Accepted Manuscript

Running Related Gluteus Medius Function in Health and Injury: A Systematic

Review with Meta-analysis

Adam Semciw, Racheal Neate, Tania Pizzari

PII: S1050-6411(16)30053-0

DOI: http://dx.doi.org/10.1016/j.jelekin.2016.06.005

Reference: JJEK 1983

To appear in: Journal of Electromyography and Kinesiology

Received Date: 24 February 2016

Revised Date: 21 May 2016

Accepted Date: 14 June 2016

Please cite this article as: A. Semciw, R. Neate, T. Pizzari, Running Related Gluteus Medius Function in Health and

Injury: A Systematic Review with Meta-analysis, Journal of Electromyography and Kinesiology (2016), doi: http://

dx.doi.org/10.1016/j.jelekin.2016.06.005

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1

Running Related Gluteus Medius Function in Health and Injury: A Systematic Review

with Meta-analysis

Adam Semciw1,2,*

Racheal Neate2,

Tania Pizzari2,

1School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane,

Queensland, Australia

2La Trobe Sports and Exercise Medicine Research Centre, La Trobe University, Bundoora,

Victoria, Australia.

*Corresponding author: Dr Adam Semciw,

School of Health and Rehabilitation Sciences,

The University of Queensland,

St Lucia, Brisbane 4067

Queensland, Australia

Ph +61 7 3365 4592; E: adam.semciw@gmail.com

1

Abstract

Running is a popular sport and recreational physical activity worldwide. Musculoskeletal

injuries in runners are common and may be attributed to the inability to control pelvic

equilibrium in the coronal plane. This lack of pelvic control in the frontal plane can stem

from dysfunction of the gluteus medius. The aim of this systematic review was therefore

to: (i) compile evidence of the activity profile of gluteus medius when running; (ii) identify

how gluteus medius activity (electromyography) varies with speed, cadence and gender

when running; (iii) compare gluteus medius activity in injured runners to matched controls.

Seven electronic databases were search from their earliest date until March 2015. Thirteen

studies met our eligibility criteria. The activity profile was mono-phasic with a peak during

initial loading (four studies). Gluteus medius amplitude increases with running speed; this

is most evident in females. The muscles’ activity has been recorded in injured runners with

Achilles tendinopathy (two studies) and patellofemoral pain syndrome (three studies). The

strongest evidence indicates a moderate and significant reduction in gluteus medius

duration of activity when running in people with patellofemoral pain syndrome. This

dysfunction can potentially be mediated with running retraining strategies.

2

1 Introduction

Running is an increasingly popular recreational and competitive sport that is associated

with many cardiovascular and musculoskeletal benefits. In 2009-2010, over 1.1 million

Australians (6.5% of the population) participated in running or jogging as a form of

exercise and this was a significant jump in participation from 5 years earlier (0.68 million,

4.3% of the population) (Australian Bureau of Statistics, 2010). In 2013, over 50 million

Americans participated in running or jogging, a rise of 5% since the previous year

(Running USA, 2014). Although the benefits of physical activity are well documented,

musculoskeletal injuries are common in runners of all levels. A recent meta-analysis

indicates that the incidence of running related injuries per 1000 hrs of training is 17.8% for

novice runners and 7.7% for recreational runners (Videbæk et al., 2015). Such injuries can

affect not only the ability to participate in physical and occupational activity, but also

affect the psychological wellbeing of the athlete (Leddy et al., 1994; Putukian, 2016).

Hip adduction excursion during running has been identified as a risk factor for the

development of running related injuries such as patellofemoral pain syndrome (PFPS)

(Neal et al., 2016). Arguably, gluteus medius (GMed) is one of the most important hip

muscles that controls this coronal plane motion. It is morphologically suited to generate the

large abduction torques required to maintain femoropelvic equilibrium in the coronal plane

(Dostal et al., 1986; Flack et al., 2014). It is feasible then that GMed dysfunction may

contribute to poor coronal plane pelvic control, or increased hip adduction excursion while

running and contribute to injury. Some studies have associated hip muscle strength

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(Niemuth et al., 2005) or GMed activation (Willson et al., 2011) with running related

injuries, however, there are no studies that systematically compile evidence of GMed

function while running in those who are healthy or injured.

Neuoromotor function is typically assessed using electromyography (EMG) (Basmajian

and De Luca, 1985). Surface or fine-wire electrodes can record the resultant output of

myoelectric activity from the central nervous system to a muscle for a particular task

(Basmajian et al., 1985; Konrad, 2005). It is known in some injuries that the timing and

amplitude of EMG activity differs to that of uninjured groups (e.g. lateral epicondylalgia;

Heales et al., 2016). A greater understanding of impairments in GMed EMG function when

running may therefore assist in the development of targeted strategies for managing

running related injuries (Willy and Davis, 2013). It could also help to guide approaches to

minimise soft tissue injury risk in runners, of which there is currently no proven exercise

based intervention (Yeung et al., 2011). Informed decisions on tailored intervention

strategies may also be guided by an understanding of how GMed function varies between

genders, running speed and cadence (Chumanov et al., 2008; Chumanov et al., 2012). The

aim of this systematic review was therefore to identify the electromyographic (EMG)

characteristics of GMed in healthy and injured runners. Specifically, we aimed to;

i. compile evidence of the GMed EMG activity profile when running,

ii. identify how GMed EMG amplitude and timing of activity varies between gender,

cadence and speed of running,

iii. compare GMed EMG activity of injured runners to healthy matched controls and

pool evidence with a meta-analysis (if appropriate).

4

2 Methods

2.1 Search strategy

MEDLINE, EMBASE, CINAHL, SPORTDiscus, AMED, PEDro and the Cochrane

Library databases were searched from inception until week 2 March 2015. The search was

performed using three main concepts (Appendix 1); gluteals, running and

electromyography. The search yield was exported to Endnote V.X6 (Thomson Reuters).

Reference checking of included articles and citation tracking via Google Scholar were

performed to identify relevant articles not initially detected.

2.2 Selection criteria

Studies were eligible if they reported on healthy participants, or compared healthy

participants to an injured sample. To be included, studies were required to assess muscle

activation in running on even land (either treadmill or overground; excluding cutting

manoeuvres, obstacles or stairs). Sprinting related studies were not the primary focus of

this review, however, were included if they were compared to running related speeds. All

studies were required to use EMG as a primary tool to detect muscle activation. All

experimental designs published in English language were included with the exception of

case studies, narrative reviews and systematic reviews.

Two reviewers independently applied the selection criteria to the titles and abstracts of the

yield (RN and AS reviewed studies with lead author A-M; RN and TP reviewed papers

with lead authors N-Z). Any disagreement was referred to the third independent reviewer

for consensus (AS for papers N-Z; or TP for papers A-M). Full texts were obtained from

remaining articles for further consideration of eligibility.

5

2.3 Methodological quality

A standardised quality assessment tool recommended by the Non-Randomised Studies

Group of the Cochrane Collaboration was adapted for this review (Ganderton and Pizzari,

2013; Siegfried et al., 2005). Risk of bias in non-randomised studies can be categorised in

the following dimensions; selection bias, performance bias, detection bias, attrition bias

and reporting bias (Reeves et al., 2008). Items relating to performance bias (typically

associated with intervention based research) and reporting bias (difficult to quantify

(Higgins and Altman, 2008)) were removed from this tool. The ratings for each study were

used to rate the quality of the body of evidence.

