Functional Movement Screen (FMS)

Reading time:

Abstract Contents References Back to site menu

ABSTRACT

The Functional Movement Screen (FMS) is a pre-participation screening tool designed to identify compensatory movement patterns that are indicative of increased injury risk and inefficient movement that causes reduced performance.

In most populations, the FMS sum score ranges between 13 – 15 points, and many trials have reported norms of around 14 points. 

The FMS sum score is positively associated with increased physical activity and negatively associated with higher BMI, age, and the presence of breathing pattern disorders. It is not associated with previous injury, athletic ability, postural stability, time within the competition season, or gender.

The FMS sum score appears to be very reliable between raters (inter-rater) and within raters for a video of the same test (intra-rater). However, test-retest reliability is less reliable, indicating that the same subjects may score differently on different occasions.

The FMS displays poor construct validity, poor criterion reference validity, poor content validity in respect of both injury risk and movement efficiency, and poor concurrent validity in respect of both injury risk and movement efficiency.

Many investigations have found that the odds of experiencing an injury are higher in subjects who score <14 points on the FMS sum score. However, there is considerable variation between studies in the reported results.

Although it has been suggested that the FMS can measure efficiency of movement (and this implies athletic performance), the FMS sum score is not associated with either level of athletic performance or ability in athletic tasks, such as sprint running, agility or jumping.

Various different exercise programs appear to improve FMS sum score, including yoga, resistance training, functional training, and general military training. However, since the improvements are lower than the mean difference to be considered real, care should be taken in interpreting the results.

PRACTICAL PERSPECTIVE

The Functional Movement Screen (FMS) is the most well-researched movement screen currently available. Therefore, it is likely to be the movement screen of choice for many sport teams and athletes. The mean FMS sum score is around 13 – 15 points in most groups of people, and substantially lower scores may indicate an increased risk of injury. However, higher scores should not be taken as indicative of more efficient movement, because they are not associated with better athletic performance. 



CONTENTS

Full table of contents

Click on the links below to jump down to a section:

Background

Factors affecting FMS scores

Reliability

Validity

Injury prediction

Performance prediction

Effects of exercise training programs

References

Contributors



BACKGROUND

PURPOSE

This section sets out the background to the FMS and its development and provides an explanation of what constitutes a “screen” for these purposes.

BACKGROUND

Introduction

The FMS is a pre-participation test for which an overall score is given but which comprises seven individual tests, comprising standardized, compound movements that are rated from 0 – 3 by an examiner and include the Deep Squat, Hurdle Step, In-Line Lunge, Shoulder Mobility, Active Straight Leg Raise, Trunk Stability Push-Up, and Rotary Stability. Scores on each test are determined by reference to how the movements appear to the examiner when they are performed.

History of the FMS

The FMS was introduced to the scientific community in 1997 with the publication of the two review articles by Cook et al. (2006a) and Cook et al. (2006b). However, this had been preceded by the original lay publication by Cook et al. (1998) and the follow-up book by Cook (2004), called “Athletic Body in Balance”. More recently, a two-part commentary on the FMS and the research over the intervening years has been published by the same team: Cook et al. (2014a) and Cook et al. (2014b), which describes changes in the thought processes of the inventors and seeks to address some of the criticisms that have been directed at the sum score and the testing procedure.

Scoring the FMS

For each individual test, a score of 0 is given if pain occurs. The score of 1 is given if the subject is not able to perform the movement. The score of 2 is given if the subject is able to complete the movement but compensates in some way. The score of 3 is given if the subject performs the movement correctly. The individual scores for each movement are combined into a final score out of 21 points, which is thought to predict injury risk. The designers of the test have suggested that scores ≤14 points predict individuals who are at a greater risk of injury than those with a score that is >14 points.

What is the purpose of the FMS?

Introduction

According to the designers of the test, the FMS is a screen that is intended to measure “compensatory movement patterns” within the kinetic chain (Cook et al. 2014a). These compensatory movement patterns are believed to arise from the presence of “weak links” (Cook et al. 2014a). This can be seen from a recent review published by the designers of the screen: Cook et al. (2014a) state: “if this weak link is not identified, the body will compensate…” Moreover, these compensatory movement patterns are thought to cause inefficient movements, leading to reduced performance and an increased risk of injury. Again, this can be seen from a recent review published by the designers of the screen: Cook et al. (2014a) state that when there is a weak link present: “the body will compensate, causing inefficient movements. It is this type of inefficiency that can cause a decrease in performance and an increase in injuries.” By drawing on the explanations of the designers, it is clear that the FMS is designed to: identify compensatory movement patterns that are indicative of increased injury risk and inefficient movement that causes reduced performance.

Definitions in the literature

Curiously, many reviewers have not reverted to the stated purpose of the FMS in published work by the designers but have either inferred their own definitions by observation or left the purpose of the screen undefined. For example, Kraus et al. (2014) state that “the general aim of the FMS is to assess obviously functional musculoskeletal asymmetries and postural deficits”, Stobierski et al. (2014) state that “the ability of the FMS to detect abnormal movement patterns can be useful when planning training programs”, and Krumrei et al. (2014) left the purpose of the test undefined.

Evaluating the FMS as a screen

Introduction

As noted above, the FMS is a self-titled screen that is designed to: identify compensatory movement patterns that are indicative of increased injury risk and inefficient movement that causes reduced performance. The fact that the FMS is called a screen has important implications for how we should approach evaluating it, as is explained below.

Purpose of screens

Screens are most commonly used by medical professionals to identify those individuals who either have a specific disease or who are at higher risk of developing a specific disease. In a seminal paper setting out the principles for screening, Wilson and Jungner (1968) suggested that the process of screening can be defined as “the presumptive identification of unrecognized disease or defect by the application of tests, examinations, or other procedures which can be applied rapidly.” In other words, in the medical context, a screen identifies individuals who are apparently healthy but who probably actually have a previously unrecognised disease or problem. Importantly, the screen does not provide a diagnosis but rather identifies an individual who should be referred to the relevant healthcare specialist who will then provide the diagnosis (Wilson and Jungner, 1968). This difference is appreciated by the designers of the FMS, who note that the “[FMS] serves a directional role, not a diagnostic role” (Cook et al. 2014b).

Validity of screens

Although validity has many different aspects, Wilson and Jungner (1968) identify the primary measurement of validity to be the proportion of results that are identified by the screen that are subsequently confirmed by an acceptable diagnostic procedure (i.e. criterion reference validity). In this context, the preferred statistics for measuring validity are sensitivity and specificity. Sensitivity is the percentage of subjects who have the disease confirmed by later diagnosis and who are correctly identified beforehand by the screen (Florkowski, 2008). Specificity is the percentage of subjects who do not have the disease, as confirmed by later diagnosis, and who are correctly identified by a screen (Florkowski, 2008). Both sensitivity and specificity are needed to evaluate the validity of a screen.

Reliability of screens

The two primary aspects of reliability are inter-rater (between different examiners performing the screen) and intra-rater (between different ratings by the same examiner). Inter-rater reliability is important as screens that do not have good inter-rater reliability could transpire to be valid when performed by one examiner but not by another. Similarly, intra-rater reliability is important as screens could prove to be valid on one occasion but not on another, even when performed by the same examiner.

SECTION CONCLUSIONS

The FMS is a pre-participation screening tool designed to identify compensatory movement patterns that are indicative of increased injury risk and inefficient movement that causes reduced performance.

As a self-identified screen, the FMS is not a diagnostic but rather identifies individuals who should be referred for a diagnosis. Nevertheless, as a screen, the FMS should be both valid and reliable.

Top · Contents · References


FACTORS AFFECTING FMS SUM SCORE

PURPOSE

This section details the factors that influence the FMS sum score and provides an analysis of what constitutes a “normal” score in different populations.

BACKGROUND

Introduction

The stated aim of the FMS sum score is to identify the presence of compensatory movement patterns that are indicative of increased injury risk and inefficient movement that causes reduced performance. However, a number of studies have identified various other factors that can influence the FMS sum score and which may therefore be confounding factors.

Effect of gender

While some researchers have reported differences in the FMS sum score between groups of individuals of similar age and demographic, the majority of studies have found no differences.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

Effect of age

Several investigators have shown that FMS sum score appears to reduce gradually with increasing age in the normal, healthy population (Perry and Koehle, 2013; Loudon et al. 2014). Perry and Koehle (2013) reported normative values of the FMS sum score by age, which display a steadily decreasing trend. They found that FMS sum score was correlated negatively with age.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

Effect of physical activity levels

In general, the literature shows that a higher level of physical activity is associated with higher FMS scores. Physical activity can be measured in various ways, including direct measurement by pedometer or by questionnaire. Popular questionnaires include the Healthy Physical Activity Participation Questionnaire (HPAPQ), which is a short questionnaire assessing frequency and intensity of physical activity.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

Effect of postural stability and balance

The Balance Error Scoring System (BESS) is a measure of balance and postural stability. The BESS test comprises 3 stances: a double-leg stance (with the feet together), a single-leg stance (standing on the nondominant leg), and a split stance (nondominant foot directly behind the dominant foot) performed on both firm and unstable surfaces with the eyes closed (see review by Bell et al. 2011). Raters count errors over a 20-second period. The BESS is regarded as moderately reliable although some authors have reported poor reliability for the sum score (Finnoff et al. 2009). The BESS correlates well with criterion reference measures of postural stability (see review by Bell et al. 2011) and also displays construct validity as an injury or fatigue-related measure of balance, as BESS scores increase with concussion, functional ankle instability, external ankle bracing, fatigue, and age. Perry and Koehle (2013) assessed performance on the BESS in 116 subjects and the association with the FMS sum scores. They found no significant association between performance on the BESS and on the FMS (r = 0.07).

