Association between inter-limb asymmetry and endurance running performance in healthy populations: A systematic review

Association between inter-limb asymmetry and endurance running performance in healthy populations: A systematic review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between inter-limb asymmetry and endurance running performance in healthy populations: A systematic review Joachim D'Hondt, Laurent Chapelle, Chris Bishop, Dirk Aerenhouts, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3787566/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Nov, 2024 Read the published version in Sports Medicine-Open → Version 1 posted 4 You are reading this latest preprint version Abstract Background ː The presence of inter-limb asymmetry in the human body has traditionally been perceived to be detrimental for athletic performance. However, a systematic review addressing and comprehensively assessing the association of asymmetry between the lower limbs and endurance running performance is currently lacking. Objective : The main purpose of this systematic review was to examine the relationship between lower inter-limb asymmetry and running performance in healthy endurance runners. The secondary objective was to identify possible avenues for further research in this area. Methods ː Pubmed, Web of Science and SPORTDiscus were systematically searched for studies investigating the relationship between lower inter-limb asymmetry and (determinants of) running performance in healthy and injury-free endurance runners. The quality of studies eligible for inclusion was assessed using the Downs and Black Quality Index Tool. Results ː Out of 4817 articles screened, 8 studies were included in this review. The quality score of the included research varied between 5/10 and 9/10. Except from one finding demonstrating a positive association between peak ankle dorsiflexion asymmetry and running performance, all other lower inter-limb asymmetry outcome measures were either negatively (N = 16) or not significantly (N = 30) associated with running performance. Conclusions ː A high heterogeneity across study methods and outcomes was apparent, making it difficult to draw a straightforward conclusion. Despite one study showing a positive relationship, the results demonstrate that some, but not all, metrics of functional, morphological, kinematic and kinetic inter-limb asymmetry are negatively or not associated with running performance. Thus, a more extensive high-quality body of research is essential to determine whether and to what extent asymmetry between the lower limbs could affect endurance running performance as well as to establish potential trade-off values for practitioners in developing training programs. Side-to-side differences bilateral difference between-limb Functional asymmetry Morphological asymmetry Kinematic asymmetry Kinetic asymmetry Biomechanics Running economy Distance running Figures Figure 1 Figure 2 Key points In the majority of the metrics, the magnitude of lower inter-limb asymmetry was not or negatively associated with running performance. Coaches, athletes and researchers should be attentive of the task, time- and metric-specificity as well as the inter- and intra- individual variability of magnitude outcomes, when assessing inter-limb asymmetries 1. Background The concept of lateralization is a fundamental aspect of human neurodevelopment that involves the preferential use of one side of the body over the contralateral side during voluntary movement [ 1 ]. This phenomenon, that initiates before birth and expands during early infancy, occurs in almost every individual resulting in imbalances between body sides [ 1 ]. Such inter-limb asymmetry can manifest itself in multiple dimensions, encompassing functional (e.g., strength, power, speed, range of motion and agility), morphological (e.g., muscle mass, bone mineral content and fat mass), kinematic (e.g., body centre of mass displacements, joint angles or spatiotemporal measures, such as contact time, flight time, step length and step frequency), and kinetic (e.g., peak vertical ground reaction force) [ 2 – 5 ] measures. Interestingly, previous studies have shown that these different types of asymmetry are not necessarily related to each other [ 2 , 6 ]. Furthermore, the magnitude of asymmetry has also been reported to be specific according to the task, metric, test occasion, and individual [ 7 – 12 ]. Given that the presence of inter-limb asymmetry has intuitively been considered to increase injury risk and to compromise athletic performance among sport practitioners, an abundance of research has been conducted on this topic over the last decade [ 4 , 13 – 15 ]. From a sports performance perspective, recent research has shown that a larger magnitude of functional asymmetry can be associated with impaired sport performance [ 16 – 20 ]. For instance, larger inter-limb differences resulting from the unilateral countermovement (CMJ) and drop jump (DJ) tests have been shown to be positively correlated with sprint time (CMJ: r = 0.43 to 0.71, DJ: r = 0.52 to 0.58) and change of direction time (CMJ: r = 0.61 to 0.71, DJ: r = 0.52 to 0.66), both indicating poorer performance, in youth team-sport athletes [ 19 – 22 ]. However, there are also a number of studies demonstrating no meaningful relationship between functional inter-limb asymmetry and athletic performance [ 21 , 23 – 25 ]. In the realm of morphological asymmetry, the existing literature on athletes shows that a higher degree of lean mass asymmetry between the lower limbs is also related to a decreased sport performance, in tasks such as kicking ( r = -0.31 to 0.41) [ 26 ]. Further to this, inter-limb asymmetry in calf girths has been negatively associated with cycling performance ( r = -0.461) [ 27 ], whereas asymmetries in knee and ankle widths have been reported to explain 5% of the variation in track and field performances [ 28 ]. Also regarding kinematic and kinetic asymmetry, several studies demonstrated significant relationships with sport performance [ 29 , 30 ]. For instance, Rannama et al. [ 30 ] documented negative associations between peak isokinetic torque asymmetry (at 180° sec − 1 ) ( r = -0.50) as well as trunk ( r = -0.65) and pelvis ( r = -0.63) kinematic asymmetry and power output during a 5-second maximal cycle test. Although extensive research has recently been conducted on the link between inter-limb asymmetry and sports performance, the available body of literature on inter-limb asymmetry has focused mostly on unilateral sports (i.e., sports that involve primarily one side of the body or predominantly use one limb, such as tennis, soccer or cricket) [ 4 , 31 , 32 ]. The relevance here being that athletes performing unilateral sports will likely exhibit larger magnitudes of inter-limb asymmetry due to the demands and nature of their specific sport [ 2 , 3 ]. However, research has indicated that in so-called bilateral sports (e.g., cycling and running) significant inter-limb asymmetries may also occur due to the preferential use of one side of the body during repetitive movement patterns [ 15 ]. For example, functional, kinematic and kinetic asymmetries at lower limb level, respectively ranging from 16 to 17%, 3 to 54% and 3 to 54%, have been reported in adult endurance runners [ 9 , 33 , 34 ]. In line with research focussing on unilateral sports, inter-limb asymmetry could also affect running performance. For instance, a positive relationship ( r = 0.85) between inter-limb functional asymmetry and running economy (i.e., a major determinant of long-distance running expressed as energy cost in kJ.kg − 75 .km − 1 ) was documented in female adolescent elite endurance runners, indicating that inter-limb asymmetry could impair running performance [ 35 ]. Also, regarding morphological asymmetry, Jamaican track athletes have been shown to perform better in 100 m sprinting when having more symmetrical knee and ankle widths [ 28 , 36 ]. Whilst it is acknowledged that these are non-modifiable factors, such data could have a role to play as part of the talent identification process. In contrast, previous research has shown no significant associations of maximal sprint velocity and running velocity with spatiotemporal, kinematic and kinetic asymmetry [ 5 , 37 ]. For example, Mackala et al. [ 38 ] found no significant difference in the magnitude of stride length asymmetry between novice and elite sprint athletes. Despite advancements in knowledge on the topic, it is important to note that running includes both sprinting (i.e., up to distances of 400 m) and endurance running (i.e., distances exceeding 400 m), implying that both disciplines should be examined separately in the context of inter-limb asymmetry in relation to performance. Specifically, getting out of the starting blocks alongside the subsequent acceleration phase with changing step and stride characteristics [ 38 , 39 ], makes sprinting inherently more asymmetric. Consequently, this asymmetry during sprinting could induce greater side-to-side differences to the body compared to submaximal running at a more constant velocity. Endurance running is a commonly practiced and popular sport and leisure time activity on a global level. Given the potential impact of inter-limb asymmetry in view of endurance running performance, a clear overview of the available literature is warranted to help practitioners better understand the possible role of inter-limb asymmetry in their sport. However, a systematic review on the association between inter-limb asymmetry and endurance running performance (i.e., actual running performance and/or determinants of running performance) is currently lacking. Therefore, the main aim of this systematic review was to comprehensively synthesize and appraise the available literature relating to inter-limb asymmetry and to evaluate its association with endurance running performance in healthy populations. Based on the resulting synopsis, some possible avenues for future research on the topic will be suggested. 2. Methods This systematic review was written in accordance with the “Preferred Reporting Items for Systematic reviews and Meta-Analyses” (PRISMA) guidelines [ 40 ], and was registered on Prospero on 31 October 2023 (ID: CRD42023474606). 2.1 Eligibility criteria Table 1 presents an overview of the eligibility criteria used during our systematic search in order to guide the selection procedure. All criteria were determined a priori in accordance with the P, O and S dimensions from the PICO(S) acronym [ 40 ]. Table 1 Eligibility criteria Inclusion criteria Exclusion criteria Participants • Healthy injury-free male and / or female participants of any age • Endurance runners of any level • Physical conditions that may influence running asymmetry or running performance • Sprint athletes (i.e., up to distances of 400 m) Outcome of interest • Analysis of the association between lower inter-limb asymmetry and endurance running performance (and/or its determinants) • Analysis of the association between inter-limb asymmetry and sports performance, but not specifically related to endurance running • Only reporting the magnitude of asymmetry Study design • English original peer-reviewed articles • Umbrella reviews, systematic reviews or meta-analyses, books, conference abstracts ** PLEASE INSERT Table 1 ABOUT HERE ** 2.2 Search strategy The electronic databases PubMed, Web of Science, and SPORTDiscus (EBSCOhost) were systematically screened to gather relevant literature in August 2023. Searches included all papers using “Title / Abstract” for PubMed, and “Abstract” for Web of Science and EBSCOhost. In accordance with the eligibility criteria, the search was limited to English-language articles only. Additionally, “Article” and “Early access” in Web of Science, and “Academic Journals” in EBSCOhost were selected as source type, whereas no source type could be selected in PubMed. No limit was imposed on the publication date. Forward citation tracking (i.e., screening the citations of the included studies, using Web of Science) as well as backward citation tracking (i.e., screening the reference list of the included studies) was performed for the included articles. Citations and reference lists of related (systematic) reviews identified during the search were also screened. The corresponding flow diagram is depicted in Fig. 1 and the complete search strategy with Boolean operators is detailed in Table 2 . Table 2 Schematic overview of the search strategy Data base Hits Complete search strategy using Boolean operators (as applied across all databases, N = 3) PubMed 952 (“distance runn*” OR “endurance runn*” OR “middle distance runn*” OR “jogging” OR “marathon” OR “trail runn*” OR “ultramarathon” OR “track and field” OR “5k” OR “10k” OR “run” OR “runn*” OR “sprint” OR “treadmill”) AND (“asymmetr*” OR “symmetr*” OR “imbalance” OR “side-to-side” OR “dissymmetr*” OR “interlimb” OR “inter-limb” OR “between-limb” OR “bilateral difference”) AND (“performance” OR “time trial” OR “trial” OR “speed” OR “velocity” OR “economy” OR “cost of running” OR “energy cost” OR “VO2max” OR “VO2peak” OR “maximal oxygen uptake” OR “oxygen consumption” OR “fatigue” OR “exhaustion” OR “lactate” OR “aerob*” OR “anaerob*” OR “stride length” OR “step length” OR “contact time*”) NOT (“injur*” OR “ACL” OR “anterior cruciate ligament” OR “syndrome” OR “traum*” OR “facture” OR “illness” OR “disease” OR “amput*” OR “stroke” OR “cerebral palsy” OR “tremor” OR “diagnosed” OR “disorder” OR “osteoarthritis” OR “geriatric” OR “return to sport” OR “rehabilitat*” OR “diagnosis” OR “pathology” OR “surgery”) ** PLEASE INSERT FIGURE 1 ABOUT HERE ** ** PLEASE INSERT Table 2 ABOUT HERE ** 2.3 Study selection All articles were retrieved from the three scientific databases consulted, and duplicates were removed using the Endnote software. Subsequently, all titles and abstracts were screened in a blinded and standardized manner by two independent researchers (J.D. and L.C.) using the Rayyan software [ 41 ]. Any divergency between both reviewers was resolved by consensus, or by discussion with a third reviewer (D.A.). The remaining articles were independently evaluated on full text by the same two researchers, who registered the reasons for exclusion. 2.4 Data collection process Data extraction from the included studies was also conducted by two reviewers independently (J.D. and L.C.). Any disagreement was resolved by consensus, or by discussion by a third reviewer (D.A.). A self-created form was used to collect the following data per included study: year of publication, study design, sample characteristics (i.e., sample size, mean age, gender distribution, training status [e.g., athletes or non-athletes]), type of assessments as well as the metrics used to determine inter-limb asymmetry and equations applied to calculate asymmetry magnitude, association(s) between inter-limb asymmetry and any measure or metric to express participants’ running performance. 2.5 Risk of Bias Assessment The included studies’ quality was assessed by two independent researchers (J.D. and L.C.) using the Downs and Black Quality Index Tool [ 42 ]. In accordance with two recent systematic reviews, a modified version of this tool was used by only including the items deemed relevant for this current review [ 43 , 44 ]. More specifically, the items relating to patient treatment, training interventions and group randomization processes were excluded from the assessment. Each remaining item (N = 10, see Table 3 ) was scored either a 1 (yes = ‘•’), a 0 (no = ‘○’) or was indicated as ‘-’ when unable to determine a score based on the information in the study reports. All disagreements between assessors were resolved through discussion with a third reviewer (D.A.). Table 3 Questions from the modified Downs and Black [ 40 ] checklist used to evaluate methodological quality of the included studies Item Number Question Reporting 1. Is the hypothesis/aim/objective of the study clear? 2. Are the main outcomes to be measured clearly described in the introduction or methods section? *Information outlined in introduction/methodology for both physical characteristics and running performance measure used for associative analysis pertaining to assessment(s) used, any calculations used and units of measurement 3. Are the characteristics of the subjects included in the study clearly described? *Source defined, with characteristics included 4. Are the main findings of the study clearly described? 