The Reactive Strength Index in Unilateral Hopping for Distance and Its Relationship to Sprinting Performance: How Many Hops Are Enough for a Comprehensive Evaluation?

Abstract

The reactive strength index (RSI) is used to assess reactive strength in the lower limbs. Since previous studies have mostly focused on vertical RSI (derived from drop jumps), we investigated the RSI across eight consecutive unilateral horizontal hops and its associations with sprint performance. A sample of 104 male kinesiology students (age: 19.2 ± 1.1 years) performed unilateral hops and 100 m sprints with split times recorded. RSI was determined as the ratio between contact time and subsequent flight time for each hop. On a group level, the horizontal RSI was statistically significantly (p < 0.001; η_p² = 0.49) and increased from hop 1 (1.04 ± 0.17) to hop 5 (1.41 ± 0.22), but then plateaued (1.43–1.44) for hops 6–8. However, on an individual level, variations in RSI were present all the way to the last hop. All sprint split times were in small-to-moderate correlation with RSI variables (r = 0.25–0.40), implying that individuals with higher hopping RSI presented with shorter sprint times. Future studies should expand the research on the relationship between horizontal hopping RSI and sports performance, and examine if unilateral RSI tests can provide practitioners with valuable information when performed alongside more common vertical RSI tests.

1. Introduction

Reactive strength is the ability to efficiently perform rapid eccentric–concentric contractions, also known as stretch-shortening cycles (SSC) [1,2]. The reactive strength index (RSI), expressed as the ratio between height and contact time in the drop jump task, is commonly used to assess the reactive strength ability of the lower limbs [1,3]. A recent systematic review has highlighted that RSI is associated with various proxies of sports performance [4]. Moreover, RSI has been shown to be reliable [5] and sensitive to age and sex [6,7], as well as previous injury [8]. In addition, using RSI for designing plyometric training has been suggested; specifically, the drop jump height that elicits the highest RSI was suggested to be used during training for optimizing improvements in SSC ability [1,9]. All in all, RSI can be considered as one of the most useful biomechanical metrics in the context of athletic training [10].

Recently, alternative metrics for the evaluation of reactive strength have been emerging. For instance, modified reactive strength index (RSI_mod), derived from the countermovement jump (i.e., the ratio between jump height and time to take off), has been shown as reliable [11], sensitive to sex and sport discipline [12], and associated with approach jump performance [13]. However, both RSI and RSI_mod are derived from vertical tasks and are relatively closely related, with correlation coefficients between the two metrics reported to be ~0.6–0.7 [14]. Both measures are likely useful to assess vertical reactive strength and have shown associations with a change in direction performance (to a horizontally directed task) [4]. Considering the principle of specificity, it seems reasonable to investigate if reactive strength outcomes derived from horizontal tasks offer additional information relevant for horizontal performance (e.g., sprinting, agility tasks).

Previous studies have suggested that exercise/testing outcomes have larger associations with performance tasks when they are executed in the same direction (e.g., hip thrust and sprint) than when they are not (e.g., vertical jump and sprint) [15]. Notably, single- (r = 0.74–0.76) and triple-hop (r = 0.84–0.89) distances (but not outcomes from vertical jumps) were both shown to be highly correlated with 10 m sprint performance [16]. Thus, it seems reasonable to assume that horizontal RSI would better relate to horizontally directed tasks than vertical RSI. However, the literature on RSI derived from horizontally oriented tasks is scarce. Davey et al. [17] has demonstrated the high reliability of RSI derived from a horizontal unilateral triple-hop test. They also reported consistent magnitude, and the fluctuating direction of inter-limb asymmetries derived from RSI values [17]. Recently, it was reported that RSI from triple-hop tasks is moderately related to both RSI from drop jump tasks (r = 0.54–0.66) and countermovement jump tasks (r = 0.42–0.63) [14]. This suggests that all of these variables partially reflect the same general ability (reactive strength), but also contribute unique information, likely because of their direction- and velocity-specific differences [15,18]. RSI from drop jump and triple-hop tasks, but not countermovement jump tasks, was shown to be moderately related to a change in direction performance [14].