Effect of Body Mass Index (BMI)

In general, the literature shows that a higher BMI is associated with lower FMS scores, although the strength of the relationship seems to vary extremely widely from low to moderate to high.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

Effect of previous injury

Perhaps surprisingly, given that previous injury is likely one of the best predictors of increased future injury risk, researchers have found that previous injury is not associated with FMS sum score.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

Effect of breathing pattern disorders

Introduction

Diaphragmatic breathing involves a synchronized motion of the upper rib cage, lower rib cage, and abdomen. On the other hand, thoracic breathing involves breathing from the upper chest and can be observed by reference to a greater degree of upper rib cage motion in comparison to the movement of the lower rib cage. Breathing pattern disorders are conditions that lead to symptoms that have no other apparent cause and which are observed in individuals with musculoskeletal dysfunction. It is possible that such breathing pattern disorders are caused by the onset of the musculoskeletal dysfunction, or alternatively they could be predictive risk factors of the dysfunction occurring in the first place. Breathing pattern disorders are commonly measured by a range of different tests, including: capnography-measured end tidal CO2 values of <35mmHg, Nijmegen Questionnaire scores of ≥23, a respiratory rate of >16 breaths per minute at rest, the Hi Lo assessment, and breath-hold time of <20 seconds.  End tidal CO2 values are relevant as thoracic breathing is known to cause low levels of CO2 in the blood in comparison to diaphragmatic breathing. The Hi Lo breathing test assesses the movement of the chest and abdomen motion at rest and in a seated position.

Breathing pattern disorders and the FMS sum score

Bradley & Esformes (2014) assessed the correlation between measures of breathing pattern disorders and the FMS sum score. They found correlations between active end tidal CO2 value and FMS score, and between active respiratory rate and FMS score. However, they did not find any correlations between other measures of breathing pattern disorders and FMS scores. They concluded that those individuals who displayed positive results on tests for breathing disorders were significantly more likely to score poorly on the FMS.

Effect of competition season

Recent research has identified that changes may occur in respect of the individual elements of the FMS (but not the sum score) over the course of a playing season. Sprague et al. (2014) measured FMS sum score and individual test performance in 57 NCCA Division II athletes before and after their competitive seasons. While the sum score did not change, performance in 4 individual tests did change (the deep squat and in-line lunge scores improved while the active straight leg raise and rotary stability scores got worse).

NORMATIVE VALUES FOR FMS SUM SCORE

In general normative values for the FMS sum score center around 13 – 15 points, with many populations displaying norms around the 14-point cut-off that is very popular in injury prevention trials.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

SECTION CONCLUSIONS

In most populations, norms for the FMS sum score range between 13 – 15 points and many trials have reported norms of around 14 points.

The FMS sum score is positively associated with increased physical activity and negatively associated with higher BMI, age, and the presence of breathing pattern disorders. It is not associated with previous injury, athletic ability, postural stability, time within the competition season, or gender.

Top · Contents · References


RELIABILITY

[Read more: reliability]

PURPOSE

This section sets out a summary of the research into the inter-rater, intra-rater, and test-re-test reliability of the FMS.

BACKGROUND

Introduction

The FMS is a pre-participation screening tool designed to identify compensatory movement patterns that are indicative of increased injury risk and inefficient movement that causes reduced performance. The reliability of the FMS has been assessed by several reviewers (Beardsley & Contreras, 2014; Kraus et al. 2014; Stobierski et al. 2014). Both Beardsley & Contreras (2014) and Stobierski et al. (2014) pronounced the FMS as a reliable screen both in terms of inter-rater and intra-rater, although neither differentiated between the two types of intra-rater reliability (see further below). Kraus et al. (2014) did not conclude on this question, but noted certain limitations in the test: including the apparent ability of subjects to influence the outcome (i.e. Frost et al. 2013b) and the appearance of a practice effect caused by repeated trials.

N.B. regarding intra-rater reliability

The intra-rater reliability of a test like the FMS is simply the degree to which the same rater will score the identical test performance in the same way every time. For this purpose, videos are used of subjects performing the test. Videos are used so that the effect of any differences between performances by a subject is completely removed from the equation. In contrast, test-re-test reliability is measured on two separate occasions, with the subject themselves performing the test twice. Test-re-test reliability therefore introduces an element of variability from the subject as well as from the rater. While a controlled measurement of intra-rater reliability is valuable for understanding the degree to which an individual rater can affect the results of the test, it is much less useful, because it ignores an element of variability between tests that will necessarily occur in practice.

INTER-RATER RELIABILITY

Selection criteria

Population – any

Intervention – the FMS tests as typically prescribed

Comparator – other raters

Outcome – ICC, SEM and MD of the two or more sets of FMS scores

Results

The following studies were identified: Schneiders (2011), Onate (2012), Teyhen (2012), Butler (2012), Schultz (2013), Smith (2013), Elias (2013), Parenteau-G (2014), Loudon (2014), Gulgin (2014).

Findings

In these trials, the ICCs range from 0.44 – 0.98 but in all but one case were >0.76, suggesting that the FMS displays good inter-rater reliability.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

INTRA-RATER RELIABILITY

Selection criteria

Population – any

Intervention – the FMS tests as typically prescribed

Comparator – other ratings by the same rater of the exact same FMS test performance

Outcome – ICC, SEM and MD of the two or more sets of FMS scores

Results

The following studies were identified: Onate (2012), Schultz (2013), Smith (2013), Gribble (2013), Parenteau-G (2014).

Findings

In these trials, the ICCs range from 0.75 – 0.96, suggesting that the FMS displays good intra-rater reliability.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

TEST-RE-TEST RELIABILITY

Selection criteria

The following criteria were applied:

Population – any

Intervention – the FMS tests as typically prescribed

Comparator – other ratings by the same rater of different FMS test performances by the same individual

Outcome – ICC, SEM and MD of the two or more sets of FMS scores

Results

The following studies were identified: Teyhen (2012), Schultz (2013).

Findings

In these trials, ICCs ranged from 0.60 – 0.75, suggesting that the FMS displays worse test-retest reliability than either inter-rater or intra-rater reliability. This may be because individuals perform differently on repeated attempts at the same test.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

Interpreting the SEM and MD of the FMS sum score

The SEM of the FMS sum score in the above analyses seems to be generally <1.0 points. This means that we can be confident that the true FMS sum score value of a given test will fall within ±2.0 points, 95% of the time. The MD of the FMS sum score in the above studies seems to be always <3.0 points. This means that when we take repeated measures of the FMS sum score to assess whether an athlete or individual has improved their functional movement, we can be confident that a real change has occurred when the improvement is ±6.0 points.

SECTION CONCLUSIONS

The FMS sum score appears to be very reliable between raters (inter-rater) and within raters for a video of the same test (intra-rater). However, test-retest reliability is much less reliable, indicating that the same subjects may perform the test differently on different occasions.

Top · Contents · References


VALIDITY OF THE FMS

PURPOSE

This section sets out a summary of the research into the validity of the FMS.

BACKGROUND

Introduction

The FMS is a pre-participation screening tool designed to identify compensatory movement patterns that are indicative of increased injury risk and inefficient movement that causes reduced performance. To be used as a screen, it should be both reliable and valid. Validity describes whether a test or screen actually measures what it is intended to measure (Karras, 1997b) and whether it can be used for the purpose it is designed.

Literature usage

The validity of the FMS has been assessed by several reviewers (Beardsley & Contreras, 2014; Kraus et al. 2014). Both Beardsley & Contreras (2014) and Kraus et al. (2014) drew attention to the key problem that the FMS sum score faces, which is that it is evidently not a unitary construct and should therefore not be used as such. In addition, Beardsley & Contreras (2014) noted that the validity of the FMS to measure compensatory movement patterns as they are performed in sport should be questioned, as movement patterns clearly change with load and speed (i.e. Frost et al. 2013a).

Types of validity

There are several types of validity, including construct validity, criterion-referenced validity, content validity, and concurrent validity (Baechle and Earle, 2008). In general, construct validity and criterion reference validity relate to whether the test actually measures what it is intended to measure, while content validity and concurrent validity relate to whether the test can be used for the purpose it is designed. More specifically, construct validity measures whether a test actually measures what it sets out to measure (in this case compensatory movement patterns). Criterion reference validity measures whether the standard test procedures agree with a gold standard, objective measurement. Content validity measures whether a test contains all the necessary features that might be expected from a test designed to assess  injury risk and efficiency of movement. Concurrent validity measures whether a test agrees with other tests that are known to be good of injury risk or efficiency of movement.

Evaluating the validity of the FMS

The FMS is a self-titled screen that is intended to measure “compensatory movement patterns” that result from the presence of dysfunction (Cook et al. 2014a). These compensatory movement patterns are thought to lead to both an increased risk of injury and inefficient movement. Assessing construct validity therefore requires us to assess whether the FMS actually measures the presence of these compensatory movement patterns. Assessing criterion reference validity requires us to assess whether the results of the FMS standard scoring system (using human raters) agrees with the results of an objective scoring system (perhaps using motion analysis). Assessing content validity requires us to assess whether the FMS test contains all the necessary features that might be expected from a test designed to measure injury risk and efficiency of movement. In addition, the FMS is not a single test but the sum of multiple tests and our ability to sum the results of individual tests together requires us to assess the construct validity of this summation, as well as the construct validity of the individual test components.

