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Brief Review

Efficacy of the Functional Movement Screen: A Review

Kraus, Kornelius1,2; Schütz, Elisabeth2,3; Taylor, William R.4,5; Doyscher, Ralf4,6

Author Information
Journal of Strength and Conditioning Research: December 2014 - Volume 28 - Issue 12 - p 3571-3584
doi: 10.1519/JSC.0000000000000556
  • Free



Strength and conditioning specialists' main tasks are to generate athletic profiles for athletes and other clients. These profiles are crucial to identify functional deficits. Therefore, specialists have to achieve the athletes' awareness for necessary changes in their training and performance behavior to enhance motivation for rehabilitation exercises or practicing fundamental movement patterns like squatting or jumping (personal interview with Dr. Tom Little, 2014). While under real-world conditions, simple screening tools are commonly used to assess movement quality. In research settings, however, complex and expensive methods have to be used to enable accurate measurements. For example, marker-based motion analysis and electromyography have become the gold standard for evaluating movement pattern and biomechanical musculoskeletal deficits in modern sport science. In an attempt to translate a motion assessment tool into daily sports practice, the functional movement screen (FMS) was developed.

The term “functional training” was popularized in the 1990s, when “functional” was used as an indicator for training and rehabilitation exercises that are a part of, or focused on, natural movement patterns required throughout activities of normal daily living (1). As a result, functional training attempts to practice and simulate the situational needs and constraints of real-life activities, including sport events, to enhance training effectiveness (25,36). Under the assumption that strength, movement, flexibility, and stability are prerequisites for optimal athletic performance, the FMS is a screening tool to assess functional mobility and postural stability in different settings without locomotion (14,51). The FMS consists of a series of movement tasks that assess hip flexion, external, and internal rotation, as well as core stability and abduction/adduction of the shoulder joints (Figure 1).

Figure 1
Figure 1:
Examples of the in-line lunge subtest as used within the functional movement screen examination, where a score of “1” is awarded when the individual is only able to perform the movement pattern in a limited manner (A), a score of “2” indicates that the subject needs compensatory movements (B), and a score of “3” describes the correct performance of the movement pattern (C). A score of “0” is given in cases where subjects feel pain while performing a subtest.

Currently, the FMS has reached considerable scientific attention. The intention of FMS developers Cook and Burton was to face the problem of overuse injuries and to enhance awareness of team players for preventive exercises with a simple method. However, the fact that the FMS today is widely practiced in the field of athletic and in strength and condition training does not automatically mean that the method is supported by sufficient scientific evidence.

Hence, the overreaching aim of this review was to evaluate achievements for science and practice as well as to identify problems of the FMS-driven research. Two specific aims are:

  • Conducting a thorough review of the FMS-driven research and to answer the question, how we can optimize the scientific quality of FMS-driven research
  • Provide strength and conditioning specialists with research-based recommendations how to apply and integrate this screening tool, wisely.


Search Strategy and Study Selection

PubMed and Google Scholar were used to identify studies published before December 2013. The keyword search was performed by applying a combination of following words: “functional movement screen,” “FMS and injury,” “FMS and athletic performance,” “FMS and norming data,” “reliability of FMS,” and “FMS and scoring system.” Languages were limited to English and German. However, the inclusion criteria involving professional athletes were less stringent because of inherent limitations within the Olympic and team sporting environment (52).

Author 1 screened titles and abstracts and excluded obviously irrelevant studies. According to the including criteria, all authors analyzed the remaining studies. In addition, studies by the knowledge of the authors and a manual search were undertaken on the reference lists in the included articles. Studies were included if they met determined inclusion criteria. Detailed study inclusion and exclusion criteria are presented in Table 1.

Table 1
Table 1:
Study inclusion and exclusion criteria.

Data Extraction

Information from included studies were abstracted, summarized by author 1, and verified by the coauthors. In unclear cases, all authors reviewed the data together until a consensus about the main issue and conclusion was identified.

Methodological Quality Assessment

At present, no standard for rating the quality of studies in sport science exists. To name a few: the type of study, the level of experience of the scientist with technology and methodology, the field of investigation or the sporting environment are some factors which have to be considered. The paradigm of evidence-based medicine offers a framework to assess the quality of clinical trials, but it cannot be easily transferred to the field of sports performance where practical significance is more important than statistical significance (34). In sport science, already exists a model to assess the quality of specific knowledge.

The Applied Research Model for the Sport Sciences (ARMSS) was introduced to assess the quality of specific knowledge in the field of sport science. The ARMSS offers researchers a theoretical framework for problem driven research projects and a standard to evaluate the research process (6). Eight stages beginning with definition of the problem, descriptive research, predictors of performance, experimental testing of predictors, determinants of key performance predictors, efficacy studies, examination of barriers to uptake, and implementation studies in a real sporting setting guide the scientist from theory to practice (Figure 2).

Figure 2
Figure 2:
This draft outlines the idea of the Applied Research Model for the Sport Sciences (6). It shows the iterative process from phenomenon to implementation. Every stage can be considered as an iterative loop.


More than 400 citations were identified between January 2004 and November 2013. After electronic database screening and handsearching, 33 publications seemed to be relevant for the review addressing the field of objectivity, reliability, validity, and norming that can be categorized in different research stages.

Research Stages

Thirteen descriptive studies explored the main tasks in test development like factor structure, objectivity, and reliability. They can be classified to the second stage of Bishops Model (ARMSS stage 2). Eleven studies covered the ability of the FMS to predict sporting performance and injury risk (ARMSS stages 3 and 4). Seven studies investigated the effectiveness of the FMS in designing programs (ARMSS stages 6 and 8), and 2 assessed norming data.


The screening battery includes: “deep squat,” “hurdle step,” “in-line lunge,” “shoulder mobility test,” “active straight leg raise,” “trunk stability push-up,” and a “rotary stability test.” All items are performed 3 times, although the best trial is scored. The total FMS score is the summation of all 7 scores, resulting in a maximum of 21 points (51). A 4-point ranking system is used to evaluate the movement quality. A score of “3” describes the correct performance of the movement pattern, “2” indicates that the subject needs compensatory movements to solve the exercise, and a score of “1” is given when the individual is not able to perform the movement pattern at all. In cases where subjects feel pain while performing an item, a score of “0”is given.

Five of the 7 FMS items (hurdle step, shoulder mobility, active straight leg raise, trunk stability push-up, and rotary stability test) are performed independently on the right and left sides of the body. The lowest score of the 2 sides contributes toward the overall score. For example, an in-line lunge score of 3 on the left side and 2 on the right side is rated as a score of 2 for the in-line lunge item. Using these targeted items, the examiner aims to identify specific mistakes in movement patterns, compensatory motions, lack of mobility or flexibility, coordination deficits, or muscular imbalance (51).

The demonstrated reliability of any approach, including its intrarater and interrater reliability, is critical before its widespread uptake can be expected (2). Scientists need a high precision to estimate a change in the variable to determine effects. For conditioning experts or athletic health care professionals, it is important to have less uncertainty in estimation to reduce wrong conclusions in the monitoring process. Table 2 offers an overview of the main outcomes and details some background information.

