Monitoring tests are commonly used to assess weightlifter’s preparedness for competition. Although various monitoring tests have been used, it is not clear which test is the strongest indicator of weightlifting performance. Therefore, the purpose of this study was to (1) determine the relationships between vertical jump, isometric mid-thigh pull (IMTP) and weightlifting performance; and (2) compare vertical jumps to IMTP as monitoring tests of weightlifting performance in a large cohort of male and female weightlifters. Methods: Fifty-two competitive weightlifters (31 males, 21 females) participated in squat and countermovement jump testing (SJ, CMJ), and IMTP testing performed on force plates. All laboratory testing data was correlated to a recent competition where the athletes had attempted to peak. Results: Squat jump height (SJH) was the strongest correlate for men and women with the Sinclair Total (r = 0.686, p ≤ 0.01; r = 0.487, p ≤ 0.05, respectively) compared to countermovement jump height (r = 0.642, p ≤ 0.01; r = 0.413, p = 0.063), IMTP peak force allometrically scaled to body mass (r = 0.542, p ≤ 0.01; r = −0.044, p = 0.851) and rate of force development at 200 ms (r = 0.066, p = 0.723; r = 0.086, p = 0.711), respectively. Further, SJH was a stronger correlate of relative weightlifting performance compared to IMTP peak force in females (p = 0.042), but not male weightlifters (p = 0.191). Conclusions: Although CMJ and IMTP are still considered strong indicators of weightlifting performance, SJH appears to be the most indicative measure of weightlifting performance across a wide-range of performance levels. Thus, SJH can be used as a reliable measure to monitor weightlifting performance in male and female weightlifters.

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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, Δ3-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), η2 = 0,32] and group [F(1-25) = 49,91; P<0,001, η2 = 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, Δ6-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 também em subsegmentos dos 10 km, e predizer desempenho a partir de variáveis metabólicas/respiratórias e neuromusculares. Para tal fim, 27 corredores bem treinados (idade = 26,4 ± 6,5 anos, experiência 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 três sessões de avaliação: A primeira sessão foi dedicada à determinação das variáveis mecânicas (pico de velocidade concêntrica, PV; tempo até o pico de velocidade, TPV; pico de força, PF e pico de potência, PP) nos exercícios de médio agachamento (AG) e salto com sobrecarga (SSC) e das variáveis associadas à composição corporal; durante a segunda sessão se avaliaram variáveis metabólicas [VO2max, limiares ventilatórios (VT1, primer limiar ventilatório, VT2, segundo limiar ventilatório), custo energético da corrida (CR) e velocidade máxima (SMAX)] conjuntamente com a potencialização no salto vertical (CMJ); e durante a terceira sessão se registrou o desempenho em uma prova simulada de 10 km (T10km) com monitoramento continuo da velocidade (GPS) e da frequência cardíaca (FC). Antes e depois (0, 3, 6 e 9 min) dos 10 km os atletas completaram 2 saltos verticais (CMJ) para à avaliação das variáveis mecânicas associadas ao salto [deslocamento excêntrico (DE), velocidade média excêntrica e concêntrica (VME, VMC), pico de velocidade excêntrica e concêntrica (PVE, PVC)]. O ritmo de corrida foi definido como o tempo ou velocidade a cada 1000 m, e para as análises dos fatores implicados na variância do desempenho em 10 km foi realizada uma análise de regressão múltipla hierárquica utilizando todas as variáveis disponíveis. Além disso, análises de regressão foram completadas para determinar equações de predição do T10km com variáveis independentes das registradas durante a prova. Entanto que analises por conglomerados foram utilizados para analisar os efeitos do nível de desempenho (grupo de alto desempenho, GAD; grupo de baixo desempenho, GBD) e da potencialização do salto vertical (grupo que exibiu potencialização, GP; grupo que não 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 Δ3-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 predição do T10km, com variáveis 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), η2 = 0,32] e do grupo [F(1-25) = 49,91; P<0,001, η2 = 0,66] assim como diferencias nas variáveis que explicaram a variância no T10km: para GAD [SMAX; SSCPF, FCMÉDIA, CV10km e Pós-0min CMJPVE, F(5-9) = 266,06; P <0,001; SSE = 0,09 min; r2AJUSTADO = 0,99]; GBD [VT2-%VO2max, o Δ6-Pre CMJEPV, CR; F(4-18) = 33,16; P <0,001, EES = 0,045 min; r2AJUSTADO = 0,88]. Adicionalmente, acharam-se equações diferentes para a predição 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 potencialização, se acharam diferenças significativas entre os grupos na velocidade atingida só 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 potencialização 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 conclusão, os resultados deste estudo sugerem que as variáveis mecânicas são importantes para corredores de 10 km já que permitem explicar a variância e predizer o desempenho. Além disso, o nível de desempenho parece estar associado com diferencias neuromusculares que influenciam o ritmo de corrida, entanto que a potencialização do salto vertical parece afeitar sobre tudo a percepção do esforço.

