Toppan Best-set Premedia Limited Proofreader: Mony Delivery date: 10 Sep 2015 bs_bs_query
Journal Code: SMS Article No: SMS12558 Page Extent: 9 Scand J Med Sci Sports 2015: ••: ••–•• doi: 10.1111/sms.12558
© 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
Determinant factors in climbing ability: Influence of strength, anthropometry, and neuromuscular fatigue
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G. Laffaye, G. Levernier, J.-M. Collin
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UR CIAMS – Motor Control and Perception Group, Sport Sciences Department, Université Paris-Sud, Orsay, France Corresponding author: Guillaume Laffaye, Laboratoire Contrôle Moteur et Perception, University Paris-Sud, Bât 335, 91405 Orsay Cedex, France. Tel: + 33 1 69 15 31 59, Fax: + 33 1 69 15 62 22, E-mail:
[email protected]
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Accepted for publication 23 August 2015
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The goal of this study was to (i) assess the physical and anthropometric differences between three levels of climbers and (ii) predict climbing ability by using a multiple regression model. The participants were divided into novice (n = 15), skilled (n = 16), and elite (n = 10) climbers. Anthropometric characteristics such as height, weight, percentage of body fat and muscle, bi-acromial breath, arm span, and ape index were measured. General and specific strength were assessed through an arm jump test, a bench press test, and a hand and finger grip strength test in maximal and endurance conditions. All variables were combined into components via a principal component analysis (PCA) and the components used in a
multiple regression analysis. The major finding of this study is that climbing ability is more related to specific rather than general strength. Only finger grip strength shows a higher level of initial strength between all samples while the arm jump test discriminates between climbers and non-climbers. The PCA reveals three components, labeled as training, muscle, and anthropometry, which together explain 64.22% of the variance. The regression model indicates that trainable variables explained 46% of the total variance in climbing ability, whereas anthropometry and muscle characteristics explain fewer than 4%.
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Climbing is a sport that includes different subdisciplines, such as rock climbing and bouldering. It can be performed either indoors in specially built structures or at natural outdoor sites (Watts, 2004). During the last few decades, an increased number of studies have sought to explain climbing performance (Magiera et al., 2013; Laffaye et al., 2014) by assessing the contribution of anthropometric variables (Watts et al., 1993; Mermier et al., 2000; Watts et al., 2003; Laffaye et al., 2014), physiological variables (Booth et al., 1999; Watts et al., 2000; Giles et al., 2006; Morrison & Schöffl, 2007), and psychological profiles (Sanchez et al., 2012; Magiera et al., 2013). All these studies appear to validate climbing ability as a combination of mental factor, upper-limb strength and power, technique (Sibella et al., 2007), and anthropometric characteristics. When focusing on the strength necessary to be a highly efficient climber, the literature reveals some discrepancies. Firstly, several studies have focused on specific strength, such as hand, finger, or forearm strength (Watts et al., 2008; Macdonald & Callender, 2011; Amca et al., 2012; Philippe et al., 2012), whereas few studies have investigated general upper-limb strength (Draper et al., 2011; Laffaye et al., 2014). These studies have indicated that climbers have a greater specific finger flexor maximal voluntary contraction (MVC) and strength-to-weight ratio than non-climbers (Quaine
et al., 2003; Vigouroux & Quaine, 2006; MacLeod et al., 2007; Macdonald & Callender, 2011). However, this difference is not so obvious in terms of relative hand strength. Indeed, one study found no significant difference between climbers and non-climbers for relative hand strength (Ferguson & Brown, 1997), whereas a study by Macdonald and Callender (2011) revealed that climbers’ hand and finger grip strength was better than that of non-climbers. Even if specific strength appears to be a good way to discriminate between non-climbers and climbers, it has a low predictive power with regard to the level of expertise (Watts et al., 1993). Lastly, when focusing on general upper-limb force, explosive power is significantly correlated with climbing ability (r = 0.69 to 0.70; P < 0.01), as revealed by two studies that carried out an arm jump task (Draper et al., 2011; Laffaye et al., 2014). Thus, one of the goals of our study was to assess the impact of general and specific strength on climbing ability. Moreover, the capacity to maintain a high level of strength in a specific muscular group seems to be another determinant of climbing performance. Indeed, in a study where a sample of high-level sport climbers carried out a climbing task, elite climbers were shown to have a significantly higher time to exhaustion than novices (España-Romero et al., 2009). Another study revealed that elite climbers can last twice as long as non-climbers
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Laffaye et al. (Vigouroux & Quaine, 2006) before they reach the failure point (the point at which the force can no longer be maintained at the required MVC percentage). Based on this theoretical background, the goal of our study was to (i) assess the general and specific strength and the anthropometric differences between three levels of climbers, (ii) assess the impact of expertise on neuromuscular exhaustion, and (iii) predict climbing ability by crossing the relative contribution of each variable in a multiple regression model.
