Does gymnastics practice improve vertical jump

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Does gymnastics practice improve vertical jump reliability from the age of 8 to 10 years? a

Michel Marina & Priscila Torrado

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INEFC Barcelona, Sports & Performance, Barcelona, Spain Version of record first published: 18 Feb 2013.

To cite this article: Michel Marina & Priscila Torrado (2013): Does gymnastics practice improve vertical jump reliability from the age of 8 to 10 years?, Journal of Sports Sciences, DOI:10.1080/02640414.2013.771816 To link to this article: http://dx.doi.org/10.1080/02640414.2013.771816

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Journal of Sports Sciences, 2013 http://dx.doi.org/10.1080/02640414.2013.771816

Does gymnastics practice improve vertical jump reliability from the age of 8 to 10 years?

MICHEL MARINA & PRISCILA TORRADO INEFC Barcelona, Sports & Performance, Barcelona, Spain

Downloaded by [University of Barcelona] at 00:56 19 February 2013

(Accepted 28 January 2013)

Abstract The objective of this study was to confirm whether gymnastics practice from a young age can induce greater vertical jump reliability. Fifty young female gymnasts (8.84 ± 0.62 years) and 42 females in the control group (8.58 ± 0.92 years) performed the following jump tests on a contact mat: squat jump, countermovement jump, countermovement jump with arm swing and drop jump from heights of 40 and 60 cm. The two testing sessions had three trials each and were separated by one week. A 2 (groups) × 2 (sessions) × 3 (trials) repeated measures analysis of variance (ANOVA) and a test-retest correlation analysis were used to study the reliability. There was no systematic source of error in either group for nonplyometric jumps such as squat jump, countermovement jump, and countermovement jump with arm swing. A significant group per trial interaction revealed a learning effect in gymnasts’ drop jumps from 40 cm height. Additionally, the test-retest correlation analysis and the higher minimum detectable error suggest that the quick drop jump technique was not fully consolidated in either group. At an introductory level of gymnastics and between the ages of 8–10 years, the condition of being a gymnast did not lead to conclusively higher reliability, aside from better overall vertical jump performance. Keywords: variability, reproducibility, standard error of the measurement, minimum detectable error

Introduction The reliability of a measure is concerned with its reproducibility or repeatability across multiple trials. Therefore, a reliable measure is characterised by low within-participant variation and a high testretest correlation (Hopkins, 2000). Precise, reliable assessments allow a coach to monitor parameter changes much better (Hopkins, 2000), such as jumping capacity or any other strength related capacity useful to sports performance. Without reliability, a coach cannot study the effects of training and growth in young populations and ascertain which changes can be attributed to either incidence of the sport or maturation/growth rather than measurement error (Acero et al., 2011). Artistic gymnastics requires great precision and repeatability as during competition, gymnasts perform the routines they have repeated thousands of times in their gymnasium. So, because of the particular nature of this sport, it is pointless to use tests of physical condition that are unreliable in monitoring the development of physical condition as the gymnast ages or as a tool to identify young talent. Variability in jump performance tasks requiring a stretch-shortening cycle is greatest in younger

participants and reduces as participants move toward adulthood (Gerodimos et al., 2008; Harrison & Gaffney, 2001). It is reasonable to suggest that an immature neurophysiological status may lead to greater variability in movements such as jumping that elicit rapid corrections of the pre-motor cortex by innervating the appropriate motor units to accurately maintain postural control (Lloyd, Oliver, Hughes, & Williams, 2009). Whereas it has been suggested that jumping skills in gymnasts reach the level of adult participants at the age of 15–16 years (Ishii, Ishii, & Iwai, 1983), Viitasalo (1988) found that, whatever the age, artistic gymnasts obtained higher reliability scores than their companions of matched age. Three of the four types of apparatus (vault, beam and floor) used in women’s artistic gymnastics require the use of the lower limbs to perform most of the acrobatic exercises. The body’s vertical velocity at take-off and the net vertical impulse are prime determinants of jump performance (Bobbert & van Ingen Schenau, 1988; Knudson, 2009), and strongly depend on segmental coordination and the application of a proper technique (Bobbert, Huijing, & van Ingen Schenau, 1987a). To attain reliable jump measurements, it is

Correspondence: Michel Marina, INEFC Barcelona, Sports & Performance, Barcelona, Spain. E-mail: [email protected] © 2013 Taylor & Francis