Table 2
Table 2:
Descriptive characteristics of reliability and objectivity studies.*

A number of recent studies (21,27,51,57,64,66,68) have all described the FMS movement grading method as reliable (both repeatable and reproducible) for assessing movement patterns (with intraclass correlation [ICC] values of over 0.8). Here, Teyhen et al. (3) focussed their investigation on assessing intrarater (test-retest) reliability. In using a pool of 8 raters and 64 study participants (25.2 ± 3.8 years), baseline results were compared with the scores of the repeated testing 48–72 hours later. The results (ICCs of 0.76) indicated good to excellent reliability of assessment using the FMS system between different observers.

To examine whether the observer's experience plays a role in the outcome score, 39 athletes (mean age = 20.8 years) were recorded using videotapes while performing the FMS activities. Then the recordings were assessed by either experts (10 years of experience in using the FMS development, n = 2) or novices (certified functional movement specialists with up to 1 year experience, n = 2). While the experts displayed a good κ-value of 0.68, demonstrating high levels of interrater agreement, the novices showed good to excellent agreement, and were partially better than the experts, producing a κ-value of 0.75 (9). In a similar investigation, Schneiders et al. (64) examined young and mixed collectives (n = 209; 21.9 ± 3.7 years). Interrater reliability (κ) for the composite FMS score was excellent (0.971), whereas interrater reliability for individual test components of the FMS demonstrated a good to excellent agreement (0.70–1.0).

In an assessment of 18 elite soccer players, Frohm et al. (21) provided further evidence of good interrater reliability of movement pattern assessment using 5 subtests of the FMS (deep squat, in-line lunge, active straight leg raise, trunk stability push-up, and shoulder mobility) and 4 further subitems (1-leg squat, straight leg raise, diagonal lift [modified FMS item], and seated rotation) in their movement screening. The maximum score for this assessment is 27 points. Using 8 different observers on 2 test occasions, who were either familiar (n = 4; 2–4 years of experience) or experienced (n = 4; 5–7 years of experience) with the FMS methodology, the authors found no significant difference (p = 0.31) between the baseline screening test (mean score = 18.3) and testing 7 days later (18.0). Furthermore, high interrater reliability was reported on both test occasions (ICC = 0.80 and 0.81) despite the difference in FMS experience.

Gribble et al. (27) also addressed the question of interrater reliability by comparing FMS trained certified ATs (ATExp; n = 7; 6.1 ± 5 years of experience) with ATs (n = 15; 2.5 ± 2.7 years of experience) and AT students (ATS; n = 16) without experience. The ATExp group showed excellent reliability at (ICC = 0.95), while the less experienced groups, AT had ICC = 0.76 and ATs ICC = 0.37.

Another investigation in the field of athletic training addressed the question of test-retest and interrater reliability (65). Six raters of the field of athletic training and strength and conditioning were used to determine the typical error in screening between more or less qualified raters (<1 month up to 4 years). The change in mean between the unexperienced athletic training student (17.2 ± 1.3) to the most experienced athletic trainer (16.9 ± 1.4) was 0.3. Krippendorff alpha (Kα) was used to determine the typical error of the FMS subitems between the raters. The result of this investigation indicates a low interrater reliability in this case. Other investigations used κ-value to estimate reliability (51,64). This could be a rational for this variance. Another point why we should treat this result with caution is because of the low interrater reliability of 2 experienced raters (ICC = 0.44). The authors concluded that it is appropriate to explain that the number of FMS performed previous scientific testing offers more evidence about scoring competence than the number of years. Onate et al. argued that it might be beneficial for understanding to investigate the influence of different viewing angles on the resulting FMS score (10).

The research of physiotherapist Elias (18) focused on this issue. He used 5 professional athletes and 20 experienced physiotherapists with at least 4 years of experience in sport physical therapy but not certified in the FMS method. In addition to the standardized frontal and sagittal view, an overhead video camera offered additional insights. Six of 7 FMS items (without shoulder mobility) were used to calculate an interrater reliability (ICC = 0.9). This investigation provides evidence to apply the screening tool in cases of experienced raters with a sound background in anatomy, movement science, and athletic health care. The overhead camera can assist to enhance the precision of scoring for instance to determine movement symmetry more accurate.

For further investigations, the typical error of a measure can offer new insights. It shows the difference between subject's true and observed value (35). The mathematician Hopkins argues for using the typical error “because the relationship between the typical error and the limits of agreement is straightforward. If the typical error is expressed as a coefficient of variation, the corresponding limits of agreement are then percentage limits.”

How does affect performers' knowledge about the scoring criteria the FMS outcome? Frost et al. (24) addressed the practical effect in their investigation. Their study provides evidence that reliability of the FMS is also affected by performer's knowledge of scoring criteria. In that study, the participants (n = 21) performed better after they received feedback. A clear practice effect was observed within the first 2 trials. In prefeedback the participants scored 14.1 ± 1.8 and Postfeedback they achieved 16.7 ± 1.9 points. This practice effect was observed in the field of sports performance testing as well (35).

To reduce noise of the practice effect, we recommend that at least 1 practice trial should be performed before the testing. In medical science, STARD checklist is used to improve reporting quality of diagnostic studies ( To enhance transferability of studies in the field of sporting research, this kind of clinical framework might be beneficial. Furthermore, future work will assess the potential of Hopkins typical error approach.

The approach taken to score the FMS, where all subtests possess the same weighting, and the individual results are simply summed, remains contentious. A prime example is provided by the German soccer league (Bundesliga), where 76% of all injuries occur in the lower extremities 20. In such cases, it would be reasonable for, for example, shoulder mobility not to have the same impact as lower limb strength and stability on injury prediction. Within the FMS system, 3 subtests (deep squat, in-line lunge, and hurdle step) screen complex movement patterns but are not considered with different weightings. Furthermore, a broad range of movement patterns can be scored with the same score. For instance, a perfect forward pattern of the hurdle step and compensatory backward pattern results in score 2. A compensatory forward and backward pattern also results in score 2.

In classical test theory, the dimensions of the underlying construct have to be identified. The use of FMS sum score to predict injury risk or ability of motor control indicates that the construct is treated as unidimensional. However, the factorial structure of the FMS was still unclear. A recently published factorial analysis clarified that the FMS is not a unitary construct (5). The nature of the scoring system could be a possible factor of interference for these flaws. Although, internal consistency was poor for FMS standard scoring system and excluding pain criteria, as well. These data indicate that the FMS total score cannot be treated as a cluster variable, which estimates core stability in general. Hence, when using the traditional scoring system, each item should be interpreted as distinct construct argues the researcher team. We think that it is too early to draw any conclusion of this trend. Therefore, we are advocating for another investigation that includes the noise of practice effects and the quality of the tester. Nevertheless, we have to realize that it will take some time.

Additionally, to address this system deficit, an updated 100-point scale has been introduced by Hickey et al. (31) with each exercise weighted differently, in which each correct movement pattern was assigned a point and summed. As a result, for example, a maximum score of 18 could be achieved if the deep squat was performed correctly. In this case, 6 points were awarded if the tibia and torso were parallel, and 4 points were awarded if the knees were aligned with the toes, the squat was symmetrical, and the dowel was behind the toes. If the FMS board was used to provide additional support, the maximum score was reduced to 8, and 2 points were awarded for each criterion.