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Methods
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Participants
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In this study, 41 subjects were separated into three groups according to their climbing ability (Table 1). Each subject completed all trials in the same time period of test days to eliminate any influence of circadian variation. Each volunteer signed a written informed consent statement prior to the investigation after receiving oral and written descriptions of the procedures in accordance with guidelines established by the University Human Subject Review Board. They were informed of the risks and benefits of participation in this study.
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Study overview
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The experiment took place between April 2014 and July 2015 and was divided into two sessions to avoid the effects of fatigue; the sessions were separated by 1 week. In the first session, anthropometric measurements were recorded. A bench press task was also carried out, during which a force–velocity profile was assessed. After a 10-min rest, each subject performed a Sorensen test for assessing trunk extensor muscle strength. In the second session, subjects performed a hand grip (HG) test and a finger grip (FG) resistance to fatigue test. After a 10-min rest, they performed a repetitive arm jump (AJ) resistance to fatigue test. All the variables studied when making these measurements were independent; the dependent variable was climbing ability. Climbing ability (CA) was defined according to the most difficult route ever followed: 5a to 9b + on the French scale. The French scale was then transformed into a linear scale (5a = 1, 5a + = 2 . . . 9b + = 28) to enable statistical calculation. To be included in the skill sample, the climbers’ experience had to exceed 3 years, with at least two training sessions undertaken per week. The participants were categorized (Laffaye et al., 2014) as either novice (< 6a), skilled (6c–7b), or elite (≥ 8a) climbers.
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Anthropometric measurement
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We followed the standardized techniques recommended by the International Society for the Advancement of Kinanthropometry
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(Marfell-Jones et al., 2012). Body height, biacromial breath, and arm span were measured using an anthropometer, with 0.1 cm accuracy. For measuring height, each subject stands erect, bare feet with head in Frankfort plane, and we measure with a stadiometer (Matsport, Saint-Ismier, France) the perpendicular distance from the floor to the vertex (the crown of the head). For measuring biacromial breath (Tanner et al., 1969), each subject had to stand with his shoulders relaxed to the point of slumping forward. Standing behind the subject, the measurer then felt for the outside edge of the acromial process of the shoulder blade, which can be felt as a ridge just above the shoulder joint. He then placed the edge of one arm of the anthropometer along the external border of one acromial process and brought the other arm of the anthropometer inwards until its edge rested on the opposite acromial external border. Body mass, percentage of body fat (% BF) and percentage of mass muscle (% MM) were measured using bioelectric impedance scales (Weinberger model DJ-156; Weinberger GmbH & Co, Germany), with 0.1% accuracy. Moreover, the body mass index (BMI) was calculated as the ratio of the mass (kg) to the squared height (m).
Age (year)
Weight (kg)
Height (m)
Body mass index
% of body fat
% of muscle mass
Biacromial length (cm)
Arm span (m)
Ape index
Novice Skilled Elite
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23.0 ± 11.4 26.5 ± 11.5 27.3 ± 15.2
70.5 ± 14.78 69.9 ± 10.24 67.2 ± 13.6
1.76 ± 0.09 1.76 ± 0.08 1.756 ± 0.11
22.6 ± 3.8 22.6 ± 3.1 21.8 ± 3.9
14.3 ± 5.6 12.8 ± 5.3 12.1 ± 6.8
45.9 ± 5.2 44.9 ± 4.1 45.9 ± 4.2
35.3 ± 0.02 35.9 ± 0.02 34.1 ± 0.02
1.78 ± 0.12 1.81 ± 0.06 1.81 ± 0.06
1.00 ± 0.03†‡ 1.02 ± 0.02* 1.03 ± 0.03*
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*Significantly different than novices. † Significantly different than skilled (P < 0.05). ‡ Significantly different than elite (P < 0.05).