In one of the rare studies on horizontal RSI, it was proposed that further studies are needed to investigate how many hops are needed in a horizontal hop test before flight time and distance performance plateau [14]. Namely, information obtained from each consecutive hop could have unique meaning and transfer to sports performance. Therefore, the primary purpose of this study was to investigate the behavior of RSI across multiple consecutive unilateral hops. As no similar study has previously been conducted, we arbitrarily hypothesized that contact and flight times (and by extension, the RSI) will plateau after the fifth consecutive hop. As a secondary aim, we investigated how horizontal RSI relates to contact times, flight times and RSI derived from linear sprinting, as well as sprint performance over different distances (i.e., split times from 0–10 m to 0–100 m). We hypothesized that individuals with larger horizontal hop RSI will also show larger sprint RSI and superior sprint performance (shorter split times).

2. Materials and Methods

2.1. Participants and Study Design

For this study, we recruited 103 young males attending the Faculty of Kinesiology (at University of Zagreb). All participants were healthy, injury-free, and physically active. They were recreationally involved in various sports and other recreational activities, as well as kinesiology practical courses. However, no participant was a sprinter nor had any sprint-training history. The average subject’s age was 19.2 ± 1.1 years, with a body height of 180.2 ± 6.7 cm and a body mass of 76.4 ± 7.9 kg. After a thorough explanation of the experimental protocols and before the onset of the measurements, the participants were required to sign an informed consent form. The experimental protocol was reviewed and confirmed by the Faculty of Kinesiology (University of Zagreb) (approval number: 42/2018). All procedures conformed to the latest revision of the Declaration of Helsinki.

The study was conducted as a cross-sectional experiment, wherein all participants performed unilateral hopping tasks, as well as 20 m and 100 m sprints. All subjects were running in athletics sports shoes with no spikes. Each sprinting test (100 m and 20 m) was conducted on separate days. The unilateral hopping task and 20 m sprint test were conducted on the same day, with the 20 m sprint conducted first. Each subject had a warm-up period before performing the tests twice, with a break of 15–20 min between the two trials. The unilateral hopping test was performed once before the measurement as a warm-up, after which the next trial was recorded as the test result. No familiarization session was performed, as the participants were already well familiar with the test. After the test performance was completed, all subjects undertook a cool-down protocol (consisting of light-intensity running and stretching exercises). The protocol was performed within a single session.

2.2. Unilateral Hopping Task

For the unilateral hopping task, a 20 m track (smooth and non-slippery surface), an OPTOJUMP measurement device (Microgate, Bolzano, Italy), and a photocell system (Brower Timing System, Draper, UT, USA) were provided. The task began with a participant positioned behind a clearly marked starting line at the beginning of the 20 m track. On the command “On your marks”, the participant assumed a position with the test leg pointing forward. After an additional start signal, the participant executed consecutive unilateral horizontal jumps across the 20 m distance. This distance enabled us to record 8–10 hops. We included eight hops in the analysis to keep the number of hops consistent across participants. One repetition of the test was performed before the actual measurement as a warm-up and familiarization. Due to the high load, subjects performed the task only once on each leg. Previous results from a smaller sample showed a high reliability (ICC = 0.94 [0.88–0.98]) in determining the temporal variables [19]. For each consecutive hop, we extracted contact time and flight time. Thereon, the RSI was derived by dividing the flight time by the contact time (referred to as hop-RSI from hereon). This ratio has been extensively used in drop jump tasks to assess vertical reactive strength [1]. Only recently, an interest has been rising in an analogous index for assessing horizontal reactive strength, derived from unilateral hopping for distance [14,17].

2.3. Sprinting Task

A 20 m run from the crouched (sprinting) start position over the contact mat (ErgoTester—Bosco, Italy) enabled us to record contact times for each step, and the duration of the consecutive flight times. In addition, the total sprinting time at the 20 m mark was measured with photocells (Microgate, Bolzano, Italy). Similarly to the unilateral hopping task, we also calculated the ratio between flight time and contact time to obtain the RSI associated with sprinting (referred to as sprint-RSI from hereon).

The 100 m sprint was performed on an official IAAF-approved 100 m track, and the subjects were well trained for this task. The measurement was performed with an electronic measuring device consisting of a central unit, blocks Omega, 10 pairs of photocell timing gates, and a specially developed computer program “BRZ”. The participants were required to perform three repetitions of the task. The time interval between the first and the second test was one day, and there were four days between the second and the third test. All segment times were measured to a 10 ms precision. Subjects completed warm-up training prior to test performance and cool-down training after test performance was completed. The best two of three performances were considered for statistical analysis. We extracted split times for each 10 m increment along the 100 m course.