Frost et al. further modified the system by specifically assessing the number of compensations (as described in as well as normalizing each subtest to a score of 14.3 (22)). With this modified scoring method, each compensation to the correct movement was assigned 1 point, with the left and right sides treated separately. If the test objective was met, the task score was equal to the total number of compensations present. If the objective was not met, the baseline score was raised to be 1 point higher than the total number of compensations possible (e.g., 7 for the squat). The scores for each task were weighted evenly, and a cumulative sum was given out of 100 points, where a score of 0 was considered the best, indicating that no compensation occurred, and all movements were carried out correctly. In their study, despite showing larger differences in the modified scoring compared with the original FMS system postintervention, the lack of repeatability of their baseline controls suggested that no reliable differentiation could be demonstrated between study participants (22).

Although such modifications to the scoring method therefore show promise for improving the accuracy of the FMS approach, further investigation is required to elucidate the conditions under which the benefits of such a modified scoring system might be better demonstrated. Moreover, different speeds or different loads of specific subtests of the FMS possibly provide some more insights.

The reliability of a method is a necessary prerequisite for researchers and practitioners. Scientists need a high precision to estimate any change in the variable to determine effects. For conditioning experts or ATs, it is important to have less uncertainty in estimation to reduce wrong conclusions in the monitoring process.

Reliability is affected by several factors. Studies provide evidence for the reproducibility of the FMS for experts and familiarized raters. This indicates that training of the raters is a necessary prerequisite to reduce errors in repeated trials. Furthermore, it has been shown that test-retest reliability is high, if the rater is trained and also familiar with the screening tool in practice. It sounds rational that the number of tests performed is more important than the years of experience. For that reason, we are advocating authors to report the experience of the raters by the number of tests performed.

Investigations in attempt to determine the uncertainty between the interrater differs between poor and high reliable. In some cases, high reliability was achieved despite the difference in FMS experience. To identify interrater reliability, different statistical approaches like κ-value or Kα were used. This thought can offer some rational for the conflicting results. For further investigations, the typical error of a measurement can offer new insights. To enhance methodological quality in cases of large cohorts, interrater reliability should be assessed if more than 1 individual is rating. It shows the difference between subject's true and observed value.

A clear practice effect between the first 2 trials of a test exists in the field of sports performance testing. One investigation provides evidence that the reliability of the FMS is also affected by performer's knowledge of scoring criteria. To reduce noise of the practice effect, we recommend that at least 1 practice trial should be performed before formal testing.

The scoring system remains some unsolved issues. The FMS sum score should be interpreted with caution. Results of factorial analysis demonstrated that the FMS is not a unitary construct. The heuristic “pain criterion” is another source of inconsistency. This offers evidence, to examine each item as a distinct construct. However, these data have to be verified.

To save time in practical or scientific settings using videotapes to educate raters, test the ability of the raters on different occasions and to score individuals is an appropriate procedure. In cases of uncertainty, the slowdown function of software can be beneficial.

A core criterion of sport scientific tests or screening methods is its ability to predict factors that affect performance or injury. Generally, this fact will be investigated by building relationships between predictors of sports performance (6) and injury rates.

In the next part of this review, we are evaluating predictor studies of the FMS on the basis of Bishops ARMSS. In his model, he argues for a solid work on predictor level because the experimental testing of the specific factors on performance and injury prediction has to be identified, approved, and modified, if necessary. This iterative cycle is typical between investigating and experimentally testing the predictors.

The general aim of the FMS was to assess obviously functional musculoskeletal asymmetries and postural deficits. Several researchers used the FMS to identify asymmetries in elite track and field athletes, professional soccer (Bundesliga) and American football players, without questioning the relationship of functional muscle length and FMS performance (11,39,63). In a sample of 2 different female soccer teams (Ekstraklasa, n = 21 and 1; Division, n = 22), the relationship between functional length of hamstrings, rectus femoris, and patellofemoral pain and FMS performance was investigated.

In this cohort, it has been shown that the patellofemoral pain is associated with functional elasticity of the hamstrings muscles, training experience, and level of competition. In addition, dynamical functional limitations presented by the quality of the FMS subitems deep squat, in-line lunge, and active straight leg raise of the high level players (Ekstrasklasa) were affected by functional muscle length of the hamstring, iliopsoas, and rectus femoris muscle. Interestingly, top-level players scored higher in rotary stability and shoulder mobility. This resulted in a higher FMS total score of 16.0 ± 0.5 vs. 15.5 ± 0.6. This investigation supports the thought that isolated muscle flexibility is 1 limiting factor in fundamental movement patterns (28). However, a longitudinal study without selection bias could help to identify a deeper understanding of the causal relationship.

A desired goal of a screening tool is to predict performance. How valid are predictions based on FMS data in respect to athletic performance and injury? This question is of great importance for strength and conditioning experts. A primary goal of the FMS is to identify subject specific movement and balance deficits through an evaluation of mobility and stability. One recent claim, however, found no significant relationship between FMS score, core stability, and athletic performance (56). Here, 4 trunk muscle endurance tests (18) were performed in 28 healthy adults (24.4 ± 3.9 years). Although no significant relationships were found between core stability and FMS score, the results did suggest that field tests such as overhead medicine ball throw could assist in determining athletic performance. Interestingly, more basic relationships between, for example, high or low scores awarded for deep squat, hurdle step or in-line lunge, and total FMS score were not found. Moreover, a negative correlation was determined for the in-line lunge, putting the relationship between score and agility performance into question.

The data of Parchmann and McBride (58) provides only limited support for the effectiveness of the FMS for assessing athletic performance, reporting no direct link between FMS score and agility, jumping and sprinting performance. In their study, 25 collegiate-golfers (15 men, 20.0 ± 1.2 years; 10 women, 20.5 ± 0.8 years) undertook FMS and several athletic performance tests. Here, FMS data were poorly correlated with agility T-test time (r = −0.15); 20-m sprint time (r = −0.11) and vertical jump (r = 0.25), but back squat strength appeared to be a good indicator for power (r = 0.87), and, although negatively correlated, for agility (r = −0.76) and linear speed (r = −0.87). These data should be interpreted with caution, however, because gender effects were not considered. According to these investigations, the FMS might not to be considered as a valid tool for predicting athletic performance at beginner or intermediate levels.

The demands of team sports are diverse, and it is thus difficult to conduct appropriate scientific studies that compare realistic conditions in a standard manner. Team-sport training is generally characterized by an assortment of training activities, often paired with variable environmental conditions (52). To compensate for these difficulties, McGill et al. (48) observed 14 collegiate basketball players (20.4 ± 1.6 years) over a period of 2 seasons. The FMS was part of their widespread test battery. The correlation between FMS hurdle-step results, games played (r = 0.46), points per game (r = 0.45), and rebounds per game (r = 0.41) are certainly noteworthy. Furthermore, torsion control was significantly associated with assists per game (r = 0.60) and steals per game (r = 0.54). Hip range of motion was significantly related to blocks per game (r = −0.5 to −0.7), while power correlated with minutes played (r = 0.67), rebounds (r = 0.63), and blocks (r = 0.55). Agility was associated negatively with minutes (r = −0.59), points (r = −0.60), assists (r = −0.74), and steals (r = −0.69) per game, suggesting that greater agility results in improved performance and power is linked with playing time and improved defense.