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Level
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The power and force–velocity profile of the upper-limb was assessed using an arm jump test and a bench press test. In the arm jump test, the participants performed an explosive pull-up movement. With chalked hands, they adopted a standing and motionless position, hanging at full elbow extension from the holds. They then slapped as high as possible on the scaled board above (Laffaye et al., 2014). The highest point touched by the lowest hand during this test was called the AJ. This test has been previously assessed as highly reliable and valid (Laffaye et al., 2014). A maximal performance was recorded using the best of a three-trial series. After a 5-min rest, a protocol of resistance fatigue was performed by repeating this test ten more times with a 10-s rest between each trial. During this rest period, the subjects stood on the ground. The countdown was managed using two beep signals. One preparatory signal (warm-up signal) was emitted 3 s before the jump. On hearing this signal, the subject caught the holds. A jumping signal was then given, following which the subjects performed an arm jump. An arm jump fatigue index (AJ-FI) was calculated as the first value of the arm jump performance minus the last value, divided by the first value ×100. The force–velocity profile of upper-limb strength was assessed via a one-repetition maximum (1RM) bench press, with a free weight barbell machine. Participants were instructed to take hold of the bar (step 1), position it on the chest (step 2), and raise it as fast as they could until a full extension of the elbows occurred (step 3). The initial mass was 17 kg and the increment was 10 kg until the velocity became too slow (less than 100 cm/s). The velocity, maximal power and force were calculated using an isoinertial dynamometer (Myotest S.A., Switzerland) with a frequency of 500 Hz. 1RM was assessed using a single regression equation based on the velocity recorded for each bar. This method and
Table 1. Anthropometric profile for three climbing ability group (mean ± SD)
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Upper-limb tests
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Determinant factors in climbing ability 1 2 3 4 5 6 7 8 9 10
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device have been previously validated (Jidovtseff et al., 2008). Moreover, to compare the velocity–load and power–load profiles, it was necessary to normalize the performance. For this purpose, the linear equation of the force–velocity curve was used to transform the absolute load into a percentage of 1RM for each subject. The same procedure was applied to the power–velocity curve with a second polynomial order equation. The mean correlation between raw data and the fitted equation was 0.98 (CV = 2.6%) for the velocity–load curve and 0.93 (CV = 11%) for the power–load curve.
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Finger and hand grip tests
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Hand grip and finger grip strength were measured by means of an electronic hand dynamometer (Camry EH-101©). The maximal strength in a radio-ulnar pronation and supination for both hands were recorded. Three trials for each condition were performed and then averaged (0.91 < ICC < 0.97). Based on these results, the stronger hand was used for the finger grip endurance strength (FG). During this test, subjects were asked to perform a maximal voluntary contraction 20 times over a period of 5 s each, with rest intervals of 5 s. To minimize the use of the hand flexor muscle, the following position was adopted: the subject was sitting on a chair, one arm at 45° from the trunk, the elbow positioned on the table with a 90° angle between arm and forearm and with the whole of the palm of the hand in contact with the table. Only the distal phalanx was in contact with the dynamometer force bar and the thumb in pressure with the opposite side of the handful. Test-retest ICC and CV were 0.93 and 3.2%, respectively. A finger grip fatigue index (FG-FI) was calculated as the first value of finger grip strength minus the last value, divided by the first value ×100 (España-Romero et al., 2009).
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Trunk extensor test
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Trunk extensor muscle isometric endurance was assessed using the Sorensen test (Demoulin et al., 2012), in which the subject were asked to lie on a flat table in the prone position with the pelvis aligned with the edge of the table. Calves and thighs were secured and blocked, and the subjects were asked to maintain the horizontal position (controlled by a rope touching their back) as long as possible, with the arms folded across the chest.
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and the number of components. A multiple regression analysis technique was then used to determine the part of variance explained by the components extracted from the PCA. The number of principal components in the pattern matrix extracted by the PCA was chosen with an eigenvalue greater than one (Kaiser criterion). The original matrix was rotated with a normalized VARIMAX rotation (orthogonal rotation) to extract the appropriate variables. A multiple regression analysis was then performed with climbing ability as the dependent variable and the individual scores on each component after the orthogonal rotation as the independent variables. A step-by-step backward elimination technique was applied to assess the relative contribution of each independent variable (Thompson, 1978).
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Results Anthropometry
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Only the ape index shows significant differences [F(2, 38) = 4.9; P = 0.01, η2 = 0.18, 1-β = 0.58], with skilled and elite climbers revealing higher values than novice climbers (1.03 ± 0.02 vs 1.00 ± 0.03). None of the other variables revealed any differences. All the results are presented in Table 1.