2.4 Data extraction

One author (RN) independently extracted the relevant data from the included studies and

this was checked by a second reviewer (AS). Information extracted included the condition

and comparison, participant demographics, running protocol and specific EMG data

including electrode placement and the method of processing. Temporal and/or amplitude

EMG data for GMed was also extracted.

Running activity profile: Ensemble curves were compiled to provide an overall estimate of

the major bursts, peaks and troughs of GMed throughout the gait cycle during running. To

create the ensemble graph from included studies, the x-axis was time normalised to 100

points, representing foot contact (0%) and the subsequent ipsi-lateral foot contact (100%)

of one complete stride. Amplitude values were then visually determined from magnified

images of figures within an included study at 1% increments along the x-axis using

GraphClick software (Arizona-Software, 2008; http://www.arizona-

6

software.ch/graphclick/ ), and expressed as a per cent of peak amplitude across the gait

cycle (Yang and Winter, 1984).

Effect of gender, cadence, speed and injury: To investigate the effect of gender, cadence,

speed (e.g. running vs sprinting) and injury, an effect size estimate was generated from

information within included studies. For between group, cross-sectional studies (e.g.

comparing gender or injured and uninjured groups) a standardised mean difference (SMD=

mean difference/pooled SD) and 95% confidence interval (95% CI) was calculated to

determine the magnitude of difference in running related EMG activity between groups

(Centre for Evaluation & Monitoring, n.d.). For repeated measures designs (e.g. effect of

change in cadence or speed) a standardised paired difference (SPD, or repeated measures

Cohen’s d) with 95% CI was calculated using the Comprehensive Meta-analysis Version 2

statistical software package (Biostat Inc., USA) (http://www.meta-analysis.com/)

(Borenstein et al., 2009). Where the pre-test post-test correlation (r) was not reported or

unable to be imputed, a conservative estimate of r=0.5 was used (Borenstein et al., 2009;

Negrin et al., 2012). Effect sizes of 0.2, 0.5 and 0.8 were considered small, medium and

large respectively (Cohen, 1988).

2.5 Data synthesis

Data were grouped according to outcome (e.g. cadence) and described qualitatively. Where

sufficient data were available from multiple comparative studies (e.g. injury vs control),

SMDs were pooled in a meta-analysis using fixed or random effects (Review Manager

5.3), depending on statistical heterogeneity. I2 values of 25%, 50% and 75% indicated low,

7

moderate and high levels of heterogeneity (Higgins et al., 2003). A random effects analysis

was conducted where moderate and high heterogeneity existed (I2>50%).

2.6 Assessment of the quality of the body of evidence

The Grades of Research, Assessment, Development and Evaluation (GRADE) approach

was used to evaluate the quality of evidence in each meta-analysis (Guyatt et al., 2008;

Schache et al., 2014b). Quality was defined as high, moderate, low or very low (Balshem

et al., 2011).

3 Results

3.1 Study selection

Figure 1 illustrates the flow of studies through the review. Thirteen articles satisfied the

eligibility criteria and were included in the review and of these, three were pooled in a

meta-analysis.

[Insert Fig 1 here]

3.2 Study characteristics

Study and participant characteristics are described in Table 1. Of the 13 studies included,

eight were cross sectional designs, and five were case control designs (refer to Table 1).

The mean age of participants ranged from 21 to 39 years and the running experience varied

from recreational runners (Unfried et al., 2013) to highly trained varsity track athletes

(Mann et al., 1986).

8

Insert Table 1 here

The EMG burst activity profile of GMed in healthy participants was illustrated in four

studies (Chumanov et al., 2012; Gazendam and Hof, 2007; Unfried et al., 2013;

Wall‐Scheffler et al., 2010). Other variables assessed in healthy participants include the

impact of running speed (Bartlett et al., 2014; Gazendam et al., 2007; Mann et al., 1986;

Wall‐Scheffler et al., 2010), cadence (Chumanov et al., 2012) and gender (Chumanov et

al., 2008; Willson et al., 2012) on GMed EMG activity when running.

The type of amplitude EMG measure reported varied across studies. Four studies measured

mean EMG amplitude over a specified phase of running (Table 2); four studies recorded

peak EMG amplitude (Table 2) and three studies recorded integrated EMG amplitude

(Table 2). Integrated amplitude refers to the area under the linear envelope (burst profile)

and represents the total EMG activity over a specified phase of running (Konrad, 2005).

There were five studies that compared GMed EMG amplitude of injured runners to healthy

controls. Two studies included participants with Achilles tendinopathy (Azevedo et al.,

2009; Smith et al., 2014) and the remaining three investigated PFPS (Esculier et al., 2015;

Souza and Powers, 2009; Willson et al., 2011). There were no studies that assessed the

effect of local hip joint injury on GMed running activity.

The EMG and running protocols are described in Table 2. Over-ground running was

performed in eight studies and treadmill running in six; one study used both, with

participants running on the treadmill and sprinting over-ground (Bartlett et al., 2014).

treadmill and over-ground to compare. All studies used surface electrodes to record

9

activity from GMed, however the placement of electrodes varied across studies. Four

studies used the recommended SENIAM location of midway between the iliac crest and

greater trochanter (Bartlett et al., 2014; Esculier et al., 2015; Smith et al., 2014; Unfried et

al., 2013) and two studies did not report an exact location (Azevedo et al., 2009; Mann et

al., 1986). The remaining studies placed the electrode on a line between the iliac crest and

the greater trochanter however the exact position was only described in one of these studies

(25 mm below the iliac crest; Souza et al., 2009).

Insert Table 2 here

3.3 Methodological quality

The risk of bias across studies is summarised in Table 3. Four studies failed to adequately

describe the population of interest (see Table 3). No studies described blinded analysis of

the raw EMG data and only four of the studies reported randomising trials (e.g. between

running speeds) (see Table 3). One study did not apply any statistical analysis (Mann et al.,

1986). All of the ten studies that investigated amplitude measures employed an appropriate

normalisation technique. All but two of the studies (Esculier et al., 2015; Mann et al.,

1986) that provided temporal data gave an adequate description for how they determined

onset and offset.

Insert Table 3 here

10

3.4 GMed running activity profile

Four studies generated a linear envelope of GMed EMG amplitude for a total of 104

runners (46 female) (Chumanov et al., 2012; Gazendam et al., 2007; Unfried et al., 2013;

Wall‐Scheffler et al., 2010). The runners ranged from ‘physically active’ (Gazendam et al.,

2007) to those who ran at least 15 miles per week (Chumanov et al., 2012) and running

speed ranged from 2.25 to 2.90 m/s. Gazendam et al. (2007) plotted the GMed profile at

multiple speeds, however we selected the representative ‘running’ speed as opposed to

‘jogging’ for further processing. The running speed was not reported by Unfried et al.

(2013).

When normalised to peak activity across the gait cycle and plotted alongside each other, a

consistent pattern of activity can be observed (Figure 2). GMed EMG during running was

typically presented as mono-phasic, with peak activity occurring just after initial contact

(≈5-10% GC). One study presented a bi-phasic pattern, with a second, smaller burst of

activity (63.5% peak amplitude) occurring at toe-off (Gazendam et al., 2007). During

swing, GMed EMG amplitude steadily increased from toe-off to foot contact.