It is generally accepted that the ability to recover from fatigue is a key factor in top-level sports. Besides musculoskeletal screening, the aerobic and anaerobic capacity is a general part of the preseason assessment in team sports. The Yo-Yo–intermittend recovery test is a valid test to determine the physical ability of soccer players (44). To learn more about the relationship between specific aerobic and anaerobic capacity in addition to general movement control (FMS), an elite soccer team (Germany 3. Liga) was tested measuring heart rate, rate of perceived exertion, and lactate concentration in a standardized Yo-Yo test as well performing an FMS test before. No evidence for a relationship between FMS data and physical capacity (r = 0.23; n = 20; FMS = 14.1 ± 1.1) (35). Running economy is a necessary part to achieve maximal performance of the individual (54). However, the ability of the lower extremities to develop force and storage mechanical energy might be better assessed with Bosco's test battery (8). Another criterion in shuttle running performance is the player's ability to change the direction efficiently.

However, literature provides some evidence for an indirect association between movement quality and the performance outcomes of elite track and field athletes (USATF) (11). All athletes were part of the High Performance Program 2011. They were screened between July 2010 and May 2011 and received prescribed FMS corrective exercises after screening. The inclusion criterion was to stay free of injury at time of the screening. Two highly experienced sports medicine professionals screened the athletes (n > 1,000). At the outset the data of this investigation shows that functional mobility is related to the longitudinal performance outcomes. It exists scientific evidence that musculoskeletal asymmetry is a risk factor for injury (42,71). At least 1 asymmetry was identified in more than 50% of the cases, and these asymmetries were related to performance outcomes in sprint, hurdle, distance, jumps, throws, or multi events. Athletes with no asymmetry had an improvement in performance 0.6 ± 2.9%, whereas athletes with at least 1 asymmetry lost −0.3 ± 2.1% of their best performance.

Furthermore, the data of Chapman et al. (11) indicate that deep squat score had an impact on performance of track and field athletes. In that observational study, a low score in deep squat resulted in performance change of −1 ± 2.1% (n = 22). Athletes with a moderate movement quality gained 0.1 ± 2.3% (n = 87), whereas the adequate group (DS = 3) achieved 2 ± 2.3% (n = 12).

A performance improvement <1% sounds trivial. For top-level sports, a performance improvement of 1% could mean the crucial difference between gold and silver medal. The statistician 38 analyzed the coefficients of variations (CV) from published and gray literature in track and field. The CV ranges from 0.8% in sprints and hurdles up to 3% in marathon. He suggests that the smallest change of performance in cases of practical significance for elite athletes should be at least 0.3 of the CV. The changes of Chapman asymmetry group (0.86%) is still within that range of Hopkins recommendation (33).

By contrast, there are some biases which should be mentioned. One limitation is the unknown compliance of the athletes in integrating the corrective exercises into their training schedule. A posttest could offer more information in interpreting the performance outcomes. The unknown relationship between injury and training offers room for speculations, but not enough information for an evidence-based implementation in practice. In addition, peak performance is a phenomenon, which is affected by manifold factors.

We think that these data offer insights, which are worth to consider, seriously. Especially for track and field coaches, that support athletes without regular support of medical professionals. In general, the FMS score is limited in its ability to assess or predict an individual's athletic performance in team sport scenarios. On this basis, the use of broad approaches that quantify specific performance factors might be more appropriate for undertaking such tasks.

Injuries in soccer accumulate mostly during competitive games (19), often as a result of neuromuscular fatigue (37), possibly caused by reduced control of lower extremity mechanics during cutting (69), landing (12), and running (16). Effective assessment of musculoskeletal and balance deficits is thought to offer the ability to predict subjects at high risk from injury, as well as aid in designing specific intervention programs. It would therefore seem reasonable that a measure of the efficacy of the FMS approach can be accessed through its ability to reduce injuries of the lower extremities. Here, in a season long observation of a professional American football team, Kiesel et al. (40) used the FMS to predict susceptibility to sports injury. In preseason assessment, each of the 46-men team underwent an FMS. Serious musculoskeletal injuries that occurred throughout the season were then documented, where serious injuries were classified as those with a recovery period requiring at least 3 weeks in the reserves. Thirteen players with an FMS score of 14 or below, 7 sustained a serious injury, whereas only 3 of the remaining players suffered an injury. The average FMS score of injured players was 14 ± 2.3 and the mean score of the fit athletes' amount 17.4 ± 3.1 points. However, the resulting sensitivity (0.54) and specificity (0.91) of the FMS prospective predictions demonstrated noteworthy results, and importantly suggested that the risk of suffering a serious injury increases with FMS scores of 14 or below. In recently published cohort study, Kiesel et al. (38) observed 238 professional American football players. A combination of at least 1 movement asymmetry and a score below the 14-point threshold was highly specific for injury (0.87). Based on that finding, the authors grant the fundamental movement patterns, and pattern asymmetry are identifiable risk factors for time-loss injury during the preseason in professional football players.

This sample of football players can be treated as a representative cross-section of the population, and the results confirm the findings of the first investigation. However, in this specialist cohort, it might also be important to differentiate between contact and noncontact injuries, but neither the injury circumstances nor the player's age or injury history, known risk factors for further injury (1), were reported. Because most injuries are thought to occur during competitive contests (19), it would seem imperative to consider the risk factors in specific team sports such as history of injury, fatigue, playing position, or age.

To address some of these issues, Chorba et al. (13) analyzed and compared 38 female college athletes from basketball, soccer, and volleyball. Here, 16 of the athletes examined achieved only 14 FMS points or fewer. Altogether, 18 (17 lower extremities, 1 lower back) injuries were reported. Eleven of these occurring in the athletes with FMS scores ≤14 (n = 16; r = −0.76; p = −0.02). The study revealed a strong correlation (r = 0.95, p = 0.003) between FMS score and lower extremity injury, if the shoulder mobility test was removed from the total FMS score. This resulted in a maximum FMS score of 18, rather than 21, but it was not surprising that, as an upper extremity screening exercise, shoulder mobility was not related to the lower extremity injuries. For athletes without a history of anterior cruciate ligament injury (n = 31), 73.3% sustained an injury within their competitive season, where the sensitivity and specificity of the FMS-based predictions were 0.58 and 0.74, respectively. Female soccer players had the lowest average FMS score (13.2) with 8 injured players. In basketball, the average score was 14.6 (5 injuries) and in volleyball 15.3 (5 injuries). Although a soccer team consists of 15 members, and the volleyball or basketball teams are built up of 11 or 12 players, respectively (possibly suggesting a bias based on type of sport), the number of participants was too low to detect differences of statistical significance. Furthermore, the authors reported a p-value of p = 0.05 because they applied a 1-tailed test, but they provide no justification for such an approach. This assumes that it would not be possible for subjects with a low FMS score to have a decreased injury risk. However, the authors did not provide any information on the types of injury and whether they occurred in practice or competition, or the duration of absence from team practice. In such cases, monitoring physical stress can offer new insights into the prevention of injuries and illness in team sports (10).