5. Does the study provide estimates of the random variability in the data for the main outcomes? *One of the following included for both physical characteristics and running performance measures: a) mean ± SD, b) standard error, c) confidence intervals and d) interquartile range 6. Have actual probability values been reported (e.g. 0.035 rather than < 0.05) for the main outcomes except where the probability value is less than 0.001? *Exact correlation (r) and significance (p) values provided, specific to the associative analysis External validity 7. Were the subjects to participate in the study representative of the entire population from which they were recruited? * Proportion of subjects asked to participate, relative to the sample population, explicitly stated. Unless evident, then answer "unable to determine" Internal validity 8. If any of the results of the study were based on 'data dredging,' was this made clear *Were any additional data analysis reported in the results not highlighted during the methodology 9. Were statistical tests used to assess the main outcomes appropriate 10. Were the main outcome measures accurate (valid and reliable)? ** PLEASE INSERT Table 3 ABOUT HERE ** 3. Results 3.1 Study selection The search strategy yielded a total of 4817 articles, of which 672 duplicates were removed. Subsequently, 4135 articles were excluded based on title and abstract screening. In total, 7 articles were included in this systematic review after full text screening and one additional article was included from backward citation tracking, resulting in a total of 8 studies to be included. The most common reasons for exclusion of studies (i.e., based on titles and abstracts) were: wrong study field (97.3%), wrong population (1.7%), wrong outcome measures (0.6%), and wrong study design (0.4%). In the second screening phase (i.e., based on full text articles), all further exclusions were due to a wrong study design of population (100%). 3.2 Risk of bias The risk of bias assessment is presented in Table 4 . Based on the modified assessment tool of Downs and Black [ 42 ], including only 10 items, we were unable to confirm the external validity of all the studies included due to the lack of information regarding the proportion of individuals recruited relative to the overall sample population. Furthermore, no internal validity bias was apparent, except for some studies that failed to report data on the validity and reliability of the outcome measures used. In general, total scores ranged between 5/10 and 9/10 for study methodological quality and risk of bias. Table 4 Results of the risk of bias assessment for all included studies Study Modified Downs and Black checklist item number Total score out of 10 Reporting External validity Internal validity 1 2 3 4 5 6 7 8 9 10 Beck et al., (2018) [ 45 ] • ○ • • • • - • • - 7 Blagrove et al., (2021) [ 35 ] • • • • • • - • • • 9 Joubert et al., (2020) [ 46 ] • • • • • • - • • • 9 Melo et al., (2020)[ 47 ] • • • • • • - • • - 8 Mo et al., (2020) [ 48 ] • • • • • • - • • - 8 Seminati et al., (2013) [ 49 ] ○ ○ • • • ○ - • • - 5 Stiffler-Joachim et al., (2021) [ 50 ] • • • • • • - • • - 8 Tabor et al., (2019) [ 51 ] • ○ • • • ○ - • • - 6 • = yes; ○ = no; - = unable to determine ** PLEASE INSERT Table 4 ABOUT HERE ** 3.3 Study characteristics In addition to the majority of research being performed in Europe (37.5%; GBR, ITA, POL) [ 35 , 45 , 46 ] and North America (37.5%; USA) [ 47 – 49 ], only one included study was conducted in South America (12.5%; BRA) [ 50 ] and another single study in China (12.5%; CHN) [ 51 ]. Furthermore, and except from one study being published in 2013 [ 45 ], all studies were published from 2018 on. 3.4 Sample characteristics Information regarding study characteristics is provided in Table 5 . The 8 included studies represented a total of 181 participants, including 52% males (n = 94) and 48% females (n = 87). The mean age of study participants ranged between 17.1 years [ 35 ] and 42.6 years [ 45 ]. Across studies, these participants consisted for 68% of competitive runners (54% female) [ 35 , 45 , 46 , 48 , 49 , 51 ], 22% of recreational runners (36% female) [ 45 , 47 , 50 , 51 ] and 10% of novice runners (33% female) [ 45 , 51 ]. Table 5 Overview of study sample characteristics, outcome measures, equations for calculating asymmetry magnitude, and main results of the association between inter-limb asymmetry and running performance Study and country (ISO code) Participant characteristics Asymmetry test/metric measured Running performance outcome measure(s) Equations for calculating asymmetry magnitude Associations with running performance N Age (years) Training status Beck et al. (2018) [ 47 ] USA n = 10 (♂: 6, ♀: 4) 23 ± 6 Participants ran at least 3 times per week for minimum 30 min Step time asymmetry Ground contact time asymmetry Stance average vertical ground reaction force asymmetry Peak braking ground reaction force asymmetry Peak propulsive ground reaction force asymmetry Leg stiffness asymmetry Peak vertical ground reaction force asymmetry Metabolic power Symmetry index = \(\frac{\left| \right(\text{t}\text{s}\text{t}\text{e}\text{p}, 1 – t\text{s}\text{t}\text{e}\text{p}, 2) }{ 0.5 \text{X} (\text{t}\text{s}\text{t}\text{e}\text{p}, 1 + \text{t}\text{s}\text{t}\text{e}\text{p}, 2) | }\text{X} 100\) Metabolic power vs. step time asymmetry: r 2 = NA, p < 0.001, Metabolic power = 0.35 \(\bullet\) tstep SI + 0.67 vs. ground contact time asymmetry: β = 0.78, r 2 = NA, p = 0.036, regression equation : NA vs. stance average vertical ground reaction force asymmetry: β = 0.35, r 2 = NA, p < 0.001, regression equation : NA vs. peak braking ground reaction force asymmetry: β = 0.13, r 2 = NA, p < 0.001, regression equation : NA vs. peak propulsive ground reaction force asymmetry: β = 0.20, r 2 = NA, p < 0.001, regression equation : NA vs. leg stiffness asymmetry: β = 0.39, r 2 = NA, p = 0.042, regression equation : NA vs. peak vertical ground reaction force asymmetry: p = 0.469, regression equation : NA Blagrove et al. (2021) [ 35 ] GBR n = 31 (♂:15, ♀: 16) ♂: 17 ± 1 ♀: 17 ± 1 Competitive middle and long-distance runners Non-strength trained Lower limb extensor bilateral symmetry index Hip extension strength asymmetry Hip abduction strength asymmetry Running economy Best race performance Symmetry index (%) = \(\frac{(\text{s}\text{t}\text{r}\text{o}\text{n}\text{g}\text{e}\text{r} \text{l}\text{i}\text{m}\text{b} – \text{w}\text{e}\text{a}\text{k}\text{e}\text{r} \text{l}\text{i}\text{m}\text{b})}{Total} \text{X} 100\) Strength asymmetry (%) = \(\frac{(\text{S}\text{t}\text{r}\text{o}\text{n}\text{g}\text{e}\text{r} \text{l}\text{i}\text{m}\text{b} – \text{w}\text{e}\text{a}\text{k}\text{e}\text{r} \text{l}\text{i}\text{m}\text{b})}{\text{s}\text{t}\text{r}\text{o}\text{n}\text{g}\text{e}\text{r} \text{l}\text{i}\text{m}\text{b}} \text{X} 100\) Males: - Lower limb extensor bilateral symmetry index vs. running economy: r s = 0.30, p > 0.05 vs. race performance: r s = -0.09, p > 0.05 - Hip extension strength asymmetry vs. running economy: r s = 0.02, p > 0.05 vs. race performance: r s = -0.26, p > 0.05 - Hip abduction strength asymmetry vs. running economy: r s = -0.19, p > 0.05 vs. race performance: r s = 0.05, p > 0.05 Females: - Lower limb extensor bilateral symmetry index vs. running economy: r s = 0.30, p > 0.05 vs. race performance: r s = -0.07, p > 0.05 - Hip extension strength asymmetry vs. running economy: r s = 0.11, p > 0.05 vs. race performance: r s = -0.20, p > 0.05 - Hip abduction strength asymmetry vs. running economy: r s = 0.85, p < 0.001 vs. race performance: r s = -0.47, p = 0.07 Joubert et al. (2020) [ 48 ] USA n = 11 (♂: 7, ♀: 4) ♂: 21 ± 1 ♀: 19 ± 1 NCAA Division I athletic program 1500m, 10.000m and 800m specialists Ground contact time Running Economy Ground contact imbalance = \(|\text{%} t\text{l}\text{e}\text{f}\text{t} - \text{%} t\text{r}\text{i}\text{g}\text{h}\text{t}|\) Ground contact time imbalance vs. Caloric Unit Cost (kcal kg − 1 km − 1 ): r = 0.808, r 2 = 0.66, CI : [0.37–0.93], p = 0.003 Caloric Unit Cost = 0.9649 + 0.0354 \(\bullet\) ground contact time Melo et al. (2020) [ 50 ] BRA n = 13 (♂: 8, ♀: 5) 36 ± 4 Amateur trained runners Dynamical symmetry index in vertical, mediolateral en anteroposterior directions based on body centre of mass displacements during a 10km run Mechanical efficiency Global symmetry index = \(\frac{dx \bullet \stackrel{-}{SIx}+dy \bullet \stackrel{-}{SIy}+dz \bullet \stackrel{-}{SIz}}{dx+dy+dz}\) Global symmetry index vs. Mechanical efficiency: r = 0.66, r 2 = 0.43, p = 0.015, Mechanical efficiency = 2.4 + 31.2 \(\bullet\) global symmetry index Mo et al. (2020) [ 51 ] CHN n = 31 (♂: 13, ♀: 18) Competitive runners: 32 ± 4 Recreational runners: 35 ± 7 Novice runner: 29 ± 4 Competitive, recreational and novice runners Stride asymmetry Step asymmetry Stance asymmetry Flight time asymmetry Duty factor asymmetry Fixed instrumented treadmill velocities (i.e., 8, 9, 10, 11 and 12 km/h) Symmetry index = \(\frac{| \text{X}\text{r}\text{i}\text{g}\text{h}\text{t} – Xleft| }{ 0.5 \text{X} (\text{X}\text{r}\text{i}\text{g}\text{h}\text{t} + Xleft) }\text{X}\) Running velocity vs. Flight time asymmetry: p = 0.012 - Competitive runners: r 2 = 0.949, SI = -0.7 \(\bullet\) Velocity + 10.5 - Recreational runners: r 2 = 0.947, SI = 0.5 \(\bullet\) Velocity 2 + 10.2 \(\bullet\) Velocity + 57.9 - Novice runners: r 2 = 0.644, SI = 0.5 \(\bullet\) Velocity 2 + 10.0 \(\bullet\) Speed + 57.3 vs. Time to peak vertical ground reaction force: p = 0.032 - Competitive runners: r 2 = 0.947, SI = -0.5 \(\bullet\) Velocity + 112.6 - Recreational runners: r 2 = 0.993, SI = 0.3 \(\bullet\) Velocity 2 – 6.1 \(\bullet\) Speed + 35.4 - Novice runners: r 2 = 0.780, SI = 0.6 \(\bullet\) Velocity + 0.9 vs. Vertical average loading rate asymmetry: p = 0.002 - Competitive runners: r 2 = 0.940, SI = -2.4 \(\bullet\) Velocity + 40.9 - Recreational runners: r 2 = 0.883, SI = 1.9 \(\bullet\) Velocity 2 – 37.9 \(\bullet\) Velocity + 203.4 - Novice runners: r 2 = 0.933, SI = 0.3 \(\bullet\) Velocity 2 – 5.0 \(\bullet\) Velocity + 40.3 Seminati et al. (2013) [ 45 ] ITA n = 19 (♂) Untrained runners: 33 ± 13 Occasional runners: 32 ± 12 Skilled runners: 43 ± 7 Untrained, occasional and skilled runners Anatomical symmetry (i.e., volumes of pelvis district, upper-leg district, lower-leg district and global anatomical cross correlation value) Dynamical symmetry: body centre of mass trajectory (SI x , SI y , SI z , GI) Metabolic cost Symmetry degree between 3D split volumes: \({r}_{i,j,k}= \frac{{\sum }_{x,y,z}\left[Rv\left(x,y,z\right)-\stackrel{-}{{Rv}_{i,j,k}} \right] \bullet \left[Lrv\left(x-i,y-j,z-k)-\stackrel{-}{Lrv}\right) – \stackrel{-}{{Rv}_{i,j,k}} \right]}{\sqrt{{\sum }_{x,y,z}{\left[Rv\left(x,y,z\right)-\stackrel{-}{{Rv}_{i,j,k}} \right]}^{2} \bullet \sum x,y,z {\left[Lrv\left(x-i,y-j,z-k\right)-\stackrel{-}{Lrv} \right]}^{2}}}\) Global symmetry index = \(\frac{dx \bullet \stackrel{-}{SIx}+dy \bullet \stackrel{-}{SIy}+dz \bullet \stackrel{-}{SIz}}{dx+dy+dz}\) Metabolic cost vs. anatomical symmetry: - Pelvis district: r = 0.157, p = 0.547 - Upper-leg district: r = -0.114, p = 0.662 - Lower leg district: r = 0.059, p = 0.822 - Global anatomical cross correlation value: r = 0.072, R 2 = 0.223, p = 0.055 Global symmetry index = 0.460 \(\bullet\) Global anatomical cross correlation + 0.340 vs. dynamical symmetry: - SI x : r = 0.005, p = 0.983 - SI y : r = 0.105, p = 0.668 - SI z : r = 0.211, p = 0.385 - GI: r = -0.001, p = 0.995 Stiffler-Joachim et al. (2021) [ 49 ] USA n = 54 (♂: 26, ♀: 28) ♂: 19 ± 1 ♀: 19 ± 1 Collegiate cross-country runners Ground contact time Vertical ground reaction force Average loading rate Braking impulse Propulsive impulse Foot inclination Angle Peak hip flexion Peak hip extension Peak knee flexion Peak ankle dorsiflexion Peak hip adduction Peak pelvic drop Base of gate Within-season personal records (i.e., 8 km for male runners and 6 km for female runners) Kinematic outcomes: \(| \text{X}\text{r}\text{i}\text{g}\text{h}\text{t} – Xleft|\) Kinetic outcomes: Symmetry index = \(\frac{| \text{X}\text{r}\text{i}\text{g}\text{h}\text{t} – Xleft|}{ 0.5 \text{X} (\text{X}\text{r}\text{i}\text{g}\text{h}\text{t} + Xleft) }\text{X}\text{X}\) Personal records vs. ground contact time: β = -4.5, CI : [-14.9, 6.3], r 2 : NA, p = 0.39, regression equation : NA Vertical ground reaction force: β = -2.7, CI : [-9.2, 3.7], r 2 = NA, p = 0.42, regression equation : NA Average loading rate: β = -3.3, CI : [-8.1, 1.4], r 2 = NA, p = 0.17, regression equation : NA Braking impulse : β. = 0.0 CI : [-10.4, 10.9], p = 0.99, regression equation : NA Propulsive impulse : β = 14.6, CI : [4.4, 25.0], r 2 = NA, p < 0.01, regression equation : NA Foot inclination Angle : β = -3.9; CI : [-10.9, 2.9], r 2 = NA, p = 0.27, regression equation : NA Peak hip flexion: β = -4.1, CI : [-14.4, 5.7], r 2 = NA, p = 0.41, regression equation : NA Peak hip extension: β = -3.1, CI : [-13.7, 7.2], r 2 = NA, p = 0.56, regression equation : NA Peak knee flexion: β = -1.3, CI : [-7.2, 4.3], r 2 = NA, p = 0.63, regression equation : NA Peak ankle dorsiflexion: β = -6.1, CI : [-12.9, 0.7], r 2 = NA, p = 0.08, regression equation : NA Peak hip adduction: β = 0.4; CI : [-5.5, 6.1], r 2 = NA, p = 0.90, regression equation : NA Peak pelvic drop: β = -2.8, CI : [-4.3, 9.7], r 2 = NA, p = 0.42, regression equation : NA Base of gate: β = -5.5, CI : [-18.9, 7.8], r 2 = NA, p = 0.43, regression equation : NA Tabor et al. (2019) [ 46 ] POL n = 12 (♀) Group A: 23 ± 3 Group B: 23 ± 1 Intermediate and advanced middle-distance runners Muscle strength symmetry Support phase time symmetry Swing phase time symmetry Running velocity Symmetry index = \(\frac{2 \text{X} (\text{r}\text{i}\text{g}\text{h}\text{t}-\text{l}\text{e}\text{f}\text{t}) }{(\text{r}\text{i}\text{g}\text{h}\text{t}+\text{l}\text{e}\text{f}\text{t}) }\) Asymmetry index = \(\underset{{t=t}_{1}}{\overset{{t}_{2}}{\int }}A|{x}_{r}\left(t\right)- {x}_{l}\left(t\right)|\text{d}\text{t}\) Running velocity vs. muscle strength symmetry: β = -5.77, r 2 = NA, p = 0.01, regression equation : NA vs. support phase time symmetry: β = -6.64, r 2 = NA, p = 0.03, regression equation : NA vs. swing phase time symmetry: β = -2.47, r 2 = NA, p > 0.05, regression equation : NA ♂ = male(s), ♀ = female(s), NA = not available, CI = confidence interval, β = beta value, r = Pearson correlation coefficient, r s = Spearman rank order correlation coefficient, r 2 = R-squared value, t = time; dx, dy and dz = vector displacement, SI = symmetry index for three directions (x, y and z); x anteroposterior direction, y = mediolateral direction, z = vertical direction, GI = global symmetry index, Xright = value of the right leg; Xleft = value of the left leg, \({r}_{i,j,k}\) = normalised cross-correlation coefficient, \(\stackrel{-}{Lrv}\) = voxel mean value of the left volume, \(\stackrel{-}{{Rv}_{i,j,k}}\) = voxel mean value of the right volume, \({x}_{r}\left(t\right)\) = value of specific variable recorded for the right leg at time t, \({x}_{r}\left(t\right)\) = value of specific variable recorded for the left leg at time t 3.5 Test and outcome measures 3.5.1 Asymmetry test and metrics Overall, 2 studies examined functional asymmetry [ 35 , 46 ], 1 study morphologic asymmetry [ 45 ], 6 studies kinematic asymmetry [ 46 – 51 ] and 2 studies kinetic asymmetry [ 47 , 49 ] in relation to endurance running performance and/or its determinants. Functional asymmetry was assessed using strength tests (i.e., isometric quarter-squat, isometric hip extension, isometric hip adduction, isokinetic knee flexion, isokinetic knee extension) [ 35 , 46 ], whilst morphological asymmetry was measured by performing magnetic resonance imaging [ 45 ]. Kinematic (e.g., step time, ground contact time, flight time, stride asymmetry, displacements in body centre of mass) and kinetic (e.g., ground reaction force, leg stiffness, braking impulse) variables were all collected while running [ 47 – 51 ] or during the execution of a CMJ [ 46 ]. 3.5.2 Equations for calculating asymmetry A variety of equations were used to express the magnitude of inter-limb asymmetry among participants in the 8 included studies. Four studies calculated the percentage of asymmetry related to the right versus left lower limb [ 46 , 48 , 49 , 51 ], while only one accounted for stronger and weaker lower limb in the formula [ 35 ]. Furthermore, two studies used the global symmetry index to identify the magnitude of asymmetry in body centre of mass trajectory [ 45 , 50 ], whereas one study also adopted a normalised cross-correlation coefficient to quantify the magnitude of asymmetry between 3D split volumes with magnetic resonance [ 45 ]. 3.5.3 Running performance metrics The endurance running performance variables taken into account could be divided in two specific subcategories: determinants of running performance versus actual running performance metrics based on race performances. As determinants of running performance, metabolic power (i.e., energy cost, based on O 2 consumption and CO 2 production [ 52 ]) [ 47 ], metabolic cost [ 45 ], mechanical efficiency [ 50 ], running economy [ 35 , 48 ] and running velocity [ 46 , 51 ] were examined. Furthermore, race performances or personal records were used as an actual running performance metric in two included studies [ 35 , 49 ]. Metabolic power, run economy, metabolic cost and personal records should be interpreted inversely with a view to running performance, as lower values in these metrics correspond to better running performance. 