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M. Marina & P. Torrado

important to familiarise the participants with the jump techniques used during the assessment (Nuzzo, Anning, & Scharfenberg, 2011). When young girls start gymnastics (from 5 to 8 years of age), one of the main training aims is to acquire the basic vertical jump techniques. Basically, we can distinguish three sort of jump situations in gymnastics: 1) jumps from a static or “semi-static” squat position (on very few occasions), 2) jumps with a previous countermovement, which are associated with the stretch-shortening cycle (on many occasions, mainly in beam exercises), and 3) jumps that take advantage of a previous aerial acrobatics exercise, which are commonly called plyometrics or rebound jumps (very frequent and increasingly used nowadays in floor exercises). In the present study, the squat jump was used to represent jumps without a countermovement from a static situation, the countermovement jump with and without arm swing to represent the countermovement jump situation, and the drop jump from heights of 40 and 60 cm to represent plyometric jumps. These jumps can be considered as generic jumping assessments whose specific segmental coordination does not match the specific acrobatics take-off in gymnastics (the same could be said about any other sport). Nevertheless they proved to be useful to distinguish well-trained competitive gymnasts compared to control groups, other sports (Bosco, 1992; Viitasalo, 1988), and even among themselves, if the proper parameters are used (Marina, Jemni, Rodriguez, & Jimenez, 2011). The previous considerations can be reinforced taking into account the overall simplified jump exercises used during physical conditioning at these ages. The overall hypothesis of this study was that, due to their extensive jumping practice, young gymnasts should demonstrate greater reliability in the jump tests mentioned above than a control population that was much less familiar with these techniques. The squat jump, countermovement jump and drop jump have been shown to be reliable in adults (Flanagan, Ebben, & Jensen, 2008; Markovic, Dizdar, Jukic, & Cardinale, 2004; Moir, Garcia, & Dwyer, 2009; Slinde, Suber, Suber, Edwen, & Svantesson, 2008; Viitasalo, 1985, 1988). Nevertheless, very few studies have focused on children’s jump test reliability. A high intra-trial reproducibility and questionable inter-session reliability was reported for squat jump and countermovement jump in children aged 6 to 8 years (Acero et al., 2011). These results were similar to those reported by Viitasalo (1988) with eleven-year-old children and in line with another study that found moderate reliability indexes in twelve- to fourteen-year-olds assessed with a contact platform (Lloyd et al.,

2009). Finally, well-trained gymnasts of both sexes proved to be more reliable than control groups of similar ages in drop jumps using the bouncing technique (Marina et al., 2011), which aims to minimise the impulse time (Bobbert et al., 1987a; Eloranta, 1997; Young, Prior, & Wilson, 1995). The aim of this study was to confirm the hypothesis that gymnastics jumping practice from a young age should lead to greater reliability in three basic techniques (with and without countermovement, and plyometrics), not only within a session but between two sessions. Methods Participants Much of the previous research on jump reliability has used small sample sizes. This has resulted in excessively large confidence limits, associated with reliability statistics (Baumgartner & Chung, 2001). In an attempt to resolve this matter, we recruited 50 young female gymnasts aged 8.84 ± 0.62 years old (height: 132.82 ± 10.45 cm; body mass: 28.84 ± 6.62 kg) and 42 females for the control group aged 8.58 ± 0.92 years old (height: 129.75 ± 10.21 cm; body mass: 28.80 ± 6.44 kg). The inclusion criteria for the group of gymnasts were: 1) younger than 11 years old, 2) an average of 10 to 14 h training a week, 3) at least one year of experience in competitive artistic gymnastics routines, and 4) participation in regional or national championships in their corresponding categories. The criteria for inclusion in the control group were participation in a general (not gymnastics) extracurricular and recreational physical or sports activity for two to three hours per week, such as basketball, dance, tennis, swimming, roller skating, etc. The participants who did not carry out any physical activity at all were discarded from the control group. The study was approved by the Catalan Sport Administration’s ethics committee for clinical research and written consent was given by all parents and/or guardians of the minors involved. Material To assess vertical jump performance, we used a contact mat (1.20 × 0.80 m) connected to an Ergo Jump Bosco/System ® unit and customised steps of 40 and 60 cm height for the plyometric jumps (drop jumps). The contact mat was placed on a standard wooden floor and protected by an extra layer of thin carpet to reduce the landing impact and enhance adherence, as all participants performed the jumps without sports shoes.


