Secondary Logo

Journal Logo

Original Research

Effect of Jump Interval Training on Kinematics of the Lower Limbs and Running Economy

Ache-Dias, Jonathan1; Pupo, Juliano Dal2; Dellagrana, Rodolfo A.2; Teixeira, Anderson S.2; Mochizuki, Luis3; Moro, Antônio R.P.2

Author Information
Journal of Strength and Conditioning Research: February 2018 - Volume 32 - Issue 2 - p 416-422
doi: 10.1519/JSC.0000000000002332
  • Free



Performance in distance running is primarily explained by aerobic characteristics (25); however, it has been suggested that neuromuscular properties are essential for performance (23,36,34). The potentiation of elastic mechanisms is considered one of the most important neuromuscular adaptations for running because of the locomotion pattern, in which the legs behave like a linear spring (33); therefore, musculotendinous or joint stiffness is key for running performance (15,34).

High stiffness allows runners to optimize the stretch-shortening cycle (SSC) because more elastic energy can be stored at landing and, consequently, to generate more power output at push-off (9,22). High stiffness also implies lower energetic cost and better performance reducing the amount of energy wasted in breaking forces (26). In addition, vertical stiffness (Kvert) strongly influences running technique, changing kinematic parameters such as stride frequency, stride length, contact time, and flight time (FT) (29). Lower extremity stiffness seems to suffer adaptation after exercise interventions, especially using weight (23) and jump training (11,15,23,34).

Amateur and even professional runners frequently do not have adequate time for training all physical abilities in specific sessions. In this context, low-volume training with short bouts of high-intensity exercise may be a good strategy for runners. High-intensity interval training (HIIT) is considered a time-efficient way to improve performance, stimulating both metabolic and neuromuscular systems (6). Recently, a new HIIT regimen, jump interval training (JIT), was proposed, in which vertical jumps are performed continuously at maximal intensity (1). Jump interval training induces moderate-to-large effect on vertical jump performance, power output, explosive strength, and metabolic indexes related to aerobic and anaerobic performances (1). Considering that adaptation on stiffness is primarily explained by the ability to use the SSC more effectively after training (11), JIT would be a good time-efficient alternative for stimulating the muscle elastic mechanisms related to the stiffness control and consequently, the kinematics of the lower limb and running economy (RE).

Therefore, the main objective of this study is to analyze the effect of the addition of 4 weeks of JIT to the regular endurance training of recreational runners on vertical stiffness, kinematic parameters related to running technique, and energy cost during submaximal running exercise. The second objective is to analyze the relationship between energy cost and stiffness. Our main hypothesis is that JIT improves stiffness in the lower limbs (7) and RE (34,37).


Experimental Approach to the Problem

This study used a randomized controlled trial design with 2 parallel groups to verify the effect of 4 weeks of jump interval training—JIT (independent variable)—on stiffness, kinematic of lower limbs, and RE (dependent variables) of recreational runners. For this, participants were divided into an experimental group (EG) and a control group (CG). All groups performed a standard training program consisting of submaximal running (3 times per week), but additionally, the EG performed the JIT twice a week. This study design is similar to that used in a previous study (37) aiming to analyze the isolated effect of JIT.


Twenty-six recreational runners volunteered to participate in this study. Inclusion criteria were: (a) women or men who practiced running 2–5 times per week in the previous 2 months; (b) aged from 18 to 40 years old; and (c) able to perform the jump training with good jump technique (to perform countermovement jump (CMJ) with the trunk as vertical as possible, hands placed on hips, flexing knees at ≈90° in the transition between the eccentric-concentric phases, and full knee extension during the flight phase). Exclusion criteria were: (a) jump training in the previous 3 months; and (b) lower limb injury in the previous 2 months. Participants were randomly assigned to 1 of 2 groups (EG and CG). Before randomization, peak aerobic velocity (Vpeak) was measured during a progressive maximal aerobic test. Participants of the same sex who presented a Vpeak with a difference of less than 0.5 km·h−1 were considered to have the same aerobic level and were assigned randomly (tossing a coin) into the EG or CG. Nine participants (5 women and 4 men) in the EG and 9 in the CG (Table 1) completed the training. Eight participants were excluded from the experiment because they were injured or stopped training because of work commitments. Ethical approval for the study was obtained from the Human Research Ethics Committee of Federal University of Santa Catarina, and all participants provided written informed consent.

