5 Must-read research reviews on testing by Science for Sport

SPECIAL EDITION 5 must-read research reviews on testing

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Practical Takeaways from study

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Reviewers comments on the study

Technology & Monitoring

[Abstract]

Force-velocity-profiling an athlete’s speed: How many timing gates do we need? OBJECTIVE The purpose of this study was to explore what combinations of timing gates would be best for profiling the mechanical outputs of long accelerations. Coaches and teams always want to know more about the speed of their athletes, and finding an ideal placement of timing gates would be practical for both standardisation of profiling and assessment of speed. Therefore, the objective to this study was to assess the construction of timing gate set-up and the impact of statistical confidence that the data could be used for deeper insight. WHAT THEY DID The researchers timed athletes with a double-beam system over 40 meters, with timing gates every 5 meters. By collecting data every 5 meters, the researchers could analyse every likely combination of split arrangement to find what is truly necessary to properly assess the velocity curve (See example video HERE). After a few runs, all of the athlete’s data was analysed with common statically valid approaches to ensure that the practical set-up was scientifically accurate. WHAT THEY FOUND Not surprisingly, a straightforward assessment of athletes sprint profile can be done with timing gates at the 10m, 20m, and 30m marks. The researchers concluded that the spacing of the timing gates must cover most of the acceleration zone of the sprint, but early splits of 0-5m may be very limited in use, at least statistically speaking. Finally, the authors also question the need for using 5 timing gates to analysis an athlete’s performance.

Practical Takeaways Previous research suggested that a minimum of 5 split times are required to accurately calculate sprint mechanical outputs. However, based on the findings of this study, it appears that using just 3 timing gates at 10m, 20m, and 30m is sufficient for power-force-velocity profiling athletes. The authors provided a very simple way to test and profile an athlete’s speed by reducing the amount of hardware needed (i.e. timing gates). Based on the evidence, using just three timing splits in 40m sprints can estimate potential of maximum speed as well as set-up a training plan to help improve acceleration programing. Coaches should be aware that while Radar and Lasers are excellent for research and training, those with simple data points of 10m, 20m, and 30m are excellent alternatives for most purposes. In addition to the convenience proposed by the researchers, the acceleration model proposed by them could be used with caution when estimating peak velocity. Many team-sport athletes can be tested with shorter distances because they, in most cases, are unable to hit above 10 m/s and reach their maximal speed before 40m.

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Carl’s Comments “I am a big fan of research that helps create pragmatic testing protocols that are clear and easy-to-follow. The key benefit of this research is that it enables strength and conditioning coaches to accurately test athletes with minimal equipment and effort, minimising the headaches of labourious testing. One concern of mine, however, is the indirect measurement of peak velocity as it’s both a skill as well as an athlete talent. Some athletes are able to run faster with a more gradual acceleration pattern as proposed by Warren Young (Read HERE), but some coaches may not see the same outcomes. In my experience, both acceleration and peak speed should be tested separately as a few athletes can be coached up to hit 0.3 to 0.8 m/s above what their talent is capable of.”

Technology & Monitoring

[Abstract]

Sled-resisted sprinting: A new way to test and optimise sprint speed? OBJECTIVE Horizontal force and sprint performance has gathered a lot of research interest in the past decade, along with forcevelocity (F-V) profiling. However, most F-V profiling has been studied and used for jumping, and as such, it is not directly translatable for horizontally-emphasised sports. Hence, the aim of this study was to compare F-V relationships determined via multiple resisted sprints to a single unloaded sprint trial. A secondary aim was to examine whether practical resisted sprint loading parameters could be determined from a single sprint. WHAT THEY DID 12 recreational level mixed-sport athletes and 15 highly-trained sprinters participated. 7 loading protocols were prescribed (unloaded, 20, 40, 60, 80, 100, 120% of body mass). This was to obtain a power-velocity curve where power will peak at a certain velocity; which is inversely related to the load being dragged (i.e. the lighter the load, the faster you run). Distances for each load were as follows: unloaded = 45m, 20% = 40m, 40% = 30m, 60% = 30m, 80% = 30m, 100% = 20m, 120% = 20m. These were obtained by the authors’ pilot study where each distance is approximately what would be required to reach maximal velocity under each condition. Load was added until a 50% decrement in maximal velocity was observed. WHAT THEY FOUND There were large errors associated with increased horizontal force output (F0) between the two methods. In the calculations for optimal loading, the multiple trial method presented a lesser optimal load by approximately 3.1kg. It is unclear whether or not this practically represents a worthwhile margin of error. Both methods calculated the ability to generate force at high velocities (v0) with almost perfect relation between methods (r = 0.99) while having a small margin of error. The biggest finding was the overall strong association between the F-V relationships determined through free sprinting and multiple trial resisted sprint (r = 0.71 to 0.99).