Vertical jump reliability from the age of 8 to 10

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Jump tests

Statistical analysis

The participants were asked to perform the jump tests following the protocol described by Bosco et al. (Bosco, 1992; Bosco & Komi, 1979; Bosco, Komi, & Ito, 1981; Bosco, Tihanyi, Komi, Fekete, & Apor, 1982; Bosco, Viitasalo, Komi, & Luhtanen, 1982), in the following order: 1) squat jump (SJ), 2) countermovement jump (CMJ), 3) countermovement jump with arm swing (CMJA), also known as Abalakov (Lidor, Hershko, Bilkevitz, Arnon, & Falk, 2007; Loko, Aule, Sikkut, Ereline, & Viru, 2000), and 4) drop jump (DJ). To control extraneous variables related to the squat jump and countermovement jump assessment and before considering whether the jumps were correctly executed, we followed the criteria proposed by Acero et al. (2011). The drop jump performed in this study is a modified version of the drop jump test published by Komi and Bosco (1978) for two reasons: 1) a free arm swing was allowed to help take-off in the DJ, following the recommendations of previous studies with gymnasts (Faria & Faria, 1989; Marina et al., 2011), 2) with the instruction “perform the maximum flight time combined with the shortest contact time” the participants were encouraged to take off immediately after landing, performing a “quick drop jump” similar to those described in previous studies (Bobbert et al., 1987a; Eloranta, 1997; Young et al., 1995). The flight time and contact time (FT and CT respectively, in milliseconds) were the only parameters directly collected by the system (Bosco/ System ®). We accepted the biases shown by Hatze (1998), and calculated the mechanical estimation of power in the drop jump using the flight time and contact time, according to the Bosco, Luhtanen, and Komi (1983) formulae and strictly under the conditions described by Arampatzis, Stafilidis, MoreyKlapsing, and Bruggemann (2004).

The Kolmogorov-Smirnov test was used to assess the normal distribution of the data. Measures of central tendency and the spread of the data were represented as means and standard deviations. For a more comprehensive reliability assessment, we followed a multiple approach: first, we performed a repeated measures analysis of variance (ANOVA), then we calculated test-retest correlations and related statistics (Weir, 2005). To study reproducibility in gymnasts and nonsports participants, we used a 2 (groups) × 2 (sessions) × 3 (trials) repeated measures ANOVA. The group was considered the between-participant factor and sessions (n = 2) and assessment trials (n = 3 + 3 = 6) were the within-participant factor. If the sphericity test to study the matrix proportionality of the dependent variable was significant, we used the Greenhouse-Geisser correction. If a significant effect was found, simple main effects were conducted. Post hoc analyses were carried out when appropriate, with the Sidak adjustment for multiple comparisons. No difference between the trials and sessions was interpreted as good within-participant reproducibility and low systematic error (e.g. learning and fatigue). As a complementary methodological approach, we calculated the test-retest correlations. We used a combination of statistics to check the within-participant variability. The intra-session coefficient of variation (CV) was calculated by the ratio of the standard deviation (s) and the mean value of the three trials of each participant, and expressed as a percentage (%). We also used the intra-class correlation coefficient (ICC), following Hopkins’ suggestions (2000). This author considers that the intraclass correlation coefficient is more appropriate than Pearson’s correlation (r) when more than two measures (three trials per session) are used in the reliability study, as Pearson’s correlation does not take into account the number of trials. Both statistics (coefficient of variation and intra-class correlation coefficient) were used to study the temporal withinparticipant variability between trials of the same session and between sessions, with the addition of the Pearson correlation in the between-sessions study (using one mean value from each of the two sessions). Since there is no clear consensus about the precise interpretation of the intra-class correlation coefficient (Weir, 2005), it was examined in conjunction with the coefficient of variation, following the suggestions of Ditroilo et al. (2011). The standard error of the measurement (Sx-) was calculated according to formulae by (Atkinson & Nevill, 1998) for studying inter-session reliability. Before we considered the standard error of the measurement for

Testing procedure All jump tests were carried out by the same researcher, who had extensive experience in this type of assessments, to avoid between-researcher variability. The two testing sessions took place at the same time of day (the usual afternoon training time) and were separated by one week. In each session, all participants performed three trials for each type of jump. During the assessment briefing, a short period (~ 10 min) of two/three familiarisation trials was allowed for each type of jump. Following the suggestions of previous authors about jump reliability in young participants, special care was taken with the squat jump (Acero et al., 2011) and the drop jump (Marina et al., 2011). One to two minutes rest was given between the trials.

