THE SYMMETRY ANGLE IDENTIFIES LESS CLINICALLY RELEVANT
INTER-LIMB ASYMMETRIES THAN THE SYMMETRY INDEX IN HEALTHY
Daniel J. Glassbrook1, Joel T. Fuller1, Jacqueline A. Alderson2, Jodie A. Wills1
and Tim L. A. Doyle1
Faculty of Medicine and Health Sciences, Macquarie University, Sydney,
Faculty of Science, The University of Western Australia, Perth, Australia2
There are several methods for calculating inter-limb symmetry, an inter-limb difference
≥15% has been suggested as an indicator of sporting injury risk. The purpose of this study
was to compare three common methods for determining symmetry: the Symmetry Index
(percentage difference; SI) when referenced to the left limb (SILeft) or the average of both
limbs (SIAverage), and the Symmetry Angle (vector difference; SA). 15 recreationally active
participants completed a sprint protocol on a non-motorised treadmill. Accelerometers were
positioned on both tibias to measure peak resultant acceleration (PRA). The SA identified
less clinically relevant PRA inter-limb asymmetries than the SI in healthy adults. Once an
appropriate level of asymmetry as measured by the SA is determined, this may help to
more correctly identify asymmetry in athletes and patients than the SI.
KEYWORDS: Accelerometer, non-motorised treadmill, inertial measurement unit.
INTRODUCTION: Measuring biomechanical locomotor asymmetry of the lower-limb is
relevant for athletic and general populations because inter-limb differences of ~15% may be
indicative of injury risk (Knapik, Bauman, Jones, Harris, & Vaughan, 1991; Zifchock, Davis, &
Hamill, 2006). Moreover, lower-limb asymmetry may decrease athletic performance (Bishop,
Turner, & Read, 2018). There are several different methods to calculate locomotor asymmetry
(Bishop, Read, Chavda, & Turner, 2016), two common methods are the Symmetry Index (SI),
and Symmetry Angle (SA). The SI measures the percentage difference between two limbs
relative to the non-injured or dominant limb, or an average of both limbs. However, in healthy
populations there may not be a clear reference limb available and the choice of reference limb
can influence the percentage outcome, as the reference limb may not consistently provide the
smallest or largest value in the equation for each participant measured (Zifchock, Davis,
Higginson, & Royer, 2008). A study by Zifchock et al. (2008) also showed that using an average
of both limbs as reference produces a significantly smaller percentage asymmetry than when
referenced to one limb (left) (Zifchock et al., 2008). The SA was developed as an alternative
to the SI and is calculated by plotting a measure for the right side against a measure for the
left side (Xright,Xleft). A vector line is drawn from this point through the intersection of the x and
y axes, and the angle with respect to the x-axis is calculated. Two identical values will create
a 45° angle, indicating perfect symmetry. The vector angle can be converted to a percentage
and compared to SI results (Zifchock et al., 2008).
Wearable technology such as accelerometers have been suggested as a way to measure
meaningful biomechanical asymmetries in human locomotion (Willy, 2018). They have been
shown to reliably measure impact loading (Crowell & Davis, 2011), and foot-ground collisions
(Lucas-Cuevas, Encarnacion-Martinez, Camacho-Garcia, Llana-Belloch, & Perez-Soriano,
2017). However, it is unclear whether the SI or SA is most suitable to assess these results for
The purpose of this paper was to compare the SI with reference to the left side (SILeft) and the
SI referenced to the average of left and right sides (SIAverage) with the SA in peak resultant
acceleration (PRA) obtained during running.
METHODS: Fifteen recreationally active participants (Male n = 9, 23.9 ± 3.6 yrs, 1.8 ± 0.05 m,
78.3 ± 12.0 kg; Female n = 6, 27.3 ± 6.0 yrs, 1.7 ± 0.05 m, 66.3 ± 10.7 kg) volunteered to
participate in this study. Participants were eligible to participate if, at the time of recruitment,
they were: 1) aged 18-35 years, 2) free of injury, and 3) able to run without restriction. This
study was approved by the Macquarie University Human Research Ethics Committee (ethics
protocol number: 5201700532). Written informed consent was received from each participant
prior to participation.
Participants were required to attend a total of four sessions, separated by a minimum of 24-
hours recovery, over the course of a two-week period. The first three sessions were
familiarisation sessions that allowed participants to become accustomed to running on a non-
motorised treadmill (Force 3, Woodway USA, Inc., Waukesha, WI, USA). All data collection
occurred during the final session and all running was performed on the non-motorised
treadmill. Each session lasted approximately 20 minutes and was identical for both
familiarisation and data collection sessions. Participants performed a standardised warm-up
consisting of dynamic stretches and two minutes of steady-state running at 50-60% of self-
perceived maximal effort. After a 30-60 second standing rest period, participants ran for 60
seconds at 60% of self-perceived maximal effort and then immediately completed a 15 second
sprint at 70% of self-perceived maximal effort. The participant then ran for 60 seconds at 60%
self-perceived maximal effort as an active recovery. This sequence was repeated four more
times, with the sprint efforts of 80%, 90%, 100%, and 100%, respectively. Running at self-
perceived maximal effort is a reliable method of setting running speed on a non-motorised
treadmill (Tofari, McLean, Kemp, & Cormack, 2015). The athlete was tethered to a vertical
strut at the rear of the treadmill using a belt and cable so that they remained in place while
running on the treadmill belt (belt dimensions: 55 cm wide x 173 cm long) (Brown, Brughelli, &
Cross, 2016). Two accelerometers (iMeasureU, Auckland, New Zealand) measuring 40 x 28 x
15 mm and weighing 12 g were used to measure accelerations in three axes (x, y and z) at
500 Hz. The two accelerometers were attached to the distal medial tibial malleolus using velcro
straps in accordance with manufacturer recommendations.