[Insert Figure 2 here]

3.5 Running speed

Amplitude: Three studies compared GMed EMG amplitude between running speeds (44

participants, 22 females) (Bartlett et al., 2014; Chumanov et al., 2008; Wall‐Scheffler et

al., 2010) (Table 3). Two studies reported on the same cohort of participants (Chumanov et

al., 2008; Wall‐Scheffler et al., 2010); however one of the studies presented separate

results for males and females (Chumanov et al., 2008).

11

There was a trend from limited studies to indicate that greater running speeds required

higher GMed EMG amplitude. In a cohort of 34 participants (17 females) (Chumanov et

al., 2008; Wall‐Scheffler et al., 2010), significantly greater integrated EMG amplitude

across the gait cycle was evident in the females but not males when running at higher

speeds (Chumanov et al., 2008). Bartlett et al. (2014) reported significantly higher peak

GMed EMG amplitude in the stance phase when running at higher speed (5 males and 5

females)

Temporal activity: Results from one study indicate that the duration of GMed activity

increases in males when sprinting. Mann et al. (1986) illustrated the mean temporal

duration of 15 elite male runners when running at different speeds. While no quantitative

comparisons were performed, the authors reported that sprinting (>10m/s) was

characterised by longer duration of GMed EMG activity than running related speeds

(3.4m/s, 4.5 m/s), especially in mid to late swing.

3.6 Cadence

Amplitude: One study analysed GMed EMG amplitude during running as cadence

increased (5% and 10%; 45 participants, 20 females) (Chumanov et al., 2012). Limited

evidence indicated that a higher cadence required significantly greater mean GMed EMG

amplitude in late swing, but not stance (Table 2).

3.7 Gender

Amplitude: Two studies compared GMed EMG amplitude between males and females

(Chumanov et al., 2008; Willson et al., 2012) (Table 3). At slow and medium running

12

speeds, there was no difference between gender in integrated EMG activity (Chumanov et

al., 2008). When considering fast running speed (>3.5m/s), females had significantly

greater integrated EMG activity across the gait cycle (ES=0.39; Chumanov et al., 2008),

but did not have greater peak or mean amplitude in stance (Willson et al., 2012).

Temporal activity: One study reported no significant difference in temporal variables

(duration and onset) between males and females at fast running speed (Willson et al.,

2012).

3.8 Injury- Patellofemoral pain syndrome

Three studies compared PFPS (n=62; females=41) to a healthy sample (n=60; females

=40) (Esculier et al., 2015; Souza et al., 2009; Willson et al., 2011) (Table 3). The studies

had comparable methods of PFPS diagnosis, with all participants having pain over or

adjacent to the patella of intensity ≥3/10 on a visual analogue scale (VAS) for at least 2

months during functional tasks like running and squatting.

3.8.1 Amplitude:

The results of all three studies could be pooled for mean GMed EMG amplitude during

running (Figure 3A). There is low quality evidence (Table 4) of no significant difference in

mean running activity (ES=0.05[-0.55, 0.65]).

Insert Figure 3 here

Insert Table 4 here

13

There is moderate quality evidence from two studies that peak GMed EMG amplitude in

runners with PFPS (n=41) is no different to control participants (ES=-0.06[-0.50, 0.38])

(Figure 3B) (Table 4).

3.8.2 Temporal activity:

The results of two studies could be pooled for temporal GMed EMG outcomes (Figure

3C). There is low quality evidence (Table 4) that GMed EMG onset in runners with PFPS

(n=41) is no different to control participants (ES=-0.31[-1.14, 0.52]) (Figure 3C). There is

moderate quality evidence (Table 4) that people with PFPS (n=41) have significantly

shorter GMed EMG duration of activity when running compared with control participants

(moderate ES=-0.52[-0.97, -0.08]) (Figure 3D).

3.9 Injury- Achilles Tendinopathy

Two studies compared running related GMed activity in people with Achilles tendinopathy

to control participants (Azevedo et al., 2009; Smith et al., 2014) (Table 2 and 3). Similar

diagnostic criteria were reported in each study; gradual onset of mid-portion Achilles pain

during functional tasks like running and hopping and tender on palpation. Neither study

indicated the minimum pain intensity during activity, nor did they report on the minimum

period of time that participants had been suffering from the condition.

3.9.1 Amplitude

Azevedo et al. (2009) reported significantly smaller amplitude of integrated EMG activity

in people with Achilles tendinopathy (n=21; 5 females) immediately post foot contact

(initial loading), while there was no difference immediately prior to FC (late swing).

14

3.9.2 Temporal

Smith et al. (2013) identified that people with Achilles tendinopathy (n=19 males) have

significantly shorter duration (large ES) and delayed onset (large ES) of GMed EMG

activity compared with controls (Table 3).

3.10 Summary of results

The primary aim of this review was to identify the EMG characteristics of GMed when

running in healthy and injured people. There were 13 studies included. The GMed burst

EMG activity profile while running was typically presented as monophasic, with peak

activity occurring in initial loading 5-10% of the gait cycle. There was limited evidence

from individual studies that running speed, cadence and gender can affect GMed EMG

function in healthy participants. GMed EMG activity has been assessed in runners with

Achilles tendinopathy and PFPS. Limited evidence suggests temporal and amplitude

changes in people with Achilles tendinopathy; while moderate quality evidence indicates

that people with PFPS have significantly shorter duration GMed activity when running

compared with matched controls.

4 DISCUSSION

4.1 Activity profile in healthy participants

The results support the notion that the function of GMed in running is primarily to assist

with absorbing the ground reaction force in the loading phase (Hamner et al., 2010;

Lenhart et al., 2014). Biomechanical modelling studies indicate that GMed produces the

largest mean peak muscle force of all hip muscles when running (Lenhart et al., 2014); this

15

peak force occurs in early stance (Lenhart et al., 2014); and together with gluteus maximus

and adductor magnus provide half of the vertical support during early stance when running

(Hamner et al., 2010). The EMG profiles of GMed identified in the current study concur

with those findings by identifying peak activity at approximately 5-10% of the gait cycle.

Morphologically, middle and anterior GMed is relatively large in physiological cross-

sectional area, has a large abduction moment arm and has fascicles that are aligned

relatively vertical in the coronal plane (Dostal et al., 1986; Flack et al., 2014). On a fixed

lower limb, these morphological characteristics are ideal for generating the large torques

required to absorb the vertical ground reaction forces imposed on the body and to support

coronal plane pelvic alignment during the early stance phase of running. Adequate GMed

strength and recruitment is therefore a fundamental component of running.

While the EMG profile of GMed was relatively consistent across the studies analysed in

this review, there are some intrinsic and extrinsic factors that influence GMed function

when running. These include running speed, cadence, gender, and injury.

4.2 Speed, gender, and cadence

Higher running speeds are thought to require greater GMed EMG amplitude (Bartlett et al.,

2014; Chumanov et al., 2008) and duration (Mann et al., 1986). Although this relationship

appears to depend on gender (Chumanov et al., 2008; Wall‐Scheffler et al., 2010). Of the

two cohorts that investigated GMed EMG activity across different running speeds, one

identified higher amplitude activity during stance at higher running speeds in a mixed

gender cohort (Bartlett et al., 2014), while the other cohort only found these differences in

females but not males (Chumanov et al., 2008; Wall‐Scheffler et al., 2010). The

16

discrepancies are likely due to the different speeds tested across each study, as well as the

biomechanical variability between genders. Chumanov et al. (2008) tested running speeds

of between 1.8 and 3.6 m/s. These speeds have previously been expressed as jogging (≈2.0

m/s) and slow-paced running (3.5 m/s) (Schache et al., 2014a). The predominant strategy

adopted to increase speeds in this range is typically to extend the stride through larger

ankle plantar flexor activity (Dorn et al., 2012; Schache et al., 2014a) and is therefore less

likely to have a dramatic effect on hip related activity, as seen with males in this cohort.

