Many (personal) trainers, (strength and conditioning) coaches and (physio)therapists frequently test their athletes or patients to monitor fitness, progress and to determine changes in performance following training or a specific intervention. For example, some trainers let their athletes perform a bounce drop jump on a regular basis to determine the reactive strength index (which is the ratio between ground contact time and jump height). This information is -sometimes in combination with the results of other objective and subjective measurements- used to determine whether the athlete is fit enough for the scheduled training session. Similarly, some physiotherapists use a repetition maximum (RM) tests to monitor the progress of a patient in rehabilitation.

For the examples above and many other tests it is however often unclear whether the change in the test score is the result of an improved or decreased performance of the athlete or patient, or the result of typical variation that an individual shows when a test is repeated.  In a new video, I explain how a new spreadsheet can be used to monitor the progress of individual athletes and patients.

Specifically, the spreadsheet can be used to:

  1. Determine how likely it is that your athlete of patient has changed performance on a measurement since the previous measurement. For example, it can be used to determine how likely it is that jump height or 1RM back squat performance of your athlete or patient has substantially changed since the previous test;
  2. Estimate the deviation of one measurement or the average of several measurements from a linear trend line. In this way you could determine whether an athlete performs worse than normal and thus whether he/she is fatigued. Additionally, you could determine the effect of an intervention such as caffeine supplementation on performance compared to the athlete’s or patients normal performance.
  3. Determine whether your athlete or patient is progressing fast enough for a specific long-term goal you have set. For example, if your goal is that the patient improves his 10RM back squat by 50kg over the course of 5 weeks, this spreadsheet can calculate whether the progression of the patient is faster, slower or in line with the goal.

 

Click here for the video.

Click here for a Dutch article on this topic in which I explain the spreadsheet and other issues that are relevant for monitoring an individual athlete in more detail.

 

Typical error and smallest worthwhile change

For the spreadsheet to work, you need to provide a typical error and smallest worthwhile change. In the table below I provide some data on the typical error and smallest worthwhile change for some frequently used physical performance tests in specific groups of subjects.

 

Table. Data on the typical error and smallest worthwhile change for some frequently used physical performance tests

Test Equipment Group with reference in superscript Typical error Degrees of freedom* Smallest wortwhile change
Countermovement jump Jump mat Physical education students1 1.0 cm (2.8%) 276 0.9 cm
Squat jump Jump mat Physical education students1 1.1 cm (3.3%) 276 0.84 cm
Reactive strength index from 40cm drop jump Jump mat Professional basketbal players2 2.1 (3%) 36 0.052
1-RM leg press Leg press Untrained middle-aged men and women3 8.0 kg (3.9%) 78 16.6 kg
Vo2max Treadmill Well-trained middle long distance runners4 1.3 ml∙min-1∙kg-1

(2.9%)

30 3.08 ml∙min-1∙kg-1

* The degrees of freedom can be filled in the spreadsheet as shown in the table

References

  1. Markovic G, Dizdar D, Jukic I, et al. Reliability and factorial validity of squat and countermovement jump tests. J Strenght Cond Res 2004;18(3):551-55.
  2. Markwick WJ, Bird SP, Tufano JJ, et al. The intraday reliability of the Reactive Strength Index calculated from a drop jump in professional men’s basketball. Int J Sports Physiol Perform 2015;10(4):482-8. doi: 10.1123/ijspp.2014-0265 [published Online First: 2014/11/14]
  3. Levinger I, Goodman C, Hare DL, et al. The reliability of the 1RM strength test for untrained middle-aged individuals. J Sci Med Sport 2009;12(2):310-6. doi: 10.1016/j.jsams.2007.10.007
  4. Saunders PU, Pyne DB, Telford RD, et al. Reliability and variability of running economy in elite distance runners. Med Sci Sports Exerc 2004;36(11):1972-6.
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