3.6 The association between inter-limb asymmetry and running performance Evidence for an association between inter-limb asymmetry and endurance running performance (and/or its determinants) in healthy populations was mixed. All asymmetry outcomes could be sub-divided into four dimensions (i.e., functional asymmetry, morphologic asymmetry, kinematic asymmetry and kinetic asymmetry) and were assessed independently in view of their link with running performance metrics. Table 6 summarises all (significant positive, significant negative or no significant) associations between functional, morphological, kinematic and kinetic asymmetry and running performance. 3.6.1 Functional asymmetry linked to running performance Tabor et al. [ 46 ] reported significant negative associations of asymmetry in the sum of muscle torque under static conditions in the hip, knee and ankle with maximal running velocity ( β = -5.77, p 0.05) [ 46 ]. In the study by Blagrove et al. [ 35 ], negligible associations were reported between muscle strength asymmetry and race performance as well as running economy (race performance: r = -0.20 to 0.13; running economy: r = 0.02 to 0.30), except for the correlations found between hip abduction strength asymmetry and race performance ( r = -0.47, p = 0.07) as well as running economy ( r = 0.85, p < 0.001) in female endurance runners. 3.6.2 Morphological asymmetry linked to running performance The only study documenting morphological asymmetry included in this systematic review, reported no significant associations of anatomical asymmetry (i.e., volume assessed by means of magnetic resonance images) at the pelvis and lower limb level with the metabolic cost of running ( r = 0.06 to 0.16, p = 0.55 to 0.82) [ 45 ]. 3.6.3 Kinematic asymmetry linked to running performance Significant associations were reported between kinematic asymmetry and metabolic power ( β = 0.10 to 0.80, p < 0.001) [ 47 ]. More specifically, for every 10% increase in step time asymmetry and ground contact time asymmetry an increase of 3.5% and 7.8% in metabolic power was observed, respectively [ 47 ]. Similarly, ground contact time asymmetry was strongly and positively related to caloric unit cost ( r = 0.81, p = 0.003) [ 48 ]. For every 1% increase in ground contact time asymmetry, the caloric unit cost increased by 0.0354 kcal · kg − 1 km − 1 . Symmetry in displacements of the body centre of mass during running was moderately and positively related to mechanical efficiency ( r = 0.66, p = 0.015) but not associated with metabolic cost ( r = -0.00, p = 0.995) [ 45 , 50 ]. Kinematic asymmetry was not related to within-season personal records ( β = -5.5 to 0.4, p = 0.27 to 0.90) [ 49 ], with the exception of asymmetry in peak ankle dorsiflexion (i.e., for every 1° increase in peak ankle dorsiflexion asymmetry, personal record times on distances of 8 km for male runners and 6 km for female runners decreased by 7.6 seconds). Support phase time asymmetry was negatively related to running velocity ( β = -6.60, p = 0.03) [ 46 ]. 3.6.4 Kinetic asymmetry linked to running performance It was reported that every 10% increase in peak braking ground reaction force asymmetry ( β = 0.13, p < 0.001), peak propulsive ground reaction force asymmetry ( β = 0.20, p < 0.001), stance average vertical ground reaction force ( β = 0.35, p < 0.001) and leg stiffness asymmetry ( β = 0.39, p = 0.042), respectively elicits a 1.3%, 2.0%, 3.5% and 3.9% metabolic power increase [ 47 ]. In contrast, peak vertical ground reaction force asymmetry was not found to be significantly correlated with net metabolic power ( p = 0.469) and within-season personal records ( β = -2.70, p = 0.42) [ 47 , 49 ]. Conversely, peak vertical ground reaction force asymmetry while running was reported to be significantly related to running velocity (i.e., 3 minutes at fixed velocity of 8, 9, 10, 11 and 12 km/h) ( p = 0.032; competitive runners: β = -0.5, recreational runners: β = 0.3, novice runners: β = 0.6) [ 51 ]. In competitive and recreational runners, asymmetry in peak vertical ground reaction force and vertical load rate asymmetry showed a linear and U-shaped trend across velocities, respectively [ 51 ]. In novice runners, a lower asymmetry of time peak vertical ground reaction force was associated with increasing running velocity, whilst asymmetry in vertical load rate did not differ across velocities [ 51 ]. As opposed to average loading rate and braking impulse asymmetry ( β = 0.00, p = 0.99), propulsive impulse asymmetry ( β = 14.60, p < 0.01) was positively associated with within-season personal records on distances of 8 km for male runners and 6 km for female runners [ 49 ]. Controlled for sex, for every 5% increase in propulsive impulse asymmetry, personal records times within the running season increased by 16 seconds [ 49 ]. Table 6 Summary of the (significant positive and significant negative or no significant) associations in view of inter-limb asymmetry and running performance Type of asymmetry # of asymmetry outcome measures associated with running performance Studies Functional asymmetry Significantly positive 0 / Significantly negative 3 [ 35 , 46 ] Not significant 10 [ 35 ] Morphological asymmetry Significantly positive 0 / Significantly negative 0 / Not significant 4 [ 45 ] Kinematic asymmetry Significantly positive 1 [ 49 ] Significantly negative 6 [ 46 – 48 , 50 , 51 ] Not significant 13 [ 46 , 49 ] Kinetic asymmetry Significantly positive 0 / Significantly negative 7 [ 47 , 49 , 51 ] Not significant 3 [ 47 , 49 ] Overall Significantly positive 1 [ 49 ] Significantly negative 16 [ 35 , 46 – 51 ] Not significant 30 [ 45 , 47 , 49 ] Note: Running performance includes actual running performance as well as running performance determinants. The outcome measures from the determinants metabolic power, running economy, metabolic cost and personal records were inversely interpreted given their negative relationship with running performance. Similarly, symmetry magnitudes were inversely construed as asymmetry magnitudes. ** PLEASE INSERT Table 5 ABOUT HERE ** ** PLEASE INSERT Table 6 ABOUT HERE ** 4. Discussion The main objective of this systematic review was to synthesize and evaluate the available literature regarding the associations between lower inter-limb asymmetry and endurance running performance in healthy populations. According to the risk of bias assessment, all included studies were of moderate to strong quality. To compare and evaluate the association of inter-limb asymmetry with running performance (and/or its determinants), it was necessary to differentiate between dimensions to quantify asymmetry. Therefore, this review addressed the link between functional, morphological, kinematic and kinetic inter-limb asymmetry with running performance, separately. It is important to note that the limited available literature on the topic alongside the high heterogeneity in terms of asymmetry assessments and running metrics, as well as the different mathematical equations for calculating asymmetry magnitude, made it difficult to compare studies and even impossible to conduct a meta-analysis. This discrepancy across test protocols and outcome measures resulted in inconsistent findings highlighting the task, metric, test occasion and individual specific nature of inter-limb asymmetry and its magnitude [ 7 – 12 ]. 3.7 Functional asymmetry and running performance Functional asymmetry was most commonly assessed using strength measures (e.g., isometric strength) [ 35 , 46 ]. This is unsurprising since strength training-induced neuromuscular adaptations have been demonstrated to enhance running economy (i.e., 2–8%) as well as time trial performance and maximal sprint velocity in middle and long-distance runners [ 53 – 55 ]. Moreover, larger magnitudes of inter-limb strength asymmetry have also been associated with increased gait asymmetry ( r = 0.44), indicating a transfer from functional assessments to sport-specific measures [ 56 ]. Blagrove et al. [ 35 ] examined the relationship between the magnitude of isometric muscle strength asymmetry (i.e., quarter-squat, hip extension and hip abduction) and running performance as well as running economy in male and female competitive middle- and long-distance runners. In general, this study observed group mean asymmetry values ranging between 4.6–8.4%, resulting in negligible associations between inter-limb strength asymmetry and running economy ( r = − 0.02 to 0.13) and running performance ( r = − 0.26 to 0.13). However, a larger magnitude of inter-limb asymmetry of 10% was found in hip abduction torque asymmetry for the female endurance runners. This inter-limb asymmetry in abduction strength was significantly positively correlated ( r = 0.85) with running economy (i.e., energy cost in kJ.kg − 75 .km − 1 ), indicating the potential negative impact of larger inter-limb asymmetry magnitudes on running performance as higher energy costs are detrimental to endurance performances [ 57 ]. Similarly, Tabor et al. [ 46 ] reported that reductions in the magnitude of the sum of muscle torque in knee and hip flexors and extensors were negatively correlated with running velocity in female middle-distance runners ( β = -6.64). However, given the task-dependent nature of functional asymmetry [ 7 ], it may not be advisable to add together different strength measures to determine strength asymmetry. Therefore, this latter result should be interpreted with caution. 3.8 Morphological asymmetry and running performance Previous research documented a negative relationship between asymmetry in various traits (e.g., nostrils and ears) and running performance in middle-distance runners [ 58 ]. However, the existing literature on the association between the magnitude of morphological asymmetry and (determinants of) running performance (e.g., metabolic cost) seems to be limited to only one study in untrained, occasional and skilled runners. A first important finding of this study conducted by Semanti at al. [ 47 ] was the moderate and positive correlation ( r = 0.606) between anatomical asymmetry (i.e., side-to-side differences in volume of the lower limbs measured by magnetic resonance imaging) and dynamical asymmetry (i.e., body centre of mass displacements), indicating that runners with greater magnitudes of morphological asymmetry tend to exhibit more pronounced asymmetrical running patterns. Moreover, this latter study showed that training status moderated this relationship, as more experienced runners showed smaller magnitudes of dynamic asymmetry at higher running velocities compared to their untrained peers. However, this study did not report a significant correlation between anatomical asymmetry and metabolic cost. The authors speculated that certain physiological adaptations may compensate for the relatively small anatomical asymmetry magnitudes observed (i.e., 0.77 to 0.83%), regardless of training status [ 47 ]. Given the scarcity of literature on the link between morphological inter-limb asymmetry and running performance, it is difficult to draw clear conclusions. However, it is important to note that none of the studies included in this review addressed leg length discrepancies. This is probably because leg length differences are typically reported as an absolute difference between the right and left lower limb, rather than as a relative asymmetry score (i.e., expressed as a percentage). As such, larger absolute leg length differences (> 2 cm) have been reported to increase energy expenditure during walking, to increase oxygen consumption during submaximal running and to impair running economy [ 59 , 60 ]. Moreover, and although this seems to be individual specific, absolute leg length differences have been positively associated with a more pronounced gait asymmetry ( r = 0.29 to 0.51) [ 61 , 62 ]. In contrast, leg length differences smaller than 1 cm do not appear to be significantly associated with running economy [ 48 , 63 ]. These results support the notion that the magnitude of morphological asymmetry between the lower limbs could affect running performance [ 61 ]. 3.9 Kinematic asymmetry and running performance Despite the wide range of kinematic asymmetry magnitudes observed (i.e., 3–54%), the narrative review by Carpes et al. [ 15 ] concluded in 2010 that the available studies failed to establish significant relationships between kinematic asymmetry and running performance. Due to the recently growing interest on the topic, several studies attempted to investigate the association between kinematic inter-limb asymmetry and determinants of running performance as well as personal records. For instance, two studies included in the current systematic review indicated that inter-limb asymmetry in ground contact times (i.e., the average time each foot spends in contact with the ground while running) was correlated with impaired running economy ( r = 0.808) and metabolic power (i.e., energy cost, based on O 2 consumption and CO 2 production [ 52 ]) ( β = 0.78) [ 47 , 48 ]. As discussed in a recent review by Moore et al. [ 64 ], there is still debate on whether short or long contact times are favourable in view of running performance. Whereas short ground contact times are suggested to impose a higher metabolic cost due to the need for faster force production [ 65 , 66 ], longer ground contact times are suggested to increase the metabolic cost during the increased deceleration, resulting in a lengthened braking phase [ 67 ]. However, the findings in our review indicate that inter-limb asymmetry in ground contact times has a negative impact on energy depletion and running economy, potentially impairing running performance. Similarly, step time asymmetry (i.e., including both the ground contact time and the subsequent aerial time) was significantly positively correlated ( β = 0.35) with metabolic power [ 47 ]. This result is consistent with previous research in which larger asymmetric step times were associated with increased metabolic power in walking [ 68 ]. Beck et al. [ 47 ] attributed these findings to reduced mechanical energy conservation in asymmetric step times, resulting in increased muscle mechanical work per step and an increased metabolic rate. Studies investigating the association of asymmetry in trajectories of body centre of mass and (determinants of) running performance revealed equivocal results. Whilst asymmetry in body centre of mass displacements was moderately negatively related to mechanical efficiency ( r = -0.66), no significant association was found with metabolic cost [ 45 , 50 ]. Differences in duration of the running protocol have been postulated as a possible explanation for these discrepancies. Melo et al. [ 50 ] argued that longer distance protocols (e.g., 10 km) are more suitable for detecting kinematic asymmetry, which may not be evident in shorter running bouts. Moreover, variations in running experience [ 69 ], running intensity [ 70 , 71 ] and muscle fatigue [ 72 ] could also explain these differences in findings. Previous research showed that peak ankle dorsiflexion (i.e., maximal ankle dorsiflexion angle during stance phase) later in stance was positively related to running economy and thus possibly affecting running performance [ 73 ]. In the study by Stiffler-Joachim et al. [ 49 ], inter-limb asymmetry in peak ankle dorsiflexion was the only kinematic variable that was significantly and negatively correlated with within-season personal records ( β = -6.1, CI : [-12.9, 0.7]. Every 1° increase in peak ankle dorsiflexion asymmetry was related to a 7.6 s decrease in the best running time on 8km for male and 6 km for female distance runners. Whilst the underlying mechanism for this finding is unclear, it should be noted that the magnitude for peak dorsiflexion was quantified as the absolute value of the inter-limb differences and not as a percentage. Lastly, Tabor et al. [ 46 ] demonstrated that swing phase asymmetry can impair running velocity in intermediate and advanced middle-distance runners. Given that a shorter support phase has been related to increased running velocity [ 67 , 74 ], it seems plausible that asymmetry in the extension of the swing phase could impair running velocity [ 46 ]. 