On the other hand, Bartlett et al. (2014) tested running speeds of 3 m/s and sprinting.

Increasing speed above a threshold of ≈7m/s requires a shift from an ankle to hip based

strategy in order to raise stride frequency during swing (Dorn et al., 2012). This is

primarily attributed to increases in muscle force contributions of iliopsoas, gluteus

maximus and hamstrings, however, increases in muscle forces were also evident in GMed,

particularly towards the end of swing (Dorn et al., 2012). This places greater

biomechanical demand on hip based musculature (Dorn et al., 2012; Schache et al., 2014a),

particularly during swing and may explain the large increase in GMed EMG activity of

Bartlett’s participants, regardless of gender.

The greater GMed EMG amplitude in females compared with males could be related to

biomechanical differences between genders. When activity across the whole gait cycle is

considered, Chumanov et al. (2008) described significantly greater integrated GMed EMG

amplitude in females compared to males when running at 3.6 m/s. Interestingly, Chumanov

also recognized significantly greater peak hip adduction and internal rotation kinematics

during stance, as well as hip adduction excursion across the gait cycle (accounting for

swing) in females compared to males. These kinematic observations are consistent with

previous research (Ferber et al., 2003). It is possible that females require greater GMed

17

EMG amplitude in order to control the larger frontal and transverse plane motion that

occurs when running (Chumanov et al., 2011). Inadequate GMed recruitment when

running may therefore be of more concern in females than males, even at speeds where the

hip muscles are not considered to be as heavily involved in propulsion (<7m/s) (Schache et

al., 2014a).

Altering cadence has also shown to affect GMed EMG activity. Significantly greater

amplitude is required in late swing but not stance, when cadence is increased by 10% at the

same running speed (Chumanov et al., 2012). This is consistent with a biomechanical study

that found an increase in the peak force exerted by GMed during late swing, when cadence

was increased by 10% (Lenhart et al., 2014). Pre-activation of GMed in late swing can

possibly facilitate early tensioning of the lateral hip stability mechanism (a combination of

hip abductor muscles that aid pelvic stability; Grimaldi, 2011) in preparation for stance,

aiding frontal and transverse plane control (Chumanov et al., 2012). Heiderscheit et al.,

(2011) supports this notion by reporting significantly smaller peak hip adduction angle and

abduction moment during stance when healthy participants ran at 110% of their preferred

cadence. While further work is required to support the limited evidence presented here, it is

possible that increasing cadence can be a beneficial strategy for pre-activating the main

pelvic stabiliser in preparation for the loading response, in running related conditions

where pelvic stability is thought to be compromised e.g. patellofemoral pain syndrome

(PFPS) (Heiderscheit et al., 2011; Lenhart et al., 2014).

18

4.3 Injury

4.3.1 Patellofemoral pain syndrome

Evidence of GMed EMG amplitude dysfunction in people with PFPS is not convincing.

When comparing GMed EMG amplitude while running in people with PFPS to controls,

there is low to moderate quality evidence of no difference in GMed mean and peak

amplitude respectively. The results were highly variable across the three studies,

particularly with mean amplitude, where moderate effect sizes were identified favouring

higher activity in healthy control participants (Esculier et al., 2015) or people with PFPS

(Willson et al., 2011). The discrepancy between studies may be explained by the

normalisation method employed. To facilitate comparisons between groups and studies,

raw EMG signals are typically expressed as a per cent of maximum or sub-maximum

isometric contraction (Burden, 2010). Normalising to a maximum contraction may

inevitably result in inaccurate findings in people with musculoskeletal conditions, as pain

or weakness may affect a participants’ ability to produce a ‘maximal’ effort reliably. The

two studies that favoured higher GMed EMG amplitude in people with PFPS both

normalised the raw signals to maximum isometric hip abduction (Souza et al., 2009;

Willson et al., 2011). A recent meta-analysis found strong evidence from eleven studies

that maximum isometric hip abduction is significantly weaker in people with PFPS than

control participants (Rathleff et al., 2014). It is feasible then, to find a trend for higher

amplitude activity in people with PFPS if the raw running related activity is being

expressed as a per cent of maximum hip abduction, regardless of whether GMed

recruitment was altered in running. These generic issues in EMG amplitude normalisation

for musculoskeletal conditions ultimately affect the interpretation of findings and may

19

steer researchers and clinicians to lend more weight to studies that report temporal

outcomes.

People with PFPS have shorter duration GMed EMG activity when running. This

represented moderate quality evidence from two studies (Esculier et al., 2015; Willson et

al., 2011). It is likely that the shorter duration is primarily related to delayed onset or pre-

activation, although the meta-analysis for this outcome presents low quality evidence from

two studies and lacks statistical significance (Esculier et al., 2015; Willson et al., 2011).

The shorter duration of GMed activity may impact on pelvic control during stance. The

temporal differences have been associated with increased hip adduction excursion during

stance in people with PFPS (Esculier et al., 2015; Willson et al., 2011), although in

Esculiers’ cohort (Esculier et al., 2015), this was only present in rear-foot strikers.

Nevertheless, the findings support evidence from a meta-analysis that found increased peak

hip adduction is a risk factor for the development of PFPS in runners (Neal et al., 2016),

and are also consistent with a recent prospective cohort study that identified poor eccentric

hip abduction strength as a risk factor for the development of PFPS in runners (Ramskov et

al., 2015). Combined, these studies provide justification for targeted strategies to improve

neuro-motor control and strength of hip and pelvic stabilisers in injured runners with PFPS

(Lack et al., 2015). These strategies may assist with reducing the burden of a highly

prevalent condition in runners (6% to 16%) (Lopes et al., 2012).

20

4.3.2 Achilles tendinopathy

There is limited evidence of GMed dysfunction in people with Achilles tendinopathy

(Azevedo et al., 2009; Smith et al., 2013). GMed muscle recruitment is significantly

smaller in amplitude during initial loading (Azevedo et al., 2009), delayed in onset (Smith

et al., 2014) and shorter in duration (Smith et al., 2014) in people with Achilles

tendinopathy. Azevedo et al. (2009) expressed total GMed EMG activity during the

loading response as a proportion of mean activity across the whole gait cycle. Their results

imply that GMed is proportionately less influential during loading in people who have

Achilles tendinopathy. This may have significant implications for the ability to absorb

vertical ground reaction forces and control coronal and transverse plane motion in a critical

phase of running; ultimately influencing foot and ankle biomechanics during stance. The

delayed onset (pre-activation) and shorter duration of GMed EMG activity reported by

Smith et al. could feasibly increase these biomechanical deficits at the hip, knee and ankle.

While the current evidence is only based on two studies, they each provide a link between

GMed muscle dysfunction and ankle pathology. Strategies aimed at improving proximal

neuro-motor control (e.g. enhance pre-activation of GMed) may have a role in Achilles

tendinopathy rehabilitation or prevention.