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Force–velocity profile
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The level of expertise did not affect the velocity–load profile [F(2, 38) < 1]. There was an effect of load, showing that velocity decreased linearly with relative load from 2.37 ± 0.04 m/s at 20% of 1RM to 0.66 ± 0.02 m/s at 80% of 1RM. The level of expertise did not affect the power–load profile [F(2, 38) < 1], with mean values ranging from 504 ± 57 W for elite climbers, 532 ± 54 W for novice climbers and 554 ± 54 W for skilled climbers. The load affected power output [F(3, 117) = 64, P = 0.00001, η2 = 0.21, 1-β = 0.99], with a maximum value obtained in the 40%-1RM condition (614 ± 31 W). As shown in Fig. 1, there was no effect of interaction [F(6, 117) < 1].
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Statistical analysis
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All descriptive statistics were used to verify whether the basic assumption of normality of all studied variables was met. The statistical tests were processed using STATISTICA software (version 10, Statsoft, Inc.). The effect of expertise was assessed by a one-way analysis of variance using the Fisher post-hoc test. For the fatigue test, a repetition × expertise mixed-model analysis of variance with repeated measure on the second variable was performed. The level variable (elite vs skilled vs novice) is an intersubject factor and the repetition is an intra-subject factor (10 repetitions for the arm jump and 20 repetitions for the finger grip tests). The level of significance chosen for the statistical analysis was P < 0.05 and the power (1-β) was performed. The effect size was determined by calculating partial η2 and was defined as small for η2 > 0.01, medium η2 > 0.09, and large for η2 > 0.25 (Cohen, 1988). A Pearson product moment correlation was used to determine the relation between climbing ability and the studied variables. The strength test showed the same results with or without bodyweight normalization, probably because of the similar anthropometric profile of our three samples. Consequently, only the absolute values are presented in this study. Moreover, a principal component analysis (PCA) was performed using STATISTICA 10.0 to reduce the number of variables
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General and specific strength
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None of the general strength tests (i.e., the isometric Sorensen test, and the maximal power and 1RM in bench
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Fig. 1. Power–load profile at four percentages of the maximum one repetition during a bench press test at maximum velocity.
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Laffaye et al. 1
Table 2. Physical results for three climbing ability group (mean ± SD)
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Level
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Sorensen test (s)
Power max 1RM bench Hand grip Finger grip Bench press (W) press (kg) max (N) max (N)
Finger grip fatigue Arm jump index (%) (cm)
Arm jump fatigue index (%)
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Novice 15 142.5 ± 43.8 653 ± 223 Skilled 16 139.7 ± 43.7 694 ± 110 Elite 10 131.9 ± 43.8 601 ± 162
31.4 ± 11.6 69.7 ± 22.6 450 ± 122 182 ± 77†‡ 72.8 ± 10.6 548 ± 120 256.8 ± 78.6*‡ 34.1 ± 12.1 30.6 ± 11.6 65.1 ± 12.8 567 ± 121 330 ± 78*†
37.3 ± 11.9* 28.9 ± 9.7†‡ 73.5 ± 12†‡ 12.2 ± 10.02* 76 ± 12* 11.1 ± 10*
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*Significantly different than novices. † Significantly different than skilled (P < 0.05). ‡ Significantly different than elite (P < 0.05). 1RM, one-repetition maximum; SD, standard deviation.
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Fig. 2. Resistance to fatigue during the arm jump test in novice, skilled, and elite climbers.
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press tests) showed a significant difference between the three samples, which all had quite similar values. However, specific strength increased with expertise for the finger grip strength test [F(2, 38) = 10.2; P < 0.0001, η2 = 0.57, 1-β = 0.99], with a difference showing between all categories (Table 2). The hand grip strength test revealed only a tendency with expertise F(2, 38) = 3; P = 0.06, η2 = 0.23, 1-β = 0.60].
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Neuromuscular fatigue
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With regard to the arm jump endurance test, an effect of expertise [F(2, 38) = 38.58, P < 0.001, η2 = 0.69, 1-β = 0.99] can be seen. The Fisher post-hoc test shows a difference between the novices and the two other levels (P < 0.05). There is no effect of fatigue with a mean decrease of 18.1% (Fig. 2). With regard to the finger grip endurance test, an effect of expertise [F(2, 38) = 156.17, P < 0.0001, η2 = 0.42, 1-β = 0.99] can be seen. The Fisher post-hoc test shows a difference between all levels (P < 0.05). There is an effect of fatigue [F(19, 722) = 5.33, P < 0.001, η2 = 0.19, 1-β = 0.99], with a mean decrease of 32.2%. There is an interaction effect [F(38, 722) = 2.89; P < 0.001, η2 = 0.03, 1-β = 0.43], showing that fatigue affected the three samples in different ways (Fig. 3).