Physical tasks such as scrummaging, rucking and mauling are highly specific to rugby and also place unique physiological demands on the different playing positions within the forwards. Traditionally, the recruitment and development of talented rugby union players has focused on the assessment of motor skills and game intelligence aspects of performance, with less emphasis placed on the specific physiological requirements of playing positions in rugby. The purpose of this investigation was to measure the position-specific strength, speed and power characteristics of Premier rugby forwards in order (1) to determine whether any differences existed in the physiological characteristics of the different forward playing positions (prop, lock and loose forwards) and (2) to investigate the relationship between these physiological characteristics and coaches evaluations of football playing ability. Twenty-two male Premier level competition rugby forwards, consisting of eight prop forwards, five lock forwards and nine loose-forwards participated in the study. The Grunt 3000, a rugby specific force testing device was utilised to measure the static and dynamic horizontal strength during simulated scrummaging and rucking/mauling movements. Sprint times relating to acceleration ability (0 -10m, 0-20m) and maximum running speed (20 - 40m) were measured during a 40m sprint running test. In addition, force, power and displacement characteristics of a countermovement vertical jump were calculated from trials performed on a force plate. Also, player performance skill and physical capacity scores were determined independently by experienced coaches who assessed them based on their performances during the season. One-way analysis of variance and effect size statistics evaluated differences in the measured variables between forward playing positions and linear regression analysis evaluated the relationship between the coaches' scores of player performance skill and physical capacity and game specific measures of strength speed and power. Since there were no statistical significant differences between forward groups for horizontal force and countermovement jump variables and these analyses lacked statistical power, an effect size statistic was used to establish trends for differences in force and CMJ variables between the groups. There were moderate effect size differences between groups for horizontal impact force with prop and lock forwards producing 17.7% and 12.8% more force than the loose forwards respectively. No clear differences were apparent between forward positional groups for mean dynamic horizontal force and countermovement jump displacement of the centre of gravity. A significant difference (p =0.049) was shown between forward positional groups over the 0-40m sprint distance. Also, moderate effect size differences between pairs of groups were evident in 0-10m, 0-20m, 20-40m sprint times with both loose forwards and lock forwards on average, 6% faster than the prop forwards. A backward linear regression analysis revealed that the single best predictor of coaches' physical capacity and performance skill scores was the 20 - 40m sprint performance, accounting for 28% of the variance in player's physical capacity scores and 29% of the variance in player's performance skill scores. Whole-body horizontal static strength and impact strength in prop forwards and dynamic horizontal strength (relative to body mass) and sprint acceleration ability in loose forwards represent key factors for consideration when selecting forward players to these positions in the Premier rugby competition. The vertical jumping ability of all forward positional groups needs to be confirmed in a future study utilising a line-out specific countermovement jump test (free use of arm swing and line-out lifters in the jump) on a force plate. Monitoring of performance in rugby forwards should include an acceleration sprint test (0-10m) as this is specific to the sprinting patterns of forward players during a game, and maximum sprinting speed test (20-40m) as this test has the ability to discriminate between skilled and less-skilled rugby union forwards.