4.4 Clinical implications

This review has identified a range of strategies that can be used to increase GMed EMG

amplitude and duration of activity when running. These strategies may potentially assist

with the assessment of proximal control and the integrity of the lateral hip stability

mechanism of the hip in healthy or injured athletes. For example, increasing running speed,

particularly > 7m/s requires larger hip muscle amplitude (Bartlett et al., 2014; Chumanov

21

et al., 2008; Schache et al., 2014a). Assessment of an athlete at high running speeds may

therefore be preferable to increase task complexity and identify issues in coronal pelvic

stability that may not be immediately obvious at more comfortable speeds (e.g. for the

middle or long distance athlete).

The studies identified in this review have also provided support for a targeted strategy to

correct dysfunctional GMed recruitment in injured runners. Moderate quality evidence

from two studies (Esculier et al., 2015; Willson et al., 2011) indicates that people with

PFPS have shorter duration GMed EMG activity when running, and it is likely that this is

primarily due to delayed pre-activation (prior to foot strike). Similar dysfunction has been

reported in people with Achilles tendinopathy (Smith et al., 2014). This has the potential to

affect coronal and transverse plane alignment when running and contribute to, or

exacerbate the athletes’ symptoms. It could however, also be possible to intervene with

strategies to facilitate GMed recruitment in runners. This review has provided limited

evidence from one study (Chumanov et al., 2012) that increasing cadence by 10% of

preferred running speed can facilitate larger GMed EMG amplitude immediately prior to

foot contact in healthy runners. The greater pre-activation is supported by an increase in

GMed muscle force (Lenhart et al., 2014) prior to foot contact when running and likely to

facilitate proximal control and hip biomechanics. It may prove useful then, to increase the

cadence of injured runners to facilitate proximal motor recruitment and ultimately, reduce

load in the lower limb joints during stance. Current evidence suggests that increasing

cadence can decrease patello-femoral joint stress (Willson et al., 2014) in people with and

without PFPS, however a relationship between this and hip muscle activity or

biomechanics has not yet been established. Further work is required to confirm the utility

22

of increased running cadence as a management strategy for running related injuries

(Esculier et al., 2016) but current evidence is promising.

4.5 Strengths, limitations and further research

There are a number of strengths of this systematic review. By applying a thorough search

criteria, we have identified all relevant literature related to GMed EMG function in healthy

and injured runners. Of the 13 studies identified, 12 were published in the last nine years,

potentially demonstrating an area of increasing interest in the scientific community. We

have provided moderate quality evidence that runners with PFPS have shorter duration

GMed EMG activity when running. This knowledge is useful for clinicians and researchers

to consider potential targeted rehabilitation strategies for facilitating GMed activity

(duration) in this population. We have discussed some potential examples based on recent

and proposed work that my ultimately prove effective with further research (Esculier et al.,

2016; Willson et al., 2014)

The findings of this review also need to be viewed in light of a number of methodological

limitations of the included studies. All 13 of the included studies used surface electrodes to

analyse the EMG characteristics of GMed in running. Surface electrode signals can be

contaminated with noise from surrounding musculature (cross-talk) (Perry et al., 1981).

Only a small portion of GMed is exposed superficially (Semciw et al., 2013a), being

covered anteriorly by tensor fascia lata and posteriorly by gluteus maximus. Recent work

suggests that surface electrodes placed over the middle portion of GMed records additional

myoelectric activity when compared to indwelling fine-wire electrodes, indicative of cross-

talk from surrounding muscles (Semciw et al., 2014). It is possible therefore that GMed

23

activity represented in studies included in this review are contaminated by cross-talk.

Furthermore, all studies used only one electrode to assess the function of the whole muscle.

GMed is reported to have three distinct segments (anterior, middle and posterior), each

with independent innervation (Flack et al., 2014; Gottschalk et al., 1989), morphological

characteristics (Flack et al., 2014; Semciw et al., 2013a) and function (Semciw et al.,

2013c). The large physiological cross-sectional area and moment arms of anterior and

middle GMed (Dostal et al., 1986; Flack et al., 2014; Semciw et al., 2013a) would

facilitate the ability to generate the high torques required to provide coronal plane pelvic

stability. The anterior and middle portions may therefore be of more functional relevance

than the smaller posterior portion in conditions where coronal plane stability is considered

important (e.g. PFPS). Further research using fine wire techniques may shed light on these

speculations (Semciw et al., 2013b).

4.6 Conclusion

The results of this review, from 13 available studies support the notion that GMed plays an

integral role in running. It is most active in the initial loading stage of stance, however,

pre-activation appears to be impaired in people with Achilles tendinopathy and PFPS,

ultimately affecting the runners’ ability to control coronal plane motion. There are some

strategies such as increasing running cadence that can facilitate GMed muscle recruitment

and may prove beneficial to runners with suspected dysfunction of their lateral support

mechanism. Further work is required to investigate these targeted intervention strategies

and to determine the role of different segments of GMed in healthy and injured runners.

Conflict of interest: none to declare

24

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6 Figure Captions

Figure 1: PRISMA flow diagram illustrating the flow of studies through the review

Figure 2: Ensemble curves illustrating the activity profile of gluteus medius when running

Figure 3: Meta-analysis of gluteus medius muscle activity in people with patellofemoral

pain syndrome (PFPS) compared with healthy matched control participants.

Records identified

through database search

(n = 1034)

Records identified

through other sources

(n = 3)

Screened by title and

abstract

(n = 712)

Duplicates removed

(n = 325)

Full text articles assessed

for eligibility

(n = 36)

Eligible studies

(n = 13)

Full-text articles excluded

(n = 23)

Reasons:

1. Not GMed (n=9)

2. No EMG data for

running (n=9)

3. Not comparing

injury, gender,

cadence or speed

(n=1)

4. Not peer review

journal article

(e.g. conf abstract)

(n=4) Studies included in meta-

analysis

(n = 3)

0

20

40

60

80

100

0 20 40 60 80 100

Wall-Scheffler et al. (2010)2.7m/sGazendam et al. (2007) 2.25m/s

Chumanov et al. (2012)2.9±0.5m/sUnfried et al. (2013) Self-selectedspeed

Toe-off35% Wall-Scheffler + Unfried36% Gazendam44% Chumanov

% Gait Cycle

Am

plitu

de (%

pea

k ac

tivity

)

Figure 2

A. Mean Amplitude

B. Peak amplitude

C. Onset

D. Duration

Figure 3

33

Table 1: Study characteristics, participant demographics and running experience.

Author

(year)

Study

design

Conditio

n/

comparis

on

Sample size

(Females:ma

les)

Group

demograph

ics:

Mean (SD)

(Age,

height,

BMI, mass)

Running

experienc

e

Footwear

Healthy

control

participant

s

Injured

compariso

n

demograp

hic and

diagnosis

(if

applicable

)

Azeved

o (2009)

Case

control

Injury –

AT

Control =

5:16

Injured =

5:16

Age, 38.9

(10.1);

height,

174.3 (8.0);

mass, 70.2

(10.9).

Age, 41.8

(9.7);

height,

177.8

(7.4);

mass, 77.7

(12.6).