VALIDITY SECTION CONTENTS

Click on the following links to jump down to a section:

CONSTRUCT VALIDITY – Compensatory movement patterns

CONSTRUCT VALIDITY – Sum score

CRITERION REFERENCE VALIDITY

CONTENT VALIDITY – Injury risk

CONCURRENT VALIDITY – Injury risk

CONTENT VALIDITY – Movement efficiency

CONCURRENT VALIDITY – Movement efficiency


CONSTRUCT VALIDITY: COMPENSATORY MOVEMENT PATTERNS

Background

Investigating the construct validity of the FMS is the way in which we can assess whether the FMS really measures compensatory movement patterns. Construct validity is the ability of a test to represent the underlying theoretical basis upon which outcomes of the test are interpreted (Baechle & Earle, 2008). The question for the FMS is whether the test actually measures compensatory movement patterns or whether it simply describes ways in which different people perform the same movement. Since there is no way to measure a compensatory movement pattern directly, without defining what the compensatory movement looks like a priori, the construct validity of the FMS in respect of compensatory movement patterns must be assessed indirectly. There are several ways in which this problem might be approached, which are described below.

Effect of load and speed

In order for the FMS to be a valid indicator of injury or inefficient movement in sport, it is logical that the compensatory movement patterns that are tested during the FMS must be the same or similar to those that are performed in sport (Beardsley & Contreras, 2014a). If the FMS tests movements that are performed in one way during the test but in another way during sport, then the FMS lacks construct validity for describing the tendency of an athlete to display compensatory movement patterns. Indeed, there are indications that movement patterns during similar exercises or movements to those tested in the FMS differ according to load and speed (Frost et al. 2013a; Beardsley & Contreras, 2014b). Frost et al. (2013a) reported that movement patterns were altered by both load and speed in five whole-body movements (deadlift, squat, lunge, one-arm cable push, and one-arm cable pull). Similarly, Beardsley and Contreras (2014b) reported that from a review of the literature it is apparent that movement patterns change during the deadlift, squat, lunge, as well as during running and jumping.

Effect of knowledge of test criteria

In order for the FMS to be a valid indicator of injury or inefficient movement in sport, it is logical that the compensatory movement patterns that are tested during the FMS must be the same or similar to those that are performed in sport (Beardsley & Contreras, 2014a). If the FMS tests movements that are performed in one way during the test but in another way during sport, then the FMS lacks construct validity for describing the tendency of an athlete to display compensatory movement patterns. Indeed, there are indications that subjects may deliberately alter their movement patterns during the FMS test in order to score more highly. This would lead to a difference between the way in which the raters observed the individual performing the movement and the way in which they then naturally performed the same or similar movements during sports. When playing sport, athletes will likely not attempt to move as they would in order to score well on the FMS but will most likely revert to their normal way of moving. Frost et al. (2013b) found differences in FMS sum scores in a group of subjects before and after telling them how to achieve a perfect score. The FMS sum score improved significantly from 14.1 ± 1.8 to 16.7 ± 1.9 points. Frost et al. (2013b) concluded that changes in FMS score may not therefore reflect actual changes in the mobility, stability or co-ordination of an athlete but rather simply a knowledge of what the rater requires.

CONSTRUCT VALIDITY: SUM SCORE

Background

Sum scores are scores that are made up of the total of a number of other, individual scores. Sum scores are not often used within mainstream sports science, although they are very frequently used in medicine, the social sciences, psychology and gerontology. Wherever multiple-choice questionnaires or tests are used, it can usually be assumed that a sum score will be created based on the responses to the individual questions, or the performances in the individual tests. Sum scores are used wherever a group of individual tests provide information about an underlying key quality (also called a “latent” variable) that cannot be measured directly. The test is often specific to an individual population. To understand the purpose of sum scores, it is useful to consider one or two different examples.

Example sum score: anxiety

The Hamilton Anxiety Rating Scale (HAM-A) was first presented in 1959 by Max Hamilton (Hamilton, 1959) in order to provide a clinician-rated assessment of anxiety in the general population. It comprises 14 items that are each scored on a scale of 0 (not present), 1 (mild presence), 2 (moderate presence), 3 (severe presence), and 4 (very severe presence). The sum score is then calculated out of a possible total 56 points. The bands are provided as <17 indicates mild anxiety severity, 18 – 24 mild-to- moderate anxiety severity and 25 – 30 moderate-to- severe. The 14 questions include assessments of: anxious mood, tension, fears, insomnia, cognitive impairment, depression, musculoskeletal discomfort, heightened sensations, increased cardiovascular activity, respiratory constriction, gastrointestinal problems, genitourinary symptoms, increased sympathetic activity, and restless behavior. All of these individual tests are direct measurements of a specific symptom in their own right, for any population. However, they also provide secondary information about the presence of an underlying or “latent” variable, which is anxiety.

Example sum score: disability

The Health Assessment Questionnaire Disability Index (HAQ-DI), which was developed by Fries et al. (1980) to measure the status of patients with rheumatic conditions, is now commonly used to measure the extent of disability in many different populations. The HAQ-DI comprises 20 questions relating to the specific limitations that individuals experience when carrying out activities of daily living, where the answers are scored from 0 (no difficulty) to 3 (impossible). The questions are grouped into 8 categories and the highest score in each category is used to form the answer to the sub-scale, subject to a follow-up question regarding the use of assistance or aids, which leads to an added point on the score for that sub-scale. The sub-scales are: rising, walking, dressing and grooming, reaching, eating, grip, activities, and hygiene. The sub-scale scores are averaged to produce the sum score. All of these individual tests are direct measurements of a specific symptom in their own right but they also provide secondary information about the presence of an underlying or “latent” variable, which is disability during activities of daily living, in patients with certain conditions.

Measuring validity of sum scores

There are two main ways of measuring the validity of sum scores. Firstly, we can calculate Cronbach’s alpha, which is essentially a measure of internal consistency (Bland and Altman, 1997). Secondly, we can conduct exploratory factor analysis. Cronbach’s alpha is a standardized index of a scale’s reliability based on the  consistency between the individual test scores. Cronbach’s alpha therefore provides an estimate of the proportion of variance that is caused by a common, underlying or “latent” variable. If Cronbach’s alpha produces a low result, we can assume that the individual test scores do not provide information about the same underlying variable, which in the case of the FMS is the presence of compensatory movement patterns. Several categories for interpreting the results of Cronbach’s alpha but it is generally regarded that <0.5 is an unacceptable level of internal consistency, 0.5 – 0.6 is poor, 0.6 – 0.7 is acceptable, 0.7 – 0.9 is good, 0.9 is very good (Bland and Altman, 1997; DeVellis, 2012; Kazman et al. 2014).

Construct validity of the FMS sum score

In order to use the FMS sum score, we must therefore see some internal consistency between the test results of the FMS, as measured by Cronbach’s Two studies have assessed the construct validity results for the FMS sum score by reference to Cronbach’s alpha and found it poor (0.39 – 0.58). Frohm et al. (2010) also assessed the construct validity of the FMS sum score by reference to Cronbach’s alpha (α = 0.43).

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

It seems that the designers of the FMS have noted the recent research relating to the use of the FMS sum score. In their recent review of the literature relating to the validity of the FMS, Cook et al. (2014b) state: “the use of a total FMS score for predicting injury risk should be avoided, as the individual components of the test are not correlated with one another and are therefore not measuring the same underlying variable.” This statement appears to acknowledge the findings of the above research by Kazman et al. (2014) and Li et al. (2014) in respect of the interpretation of the FMS sum score.


CRITERION REFERENCE VALIDITY

Introduction

Convergent criterion-referenced validity describes the extent to which a test is correlated with other gold-standard tests (Baechle and Earle, 2008). For example, when measuring muscle size, new tests are judged based on their ability to produce measurements for cross-sectional area or volume relative to either computed tomography (CT) or magnetic resonance imaging (MRI) scans.

.

Criterion reference validity of the FMS

The criterion reference validity of the FMS has been assessed by Whiteside et al. (2014). The investigators compared the results of FMS scores assigned by a certified FMS rater (the standard testing procedure) to those measured by an objective inertial-based motion capture system (the criterion reference). For this purpose, the researchers had to devise specific thresholds for each of the joint angles that corresponded to each of the set grading criteria. However, the researchers found that there was poor agreement between the standard grading method (by the rater) and the criterion reference method (objective inertial-based motion capture system). Whiteside et al. (2014) concluded that the FMS did not satisfy their test of criterion reference validity.


CONTENT VALIDITY: INJURY RISK

Introduction

Content validity describes the extent to which a test includes all those aspects that it might be expected to include in order to measure what it purports to measure (Baechle and Earle, 2008).

Assessing content validity of the FMS

The content validity of the FMS for assessing injury risk has been called into question on the basis that it does not include several factors that are thought to contribute to injury risk, including cognition, neural pathways, and proprioception (Whiteside et al. 2014).

CONCURRENT VALIDITY: INJURY RISK

Introduction

Concurrent validity describes the extent to which a test is correlated with other similar tests (Baechle and Earle, 2008). The FMS is primarily used as an injury prevention screen. However, there are unfortunately very few strong predictors of injury in sport. When Dallinga et al. (2012) reviewed the literature in respect of the tests that could predict a greater risk of injury, they reported that general joint laxity, the Star Excursion Balance test, age, a lower hamstring-to-quadriceps strength ratio, and a reduced hip abduction range of motion could all predict a higher risk of lower body injuries. Murphy et al. (2003) reviewed the literature in respect of the intrinsic risk factors for increased risk of lower body injury and reported that there is strong evidence that previous injury combined with inadequate rehabilitation is a risk factor for re-injury of the same type and location. Indeed, many studies have found that previous injury is a good predictor of future injury risk (Dvorak et al. 2000; Arnason et al. 2004; Kucera et al. 2005; Hägglund et al. 2006). Indeed, when McCall et al. (2015) reviewed the literature in respect of soccer, they gave the highest grading to previous injury out of all the factors investigated.