Introduction : Les joueurs de hockey sur glace effectuent des mouvements asymétriques en utilisant la rotation du torse et des hanches lors des passes et des tirs. Les asymétries entre les segments corporels peuvent avoir un impact sur la capacité de sprints répétés, la puissance verticale lors de sauts à contre-bas et les performances sportives générales. La force isométrique de la hanche et le saut à contre-bas sont couramment évalués chez les joueurs de hockey sur glace en raison de leurs relations avec les performances en patinage et l'incidence des blessures à l'aine, respectivement. Objectif : Cette étude vise explorer si les asymétries obtenues lors des évaluations des forces isométriques de la hanche et des sauts à contre-bas sont associées à celles observées lors du patinage. Méthodes : Trente-sept joueurs professionnels de l'hockey sur glace ont effectué des évaluations hebdomadaires de force et de saut de la hanche et ont porté des centrales inertielles pendant les séances sur glace tout au long des saisons pré et compétitives. Les accélérations sont mesurées pour les deux jambes et ont été utilisées pour calculer les asymétries inter-membres, en pourcentage. Résultats : Parmi tous les paramètres mesurés, seule l'asymétrie dans le pic de la force de l’attérissage du saut à contre-bas a dépassé 10% pour toutes les positions (22,1%) et par position (21,3% - 22,6%). Les joueurs de centre et à la défense ont mené à plusieurs relations modérées à grandes entre différentes asymétries lors des évaluations hors-glace (r: -0,67 - 0,38, p <0,01). Toutes les positions ont montré des relations modérées à grandes entre la résistance à la hanche et la charge de patinage sur la glace et la force moyenne par foulée (r: -0,32 - 0,56, p <0,05). Les joueurs de centre ont montré des relations modérées entre le saut à contre-bas et des asymétries sur la glace (r: -0,31 - 0,43, p <0,01). Conclusion Cette étude a révélé qu'il existe des relations significatives entre les asymétries sur- et hors glace dans le hockey professionnel masculin. Les résultats de cette étude fournissent également aux intervenants auprès de cette population des valeurs de référence pour les asymétries sur glace et hors glace. / Introduction Ice-hockey players develop asymmetrical movement patterns by favoring rotation through the torso and hips while passing and shooting. Interlimb asymmetries have been shown to affect repeated sprint ability, vertical and horizontal countermovement jump power, and general athletic performance. Isometric hip strength and the countermovement jump are commonly assessed in ice-hockey players because of their relationships with skating performance and incidence of groin injuries, respectively. Purpose: This study explored whether asymmetries returned during isometric hip strength and countermovement jump assessments relate to those from stride-by-stride analyses. Methods: Thirty-seven professional ice-hockey players performed weekly hip strength and jump assessments and wore inertial momentum units during on-ice sessions throughout the pre- and competitive seasons. Data were either available for both limbs and were utilized to calculate inter-limb asymmetries, or as an asymmetry percentage. Results: Among all parameters measured, only the CMJ peak landing force asymmetry exceeded 10% for all positions (22.1%) and by position (21.3% - 22.6%). Centers and Defense positions returned several moderate to large relationships between fitness assessment asymmetries (r: -0.67 – 0.38, p < 0.01). All positions returned moderate to large relationships between hip strength and on-ice skating load and average force per stride (r: -0.32 – 0.56, p < 0.05). Centers returned moderate countermovement jump and on-ice asymmetries (r: -0.31 – 0.43, p < 0.01). Conclusion: This study revealed that significant relationships exist between on- and off-ice asymmetries in men’s professional ice-hockey. The results from this study also provide practitioners with reference values for on- and off-ice asymmetries.