Run

>15km/w

k for ≥3

years

ND

34

AT:

progressive

pain over

Achilles;

morning

pain/stiffne

ss; history

of swelling

over

Achilles;

tender on

palpation;

palpable

nodular

thickening;

movement

of nodule

with

plantar-

dorsiflexio

n.

Bartlett

(2014)

Running

speed

5:5 Age, 31 (9);

height, 171

N/A ND ND

35

Cross-

sectiona

l, single

group

(12); mass,

62.9 (10.7).

Chuman

ov

(2008)

Cross-

sectiona

l, 2

group

Gender 17:17 Female:

Age, 29.4

(4.8);

height,

165.9 (8.5);

mass, 60.1

(79.8)

Male: Age,

22.0 (4.8);

height,

182.3 (8.0);

mass, 79.8

(13.0)

N/A Experienc

ed runner

or

participate

d in

aerobic

conditioni

ng.

Familiar

with

treadmill

running.

ND

Chuman

ov

(2012)

Cross-

sectiona

l, single

group

Running

cadence

EMG

burst

profile

20:25 Age, 32.7

(15.5);

height,

176.3

(10.3);

mass, 69.5

(13.1)

N/A Ran at

least

24.1km

(15

miles)/wk

for ≥3

months.

ND

36

Familiar

with

treadmill

running.

Esculier

(2015)

Case

control

Injury-

PFPS.

Control =

15:5

Injured =

16:5

Age, 33.2

(6); height,

169.1 (7.1);

mass, 62.8

(9.4)

Age, 31.4

(6.0);

height,

167.8

(6.7);

mass, 67.4

(11.5).

PFPS:

history of

anterior

knee pain

≥3 months.

Pain

running ≥

3/10 on

VAS and

pain on at

least three

functional

Ran at

least

15km per

wk

Usual

footwear

37

activities

(stairs,

kneeling,

squatting,

resisted

knee

extension,

prolonged

sitting)

Gazenda

m

(2007)

Cross-

sectiona

l, single

group

Running

speed

EMG

burst

profile

0:10 Age, 20.8

(1.2);

height, 184

(7); mass,

71.3 (6.3)

N/A Physically

active

Sporting

shoes

Mann

(1986)

Cross-

sectiona

l, single

group

Running

speed

0:15 Age, range

196.

N/A Highly

trained

varsity

trackmen

specialisin

g in

100m-

800m

ND

38

Smith

(2014)

Case

control

Injury –

AT

Control =

0:19

Injured =

0:14

Age, 37 (8);

height, 179

(6); mass,

77.4 (10.2).

Age, 43

(8); height,

179 (5);

weight,

77.4

(10.2);

mass, 82.3

(11.1).

AT:

Achilles

tendon

pain with

running,

hopping,

palpation.

Morning

stiffness.

Tendon

thickening.

Symptoms

affecting

exercise

activity.

Running

activities

>20km

per week

ND

39

Confirmati

on of

midportion

AT made

with

diagnostic

ultrasound.

Souza

(2009)

Case

control

Injury-

PFPS

Control =

20:0

Injured =

21:0

Age, 26 (5);

height, 170

(6); mass,

62.9 (6.6).

Age, 27

(6); height,

170 (8);

mass, 64.7

(10.4).

PFPS: (1)

Pain over

the

patellofem

oral

articulation

; (2) ≥3/10

VAS with

at least 2 of

the

following

Young

active

adults-

Participan

ts wore

appropriat

ely sized

athletic

shoes of

the same

style

40

tasks- stair

ascent or

descent,

squatting,

kneeling,

prolonged

sitting, or

isometric

quadriceps

contraction

; (3) pain >

3 months

duration.

Unfried

(2013)

Cross-

sectiona

l, single

group

EMG

burst

profile

9:6 Age, 27.6

(6.6);

height,

174.1 (9.6);

mass, 71.2

(13.7)

NA Recreatio

nal

runners.

Minimum

of 10

miles

(16km)

per week

or 2-3

times per

week.

ND

41

Wall-

Scheffle

r (2010)

Cross-

sectiona

l, single

group

Running

speed

EMG

burst

profile

17:17 Age, 22.9

(18-37);

height: F =

166.5 (9.1),

M = 182.3

(8.0); mass,

F = 61.0

(6.9), M =

79.8 (13.0)

NA ND ND

Willson

(2011)

Case

control

Injury-

PFPS

Control =

20:0

Injured =

20:0

Age, 21.6

(4.5);

height, 169

(9); mass,

62.1 (8.9).

Age, 21.3

(2.6);

height, 168

(6); mass,

62.9 (7.7)

PFPS: pain

behind or

adjacent to

patella

≥3/10 VAS

with

running

and

squatting,

Ran at

least 10

miles (16

km) per

week.

Same

style of

shoe

42

prolonged

sitting,

ascending

or

descending

stairs or

jumping.

Pain > 2

months

duration.

Exacerbate

d with

manual

compressio

n of patella

Willson

(2012)

Cross-

sectiona

l, 2

group

Gender 20:20 Age, F =

20.7 (1.5),

M = 20.4

(1.8);

Height, F =

168 (10), M

= 180 (8);

mass, F =

61.8 (9.2),

NA Ran

≥16km/w

k

ND

43

M = 74.9

(9.5).

Abbreviations: AT, Achilles tendinopathy; F, female; M, male; PFPS, patellofemoral pain

syndrome; NA, not applicable; ND, not described; VAS, visual analogue scale.

44

Table 2: EMG protocol and results

Author

(year)

Running

protocol

EMG protocol Results

Electrode type

and placement

EMG

processing

Temporal

activity

Amplitude

Azevedo et

al. (2009)

Over

ground

(10m).

Self-

selected

speed

Control = 3

(0.41) m/s

AT = 2.97

(0.37) m/s

Surface electrode

Visual mid-point

of the contracted

muscle belly.

AT: symptomatic

leg, 8L, 13R

Control: R

Band-pass

filtered at

15-500Hz.

Amplitude

normalized

to % mean

activity.

FC

identified

by force

plate

Achilles

tendinopathy

(+’ve

indicates

AT>control)

Integrated

EMG activity

Pre (100ms

before FC)

FC = -0.10

(-0.71, 0.50),

Post (100ms

after FC) FC

= -1.05 (-

1.69, -0.40)

Bartlett et al.

(2014)

Treadmill

3.0m/s

Sprint

(maximal):

Surface electrode.

50% distance

along a line from

the iliac crest to

Band-pass

filtered at

20-450Hz.

RMS

Speed (+’ve

indicates

higher speed

has higher

45

Over

ground

(30m)

the greater

trochanter

(SENIAM

guidelines).

Right limb

filtered

(100ms

moving

average) to

generate

linear

envelope.

Amplitude

normalized

to normal

walking.

FC

identified

by

footswitch

signals.

Stance =

FC to TO

EMG

amplitude)

Peak EMG

amplitude

Stance =

2.57 (1.88,

3.27)

Chumanov et

al. (2008)

Treadmill

1.8 ‘slow’,

2.7

‘medium’,

and 3.6m/s

‘fast’.

Surface electrode.

Mid-way between

ASIS and PSIS,

below iliac crest.

(Basmajian and

Blumenstein,

Full-wave

rectified

and low

pass

filtered

using a

Speed

Integrated

EMG

amplitude

over the gait

cycle

46

1980).

Right limb

bidirectiona

l, 6th order

Butterworth

filter (cut-

off 50Hz).