3.10 Kinetic asymmetry and running performance Stiffler-Joachim et al. [ 49 ] documented varying kinetic asymmetry percentages in National Collegiate Athletic Association (NCAA) Division I runners, ranging from 3% for peak vertical ground reaction force up to 20% for average vertical loading rate. Propulsive impulse asymmetry has been reported to be related to impaired race performance in distance running [ 49 ]. Given that metabolic cost during running is to a large extent determined by propulsive impulse [ 64 ], practitioners should not only consider to improve runners’ overall propulsive impulse but also aim to minimize side-to-side differences in this respect. In contrast, average vertical loading rate, braking impulse and peak vertical loading rate were not found to be significant predictors for race performance [ 64 ]. This could potentially be attributed to the fact that these associations were investigated in elite runners, who exhibited low overall asymmetry scores for these particular metrics. Regarding the relationship between kinetic inter-limb asymmetry and determinants of running performance, results indicated that stance average vertical ground reaction force, peak propulsive ground reaction force and leg stiffness asymmetry were positively associated with metabolic power in recreational runners [ 47 ]. This suggests that more pronounced kinetic inter-limb asymmetry could eventually increase energy expenditure while running and thus potentially have an adverse effect on running performance. [ 47 ]. In contrast, peak ground reaction force asymmetry was not found to be significantly associated with metabolic power, demonstrating the variable nature of asymmetry [ 47 ]. This variability in asymmetry metrics and their associations with running performance was further emphasized in the study conducted by Mo et al. [ 51 ]. The latter study indicated that the association between inter-limb kinematic asymmetry and running performance highly depends on the velocity of the running test, the running experience of the participants and the parameter of interest assessed. Consistent with previous research, the magnitude of asymmetry not only varied considerably within kinetic variables, but also appeared to be more pronounced compared to kinematic variables [ 51 , 75 ]. 3.11 Limitations and strengths Although this is the first systematic review to provide a holistic view on the available evidence concerning lower inter-limb asymmetry and the association with (determinants of) running performance in endurance runners, this research effort is not without limitations. First, it is difficult to draw a definitive conclusion due to the high heterogeneity among the included studies. More specifically, the heterogeneity was evident in a variety of dimensions, (e.g., functional, morphological, kinematic or kinetic), assessments, equations and metrics being used to assess and express inter-limb asymmetry, as well as in a diversity of population characteristics (e.g., sex, age and/or training status of participants. Furthermore, caution is warranted when interpreting these results because of the scarcity of eligible studies, making comparisons between study results difficult and less robust. Moreover, only a quarter of the studies documenting Pearson’s or Spearman’s rank order correlations also reported an assessment of normality on their raw data. Since falsely (i.e., with non-normal data) using a Pearson’s correlation coefficient highly increases type I error rates, justification for the use of parametric statistics by means of normality tests (e.g., Shapiro-Wilk test) is essential [ 76 ]. Lastly, only one study reported the reliability of the asymmetry metric of interest. Given the inherently high variably nature of inter-limb asymmetry [ 13 ], a good test-rest as well as inter-rater reliability is necessary to ensure the quality of the data. 3.12 Directions for future research By analogy with the work of Afonso et al. [ 13 ], the synopsis of literature on the topic presented in the current systematic review is important to identify a research agenda highlighting some key areas for future research on inter-limb asymmetry in endurance runners (see Fig. 2 for an illustrative overview): In literature, inter-limb asymmetry is often not reported using sport-specific and field-based assessments in endurance runners. Whilst functional asymmetry is generally measured using maximal (isometric) strength, (repeated) hop tests are presumed to have a greater ecological validity for assessing inter-limb asymmetry in runners due to their ability to measure various facets related to the stretch and shortening cycle [ 77 ]. Notably, storing and returning mechanical energy in the process of elastic energy utilization plays a key role in the metabolic energy-saving mechanism, and consequently running economy [ 78 ]. In this regard, leg stiffness (i.e., resistance to deformation of the limb) and reactive strength (i.e., the ability to effectively use the stretch and shortening cycle as well as the energy produced by the muscle-tendon complex) have been proposed to be important neuromuscular factors contributing to the elastic energy utilization [ 64 , 79 , 80 ]. Given that these factors can be measured using (repeated) hop tests and rebound jump protocols, moderate to large correlations between a countermovement jump and running economy have been previously reported [ 60 ]. Hence, future research should consider investigating the effect of functional inter-limb asymmetry in leg stiffness and reactive strength using unilateral (repeated) hop tests. Moreover, the use of sport-specific, valid and reliable field-based assessments of functional asymmetry is needed to enhance the ecological validity and applicability among practitioners. Disparities in asymmetry outcomes and magnitudes between different types of runners underscore the necessity for practitioners to account for inter-individual differences. Variables such as type of running (e.g., track versus road running), training status of runners (e.g., trained versus untrained) and injury history of runners (i.e., injured versus non-injured) should be considered when assessing lower inter-limb asymmetries [ 51 , 81 – 83 ]. Although larger magnitudes of inter-limb asymmetry are expected in novice endurance runners than in elite endurance runners [ 51 ], the results of the present review indicate that – based on the included studies – 68% of research has been conducted in competitive runners and only 22% in recreational runners and 10% in novice runners. Therefore, addressing a more diverse range of running populations in terms of training status, age and sex, while acknowledging the high inter- and intra-variability, is warranted. The direction of asymmetry is highly variable between tasks and between test occasions [ 84 , 85 ]. For instance, a distance runner may favour his right limb on a first test occasion whilst favouring his left limb on a second test occasion. Given that asymmetry is a ratio metric, reporting kappa values is highly recommended to assess differences in the direction of asymmetry between different tasks and/or test occasions. Several factors relating to test protocols, such as running velocity, test intensity or fatigue, will likely induce intra-individual differences in asymmetry [ 51 , 70 , 86 ]. In addition, also environmental factors such as the running underground, air humidity and ambient temperature may possibly lead to different asymmetry magnitudes and/or running performances. This accentuates the need for a standardized approach under stable conditions when evaluating inter-limb asymmetry. Recognizing the highly variable nature of inter-limb asymmetry, researchers are urged to report the reliability of their tests and related outcome measures (e.g., test-retest or inter-rater reliability) to mitigate the impact of fluctuations on asymmetry due to test errors. A standardized approach for expressing asymmetry magnitude across studies is also needed, preferably using “stronger” and “weaker” limb instead of “right” and left “limb” [ 87 ]. ** PLEASE INSERT FIGURE 2 ABOUT HERE ** 5. Conclusion In summary, the limited literature on inter-limb asymmetry in endurance runners displayed a high heterogeneity regarding study samples, study methods, assessments and metrics used, making direct comparisons extremely difficult. With the exception of one study demonstrating a positive association between peak ankle dorsiflexion asymmetry and running performance, the majority of findings either suggest inter-limb asymmetries to be negatively associated or not affect running performance or its determinants in healthy populations. However, more research across diverse running populations is needed to confirm these assertions and to establish critical thresholds within this respect. Practitioners should be mindful of the task, test occasion and metric specificity as well as the inter- and intra-individual variability when monitoring inter-limb asymmetry. Abbreviations CMJ countermovement jump DJ drop jump Declarations Ethical approval and consent to participate: Not applicable. Consent for publication: Not applicable. Availability of data and material: Not applicable. Competing interests: Joachim D’Hondt, Laurent Chapelle, Chris Bishop, Dirk Aerenhouts, Kevin de Pauw, Peter Clarys and Eva D’Hondt have no conflicts of interest relevant to this review. Funding: No funding was received for this study Author contributions: All authors contributed to the development of the present systematic review. The initial design of the search strategy was performed by J.D. and revised by L.C., C.B., D.A., K.D.P., P.C. and E.D.. The screening process as well as the data-analysis and risk of bias assessment was conducted by J.D. and L.C.. In case of divergency, D.A. was involved. The first version of the manuscript was drafted by J.D. and revised and edited by all authors. All authors read and approved the final version of the manuscript prior to submission. Acknowledgements: None to disclosure. References McCartney G, Hepper P. Development of lateralized behaviour in the human fetus from 12 to 27 weeks' gestation. Dev Med Child Neurol. 1999;41(2):83–6. Chapelle L, Bishop C, D'Hondt J, D'Hondt E, Clarys P. Morphological and functional asymmetry in elite youth tennis players compared to sex- and age-matched controls. J Sports Sci. 2022;40(14):1618–28. D'Hondt J, Chapelle L, Van Droogenbroeck L, Aerenhouts D, Clarys P, D'Hondt E. Bioelectrical impedance analysis as a means of quantifying upper and lower limb asymmetry in youth elite tennis players: An explorative study. Eur J Sport Sci. 2022;22(9):1343–54. Bishop C, Turner A, Read P. Effects of inter-limb asymmetries on physical and sports performance: a systematic review. J Sports Sci. 2018;36(10):1135–44. Nagahara R, Gleadhill S. Asymmetries of kinematics and kinetics in female and male sprinting. J Sports Med Phys Fitness. 2023;63(8):891–8. Chapelle L, Bishop C, Clarys P, D'Hondt E. No Relationship between Lean Mass and Functional Asymmetry in High-Level Female Tennis Players. Int J Env Res Pub He. 2021;18(22). Virgile A, Bishop C. A Narrative Review of Limb Dominance: Task Specificity and the Importance of Fitness Testing. J Strength Cond Res. 2021;35(3):846–58. Stiffler-Joachim MR, Lukes DH, Kliethermes SA, Heiderscheit BC. Lower Extremity Kinematic and Kinetic Asymmetries during Running. Med Sci Sports Exerc. 2021;53(5):945–50. Karamanidis K, Arampatzis A, Bruggemann GP. Symmetry and reproducibility of kinematic parameters during various running techniques. Med Sci Sport Exer. 2003;35(6):1009–16. Bissas A, Walker J, Paradisis GP, Hanley B, Tucker CB, Jongerius N, et al. Asymmetry in sprinting: An insight into sub-10 and sub-11 s men and women sprinters. Scand J Med Sci Sports. 2022;32(1):69–82. Dos'Santos T, Thomas C, Jones PA. Assessing Interlimb Asymmetries: Are We Heading in the Right Direction? Strength Cond J. 2021;43(3):91–100. Chapelle L, Bishop C, D'Hondt J, Rommers N, D'Hondt E, Clarys P. Development of Upper and Lower Extremity Functional Asymmetries in Male and Female Elite Youth Tennis Players: A Longitudinal Study. 2023:[unpublished observations]. Afonso J, Pena J, Sa M, Virgile A, Garcia-de-Alcaraz A, Bishop C. Why Sports Should Embrace Bilateral Asymmetry: A Narrative Review. Symmetry-Basel. 2022;14(10). Helme M, Tee J, Emmonds S, Low C. Does lower-limb asymmetry increase injury risk in sport? A systematic review. Phys Ther Sport. 2021;49:204–13. Carpes FP, Mota CB, Faria IE. On the bilateral asymmetry during running and cycling - A review considering leg preference. Phys Ther Sport. 2010;11(4):136–42. Philipp NM, Garver MJ, Crawford DA, Davis DW, Hair JN. Interlimb asymmetry in collegiate American football players: Effects on combine-related performance. J Hum Sport Exerc. 2022;17(3):708–18. Madruga-Parera M, Bishop C, Read P, Lake J, Brazier J, Romero-Rodriguez D. Jumping-based Asymmetries are Negatively Associated with Jump, Change of Direction, and Repeated Sprint Performance, but not Linear Speed, in Adolescent Handball Athletes. J Hum Kinet. 2020;71:47–58. Fort-Vanmeerhaeghe A, Bishop C, Buscà B, Aguilera-Castells J, Vicens-Bordas J, Gonzalo-Skok O. Inter-limb asymmetries are associated with decrements in physical performance in youth elite team sports athletes. PLoS ONE. 2020;15(3):e0229440. Bishop C, Read P, McCubbine J, Turner A. Vertical and Horizontal Asymmetries Are Related to Slower Sprinting and Jump Performance in Elite Youth Female Soccer Players. J Strength Cond Res. 2021;35(1):56–63. Bishop C, Turner A, Maloney S, Lake J, Loturco I, Bromley T et al. Drop Jump Asymmetry is Associated with Reduced Sprint and Change-of-Direction Speed Performance in Adult Female Soccer Players. Sports (Basel). 2019;7(1). Bishop C, Brashill C, Abbott W, Read P, Lake J, Turner A. Jumping Asymmetries Are Associated With Speed, Change of Direction Speed, and Jump Performance in Elite Academy Soccer Players. J Strength Cond Res. 2021;35(7):1841–7. Bishop C, Read P, Bromley T, Brazier J, Jarvis P, Chavda S, et al. The Association Between Interlimb Asymmetry and Athletic Performance Tasks: A Season-Long Study in Elite Academy Soccer Players. J Strength Conditioning Res. 2022;36(3):787–95. Loturco I, Pereira LA, Kobal R, Abad CCC, Rosseti M, Carpes FP, et al. Do asymmetry scores influence speed and power performance in elite female soccer players? Biol Sport. 2019;36(3):209–16. Lockie RG, Callaghan SJ, Berry SP, Cooke ER, Jordan CA, Luczo TM, et al. Relationship between unilateral jumping ability and asymmetry on multidirectional speed in team-sport athletes. J Strength Cond Res. 2014;28(12):3557–66. Michailidis Y, Pirounakis V, Savvakis C, Margonis K, Metaxas T. The influence of unilateral jumping asymmetry on acceleration and speed performance, in U10 and U15 groups of youth soccer players. Trends in Sport Sciences. 2019;26(4):145–51. Hart NH, Nimphius S, Spiteri T, Newton RU. Leg Strength and Lean Mass Symmetry Influences Kicking Performance in Australian Football. J Sport Sci Med. 2014;13(1):157–65. Rauter S, Simenko J. Morphological Asymmetries Profile and the Difference between Low- and High-Performing Road Cyclists Using 3D Scanning. Biology (Basel). 2021;10(11). Trivers R, Fink B, Russell M, McCarty K, James B, Palestis BG. Lower body symmetry and running performance in elite Jamaican track and field athletes. PLoS ONE. 2014;9(11):e113106. Bini RR, Hume PA. Relationship between pedal force asymmetry and performance in cycling time trial. J Sports Med Phys Fitness. 2015;55:892–8. Rannama I, Port K, Bazanov B, Pedak K. Sprint cycling performance and asymmetry. J Hum Sport Exerc. 2015;10:247–S58. Fox KT, Pearson LT, Hicks KM. The effect of lower inter-limb asymmetries on athletic performance: A systematic review and meta-analysis. PLoS ONE. 2023;18(6):e0286942. Guan Y, Bredin SSD, Taunton J, Jiang Q, Wu N, Warburton DER. Association between Inter-Limb Asymmetries in Lower-Limb Functional Performance and Sport Injury: A Systematic Review of Prospective Cohort Studies. J Clin Med. 2022;11(2). Yanci J, Los Arcos A. Muscle strength and leg asymmetries in elite runners and cyclists. Int SportMed J. 2014;15(3):285–97. Zifchock RA, Davis I, Hamill J. Kinetic asymmetry in female runners with and without retrospective tibial stress fractures. J Biomech. 2006;39(15):2792–7. Blagrove RC, Bishop C, Howatson G, Hayes PR. Inter-limb strength asymmetry in adolescent distance runners: Test-retest reliability and relationships with performance and running economy. J Sports Sci. 2021;39(3):312–21. Trivers R, Palestis BG, Manning JT. The symmetry of children's knees is linked to their adult sprinting speed and their willingness to sprint in a long-term Jamaican study. PLoS ONE. 2013;8(8):e72244. Meyers RW, Oliver JL, Hughes MG, Lloyd RS, Cronin JB. Asymmetry During Maximal Sprint Performance in 11- to 16-Year-Old Boys. Pediatr Exerc Sci. 2017;29(1):94–102. Mackala K, Michalski R, Coh M. Asymmetry of Step Length in Relationship to Leg Strength in 200 meters Sprint of different Performance Levels. J Hum Kinetics. 2010;25:101–8. Debaere S, Delecluse C, Aerenhouts D, Hagman F, Jonkers I. From block clearance to sprint running: characteristics underlying an effective transition. J Sports Sci. 2013;31(2):137–49. Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021;372:n160. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Commun H. 1998;52(6):377–84. Jarvis P, Turner A, Read P, Bishop C. Reactive Strength Index and its Associations with Measures of Physical and Sports Performance: A Systematic Review with Meta-Analysis. Sports Med. 2022;52(2):301–30. Fox JL, Stanton R, Sargent C, Wintour SA, Scanlan AT. The Association Between Training Load and Performance in Team Sports: A Systematic Review. Sports Med. 2018;48(12):2743–74. Seminati E, Nardello F, Zamparo P, Ardigò LP, Faccioli N, Minetti AE. Anatomically asymmetrical runners move more asymmetrically at the same metabolic cost. PLoS ONE. 2013;8(9):e74134. Tabor P, Mastalerz A, Iwańska D, Grabowska O. Asymmetry Indices in Female Runners as Predictors of Running Velocity. Pol J Sport Tourism. 