2.4. Statistical Analysis

The statistical analysis was carried out in SPSS software (version 25.0; SPSS Inc., Chicago, IL, USA). Descriptive statistics were calculated and reported as mean ± standard deviation. The normality of the data distribution was verified with Shapiro–Wilk tests (all p ≤ 0.110) and, consequently, parametric statistics were used. A one-way repeated measures analysis of variance (ANOVA) was run to explore the differences among consecutive steps in the sprinting task or consecutive hops in the horizontal hopping task. The effect sizes were expressed as partial eta-squared (η_p²). The correlations between the sprinting and hopping tasks were assessed with Pearson’s correlation coefficient and interpreted as negligible (<0.1), weak (0.1–0.4), moderate (0.4–0.7), strong (0.7–0.9), or very strong (>0.9) [20]. The effect sizes (η_p²) were interpreted as small (<0.13), medium (0.13–0.26), or large (>0.26) [21]. Effect sizes >0.13 were considered as practically relevant [21]. For all analyses, statistical significance was accepted at α < 0.05.

3. Results

3.1. Differences among Consecutive Hops in the Unilateral Hopping Task

In the unilateral hopping task, very similar results were obtained for each leg on a group level (p > 0.114 for all between-leg comparisons). Thus, only the results averaged between the legs are presented here. Three participants for hop 7 and eighteen participants for hop 8 were removed, as they presented as outliers.

Contact times decreased with consecutive hops (F = 136.8; p < 0.001; η_p² = 0.58; large effect). A post hoc t-test showed that the decreases with adjacent hops were statistically significant (all p < 0.05) from hop 1 (240.3 ± 31.8 ms) to hop 5 (196.1 ± 21.7 ms), but then plateaued (193.8–194.2 ms) for hops 6–8. The main effect of the hop was also statistically significant for flight times (F = 14.4; p < 0.001; η_p² = 0.15; medium effect). In this case, the changes between adjacent hops were smaller and not statistically significant. However, statistically significant differences were observed between hop 1 and hops 4–8 (p < 0.001), hop 2 and hops 5–8 (p = 0.002–0.043), as well as hop 3 and hops 7–8 (p = 0.008). As expected from contact time and flight time results, RSI statistically significantly increased with each hop (F = 78.3; p < 0.001; η_p² = 0.49; large effect). Specifically, the RSI increased with each consecutive hop from hop 1 (1.04 ± 0.17) to hop 5 (1.41 ± 0.22), but then plateaued (1.43–1.44) for hops 6–8 (Figure 1).

We further assessed the proportion of participants that achieved stable performance at a given hop. Namely, although group data plateaued at hop 5, it could be that hop-by-hop changes in individual participants were different.

Table 1. The proportions of participants with stable hop-RSI scores across consecutive hops.

	Hop 1–Hop 2	Hop 2–Hop 3	Hop 3–Hop 4	Hop 4–Hop 5	Hop 5–Hop 6	Hop 6–Hop 7	Hop 7–Hop 8
Mean RSI score change (%)	16.0	7.3	5.3	5.6	2.3	2.5	0.7
Total participant data (n)	103	103	102	103	103	100	85
Participants with inter-hop increase < 20%	62 (60%)	82 (79%)	85 (83%)	91 (88%)	92 (89%)	91 (91%)	77 (91%)
Participants with inter-hop increase < 10%	35 (34%)	67 (65%)	71 (69%)	68 (66%)	78 (75%)	75 (68%)	67 (79%)
Participants with inter-hop increase < 5%	25 (24%)	53 (51%)	53 (53%)	50 (48%)	66 (64%)	68 (68%)	52 (61%)

Note that the data for some participants were not available for later hops as they jumped out of the video analysis frame.

shows the proportion of the participants that achieved stable performance (no further increase in RSI in comparison to the previous repetition), using <20%, <10%, and <5% change as cut-off criteria for stable performance. The associated data are shown in Table 1. If the least strict threshold was used, 88% of the participants reached stable performance at hops 4–5, and the number did not further increase (90–91%). When the thresholds for stable performance were stricter, the trend was similar (i.e., the proportion of the participants with stable performance increased to about hop 5); however, the final proportion of participants with stable performance was lower (68–78% with 10% threshold and 50–68% for 5% threshold).