Table 1.:
Mean values and SDs of anthropometric characteristics and physiological measures obtained during maximal progressive test.*


Both groups (EG and CG) performed a 4-week training program that consisted of running on a treadmill (Inbramed Millenium 10.200; INBRAMED, Porto Alegre, Brazil) at 70% of Vpeak, for 40 minutes, 3 times per week (Monday, Wednesday, and Friday). In addition, the EG performed high-intensity JIT twice a week (Tuesday and Thursday). Jump interval training sessions were comprised intermittent bouts of continuous maximal jump for 30 seconds (CJ30) with 5-minute intervals (2-minute walking on a treadmill at 5 km·h−1 and 3-minute passive), this time of effort rest is commonly used in sprint training models (16). The number of bouts was progressively increased from 4 to 6 in the first 3 weeks and decreased to 5 in the last week. All JIT sessions were preceded by: (a) dynamic stretching of the quadriceps, hamstring, and triceps surae during 2–3 full squat movements; (b) 3-minute walk on the treadmill at 5 km·h−1; (c) hopping for 10 seconds; and (d) 5 submaximal CMJs. Participants were asked to perform no type of physical training other than what was planned.

Test sessions were conducted over 2 days, 72 hours before and after the training program. On the first day, an anthropometric evaluation was performed and followed by a progressive test on a motorized treadmill. On the second day, a submaximal constant-load test was used to determine the energy cost of running and to analyze the kinematics of running. Participants were instructed to refrain from training for 24 hours before the testing sessions, to maintain their regular diet, and to avoid caffeinated drinks. All procedures were conducted in a laboratory at 24° C.

During the progressive maximal exercise test, peak oxygen uptake (V̇o2peak), velocity at V̇o2peak (V̇o2peak), maximal heart rate (HRmax), and velocity at onset of blood lactate accumulation (vOBLA) were measured. Respiratory measures were obtained using a gas analysis system (Quark PFT Ergo; Cosmed, Rome, Italy). The treadmill (model ATL 10200; IMBRAMED) was set at a 1% gradient (20) with an initial starting velocity of 7 km·h−1 for women and 8 km·h−1 for men, which was subsequently increased by 1 km·h−1 every 3 min until volitional exhaustion (7). The participants were verbally encouraged to exert maximum effort. V̇o2peak was considered as the highest value of V̇o2 obtained during the progressive maximal test (7). Maximum effort was deemed to be achieved if the incremental test met 2 of the following criteria (4): (a) respiratory exchange ratio ≥1.15; (b) HRmax (age predicted) ≥90%; and (c) LACmax ≥8 mmol·L−1. Between each stage, there was a rest interval of 30 seconds, in which 25 μl of capillary blood was collected from the earlobe to measure blood lactate concentration ([La]) (7). The blood lactate samples were analyzed using an electrochemical analyzer (YSI 2300 STAT; Yellow Springs, OH, USA). The vOBLA was determined by linear interpolation using the intensity at a fixed [La] of 3.5 mmol·L−1. Heart rate was recorded every 5 seconds throughout the test (model RS800sd; Polar, Kempele, Finland).

The submaximal constant-load test was performed on a motorized treadmill to evaluate RE. First, participants walked at 4 km·h−1 for 3 minutes as a warm-up, followed by 6 minutes of running at 9 km·h−1. Oxygen uptake (V̇o2), carbon dioxide (V̇co2), and HR were continuously measured during the test. Running economy was determined by V̇o2 during the last minute of the test. Running was recorded in the last minute of the test using a camera (Canon SX510 HS; Canon, Tokyo, Japan) that was calibrated and recorded images at 120 Hz; the camera was positioned perpendicular to and 6 m from the treadmill.

Three stride cycles during the middle of the last minute of the submaximal constant-load test were selected from the recorded images, and the average of these 3 cycles was used for analysis. Stride cycle was normalized as 100%. Stride cycle was defined as the period from 1 left foot ground contact (foot strike) to the next left foot strike and it was divided into stance (left foot strike to left foot toe-off) and swing (left foot toe-off to left foot strike) phases. The time of foot strike and foot toe-off was defined by visual inspection using zoom. The kinematic variables obtained were step length (SL); step rate (SR); support time (ST), time from right foot strike to right foot toe-off; and FT, time from right foot toe-off to left foot strike.