Despite these promising results, it remains difficult to predict the rates of injury, after all, alone the identification of injury cause is challenging. To compensate for this lack of information, Bahr (3) exploited a multifactorial model for assessing injury risk (based on the injury causation model of Meeuwisse (50)), which accounted for both internal and external risk factors, as well as the type of competition. By considering the possible injury scenarios of each player, including position, playing situation, playing time, opponent behavior the authors suggested that the approach can account for cause of injury to improve the potential for prevention. Nevertheless, the efficacy of these approaches remains to be demonstrated. From a methodological point of view, a test should be compared with other screening tools or tests as well. This direct comparison with other screenings such as Landing Error Scoring System is missing in literature (approximately ARMSS—Level 4-Bishop, 2008).

The ultimate goal of sport science is the application of research findings in real-sport setting. Here, the findings can be applied to the target population. The constraints of limited time and resources such as different quality in the coaching staff may affect the practical outcome. The implementation stage shows how effective the innovation under real conditions works. However, real-world conditions offer some variance in methodological design and uncontrolled biases (6). In a field experiment with 10 teams from various sports (men, n = 118; women, n = 65), the FMS was part of a test battery, which was used to test an injury risk algorithm for noncontact injuries. In addition to the field-expedient tests, historical and demographic risk factors like injury history, age, gender, or sport were part of the injury risk algorithm. For instance, FMS asymmetry and a score below the injury threshold were 2 research based components in this algorithm. Sixty-four of the athletes were categorized in the group of high risk and 119 in the group of low risk. The high-risk group was associated with a greater risk to sustain a noncontact lower extremity injury (27 of 64; 43%) as the low-risk group (12 of 119; 13%). We agree that this algorithm is a low-cost strategy, which can bring more objective information for return to sport decisions (45). However, general fatigue and neuromuscular coordination of eccentric movement patterns are important science-based injury risk factors (53). These factors should be included as well. In addition, a monitoring of the FMS score over the period of the scientific observation is an important information for evaluating the findings.

To examine the ability of the FMS to predict overuse injuries in individual sport, where no contact was involved, 60 participants were recruited from a pool of 35,000 individuals registered in the 2006 Indianapolis half-marathon (32). Of these, 49 runners successfully completed the weekly surveys addressing injury and training status over 10 weeks. Within the study, the FMS score ranged from 11 to 20, and 12 individuals reported an overuse injury in this period, but only 1 of the 12 injured runners scored below 14. In examining different threshold scores for successfully identifying overuse injuries, a score of 20 generated the highest sensitivity (100%) but resulted in a specificity of only 27%. A threshold score of 11 achieved the highest specificity (97.2%) but had a sensitivity of 0%. They reported a threshold of 17 as providing the highest combination (41.6 and 56.7%), but this was not considered sufficient for a definite prediction of injury. These data, together with the previous findings in runners, suggest that the FMS possesses only limited efficacy in predicting overuse injury in runners.

O'Connor et al. (55) examined the use of the FMS as a tool to predict injuries in a cohort of 874 marine officer candidates (18–30 years) over a training period of either 6 or 10 weeks. The mean FMS score of the military personnel was 16.6 ± 1.7 with only 10% of participants below the cutoff score of 14 or less. These subjects with an FMS score ≤14 were 1.7- to 1.9-fold times more likely to be injured than their colleagues with more than 14 points. Similarly, Lisman et al. investigation addressed the relationship between the injury risk factor, aerobic fitness, and movement quality. In a young and fit cohort (22.4 ± 2.7 years), their results showed that FMS scores ≤14 combined with slow running times (3 miles ≥20.5 minutes), resulted in an increased incidence of injury (46). In detail, the risk was 4.19, 3.77, and 1.85 more likely to suffer from any injury, a traumatic injury and/or overuse injury, respectively.

Research suggests that a decreased range of motion and reduced neuromuscular control can decrease movement quality and increase the risk of overuse injury (1,30). The observation of movement and stability compensations during FMS activities could therefore provide subtle but important indicators of musculoskeletal deficits in individuals that might be critical for predicting musculoskeletal injury or aiding, for example, physiotherapists identify and treat muscular or joint dysfunction, as well as designing intervention programs.

Kiesel et al. (39) indicated that the FMS might be able to aid in the design of appropriate intervention programs to address specific musculoskeletal and balance deficits identified using the FMS. In their intervention study with 62 football players, which was focused on improving subject specific deficits, the majority of the squad were able to improve their FMS scores. Of their 62 athletes, only 7 were assessed with a preintervention score of 14 or more, and 31 subjects possessed asymmetries. The following 7 weeks intervention program, was laid out to correct all identified movement deficits and was part of the athletes' strength and conditioning offseason program. It included “movement preparation” with individual as well as partnered stretching and self-administered trigger point treatment using The Stick (RPI, Atlanta, GA, USA). Furthermore, supervised corrective exercises were used to transfer the gained mobility into enhanced motor learning. The result of this offseason training program was a mean of 11% increase in their total FMS scores, with 30 athletes possessing a postintervention score of 14 or more (p < 0.01), and a reduction from 31 to 20 subjects who possessed asymmetries.

Sufficient flexibility is thought to play a key role in avoiding injury (70), which has been supported by Arnason et al. (1), who reported that soccer players with a low range of hip abduction are at increased risk of groin strains. To this end, Cowen (15) assessed the possible benefits of yoga using the FMS. In her setting, 77 firefighters with no previous experience in the specific stretching techniques underwent yoga classes that focused on breathing, relaxation, and posture over a period of 6 weeks. This study observed a large positive impact on the total FMS scores (+3.2), with significant improvements in trunk flexibility, but also a reduction in musculoskeletal pain, reported postintervention. However, it is difficult to interpret these findings because the progress was based on an average attendance of only 4 yoga sessions.

Bodden et al. (7) used the insights of the FMS to design a preventive training program for mixed martial arts fighters (n = 25, 24.3 ± 4.5 years). They used a control and an intervention group to distinguish the effects of their program. The goal was to enhance motor control and to reduce musculoskeletal asymmetries. After 8 weeks, the intervention group achieved in general 2 points more (pre: 13.2 ± 0.8; post: 15.3 ± 1.4), whereas the control group showed no worthwhile change (pre: 13.2 ± 0.8; post: 13.3 ± 0.9).

Klusemann et al. (41) used a modified FMS (FMSmod), including 7 FMS criteria and a landing screen (ICC = 0.82), to assess the effects of different training intervention settings. Thirty-nine adolescent basketball players (17 boys, 14 ± 1 years; 22 girls, 15 ± 1 years, 3 dropouts) were physically tested and observed over a period of 6 weeks. All subjects were beginners in strength training and attended at least 3 basketball training sessions and a competitive match per week. The subjects were divided into a supervised (n = 13; dropouts 0; FMSmod = 14 ± 1), video (n = 13; dropouts 2; FMSmod = 14 ± 2) and a control group (n = 13, dropout 1; FMSmod = 14 ± 2). All chosen exercises met the basketball training criteria, where landing technique, squatting, jumping, change of direction, pull-ups, and trunk stability elements were all used to improve the functional fitness of the participants. All subjects in the supervised group received verbal, visual, and kinesthetic feedback on their movement technique from experienced strength and conditioning specialists. The online-video group received their training program from a web site. Every week, a new training intervention was uploaded, which included the full exercise description and video clip demonstration with tips. Although the intervention of the control group and video group showed no significant differences, the supervised group achieved a substantial change, with the score increasing from 14 ± 1 to 16 ± 2. Unfortunately, the authors did not provide a result for the 8 FMSmod items, making it difficult to compare the results with other studies, but the study suggests FMS sensitivity to training intervention, as well as the need for expert supervision and expert program design for ensuring effective training.