Practical Takeaways The strong relationship in calculating optimal velocity provides a potential avenue for assessment and prescription of training load from a single sprint, without the need of multiple taxing resisted sprints. However, it is important to note that for the optimal velocity to be translated into a useable training resistance accurately (i.e. sled load or pulley resistance), surface conditions and equipment would have to be standardised due to changes in friction. Being able to calculate the optimal load to maximise velocity in one single sprint could allow coaches to individualise resisted sprint loads to target a particular side of the F-V relationship. What is also interesting is the similar nature of the unloaded and resisted sprints, as shown by the strong relationship. Maintaining a maximal sprint effort at a given load may replicate the conditions experienced during an unresisted sprint, but at different parts of the F-V relationship. For example, an athlete towing an individualised optimal load of 82% body mass (load taken from a previous paper) would mimic the moment power is maximised during an unloaded sprint (i.e. steps 2-3 or early acceleration).

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James’s Comments “This new paper provides valuable insight for coaches when it comes to sprint training and testing. Rather than the typical speed test where the only information gathered is time, some further calculation (with the right data collecting equipment) can make your data actionable when looking to maximise the velocity side of the F-V relationship. The fact that resisted sprints replicate an unresisted sprint at different parts of the F-V relationship is an exciting finding for the future, especially when the calculation for an optimal horizontal F-V profile gets published later this year and added to apps and devices alike. This means that ultra-heavy sledsprinting to maximise the force end of the F-V relationship doesn’t have to be vilified as a sprint mechanic destroying exercise. Instead, it opens up the thought process of maximising horizontal force capabilities for athletes who’re lacking this quality in order to improve sprint performance,”

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Technology & Monitoring

[Abstract]

My Jump 2 App: A great tool for measuring the reactive strength index or all hype? OBJECTIVE The reactive strength index (RSI) is one metric commonly analysed from the drop jump (DJ). It identifies an athlete’s ability to quickly switch from an eccentric to a concentric contraction, and how much force the athlete is able to produce in the shortest possible time. RSI has also been correlated to change of direction speed, and attacking and defensive agility. Testing the drop jump is now easier than ever with the iPhone app My Jump 2. Therefore, the aim of the study was to analyse the validity and reliability of the My Jump 2 app for measuring RSI and DJ performance. WHAT THEY DID 14 active male students with at least one year of jump training experience (including DJ) participated. Leg length was measured as per previous force-velocity-power studies to calculate force and power variables. After a standardised warm-up, subjects performed 3 DJ onto a force platform whilst simultaneously being recorded with a smartphone using the My Jump 2 app. Drop heights of 20cm and 40cm were used. Jump height, contact time, mean power, flight time, and RSI were recorded on both devices. WHAT THEY FOUND Near perfect levels of agreement were seen between the My Jump 2 app and force platform measures of RSI at 20cm and at 40cm (ICC = 0.95 and 0.98, respectively). Furthermore, near perfect agreement was seen in measures of jump height and contact time (ICC = 0.96 and 0.92, respectively). Mean power in both tests had a weaker agreement (ICC = 0.67). Near perfect correlations were seen in RSI measures at 20cm and 40cm (r = 0.94 and 0.97, respectively) between the My Jump 2 app and force platform. Furthermore, near perfect correlations in both jump height and contact time between measuring devices (r = 0.96 and 0.98, respectively). Conversely, mean power showed weaker correlations (r = 0.66). My Jump 2 showed good intra-session reliability when measuring RSI at 20cm and 40cm (CV = 6.71% and 10.32%, respectively).

Practical Takeaways The near perfect agreement seen between the My Jump 2 app and force platform for RSI, jump height, and contact time all support the validity of the app as a valid tool for measuring drop jump performance. These findings suggest that even though the take-off and landing frames are manually selected, the app can still accurately measure contact time and jump height. Mean power was the only variable which did not correlate well between the two devices. This could be due to the app’s calculation of power, as the force plate measures force directly, whilst the app uses contact time, flight time, and body mass to estimate power. The slightly larger variation in RSI measurements (RSI at 40cm) could be due to the fact that RSI is multi-factorial, with the error on flight time being compounded by error on contact time. Similar findings in this study and previous research show the My Jump 2 app is able to reliably measure DJ performance in a wide range of populations from recreational to elite-level athletes.

