FMS Validity, PAP Meta-Analysis

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Everyone has their own gamut of screening they may perform with their athletes/clients. Over the years, the FMS has gained much popularity in a variety of sectors. It is believed that the FMS was introduced to identify movement dysfunction that may be correlated to future injury. Last month this study was published delving into the validity of this notion. “The aim of this study was therefore to evaluate the measurement and predictive validity for all components of the FMS.”

Subjects

25 male football(soccer) players from the British Universities and College Sports league were recruited; 24 finished with one dropout. All subjects were healthy, and non-injured at the start of the study. Of the 24 participants who completed the study, the mean age was 19 years (range 19–22) with a football training age of 12.13 years (SD ±2.1).

Methods

The FMS score was obtained from an experienced assessor. Along with this, a Vicon motion capture system was used concurrently to obtain a reference score analyzing 3D data. Prior to being screened, the participants completed a warm-up to familiarize themselves with the FMS test. During the testing, participants were required to complete three attempts for each movement, beginning with the left side first. Therefore, the individual was given two scores: one by the assessor and one calculated with the 3D kinematic data. It is expected that with a higher FMS score, a subject has a lower susceptibility to injury.

Results

Anthropometric profiles of the participants were a mean height of 1.79 m (SD ±0.06), mean weight of 77.75 kg (SD ±9.7) and a mean skinfold thickness (sum of four sites: biceps, triceps, subscapular and anterior superior iliac spine) of 40.98 mm (SD ±17.0). 22 of the 24 reported their dominant kicking leg to be right-sided.

The real-time assessor score was higher than the photogrammetric system for 22 of the 24 subjects, 1 subject’s assessor score matched the photogrammetric system score, and 1 subject’s assessor score was lower than the photogrammetric system. “Additionally, neither method of score determination was able to prospectively identify players at risk of serious injury, as per table 2.”

The results have shown poor agreement between subjective assessor scores and the encoded kinematic measures. Ironically, the FMS was designed to be a simple, 7-step assessment when it actually is far more complex “…evidenced by some subtests of the FMS, such as the Rotary Stability test, which requires the assessor to consider up to 23 criteria and rules for the assessment process.” It may be more accurate to use multiple assessors and fulfill all subtest criteria, but at this point the practicality is greatly diminished.

Takeaway

The FMS has shown ambiguity and poor correlation to what is was intended to measure. Oddly enough, subjects “…who scored above the previously used threshold of 14 sustained a higher number of total and serious injuries within this study.” This agrees with previous literature that the FMS is not a valid predictor of injury. “The clinical application of the FMS in any capacity, rating and ranking movement patterns or injury prediction is therefore questionable.”

I was forwarded this article on post-activation potentiation from Jarred. “Meta-analysis of postactivation potentiation and power: effects of conditioning activity, volume, gender, rest periods, and training status

Eliciting PAP (post-activation potentiation) may an individualized process as it walks a fine line between inducing fatigue and enhancing performance. A certain level of fatigue resistance is a requisite characteristic for this effect. “Thus, the primary objective of this investigation was to quantitatively identify which components of conditioning activities optimize power output.”

Methods

Literature was reviewed to find how factors such as training status, volume, rest periods, conditioning activity, and gender may influence power and PAP. Primary search criteria were four-fold:

  • “First, the primary focus of the study was to investigate the effects of a conditioning activity on a specific criterion power task (force x velocity).”
  • “Second, the conditioning activity had to be performed at a greater load than the criterion task (e.g., a free weight squat performed before a vertical jump).”
  • “Third, the study could not use any outside electrically elicited stimuli during the conditioning activity.”
  • “Fourth, all studies were limited to human controlled randomized trials.”

In final, a total of 32 studies were used to collect data on 141 subjects, both male and female. Subjects ranged between 18-35 years of age, with a mean age of 20 years.

Conditioning activity protocol was coded for mean intensity (low ≤60 % 1RM, moderate = 60–84% 1RM, and heavy ≥85% 1RM), sets (single vs. multiple), mode (isometric vs. dynamic), and rest period between the end of the conditioning activity and performance of the criterion task. Rest periods were coded as immediate (<2 minutes), short (3–7 minutes), moderate (7–10 minutes), and long (>10 minutes). Training status was coded as recreationally trained (active but not currently resistance training), trained (at least 1 year of resistance training experience), and athlete (criteria included either 3 years resistance training experience, NCAA college or pro level athlete, or competitive power or weight lifter).

Results

No significant differences were found between gender groups; males (ES= 0.42, n: 123) and females (ES=0.20, n= 16). This was also true with dynamic (ES= 0.42, n=107) and static (ES=0.35, n=14) conditioning activities.

Significant differences were found between moderate intensity (ES=1.06, n=15) and heavy intensity (ES=0.31, n=121); single sets (ES=0.24, n=95) and multiple sets (ES=0.66, n=46); rest periods of 3-7 minutes (ES=0.54, n=75) and greater than 10 minutes (ES=0.02. n=31).

 “Our results indicate that moderate intensity (60–84% 1RM) (ES = 1.06) exercise is ideal for eliciting PAP when compared with very high intensities (.85% 1RM) (ES = 0.31), independent of training experience.” PAP likely dissipates within less than 30 minutes. This study, as well as others, have also shown that athletic populations require less rest time than trained and untrained populations, respectively. Athletic subjects may require only 3-7 minutes rest where less trained populations should consider 7-10 minutes.

Takeaway

It is likely that decreased fatigue and increased potentiation is optimized with greater training experience. Athletes with greater than 3 years of resistance training experience appear to respond optimally. Power was enhanced after the conditioning activity in untrained (ES=0.14), trained (ES=0.29), and athletic (ES=0.89) populations. “Moreover, potentiation was optimal following multiple (vs. single) sets, performed at moderate intensities (60–84%), and using moderate rest periods lengths (7–10 minutes).”

  • Less experienced individuals may consider a moderate intensity (60-85% 1RM) with only one set prior to the power-related task, with 7-10 minutes rest.
  • More experienced individuals may utilize multiple sets, still at a moderate intensity (60-85% 1RM), with rest periods of 3-7 minutes.

 

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Thanks, 
AJ

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