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discussion, we checked the heteroscedasticity of each variable, to avoid misleading interpretations (Atkinson & Nevill, 1998; Weir, 2005). Whereas heteroscedasticity implies that the size of the error correlates with the magnitude of the observed score (Weir, 2005), homoscedasticity assumes that the amount of maximal error is the same for individuals with very high or very low values in the sample (Atkinson & Nevill, 1998). The minimum detectable change (MDC) was considered as the minimum change in a variable over time that is meaningful and represents a true change (Ditroilo et al., 2011; Weir, 2005). The minimum detectable change was calculated according to Equation 1:


MDC ¼ 1:96

pffiffiffi 2 Sx

(1)

To allow comparisons among measures and studies, the minimum detectable change was also expressed as a percentage MDC (MDC%), which was calculated as the percentage ratio between the minimum detectable change and the mean of all observations (Webber & Porter, 2010). Ninety-five per cent confidence intervals (95% CI) were reported for the coefficient of variation and the intra-class correlation coefficient. The level of significance was set at P ≤ 0.05. The statistical analysis was performed with PASW 18 and Excel 2010 software.

Results The normal distribution and homoscedasticity of the data was confirmed before the statistical analysis. Using the first methodological approach, the 2 (groups) × 2 (sessions) × 3 (trials) repeated measures ANOVA revealed a significant group effect in all jump tests for FT and mechanical power (Tables I and II respectively). Even though a trial effect was observed for flight time in squat jump and countermovement jump, the lack of significant trial per group interactions confirms that when both groups were analysed separately, no significant differences were found between trials (Table I), as for countermovement jump with arm swing. In contrast, a significant trial per group interaction for flight time in drop jump from 40 cm (DJ40) confirms that the gymnasts performed better in the third trial than in the first one (Table I). Nevertheless, the absence of triple interactions (groups per sessions per trials) meant that we could not evaluate whether the gymnasts increased their DJ40’s flight time outcome from trial to trial, preferably in the first or second session (Table I). With respect to the estimated mechanical power, the significant trial per group interaction in drop jump from 40 cm was explained

by higher values in the third trial than in the first one in the gymnasts group only (Table II). Using the second statistical approach, we present the intra-session reliability results for both sessions in Table III and the inter-session reliability results in Table IV. Gymnasts and controls had good reliability results in general. Table V indicates how many participants achieved maximum flight time and power in each jump test during specified trials. We can see that the participants always achieved their best performance in the second and third trials, rather than the first trial. There was no clear predominance of the second or third trial in terms of the best performance. Intra-session reliability From a descriptive point of view, the coefficient of variation (CV) was almost always lower in gymnasts than in controls and lower in the non-plyometric jump tests (SJ, CMJ and CMJA) than in the plyometric jumps (drop jump from 40 and 60 cm). However, when plyometric jumps (DJs) were analysed, the coefficient of variation was lower for the flight time (3.26–5.82%) than for the estimated power (6.16–10.56%). The only acceptable coefficient of variation for the estimated power was found in the drop jump from 60 cm of the control group (7.37–13.75%). The intra-class correlation coefficient (ICC) was moderate to high (0.82–0.96) in both sessions and values were similar in both groups. Nevertheless, the lowest intra-class correlation coefficient value (0.82) was reported in the first session for DJ60 in the control group (Table III). Inter-session reliability The coefficient of variation was very low in both groups for the flight time of the non-plyometric jumps (1.88–2.60%). The flight time for DJs had a slightly higher coefficient of variation than that of the non-plyometric jumps. However, it was still within the range of values that are considered low (3.43– 4.40%). Again, the estimated power in DJs had higher coefficient of variation values (7.68–9.74%) than the flight time. The inter-session ICC was moderate to high (0.90–0.96) for all jump tests and parameters (flight time and estimated power), with very similar values in both groups (Table IV). This observation was supported by moderate-to-high r-values (0.72–0.88) for flight time, which were a little bit lower for the estimated power (0.63–0.83). The minimum detectable errors expressed as a percentage (MDC%) were quite similar between groups, although they tended to be higher in the

Vertical jump reliability from the age of 8 to 10 Table I. Two (groups) × two (sessions) × three (trials) ANOVA of repeated measures. The variable analysed is flight time (ms). Jump Test SJ

CMJ


CMJA

DJ40

DJ60

Effect g × ses × g × ses Tr × ses Tr × g Tr ses g g × ses × g × ses Tr × ses Tr × g Tr ses g g × ses × g × ses Tr × ses Tr × g Tr ses g g × ses × g × ses Tr × ses Tr × g Tr ses g g × ses × g × ses Tr × ses Tr × g Tr ses g