Concurrent validity of the FMS

Previous injury

The FMS sum score is not associated with incidence of previous injury. Schneiders et al. (2011) assessed the FMS sum score in 209 subjects (108 females and 101 males), of whom a proportion had sustained an injury in the preceding 6 months but were currently injury free. There was no significant difference in the FMS sum score between individuals who had an injury during the 6 last months and for those who had not. Agresta et al. (2014) assessed the FMS sum score in 45 healthy runners (24 males and 21 females). There was no significant difference in the FMS sum score between individuals who had an injury during the 12 last months and for those who had not (13.6 ± 0.4 vs. 12.9 ± 0.3 points).

General joint laxity

The FMS sum score is not associated with measures of general joint laxity. Paszkewicz et al. (2013) investigated the association between total FMS score and Beighton and Horan joint mobility index (BHJMI) in 66 adolescent athletes aged 8 – 14 years. They found no correlation between total FMS score and BHJMI index.

Postural stability and balance

The FMS sum score is not associated with measures of postural stability and balance, irrespective of the method used to measure these outcome measures. Clifton et al. (2013) recorded FMS sum scores and changes in measures of static balance as a result of exercise, as measured by center of pressure. They found no correlations between pre-exercise FMS scores and the changes in center of pressure that occurred following exercise. This implies that the FMS was not sensitive enough to detect the reductions in postural stability that occurred following fatiguing exercise and which are thought to be involved in increasing injury risk. Perry and Koehle (2013) assessed performance on the Balance Error Scoring System (BESS) in 116 subjects and the association with the FMS sum scores. The BESS is regarded as a moderately reliable measure of postural stability, which correlates well with criterion reference measures (see review by Bell et al. 2011). Perry and Koehle (2013) found no significant association between performance on the BESS and on the FMS sum score.

Risky movement behavior

Certain biomechanical movement patterns have been identified as predisposing individuals to greater risk of injury. For example, in respect of anterior cruciate ligament (ACL) injury, some investigations have indicated that greater knee valgus leads to greater risk of injury (see review by Murphy et al. 2003; Hewett et al. 2005). In addition, certain spine movements have also been identified as potentially risky for disc herniations (Marshall and McGill, 2010). Consequently, Frost et al. (2014) assessed the relationship between the FMS sum score and both spine (flexion-extension, lateral bend, and axial twist) and frontal plane knee motions that have previously been identified as risky. They reported that although individuals who scored >14 points on the FMS sum score displayed significantly less spine and frontal plane knee motion, there was substantial inter-individual variation in the display of these biomechanical characteristics between the high and low scorers on the FMS sum score.

CONTENT VALIDITY: MOVEMENT EFFICIENCY

Introduction

Content validity describes the extent to which a test includes all those aspects that it might be expected to include in order to measure what it purports to measure (Baechle and Earle, 2008).

Content validity of the FMS

The content validity of the FMS for assessing movement efficiency might be called into question on the basis that it does not directly measure whether a given movement is more or less efficient as it only assesses the kinematics (joint angle movements) and not the kinetics (force-related variables). It is therefore unclear whether a movement that scores poorly on the FMS is actually objectively less efficient (i.e. requires less energy) than a movement that scores well. For example, it is commonly claimed that round-back deadlifts are more efficient (i.e. require less force to lift the same weight) than straight-back deadlifts and indeed they do seem to be common in maximal lifts (Hales, 2010). However, straight-back deadlifts are usually regarded as displaying a better quality of movement. The appearance of a movement may therefore be unrelated to its efficiency.

CONCURRENT VALIDITY: MOVEMENT EFFICIENCY

Introduction

Concurrent validity describes the extent to which a test is correlated with other similar tests (Baechle and Earle, 2008). The FMS is primarily used as an injury prevention screen. However, it is also intended to measure the efficiency of movement. Athletes who display greater movement efficiency would be expected to display superior athletic performance measures.

Concurrent validity of the FMS

There are many good predictors of athletic performance in sport, including sprint running, vertical jumping, throwing, and agility. In addition, athletic performance can be assessed by reference to athletic ability level (i.e. elite vs. sub-elite). Several researchers have assessed the FMS sum scores of different ability levels of athlete and all have found no differences between levels (Fox et al. 2013; Grygorowicz et al. 2013; and Loudon et al. 2014). In addition, many investigations have been performed to assess the correlations between sprint running, jumping, throwing and agility performances and FMS score and most have found no relationship between any measure of athletic performance and FMS score (Okada et al. 2011; Parchman et al. 2011; Lockie et al. 2015b). However, some studies indicate that there may be a relationship in certain populations, particularly youth (e.g. Lloyd et al. 2015).

SECTION CONCLUSIONS

The FMS displays poor construct validity, poor criterion reference validity, poor content validity in respect of both injury risk and movement efficiency, and poor concurrent validity in respect of both injury risk and movement efficiency.

Top · Contents · References


INJURY PREDICTIVE ABILITY OF THE FMS

PURPOSE

This section sets out a summary of the research into the predictive ability of the FMS sum score for injury risk.

BACKGROUND

Introduction

The FMS has been advocated as a pre-participation screen suitable for providing an indication of injury risk (Cook, 2004; Cook et al. 2006a; Cook et et al. 2014). In a recent review, Cook et al. (2014a) state that screening using a tool like the FMS can help to identify weak links and that “if this weak link is not identified, the body will compensate, causing inefficient movements. It is this type of inefficiency that can cause a decrease in performance and an increase in injuries.” This therefore implies that athletes with higher FMS sum scores will display more efficient movement patterns and be less likely to experience injuries. The ability of a screening tool to assess injury risk is conventionally measured by several different statistics, including the odds ratio, relative risk, specificity and sensitivity, among others.

Definitions

Odds ratios

In general, prospective cohort trials measuring the ability of a screen like the FMS to predict injury risk use either odds ratios or relative risks. These are very different calculations and should not be confused. Odds ratios simply measure the probability of an event occurring relative to the probability that it does not occur (Bland and Altman, 2000).

Relative risk

Relative risk is the multiple of the risk of an outcome occurring in one group as a result of an exposure, compared to the risk of the same outcome in another group (Zhang and Yu, 1998). The main difference between odds ratios and relative risks is that relative risks also take into account the amount of exposure. In general, relative risks are considered to be a slightly superior way of assessing risk factors. Typically, the relative risk is lower than an odds ratio.

Sensitivity

Sensitivity is the percentage of subjects who have an underlying predisposition to injury and who are correctly identified beforehand by the test (Florkowski, 2008). Sensitivity is sometimes called a test of “true positives” because it rates a test solely based on its ability to flag positive results. Where sensitivity is high, there is little chance of failing to detect an underlying predisposition to injury. However, a test with good sensitivity can still be poor at clearing subjects who do not have an underlying predisposition to injury.

Specificity

Specificity refers to the percentage of subjects who do not incur an injury and who are correctly identified by a test (Florkowski, 2008). Specificity is sometimes called a test of “true negatives” because it rates a test solely based on its ability to flag negative results. Where specificity is high, there is little chance of failing to clear a subject who is not at risk. A test with good specificity can therefore still be very poor at identifying subjects who are actually at risk.

Receiver Operating Characteristic (ROC)

Most studies exploring the injury risk of the FMS sum score make use of cut-off values. The most commonly-used cut-off value is to differentiate between individuals scoring above or below 14 points. Cut-off values are often determined statistically using a method known as a Receiver Operating Characteristic (ROC) curve. This technique allows the identification of the number of points that maximizes the correct prediction of injury classification, by compromising the point between sensitivity and specificity (Zweig and Campbell, 1993). However, not all researchers investigating the ability of the FMS sum score have used ROC curves to identify the cut-off points to report. Since several influential early studies identified 14 points as the cut-off point and perhaps also since 14 points has the benefit of being a logical value (14 points = 2 points per test on the 7 tests), some researchers have simply adopted this as the cut-off point to use (e.g. Chorba et al. 2010; Kiesel et al. 2014).

Literature usage

Several reviews have assessed the ability of the FMS sum score to predict injury. McCall et al. (2015) performed a systematic review of the scientific level of evidence for the three most commonly-reported risk factors, screens, and injury prevention exercises in a previously published survey of 44 premier league soccer teams. The FMS was one of the identified screens. They assigned the FMS a grade D, where D = insufficient evidence to assign a specific recommendation. However, this was for a very specific sporting population. Kraus et al. (2014) performed a systematic review of the literature regarding the ability of the FMS to assess injury risk and concluded that it was able to function as an injury risk screen in collision sports, team sports, firefighters and tactical professions. Krumrei et al. (2014) performed a clinical review of the ability of the FMS sum score to predict injury risk and concluded that “preliminary, moderate-quality evidence suggests that the FMS can accurately identify individuals with an elevated risk of musculoskeletal injury amongst male professional football players, male marine officer candidates, and female collegiate basketball, soccer, or volleyball players.” Beardsley and Contreras (2014) also confirmed that the FMS has some predictive ability for identifying individuals at greater risk of musculoskeletal injury.