Frost et al. (23) used a control group to investigate the effectiveness of an FMS-based program design in adults. After prescreening, 60 firefighters (37.5 ± 9.6 years; all men) were allocated into 1 of 3 groups: intervention group 1 (INT1; n = 21), intervention group 2 (INT2; n = 19), or control (CTL; n = 20). Each group was matched for age, height, body mass, and total FMS score. All subjects were required to attend at least 30 of 36 training sessions to be included in the analyses. Three sessions per week (each 1.5 hours) for a total of 12 weeks, were offered to the firefighters. Each group was coached by a strength and conditioning professional, who was blinded to the results of the FMS. The aim of INT1 was to reduce the risk of injury (29,47), whereas the main objective of the INT2 was to make the professionals as “fit” as possible. The control group received no guidance or feedback to its exercise. No significant posttraining changes were observed for any group (p > 0.176; group INT1, pre: 13.1 ± 2.7, post: 13.5 ± 2.3; group CTL, pre: 13.3 ± 2.5, post: 13.0 ± 2.4). These results did not support the hypothesis that the FMS can be used for rating movement quality. However, 85% of the control group had a different composite score posttraining. Interestingly, 17 improvements were identified in the control group, but only 9 in INT1 and 10 in INT2. Nevertheless, these data suggest that the standard 4-point FMS scale prohibits evaluation of the influence of training on movement quality of the firefighters in this study.

To avoid the influence of sporting contact injuries, the FMS has also been used in a non-sports context as an approach to predict possible musculoskeletal injuries in 433 professional firefighters. In their study design, Peate et al. (61) designed a subject specific training program that was based on FMS assessment. The outcome was a reduction of injuries (preintervention 62% vs. postintervention 42%), which included a reduction in number of injuries to the back (p = 0.02) and upper extremities (p = 0.03). However, it remains unclear whether the reduction in postintervention injuries was a result of the FMS specific training schedule or simply through the implementation of a strength workout that may have also improved their lifting techniques.

In the field of military medicine, the FMS was applied to assess the motor control improvement of a strength training and conditioning program that was designed after the principles of functional training (26). It included assisted resistance, free and bodyweight as well as flexibility exercises. Although the compliance of the participants were mediocre (10/18 sessions), the intervention with a duration of 6 weeks showed a significant effect. The group (80 men and 10 women) scored significant higher (pre: 15.1; post: 17.6; mean not mentioned). The missing reliability analysis and the missing of a control group make it difficult to generalize the data.

In an attempt to characterize functional ability in normal populations, Perry and Koehle (66) assessed 622 adults (50.9 ± 10.8 years; range, 21–82 years) and showed that young subjects have improved balance and movement ability (higher FMS score) than older populations, although the association was low (r = 0.25). Men and women had mean FMS scores of 14.8 ± 2.8 and 15.4 ± 2.4, respectively. Although the samples' sizes in the elderly groups were relatively low (n = 27 for 60–64 years; n = 12 for 65+ years), this study currently provides the largest reference repository for normative FMS data. Their normative data for the general population compare favorably with subjects from, for example, college sports (13) or professional footballers, where mean FMS scores of 16.9 ± 3.0 have been reported 25, suggesting that active sports participation is indeed reflected in the FMS outcome score.

At the outset, the screening tool can be used to assess general functional asymmetries and postural stability. Scientists and coaches agree that performance and injury prevention are composed by multiple factors. If the pros and cons are weighted up, it must be mentioned that the ability of FMS to predict athletic performance is not supported with sound scientific evidence. However, postural stability and functional asymmetries might be a contributing factor in the long-term development of an athlete.

Health is a necessary prerequisite for a successful participation in sport events. To assess musculoskeletal health, research findings indicate that the FMS is a low-cost and time-efficient screening tool for strength and conditioning specialists. In the field of injury risk, screening the method has shown some efficiency. In literature, studies do not agree in their findings. In female team sports on collegiate level and professional team sports, the FMS threshold score (≤14) predicted injuries of the lower extremities successfully. However, at least 1 interesting information is missing—the courses of distinct FMS values. This could offer interesting insights and assist to interpret findings.

In the field of military, the threshold score demonstrated in combination with a low endurance capacity its ability to predict overuse injuries. On the contrary, in recreational sports like running, the FMS threshold score failed to predict overuse injuries. From a methodological point of view, the quality of the most “isolated” FMS investigations is on moderate level and need to be confirmed on a higher reporting and study design level.

To enhance methodological quality in FMS-driven research, we recommend using a multifactorial approach including evidence-based risk factors like fatigue level, level of competition, sport event, or injury history. A theoretical framework might contribute to an appropriate methodological design.

Some investigations have shown that FMS based training programs can lead to reduce functional imbalances and enhance general motor control in professional, recreational sports, firefighters, and military. The effects of FMS-based interventions were achieved in samples of weak or moderate motor control. These findings are supported by limited evidence because of variance in reporting and methodological quality by reason of a missing control group or sample size. However, field conditions are manifold, and the presented effects could be caused by several circumstances like the quality of the coach, different training designs that based on additional tests, knowledge of the tester or performers' knowledge of the test criteria.

Thus, it is not surprising that the effects of field conditions were not verified on a higher evidence level. Future investigations have to assess clearly for different levels of general motor control. It might be beneficial to investigate some underlying factors of motor control with more sophisticated approaches.

Practical Applications

This review offers some evidence that strength and conditioning specialists can use the FMS to assess general functional asymmetries and postural stability in different populations. To reduce error in screening, the specialist should be instructed and familiar with the screening tool (>100 trials). In addition, a sound background in functional anatomy and motor learning is advantageous. A clear practice effect is common in athletic performance testing this fact should always be considered in interpreting the results of FMS freshmen. A recently published factor analysis has shown that the FMS is not a unitary construct. Hence, it is better to evaluate and draw conclusion from every subitem.

The ability to predict injuries and sport performance is of great interest of athletes and coaches. Strength and conditioning specialists can integrate the FMS as low-cost and time-efficient screening tool as part of their monitoring battery. Scientific findings support the FMS threshold score (≤14) as valid for injury risk screening in collision, team sports, firefighters, and tactical professions. Its ability to predict sporting performance is not supported by strong evidence. General motor control seems not be a key predictor in team sports, where many factors constitutes performance. However, in elite track and field athletes exists some evidence. Especially, the deep squat score was related to the improvement of sport performance.

Some investigations have shown that FMS-based training programs can lead to reduced functional imbalances and enhanced general motor control in professional, recreational sports, firefighters, and military. The effects of FMS-based interventions were achieved in samples of weak or moderate motor control. These findings are supported by limited evidence because of variance in reporting and methodological quality by reason of a missing control group or sample size. However, field conditions are manifold, and the presented effects could be caused by several circumstances like the quality of the coach, different training designs based on additional tests, knowledge of the tester or performers' knowledge of the test criteria.