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PCA and regression model
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The single correlation study shows a significant correlation between climbing ability and ape index (r = 0.45;
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Fig. 3. Finger grip fatigue index during the finger grip test in novice, skilled, and elite climbers.
P < 0.01, η2 = 0.17, 1-β = 0.67), arm jump performance (r = 0.79; P < 0.001, η2 = 0.62, 1-β = 0.99), hand grip strength (r = 0.34; P < 0.01), finger grip strength (r = 0.72; P < 0.001, η2 = 0.54, 1-β = 0.99) and arm jump fatigue index (r = −0.64; P < 0.001, η2 = 0.48, 1-β = 0.99). The Bartlett’s test of sphericity was significant (P < 0.005), indicating that a PCA process could be driven. Based on the scree plot analysis (eigenvalue greater than one), three components were extracted. Variables were well defined by the model, as revealed by the moderate to high values of the commonalities (Table 3). A cut-off criterion of 0.40 for including a variable in the model was established (Tabachnick & Fidell, 2001); thus, only two variables were not integrated in the model (Sorensen test and percentage of hand grip loss). In sum, the three components explain 64.22% of the total variance. The first component, which explains 26.88% of the total variance, includes anthropometric variables related to body composition, such as fat and muscle percentage, BMI, weight and 1RM and maximal power output during the force–velocity test. We have labeled this the “muscle component.” The second component, which explains 24.01% of the total variance, is linked with the trainable variables, such as hand and finger grip strength, arm jump test, ape index and percentage of AJ loss, and has a negative sign, meaning that the higher the initial upper-limb strength, the lower the loss of strength in the endurance arm jump test. The last component, which explains 13.32% of the total variance, linked anthropometric variables, height, weight, and biacromial breath.
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Determinant factors in climbing ability 1
Table 3. Principal component analysis: factor loading
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Variables
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Commonalities, eigenvalue for each variable after normalized varimax rotation. Component 1 is labeled as muscle, component 2 as training, and component 3 as anthropometry. 1RM, one-repetition maximum; BMI, body mass index; BP, bench press; FI, fatigue index.
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Muscle Training Anthropometry Commonalities
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Weight 0.828 Height BMI 0.970 %fat 0.949 %muscle −0.857 Arm Span Ape index 0.591 Biacromial length Sorensen Power max BP 0.464 1RM BP 0.524 Hand grip max 0.736 Hand grip FI Finger grip max 0.850 Arm jump 0.932 Arm jump FI −0.605 Eigenvalue 4.3 3.84 % of total 26.88 24.01 variance cumulative % 26.88 50.90
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0.93 0.81 0.93 0.89 0.72 0.97 0.54 0.57 0.05 0.21 0.27 0.56 0.15 0.73 0.87 0.40
0.883
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2.13 13.32 64.22
Table 4. Multiple regression model on principal component (n = 41)
Component
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t
P
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Constant Training Muscle Anthropometry
9.55 1.998 −0.221 −1.005
0.00 0.621 −0.074 −0.246
0.46 0.04 0.04
12.34 3.08 −4.03 −2.68
0.001 0.001 0.29 0.03
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Multiple regression was performed (Table 4), with the individual coordinates on the three components as the independent variables and the climbing ability as the dependent variable. The model identified accounts for 54% of climbing ability, with a greater part extracted from the training component (r2 = 0.46) and a smaller one from the muscle and anthropometric components. The standard error of estimate is 4.96.