Tr

Tr

Tr

Tr

Tr

F

df

P

0.57 0.13 0.93 0.27 3.9 0.99 42.99 0.84 0.3 1.65 1.43 4.72 0.15 66.13 0.13 0.57 0.16 0.12 1.18 0.02 86.67 0.83 0 1.13 6 2.29 0.29 58.33 1.3 0.1 2.24 0.66 1.76 0.44 54.08

2,89 1,90 2,89 2,89 2,89 1,90 1,90 2,89 1,90 2,89 2,89 2,89 1,90 1,90 2,89 1,90 2,89 2,89 2,89 1,90 1,90 2,89 1,90 2,89 2,89 2,89 1,90 1,90 2,89 1,90 2,89 2,89 2,89 1,90 1,90

ns ns ns ns 0.022 ns ≤ 0.001 ns ns ns ns 0.01 ns ≤ 0.001 ns ns ns ns ns ns ≤ 0.001 ns ns ns 0.003 ns ns ≤ 0.001 ns ns ns ns ns ns ≤ 0.001

Post Hoc

P

G1: Tr1 < Tr3

≤ 0.001

Tr = trial; ses = session; g = group (G1: gymnasts; G2: control); SJ = squat jump; CMJ = counter movement jump; CMJA = counter movement jump with arm swing; DJ40, DJ60 = drop jump from 40 and 60 cm drop height.

Table II. Two (groups) × two (sessions) × three (trials) Anova of repeated measures. The variable analysed is estimated mechanical power (ms). Jump Test DJ40

DJ60

Effect g × ses × Tr g × ses Tr × ses Tr × g Tr ses g g × ses × Tr g × ses Tr × ses Tr × g Tr ses g

F 2.35 0.31 0.17 5.18 3.71 2.72 47.67 0.16 0.07 1.25 0.14 1.13 2.94 51.51

df 2, 1, 2, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 1,

89 90 89 89 89 90 90 89 90 89 89 89 90 90

P ns ns ns 0.006 0.26 ns ≤ 0.001 ns ns ns ns ns ns ≤ 0.001

Post Hoc

P

g1: Tr1 < Tr3

≤ 0.001

Tr = trial; ses = session; g = group (G1: gymnasts; G2: control); DJ40, DJ60 = drop jump from 40 and 60 cm drop height

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M. Marina & P. Torrado Table III. Session 1 and Session 2 intra-session reliability in young women gymnasts (G1) and control group (G2). Intra-session reliability Session 1

Test

g

CV (95% CI)

SJ

G1 G2 G1 G2 G1 G2 G1 G2 G1 G2

2.53 3.82 1.96 2.46 1.87 3.23 4.03 5.82 3.26 5.75

G1 G2 G1 G2

Estimated 6.57 (5.28–7.87) 9.49 (6.62–12.36) 6.16 (4.95–7.37) 10.56 (7.37–13.75)

CMJ CMJA DJ40


DJ60

DJ40 DJ60

Intra-session reliability Session 2

ICC (95% CI) Flight Time (ms) 0.90 (0.84–0.94) 0.89 (0.82–0.94) 0.94 (0.92–0.97) 0.96 (0.93–0.97) 0.96 (0.94–0.98) 0.90 (0.83–0.94) 0.93 (0.89–0.96) 0.89 (0.81–0.93) 0.95 (0.92–0.97) 0.82 (0.69–0.89)

(2.08–2.99) (2.67–4.98) (1.57–2.35) (1.71–3.20) (1.46–2.27) (2.26–4.21) (3.35–4.71) (4.06–7.58) (2.60–3.93) (4.01–7.49)

mechanical power (w/kg) 0.96 (0.94–0.98) 0.94 (0.90–0.97) 0.96 (0.94–0.98) 0.93 (0.89–0.96)

CV (95% CI)

ICC (95% CI)

2.40 2.34 1.97 3.02 1.86 3.05 3.85 4.51 3.62 4.90

(1.92–2.87) (1.63–3.05) (1.55–2.39) (2.11–3.94) (1.50–2.22) (2.13–3.98) (3.26–4.44) (3.15–5.87) (3.02–4.23) (3.42–6.38)

0.94 0.96 0.94 0.90 0.95 0.92 0.92 0.91 0.93 0.90

(0.90–0.96) (0.94–0.98) (0.90–0.98) (0.84–0.94) (0.92–0.97) (0.86–0.95) (0.88–0.95) (0.84–0.95) (0.89–0.96) (0.83–0.96)