3.2. Differences among Consecutive Steps in Sprinting

Contact times in sprinting were also progressively shorter with each step (F = 698.8; p < 0.001; η_p² = 0.87; large effect), with all steps statistically significantly differing from each other (all p < 0.001) (first step: 216.1 ± 27.8 ms; last step: 130.3 ± 11.1 ms). Flight times increased with step number (F = 186.7; p < 0.001; η_p² = 0.64, large effect; first step: 66.4 ± 16.8 ms; last step: 103.8 ± 11.4 ms). A post hoc t-test showed that the differences between consecutive steps were statistically significant for every pair except between steps 4 and 5, and between steps 7 and 8. Figure 2 depicts the changes in contact time, flight time, and sprint-RSI as a function of step number. Finally, the RSI derived from sprint data was statistically significantly different among the eight sprinting steps (F = 566.4; p < 0.001; η_p² = 0.84). The sprint-RSI increased from 0.32 ± 0.09 in the first step to 0.80 ± 0.11 in the last (eighth) step. The post hoc tests showed that the differences in sprint-RSI were statistically significant among all steps (all p < 0.001).

3.3. Correlations between Hopping and Sprinting Reactive Strength Index

When correlating all eight sprint-RSI with all hop-RSI (64 pairs of variables), 46 statistically significant positive correlations were found, mostly small to moderate (r = 0.21–0.41). On the other hand, all contact times from sprinting were statistically significantly related to contact times from hops (64 correlations; r = 0.31–0.58). Finally, only 21/64 correlations among flight times from sprint and flight times from hops were statistically significant (r = 0.19–0.34).

3.4. Correlations between Hopping Reactive Strength Index and Sprint Performance

Correlations between sprint times (from 10 m to 100 m in 10 m split times) and variables obtained in hopping tasks were calculated. All sprint split times were statistically significantly related to most hop-RSI outcomes (r = 0.25–0.40) (

Table 2. Association between the hop-RSI metrics (8 hops) and sprint split times (100 m with 10 m increments).

	Sprint Split Times
	10 m	20 m	30 m	40 m	50 m	60 m	70 m	80 m	90 m	100 m
RSI (Hop 1)	−0.23	−0.20	−0.19	−0.19	−0.17	−0.16	−0.15	−0.14	−0.13	−0.12
RSI (Hop 2)	−0.10	−0.08	−0.06	−0.06	−0.03	−0.02	−0.02	−0.01	−0.01	0.00
RSI (Hop 3)	−0.21	−0.21	−0.22	−0.23	−0.22	−0.22	−0.21	−0.21	−0.20	−0.19
RSI (Hop 4)	−0.17	−0.18	−0.20	−0.21	−0.20	−0.21	−0.21	−0.21	−0.21	−0.20
RSI (Hop 5)	−0.32	−0.34	−0.36	−0.38	−0.38	−0.38	−0.38	−0.38	−0.37	−0.38
RSI (Hop 6)	−0.27	−0.29	−0.32	−0.34	−0.34	−0.34	−0.34	−0.34	−0.34	−0.34
RSI (Hop 7)	−0.24	−0.24	−0.26	−0.27	−0.27	−0.28	−0.28	−0.29	−0.29	−0.29
RSI (Hop 8)	−0.14	−0.12	−0.14	−0.15	−0.15	−0.15	−0.15	−0.15	−0.16	−0.16

), implying that individuals with higher hop-RSI presented with shorter sprint times. Inter-limb asymmetries in RSI were not related to sprint performance (all p > 0.05).

4. Discussion

This study was conducted to investigate the changes in hop-RSI across multiple consecutive unilateral horizontal hops. Only a limited number of studies to date on hop-RSI have been conducted, and all have used only triple hops. Thus, it was important to investigate how many successive hops should be performed for comprehensive evaluation. On a group level, the RSI (and the underlying contact and flight times), plateaued around hop 5, which was in accordance with our hypothesis. However, a significant proportion of individuals exhibited variable performance all the way to the last hop. Therefore, the hop-by-hop behavior of RSI seems to be stable on a group level after five hops, but still exhibits within-individual variability in later hops. As a secondary goal, we aimed to examine if hop-RSI is related to sprint-RSI and sprint performance. Our hypothesis was confirmed, however, the associations were mostly small to moderate.