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Discussion Anthropometry
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Slight differences were observed when comparing the three samples. Indeed, only the ape index shows a difference between novice and other climbers (1.02 ± 0.03 for elite and skilled climbers vs 1.00 ± 0.03 for novices) with moderate size effect. Thus, a high arm span to height ratio may be advantageous in climbing (Watts et al., 2003; Laffaye et al., 2014). Moreover, the anthropometric characteristics of our sample, which include a BMI between 21.9 and 22.6, body fat percentage between 12.3% and 13.3% and a height of 1.76 m, are close to the values found in the literature (Mermier et al.,
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2000; Watts, 2004; España-Romero et al., 2012; Laffaye et al., 2014). The role of anthropometry in climbing ability will be discussed further. General and specific strength
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One of the major findings of this study is that climbing ability is related more to specific strength than to general strength. Indeed, none of the studied parameters in the bench press test was able to discriminate between the three samples. The 1RM performance was fairly comparable, with values ranging from 65.1 ± 12.8 kg for elite climbers to 72.8 ± 10.6 kg for skilled climbers. In addition, the maximal power output during the test ranged from 601 ± 162 W to 694 ± 110 W. Moreover, the force– velocity profile reveals a similar curve, showing that force is specific to the ecological context of development. Indeed, bench press exercises necessitated the use of muscular force from the pectoralis major and triceps brachii (Padulo et al., 2014), muscles that are rarely used in climbing. Specific strength tests show a significant effect of expertise. Indeed, the finger grip strength test shows a higher level of initial strength depending on expertise. Thus, this variable is shown to be a key factor in climbing ability, with a 22.4% higher value for elite climbers compared with skilled climbers and a 29.1% higher value for skilled climbers compared with novices. This finding contrasts with the results of the hand grip strength tests, which show a tendency rather than an actual difference between samples. The average value of hand grip strength (516 N) for climbers is quite similar with comparable samples (Watts et al., 1993, 2000; Grant et al., 1996; Ferguson & Brown, 1997). Moreover, finger grip strength is a better predictive variable of climbing ability; indeed, it explains 52% of the total variance. However, in our experiment, the hand grip strength test only explained 12% of the total variance. This weak association between hand grip strength and rock climbing performance has been noted previously (Watts, 2004) and can be explained by an analysis of basic hand positioning when climbing. Indeed, the classic “grips” used in rock climbing are open, pocket, crimp, and pinch grips, all of which involve more finger flexor strength than forearm strength. More specifically, the thumb or palm are brought into play more often than the fingers, which are rarely used in climbing. This explains why finger grip strength is more predictive because it is closer to the ecological context of force development. Furthermore, hand grip strength requires the fingers to be closed, which requires concentric contractions of the fingers. In climbing, however, the muscular contraction of the fingers is almost isometric. Indeed, this assertion is confirmed when calculating the strength ratio for hand and finger grips, which for novices is 2.5, compared with 2.14 for skilled climbers and 1.7 for elite climbers. Thus, the higher the level of
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Laffaye et al. climbing ability, the more specific the strength required. Indeed, when the level of climbing ability increases, so too does the technical ability to use small grips, which requires a higher level of finger grip. Indeed, a recent study (Amca et al., 2012) has shown that maximal vertical forces increase with hold depth and grip technique. Finally, arm jump performance reveals that, while there is a difference between climbers and non-climbers, similar values are recorded for elite and skilled climbers (76 cm vs 73.5 cm). This suggests that a minimum value of upper-limb power is required for climbing performance (Laffaye et al., 2014), but this variable is less important to raise the elite level than the finger grip strength. Indeed, it has been shown that climbers use two principal strategies: the first is to use agility more than force, whereas the second is to use more force than agility (Sibella et al., 2007). Thus, at a high level, climbers can compensate for moderate upper-limb force with greater agility. This explains why the power output during our test was not able to discriminate between elite and skilled climbers, as shown by previous studies (Laffaye et al., 2014).
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Neuromuscular fatigue
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Three tests were performed to assess neuromuscular fatigue. The Sorensen test, which is a trunk extensor muscle isometric endurance test, showed no differences between the samples. This counterintuitive result reveals that isometric trunk force may only be linked to the direction of application. Indeed, this test involves isometric force in a sagittal plane, requiring only the use of the force of the trunk extensor muscle (Demoulin et al., 2012), whereas climbing requires the use of isometric core force and mobility in different planes because of the several changes of direction during climbing movements (Muehlbauer et al., 2012). The second endurance test was performed with intermittent isometric finger grip contractions. Here, finger grip index fatigue decreased with repetitions, with a mean loss of strength of about 34% for all samples. This differs from previous studies in which climbers showed a greater ability to maintain a high level of isometric contraction. Indeed, it has been shown that elite climbers maintain a fingertip force of 80% of their maximal voluntary force for twice as long as non-climbers (Vigouroux & Quaine, 2006), with a difference occurring after six repetitions. Similar results were obtained while using contractions of 40% of MVC (MacLeod et al., 2007; Philippe et al., 2012). This difference may be explained by the level of MVC required in each task. Indeed, our participants were asked to maintain 100% of the MVC, in contrast with lower values in other studies. In terms of the number of repetitions made until maintaining at least 80% of MVC, the results appear to be independent of climbing ability [F(2, 38) < 1], with a large individual variability, from 5 to 20 repetitions. It is