Analysis of step-by-step acceleration data for each accelerometer was performed using
proprietary software (IMU_Step, version 1.0, iMeasureU, Auckland, New Zealand). The
variable of interest was the PRA for each step during the 100% sprint. Only the best quality
100% sprint effort for each participant was used for analysis. Data from each 100% sprint were
visually inspected to qualitatively determine the sprint with highest data quality. For each
participant the selected 100% sprint contained a minimum of 19 steps, and a maximum of 32
steps. Distal tibial PRA provides an indication of lower-limb loading, and increased lower-limb
loading is often suggested to increase injury risk (Crowell & Davis, 2011). Using the mean
absolute PRA value for the 100% sprint, the SILeft (equation 1), SIAverage (equation 2) and SA
(equation 3) were calculated (Zifchock et al., 2008):
Consistent with previous research, the left side was chosen for the SI equation with a single
side as a reference value as opposed to the right side (Zifchock et al., 2008).
Paired t-tests were used to assess any systematic bias between SILeft and SA, and between
SIAverage and SA. Pearson’s correlations were calculated to determine the relationship between
SILeft and SA, and between SIAverage and SA. Consistent with prior research, 15% was used as
the threshold for clinically significant asymmetry (Knapik et al., 1991; Zifchock et al., 2006). All
analysis was completed using SPSS software (version 23, IBM, Armonk, NY, USA). The alpha
level was set at 0.05.
RESULTS and DISCUSSION: Symmetry results for all participants, the number of participants
presenting with a clinically significant asymmetry for each equation, and the difference
between the SILeft and SA, and SIAverage and SA symmetry angles are presented in Table 1.
Table 1: Asymmetry results for each method for all participants.
No. of participants >15% asymmetry
9.7 ± 7.6
6.6 ± 5.2
9.8 ± 7.6
6.7 ± 5.3
3.1 ± 2.4
SILeft, Symmetry Index with reference to the left side; SIAverage, Symmetry Index with reference to the
average of left and right sides; SA, Symmetry Angle. Data are presented as mean ± SD.
Significant differences were observed between SILeft and SA (t(14) = 4.927, p < 0.001) and
between SIAverage and SA (t(14) = 4.943, p < 0.001). The SA was positively correlated with
SILeft (r = 0.989) and SIAverage (r = 1.000) (Figure 1).
Figure 1: Relationship between Symmetry Angle and Symmetry Index.
The results of this study support previous research that suggested the SA produces
significantly less asymmetry values than the SI, regardless of whether the left limb or average
of both limbs is used as a reference (Błażkiewicz, Wiszomirska, & Wit, 2014). If only the SI
equations were used, this study would have found that one third (5/15) of the participants in
this study had a clinically significant asymmetry between limbs. However, if the SA was used,
no participants would have been found to have inter-limb asymmetry >15%. Clinical significant
asymmetries were not expected because participants were only eligible to take part in this
study if they were free of injury and able to run without restriction. However, it is possible that
each participant may have presented with a level of inherent inter-limb asymmetry, resulting
from their training or sporting history, limb dominance, or leg-length discrepancies (Perttunen,
Anttila, Södergård, Merikanto, & Komi, 2004; Sadeghi, Allard, & Duhaime, 1997). Indeed, the
mean SI of resultant force metrics during sprinting were 3.9-9.6% in a previous study involving
healthy adults (Korhonen et al., 2010). As a result, it may not be reasonable to expect that
each participant in the present study should be without clinically significant asymmetry
between limbs in PRA.
The results of the SA should be interpreted with some caution because a clinically significant
asymmetry of >15% may not apply to these results (Zifchock et al., 2008). Knapik et al. (1991),
found that inter-limb asymmetries in isokinetic hip and knee strength >15% were a predictor of
injury in female collegiate athletes. Since then, a value of 15% asymmetry has been applied
to a variety of lower-limb measures. The SA was developed more recently (Zifchock et al.,
2008) and it is not known whether a 15% threshold for defining clinically significant asymmetry
is still appropriate. Therefore, when using the SA, the percentage chosen as being
representative of a clinically significant asymmetry may need to be investigated and modified.
Findings from the present study suggest that a threshold of 10% might be more appropriate
because SA values were approximately 6-7% lower than SI values.
CONCLUSION: This study used accelerometers attached to the distal tibia to measure inter-
limb asymmetries in peak acceleration during running. When deriving the percentage
difference between limbs, the result depends on the equation used to calculate the inter-limb
difference. The SA equation identifies less clinically relevant asymmetries than the SI equation
when referenced to the left lower-limb and the average of both lower-limbs. Future research
should validate a clinically relevant threshold of asymmetry using the SA because the 15%
threshold that is commonly used for the SI may not be appropriate.
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