50byBvdSBvdXRybyBvcmdhbmlzbW8gcXVlIG4/P28gc2VqYSBhIFVDQiwgdm9jPz8gZGVjbGFyYSBxdWUgcmVzcGVpdG91IHRvZG9zIGUgcXVhaXNxdWVyIGRpcmVpdG9zIGRlIHJldmlzPz9vIGNvbW8gdGFtYj8/bSBhcyBkZW1haXMgb2JyaWdhPz8/P2VzIGV4aWdpZGFzIHBvciBjb250cmF0byBvdSBhY29yZG8uCgpBIFVDQiBzZSBjb21wcm9tZXRlIGEgaWRlbnRpZmljYXIgY2xhcmFtZW50ZSBvIHNldSBub21lIChzKSBvdSBvKHMpIG5vbWUocykgZG8ocykgZGV0ZW50b3IoZXMpIGRvcyBkaXJlaXRvcyBhdXRvcmFpcyBkYSB0ZXNlIG91IGRpc3NlcnRhPz8/P28sIGUgbj8/byBmYXI/PyBxdWFscXVlciBhbHRlcmE/Pz8/bywgYWw/P20gZGFxdWVsYXMgY29uY2VkaWRhcyBwb3IgZXN0YSBsaWNlbj8/YS4KBiblioteca Digital de Teses e Dissertaçõeshttps://bdtd.ucb.br:8443/jspui/ |
dc.title.por.fl_str_mv |
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 |
title |
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 |
spellingShingle |
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 Del Rosso, Sebasti??n For??a Ritmo de corrida Economia de corrida Salto com contramovimento Desempenho de resist??ncia Potencializa????o p??s-ativa????o Post-activation potentiation Countermovement jump Endurance performance Running economy Strength Pacing CNPQ::CIENCIAS DA SAUDE::EDUCACAO FISICA |
title_short |
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 |
title_full |
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 |
title_fullStr |
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 |
title_full_unstemmed |
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 |
title_sort |
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 |
author |
Del Rosso, Sebasti??n |
author_facet |
Del Rosso, Sebasti??n |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Boullosa, Daniel A. |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/9059576682332603 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/3239120088596927 |
dc.contributor.author.fl_str_mv |
Del Rosso, Sebasti??n |
contributor_str_mv |
Boullosa, Daniel A. |
dc.subject.por.fl_str_mv |
For??a Ritmo de corrida Economia de corrida Salto com contramovimento Desempenho de resist??ncia Potencializa????o p??s-ativa????o Post-activation potentiation Countermovement jump Endurance performance Running economy Strength Pacing |
topic |
For??a Ritmo de corrida Economia de corrida Salto com contramovimento Desempenho de resist??ncia Potencializa????o p??s-ativa????o Post-activation potentiation Countermovement jump Endurance performance Running economy Strength Pacing CNPQ::CIENCIAS DA SAUDE::EDUCACAO FISICA |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS DA SAUDE::EDUCACAO FISICA |
dc.description.abstract.eng.fl_txt_mv |
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<0,001), ??2 = 0,32] and group [F(1-25) = 49,91; P<0,001, ??2 = 0,66] and also differences in the explaining variables for T10km: GAD [SMAX; SSCPF, HRMEAN, CV10km e Post-0min CMJPVE, F(5-9) = 266,06; P <0,001; SSE = 0,09 min; r2ADJUSTED = 0,99]; GBD [VT2-%VO2max, ??6-Pre CMJEPV, CR; F(4-18) = 33,16; P <0,001, EES = 0,045 min; r2ADJUSTED = 0,88]. Furthermore, different prediction equations were found for each group: GAD ??? [T10km (min) = 68,65 ??? (1,084 ?? SMAX) ??? (0,008 ?? SSCPF) + (0,083 ?? AGCARGA); r2 = 0,98]; GBD - T10km (min) = 44,75 ??? (1,05?? SMAX) + (0,17??VT2-%VO2max) + (1,89 ?? CMJVME) ??? (0,061 ?? Age); r2 = 0,89]. For jump potentiation groups there were significant differences only in the last 400 m and RPE (GNP = 8,36 ?? 1,6 vs. GP = 6,8 ?? 1,7; P = 0,03). Also, jump potentiation correlated with the final 400 m time in the whole sample (r = -0.42; P = 0,031) and with RPE for the GAD group (r = -0,75; P = 0,032). In conclusion, the results of the present study suggest that mechanical factors are significant for endurance runners given that explain part of the variance in the T10km while allowed for performance prediction. Moreover, performance level appears to be related to neuromuscular differences influencing pacing whereas jump potentiation likely affects effort perception. |