Vertical stiffness (Kvert) and leg stiffness (Kleg) were obtained from the kinematic data. Kvert was calculated as the ratio between maximum vertical force (Fmax) and vertical displacement of the center of mass (∆y) (equation 1) (32):

Maximal force and ∆y used in equation 4 were estimated by the equations 2 and 3, respectively:

in which: m was the body mass of participants, g was gravity acceleration, and FT and ST were flight time and support time, respectively.

Kleg was calculated by the equation 4 (37):

∆L is leg deformation as a spring and is calculated by equation 5:

in which L is lower limb length (distance between the greater trochanter and ground in an upright standing posture), v is running velocity, and ST is support time. L was defined by L = 0.53 × height of individual, as proposed by Winter (38).

Statistical Analyses

Data are presented as mean ± SD. The Shapiro-Wilks test was used to check data distribution. To determine the effect of JIT training, split-plot analysis of variance with repeated measures was used with a between-participant factor (group: EG and CG) and a within-participant factor (time: pretraining and posttraining). A Box-Cox test was used to verify the data's heteroscedasticity. Bonferroni's correction was used for post hoc analysis (pairwise comparison). Pearson's correlation was used to verify the correlation between RE and stiffness. All tests were performed using SPSS 17.0 software (SPSS, Inc., Chicago, IL, USA) with significance levels set at p ≤ 0.05.

Magnitude-based inference analyses were also used to examine practical significance. For the kinematic variables, within/between-group comparisons were performed, where the chances that the true (unknown) mean changes were beneficial/better (i.e., greater than the smallest worthwhile change [0.2 multiplied by the between-participant SD]), trivial, or harmful/worse were determined. Quantitative chances of a beneficial/better or harmful/worse effect were assessed qualitatively, as follows: <1%, almost certainly not; 1–5%, very unlikely; 5–25%, unlikely; 25–75%, possibly; 75–95%, likely; 95–99%, very likely; and >99%, almost certainly. If the chances of beneficial/better and harmful/worse effects were both >5%, then the true difference was assessed as unclear (5,18). In addition, the standardized difference, or effect size (ES), of changes for each kinematic variable between the EG and CG was calculated using the pooled pretraining SD. The criteria to interpret the magnitude of the ES were: ≤0.2 trivial, >0.2–0.6 small, >0.6–1.2 moderate, >1.2–2.0 large, and >2.0–4.0 very large (17).


Physiological measures obtained from the maximal progressive test were similar between CG and EG (Table 1). Baseline values of kinematics variables (pretraining) for all variables were similar between groups (Table 2).

Table 2.:
Changes in running economy and kinematic variables during submaximal running exercise for experimental and control groups, and results of analyses of practical significance (qualitative inference).*†

Neither an interaction (F = 1.8; p > 0.05) nor a time effect (F = 3.0; p > 0.05) from training was found for RE (Table 2). In addition, there was no interaction or group effect for the kinematic variables (p > 0.05); however, most of the kinematic variables (Table 2) had a time effect (ST [F = 13.4; p ≤ 0.05], SR [F = 10.7; p ≤ 0.05], and SL [F = 13.0; p ≤ 0.05]). There was no difference in Kvert (F = 9.7; p > 0.05) and Kleg (F = 9.7; p > 0.05) between pretraining and posttraining for the CG and EG.

Although the “traditional” statistical analyses revealed no significant changes from the pretraining to posttraining moment for Kvert and Kleg in both groups, magnitude-based inference analyses showed some meaningful differences. This analysis of practical significance found that Kvert and Kleg were very likely (98/02/00%) improved after JIT, whereas changes for the CG were unclear (56/38/06%) (Table 2). Changes in SR, SL, and ST were very likely beneficial for the EG (small-to-moderate ES's), but only possibly positive for the CG (trivial-to-small ES's). Changes in FT were possibly beneficial for the EG (small ES) but unclear for the CG (trivial ES) (Table 2). Between-group comparisons suggested that improvements in Kvert, Kleg, SR, and SL for the EG (small ES's) were possibly (64–73% chance of a beneficial difference) greater than those observed in the CG (Table 3). No significant correlation was verified between RE and Kvert (r = 0.12; p > 0.05) and Kleg (r = 0.12; p > 0.05).