INJURY PREDICTIVE ABILITY: 14 POINT CUT-OFF

Selection criteria

Population – any

Intervention – the FMS tests as typically prescribed

Comparator – groups of individuals allocated according to their FMS sum score (>14 points and <14 points)

Outcome – injury incidence recorded over a long-term period of time

Results

The following studies were identified: Kiesel (2007), Chorba (2010), O’Connor (2011), Butler (2013), Kiesel (2014), Warren (2014), Knapik (2014), Garrison (2015).

Findings

In these studies, relative risks ranged from 1.6 – 4.2 and odds ratios ranged from 1.0 – 11.7 times. These studies indicate that the FMS sum score appears to be able to predict injury incidence based upon a cut-point of 14 points.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

INJURY PREDICTIVE ABILITY: 17 POINT CUT-OFF

Selection criteria

Population – any

Intervention – the FMS tests as typically prescribed

Comparator – groups of individuals allocated according to their FMS sum score (>17 points and <17 points)

Outcome – injury incidence recorded over a long-term period of time

Results

The following studies were identified: Peate (2007), Shojaedin (2013), Letafatkar (2014), Knapik (2014).

Findings

In these studies, the relative risks ranged from 1.3 – 1.8 and odds ratios ranged from 0.8 – 4.7 times. These studies indicate that the FMS sum score might be able to predict injury incidence based upon a cut-point of 17 points but the results are less conclusive than those using 14 points as the cut-off.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

SECTION CONCLUSIONS

Many investigations have found that the odds of experiencing an injury are higher in subjects who score <14 points on the FMS sum score. However, there is considerable variation between studies in the reported results.

Top · Contents · References


ATHLETIC PERFORMANCE PREDICTIVE ABILITY OF THE FMS

PURPOSE

This section sets out a summary of the research into the predictive ability of the FMS sum score for athletic performance.

BACKGROUND

Introduction

Although it is more widely known as a pre-participation screen intended to provide an indication of injury risk, the FMS is also intended to measure efficiency of movement (Cook, 2004; Cook et al. 2006a; Cook et et al. 2014). In a recent review, Cook et al. (2014a) state that screening using a tool like the FMS can help to identify weak links and that “if this weak link is not identified, the body will compensate, causing inefficient movements. It is this type of inefficiency that can cause a decrease in performance…” This therefore implies that athletes with higher FMS sum scores will display more efficient movement patterns and should therefore be statistically more likely to be (a) better athletes, and (b) perform better in athletic tasks, such as sprint running and vertical jumping.

Literature usage

The ability of the FMS to predict injury been assessed by several reviewers (Beardsley & Contreras, 2014; Kraus et al. 2014). Both Beardsley & Contreras (2014) and Krumrei et al. (2014) noted that the evidence for the FMS as a test able to predict athletic performance was extremely limited.

ATHLETIC PERFORMANCE LEVEL PREDICTIVE ABILITY

Selection criteria

Population – athletes

Intervention – the FMS tests as typically prescribed

Comparator – groups of individuals allocated according to their athletic performance level (elite vs. sub-elite performance)

Outcome – differences in FMS sum score between groups

Results

The following studies were identified: Fox (2013), Grygorowicz (2013), Loudon (2014).

Findings

In these studies, it was found that FMS sum scores were not higher in elite athletes than in sub-elite athletes.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

ATHLETIC PERFORMANCE TEST PREDICTIVE ABILITY

Selection criteria

Population – athletes

Intervention – the FMS tests as typically prescribed; various athletic performance tests

Comparator – groups of individuals allocated according to their athletic performance ability (as measured by a test)

Outcome – relationships between FMS sum score and athletic performance test measurements

Results

The following 6 studies were identified (click to read): Okada (2011), Parchman (2011), Chapman (2014), Teyhen (2014), Lockie (2015b), Lloyd (2015).

Findings

There were no clear relationships between FMS sum score and any of the performance measures (e.g. jumping height or sprinting times), although there were correlations between some of the individual FMS test scores and performance measures. This suggests that the FMS sum score is not measuring “efficiency” as better performers will necessarily be more efficient in their movement patterns, as this is part of what makes them a superior athlete.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

Individual FMS tests

Several investigations have assessed the relationship between individual FMS tests and tests of athletic performance. Hartigan et al. (2014) assessed whether there were relationships between the in-line lunge of the FMS and measures of balance, power, and speed in 37 healthy, active adult subjects. They tested performance in unilateral drop jumps and 36.6m sprint running as well as in the in-line lunge according to FMS guidelines. However, they found no associations between either of these performance variables and the in-line lunge test scores. Lockie et al. (2015a) recorded performance in 18 tests of power, strength, flexibility, balance, as well as scores in each of the individual FMS tests, in 9 female team sports athletes. They found that unilateral sit-and-reach test was associated with the left-leg active straight-leg raise test score (r = 0.70 – 0.73). On the other hand, they noted that higher-scoring hurdle step, in-line lunge, and active straight-leg raise test scores were associated with to worse 505 and T-test performances (r = 0.72 – 0.83). Finally, they noted that a higher-scored left-leg active straight-leg raise test score was associated with worse unilateral vertical and standing broad jump performances. In contrast, Lloyd et al. (2015) assessed whether there were relationships between each of the individual FMS test scores and squat jump height, reactive strength index, and reactive agility test in 30 young male soccer players, aged 11 – 16 years. They found that most of the individual FMS tests displayed significant moderate-to-strong relationships (r = 0.4 –0.7) with one or all of the performance tests.

SECTION CONCLUSIONS

Although it has been suggested to measure efficiency of movement by the designers of the test (and this has been interpreted to mean also athletic performance), the FMS sum score is not associated with either level of athletic performance or ability in athletic tasks, such as sprint running, agility or jumping.

Top · Contents · References


EFFECT OF EXERCISE ON FMS SUM SCORES

PURPOSE

This section sets out a summary of the research into the effects of exercise on FMS sum scores.

BACKGROUND

Introduction

There are two areas of interest when considering the effect of exercise on FMS sum scores. Firstly, it is of interest whether any type of exercise can improve FMS scores. This would be logical given that physical activity levels are positively associated with higher FMS sum scores (Duncan et al. 2012; Perry and Koehle, 2013). Secondly, it is of interest whether corrective exercise programs devised based on an individual’s FMS test performance can improve FMS sum score to a greater extent than any other type of exercise. This is particularly important if the FMS is to be used as a valid screen. According to Wilson and Jungner (1968), in order to use a screen, it should always have an acceptable treatment that can be applied when the screen produces a positive result.

How big do changes need to be to be considered real?

As reported in the reliability section above, the SEM of the FMS sum score in the literature is generally <1.0 points. This means that we can be confident that the true FMS sum score value of a given test will fall within ±2.0 points, 95% of the time. The MD of the FMS sum score in the literature is generally <3.0 points. This means that when we take repeated measures of the FMS sum score to assess whether an athlete or individual has improved their functional movement, we can be confident that a real change has occurred when the improvement is ±6.0 points. Therefore, where studies exploring changes in FMS sum score find differences that are <6.0 points, we should be cautious. Such changes could easily be caused by rater error and therefore produced by chance rather than by the exercise program itself.

Literature usage

Several researchers have reviewed the ability of exercise programs to affect the FMS sum score (Beardsley & Contreras, 2014; Minthorn et al. 2014; Kraus et al. 2014). Minthorn et al. (2014) specifically reviewed the ability of corrective exercise programs to increase FMS sum score and reported that  there was only “inconsistent evidence that an individualized training program may improve movement pattern limitations, as identified by the FMS” while Beardsley and Contreras (2014) noted that while exercise in general appears to improve FMS sum scores, there is no evidence that a specific or “more functional” exercise program is superior to a standard exercise program. While Kraus et al. (2014) concluded that “some investigations have shown that FMS based-training programs can lead to reduced  functional imbalances and enhance general motor control in professional, recreational sports, firefighters and military,” they did not state whether there was any indication that such training programs might be superior to standard exercise programs.

EFFECTS OF EXERCISE ON FMS SUM SCORES

Selection criteria

Population – any

Intervention – a long-term exercise program of any kind

Comparator – baseline or a non-training control group

Outcome – changes in FMS sum score

Results

The following studies were identified: Goss (2009), Cowen (2010), Kiesel (2011), Frost (2012), Klusemann (2012), Pacheco (2013), Bodden (2014), Beach (2014), Wright (2015).

Findings

Of these 9 studies, 6 found that exercise improved FMS sum scores, which is logical given that physical activity levels have been found to correlate with FMS sum score. If a greater FMS sum score is desirable, then exercise appears to be an effective way to achieve that goal.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

EFFECT OF CORRECTIVE EXERCISE PROGRAMS FOR IMPROVING FMS SUM SCORES

Selection criteria

Population – any

Intervention – a long-term corrective exercise program

Comparator – a non-corrective “general exercise” control group

Outcome – changes in FMS sum score

Results

The following studies were identified: Kiesel (2011), Frost (2012), Bodden (2014).

Findings

Of these 3 studies, only 1 had a comparative general exercise control group. This trial found no differences in the changes in FMS sum scores between the corrective exercise and the general exercise group. The other 2 studies found that corrective exercise improved FMS sum score. However, whether they would have improved relative to a general exercise control group is unclear. It is therefore currently the state of the evidence that corrective exercise is no more effective than general exercise for changing FMS sum scores.