Thus, it is not surprising that the effects of field conditions were not verified on a higher evidence level. An evidence-based scientist would argue for further testing until more evidence is gained. On the contrary, a designer would argue handling, function, and result values more in a world of uncertainty. Our position is in between of these 2 extremists. We argue for evidence- and experience-based application of the FMS in the field of strength and conditioning. For instance, the heuristic pain criterion is scientifically doubtful, but on practical level, it could be highly effective. In our view, the heuristic criterion is an appropriate design for real-world conditions. Especially, men seem to be motivated through numbers. At the outset, the application of the FMS has to focus on the specific needs of the individual and the demands of their profession. Therefore, a monitoring battery is crucial. In this series of the test, the FMS can be a meaningful start in musculoskeletal screening and motion analysis in cases or cohorts of lower or moderate general motor quality. For athletes performing on a higher motor skill level, more sophisticated methods should be used due to the lacking scientific knowledge.


The authors thank their colleagues for critical reading and their reviewers to enhance the quality of this review.


1. Arnason A, Sigurdsson SB, Gudmundsson A, Holme I, Engebretsen L, Bahr R. Risk factors for injuries in football. Am J Sports Med 32(1 Suppl): 5S–16S, 2004.
2. Ary D. Introduction to Research in Education (9th ed.). Belmont, CA: Wadsworth Cengage Learning, 2014.
3. Bahr R, Krosshaug T. Understanding injury mechanisms: A key component of preventing injuries in sport. Br J Sports Med 39: 324–329, 2005.
4. Beach TAC, Frost DM, Callaghan JP. FMS scores and low-back loading during lifting—Whole-body movement screening as an ergonomic tool? Appl Ergon 45: 482–489, 2014.
    5. Ben Kazman J, Galecki J, Lisman P, Deuster PA, O'Connor FG. Factor structure of the functional movement screen in marine officer candidates. J Strength Cond Res 28: 672–678, 2014. doi:10.1519/JSC.0b013e3182a6dd83.
    6. Bishop D. An applied research model for the sport sciences. Sports Med 38: 253–263, 2008.
    7. Bodden JG, Needham RA, Chockalingam N. The effect of an intervention program on functional movement screen test scores in mixed martial arts athletes. J Strength Cond Res 2013. doi:10.1519/JSC.0b013e3182a480bf.
    8. Bosco C, Luhtanen P, Komi PV. A simple method for measurement of mechanical power in jumping. Eur J Appl Physiol Occup Physiol 50: 273–282, 1983.
    9. Boyle M. Functional Training for Sports. Champaign, IL: Human Kinetics; 2004.
    10. Brink MS, Visscher C, Arends S, Zwerver J, Post WJ, Lemmink KA. Monitoring stress and recovery: New insights for the prevention of injuries and illnesses in elite youth soccer players. Br J Sports Med 44: 809–815, 2010.
    11. Chapman RF, Laymon AS, Arnold T. Functional movement scores and longitudinal performance outcomes in elite track and field athletes. Int J Sports Physiol Perform 9: 203–211, 2013.
    12. Chappell JD, Herman DC, Knight BS, Kirkendall DT, Garrett WE, Yu B. Effect of fatigue on knee kinetics and kinematics in stop-jump tasks. Am J Sports Med 33: 1022–1029, 2005.
    13. Chorba RS, Chorba DJ, Bouillon LE, Overmyer CA, Landis JA. Use of a functional movement screening tool to determine injury risk in female collegiate athletes. N Am J Sports Phys Ther 5: 47–54, 2010.
    14. Cook G, Burton L, Hoogenboom B. Pre-participation screening: The use of fundamental movements as an assessment of function—Part 1. N Am J Sports Phys Ther 1: 62–72, 2006.
    15. Cowen VS. Functional fitness improvements after a worksite-based yoga initiative. J Bodyw Mov Ther 14: 50–54, 2010.
    16. Derrick TR, Dereu D, McLean S. Impacts and kinematic adjustments during an exhaustive run. Med Sci Sports Exerc 34: 998–1002, 2002.
    17. Duncan MJ, Stanley M. Functional movement is negatively associated with weight status and positively associated with physical activity in british primary school children. J Obes 2012: 697563, 2012.
    18. Elias JE. The inter-rater reliability of the functional movement screen within an athletic population using untrained raters. J Strength Cond Res 1, 2013. doi:10.1519/JSC.0b013e3182a1ff1d.
    19. Faude O, Meyer T, Federspiel B, Kindermann W. Injuries in elite german football—A media-based analysis. J German Sports Med 60: 139–143.
    20. Fischer K, David J, Paul K, Leonard R. The effect of functional movement screen-based circuit training on balance and postural stability. Int J Exerc Sci 9: 19, 2013. In: Conference abstract Submissions.
      21. Frohm A, Heijne A, Kowalski J, Svensson P, Myklebust G. A nine-test screening battery for athletes: A reliability study. Scand J Med Sci Sports 22: 306–315, 2012.
      22. Frost D, Andersen J, Lam T, Finlay T, Darby K, McGill S. The relationship between general measures of fitness, passive range of motion and whole-body movement quality. Ergonomics 56: 637–649, 2013.
      23. Frost DM, Beach TAC, Callaghan JP, McGill SM. Using the functional movement screen to evaluate the effectiveness of training. J Strength Cond Res 1, 2012. doi:10.1519/JSC.0b013e318234ec59.
      24. Frost DM, Beach TA, Callaghan JP, McGill SM. FMSscores change with performers' knowledge of the grading criteria—Are general whole-body movement screens capturing “dysfunction”? J Strength Cond Res 2013. doi:10.1519/JSC.0b013e3182a95343.
      25. Gambetta V. Athletic Development: The Art and Science of Functional Sports Conditioning. Champaign, IL: Human Kinetics; 2007.
      26. Goss DL, Christopher GE, Faulk RT, Moore J. Functional training program bridges rehabilitation and return to duty. J Spec Oper Med 9: 29–48, 2009.
      27. Gribble PA, Brigle J, Pietrosimone BG, Pfile KR, Webster KA. Intrarater reliability of the functional movement screen. J Strength Cond Res 27: 978–981, 2013.
      28. Grygorowicz M, Piontek T, Dudzinski W. Evaluation of functional limitations in female soccer players and their relationship with sports level—A cross sectional study. PLoS One 8: e66871, 2013.
      29. Hewett TE. Biomechanical measures of neuromuscular control and valgus loading of the knee predict anterior cruciate ligament injury risk in female athletes: A prospective study. Am J Sports Med 33: 492–501, 2005.
      30. Hewett TE, Lindenfeld TN, Riccobene JV, Noyes FR. The effect of neuromuscular training on the incidence of knee injury in female athletes. A prospective study. Am J Sports Med 27: 699–706, 1999.
      31. Hickey J, Barrett BA, Butler R, Kiesel K, Plisky PJ. Reliability of the functional movement screen using a 100-point grading scale. Med Sci Sports Exerc 42(5 Suppl 1): 392, 2010.
      32. Hoover D, Clyde KB, Bourcier B, Shannon L, Jenny T. Predictive validity of the functional movement screen in a population of recreational runners training for half marathon. Med Sci Sports Exerc 40: 219, 2008.
      33. Hopkins WG. Competitive performance of track and field athletes: Variability and smallest worthwile enhancements. Sportscience 17–20, 2005.