2019;26(3):3–8. Beck ON, Azua EN, Grabowski AM. Step time asymmetry increases metabolic energy expenditure during running. Eur J Appl Physiol. 2018;118(10):2147–54. Joubert DP, Guerra NA, Jones EJ, Knowles EG, Piper AD. Ground Contact Time Imbalances Strongly Related to Impaired Running Economy. Int J Exerc Sci. 2020;13(4):427–37. Stiffler-Joachim MR, Kliethermes SA, Martin JA, Tanaka CS, Benkert R, Heiderscheit BC. Longitudinal Changes in Running Gait Asymmetries and Their Relationship to Personal Record Race Times in Collegiate Cross Country Runners. Symmetry-Basel. 2021;13(9). Melo CC, Carpes FP, Vieira TM, Mendes TT, de Paula LV, Chagas MH, et al. Correlation between running asymmetry, mechanical efficiency, and performance during a 10 km run. J Biomech. 2020;109:109913. Mo S, Lau FOY, Lok AKY, Chan ZYS, Zhang JH, Shum G, et al. Bilateral asymmetry of running gait in competitive, recreational and novice runners at different speeds. Hum Mov Sci. 2020;71:102600. Brockway JM. Derivation of Formulas Used to Calculate Energy-Expenditure in Man. Hum Nutr-Clin Nutr. 1987;41c(6):463–71. Guglielmo LGA, Greco CC, Denadai BS. Effects of Strength Training on Running Economy. Int J Sports Med. 2009;30(1):27–32. Storen O, Helgerud J, Stoa EM, Hoff J. Maximal strength training improves running economy in distance runners. Med Sci Sport Exer. 2008;40(6):1087–92. Blagrove RC, Howatson G, Hayes PR. Effects of Strength Training on the Physiological Determinants of Middle- and Long-Distance Running Performance: A Systematic Review. Sports Med. 2018;48(5):1117–49. Laroche DP, Cook SB, Mackala K. Strength asymmetry increases gait asymmetry and variability in older women. Med Sci Sports Exerc. 2012;44(11):2172–81. Conley DL, Krahenbuhl GS. Running economy and distance running performance of highly trained athletes. Med Sci Sports Exerc. 1980;12(5):357–60. Manning JT, Pickup LJ. Symmetry and performance in middle distance runners. / Symetrie et performance chez des coureurs de demi-fond. Int J Sports Med. 1998;19(3):205–9. Delacerda FG, McCrory ML. A case report: effect of a leg length differential on oxygen consumption. J Orthop Sports Phys Ther. 1981;3(1):17–20. Gurney B, Mermier C, Robergs R, Gibson A, Rivero D. Effects of limb-length discrepancy on gait economy and lower-extremity muscle activity in older adults. J Bone Joint Surg Am. 2001;83(6):907–15. Kaufman KR, Miller LS, Sutherland DH. Gait asymmetry in patients with limb-length inequality. J Pediatr Orthop. 1996 Mar-Apr;16(2):144–50. Seeley MK, Umberger BR, Clasey JL, Shapiro R. The relation between mild leg-length inequality and able-bodied gait asymmetry. J Sport Sci Med. 2010;9(4):572–9. Perttunen JR, Anttila E, Sodergard J, Merikanto J, Komi PV. Gait asymmetry in patients with limb length discrepancy. Scand J Med Sci Sports. 2004;14(1):49–56. Moore IS. Is There an Economical Running Technique? A Review of Modifiable Biomechanical Factors Affecting Running Economy. Sports Med. 2016;46(6):793–807. Roberts TJ, Kram R, Weyand PG, Taylor CR. Energetics of bipedal running I. Metabolic cost of generating force. J Exp Biol. 1998;201(19):2745–51. Kram R, Taylor CR. Energetics of Running - a New Perspective. Nature. 1990;346(6281):265–7. Nummela A, Keränen T, Mikkelsson LO. Factors related to top running speed and economy. Int J Sports Med. 2007;28(8):655–61. Ellis RG, Howard KC, Kram R. The metabolic and mechanical costs of step time asymmetry in walking. Proc Biol Sci. 2013;280(1756):20122784. Cavanagh PR, Pollock ML, Landa J. A biomechanical comparison of elite and good distance runners. Ann N Y Acad Sci. 1977;301:328–45. Lee JB, Sutter KJ, Askew CD, Burkett BJ. Identifying symmetry in running gait using a single inertial sensor. J Sci Med Sport. 2010;13(5):559–63. Arampatzis A, Brüggemann GP, Metzler V. The effect of speed on leg stiffness and joint kinetics in human running. J Biomech. 1999;32(12):1349–53. Mizrahi J, Verbitsky O, Isakov E, Daily D. Effect of fatigue on leg kinematics and impact acceleration in long distance running. Hum Mov Sci. 2000;19(2):139–51. Moore IS, Jones AM, Dixon SJ. Mechanisms for Improved Running Economy in Beginner Runners. Med Sci Sport Exer. 2012;44(9):1756–63. Brughelli M, Cronin J, Chaouachi A. Effects of Running Velocity on Running Kinetics and Kinematics. J Strength Conditioning Res. 2011;25(4):933–9. Furlong LM, Egginton NL. Kinetic Asymmetry during Running at Preferred and Nonpreferred Speeds. Med Sci Sports Exerc. 2018;50(6):1241–8. Bishara AJ, Hittner JB. Testing the significance of a correlation with nonnormal data: comparison of Pearson, Spearman, transformation, and resampling approaches. Psychol Methods. 2012;17(3):399–417. Markstrom JL, Olsson CJ. Countermovement jump peak force relative to body weight and jump height as predictors for sprint running performances: (in)homogeneity of track and field athletes? J Strength Cond Res. 2013;27(4):944–53. Sasaki K, Neptune RR. Muscle mechanical work and elastic energy utilization during walking and running near the preferred gait transition speed. Gait Posture. 2006;23(3):383–90. Vogt M, Hoppeler HH. Eccentric exercise: mechanisms and effects when used as training regime or training adjunct. J Appl Physiol. 2014;116(11):1446–54. Li F, Newton RU, Shi Y, Sutton D, Ding HY. Correlation of Eccentric Strength, Reactive Strength, and Leg Stiffness With Running Economy in Well-Trained Distance Runners. J Strength Conditioning Res. 2021;35(6):1491–9. Exell T, Irwin G, Gittoes M, Kerwin D. Strength and performance asymmetry during maximal velocity sprint running. Scand J Med Sci Sports. 2017;27(11):1273–82. Ishimura K, Sakurai S. Asymmetry in Determinants of Running Speed During Curved Sprinting. J Appl Biomech. 2016;32(4):394–400. Bredeweg SW, Buist I, Kluitenberg B. Differences in kinetic asymmetry between injured and noninjured novice runners: a prospective cohort study. Gait Posture. 2013;38(4):847–52. Bishop C, Lake J, Loturco I, Papadopoulos K, Turner A, Read P. Interlimb Asymmetries: The Need for an Individual Approach to Data Analysis. J Strength Cond Res. 2021;35(3):695–701. Bishop C, Read P, Chavda S, Jarvis P, Brazier J, Bromley T, et al. Magnitude or Direction? Seasonal Variation of Interlimb Asymmetry in Elite Academy Soccer Players. J Strength Cond Res. 2022;36(4):1031–7. Heil J, Loffing F, Büsch D. The Influence of Exercise-Induced Fatigue on Inter-Limb Asymmetries: a Systematic Review. Sports Med-Open. 2020;6(1). Bishop C, Read P, Chavda S, Turner A. Asymmetries of the Lower Limb: The Calculation Conundrum in Strength Training and Conditioning. Strength Cond J. 2016;38(6):27–32. Supplementary Files PRISMA2020checklist.docx Cite Share Download PDF Status: Published Journal Publication published 26 Nov, 2024 Read the published version in Sports Medicine-Open → Version 1 posted Reviewers agreed at journal 11 Mar, 2024 Reviewers invited by journal 08 Jan, 2024 Editor assigned by journal 04 Jan, 2024 First submitted to journal 04 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3787566","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265925454,"identity":"a2c6884e-73f1-450c-84d3-35b0eb2d3f4c","order_by":0,"name":"Joachim D'Hondt","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBAC9gYGBmYGAyDrBhDzMNgwMEgwsOHVwoiqJSGNWC0McC2HidDS3vzwc0HBNga+283HHrz9cT5xw+0Gtgcf8GnpOWYsPcPgNoPknWPphnMSbiduuHOA3XAGPi0zEsyYeYBaDG7kmEnzJNw2NriRwCbNg1dL+jdkLecgWv7g0SI4IwfFlgNyYC34vC/Nc6ZYGqiFR/JGWprknLRkOck7B9sNe/Bo4WNv3/iZ589tOb4bycck3tjY8YCD7gc+a6AA2b+guBoFo2AUjIJRQBEAAB5jTM/EjSi4AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-7870-2035","institution":"Vrije Universiteit Brussel","correspondingAuthor":true,"prefix":"","firstName":"Joachim","middleName":"","lastName":"D'Hondt","suffix":""},{"id":265925455,"identity":"da08cabe-f98e-4dc6-9006-b8f55527bedd","order_by":1,"name":"Laurent Chapelle","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Laurent","middleName":"","lastName":"Chapelle","suffix":""},{"id":265925456,"identity":"28b71c91-b3f4-4d0b-bfb4-559546b49308","order_by":2,"name":"Chris Bishop","email":"","orcid":"","institution":"Middlesex University","correspondingAuthor":false,"prefix":"","firstName":"Chris","middleName":"","lastName":"Bishop","suffix":""},{"id":265925457,"identity":"928fa86e-b2a2-4515-a1e1-7039578994ad","order_by":3,"name":"Dirk Aerenhouts","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Dirk","middleName":"","lastName":"Aerenhouts","suffix":""},{"id":265925458,"identity":"a35fd46c-3763-49fa-9188-02ec52a87aaf","order_by":4,"name":"Kevin De Pauw","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"","lastName":"De Pauw","suffix":""},{"id":265925459,"identity":"3359a7e4-e77b-4ca7-b2ee-549ad6dae1c1","order_by":5,"name":"Peter Clarys","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Clarys","suffix":""},{"id":265925460,"identity":"580c5c6f-95be-4928-8f79-0845a9a50ed4","order_by":6,"name":"Eva D'Hondt","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"","lastName":"D'Hondt","suffix":""}],"badges":[],"createdAt":"2023-12-21 14:57:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3787566/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3787566/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40798-024-00790-w","type":"published","date":"2024-11-26T15:57:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49433261,"identity":"ebf775f7-faf5-4b54-b77c-79a234e471fc","added_by":"auto","created_at":"2024-01-10 18:58:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26752,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart screening proces\u003c/p\u003e","description":"","filename":"OnlineFlowchartscreeningproces.png","url":"https://assets-eu.researchsquare.com/files/rs-3787566/v1/c1e1bf8f453586bf6fe3b149.png"},{"id":49433263,"identity":"f6a094a2-b087-4246-9c2f-4994a6057315","added_by":"auto","created_at":"2024-01-10 18:58:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47651,"visible":true,"origin":"","legend":"\u003cp\u003eResearch agenda\u003c/p\u003e","description":"","filename":"OnlineResearchagenda.png","url":"https://assets-eu.researchsquare.com/files/rs-3787566/v1/a0d385228bf6aed185a44c0b.png"},{"id":70382823,"identity":"63f1c9c6-1f9a-4ce9-9004-ad7c52c64000","added_by":"auto","created_at":"2024-12-02 16:32:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1917361,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3787566/v1/1c4593c5-55e5-4c57-8a1d-64c21cafb4f1.pdf"},{"id":49433262,"identity":"05dad9b7-9569-416a-9cd5-1671ad4b2019","added_by":"auto","created_at":"2024-01-10 18:58:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32694,"visible":true,"origin":"","legend":"","description":"","filename":"PRISMA2020checklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-3787566/v1/086768c2a331cdecd65d3345.docx"}],"financialInterests":"","formattedTitle":"Association between inter-limb asymmetry and endurance running performance in healthy populations: A systematic review","fulltext":[{"header":"Key points","content":"\u003cul\u003e\n \u003cli\u003eIn the majority of the metrics, the magnitude of lower inter-limb asymmetry was not or negatively associated with running performance.\u003c/li\u003e\n\u003cli\u003eCoaches, athletes and researchers should be attentive of the task, time- and metric-specificity as well as the inter- and intra- individual variability of magnitude outcomes, when assessing inter-limb asymmetries\u003c/li\u003e\u003c/ul\u003e"},{"header":"1. Background","content":"\u003cp\u003eThe concept of lateralization is a fundamental aspect of human neurodevelopment that involves the preferential use of one side of the body over the contralateral side during voluntary movement [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This phenomenon, that initiates before birth and expands during early infancy, occurs in almost every individual resulting in imbalances between body sides [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Such inter-limb asymmetry can manifest itself in multiple dimensions, encompassing functional (e.g., strength, power, speed, range of motion and agility), morphological (e.g., muscle mass, bone mineral content and fat mass), kinematic (e.g., body centre of mass displacements, joint angles or spatiotemporal measures, such as contact time, flight time, step length and step frequency), and kinetic (e.g., peak vertical ground reaction force) [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] measures. Interestingly, previous studies have shown that these different types of asymmetry are not necessarily related to each other [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, the magnitude of asymmetry has also been reported to be specific according to the task, metric, test occasion, and individual [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Given that the presence of inter-limb asymmetry has intuitively been considered to increase injury risk and to compromise athletic performance among sport practitioners, an abundance of research has been conducted on this topic over the last decade [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a sports performance perspective, recent research has shown that a larger magnitude of functional asymmetry can be associated with impaired sport performance [\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For instance, larger inter-limb differences resulting from the unilateral countermovement (CMJ) and drop jump (DJ) tests have been shown to be positively correlated with sprint time (CMJ: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43 to 0.71, DJ: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.52 to 0.58) and change of direction time (CMJ: r\u0026thinsp;=\u0026thinsp;0.61 to 0.71, DJ: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.52 to 0.66), both indicating poorer performance, in youth team-sport athletes [\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, there are also a number of studies demonstrating no meaningful relationship between functional inter-limb asymmetry and athletic performance [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In the realm of morphological asymmetry, the existing literature on athletes shows that a higher degree of lean mass asymmetry between the lower limbs is also related to a decreased sport performance, in tasks such as kicking (\u003cem\u003er\u003c/em\u003e = -0.31 to 0.41) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Further to this, inter-limb asymmetry in calf girths has been negatively associated with cycling performance (\u003cem\u003er\u003c/em\u003e = -0.461) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], whereas asymmetries in knee and ankle widths have been reported to explain 5% of the variation in track and field performances [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Also regarding kinematic and kinetic asymmetry, several studies demonstrated significant relationships with sport performance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For instance, Rannama et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] documented negative associations between peak isokinetic torque asymmetry (at 180\u0026deg; sec \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (\u003cem\u003er\u003c/em\u003e = -0.50) as well as trunk (\u003cem\u003er\u003c/em\u003e = -0.65) and pelvis (\u003cem\u003er\u003c/em\u003e = -0.63) kinematic asymmetry and power output during a 5-second maximal cycle test.\u003c/p\u003e \u003cp\u003eAlthough extensive research has recently been conducted on the link between inter-limb asymmetry and sports performance, the available body of literature on inter-limb asymmetry has focused mostly on unilateral sports (i.e., sports that involve primarily one side of the body or predominantly use one limb, such as tennis, soccer or cricket) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The relevance here being that athletes performing unilateral sports will likely exhibit larger magnitudes of inter-limb asymmetry due to the demands and nature of their specific sport [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, research has indicated that in so-called bilateral sports (e.g., cycling and running) significant inter-limb asymmetries may also occur due to the preferential use of one side of the body during repetitive movement patterns [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For example, functional, kinematic and kinetic asymmetries at lower limb level, respectively ranging from 16 to 17%, 3 to 54% and 3 to 54%, have been reported in adult endurance runners [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn line with research focussing on unilateral sports, inter-limb asymmetry could also affect running performance. For instance, a positive relationship (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.85) between inter-limb functional asymmetry and running economy (i.e., a major determinant of long-distance running expressed as energy cost in kJ.kg\u003csup\u003e\u0026minus;\u0026thinsp;75\u003c/sup\u003e.km\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was documented in female adolescent elite endurance runners, indicating that inter-limb asymmetry could impair running performance [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Also, regarding morphological asymmetry, Jamaican track athletes have been shown to perform better in 100 m sprinting when having more symmetrical knee and ankle widths [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Whilst it is acknowledged that these are non-modifiable factors, such data could have a role to play as part of the talent identification process. In contrast, previous research has shown no significant associations of maximal sprint velocity and running velocity with spatiotemporal, kinematic and kinetic asymmetry [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. For example, Mackala et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] found no significant difference in the magnitude of stride length asymmetry between novice and elite sprint athletes. Despite advancements in knowledge on the topic, it is important to note that running includes both sprinting (i.e., up to distances of 400 m) and endurance running (i.e., distances exceeding 400 m), implying that both disciplines should be examined separately in the context of inter-limb asymmetry in relation to performance. Specifically, getting out of the starting blocks alongside the subsequent acceleration phase with changing step and stride characteristics [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], makes sprinting inherently more asymmetric. Consequently, this asymmetry during sprinting could induce greater side-to-side differences to the body compared to submaximal running at a more constant velocity.