Integrated

EMG

amplitude

determined

for: whole

gait cycle;

terminal

swing to

initial

loading

(final 10%

of gait

cycle to

first 20%

gait cycle).

Normalised

to ave

activity

across gait

(+’ve

indicates-

higher speed

has higher

amplitude)

Medium vs

fast (males)

= 0.05 (-

0.43, 0.53);

medium vs

fast

(females) =

0.65 (0.12,

1.17), slow

vs fast

(males) =

0.05 (-0.43,

0.52), slow

vs fast

(females) =

0.97 (0.39,

1.54), slow

vs medium

(males) =

47

cycle

during

slowest

walking

speed.

0.00 (-0.47,

0.48), slow

vs medium

(females) = -

0.39 (-0.11,

0.88).

Gender

Integrated

EMG

amplitude

over the gait

cycle

(+’ve

indicates-

higher

amplitude for

females)

Gender fast

speed = 0.82

(0.12, 1.52);

Gender

medium

speed = 0.39

(-0.28, 1.08);

48

Gender slow

speed = 0.05

(-0.62, 0.73).

Chumanov et

al. (2012)

Treadmill.

Preferred

cadence,

+5%, +10%

during

unchanged

preferred

speed.

Preferred

speed 2.9

(0.5) m/s

Preferred

cadence

172.6 (8.8)

steps/min

Surface electrode.

Mid-way between

ASIS and PSIS,

below iliac crest.

(Basmajian and

Blumenstein,

1980).

Right limb

Full-wave

rectified

and low

pass

filtered

using a

bidirectiona

l, 6th order

Butterworth

filter (cut-

off 50Hz).

Mean EMG

activity:

stance,

loading

phase (FC

to peak

knee

flexion

angle); late

swing (80-

Cadence

Mean EMG

amplitude

Cadence

(+’ve

indicates-

higher

amplitude for

higher

cadence)

Cadence

preferred vs

+10%:

Stance (0-

15% GC) =

0.29 (0.00,

0.59), Swing

(80-90%GC)

= 0.70 (0.37,

1.02), Swing

(90-100%) =

49

90% and

90-100%

gait cycle).

Normalised

to mean

EMG

across

whole gait

cycle

during

preferred

cadence.

0.45 (0.14,

0.76)

Cadence

preferred vs

+5%:

Stance (0-

15%GC) =

0.09 (-0.19,

0.39), Swing

(80-90%) =

0.33 (0.03,

0.63), Swing

(90-100%) =

0.12 (-0.17,

0.42)

Esculier et

al. (2015)

Treadmill.

Preferred

running

speed

between 2.2

and 2.8m/s

for 5

minutes

Surface

electrodes.

50% distance

along a line from

the iliac crest to

the greater

trochanter

SENIAM

guidelines.

Band-pass

filtered (30-

450Hz), 4th

order zero-

lag

Butterworth

.

Temporal

onset

PFPS

(+’ve =

PFPS

>control)

Duration

(% gait

cycle)

-0.85 (-

1.50, -

PFPS

(+’ve

indicates

PFPS>contro

l)

Mean EMG

amplitude

Stance = -

0.52 (-1.15,

50

PFPS=symptomat

ic limb.

Control

participants

testing limb

matched to PFPS

detection:

amplitude

above 5 SD

of resting

mean for 25

ms.

Amplitude

normalized

to a

standardize

d submax

task; prone

hip

extension

with knee

in extended

position.

2kg weight

attached to

ank. Held

for 5s x 3

repetitions.

0.2),

Onset (%

gait cycle)

0.11 (-

0.51,

0.72)

0.10)

Peak

amplitude

Stance = -

0.32 (-0.94,

0.3)

Gazendam

and Hof

Treadmill.

2.25, 3.0,

Surface electrode.

50% distance

Highpass

filtered

Burst

activity

Burst activity

profile

51

(2007) 3.5, 4.0 and

4.5m/s

along a line from

the iliac crest to

the greater

trochanter

SENIAM

guidelines.

Right limb

(Butterwort

h 4th order,

20Hz) to

remove

artifact.

Generate

linear

envelope

with low-

pass filter

after

rectifying

(Butterwort

h 4th order,

24Hz).

profile

Mann et al.

(1986)

Overground

Run 3.4m/s,

4.5 m/s,

Sprint

<10sec/100

m

Surface

electrodes.

Location not

reported.

Limb not

specified

Processing

not

reported

Speed

Duration

Qualitativ

e

descriptio

n.

Smith et al.

(2014)

Over

ground

Surface

electrodes.

High-pass

filtered

AT

Onset

52

(25m)

4m/s ± 10%

50% distance

along a line from

the iliac crest to

the greater

trochanter

SENIAM

guidelines.

AT: most

symptomatic limb

Control:

Dominant limb

(Butterwort

h 4th Order,

10Hz).

Smoothed

with a

moving

average

over a 20-

ms

window.

Temporal

onset

detection:

amplitude

above 15%

peak

activity

across GC

for at least

10% GC.

Expressed

as ms.

(+’ve =

AT later

onset)

Pre FC =

2.09

(1.21,

2.97)

Offset

(+’ve =

AT later

offset)

= -0.69 (-

1.43,

0.03)

Duration

(+’ve =

AD longer

duration)

= -2.29 (-

1.39, -

3.21)

Souza and Over Surface Band pass PFPS

53

Powers

(2009)

ground

(15m)

3m/s

electrodes.

25mm inferior to

the iliac crest,

directly superior

to the greater

trochanter.

PFPS: 13 right, 8

left

Control: 13 right,

7 left

filtered (35-

500Hz).

60Hz notch

filter

applied.

Rectified

and

smoothed

with a

moving

average

(75ms

window) to

generate

linear

envelope.

Amplitude

normalized

to %MVIC

Mean EMG

amplitude

(+’ve =

PFPS>contro

l)

Stance =

0.15 (-0.47,

0.76)

Unfried et al.

(2013)

Over

ground

(50m)

Self-

selected

Surface

electrodes.

50% distance

along a line from

the iliac crest to

Band pass

filtered (40-

450Hz) and

rectified.

Amplitude

Burst

activity

profile

Burst activity

profile

54

speed ± 5% the greater

trochanter

SENIAM

guidelines

Right limb

normalized

to %MVIC

(position

not

described)

Wall‐Scheffl

er et al.

(2010)

Treadmill

1.8, 2.7 and

3.6 m/s

Surface

electrodes.

Mid-way between

ASIS and PSIS,

below iliac crest.

(Basmajian and

Blumenstein,

1980)

Right limb

Rectified

and low

pass

filtered

(Butterwort

h 6th order,

5-Hz).

Amplitude

normalized

to mean

activity at

slowest

walking

speed.

Integrated

amplitude

determined

across the

gait cycle

Speed

Integrated

EMG

amplitude

(gait cycle)

(+’ve

indicates

higher speed

has higher

amplitude)

Medium vs

fast = 0.16 (-

0.18, 0.49),

Slow vs fast

= 0.27 (-

0.07, 0.61),

Slow vs

medium =

0.14 (-0.19,

55

0.48)

Willson et al.

(2011)

Over

ground

(20m)

Between

3.52m/s and

3.89m/s

Surface

electrodes.

Superior to the

greater trochanter

on a line to the

most lateral

aspect of the iliac

crest.

PFPS = most

symptomatic limb

Control = right

limb

Band pass

filtered (20-

450Hz).