7.08 8.11 6.35 9.40

(5.71–8.44) (5.66–10.57) (5.12–7.58) (6.56–12.24)

0.93 0.95 0.95 0.94

(0.89–0.96) (0.91–0.97) (0.93–0.97) (0.90–0.96)

CV = coefficient of variation expressed in percentage; ICC = intraclass correlation coefficient; CI = confidence interval; g = group

Table IV. Session 1 and Session 2 inter-session reliability in young women gymnasts (G1) and control group (G2). Session 1

Session 2

Test

g

mean ± s

mean ± s

SJ

G1 G2 G1 G2 G1 G2 G1 G2 G1 G2

447 406 465 417 509 446 485 417 474 407

449 409 467 417 510 444 483 415 470 407

G1 G2 G1 G2

46 34 41 30

CMJ CMJA DJ40 DJ60

DJ40 DJ60

± ± ± ± ± ± ± ± ± ±

± ± ± ±

31.2 32.2 28.3 33.2 33.6 35.4 47.1 47.3 49.3 43.2

10.5 8.4 9.6 7.7

44 33 40 28

± ± ± ± ± ± ± ± ± ±

± ± ± ±

30.3 31.3 26.9 30.3 30.0 36.8 41.5 42.1 43.1 43.2

8.5 7.3 8.2 7.2

Inter-session reliability (Session 1 – Session 2) r

CV (95% CI)

ICC (95% CI)

Flight Time (ms) 0.76 2.60 (2.01–3.20) 0.93 0.81 2.51 (1.75–3.27) 0.95 0.78 2.10 (1.57–2.63) 0.95 0.88 1.99 (1.39–2.59) 0.96 0.85 1.88 (1.41–2.36) 0.96 0.84 2.49 (1.73–3.24) 0.94 0.8 3.43 (2.68–4.19) 0.94 0.72 4.40 (3.07–5.73) 0.92 0.81 3.50 (2.66–4.33) 0.95 0.72 4.24 (2.96–5.53) 0.90 Estimated mechanical power (w/kg) 0.63 9.74 (7.21–12.27) 0.93 0.85 7.98 (5.57–10.40) 0.96 0.73 7.68 (5.64–9.73) 0.95 0.83 8.98 (6.26–11.69) 0.95

Sx-

(0.90–0.96) (0.92–0.97) (0.92–0.97) (0.93–0.97) (0.94–0.98) (0.91–0.97) (0.92–0.96) (0.87–0.95) (0.93–0.97) (0.84–0.94)

7.55 6.76 5.84 6.16 6.14 8.49 10.26 11.73 9.83 12.41

(0.89–0.95) (0.93–0.97) (0.92–0.97) (0.93–0.97)

2.25 1.51 1.84 1.59

MDCms

21 19 16 17 17 24 28 32 27 34 6.2 4.2 5.1 4.4

MDC%

4.7 4.6 3.5 4.1 3.3 5.3 5.9 7.8 5.8 8.5 13.9 12.6 12.5 15.2

CV = coefficient of variation; CI = confidence interval; ICC = intraclass correlation coefficient; g = group; r = Pearson correlation (calculated from the average value of the first and second session); Sx- = standard error of the measurement; MDCms = minimal detectable change expressed in ms; MDC% = minimal detectable change expressed as a percentage.

controls, except for the squat jump. Moreover, MDC% seemed to be higher in the plyometric jumps (DJs) than in the non-plyometric ones (SJ, CMJ and CMJA) and have higher values for the estimated power (12.5–15.2%) than for the flight time (5.8–8.5%) (Table IV). Discussion When comparing jumps between control groups and specific sport populations, we must choose tests with

a minimum technical component. Otherwise it could happen that no control participant could execute the movement selected for the assessment. This is particularly true in acrobatic jumps, even the ones considered as fundamental in artistic gymnastics. So, we have to accept the logical differences between basic acrobatic jumps and more generic ones chosen to assess the jumping capacity. But in order to be as close as possible to the gymnastic jump specificity, it is important to select jumps with both feet, a predominant vertical component, and a right-left side

Vertical jump reliability from the age of 8 to 10 Table V. Frequency table describing how many participants achieved their best performance (flight time and power) during the specified trials in the two testing sessions.