To open a new window and view detailed information in a large table, click HERE (not recommended for small screens)

SECTION CONCLUSIONS

Various different exercise programs appear to improve FMS sum score, including yoga, resistance training, functional training, and general military training. However, since the improvements are lower than the mean difference to be considered real, care should be taken in interpreting the results.

Top · Contents · References


REFERENCES

  1. Abraham, A., Sannasi, R., & Nair, R. (2015). Normative values for the Functional Movement ScreenTM in adolescent school-aged children. International journal of sports physical therapy, 10(1), 29.[PubMed]
  2. Agresta, C., Slobodinsky, M., & Tucker, C. (2014). Functional Movement Screen TM–Normative Values in Healthy Distance Runners. International journal of sports medicine, 35(14), 1203-1207.[PubMed]
  3. Anderson, B. E., Neumann, M., & Huxel, B. K. (2014). Functional Movement Screen™ Differences Between Male and Female Secondary School Athletes. Journal of strength and conditioning research.[PubMed]
  4. Arnason, A., Sigurdsson, S. B., Gudmundsson, A., Holme, I., Engebretsen, L., & Bahr, R. (2004). Risk factors for injuries in football. The American Journal of Sports Medicine, 32(1 suppl), 5S-16S.[PubMed]
  5. Baechle, T. R., & Earle, R. W. (Eds.). (2008). Essentials of Strength Training and Conditioning. Human kinetics, p239 – 40.
  6. Beardsley, C., & Contreras, B. (2014a). The Functional Movement Screen: A Review. Strength & Conditioning Journal, 36(5), 72-80.[Citation]
  7. Beardsley, C., & Contreras, B. (2014b). The increasing role of the hip extensor musculature with heavier compound lower-body movements and more explosive sport actions. Strength & Conditioning Journal, 36(2), 49-55.[Citation]
  8. Bell, D. R., Guskiewicz, K. M., Clark, M. A., & Padua, D. A. (2011). Systematic review of the balance error scoring system. Sports Health: A Multidisciplinary Approach, 3(3), 287-295.[PubMed]
  9. Beach, T. A., Frost, D. M., McGill, S. M., & Callaghan, J. P. (2014). Physical fitness improvements and occupational low-back loading–an exercise intervention study with firefighters. Ergonomics, 57(5), 744-763.[PubMed]
  10. Bland, J. M., & Altman, D. G. (1997). Statistics notes: Cronbach’s alpha. Bmj, 314(7080), 572.[PubMed]
  11. Bland, J. M., & Altman, D. G. (2000). The odds ratio. Bmj, 320(7247), 1468.[PubMed]
  12. Bodden, J. G., Needham, R. A., & Chockalingam, N. (2014). The effect of an intervention program on functional movement screen test scores in mixed martial arts athletes. The Journal of Strength & Conditioning Research. [PubMed]
  13. Boyle, M. J., Butler, R. J., & Queen, R. M. (2015). Functional Movement Competency and Dynamic Balance After Anterior Cruciate Ligament Reconstruction in Adolescent Patients. Journal of Pediatric Orthopaedics.[PubMed]
  14. Bradley, H., & Esformes, J. D. (2014). Breathing pattern disorders and functional movement. International journal of sports physical therapy, 9(1), 28.[PubMed]
  15. Brockett, C. L., Morgan, D. L., & Proske, U. (2004). Predicting hamstring strain injury in elite athletes. Medicine & science in sports & exercise, 36(3), 379.[PubMed]
  16. Burton, R., Elkins, K., Kiesel, K. B., & Plisky, P. J. (2009). Gender differences in functional movement screen and Y-balance test scores in middle aged school children. Medicine & Science in Sports & Exercise, 41, 183.
  17. Butler, R. J., Contreras, M., Burton, L. C., Plisky, P. J., Goode, A., & Kiesel, K. (2013). Modifiable risk factors predict injuries in firefighters during training academies. Work: A Journal of Prevention, Assessment and Rehabilitation, 46(1):11-7.[PubMed]
  18. Butler, R. J., Plisky, P. J., & Kiesel, K. B. (2012). Interrater reliability of videotaped performance on the functional movement screen using the 100-point scoring scale. Athletic Training & Sports Health Care 2012a, 4(3), 103-109.[Citation]
  19. Chapman, R. F., Laymon, A. S., & Arnold, T. (2014). Functional Movement Scores and Longitudinal Performance Outcomes in Elite Track and Field Athletes. International Journal of Sports Physiology and Performance. [PubMed]
  20. Chorba, R. S., Chorba, D. J., Bouillon, L. E., Overmyer, C. A., & Landis, J. A. (2010). Use of a functional movement screening tool to determine injury risk in female collegiate athletes. North American Journal of Sports Physical Therapy: NAJSPT, 5(2), 47.[PubMed]
  21. Cook, G. Athletic body in balance – optimal movement skills and conditioning for performance. (2004). Human Kinetics, 27, 28.
  22. Cook, G., Burton, L., Fields, K., & Kiesel, K. (1998). The functional movement screen. Danville, VA: Athletic Testing Services, Inc.
  23. Cook, G., Burton, L., & Hoogenboom, B. (2006a). Pre-participation screening: The use of fundamental movements as an assessment of function–Part 1. North American Journal of Sports Physical Therapy: NAJSPT, 1(2), 62.[PubMed]
  24. Cook, G., Burton, L., & Hoogenboom, B. (2006b). Pre-participation screening: The use of fundamental movements as an assessment of function–Part 2. North American Journal of Sports Physical Therapy: NAJSPT, 1(3), 132.[PubMed]
  25. Cook, G., Burton, L., Hoogenboom, B. J., & Voight, M. (2014a). Functional movement screening: the use of fundamental movements as an assessment of function-part 1. International journal of sports physical therapy, 9(3), 396-409.[PubMed]
  26. Cook, G., Burton, L., Hoogenboom, B. J., & Voight, M. (2014b). Functional movement screening: the use of fundamental movements as an assessment of function-part 2. International journal of sports physical therapy, 9(4), 549-563.[PubMed]
  27. Cowen, V. S. (2010). Functional fitness improvements after a worksite-based yoga initiative. Journal of Bodywork and Movement Therapies, 14(1), 50-54.[PubMed]
  28. Dallinga, J. M., Benjaminse, A., & Lemmink, K. A. (2012). Which Screening Tools Can Predict Injury to the Lower Extremities in Team Sports?. Sports medicine, 42(9), 791-815.[PubMed]
  29. DeVellis. (2012). Scale development: Theory and applications (Vol. 26). Sage publications.
  30. Dossa, K., Cashman, G., Howitt, S., West, B., & Murray, N. (2014). Can injury in major junior hockey players be predicted by a pre-season functional movement screen–a prospective cohort study. The Journal of the Canadian Chiropractic Association, 58(4), 421.[PubMed]
  31. Duncan, M. J., & Stanley, M. (2012). Functional Movement Is Negatively Associated with Weight Status and Positively Associated with Physical Activity in British Primary School Children. Journal of Obesity, 2012.[PubMed]
  32. Duncan, M. J., Stanley, M., & Wright, S. L. (2013). The association between functional movement and overweight and obesity in British primary school children. BMC Sports Science, Medicine and Rehabilitation, 5(1), 11.[PubMed]
  33. Dvorak, J., Junge, A., Chomiak, J., Graf-Baumann, T., Peterson, L., Rösch, D., & Hodgson, R. (2000). Risk factor analysis for injuries in football players possibilities for a prevention program. The American Journal of Sports Medicine, 28(suppl 5), S-69.[PubMed]
  34. Elias, J. E. (2013). The Inter-rater Reliability of the Functional Movement Screen within an athletic population using Untrained Raters. The Journal of Strength & Conditioning Research. [PubMed]
  35. Faude, O., Junge, A., Kindermann, W., & Dvorak, J. (2006). Risk factors for injuries in elite female soccer players. British Journal of Sports Medicine, 40(9), 785-790.[PubMed]
  36. Finnoff, J. T., Peterson, V. J., Hollman, J. H., & Smith, J. (2009). Intrarater and interrater reliability of the Balance Error Scoring System (BESS). PM&R, 1(1), 50-54.[PubMed]
  37. Florkowski, C. M. (2008). Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. The Clinical Biochemist Reviews, 29(Supplement (i)), S83.[PubMed]
  38. Fox, D., O’Malley, E., & Blake, C. (2014). Normative Data for the Functional Movement Screen™ in Male Gaelic Field Sports. Physical Therapy in Sport. [PubMed]
  39. Frohm, A., Heijne, A., Kowalski, J., Svensson, P., & Myklebust, G. (2012). A nine‐test screening battery for athletes: a reliability study. Scandinavian Journal of Medicine & Science in Sports, 22(3), 306-315.[PubMed]
  40. Frost, D. M., Beach, T. A., Callaghan, J. P., & McGill, S. M. (2012). Using the Functional Movement Screen™ to evaluate the effectiveness of training. The Journal of Strength & Conditioning Research, 26(6), 1620-1630.[PubMed]
  41. Frost, D. M., Beach, T. A., Callaghan, J. P., & McGill, S. M. (2013a). The influence of load and speed on individuals’ movement behavior. The Journal of strength and conditioning research. [PubMed].
  42. Frost, D. M., Beach, T. A., Callaghan, J. P., & McGill, S. M. (2013b). FMS™ scores change with performers’ knowledge of the grading criteria-Are general whole-body movement screens capturing” dysfunction”? The Journal of Strength & Conditioning Research.[PubMed].