High pass

filtered (bi-

directional

Butterworth

4th order,

30Hz).

Full-wave

rectified

and low

pass

filtered (bi-

directional

Butterworth

4th order,

30Hz, 6Hz)

Temporal

onset

detection:

amplitude

above 5 SD

PFPS

Onset

(+’ve =

PFPS later

onset)

Pre FC =

0.76

(1.40,

0.11)

Duration

(+’ve =

PFPS

longer

duration)

= -0.86 (-

1.5, -0.21)

PFPS

Peak EMG

amplitude

(+’ve =

PFPS

>control)

Stance (onset

to offset) =

0.21 (-0.41,

0.83)

Mean EMG

amplitude

(+’ve =

PFPS>contro

l)

Stance

(Onset to

offset) = 0.55

(-0.08, 1.18)

56

of resting

mean for 25

ms.

Amplitude

normalized

to %MVIC

(resisted

side-lying

hip

abduction

(knee

extended)).

Willson et al.

(2012)

Over

ground

(20m)

Between

3.52m/s and

3.89m/s

Surface

electrodes.

Applied inferiorly

to the most lateral

aspect of the iliac

crest on a line to

the ipsilateral

greater

trochanter.

Limb not

reported

Band pass

filtered (20-

450Hz).

High pass

filtered (bi-

directional

Butterworth

4th order,

30Hz).

Full-wave

rectified

and low

Gender

Onset

(+’ve =

females

later

onset)

Pre FC = -

0.04

(0.58, -

0.66)

Duration

(+’ve =

Gender

(+’ve =

females

higher

amplitude)

Peak EMG

amplitude

Stance (onset

to offset) =

0.10 (-0.52,

0.72)

Mean EMG

57

pass

filtered (bi-

directional

Butterworth

4th order,

30Hz, 6Hz)

Temporal

onset

detection:

amplitude

above 5 SD

of resting

mean for 25

ms.

Amplitude

normalized

to %MVIC

(resisted

side-lying

hip

abduction

(knee

extended)).

females

longer

duration)

= -0.12 (-

0.74,

0.50)

amplitude

(+’ve =

females

higher

amplitude)

Stance (onset

to offset) =

0.18 (-0.44,

0.80)

Abbreviations: ASIS, anterior superior iliac spine; AT, Achilles tendinopathy; FC, foot

58

contact; GC, gait cycle; L, left; MVIC, maximum voluntary isometric contraction; PFPS,

patellofemoral pain syndrome; PSIS, patellofemoral pain syndrome; R, right; TO, toe-off;

Basmajian, J.V., Blumenstein, R., 1980. Electrode placement in EMG biofeedback.

Williams & Wilkins.

59

Table 3: Risk of bias assessment

Study External

Validity

Internal Validity

Detection Attrition Selection bias / Control of confounding

1 2 3 4 5 6 7 8

Azevedo et al. (2009) N/A N/A

Bartlett et al. (2014) N/A

Chumanov et al. (2008) N/A

Chumanov et al. (2012) N/A

Esculier et al. (2015)

Gazendam et al. (2007) N/A

Mann et al. (1986) N/A

Smith et al. (2014) N/A N/A

Souza et al. (2009) N/A N/A

Unfried et al. (2013) N/A

Wall-Scheffler et al. (2010) N/A

Willson et al. (2011) N/A

Willson et al. (2012) N/A

Note: indicates the quality measure was addressed adequately, indicates the quality measure was not addressed adequately or not reported clearly in the study

(1) Representative: if the study describes demographic details (age, gender) and running experience (must include distance per week, or describe as recreational or elite); (2) Blinded assessor: if data

assessed or processed by a blinded assessor (blind to group allocation (e.g. injury) or condition (e.g. speed); (3) Attrition: if more than 80% of data available from those recruited; (4) Appropriate muscle

localisation: if surface electrodes were described being placed according to SENIAM guidelines or anatomy atlas; (5) Randomisation: if described, e.g. between conditions (speed), if no randomisation occurred, N/A if not applicable; (6) Appropriate normalisation: if between group comparisons normalised to a standard reference is described (pathology vs. normal or gender comparisons), N/A for within group comparisons. (7) Appropriate statistical tests used: if describes type of tests e.g. parametric/ non-parametric according to normality, or adjusting for confounding; (8) Appropriate description of temporal measures: if the study explicitly identifies the method of determining onset of muscle activation, if not described, N/A if not applicable

60

TABLE 4. GRADE: Quality of Body of Evidence

Outcome Inconsistency Indirectedness Imprecision Risk

of

bias

Rating

PFPS

Amplitude- average ✗ ✔ ✗ ✔ Low

Amplitude- peak ✗ ✔ ✔ ✔ Moderate

Temporal- onset ✗ ✔ ✗ ✔ Low

Temporal- duration ✗ ✔ ✔ ✔ Moderate

Note:

Inconsistency, downgrade if I² ≥ 25%; 2. Indirectness, downgraded if clinically

heterogeneous (e.g. differences in diagnostic criteria for injured runners); 3. Imprecision,

downgrade if upper or lower confidence interval spanned an effect size of 0.5 in either

direction; and 4. Reporting bias, downgrade if quality appraisal score average < 60%.

61

Appendix 1: Search terms

Concept 1: Gluteals Concept 2: Running Concept 3:

Electromyography

Buttocks [mesh] Running [mesh] Electromyography [mesh]

Hip [mesh] Run* (ti, ab) Electromyograph*

Hip abductor* (ti, ab) Jog* (ti, ab) EMG

Hip stabiliz* (ti, ab) Ambulat* (ti, ab) Muscle onset

Hip stabilis* (ti, ab) Locomotion (ti, ab) Muscle amplitude

Gluteus med* (ti, ab) Gait (ti, ab) Muscle timing

Gluteus min* (ti, ab) Muscle activ*

Glut* (ti, ab)

Terms combined with ‘OR’ within concepts. Terms combined with ‘AND’ between

concepts.

62

Biography

Dr Adam Ivan Semciw

Adam was awarded a BAppSc (Physiotherapy) with Honours in 2001 from the University

of Sydney, Australia, and a PhD from La Trobe University, Australia in 2013. He is

currently working as a Research Fellow in Physiotherapy at the University of Queensland.

His research interests include hip and lower limb muscle activity in health and dysfunction.

Miss Rachel Neate

Rachel graduated with a Master of Physiotherapy Practice (Honours) in 2013, from La

Trobe University, Australia. Her Honours research analysed the role of the deep hip

muscles during running with the aim to better direct rehabilitation programs. Prior to

commencing her Physiotherapy Degree, Rachel completed a Bachelor of Exercise Science

at Victoria University in 2009. Rachel currently works as a physiotherapist in private

practice.

Dr Tania Pizzari

Dr Tania Pizzari is a part-time Lecturer in the Department of Physiotherapy at La Trobe

University. She graduated from La Trobe University with a Bachelor of

Physiotherapy(Hons) in 1997 and with a PhD in 2002. Her research interests include

rehabilitation for shoulder instability, EMG of the shoulder, groin pain and hamstring

injuries in football, and hip muscle structure and function. She works part-time in her own

private practice as a physiotherapist, presents lectures on knee, hamstring and shoulder

management for the Australian Physiotherapy Association and consults to the Victorian

worker’s compensation association.

63

64

65