Session 1 Trial 1

Trial 2

SJ CMJ CMJA DJ40 DJ60

25 26 30 33 29

35 28 33 21 31

DJ40w DJ60w

25 30

Session 2 Trial 3

Trial 1

Trial 2

Trial 3

43 35 33 32 25

23 24 26 32 42

Estimated mechanical Power (w/kg) 34 33 21 35 38 24 32 29

36 31

Flight Time (ms) 32 26 38 33 29 33 38 28 32 25

symmetry of the segmental coordination before takeoff. We think that SJ, CMJ, CMJA and DJ tests satisfy the previous considerations. Table V confirms the importance of administering a minimum of two trials in young participants, whereas the lack of a clear prevalence between the second and third trial suggests the adequacy of using an intra-class correlation coefficient of average measures instead of a single measure. Although the ANOVA approach confirmed that the gymnasts performed better than the control group, the key point of this study was to see whether the condition of being a gymnast led to better intrasession and inter-session reliability at around 8–10 years of age. With a participant sample ranging from 10 to 16 years, Viitasalo (1988) reported that gymnasts achieved better intra-session reliability than participants from six other sports events. To verify this hypothesis, we used basically two methodological approaches: 1) multiple comparisons (ANOVA), 2) test-retest correlations (CV, ICC, r). Using the 2x2x3 ANOVA, the absence of trial per group interactions and group per session interactions suggests that in non-plyometric jumps, such as SJ, CMJ and CMJA, gymnastic practice from a very young age did not improve intra- and inter-session reliability, respectively. The absence of significant differences among trials and sessions in both groups indicates that there was no systematic source of error, such as the learning process. This could be explained by the fact that the intra-session and inter-session coefficient of variation and intra-class correlation coefficient were already quite low and high respectively in the control group. Therefore, only more training hours per week, and accordingly a higher gymnastics level, may induce greater reliability in non-plyometric jumps in comparison with a control group. It is very important to take into account that none of the young gymnasts in this study can be considered elite, not only because of their age, but also because none of them trained for 20 to 30 h per week.

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The statistical results reported in the plyometric jumps invite a different interpretation to the nonplyometric jumps. The significant trial per group interaction for flight time and the estimated power in drop jump from 40 cm, reported by the 2x2x3 ANOVA, suggest a learning effect in the gymnasts. Moreover, inter-session CV, Sx- and MDC% seem to increase in drop jump tests in comparison with the non-plyometric jumps. Young gymnasts, at the beginning of their sports life may be more prepared than controls to explore different combinations of flight time and contact time to obtain the best power in drop jump. The quick drop jump technique requires a lot of stiffness (Young et al., 1995), maximal force (Christou et al., 2006; Schmidtbleicher, 1992), neuromuscular pre-activation (Eloranta, 1997), and a particular technique (Bobbert et al., 1987a; Bobbert, Huijing, & van Ingen Schenau, 1987b). The control group may not be able to meet these requirements as effectively as young gymnasts, to optimise the flight time and contact time in drop jump. From a technical and coordination perspective, the difficulty and complexity of jumping high with the shortest contact time in drop jump has been reported (Bobbert et al., 1987a, 1987b). This difficult relationship between flight time and contact time is critical in gymnasts to reach the highest estimated power, according to the cited formula (Marina et al., 2011). The occurrence of a higher coefficient of variation in drop jump, particularly for the estimated power, suggests that young participants have a jumping technique in drop jump that is less consolidated than that of non-plyometric jumps. Their young age and variations in biological maturity could explain different developmental levels of motor coordination (Jones, Hitchen, & Stratton, 2000) and consequently their inability to consistently coordinate rebound jumps during the ground contact phase. This leads to a greater variation in the “spring” deformation and therefore a greater variation in leg stiffness (Lloyd et al., 2009). The results of Marina et al. (2011) support the idea that more mature and grown-up gymnasts, with a higher volume of training and gymnastic experience, have a more consolidated, reproducible quick drop jump technique. That is, they have a technique with very short contact times, whatever the drop height. This suggestion support the results of (Laffaye, Bardy, & Durey, 2005), who observed clearly different jumping profiles characterised by specific, sportrelated impulse parameters. The two methodological approaches used in this study suggest that in both groups a second session did not help to perform longer flight times, especially in the non-plyometric jumps (SJ, CMJ and CMJA). The 2x2x3 ANOVA did not report the session as a decisive factor, whereas the inter-session CV was always within an acceptable range (below 5%), and