dc.description.abstract.por.fl_txt_mv |
O objetivo do presente estudo foi analisar os diversos fatores que podem influenciar e por tanto explicar o desempenho em uma prova de corrida de 10 km assim como tamb??m em subsegmentos dos 10 km, e predizer desempenho a partir de vari??veis metab??licas/respirat??rias e neuromusculares. Para tal fim, 27 corredores bem treinados (idade = 26,4 ?? 6,5 anos, experi??ncia de treinamento = 7,4 ?? 5,9 anos, volume de treinamento = 89,1 ?? 39,1 km??semana-1, VO2max = 62,3 ?? 4,5 mL??kg-1??min-1) completaram tr??s sess??es de avalia????o: A primeira sess??o foi dedicada ?? determina????o das vari??veis mec??nicas (pico de velocidade conc??ntrica, PV; tempo at?? o pico de velocidade, TPV; pico de for??a, PF e pico de pot??ncia, PP) nos exerc??cios de m??dio agachamento (AG) e salto com sobrecarga (SSC) e das vari??veis associadas ?? composi????o corporal; durante a segunda sess??o se avaliaram vari??veis metab??licas [VO2max, limiares ventilat??rios (VT1, primer limiar ventilat??rio, VT2, segundo limiar ventilat??rio), custo energ??tico da corrida (CR) e velocidade m??xima (SMAX)] conjuntamente com a potencializa????o no salto vertical (CMJ); e durante a terceira sess??o se registrou o desempenho em uma prova simulada de 10 km (T10km) com monitoramento continuo da velocidade (GPS) e da frequ??ncia card??aca (FC). Antes e depois (0, 3, 6 e 9 min) dos 10 km os atletas completaram 2 saltos verticais (CMJ) para ?? avalia????o das vari??veis mec??nicas associadas ao salto [deslocamento exc??ntrico (DE), velocidade m??dia exc??ntrica e conc??ntrica (VME, VMC), pico de velocidade exc??ntrica e conc??ntrica (PVE, PVC)]. O ritmo de corrida foi definido como o tempo ou velocidade a cada 1000 m, e para as an??lises dos fatores implicados na vari??ncia do desempenho em 10 km foi realizada uma an??lise de regress??o m??ltipla hier??rquica utilizando todas as vari??veis dispon??veis. Al??m disso, an??lises de regress??o foram completadas para determinar equa????es de predi????o do T10km com vari??veis independentes das registradas durante a prova. Entanto que analises por conglomerados foram utilizados para analisar os efeitos do n??vel de desempenho (grupo de alto desempenho, GAD; grupo de baixo desempenho, GBD) e da potencializa????o do salto vertical (grupo que exibiu potencializa????o, GP; grupo que n??o potencializou, GNP). Para o total de 27 atletas o modelo final que incluiu a SMAX (km??h-1), a CR (mL??kg-1??m-1), o a AGVP (m??s-1), o ??3-Pre CMJPVE (m??s-1), a FCmax (bpm) e a SSCPF (N) foi estatisticamente significativo; r2 = 0,91, F(6-26) = 35,64, P < 0,001, EES = 0,76, r2ajustado = 0,89. Por outra parte, o modelo para a predi????o do T10km, com vari??veis independentes da prova de 10 km, incluiu a SMAX, o CR e AGVP [r2 = 0,75; F(3-26) = 22,52; P < 0,001; EES = 1,23]. As analises por grupo de desempenho indicaram efeitos principais do tempo (Tempos parciais, Laps) [F(2-52) = 12,20, P<0,001), ??2 = 0,32] e do grupo [F(1-25) = 49,91; P<0,001, ??2 = 0,66] assim como diferencias nas vari??veis que explicaram a vari??ncia no T10km: para GAD [SMAX; SSCPF, FCM??DIA, CV10km e P??s-0min CMJPVE, F(5-9) = 266,06; P <0,001; SSE = 0,09 min; r2AJUSTADO = 0,99]; GBD [VT2-%VO2max, o ??6-Pre CMJEPV, CR; F(4-18) = 33,16; P <0,001, EES = 0,045 min; r2AJUSTADO = 0,88]. Adicionalmente, acharam-se equa????es diferentes para a predi????o do T10km em cada um dos grupos: GAD ??? [T10km (min) = 68,65 ??? (1,084 ?? SMAX) ??? (0,008 ?? SSCPF) + (0,083 ?? AGCARGA); r2 = 0,98]; GBD - T10km (min) = 44,75 ??? (1,05?? SMAX) + (0,17??VT2-%VO2max) + (1,89 ?? CMJVME) ??? (0,061 ?? Idade); r2 = 0,89]. Enquanto aos grupos de potencializa????o, se acharam diferen??as significativas entre os grupos na velocidade atingida s?? no segmento de 400 m finais e na PSE final (GNP = 8,36 ?? 