Table 3.:
Between-group comparisons of running economy and kinematic variables during submaximal running exercise.*†


This study examined the additional effect of 4 weeks of a JIT regimen on stiffness, kinematics of lower limbs, and energy cost of running compared with a traditional endurance training program alone. The major original finding was that the addition of JIT to a traditional endurance training program (40-minute running at 70% of Vpeak) resulted in possibly superior changes (64–73% chance of a beneficial difference for some variable) than those observed for the CG on stiffness and kinematics of the lower limbs of recreational runners during constant submaximal velocity in running.

Some studies have suggested that jump training may increase lower limb stiffness. For example, Cormie et al. (11) showed that a power training regime performed for 10 weeks (consisting of jump squats at 0–30% of 1 repetition maximum) resulted in increased Kleg during jump squat exercise. Kubo et al. (23) showed that tendon stiffness increased significantly after weight training, but not after jump training; conversely, joint stiffness increased significantly after jump training, but not for weight training. Knee joint stiffness is also positively affected by jump training combined with weight training (leg press exercise), as showed by Toumi et al. (36). According to Malisoux et al. (27), the increase in joint stiffness after jump training is primarily related to the mechanical properties of the muscle-tendon complex and the ability to use the SSC more effectively during jump exercise. According to Nicol et al. (30), increased stiffness facilitates the stretch reflex, which is important for force or power optimization during SSC (21).

It has been shown that Kleg influences various parameters of functional performance, with higher and lower Kleg being more and less beneficial, respectively (10). During running, the body can be modeled as a single-linear spring mass system, in which the average stiffness of the overall musculoskeletal system is represented during the ground-contact phase (14). Stiffness influences the body-ground interaction, in which greater leg stiffness is associated with increased running velocity (8) and leads to shorter ST, a higher SR (14), and a smaller vertical excursion of the body's center of mass during the ground-contact phase (16). According to Girard et al. (16), changes in stiffness during running may be associated with stride rate and stride length modulation, which are common strategies for the modulation of movement technique.

If running is performed with high Kleg, ground-contact time will decrease, reducing the time available for force production and consequently, impulse. A reduction in the ground reaction force during ground contact would likely cause a reduction in stride length (13). Therefore, as suggested by McMahon et al. (28), athletes should focus on reducing ground-contact time while still producing a large enough ground reaction force to maintain/increase impulse, and thus stride length. Accordingly, an example of good practice during jump training would be to focus on completing the drive phase by achieving a full-triple extension of joints, then maintaining this extension during the flight phase, avoiding a “tuck” during the flight phase and facilitating impulse generation.

In this study, some kinematic variables of running were affected by time, such as SR and stride length, that are common variables to describe a global kinematic pattern of running. Although our inferential statistics indicates that there was no significant “time-group” interaction for the kinematic variables of running, practical significance analysis suggested that JIT might induce possibly superior increases in SR (71/27/2%; ES = 0.32) and decreases in v (64/33/3%; ES = −0.27). This may be explained by the dependence of these variables on the high activation level of motor units and on lower limb muscle power; the latter of which was improved by JIT training (1).

Improvements in RE were expected in this study because jump training induces changes in muscle elastic properties, such as stiffness (11,15,34), which are important for muscle efficiency during running. It has been reported that high stiffness is associated with low energy cost (24); however, no significant correlation was observed in our study between stiffness and RE. In addition, JIT had no effect on RE during the submaximal constant-load exercise. Taipale et al. (35) also found no improvements in RE after jump training when added to an endurance training program for recreational runners, whereas others have shown that RE improves after jump training in moderately trained (34) and recreational runners (37). A recent meta-analysis study showed that power training (combining strength and jump exercises) added to endurance training seems to have positive effects on RE after a short-to-medium training period (12).

The lack of effect of JIT on RE in this study may be attributed to factors such as the length of training program and the duration of bouts during jump exercise. Previous studies showed a positive effect of jump training (34,37) or power training (12) on RE with 6 weeks or longer. In our study, runners trained for only 4 weeks. Also, these studies used jump exercises with short duration efforts (less than 5 seconds), whereas jump training performed here was based on a HIIT model (short-term all-out exercise) with bouts of 30 seconds. Ache-Dias et al. (1) verified that this training model induces important metabolic adaptations but also provides improvements in muscle power. However, the all-out characteristic with fatigue occurrence at the end of the bouts may be nonideal condition to induce improvements on RE.