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interesting to notice that the higher the initial level of strength, the fewer the number of repetitions (r2 = −0.46). Some authors have suggested that the decrease in MVC percentage is due to the accumulation of interstitial biochemical by-products (Vigouroux & Quaine, 2006). It has been demonstrated that elite climbers show a greater peripheral vascular characteristic at 60% MVC, which allows blood flow to increase between each contraction, facilitating the removal of a larger part of the biochemical by-products (Ferguson & Brown, 1997; Usaj, 2002). Thus, it can be hypothesized that the repetition of maximal voluntary contractions with only 5-s rest in between did not allow the restoration of vasodilator capacities, whatever the level of expertise. Consequently, the profile of fatigue decrease is more dependent on the individual profile of muscle properties than on the level of expertise. Pitcher and Miles (1997) suggested that the reduction in induced muscle force is a combination of profound short-term fatigue in anaerobic muscle fibers (caused by the consumption of their shortterm energy supplies) and a decline in force production by aerobic muscle fibers (the consequence of hypoxia). Indeed, a climber with predominantly type II muscular fibers (fast-twitch fibers) is able to produce a high initial level of force but is unable to maintain a high level of force after the failure point. After this point, there is a predominance of type I muscle contractions (slow-twitch oxidative fibers; Pitcher & Miles, 1997). Several studies have revealed an explosive profile for boulderers, who have a higher rate of force development (Macdonald & Callender, 2011; Fanchini et al., 2013; Laffaye et al., 2014). This suggests a predominance of type II muscular fibers. On the other hand, route climbers are less explosive but are more capable of longer routes, suggesting a predominance of type I muscle fibers. To check this hypothesis, we used an additional statistical test on only elite and skilled climbers. They were separated into two samples: the boulderers (n = 6) and the route climbers (n = 13). To be included in one of these samples, we used a criterion of two levels of difference and the climbers performed this subdiscipline exclusively in competition. Then, we used the initial maximal finger grip strength and the number of repetitions performed higher than 80% of the maximum finger grip strength as dependent variables (Fig. 4). A t-test [F(2, 16) = 6.9; P < 0.05] reveals a higher initial strength for boulderers compared with route climbers (354 ± 53.3 N vs 284.9 ± 53 N) but a higher number of repetitions [F(2, 16) = 5.18; P < 0.05] in route climbers (n = 11.7 ± 4.6) than in boulderers (n = 6.5 ± 4.6). This results confirms the hypothesis put forward by Carlson (1969) and Carlson and McCraw (1971), which suggested that a negative relationship between MVC and isometric endurance is due to a greater blood flow occlusion in climbers with higher absolute force. Furthermore, this suggests that the subdiscipline developed specific muscular profile, with a higher strength for boulderers, but a higher capacity for
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Determinant factors in climbing ability
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Fig. 4. Maximal finger grip strength (black) and number of repetitions (gray) higher than 80% of the maximal value observed in boulderer (BO) and route climbers (RO) during an endurance finger grip test (20 repetitions of 5-s effort and 5-s rest). *For difference at P < 0.05.
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maintaining a high level of strength (> 80% of the maximal value) for route climbers. The final endurance strength test consisted of jumping with the arms in an explosive manner for 10 repetitions, with a 5-s rest between each repetition. The loss of performance was significantly lower in climbers (an average decrease of −11.73%) than in non-climbers (−28.9%). The fall in performance for elite climbers suggests that a resting time of 10 s allowed incomplete recoveries. A state of fatigue was created because of a combination of physiological and neuromuscular adaptations. Firstly, the lower decrease in the performance of trained climbers compared with non-climbers suggests a higher capacity to deplete and resynthesize high-energy phosphate stores, the resting time being inadequate for the full recovery of creatine phosphate and the maintenance of a high level of upper-limb power (Rehunen et al., 1982). Indeed, previous study (Mermier et al., 1997) has revealed that the most of the lactate production in short-term high-intensity exercise is in the forearms and the upper body. Considering that the arm jump test necessitates only this main muscle groups, it is probable that the higher decrease of the performance in nonclimbers is due to a lower capacity to remove lactate, while climbers increased ability to tolerate and to use lactate as a fuel across muscle fibers (Giles et al., 2006). Lastly, the diminution of force could be caused by a neuromuscular factor, through a change in muscle contraction (Boyas & Guével, 2011). To better understand the factors that explain this fatigue phenomenon, we separated the climbers into two samples, as for the previous test: the boulderers (n = 6) and the route climbers (n = 13). Firstly, boulderers had a better but insignificant initial level of performance (77.8 ± 5 cm vs 72.8 ± 8.9 cm). Secondly, the percentage of loss was quite similar (16.6 ± 5% vs 14.6 ± 5%). However, the main difference between both samples was that, for the boulderers, there was a good correlation (r = 0.75, P < .01) between the initial level of performance and the slope of loss of performance. This suggests that during the arm jump test, the performance of the boulderers was due to the use of fast-twitch fibers, which are unable to maintain a high level of contraction. Indeed, this task
requires muscular power, as suggested by the coefficient of determination of r2 = 0.49 with the performance (Laffaye et al., 2014). Indeed, boulderers’ training is based on strength and power in order to be able to move quickly, with small or distant holds. Previous studies have revealed a powerful profile and a better rate of force development in boulderers compared with route climbers (Fanchini et al., 2013; Laffaye et al., 2014). On the other hand, boulderers probably have a lower capacity for recovery between efforts than route climbers. The lack of correlation between the initial level of force and the slope of percentage of loss in route climbers (r = 0.03) could suggest either a predominance of slowtwitch fibers, which are more able to maintain a high level of force and/or a more effective recovery between two intensive efforts.