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James’s Comments “I use My Jump 2 pretty extensively with my rugby guys. The DJ measurement is a quick and easy test to get accurate RSI measurements, as well as CMJ, SJ, and force-velocity profiling. Not only does the app give you RSI, but also contact time, flight time, jump height, and stiffness. The easiest way to test a team of players is to record all jumps in slow-motion and analyse the jumps later due to the time-consuming nature of manually selecting ground contacts. It is for this reason that using the DJ as a measure of “readiness” with this app isn’t practical in a team setting. Furthermore, when testing the DJ, it will take a few sessions for the athletes to learn how to DJ correctly. Often, athletes newer to the DJ struggle to land with both feet at the same time which will skew your RSI and contact time results. As a result, test familiarisation is vital.”

Strength & Conditioning

[Abstract]

Velocity based training: Can bar-power output be used instead of 1RMs? OBJECTIVE 1-repetition maximum (1RM) tests are widely used by coaches to evaluate performance and determine training loads. Due to the inherent difficulties in applying 1RM tests, velocity based training (VBT) has emerged as a practical alternative to control resistance training intensity. Several investigations have provided useful information on VBT and have correlated movement velocities with 1RM measures. Furthermore, 1RM does not reflect the force and velocity applied by an athlete against a load, which in high-performance sport, time and velocity play a critical role in determining the effectiveness of force application. Therefore, the aims of this study were to: 1) analyse the correlations between bar-power outputs and 1RM values; and 2) assess the sensitivity and specificity of the bar-power approach for athlete testing and monitoring. WHAT THEY DID 61 elite athletes from 4 different sports (14 track & field sprinters and jumpers, 18 rugby 7s players, 8 bobsled athletes and 21 professional soccer players) participated in this study. The sample comprised of 15 athletes who participated in the previous Summer and Winter Olympic Games. Physical tests were performed on 2 non-consecutive days. Day 1) squat jumps (SJ), countermovement jumps (CMJ), and 1RM half-squat (HS); Day 2) maximum power outputs in the HS and jump squat (JS) exercises and a sprint test. SJ and CMJ were performed on a contact mat while the 1RM HS was performed on a Smith machine. Mean power (MP), mean propulsive power (MPP), and peak power (PP) were assessed with a linear position transducer for the HS and JS exercises. Optimal power load was also determined starting with a load corresponding to 40% body mass with a 10% body mass increase in external load for each set until a clear decrement in power was observed. For the sprint test, track & field athletes performed a 60m sprint while the other athletes performed a 40m sprint where timing gates were set at 10, 20, 30, 40, and 60m. WHAT THEY FOUND All power output measures from both HS and JS correlated significantly with SJ and CMJ heights (varying between 0.58 and 0.82). All power output measures significantly correlated with sprint time over each distance (varying between -0.35 and 0.91). No significant correlations were found between 1RM and the SJ and CMJ. The highest correlation values were observed between the power output measures and 60m sprint time (varying between -0.80 and 0.91) while the correlation between the 1RM with the same sprint distance was -0.63.

Practical Takeaways The most interesting take away from this study was the greater correlations found between power output from HS and JS exercises and sprint times compared to 1RM and sprint times. This suggests that optimal power output may be a novel and alternative method to effectively assess elite athletes. Due to the high levels of precision and consistency that can be obtained through velocity tracking devices, practitioners can use MP, MPP, or PP to estimate and define optimal power zones. More importantly, practitioners can measure the athlete’s ability to efficiently accelerate relative loads (thus reaching higher movements velocities), which is a selective factor in different sporting disciplines. Furthermore, strength training is velocity specific, meaning training at certain velocities will improve performance in that velocity range (as specified in the infographic below). This shows the potential limitations when considering a strength measurement such as external load on the bar which only provides a mass moved through space and doesn’t provide the “how it was moved”. Furthermore, there are potential risks with 1RM testing due to maximum loading and it can be difficult to test 1RM during a competitive period where athletes need to be fresh for future competition. Here are some guidelines/ideas that you can potentially play with in your practice with your athletes using this method. After performing a load-power profile (as per the methods above in the “what they did” section) and/or a ballistic force-velocity profile using the MyJump 2 app. Idea 1: Rank your athletes based on their bar power-output and look to use the optimal load that expresses maximal power to improve the overall mean output of the team or improve individual athletes that are lagging behind others. An example day may look like: A1) Jump Squat 3x3 w/ Optimal Power Load B1) Back Squat 4x4 @ 0.45-0.50m/s average velocity or 80-85% 1RM or RPE 7-8 B2) Box Jump 4x2-3 C1) Posterior Hip or Knee Dominant 3x6-10 Idea 2: Create a force-velocity profile for your athlete as a separate test. Using this information, the MyJump 2 app can determine whether the athlete is force- or velocity-deficient by comparing their force-velocity profile to their “optimum profile” where they would maximise power. The imbalance of the profile will determine how the training programme will be structured (I’ve linked a paper below that has a table with guidelines of how to structure a session depending on the profile [e.g. a force-deficient profile will have more force-based exercises compared to a velocitydeficient profile]). A well-balanced profile, however, will have an even number of exercises throughout the strength-speed continuum to try and shift the entire slope to the right (i.e. improving force and velocity). Instead of guessing the loads that maximise power for that athlete (general guidelines sit between 30-50% 1RM, in most exercises), the optimal power load determined through the bar-power method may allow you to maximise the training effect, especially in a wellbalanced athlete with regards to a force-velocity profile.