Im Zeitraum 2004-2006 wurde in Georgien eine beispiellose Deregulierung des Arbeitsmarktes vorgenommen, um die internationale Wettbewerbsfähigkeit zu verbessern. Diese Maßnahmen waren Teil umfassenderer Vermarktlichung- und Privatisierungsbemühungen, die von den so genannten "Rosenrevolutionären" Eliten angeführt wurden. Diese Studie, die sich auf Karl Polanyis Konzept der Arbeit als "fiktive Ware" stützt, untersucht, wie eine solche Kommodifizierung die Wirtschaft von der Gesellschaft entkoppelt und gesellschaftliche Gegenbewegungen hervorruft, die sich gegen die Expansion des Marktes schützen wollen. Polanyis "Doppelbewegung" beschreibt gesellschaftspolitische Kräfte, die eine demokratische Kontrolle des politischen und wirtschaftlichen Lebens anstreben und die künstliche Trennung zwischen beiden in Frage stellen. Die wichtigsten empirischen Fragen lauten, ob die georgische Gesellschaft auf die Marktöffnung reagiert hat und welche Konflikte daraus entstanden sind. Die Studie beleuchtet zwei Protestbewegungen, die sich gegen die Marktideologie wandten und versuchten, die schädlichen Auswirkungen der Deregulierung des Arbeitsmarktes abzumildern. Trotz ihrer Bemühungen gelang es diesen Bewegungen nicht, eine breitere demokratische Kontrolle über das Wirtschaftsleben zu etablieren. Wenn es diesen Bewegungen nicht gelungen ist, die Wirtschaft wieder in die Gesellschaft einzubinden, wie kann dann eine demokratische Kontrolle der wirtschaftlichen Sphäre theoretisiert und erreicht werden? Die Studie bezieht die Gramscianischen Analysen der "Beziehungen der sozialen Kräfte" mit ein und argumentiert, dass die Infragestellung der marktgetriebenen Ordnung mehr als nur spezifische, themenbezogene Antworten erfordert. Durch die Integration von Polanyi und Gramsci schlägt die Studie die Notwendigkeit eines politisch sinnvolleren Ansatzes vor, um der Marktideologie zu begegnen und eine demokratische Kontrolle über die Wirtschaft zu etablieren. / In the period of 2004-2006, Georgia underwent unprecedented labor deregulation to enhance international competitiveness and attract foreign investments. These policies, leaving most workers unprotected, were part of broader marketization and privatization efforts led by the so-called "Revolutionary" political elites following the Rose Revolution. This study, drawing on Karl Polanyi's concept of labor as a "fictitious commodity," explores how such commodification disembedds the economy from society, prompting societal countermovements seeking to protect against market expansion. Polanyi's "double movement" describes sociopolitical forces aiming for democratic control over political and economic life, challenging the artificial separation between the two. The key empirical questions addressed are whether Georgian society responded to labor marketization and what social conflicts ensued. The study focuses on two protest movements—the Tbilisi Metro strikes and Chiatura miners’ strike—which opposed market ideology and sought to mitigate the harmful effects of labor deregulation. Despite their efforts, these movements failed to establish broader democratic control over economic life, representing "corporatist re-embedding" and "pre-political" responses rather than achieving full "re-embedding" of the economy into society. The study poses a theoretical question: if these movements couldn't successfully re-embed the economy in society, how can democratic control over the economic sphere be theorized and achieved? To address this, the work incorporates Gramscian analyses of “relations of social forces” and hegemony, arguing that challenging market-driven order requires more than specific issue-driven responses. By integrating Polanyi with the Gramscian framework, the study suggests the need for a politically meaningful approach to countering market ideology and establishing democratic control over the economy.

Title: A comparison of dynamic abilities in nordic ski disciplines Aim: This dissertation aims to compare the dynamic abilities of cross country skiers and ski jumper in age between 12 to 15. Clarification of whether or not a training process of those two athlete groups has a crucial impact on the development of mentioned abilities compared to the other hobby sports group (target group). Methodology: This dissertation is conceived as empiric-theoretical. It analyses the results of samples to verify the hypothesis. There were three groups consisting of 10 samples (athletes) in total. The samples were tested in horizontal jumps (long jump, triple jump, multiple jumps) and vertical jumps (squat jump, counter movement jump). In all tests, it was decided to use only the best of all three attempts for the results. Findings: Development of dynamic abilities during the training process has a positive impact on their development in general. Both cross country skiers and ski jumpers achieved better results than the target group. Furthermore, ski jumpers performed better than cross country skiers. Keywords: physical health, sport performance, cross country skiing, ski jumping, squat jump (SJ), countermovement jump (CMJ), long jump, triple jump, multiple jumps