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The PCA revealed three components: training, muscle, and anthropometry. Only training and anthropometry were significant predictors of climbing ability. The model identified explains 54% of the total variance. This figure is higher than that explained by a model with two variables (Watts, 2004). It includes relative hand grip strength and percentage of body fat, which shows a low predictive power (r2 = 0.33). However, it is slightly lower than that identified by the model put forward by Mermier et al. (2000), which adopted the same statistical approach (r2 = 0.63). Our model revealed that climbing ability can be explained by a combination of trainable and anthropometric variables, which is in contrast with Mermier’s model, whereby only trainable variables are linked to climbing ability. Despite this difference, our result shows that the training component explains 46% of the total variance, including finger and hand grip strength, upper-limb power and the arm jump fatigue index. However, the anthropometric variable explains only 4% of the total variance. This shows first that climbing ability is due to a combination of variables. Thus, the relative contribution of the first component means that it is necessary to have a high level of specific strength to attain an elite level. Also necessary is the ability to maintain a high level of strength during repetitive movement, which is not the case for anthropometric and general strength. This impacts on future training programs for coaches, which need to include routines that include specific finger strength exercises, as well as those focused on upper-limb power and endurance. Another important finding of our study is the low level of prediction of the anthropometric variable. While several studies have revealed a specific body somatotype for climbers (Mermier et al., 2000; Macdonald & Callender, 2011), including a low percentage of body fat and low body weight, the most important link between strength and anthropometry is related to the ratio of grip strength to body mass, which is considered as a
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Laffaye et al. significant predictor of climbing ability (Mermier et al., 2000). In our study, we found no difference when the strength to body mass ratio was normalized, probably because of a comparable anthropometric profile between samples. Furthermore, our study agrees with previous research (Watts et al., 1993; Mermier et al., 2000) which has suggested that elite climbers do not necessarily require comparable anthropometric characteristics to reach a higher level of climbing performance. Instead, trainable variables are most important. This model is limited in terms of the parts of variance that it does not explain, which equates to about 46%. Thus, a large part of climbing ability is linked to other factors, such as level of technique and psychological factors, as suggested by a study by Magiera et al. (2013). Indeed, in this study, a canonical analysis revealed climbers’ performance capacity to be 96%, highlighting the necessity for the harmonious development of physical fitness, technical and tactical skills and mental preparation. Furthermore, a large proportion of this (38%) can be explained by a combination of physical fitness (finger and arm strength) and anthropometric factors (body mass, ape index, fat mass, and hip flexion). This study is also limited by the specific sample used. Indeed, climb-
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ing includes several types of variations, such as bouldering, route climbing, and ice climbing. Such variations require slightly differences in individual qualities, as shown by comparative studies of bouldering and route climbing (Michailov et al., 2009; Macdonald & Callender, 2011; Laffaye et al., 2014).
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Our study reveals that much of the variance in climbing ability can be explained by trainable variables, such as finger grip and hand grip strength, upper-limb power and endurance, rather than anthropometric characteristics. Furthermore, the finger grip endurance test reveals that the ability to maintain a high level of force is more dependent on the subdiscipline (route climbers vs boulderers) rather than the level of expertise. We encourage future research to focus on the impact of training programs that focus on specific maximal strength and endurance, together with subdiscipline specificity.
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Key words: Finger grip strength, endurance test, hand grip, boulderer, neuromuscular fatigue.
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