\u003c/p\u003e \u003cp\u003eEndurance running is a commonly practiced and popular sport and leisure time activity on a global level. Given the potential impact of inter-limb asymmetry in view of endurance running performance, a clear overview of the available literature is warranted to help practitioners better understand the possible role of inter-limb asymmetry in their sport. However, a systematic review on the association between inter-limb asymmetry and endurance running performance (i.e., actual running performance and/or determinants of running performance) is currently lacking. Therefore, the main aim of this systematic review was to comprehensively synthesize and appraise the available literature relating to inter-limb asymmetry and to evaluate its association with endurance running performance in healthy populations. Based on the resulting synopsis, some possible avenues for future research on the topic will be suggested.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis systematic review was written in accordance with the \u0026ldquo;Preferred Reporting Items for Systematic reviews and Meta-Analyses\u0026rdquo; (PRISMA) guidelines [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and was registered on Prospero on 31 October 2023 (ID: CRD42023474606).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Eligibility criteria\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents an overview of the eligibility criteria used during our systematic search in order to guide the selection procedure. All criteria were determined a priori in accordance with the P, O and S dimensions from the PICO(S) acronym [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEligibility criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInclusion criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExclusion criteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Healthy injury-free male and / or female participants of any age\u003c/p\u003e \u003cp\u003e\u0026bull; Endurance runners of any level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; Physical conditions that may influence running asymmetry or running performance\u003c/p\u003e \u003cp\u003e\u0026bull; Sprint athletes (i.e., up to distances of 400 m)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome of interest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Analysis of the association between lower inter-limb asymmetry and endurance running performance (and/or its determinants)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; Analysis of the association between inter-limb asymmetry and sports performance, but not specifically related to endurance running\u003c/p\u003e \u003cp\u003e\u0026bull; Only reporting the magnitude of asymmetry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; English original peer-reviewed articles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; Umbrella reviews, systematic reviews or meta-analyses, books, conference abstracts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e** PLEASE INSERT Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e ABOUT HERE **\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Search strategy\u003c/h2\u003e \u003cp\u003eThe electronic databases PubMed, Web of Science, and SPORTDiscus (EBSCOhost) were systematically screened to gather relevant literature in August 2023. Searches included all papers using \u0026ldquo;Title / Abstract\u0026rdquo; for PubMed, and \u0026ldquo;Abstract\u0026rdquo; for Web of Science and EBSCOhost. In accordance with the eligibility criteria, the search was limited to English-language articles only. Additionally, \u0026ldquo;Article\u0026rdquo; and \u0026ldquo;Early access\u0026rdquo; in Web of Science, and \u0026ldquo;Academic Journals\u0026rdquo; in EBSCOhost were selected as source type, whereas no source type could be selected in PubMed. No limit was imposed on the publication date. Forward citation tracking (i.e., screening the citations of the included studies, using Web of Science) as well as backward citation tracking (i.e., screening the reference list of the included studies) was performed for the included articles. Citations and reference lists of related (systematic) reviews identified during the search were also screened. The corresponding flow diagram is depicted in Fig.\u0026nbsp;1 and the complete search strategy with Boolean operators is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSchematic overview of the search strategy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData base\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComplete search strategy using Boolean operators (as applied across all databases, N\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePubMed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e952\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u0026ldquo;distance runn*\u0026rdquo; OR \u0026ldquo;endurance runn*\u0026rdquo; OR \u0026ldquo;middle distance runn*\u0026rdquo; OR \u0026ldquo;jogging\u0026rdquo; OR \u0026ldquo;marathon\u0026rdquo; OR \u0026ldquo;trail runn*\u0026rdquo; OR \u0026ldquo;ultramarathon\u0026rdquo; OR \u0026ldquo;track and field\u0026rdquo; OR \u0026ldquo;5k\u0026rdquo; OR \u0026ldquo;10k\u0026rdquo; OR \u0026ldquo;run\u0026rdquo; OR \u0026ldquo;runn*\u0026rdquo; OR \u0026ldquo;sprint\u0026rdquo; OR \u0026ldquo;treadmill\u0026rdquo;)\u003c/p\u003e \u003cp\u003eAND\u003c/p\u003e \u003cp\u003e(\u0026ldquo;asymmetr*\u0026rdquo; OR \u0026ldquo;symmetr*\u0026rdquo; OR \u0026ldquo;imbalance\u0026rdquo; OR \u0026ldquo;side-to-side\u0026rdquo; OR \u0026ldquo;dissymmetr*\u0026rdquo; OR \u0026ldquo;interlimb\u0026rdquo; OR \u0026ldquo;inter-limb\u0026rdquo; OR \u0026ldquo;between-limb\u0026rdquo; OR \u0026ldquo;bilateral difference\u0026rdquo;)\u003c/p\u003e \u003cp\u003eAND\u003c/p\u003e \u003cp\u003e(\u0026ldquo;performance\u0026rdquo; OR \u0026ldquo;time trial\u0026rdquo; OR \u0026ldquo;trial\u0026rdquo; OR \u0026ldquo;speed\u0026rdquo; OR \u0026ldquo;velocity\u0026rdquo; OR \u0026ldquo;economy\u0026rdquo; OR \u0026ldquo;cost of running\u0026rdquo; OR \u0026ldquo;energy cost\u0026rdquo; OR \u0026ldquo;VO2max\u0026rdquo; OR \u0026ldquo;VO2peak\u0026rdquo; OR \u0026ldquo;maximal oxygen uptake\u0026rdquo; OR \u0026ldquo;oxygen consumption\u0026rdquo; OR \u0026ldquo;fatigue\u0026rdquo; OR \u0026ldquo;exhaustion\u0026rdquo; OR \u0026ldquo;lactate\u0026rdquo; OR \u0026ldquo;aerob*\u0026rdquo; OR \u0026ldquo;anaerob*\u0026rdquo; OR \u0026ldquo;stride length\u0026rdquo; OR \u0026ldquo;step length\u0026rdquo; OR \u0026ldquo;contact time*\u0026rdquo;)\u003c/p\u003e \u003cp\u003eNOT\u003c/p\u003e \u003cp\u003e(\u0026ldquo;injur*\u0026rdquo; OR \u0026ldquo;ACL\u0026rdquo; OR \u0026ldquo;anterior cruciate ligament\u0026rdquo; OR \u0026ldquo;syndrome\u0026rdquo; OR \u0026ldquo;traum*\u0026rdquo; OR \u0026ldquo;facture\u0026rdquo; OR \u0026ldquo;illness\u0026rdquo; OR \u0026ldquo;disease\u0026rdquo; OR \u0026ldquo;amput*\u0026rdquo; OR \u0026ldquo;stroke\u0026rdquo; OR \u0026ldquo;cerebral palsy\u0026rdquo; OR \u0026ldquo;tremor\u0026rdquo; OR \u0026ldquo;diagnosed\u0026rdquo; OR \u0026ldquo;disorder\u0026rdquo; OR \u0026ldquo;osteoarthritis\u0026rdquo; OR \u0026ldquo;geriatric\u0026rdquo; OR \u0026ldquo;return to sport\u0026rdquo; OR \u0026ldquo;rehabilitat*\u0026rdquo; OR \u0026ldquo;diagnosis\u0026rdquo; OR \u0026ldquo;pathology\u0026rdquo; OR \u0026ldquo;surgery\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e** PLEASE INSERT FIGURE 1 ABOUT HERE **\u003c/p\u003e \u003cp\u003e** PLEASE INSERT Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ABOUT HERE **\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Study selection\u003c/h2\u003e \u003cp\u003eAll articles were retrieved from the three scientific databases consulted, and duplicates were removed using the Endnote software. Subsequently, all titles and abstracts were screened in a blinded and standardized manner by two independent researchers (J.D. and L.C.) using the Rayyan software [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Any divergency between both reviewers was resolved by consensus, or by discussion with a third reviewer (D.A.). The remaining articles were independently evaluated on full text by the same two researchers, who registered the reasons for exclusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data collection process\u003c/h2\u003e \u003cp\u003eData extraction from the included studies was also conducted by two reviewers independently (J.D. and L.C.). Any disagreement was resolved by consensus, or by discussion by a third reviewer (D.A.). A self-created form was used to collect the following data per included study: year of publication, study design, sample characteristics (i.e., sample size, mean age, gender distribution, training status [e.g., athletes or non-athletes]), type of assessments as well as the metrics used to determine inter-limb asymmetry and equations applied to calculate asymmetry magnitude, association(s) between inter-limb asymmetry and any measure or metric to express participants\u0026rsquo; running performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Risk of Bias Assessment\u003c/h2\u003e \u003cp\u003eThe included studies\u0026rsquo; quality was assessed by two independent researchers (J.D. and L.C.) using the Downs and Black Quality Index Tool [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In accordance with two recent systematic reviews, a modified version of this tool was used by only including the items deemed relevant for this current review [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. More specifically, the items relating to patient treatment, training interventions and group randomization processes were excluded from the assessment. Each remaining item (N\u0026thinsp;=\u0026thinsp;10, see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was scored either a 1 (yes = \u0026lsquo;\u0026bull;\u0026rsquo;), a 0 (no = \u0026lsquo;○\u0026rsquo;) or was indicated as \u0026lsquo;-\u0026rsquo; when unable to determine a score based on the information in the study reports. All disagreements between assessors were resolved through discussion with a third reviewer (D.A.).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuestions from the modified Downs and Black [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] checklist used to evaluate methodological quality of the included studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuestion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReporting\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIs the hypothesis/aim/objective of the study clear?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAre the main outcomes to be measured clearly described in the introduction or\u003c/b\u003e \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e\u003cb\u003emethods\u003c/b\u003e\u003c/span\u003e \u003cb\u003esection?\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e*Information outlined in introduction/methodology for both physical characteristics and running performance measure used for associative analysis pertaining to assessment(s) used, any calculations used and units of measurement\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAre the characteristics of the subjects included in the study clearly described?\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e*Source defined, with characteristics included\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAre the main findings of the study clearly described?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDoes the study provide estimates of the random variability in the data for the main outcomes?\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e*One of the following included for both physical characteristics and running performance measures: a) mean \u0026plusmn; SD, b) standard error, c) confidence intervals and d) interquartile range\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHave actual probability values been reported (e.g. 0.035 rather than \u0026lt;\u0026thinsp;0.05) for the main outcomes except where the probability value is less than 0.001?\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e*Exact correlation (r) and significance (p) values provided, specific to the associative analysis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eExternal validity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWere the subjects to participate in the study representative of the entire population from which they were recruited?\u003c/b\u003e\u003c/p\u003e \u003cp\u003e* Proportion of subjects asked to participate, relative to the sample population, explicitly stated. Unless evident, then answer \"unable to determine\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInternal validity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIf any of the results of the study were based on 'data dredging,' was this made clear\u003c/b\u003e\u003c/p\u003e \u003cp\u003e*Were any additional data analysis reported in the results not highlighted during the methodology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWere statistical tests used to assess the main outcomes appropriate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWere the main outcome measures accurate (valid and reliable)?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e** PLEASE INSERT Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e ABOUT HERE **\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study selection\u003c/h2\u003e \u003cp\u003eThe search strategy yielded a total of 4817 articles, of which 672 duplicates were removed. Subsequently, 4135 articles were excluded based on title and abstract screening. In total, 7 articles were included in this systematic review after full text screening and one additional article was included from backward citation tracking, resulting in a total of 8 studies to be included. The most common reasons for exclusion of studies (i.e., based on titles and abstracts) were: wrong study field (97.3%), wrong population (1.7%), wrong outcome measures (0.6%), and wrong study design (0.4%). In the second screening phase (i.e., based on full text articles), all further exclusions were due to a wrong study design of population (100%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Risk of bias\u003c/h2\u003e \u003cp\u003eThe risk of bias assessment is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Based on the modified assessment tool of Downs and Black [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], including only 10 items, we were unable to confirm the external validity of all the studies included due to the lack of information regarding the proportion of individuals recruited relative to the overall sample population. Furthermore, no internal validity bias was apparent, except for some studies that failed to report data on the validity and reliability of the outcome measures used. In general, total scores ranged between 5/10 and 9/10 for study methodological quality and risk of bias.