Horizontal hops for distance have been commonly applied in a rehabilitation context as a method of analyzing the progression of lower limb injury rehabilitation [22,23], with some interest to apply hop tests to assessments of athletes as well [24]. Lloyd et al. [25] reported that hop-RSI and hop flight times showed larger differences between the operated and non-operated leg (effect size = 0.65–0.85) than hop distance (effect size = 0.45), indicating the potential utility of calculating additional outcomes other than hop distance. Subsequently, Davey et al. [17] and Šarabon et al. [14] reported moderate-to-excellent reliability of the hop-RSI. However, all of the previous studies were limited to triple-hop tests, wherein only two RSI values can be calculated [14,17,25]. We aimed to extend the protocol from the previous studies by including multiple hops and examining how RSI behaves across consecutive hops. In previous studies, the mean RSI values from the first and second hops were ~1.0 and ~1.3–1.4, respectively [14,17], which is very close to our results (Figure 1, bottom chart). The primary novelty of the present study is the finding that hop-RSI values continue to increase up until about the fifth hop. This implies that after the fifth hop, the performance of the hops is relatively stable, which means that a five-hop test could be sufficient for a comprehensive evaluation of single-leg horizontal reactive strength. Further studies are needed to determine the test–retest reliability of hop-RSI across multiple hops, and then to investigate if (and which) hop-RSI metrics best predict performance.

As a secondary finding, we reported small-to-moderate correlations between hop-RSI and sprint-RSI, as well as between hop-RSI and sprint performance. The only previous study to assess the relationship between hop-RSI and sprint performance reported a small-to-moderate correlation with change in direction ability (assessed by a 5-0-5 change in direction test), but not with 10 m sprint times [14]. However, the former study was performed on a homogenous sample of volleyball players who rarely performed linear sprinting. A recent systematic review reported small-to-moderate associations between RSI from drop jumps and acceleration (pooled r = 0.42) and top speed (pooled r = 0.32) [4]; however, this finding is not consistent across all studies [7]. The study is the first to point out that horizontal hop-RSI could also be related to sprinting performance across a 0–100 m distance, which reflects both acceleration ability and the maintenance of high velocity. Previous reports showed high correlation (r = 0.84–0.89) between triple-hop distance and 10 m sprint [16], which suggests that the horizontal-RSI particularly relates to sprint acceleration performance. Looking at the correlation coefficients between RSI and underlying metrics across hopping and sprinting tasks (Section 3.3), it seems that these associations are primarily driven by associations in contact times. This is expected, as contact times reflect joint/leg stiffness [26], which has been shown to be important for sprint performance [27]. Perhaps even larger associations with sprint would be achieved if we used horizontal hopping tasks with alternating legs, which is a movement that resembles sprinting more closely than unilateral hopping on a single leg. We also checked if inter-limb asymmetries in hop-RSI are related to sprint performance. Although previous studies have indicated that inter-limb asymmetries can be detrimental to athletic performance [28], we found no statistically significant associations between inter-limb asymmetries and sprint performance.

There are some limitations of the study that must be acknowledged. Reliability analysis could not be performed, thus, the reliability of the hop-RSI metrics beyond triple hop tasks remains unknown. It would be reasonable to investigate if a consistent performance (stable RSI metric) is obtained on the level of the individual with multiple repetitions. Furthermore, we recruited a convenient sample of male kinesiology students for this study. As previous studies found significant differences in RSI among sports and between sexes [7,11,12], our findings cannot be generalized to elite athletes and females. Further studies are needed to investigate the behavior of RSI across consecutive hops in female populations and in elite athletes in general.

5. Conclusions

This study demonstrates that at least five hops should be executed in a unilateral hopping task for the comprehensive assessment of horizontal reactive strength in a male non-athletic but physically active population. The RSI derived from unilateral hopping shows a small association with linear sprinting performance across 10 m to 100 m distances. Future studies should expand the research on the relationship between horizontal hopping RSI and sports performance, including female participants and elite athletes.