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Øyvind Sandbakk, Thomas Haugen, and Gertjan Ettema

Purpose: To provide novel insight regarding the influence of exercise modality on training load management by (1) providing a theoretical framework for the impact of physiological and biomechanical mechanisms associated with different exercise modalities on training load management in endurance exercise and (2) comparing effort-matched low-intensity training sessions performed by top-level athletes in endurance sports with similar energy demands. Practical Applications and Conclusions: The ability to perform endurance training with manageable muscular loads and low injury risks in different exercise modalities is influenced both by mechanical factors and by muscular state and coordination, which interrelate in optimizing power production while reducing friction and/or drag. Consequently, the choice of exercise modality in endurance training influences effort beyond commonly used external and internal load measurements and should be considered alongside duration, frequency, and intensity when managing training load. By comparing effort-matched low- to moderate-intensity sessions performed by top-level athletes in endurance sports, this study exemplifies how endurance exercise with varying modalities leads to different tolerable volumes. For example, the weight-bearing exercise and high-impact forces in long-distance running put high loads on muscles and tendons, leading to relatively low training volume tolerance. In speed skating, the flexed knee and hip position required for effective speed skating leads to occlusion of thighs and low volume tolerance. In contrast, the non-weight-bearing, low-contraction exercises in cycling or swimming allow for large volumes in the specific exercise modalities. Overall, these differences have major implications on training load management in sports.

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Melissa DiFabio, Lindsay V. Slater, Grant Norte, John Goetschius, Joseph M. Hart, and Jay Hertel

measures (Table  3 ). The factors accounted for 83.8% of the variance in the data set. All variables within factors had significant moderate to strong correlations. Table 3 Variance Explained (%) for Involved (Reconstructed Group) and Nondominant (Healthy Group) Limb Factor Loading Factors 1 2 3 4 5 6

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Glauber Carvalho Nobre, Marcelo Gonçalves Duarte, Rodrigo Flores Sartori, Maike Tietjens, and Nadia Cristina Valentini

factor for the PSPPS-BR model. Figure 1 —Load factors for PSPPS-BR model. PSPPS-BR = Pictorial Scale of Physical Self-Concept for Brazilian Children. The invariance of the model for boys and girls and for ages was tested using multigroup analysis. The model without constriction demonstrated

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Richard Johnston, Roisin Cahalan, Laura Bonnett, Matthew Maguire, Alan Nevill, Philip Glasgow, Kieran O’Sullivan, and Thomas Comyns

). 22 TL factors (Table  2 ) were calculated using Microsoft Excel (Microsoft Corp, Redmond, WA) Table 2 Training-Load-Factor Definitions and Calculations Training-load factor Definition Calculation Session training load (sRPE) 15 , 19 Measure of session internal and external training load sRPE

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Johanna M. Hoch, Shelby E. Baez, Robert J. Cramer, and Matthew C. Hoch

all items had a loading factor of >.58. 13 Although the model fit indices did not fall within ideal fit ranges in the literature, 18 , 19 the confirmatory factor analysis, root mean square error of approximation, and standardized root mean square residual were within acceptable ranges of adequate

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Laurent Schmitt, Stéphane Bouthiaux, and Grégoire P. Millet

content. 7 However, to our knowledge, there is no study reporting training characteristics and HRV data over a long period (>10 y) in athletes of this performance level. So, the aim of this study was to describe the overall training, as well as the relationship between the development of key load factors

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Megan B. Shreffler, Adam R. Cocco, Regina G. Presley, and Chelsea C. Police

results from this factor analysis. Table 3 Summary Results for Factor Analysis of ASSIST Item M SD h 2 Factor 1: strategic factor loading Factor 2: deep factor loading Factor 3: surface factor loading SM 13.18 2.75 .66 .23 .76 −.16 RI 14.16 2.36 .61 .14 .76 .06 UE 13.75 2.20 .68 .13 .79 .17 II 13.97 2

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Pawel R. Golyski, Elizabeth M. Bell, Elizabeth M. Husson, Erik J. Wolf, and Brad D. Hendershot

Moreover, the mechanical and biological response of joint tissues are also modulated by other (nonpeak) loading factors such as rate and duration. Given this, one might presume that such consideration for joint loading characterization would likely also apply in the context of movement-based rehabilitation

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Olfa Turki, Wissem Dhahbi, Sabri Gueid, Sami Hmaied, Marouen Souaifi, and Riadh Khalifa

2 = 0.49 [small]) and time ( P  < .001, η p 2 = 0.64 [moderate]) factors with a significant interaction between time and load factors ( P  = .004, η p 2 = 0.16 [trivial]) (Table  3 ). Discussion The present study aimed to (a) explore the effect of 4 different warm-up strategies: weighted vest

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Amanda Timler, Fleur McIntyre, and Beth Hands

.796. Table 1 Factor Analysis (Principal Components With Varimax Rotation) and Loading Factors of the Adolescent Motor Competence Questionnaire Component Factor Items 1 2 3 4 Participation in physical activity and sports Participate in sports game .876 Hit a ball with bat .803 Kick a ball .802