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the risk of bias assessment for all included studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"14\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"12\" nameend=\"c13\" namest=\"c2\"\u003e \u003cp\u003eModified Downs and Black checklist item number\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal score out of 10\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eReporting\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eExternal validity\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003e\u003cem\u003eInternal\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003evalidity\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeck et al., (2018) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlagrove et al., (2021) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJoubert et al., (2020) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelo et al., (2020)[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMo et al., (2020) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeminati et al., (2013) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStiffler-Joachim et al., (2021) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTabor et al., (2019) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e•\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"14\"\u003e• \u003cem\u003e= yes;\u003c/em\u003e ○ \u003cem\u003e= no; - = unable to determine\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e** PLEASE INSERT Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e ABOUT HERE **\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Study characteristics\u003c/h2\u003e \u003cp\u003eIn addition to the majority of research being performed in Europe (37.5%; GBR, ITA, POL) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and North America (37.5%; USA) [\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e–\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], only one included study was conducted in South America (12.5%; BRA) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and another single study in China (12.5%; CHN) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Furthermore, and except from one study being published in 2013 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], all studies were published from 2018 on.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Sample characteristics\u003c/h2\u003e \u003cp\u003eInformation regarding study characteristics is provided in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The 8 included studies represented a total of 181 participants, including 52% males (n = 94) and 48% females (n = 87). The mean age of study participants ranged between 17.1 years [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and 42.6 years [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Across studies, these participants consisted for 68% of competitive runners (54% female) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], 22% of recreational runners (36% female) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and 10% of novice runners (33% female) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of study sample characteristics, outcome measures, equations for calculating asymmetry magnitude, and main results of the association between inter-limb asymmetry and running performance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy and country (ISO code)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eParticipant characteristics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsymmetry test/metric measured\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRunning performance outcome measure(s)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEquations for calculating asymmetry magnitude\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAssociations with running performance\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eTraining status\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeck et al. (2018) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e = 10 (♂: 6, ♀: 4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 ± 6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eParticipants ran at least 3 times per week for minimum 30 min\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStep time asymmetry\u003c/p\u003e \u003cp\u003eGround contact time asymmetry\u003c/p\u003e \u003cp\u003eStance average vertical ground reaction force asymmetry\u003c/p\u003e \u003cp\u003ePeak braking ground reaction force asymmetry\u003c/p\u003e \u003cp\u003ePeak propulsive ground reaction force asymmetry\u003c/p\u003e \u003cp\u003eLeg stiffness asymmetry\u003c/p\u003e \u003cp\u003ePeak vertical ground reaction force asymmetry\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMetabolic power\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSymmetry index =\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\left| \\right(\\text{t}\\text{s}\\text{t}\\text{e}\\text{p}, 1 – t\\text{s}\\text{t}\\text{e}\\text{p}, 2) }{ 0.5 \\text{X} (\\text{t}\\text{s}\\text{t}\\text{e}\\text{p}, 1 + \\text{t}\\text{s}\\text{t}\\text{e}\\text{p}, 2) | }\\text{X} 100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMetabolic power\u003c/p\u003e \u003cp\u003evs. step time asymmetry: \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003eMetabolic power = 0.35\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003etstep SI + 0.67\u003c/em\u003e\u003c/p\u003e \u003cp\u003evs. ground contact time asymmetry: \u003cem\u003eβ\u003c/em\u003e = 0.78, \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, p = 0.036, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003evs. stance average vertical ground reaction force asymmetry: \u003cem\u003eβ\u003c/em\u003e = 0.35, \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003evs. peak braking ground reaction force asymmetry: \u003cem\u003eβ\u003c/em\u003e = 0.13, \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003evs. peak propulsive ground reaction force asymmetry: \u003cem\u003eβ\u003c/em\u003e = 0.20, \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003evs. leg stiffness asymmetry: \u003cem\u003eβ\u003c/em\u003e = 0.39, \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, p = 0.042, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003evs. peak vertical ground reaction force asymmetry: p = 0.469, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlagrove et al. (2021) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eGBR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e = 31 (♂:15, ♀: 16)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e♂: 17 ± 1\u003c/p\u003e \u003cp\u003e♀: 17 ± 1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCompetitive middle and long-distance runners\u003c/p\u003e \u003cp\u003eNon-strength trained\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower limb extensor bilateral symmetry index\u003c/p\u003e \u003cp\u003eHip extension strength asymmetry\u003c/p\u003e \u003cp\u003eHip abduction strength asymmetry\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRunning economy\u003c/p\u003e \u003cp\u003eBest race performance\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSymmetry index (%) =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{(\\text{s}\\text{t}\\text{r}\\text{o}\\text{n}\\text{g}\\text{e}\\text{r} \\text{l}\\text{i}\\text{m}\\text{b} – \\text{w}\\text{e}\\text{a}\\text{k}\\text{e}\\text{r} \\text{l}\\text{i}\\text{m}\\text{b})}{Total} \\text{X} 100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eStrength asymmetry (%) =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{(\\text{S}\\text{t}\\text{r}\\text{o}\\text{n}\\text{g}\\text{e}\\text{r} \\text{l}\\text{i}\\text{m}\\text{b} – \\text{w}\\text{e}\\text{a}\\text{k}\\text{e}\\text{r} \\text{l}\\text{i}\\text{m}\\text{b})}{\\text{s}\\text{t}\\text{r}\\text{o}\\text{n}\\text{g}\\text{e}\\text{r} \\text{l}\\text{i}\\text{m}\\text{b}} \\text{X} 100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMales:\u003c/p\u003e \u003cp\u003e- Lower limb extensor bilateral symmetry index\u003c/p\u003e \u003cp\u003evs. running economy: \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.30, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e \u003cp\u003evs. race performance: \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.09, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e \u003cp\u003e- Hip extension strength asymmetry\u003c/p\u003e \u003cp\u003evs. running economy: \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.02, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e \u003cp\u003evs. race performance: \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.26, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e \u003cp\u003e- Hip abduction strength asymmetry\u003c/p\u003e \u003cp\u003evs. running economy: \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.19, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e \u003cp\u003evs. race performance: \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.05, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e \u003cp\u003eFemales:\u003c/p\u003e \u003cp\u003e- Lower limb extensor bilateral symmetry index\u003c/p\u003e \u003cp\u003evs. running economy: \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.30, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e \u003cp\u003evs. race performance: \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.07, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e \u003cp\u003e- Hip extension strength asymmetry\u003c/p\u003e \u003cp\u003evs. running economy: \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.11, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e \u003cp\u003evs. race performance: \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.20, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05\u003c/p\u003e \u003cp\u003e- Hip abduction strength asymmetry\u003c/p\u003e \u003cp\u003evs. running economy: \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = 0.85, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e \u003cp\u003evs. race performance: \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = -0.47, \u003cem\u003ep\u003c/em\u003e = 0.07\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJoubert et al. (2020) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e = 11 (♂: 7, ♀: 4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e♂: 21 ± 1\u003c/p\u003e \u003cp\u003e♀: 19 ± 1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNCAA Division I athletic program\u003c/p\u003e \u003cp\u003e1500m, 10.000m and 800m specialists\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGround contact time\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRunning Economy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGround contact imbalance =\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(|\\text{%} t\\text{l}\\text{e}\\text{f}\\text{t} - \\text{%} t\\text{r}\\text{i}\\text{g}\\text{h}\\text{t}|\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGround contact time imbalance\u003c/p\u003e \u003cp\u003evs. Caloric Unit Cost (kcal kg\u003csup\u003e− 1\u003c/sup\u003e km\u003csup\u003e− 1\u003c/sup\u003e): \u003cem\u003er\u003c/em\u003e = 0.808, \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.66, \u003cem\u003eCI\u003c/em\u003e : [0.37–0.93], \u003cem\u003ep\u003c/em\u003e = 0.003\u003c/p\u003e \u003cp\u003e\u003cem\u003eCaloric Unit Cost = 0.9649 + 0.0354\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eground contact time\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelo et al. (2020) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eBRA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e = 13 (♂: 8, ♀: 5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 ± 4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAmateur trained runners\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDynamical symmetry index in vertical, mediolateral en anteroposterior directions based on body centre of mass displacements during a 10km run\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMechanical efficiency\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGlobal symmetry index =\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{dx \\bullet \\stackrel{-}{SIx}+dy \\bullet \\stackrel{-}{SIy}+dz \\bullet \\stackrel{-}{SIz}}{dx+dy+dz}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGlobal symmetry index vs. Mechanical efficiency: \u003cem\u003er\u003c/em\u003e = 0.66, \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.43, \u003cem\u003ep\u003c/em\u003e = 0.015, \u003cem\u003eMechanical efficiency = 2.4 + 31.2\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eglobal symmetry index\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMo et al. (2020) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eCHN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e = 31 (♂: 13, ♀: 18)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompetitive runners: 32 ± 4\u003c/p\u003e \u003cp\u003eRecreational runners: 35 ± 7\u003c/p\u003e \u003cp\u003eNovice runner: 29 ± 4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCompetitive, recreational and novice runners\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStride asymmetry\u003c/p\u003e \u003cp\u003eStep asymmetry\u003c/p\u003e \u003cp\u003eStance asymmetry\u003c/p\u003e \u003cp\u003eFlight time asymmetry\u003c/p\u003e \u003cp\u003eDuty factor asymmetry\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFixed instrumented treadmill velocities (i.e., 8, 9, 10, 11 and 12 km/h)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSymmetry index =\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{| \\text{X}\\text{r}\\text{i}\\text{g}\\text{h}\\text{t} – Xleft| }{ 0.5 \\text{X} (\\text{X}\\text{r}\\text{i}\\text{g}\\text{h}\\text{t} + Xleft) }\\text{X}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRunning velocity\u003c/p\u003e \u003cp\u003evs. Flight time asymmetry: \u003cem\u003ep\u003c/em\u003e = 0.012\u003c/p\u003e \u003cp\u003e- Competitive runners: \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.949, \u003cem\u003eSI = -0.7\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eVelocity + 10.5\u003c/em\u003e\u003c/p\u003e \u003cp\u003e- Recreational runners: \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.947, \u003cem\u003eSI = 0.5\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eVelocity\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e+ 10.2\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eVelocity + 57.9\u003c/em\u003e\u003c/p\u003e \u003cp\u003e- \u003cem\u003eNovice runners: r\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.644, \u003cem\u003eSI = 0.5\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eVelocity\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e+ 10.0\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eSpeed + 57.3\u003c/em\u003e\u003c/p\u003e \u003cp\u003evs. Time to peak vertical ground reaction force: \u003cem\u003ep\u003c/em\u003e = 0.032\u003c/p\u003e \u003cp\u003e- Competitive runners: \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.947, \u003cem\u003eSI = -0.5\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eVelocity + 112.6\u003c/em\u003e\u003c/p\u003e \u003cp\u003e- Recreational runners: \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.993, \u003cem\u003eSI = 0.3\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eVelocity\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e– 6.1\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eSpeed + 35.4\u003c/em\u003e\u003c/p\u003e \u003cp\u003e- Novice runners: \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.780,\u003c/p\u003e \u003cp\u003e\u003cem\u003eSI = 0.6\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eVelocity + 0.9\u003c/em\u003e\u003c/p\u003e \u003cp\u003evs. Vertical average loading rate asymmetry: \u003cem\u003ep\u003c/em\u003e = 0.002\u003c/p\u003e \u003cp\u003e- Competitive runners: \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.940, \u003cem\u003eSI = -2.4\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eVelocity + 40.9\u003c/em\u003e\u003c/p\u003e \u003cp\u003e- Recreational runners: \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.883, \u003cem\u003eSI = 1.9\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eVelocity\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e– 37.9\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eVelocity + 203.4\u003c/em\u003e\u003c/p\u003e \u003cp\u003e- Novice runners: \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.933, \u003cem\u003eSI = 0.3\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eVelocity\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e– 5.0\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eVelocity + 40.