1,6 vs. GP = 6,8 ?? 1,7; P = 0,03). Ademais, na amostra completa a potencializa????o correlacionou com o tempo nos 400 m finais (r = -0.42; P = 0,031) e no grupo GAD, correlacionou com a PSE (r = -0,75; P = 0,032). Em conclus??o, os resultados deste estudo sugerem que as vari??veis mec??nicas s??o importantes para corredores de 10 km j?? que permitem explicar a vari??ncia e predizer o desempenho. Al??m disso, o n??vel de desempenho parece estar associado com diferencias neuromusculares que influenciam o ritmo de corrida, entanto que a potencializa????o do salto vertical parece afeitar sobre tudo a percep????o do esfor??o. |
description |
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<0,001), ??2 = 0,32] and group [F(1-25) = 49,91; P<0,001, ??2 = 0,66] and also differences in the explaining variables for T10km: GAD [SMAX; SSCPF, HRMEAN, CV10km e Post-0min CMJPVE, F(5-9) = 266,06; P <0,001; SSE = 0,09 min; r2ADJUSTED = 0,99]; GBD [VT2-%VO2max, ??6-Pre CMJEPV, CR; F(4-18) = 33,16; P <0,001, EES = 0,045 min; r2ADJUSTED = 0,88]. Furthermore, different prediction equations were found for each group: GAD ??? [T10km (min) = 68,65 ??? (1,084 ?? SMAX) ??? (0,008 ?? SSCPF) + (0,083 ?? AGCARGA); r2 = 0,98]; GBD - T10km (min) = 44,75 ??? (1,05?? SMAX) + (0,17??VT2-%VO2max) + (1,89 ?? CMJVME) ??? (0,061 ?? Age); r2 = 0,89]. For jump potentiation groups there were significant differences only in the last 400 m and RPE (GNP = 8,36 ?? 1,6 vs. GP = 6,8 ?? 1,7; P = 0,03). Also, jump potentiation correlated with the final 400 m time in the whole sample (r = -0.42; P = 0,031) and with RPE for the GAD group (r = -0,75; P = 0,032). In conclusion, the results of the present study suggest that mechanical factors are significant for endurance runners given that explain part of the variance in the T10km while allowed for performance prediction. Moreover, performance level appears to be related to neuromuscular differences influencing pacing whereas jump potentiation likely affects effort perception. |
publishDate |
2018 |
dc.date.accessioned.fl_str_mv |
2018-08-08T17:35:47Z |
dc.date.issued.fl_str_mv |
2018-02-28 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
status_str |
publishedVersion |
format |
masterThesis |
dc.identifier.citation.fl_str_mv |
DEL ROSSO, Sebasti??n. 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. 2018. 73 f. Disserta????o (Programa Stricto Sensu em Educa????o F??sica) - Universidade Cat??lica de Bras??lia, Bras??lia, 2018. |
dc.identifier.uri.fl_str_mv |
https://bdtd.ucb.br:8443/jspui/handle/tede/2441 |
identifier_str_mv |
DEL ROSSO, Sebasti??n. 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. 2018. 73 f. Disserta????o (Programa Stricto Sensu em Educa????o F??sica) - Universidade Cat??lica de Bras??lia, Bras??lia, 2018. |
url |
https://bdtd.ucb.br:8443/jspui/handle/tede/2441 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Cat??lica de Bras??lia |
dc.publisher.program.fl_str_mv |
Programa Stricto Sensu em Educa????o F??sica |
dc.publisher.initials.fl_str_mv |
UCB |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Escola de Sa??de e Medicina |
publisher.none.fl_str_mv |
Universidade Cat??lica de Bras??lia |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da UCB instname:Universidade Católica de Brasília (UCB) instacron:UCB |
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Universidade Católica de Brasília (UCB) |
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UCB |
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UCB |
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Biblioteca Digital de Teses e Dissertações da UCB |
collection |
Biblioteca Digital de Teses e Dissertações da UCB |
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