  43. Frost, D. M., Beach, T. A., Campbell, T. L., Callaghan, J. P., & McGill, S. M. (2015). An appraisal of the Functional Movement Screen™ grading criteria–Is the composite score sensitive to risky movement behavior?. Physical Therapy in Sport.[Citation]
  44. Galli, M., Crivellini, M., Sibella, F., Montesano, A., Bertocco, P., & Parisio, C. (2000). Sit-to-stand movement analysis in obese subjects. International journal of obesity and related metabolic disorders, 24(11), 1488-1492.[PubMed]
  45. Garrison, M., Westrick, R., Johnson, M. R., & Benenson, J. (2015). Association between the Functional Movement Screen and injury development in college athletes. International journal of sports physical therapy, 10(1), 21.[PubMed]
  46. Gilleard, W., & Smith, T. (2006). Effect of obesity on posture and hip joint moments during a standing task, and trunk forward flexion motion. International Journal of Obesity, 31(2), 267-271.[PubMed]
  47. Goss, D. L., Christopher, G. E., Faulk, R. T., & Moore, J. (2009). Functional training program bridges rehabilitation and return to duty. Journal of Special Operations Medicine: a Peer Reviewed Journal for SOF Medical Professionals, 9(2), 29.[PubMed]
  48. Gribble, P. A., Brigle, J., Pietrosimone, B. G., Pfile, K. R., & Webster, K. A. (2013). Intrarater reliability of the functional movement Screen. The Journal of Strength & Conditioning Research, 27(4), 978-981.[PubMed]
  49. Grygorowicz, M., Piontek, T., & Dudzinski, W. (2013). Evaluation of Functional Limitations in Female Soccer Players and Their Relationship with Sports Level–A Cross Sectional Study. PloS one, 8(6), e66871.[PubMed]
  50. Gulgin, H., & Hoogenboom, B. (2014). The Functional Movement Screening (FMS)™: An inter‐rater reliability study between raters of varied experience. International journal of sports physical therapy, 9(1), 14.[PubMed]
  51. Hägglund, M., Waldén, M., & Ekstrand, J. (2006). Previous injury as a risk factor for injury in elite football: a prospective study over two consecutive seasons. British journal of sports medicine, 40(9), 767-772.[PubMed]
  52. Hales, M. (2010). Improving the deadlift: Understanding biomechanical constraints and physiological adaptations to resistance exercise. Strength & Conditioning Journal, 32(4), 44-51.[Citation]
  53. Hamilton, M. A. X. (1959). The assessment of anxiety states by rating. British journal of medical psychology, 32(1), 50-55.[PubMed]
  54. Hartigan, E. H., Lawrence, M., Bisson, B. M., Torgerson, E., & Knight, R. C. (2014). Relationship of the Functional Movement Screen In-Line Lunge to Power, Speed, and Balance Measures. Sports Health: A Multidisciplinary Approach, 1941738114522412.[PubMed]
  55. Hayes, A. F., & Krippendorff, K. (2007). Answering the call for a standard reliability measure for coding data. Communication Methods and Measures, 1(1), 77-89.[Citation]
  56. Hills, A. P., Hennig, E. M., Byrne, N. M., & Steele, J. R. (2002). The biomechanics of adiposity–structural and functional limitations of obesity and implications for movement. Obesity reviews: An Official Journal of the International Association for the Study of Obesity, 3(1), 35-43.[PubMed]
  57. Hewett, T. E., Myer, G. D., Ford, K. R., Heidt, R. S., Colosimo, A. J., McLean, S. G., & Succop, P. (2005). Biomechanical measures of neuromuscular control and valgus loading of the knee predict anterior cruciate ligament injury risk in female athletes a prospective study. The American journal of sports medicine, 33(4), 492-501.[PubMed]
  58. Kale, M., Asçi, A., Bayrak, C., & Açikada, C. (2009). Relationships among jumping performances and sprint parameters during maximum speed phase in sprinters. The Journal of Strength & Conditioning Research, 23(8), 2272.[PubMed]
  59. Kazman, J. B., Galecki, J. M., Lisman, P., Deuster, P. A., & OʼConnor, F. G. (2014). Factor structure of the functional movement screen in marine officer candidates. Journal of strength and conditioning research, 28(3), 672-678.[PubMed]
  60. Karras, D. J. (1997a). Statistical Methodology: II. Reliability and Validity Assessment in Study Design, Part A. Academic Emergency Medicine, 4(1):64-71.[PubMed]
  61. Karras, D. J. (1997b). Statistical Methodology: II. Reliability and Validity Assessment in Study Design, Part B. Academic Emergency Medicine, 4(2):144-7 [PubMed]
  62. Kiesel, K., Plisky, P. J., & Voight, M. L. (2007). Can serious injury in professional football be predicted by a preseason functional movement screen? North American Journal of Sports Physical Therapy: NAJSPT, 2(3), 147.[PubMed]
  63. Kiesel, K., Plisky, P., & Butler, R. (2011). Functional movement test scores improve following a standardized off‐season intervention program in professional football players. Scandinavian Journal of Medicine & Science in Sports, 21(2), 287-292.[PubMed]
  64. Kiesel, K. B., Butler, R. J., & Plisky, P. J. (2014). Prediction of injury by limited and asymmetrical fundamental movement patterns in American football players. Journal of sport rehabilitation, 23(2), 88.[PubMed].
  65. Klusemann, M. J., Pyne, D. B., Fay, T. S., & Drinkwater, E. J. (2012). Online Video–Based Resistance Training Improves the Physical Capacity of Junior Basketball Athletes. The Journal of Strength & Conditioning Research, 26(10), 2677-2684.
  66. Knapik, J. J., Cosio-Lima, L. M., Reynolds, K. L., & Shumway, R. S. (2014). Efficacy of functional movement screening for predicting injuries in Coast Guard cadets. Journal of strength and conditioning research.[PubMed]
  67. Kraus, K., Schütz, E., Taylor, W. R., & Doyscher, R. (2014). Efficacy of the functional movement screen: A review. The Journal of Strength & Conditioning Research, 28(12), 3571-3584.[PubMed]
  68. Krumrei, K., Flanagan, M., Bruner, J., & Durall, C. (2014). The Accuracy of the Functional Movement Screen™ to Identify Individuals with an Elevated Risk of Musculoskeletal Injury. Journal of sport rehabilitation.[PubMed]
  69. Kucera, K. L., Marshall, S. W., Kirkendall, D. T., Marchak, P. M., & Garrett, W. E. (2005). Injury history as a risk factor for incident injury in youth soccer. British journal of sports medicine, 39(7), 462-462.[PubMed]
  70. Lehr, M. E., Plisky, P. J., Butler, R. J., Fink, M. L., Kiesel, K. B., & Underwood, F. B. (2013). Field‐expedient screening and injury risk algorithm categories as predictors of noncontact lower extremity injury. Scandinavian Journal of Medicine & Science in Sports, 23(4):e225-32.[PubMed]
  71. Letafatkar, A., Hadadnezhad, M., Shojaedin, S., & Mohamadi, E. (2014). Relationship between functional movement screening score and history of injury. International journal of sports physical therapy, 9(1), 21.[PubMed]
  72. Li, Y., Wang, X., Chen, X., & Dai, B. (2014). Exploratory factor analysis of the functional movement screen in elite athletes. Journal of sports sciences, (ahead-of-print), 1-7.[PubMed]
  73. Lisman, P., O’Connor, F. G., Deuster, P. A., & Knapik, J. J. (2012). Functional Movement Screen and Aerobic Fitness Predict Injuries in Military Training. Medicine and Science in Sports and Exercise, 45(4):636-43.[PubMed]
  74. Lloyd, R. S., Oliver, J. L., Radnor, J. M., Rhodes, B. C., Faigenbaum, A. D., & Myer, G. D. (2015). Relationships between functional movement screen scores, maturation and physical performance in young soccer players. Journal of sports sciences, 33(1), 11-19.[PubMed]
  75. Lockie, R. G., Schultz, A. B., Callaghan, S. J., Jordan, C. A., Luczo, T. M., & Jeffriess, M. D. (2015a). A preliminary investigation into the relationship between functional movement screen scores and athletic physical performance in female team sport athletes. Biol Sport, 32(1), 41-51.[Citation]
  76. Lockie, R. G., Schultz, A. B., Jordan, C. A., Callaghan, S. J., Jeffriess, M. D., & Luczo, T. M. (2015b). Can selected functional movement screen assessments be used to identify movement deficiencies that could affect multidirectional speed and jump performance?. The Journal of Strength & Conditioning Research, 29(1), 195-205.[PubMed]
  77. Loudon, J. K., Parkerson-Mitchell, A. J., Hildebrand, L. D., & Teague, C. (2014). Functional movement screen scores in a group of running athletes. The Journal of Strength & Conditioning Research, 28(4), 909-913.[PubMed]
  78. Marshall, L. W., & McGill, S. M. (2010). The role of axial torque in disc herniation. Clinical Biomechanics, 25(1), 6-9.[PubMed]
  79. McCall, A., Carling, C., Davison, M., Nedelec, M., Le Gall, F., Berthoin, S., & Dupont, G. (2015). Injury risk factors, screening tests and preventative strategies: a systematic review of the evidence that underpins the perceptions and practices of 44 football (soccer) teams from various premier leagues. British journal of sports medicine.[PubMed]
  80. McGill, S., Frost, D., Andersen, J., Crosby, I., & Gardiner, D. (2012). Movement quality and links to measures of fitness in firefighters. Work: A Journal of Prevention, Assessment and Rehabilitation, 45(3):357-66.[PubMed]