In addition, our participants were recreational runners with less experience and training volume than moderately trained runners. Barnes and Kilding (2) suggest that running experience (number of years) and high-training volumes are important for the improvement of RE. Furthermore, RE is affected by metabolic, cardiorespiratory, biomechanical, and neuromuscular efficiency during running (3). Improvements in a given determining factor may be beneficial for 1 runner but insufficient for the improvement of RE in another because of physiological and biomechanical differences (3). Future studies that use JIT in continuous endurance training should consider a longer training period and also assess other parameters related to RE.

In summary, conventional statistical analysis showed no effect of JIT training, but the qualitative analysis showed that the addition of 4 weeks of JIT into a continuous endurance training program was suggestive of beneficial changes in the kinematics of the lower limbs, such as an increase in stiffness and SR and a decrease in SL. These changes did not affect RE. No relationship between RE and stiffness was found in this study.

Practical Applications

Jump interval training is able to induce metabolic adaptations related to running performance, as previously shown by Ache-Dias et al. (1). In the current study, we also showed that JIT allows positive changes on lower limb kinematic variables, particularly the musculoskeletal stiffness, which is commonly related to RE. However, if the main objective is to use JIT as a training strategy to improve RE, physical conditioning professionals should consider to use the longer training period (>4 weeks) and possibly to adopt shorter time efforts (e.g., bouts of continuous jumps between until 10–15 seconds). Finally, it is important to highlight that JIT is practical and applicable, especially because it can be performed within any square meter of hard surface and does not require any kind of ergometer.


The authors thank all participants for their involvement with this study and acknowledge the staff from the Physical Effort Laboratory of the Federal University of Santa Catarina for their support. The authors have no conflicts of interest to disclose.