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accompanied by high inter-session ICC values for the flight time. Nevertheless, in drop jump, a slight increase of CV matched with Sx- and MDC% increases suggests a less stable, repetitive flight time outcome than in the non-plyometric jumps. This observation in drop jump is more remarkable for the estimated power, where the MDC% is higher than 12% in both groups. In other words, when the estimated power is recorded in drop jump, a higher percentage of change is necessary to be considered a true change instead of a change due to measurement error. There are several explanations for estimated power having lower reliability than the flight time. A low variation in the outcome measures associated with the movement tasks can be achieved despite considerable variation in the execution of the movements themselves (Button, MacLeod, Sanders, & Coleman, 2003). In vertical jumps, jump height and, for instance, flight time can be considered the outcome of the movement, while variables such as rate of force development, net impulse and vertical stiffness contribute to the performance but can be regarded as variables that characterise the execution of the jump (Moir et al., 2009). In adult jumpers, a large variation was observed in certain mechanical variables, despite low variation in the jump outcome (jump height) (Aragon-Vargas & Gross, 1997). In our study, taking into account the previous considerations, we could consider that estimated mechanical power is the variable that characterises the quick drop jump execution. This is because of its dependency on the flight time and contact time relationship (Marina et al., 2011) and the necessarily short knee flexo-extension (Bosco & Komi, 1979), which is only possible when performing a difficult technique and coordination (Bobbert et al., 1987a, 1987b). Therefore, the fact that the reliability of the drop jump’s power is worse than that of the flight time could be because power better characterises the drop jump technique, whereas flight time could be considered the outcome of the jump. Some comparisons can be made with the literature on jump reliability in young participants. Our intersession CV and ICC in squat jump and countermovement jump were lower and higher respectively than those (CV ≥ 7.47%, ICC ≥ 0.62) reported by Lloyd et al. (2009) with 13.5-year-old boys. Previous studies (Markovic et al., 2004; Nuzzo et al., 2011) carried out with adults aged 19–20 years reported CV and ICC results very similar to ours in nonplyometric jumps. Nevertheless, Nuzzo et al. (2011), who used only the countermovement jump test, had to transform by means of the natural logarithmic scale because of the heteroscedasticity of their data. We cannot perform direct Sx- comparisons as they used estimated jump height (cm). Acero et al. (2011) reported, in school children aged 6 to 8

years, higher intra-session CV (8.48–11.02%) and similar intra-session ICC (0.83–99) to ours, using a contact mat, squat jump and countermovement jump. Their inter-session reliability was also a little bit lower (r: 0.60–0.78; ICC: 0.70–0.86) and they recommended a familiarisation session for squat jumps with children aged 6 to 8 years, to ensure good reliability (Acero et al., 2011). Their overall lower reliability results can be explained by the younger age of their participants. This indicates that young people can improve their reliability in non-plyometric vertical jump tests up to the age of 11–12 years, as suggested by Viitasalo (1988). In additional support of Viitasalo’s results (1988), we suggest that even at slightly younger ages (10 years approx.) the inter-session and intra-session reliability is comparable to adults in non-plyometric jumps such as the squat jump (SJ), countermovement jump (CMJ), and countermovement jump with arm swing (CMJA). In contrast, plyometric jumps, such as drop jumps, need more time and experience to be considered as really reliable and a higher MDC% is required at young ages to consider a change a true change due to the participant and not the error of measurement. According to Marina et al. (2011), it seems that only an intensive gymnastic routine (more than 20 h per week) can ensure good reliability in drop jumps from the age of 11 years. Practical guidelines If a gymnast is not successful doing an acrobatic jump, the main problem could be either related to the jumping capacity, the specific technique and coordination of the movement, or both. Before deciding the main nature of the problem it is important to know if the difference between two jump tests performed on different occasions can be considered as a true change in order to draw up the correct training programme. The decision of prioritising physical conditioning over specific technical work or vice versa could be wrong. Conclusions The absence of differences among trials and sessions in both groups suggests that a systematic source of error, such as the learning process, is not present for flight time in non-plyometric jumps such as squat jump, countermovement jump, and countermovement jump with arm swing, and that girls from the age of 8 years have similar reliability to adults. However, the ANOVA analysis revealed learning and scouting effects in gymnasts’ drop jumps from 40 cm for flight time and estimated power. Moreover, in comparison to non-plyometric jumps,

Vertical jump reliability from the age of 8 to 10 the test-retest correlation analysis suggests that in both groups the quick drop jump technique is not fully consolidated. The higher MDC% in drop jumps confirms the previous observations, and suggests that higher flight time and particularly power increments should be considered true changes. At age 8–10 and an introductory gymnastics level, the condition of being a gymnast did not lead to definite higher reliability, but was associated with a better overall vertical jump performance.


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