  81. Minick, K. I., Kiesel, K. B., Burton, L., Taylor, A., Plisky, P., & Butler, R. J. (2010). Interrater reliability of the functional movement screen. The Journal of Strength & Conditioning Research, 24(2), 479-486.[PubMed]
  82. Minthorn, L. M., Fayson, S. D., Stobierski, L. M., Welch, C. E., & Anderso, B. E. (2014). An Individualized Training Program May Improve Functional Movement Patterns Among Adults. Journal of sport rehabilitation.[PubMed]
  83. Mitchell, U. H., Johnson, A. W., & Adamson, B. (2015). Relationship between Functional Movement Screen Scores, Core Strength, Posture, and BMI in School Children in Moldova. The Journal of Strength & Conditioning Research.[PubMed]
  84. Murphy, D., Connolly, D., & Beynnon, B. (2003). Risk factors for lower extremity injury: a review of the literature. British Journal of Sports Medicine, 37(1), 13.[PubMed]
  85. O’Connor, F. G., Deuster, P. A., Davis, J., Pappas, C. G., & Knapik, J. J. (2011). Functional movement screening: predicting injuries in officer candidates. Medicine and Science in Sports and Exercise, 43(12), 2224-30.[PubMed]
  86. Okada, T., Huxel, K. C., & Nesser, T. W. (2011). Relationship between core stability, functional movement, and performance. The Journal of Strength & Conditioning Research, 25(1), 252-261.[PubMed]
  87. Onate, J. A., Dewey, T., Kollock, R. O., Thomas, K. S., Van Lunen, B. L., DeMaio, M., & Ringleb, S. I. (2012). Real-time intersession and interrater reliability of the functional movement screen. The Journal of Strength & Conditioning Research, 26(2), 408-415.[PubMed]
  88. Pacheco, M. M., Teixeira, L. A., Franchini, E., & Takito, M. Y. (2013). Functional vs. Strength training in adults: specific needs define the best intervention. International Journal of Sports Physical Therapy, 8(1), 34.[PubMed]
  89. Parchmann, C. J., & McBride, J. M. (2011). Relationship between functional movement screen and athletic performance. The Journal of Strength & Conditioning Research, 25(12), 3378-3384.[PubMed]
  90. Parenteau-G, E., Gaudreault, N., Chambers, S., Boisvert, C., Grenier, A., Gagné, G., & Balg, F. (2013). Functional movement screen test: A reliable screening test for young elite ice hockey players. Physical therapy in sport: official journal of the Association of Chartered Physiotherapists in Sports Medicine.[PubMed]
  91. Paszkewicz, J. R., & Cailee Welch McCarty, D. (2013). Comparison of Functional and Static Evaluation Tools Among Adolescent Athletes. The Journal of Strength & Conditioning Research. [PubMed].
  92. Peate, W. F., Bates, G., Lunda, K., Francis, S., & Bellamy, K. (2007). Core strength: A new model for injury prediction and prevention. Journal of Occupational Medicine and Toxicology, 2(3), 1-9.[PubMed]
  93. Perry, F. T., & Koehle, M. S. (2013). Normative data for the functional movement screen in middle-aged adults. The Journal of Strength & Conditioning Research, 27(2), 458-462.[PubMed]
  94. Portney, L. G., & Watkins, M. P. (2008). Foundations of clinical research: applications to practice. Prentice Hall, Upper Saddle River, NJ.[Citation]
  95. Ristanis, S., Stergiou, N., Patras, K., Vasiliadis, H. S., Giakas, G., & Georgoulis, A. D. (2005). Excessive tibial rotation during high-demand activities is not restored by anterior cruciate ligament reconstruction. Arthroscopy: The Journal of Arthroscopic & Related Surgery, 21(11), 1323-1329.[PubMed]
  96. Schneiders, A. G., Davidsson, Å., Hörman, E., & Sullivan, S. J. (2011). Functional movement screenTM normative values in a young, active population. International Journal of Sports Physical Therapy, 6(2), 75.[PubMed]
  97. Shojaedin, S. S., Letafatkar, A., Hadadnezhad, M., & Dehkhoda, M. R. (2013). Relationship between functional movement screening score and history of injury and identifying the predictive value of the FMS for injury. International Journal of Injury Control and Safety Promotion, (ahead-of-print), 1-6.[PubMed]
  98. Shultz, R., Anderson, S. C., Matheson, G. O., Marcello, B., & Besier, T. (2013). Test-Retest and Interrater Reliability of the Functional Movement Screen. Journal of athletic training, 48(3), 331-336.[PubMed]
  99. Sibella, F., Galli, M., Romei, M., Montesano, A., & Crivellini, M. (2003). Biomechanical analysis of sit-to-stand movement in normal and obese subjects. Clinical Biomechanics, 18(8), 745-750.[PubMed]
  100. Smith, C. A., Chimera, N. J., Wright, N. J., & Warren, M. (2013). Interrater and intrarater reliability of the functional movement screen. The Journal of Strength & Conditioning Research, 27(4), 982-987.[PubMed]
  101. Song, H. S., Woo, S. S., So, W. Y., Kim, K. J., Lee, J., & Kim, J. Y. (2014). Effects of 16-week functional movement screen training program on strength and flexibility of elite high school baseball players. Journal of exercise rehabilitation, 10(2), 124.[PubMed]
  102. Sprague, P. A., Mokha, G. M., & Gatens, D. R. (2014). Changes in functional movement screen scores over a season in collegiate soccer and volleyball athletes. Journal of strength and conditioning research/National Strength & Conditioning Association, 28(11), 3155-3163.[PubMed]
  103. Stergiou, N., Ristanis, S., Moraiti, C., & Georgoulis, A. D. (2007). Tibial rotation in anterior cruciate ligament (ACL)-deficient and ACL-reconstructed knees: a theoretical proposition for the development of osteoarthritis. Sports Medicine, 37(7), 601.[PubMed]
  104. Stobierski, L. M., Fayson, S. D., Minthorn, L. M., Valovich, M. T., & Welch, C. E. (2014). Clinician Scoring of the Functional Movement Screen™ is Reliable to Assess Movement Patterns. Journal of sport rehabilitation.[PubMed]
  105. Teyhen, D. S., Shaffer, S. W., Lorenson, C. L., Halfpap, J. P., Donofry, D. F., Walker, M. J., & Childs, J. D. (2012). The Functional Movement Screen: a reliability study. The Journal of Orthopaedic and Sports Physical Therapy, 42(6), 530-40.[PubMed]
  106. Teyhen, D. S., Shaffer, S. W., Lorenson, C. L., Greenberg, M. D., Rogers, S. M., Koreerat, C. M., & Childs, J. C. (2014). Clinical measures associated with dynamic balance and functional movement. The Journal of Strength & Conditioning Research, 28(5), 1272-1283.[PubMed]
  107. Teyhen, D. S., Riebel, M. A., McArthur, D. R., Savini, M., Jones, M. J., Goffar, S. L., & Plisky, P. J. (2014a). Normative data and the influence of age and gender on power, balance, flexibility, and functional movement in healthy service members. Military medicine, 179(4), 413-420.[PubMed]
  108. Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: the kappa statistic. Family Medicine, 37(5), 360-363.[PubMed]
  109. Waldén, M., Hägglund, M., & Ekstrand, J. (2006). High risk of new knee injury in elite footballers with previous anterior cruciate ligament injury. British Journal of Sports Medicine, 40(2), 158-162.[PubMed]
  110. Warren, M., Smith, C. A., & Chimera, N. J. (2014). Association of Functional Movement Screen™ With Injuries in Division I Athletes. Journal of sport rehabilitation.[PubMed]
  111. Wearing, S. C., Hennig, E. M., Byrne, N. M., Steele, J. R., & Hills, A. P. (2006). The biomechanics of restricted movement in adult obesity. Obesity Reviews, 7(1), 13-24.[PubMed]
  112. Weir, J. P. (2005). Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. The Journal of Strength & Conditioning Research, 19(1), 231-240.[PubMed]
  113. Whiteside, D., Deneweth, J. M., Pohorence, M. A., Sandoval, B., Russell, J. R., McLean, S. G., … & Goulet, G. C. (2014). Grading the Functional Movement Screen™: A Comparison of Manual (Real-Time) and Objective Methods. Journal of strength and conditioning research.[PubMed]
  114. Wilson, J. M. G., & Jungner, G. (1968). Principles and practice of screening for disease. In Public Health Papers (WHO) (No. 34). World Health Organization.[PubMed]
  115. Wright, M. D., Portas, M. D., Evans, V. J., & Weston, M. (2015). The Effectiveness of 4 Weeks of Fundamental Movement Training on Functional Movement Screen and Physiological Performance in Physically Active Children. The Journal of Strength & Conditioning Research, 29(1), 254-261.[PubMed]
  116. Xu, X., Mirka, G. A., & Hsiang, S. M. (2008). The effects of obesity on lifting performance. Applied Ergonomics, 39(1), 93-98.
  117. Zalai, D., Panics, G., Bobak, P., Csáki, I., & Hamar, P. (2014). Quality of functional movement patterns and injury examination in elite-level male professional football players. Acta Physiologica Hungarica, 1-9.[PubMed]
  118. Zhang, J., & Yu, K. F. (1998). What’s the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA: The Journal of the American Medical Association, 280(19), 1690.[PubMed]
  119. Zweig, M. H., & Campbell, G. (1993). Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry, 39(4), 561-577.[PubMed]


CONTRIBUTORS

To see the authorship and review status of this page, click HERE. To provide feedback, please click HERE.

Top · Contents · References