1. Ache-Dias J, Dellagrana RA, Teixeira AS, Dal Pupo J, Moro AR. Effect of jumping interval training on neuromuscular and physiological parameters: A randomized controlled study. Appl Physiol Nutr Metab 41: 20–25, 2016.
2. Barnes KR, Kilding AE. Running economy: Measurement, norms, and determining factors. Sports Med Open 1: 8, 2015.
3. Barnes KR, Kilding AE. Strategies to improve running economy. Sports Med 45: 37–56, 2015.
4. Bassett DR, Howley ET. Limiting factors for maximum oxygen uptake and determinants of endurance performance. Med Sci Sports Exerc 32: 70–84, 2000.
5. Batterham AM, Hopkins WG. Making meaningful inferences about magnitudes. Int J Sports Physiol Perform 1: 50–57, 2006.
6. Billat LV. Interval training for performance: A scientific and empirical practice. Special recommendations for middle- and long-distance running. Part I: Aerobic interval training. Sports Med 31: 13–31, 2001.
7. Billat LV. Oxygen kinetics and modelling of time to exhaustion whilst running at various velocities at maximal oxygen uptake. Eur J Appl Physiol 82: 178–187, 2000.
8. Bobbert MF. Drop jumping as a training method for jumping ability. Sports Med 9: 7–22, 1990.
9. Brazier J, Bishop C, Simons C, Antrobus M, Read PJ, Turner AN. Lower extremity stiffness: Effects on performance and injury and implications for training. Strength Cond 36: 103–112, 2014.
10. Bret C, Rahmani A, Dufour AB, Messonnier L, Lacour JR. Leg strength and stiffness as ability factors in 100 m sprint running. J Sports Med Phys Fitness 42: 274–281, 2002.
11. Cormie P, McGuigan MR, Newton RU. Changes in the eccentric phase contribute to improved stretch-shorten cycle performance after training. Med Sci Sports Exerc 42: 1731–1744, 2010.
12. Denadai BS, de Aguiar RA, de Lima LC, Greco CC, Caputo F. Explosive training and heavy weight training are effective for improving running economy in endurance athletes: A systematic review and meta-analysis. Sports Med 47: 545–554, 2016.
13. Derrick TR, Caldwell GE, Hamill J. Modeling the stiffness characteristics of the human body while running with various stride lengths. J Appl Biomech 16: 36–51, 2000.
14. Farley CT, Morgenroth DC. Leg stiffness primarily depends on ankle stiffness during human hopping. J Biomech 32: 267–273, 1999.
15. Fouré A, Nordez A, Cornu C. Plyometric training effects on Achilles tendon stiffness and dissipative properties. J Appl Physiol (1985) 109: 849–854, 2010.
16. Girard O, Micallef JP, Millet GP. Changes in spring-mass model characteristics during repeated running sprints. Eur J Appl Physiol 111: 125–134, 2011.
17. Hazell TJ, Macpherson RE, Gravelle BM, Lemon PW. 10 or 30-s sprint interval training bouts enhance both aerobic and anaerobic performance. Eur J Appl Physiol 110: 153–160, 2010.
18. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 41: 3–13, 2009.
19. Hudgins B, Scharfenberg J, Triplett NT, McBride JM. Relationship between jumping ability and running performance in events of varying distance. J Strength Cond Res 27: 563–567, 2013.
20. Jones AM, Doust JH. A 1% treadmill grade most accurately reflects the energetic cost of outdoor running. J Sports Sci 14: 321–327, 1996.
21. Komi PV, Gollhofer A. Stretch reflexes can have an important role in force enhancement during SSC exercise. J Appl Biomech 13: 451–460, 1997.
22. Komi PV. Stretch-shortening cycle: A powerful model to study normal and fatigued muscle. J Biomech 33: 1197–1206, 2000.
23. Kubo K, Morimoto M, Komuro T, Yata H, Tsunoda N, Kanehisa H, Fukunaga T. Effects of plyometric and weight training on muscle-tendon complex and jump performance. Med Sci Sports Exerc 39: 1801–1810, 2007.
24. Lacour JR, Bourdin M. Factors affecting the energy cost of level running at submaximal speed. Eur J Appl Physiol 115: 651–673, 2015.
25. Lacour JR, Padilla-Magunacelaya S, Barthélémy JC, Dormois D. The energetics of middle-distance running. Eur J Appl Physiol Occup Physiol 60: 38–43, 1990.
26. Lazzer S, Taboga P, Salvadego D, Rejc E, Simunic B, Narici MV, Buglione A, Giovanelli N, Antonutto G, Grassi B, Pisot R, di Prampero PE. Factors affecting metabolic cost of transport during a multi-stage running race. J Exp Biol 217: 787–795, 2014.
27. Malisoux L, Francaux M, Nielens H, Theisen D. Stretch-shortening cycle exercises: An effective training paradigm to enhance power output of human single muscle fibers. J Appl Physiol (1985) 100: 771–779, 2006.
28. McMahon JJ, Comfort P, Pearson S. Lower limb stiffness: Effect on performance and training considerations. Strength Cond J 34: 94–101, 2012.
29. Morin JB, Jeannin T, Chevallier B, Belli A. Spring-mass model characteristics during sprint running: Correlation with performance and fatigue-induced changes. Int J Sports Med 27: 158–165, 2006.
30. Nicol C, Avela J, Komi PV. The stretch-shortening cycle: A model to study naturally occurring neuromuscular fatigue. Sports Med 36: 977–999, 2006.
31. Paavolainen L, Nummela A, Rusko H. Muscle power factors and VO2max as determinants of horizontal and uphill running performance. Scand J Med Sci Sports 10: 286–291, 2000.
32. Pappas P, Paradisis G, Tsolakis C, Smirniotou A, Morin JB. Reliabilities of leg and vertical stiffness during treadmill running. Sports Biomech 13: 391–399, 2014.
33. Saibene F, Minetti AE. Biomechanical and physiological aspects of legged locomotion in humans. Eur J Appl Physiol 88: 297–316, 2003.
34. Spurrs RW, Murphy AJ, Watsford ML. The effect of plyometric training on distance running performance. Eur J Appl Physiol 89: 1–7, 2003.
35. Taipale RS, Mikkola J, Vesterinen V, Nummela A, Hakkinen K. Neuromuscular adaptations during combined strength and endurance training in endurance runners: Maximal versus explosive strength training or a mix of both. Eur J Appl Physiol 113: 325–335, 2013.
36. Toumi H, Best TM, Martin A, Poumarat G. Muscle plasticity after weight and combined (weight + jump) training. Med Sci Sports Exerc 36: 1580–1588, 2004.
37. Turner AM, Owings M, Schwane JA. Improvement in running economy after 6 Weeks of plyometric training. J Strength Cond Res 17: 60–67, 2003.
38. Winter DA. Biomechanics of Human Movement. New York, NY: Wiley & Sons, 1979.

biomechanics; plyometric training; endurance; stiffness; runners

© 2017 National Strength and Conditioning Association