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James’s Comments “Velocity trackers I feel are such a useful tool for a strength coach as it provides practitioners with descriptive and prescriptive information without the need to maximally load an athlete. Furthermore, it provides a way to autoregulate training by providing velocity ranges to work off of, rather than percentages of 1RM. For example, you prescribe a velocity range of 1.0-1.05m/s for the squat for that day over a training cycle. Week 1 the athlete feels fresh and completes their sets with 140kg. However, in week 2, the athlete has accumulated massive fatigue from the previous match and is struggling in the weight room. So this week they hit their velocity range with 120kg. If percentages were prescribed, the athlete may struggle with the prescribed weight or may not even complete the prescribed work. The bar-power output approach used in the study is a way to rank your athletes through more meaningful measures than just load on the bar. Using the bar-power approach with a force-velocity profile may not only provide optimal loading for power output, but also give useful information for optimising the athletes training programme to increase power output.”

Technology & Monitoring

[Abstract]

The best method for predicting 1RM using a load-velocity relationship WHAT THEY FOUND The key findings of the study were:

OBJECTIVE

WHAT THEY DID

The inverse relationship between a load lifted and the concentric velocity of a repetition helps reduce the time and stress demands associated with identifying maximal strength. Numerous methods have been proposed to predict the 1RM of an exercise based on the load -velocity relationship, but research is limited in free-weight exercises.

Twenty male subjects completed a ⇒ Predicting 1RM based on MVT familiarisation trial, which included exhibited the highest inter-session determining back squat 1RM and the reliability and very strong associated minimum velocity threshold correlations with measured 1RM, but (MVT). During two subsequent trials, each it consistently overestimated 1RM. separated by one week, participants completed multiple sets of the back  Using the LD0 demonstrated high squat, ranging from 20-90% 1RM while inter-session reliability and concentric velocity was monitored to correlations between predicted and establish load-velocity relationships. measured 1RM.

The aim of this study was to examine the reliability and validity of current velocitybased 1RM predictions using the back squat.

⇒ No significant positive relationship Using multiple linear regression methods, between predicted and measured MVT from the initial familiarisation 1RM. session, the predicted load that  Analysis highlighted substantial intracorresponds with a velocity of 0 m∙s-1 individual variations between (LD0), and measures of force were used predicted and measured 1RM values. to predict 1RM. The test-retest reliability of 1RM predictions between trials, as well as the validity of these predictions comparing predicted and measured 1RM scores, was completed.

Practical Takeaways Applying these results in practice, it would be best practice to perform at least four progressively heavier sets in the 40-80% 1RM range. Performing at least three repetitions at each load to find the fastest repetition at each sub-maximal load. This would provide the most reliable and accurate load-velocity profile. Further, if MVT is unknown for an individual or exercise, utilising LD0 predict 1RM is an alternative that shows potentially less overestimation (i.e. ~4 kg compared to ~14 kg with the MVT method), Be cautious as that these values are a prediction of potential and are consistently overestimated. Ultimately, both the MVT and LD0 methods show reliability and allow practitioners to note performance improvements, but do not replace an actual 1RM assessment used to accurately prescribe relative intensities.

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Cody’s Comments “Maximal strength is a specific skill and it is important for athletes to develop experience at loads ≥80% 1RM. Experiencing work at these intensities serves an athlete well in developing necessary recruitment and motor patterns to improve strength and performance. The more experience an athlete has with a barbell and specific exercises, the more accurate the prediction and less variability. This is why the results showed an underestimation in weaker individuals and an overestimation in stronger individuals; as work with heavier loads ≥60% 1RM was novel and challenging for weaker individuals, and loads ≤60% were too easy and magnified for stronger individuals.”

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