      34. 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.
      35. Hopkins WG, Schabort EJ, Hawley JA. Reliability of power in physical performance tests. Sports Med 31: 211–234, 2001.
      36. Ives JC, Shelley GA. Psychophysics in functional strength and power training: Review and implementation framework. J Strength Cond Res 17: 177–186, 2003.
      37. Kernozek TW, Torry MR, Iwasaki M. Gender differences in lower extremity landing mechanics caused by neuromuscular fatigue. Am J Sports Med 36: 554–565, 2008.
      38. Kiesel KB, Butler RJ, Plisky PJ. Limited and asymmetrical fundamental movement patterns predict injury in American football players. J Sport Rehabil 23: 88–94, 2013.
      39. Kiesel K, Plisky P, Butler R. Functional movement test scores improve following a standardized off-season intervention program in professional football players. Scand J Med Sci Sports 21: 287–292, 2011.
      40. Kiesel K, Plisky PJ, Voight ML. Can serious injury in professional football be predicted by a preseason functional movement screen? N Am J Sports Phys Ther 2: 147–158, 2007.
      41. Klusemann MJ, Pyne DB, Fay TS, Drinkwater EJ. Online video–based resistance training improves the physical capacity of junior basketball athletes. J Strength Cond Res 26: 2677–2684, 2012.
      42. Knapik JJ, Bauman CL, Jones BH, Harris JM, Vaughan L. Preseason strength and flexibility imbalances associated with athletic injuries in female collegiate athletes. Am J Sports Med 19: 76–81, 1991.
      43. Kraus K, Doyscher R. Evaluation and application of motor screening systems for the long-term athlete development. In: Proceedings of 15th Summer School. Technology of Information and Communication in Applied Training Science. I. Fichtner, ed. Leipzig, Germany, IAT, 2013. pp. 50–58.
        44. Krustrup P, Mohr M, Amstrup T, Rysgaard T, Johansen J, Steensberg A, Pedersen PK, Bangsbo J. The yo-yo intermittent recovery test: Physiological response, reliability, and validity. Med Sci Sports Exerc 35: 697–705, 2003.
        45. Lehr ME, Plisky PJ, Butler RJ, Fink ML, Kiesel KB, Underwood FB. Field-expedient screening and injury risk algorithm categories as predictors of noncontact lower extremity injury. Scand J Med Sci Sports 23: e225–e232, 2013.
        46. Lisman P, O'Connor FG, Deuster PA, Knapik JJ. Functional movement screen and aerobic fitness predict injuries in military training. Med Sci Sports Exerc 45: 636–643, 2013.
        47. McGill S. Low Back Disorders: Evidence-based Prevention and Rehabilitation (2nd ed.). Champaign, IL: Human Kinetics; 2007.
        48. McGill SM, Andersen JT, Horne AD. Predicting performance and injury resilience from movement quality and fitness scores in a basketball team over 2 years. J Strength Cond Res 26: 1731–1739, 2012.
        49. McGill S, Frost D, Andersen J, Crosby I, Gardiner D. Movement quality and links to measures of fitness in firefighters. Work 2012. doi:10.3233/WOR-121538.
          50. Meeuwisse W. Assessing causation in sport injury: A multifactorial model. Clin J Sport Med 4: 166–170, 1994.
          51. Minick KI, Kiesel KB, Burton L, Taylor A, Plisky P, Butler RJ. Interrater reliability of the functional movement screen. J Strength Cond Res 24: 479–486, 2010.
          52. Mujika I. Challenges of team-sport research. Int J Sports Physiol Perform 2: 221–222, 2007.
          53. Noyes FR, Barber-Westin SD, Fleckenstein C, Walsh C, West J. The drop-jump screening test: Difference in lower limb control by gender and effect of neuromuscular training in female athletes. Am J Sports Med 33: 197–207, 2005.
          54. Nummela AT, Paavolainen LM, Sharwood KA, Lambert MI, Noakes TD, Rusko HK. Neuromuscular factors determining 5 km running performance and running economy in well-trained athletes. Eur J Appl Physiol 97: 1–8, 2006.
          55. O'connor FG, Deuster PA, Davis J, Pappas CG, Knapik JJ. Functional movement screening. Med Sci Sports Exerc 43: 2224–2230, 2011.
          56. Okada T, Huxel KC, Nesser TW. Relationship between core stability, functional movement, and performance. J Strength Cond Res 25: 252–261, 2011.
          57. Onate JA, Dewey T, Kollock RO, Thomas KS, Van Lunen BL, DeMaio M, Ringleb SI. Real-time intersession and interrater reliability of the functional movement screen. J Strength Cond Res 26: 408–415, 2012.
          58. Parchmann CJ, McBride JM. Relationship between functional movement screen and athletic performance. J Strength Cond Res 25: 3378–3384, 2011.
          59. Parenteau-G E, Gaudreault N, Chambers S, Boisvert C, Grenier A, Gagné G, Balg F. Functional movement screen test: A reliable screening test for young elite ice hockey players. Phys Ther Sport 2013. doi:10.1016/j.ptsp.2013.10.001.
            60. Paszkewicz JR, McCarty CW, Van Lunen BL. Comparison of functional and static evaluation tools among adolescent athletes. J Strength Cond Res 27: 2842–2850, 2013.
            61. Peate WF, Bates G, Lunda K, Francis S, Bellamy K. Core strength: A new model for injury prediction and prevention. J Occup Med Toxicol 2: 3, 2007.
            62. Perry FT, Koehle MS. Normative data for the functional movement screen in middle-aged adults. J Strength Cond Res 27: 458–462, 2013.
            63. Schmidtlein O, Keller M, Kurz E. Asymmetric FMS patterns in Germany's bundesliga soccer players. Paper presented at: Ghent 2012 3rd World Conference on science and soccer (WCSS); 2012; Ghent, Belgium.
            64. Schneiders AG, Davidsson A, Hörman E, Sullivan SJ. Functional movement screen normative values in a young, active population. Int J Sports Phys Ther 6: 75–82, 2011.
            65. Shultz R, Anderson SC, Matheson GO, Marcello B, Besier T. Test-retest and interrater reliability of the functional movement screen. J Athl Train 48: 331–336, 2013.
            66. Smith CA, Chimera NJ, Wright NJ, Warren M. Interrater and intrarater reliability of the functional movement screen. J Strength Cond Res 27: 982–987, 2013.
            67. Sorenson EA. Functional movement screen as a predictor of injury in high school basketball athletes. Master thesis, University of Oregon, 2009.
              68. Teyhen DS, Shaffer SW, Lorenson CL, Halfpap JP, Donofry DF, Walker MJ, Dugan JL, Childs JD. The functional movement screen: A reliability study. J Orthop Sports Phys Ther 42: 530–540, 2012.
              69. Tsai L, Sigward SM, Pollard CD, Fletcher MJ, Powers CM. Effects of fatigue and recovery on knee mechanics during side-step cutting. Med Sci Sports Exerc 41: 1952–1957, 2009.
              70. Witvrouw E, Danneels L, Asselman P, D'Have T, Cambier D. Muscle flexibility as a risk factor for developing muscle injuries in male professional soccer players. A prospective study. Am J Sports Med 31: 41–46, 2003.
              71. Yeung SS, Suen AMY, Yeung EW. A prospective cohort study of hamstring injuries in competitive sprinters: Preseason muscle imbalance as a possible risk factor. Br J Sports Med 43: 589–594, 2009.

              assessment; motor control; sports performance; sports injury; pre-participation screening

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