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeminati et al. (2013) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eITA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e = 19 (♂)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUntrained runners: 33 ± 13\u003c/p\u003e \u003cp\u003eOccasional runners: 32 ± 12\u003c/p\u003e \u003cp\u003eSkilled runners: 43 ± 7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUntrained, occasional and skilled runners\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAnatomical symmetry (i.e., volumes of pelvis district, upper-leg district, lower-leg district and global anatomical cross correlation value)\u003c/p\u003e \u003cp\u003eDynamical symmetry: body centre of mass trajectory (SI\u003csup\u003ex\u003c/sup\u003e, SI\u003csup\u003ey\u003c/sup\u003e, SI\u003csup\u003ez\u003c/sup\u003e, GI)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMetabolic cost\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSymmetry degree between 3D split volumes:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i,j,k}= \\frac{{\\sum }_{x,y,z}\\left[Rv\\left(x,y,z\\right)-\\stackrel{-}{{Rv}_{i,j,k}} \\right] \\bullet \\left[Lrv\\left(x-i,y-j,z-k)-\\stackrel{-}{Lrv}\\right) – \\stackrel{-}{{Rv}_{i,j,k}} \\right]}{\\sqrt{{\\sum }_{x,y,z}{\\left[Rv\\left(x,y,z\\right)-\\stackrel{-}{{Rv}_{i,j,k}} \\right]}^{2} \\bullet \\sum x,y,z {\\left[Lrv\\left(x-i,y-j,z-k\\right)-\\stackrel{-}{Lrv} \\right]}^{2}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eGlobal symmetry index =\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{dx \\bullet \\stackrel{-}{SIx}+dy \\bullet \\stackrel{-}{SIy}+dz \\bullet \\stackrel{-}{SIz}}{dx+dy+dz}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMetabolic cost\u003c/p\u003e \u003cp\u003evs. anatomical symmetry:\u003c/p\u003e \u003cp\u003e- Pelvis district: \u003cem\u003er\u003c/em\u003e = 0.157, \u003cem\u003ep\u003c/em\u003e = 0.547\u003c/p\u003e \u003cp\u003e- Upper-leg district: \u003cem\u003er\u003c/em\u003e = -0.114, \u003cem\u003ep\u003c/em\u003e = 0.662\u003c/p\u003e \u003cp\u003e- Lower leg district: \u003cem\u003er\u003c/em\u003e = 0.059, \u003cem\u003ep\u003c/em\u003e = 0.822\u003c/p\u003e \u003cp\u003e- Global anatomical cross correlation value: \u003cem\u003er\u003c/em\u003e = 0.072, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.223, \u003cem\u003ep\u003c/em\u003e = 0.055\u003c/p\u003e \u003cp\u003eGlobal symmetry index \u003cem\u003e= 0.460\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\bullet\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eGlobal anatomical cross correlation + 0.340\u003c/em\u003e\u003c/p\u003e \u003cp\u003evs. dynamical symmetry:\u003c/p\u003e \u003cp\u003e- \u003cem\u003eSI\u003c/em\u003e\u003csup\u003ex\u003c/sup\u003e: \u003cem\u003er\u003c/em\u003e = 0.005, \u003cem\u003ep\u003c/em\u003e = 0.983\u003c/p\u003e \u003cp\u003e- \u003cem\u003eSI\u003c/em\u003e\u003csup\u003ey\u003c/sup\u003e: \u003cem\u003er\u003c/em\u003e = 0.105, \u003cem\u003ep\u003c/em\u003e = 0.668\u003c/p\u003e \u003cp\u003e- \u003cem\u003eSI\u003c/em\u003e\u003csup\u003ez\u003c/sup\u003e: \u003cem\u003er\u003c/em\u003e = 0.211, \u003cem\u003ep\u003c/em\u003e = 0.385\u003c/p\u003e \u003cp\u003e- GI: \u003cem\u003er\u003c/em\u003e = -0.001, \u003cem\u003ep\u003c/em\u003e = 0.995\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStiffler-Joachim et al. (2021) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e = 54 (♂: 26, ♀: 28)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e♂: 19 ± 1\u003c/p\u003e \u003cp\u003e♀: 19 ± 1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCollegiate cross-country runners\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGround contact time\u003c/p\u003e \u003cp\u003eVertical ground reaction force\u003c/p\u003e \u003cp\u003eAverage loading rate\u003c/p\u003e \u003cp\u003eBraking impulse\u003c/p\u003e \u003cp\u003ePropulsive impulse\u003c/p\u003e \u003cp\u003eFoot inclination Angle\u003c/p\u003e \u003cp\u003ePeak hip flexion\u003c/p\u003e \u003cp\u003ePeak hip extension\u003c/p\u003e \u003cp\u003ePeak knee flexion\u003c/p\u003e \u003cp\u003ePeak ankle dorsiflexion\u003c/p\u003e \u003cp\u003ePeak hip adduction\u003c/p\u003e \u003cp\u003ePeak pelvic drop\u003c/p\u003e \u003cp\u003eBase of gate\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWithin-season personal records\u003c/p\u003e \u003cp\u003e(i.e., 8 km for male runners and 6 km for female runners)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKinematic outcomes:\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(| \\text{X}\\text{r}\\text{i}\\text{g}\\text{h}\\text{t} – Xleft|\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eKinetic outcomes:\u003c/p\u003e \u003cp\u003eSymmetry index =\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{| \\text{X}\\text{r}\\text{i}\\text{g}\\text{h}\\text{t} – Xleft|}{ 0.5 \\text{X} (\\text{X}\\text{r}\\text{i}\\text{g}\\text{h}\\text{t} + Xleft) }\\text{X}\\text{X}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePersonal records\u003c/p\u003e \u003cp\u003evs. ground contact time:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = -4.5, \u003cem\u003eCI\u003c/em\u003e : [-14.9, 6.3], \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e: NA, \u003cem\u003ep\u003c/em\u003e = 0.39,\u003c/p\u003e \u003cp\u003e\u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003eVertical ground reaction force:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = -2.7, \u003cem\u003eCI\u003c/em\u003e : [-9.2, 3.7], \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e = 0.42, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003eAverage loading rate:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = -3.3, \u003cem\u003eCI\u003c/em\u003e : [-8.1, 1.4], \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e = 0.17, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003eBraking impulse\u0026nbsp;:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ.\u003c/em\u003e = 0.0 \u003cem\u003eCI\u003c/em\u003e : [-10.4, 10.9], \u003cem\u003ep\u003c/em\u003e = 0.99, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003ePropulsive impulse\u0026nbsp;:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = 14.6, \u003cem\u003eCI\u003c/em\u003e : [4.4, 25.0], \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003eFoot inclination Angle\u0026nbsp;:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = -3.9; \u003cem\u003eCI\u003c/em\u003e : [-10.9, 2.9], \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e = 0.27, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003ePeak hip flexion:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = -4.1, \u003cem\u003eCI\u003c/em\u003e\u0026nbsp;: [-14.4, 5.7], \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e = 0.41, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003ePeak hip extension:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = -3.1, \u003cem\u003eCI\u003c/em\u003e : [-13.7, 7.2], \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e = 0.56, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003ePeak knee flexion:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = -1.3, \u003cem\u003eCI\u003c/em\u003e\u0026nbsp;: [-7.2, 4.3], \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e = 0.63, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003ePeak ankle dorsiflexion:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = -6.1, \u003cem\u003eCI\u003c/em\u003e : [-12.9, 0.7], \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e = 0.08, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003ePeak hip adduction:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = 0.4; \u003cem\u003eCI\u003c/em\u003e\u0026nbsp;: [-5.5, 6.1], \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e = 0.90, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003ePeak pelvic drop:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = -2.8, \u003cem\u003eCI\u003c/em\u003e : [-4.3, 9.7], \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e = 0.42, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003eBase of gate:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = -5.5, \u003cem\u003eCI\u003c/em\u003e\u0026nbsp;: [-18.9, 7.8], \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e = 0.43, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTabor et al. (2019) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e \u003cp\u003ePOL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e = 12 (♀)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup A: 23 ± 3\u003c/p\u003e \u003cp\u003eGroup B: 23 ± 1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eIntermediate and advanced middle-distance runners\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMuscle strength symmetry\u003c/p\u003e \u003cp\u003eSupport phase time symmetry\u003c/p\u003e \u003cp\u003eSwing phase time symmetry\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRunning velocity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSymmetry index =\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{2 \\text{X} (\\text{r}\\text{i}\\text{g}\\text{h}\\text{t}-\\text{l}\\text{e}\\text{f}\\text{t}) }{(\\text{r}\\text{i}\\text{g}\\text{h}\\text{t}+\\text{l}\\text{e}\\text{f}\\text{t}) }\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eAsymmetry index =\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\underset{{t=t}_{1}}{\\overset{{t}_{2}}{\\int }}A|{x}_{r}\\left(t\\right)- {x}_{l}\\left(t\\right)|\\text{d}\\text{t}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRunning velocity\u003c/p\u003e \u003cp\u003evs. muscle strength symmetry:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = -5.77, \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e = 0.01, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003evs. support phase time symmetry:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = -6.64, \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e = 0.03, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003cp\u003evs. swing phase time symmetry:\u003c/p\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e = -2.47, \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = NA, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05, \u003cem\u003eregression equation\u003c/em\u003e: NA\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e♂ = male(s), ♀ = female(s), NA = not available, CI = confidence interval, \u003cem\u003eβ\u003c/em\u003e = beta value, \u003cem\u003er\u003c/em\u003e = Pearson correlation coefficient, \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e = Spearman rank order correlation coefficient, \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = R-squared value, t = time; dx, dy and dz = vector displacement, SI = symmetry index for three directions (x, y and z); x anteroposterior direction, y = mediolateral direction, z = vertical direction, GI = global symmetry index, Xright = value of the right leg; Xleft = value of the left leg, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i,j,k}\\)\u003c/span\u003e\u003c/span\u003e = normalised cross-correlation coefficient, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{Lrv}\\)\u003c/span\u003e\u003c/span\u003e = voxel mean value of the left volume, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{{Rv}_{i,j,k}}\\)\u003c/span\u003e\u003c/span\u003e = voxel mean value of the right volume, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{r}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e = value of specific variable recorded for the right leg at time t, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{r}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e = value of specific variable recorded for the left leg at time t\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Test and outcome measures\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 Asymmetry test and metrics\u003c/h2\u003e \u003cp\u003eOverall, 2 studies examined functional asymmetry [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], 1 study morphologic asymmetry [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], 6 studies kinematic asymmetry [\u003cspan additionalcitationids=\"CR47 CR48 CR49 CR50\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and 2 studies kinetic asymmetry [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] in relation to endurance running performance and/or its determinants. Functional asymmetry was assessed using strength tests (i.e., isometric quarter-squat, isometric hip extension, isometric hip adduction, isokinetic knee flexion, isokinetic knee extension) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], whilst morphological asymmetry was measured by performing magnetic resonance imaging [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Kinematic (e.g., step time, ground contact time, flight time, stride asymmetry, displacements in body centre of mass) and kinetic (e.g., ground reaction force, leg stiffness, braking impulse) variables were all collected while running [\u003cspan additionalcitationids=\"CR48 CR49 CR50\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e–\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] or during the execution of a CMJ [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 Equations for calculating asymmetry\u003c/h2\u003e \u003cp\u003eA variety of equations were used to express the magnitude of inter-limb asymmetry among participants in the 8 included studies. Four studies calculated the percentage of asymmetry related to the right versus left lower limb [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], while only one accounted for stronger and weaker lower limb in the formula [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Furthermore, two studies used the global symmetry index to identify the magnitude of asymmetry in body centre of mass trajectory [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], whereas one study also adopted a normalised cross-correlation coefficient to quantify the magnitude of asymmetry between 3D split volumes with magnetic resonance [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 Running performance metrics\u003c/h2\u003e \u003cp\u003eThe endurance running performance variables taken into account could be divided in two specific subcategories: determinants of running performance versus actual running performance metrics based on race performances. As determinants of running performance, metabolic power (i.e., energy cost, based on O\u003csub\u003e2\u003c/sub\u003e consumption and CO\u003csub\u003e2\u003c/sub\u003e production [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], metabolic cost [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], mechanical efficiency [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], running economy [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and running velocity [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] were examined. Furthermore, race performances or personal records were used as an actual running performance metric in two included studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Metabolic power, run economy, metabolic cost and personal records should be interpreted inversely with a view to running performance, as lower values in these metrics correspond to better running performance.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 The association between inter-limb asymmetry and running performance\u003c/h2\u003e \u003cp\u003eEvidence for an association between inter-limb asymmetry and endurance running performance (and/or its determinants) in healthy populations was mixed. All asymmetry outcomes could be sub-divided into four dimensions (i.e., functional asymmetry, morphologic asymmetry, kinematic asymmetry and kinetic asymmetry) and were assessed independently in view of their link with running performance metrics. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e summarises all (significant positive, significant negative or no significant) associations between functional, morphological, kinematic and kinetic asymmetry and running performance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1 Functional asymmetry linked to running performance\u003c/h2\u003e \u003cp\u003eTabor et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] reported significant negative associations of asymmetry in the sum of muscle torque under static conditions in the hip, knee and ankle with maximal running velocity (\u003cem\u003eβ\u003c/em\u003e = -5.77, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). In contrast, swing phase time symmetry measured during a CMJ was not significantly correlated with running velocity (\u003cem\u003eβ\u003c/em\u003e = -2.50, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In the study by Blagrove et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], negligible associations were reported between muscle strength asymmetry and race performance as well as running economy (race performance: \u003cem\u003er\u003c/em\u003e = -0.20 to 0.13; running economy: \u003cem\u003er\u003c/em\u003e = 0.02 to 0.30), except for the correlations found between hip abduction strength asymmetry and race performance (\u003cem\u003er\u003c/em\u003e = -0.47, \u003cem\u003ep\u003c/em\u003e = 0.07) as well as running economy (\u003cem\u003er\u003c/em\u003e = 0.85, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) in female endurance runners.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2 Morphological asymmetry linked to running performance\u003c/h2\u003e \u003cp\u003eThe only study documenting morphological asymmetry included in this systematic review, reported no significant associations of anatomical asymmetry (i.e., volume assessed by means of magnetic resonance images) at the pelvis and lower limb level with the metabolic cost of running (\u003cem\u003er\u003c/em\u003e = 0.06 to 0.16, \u003cem\u003ep\u003c/em\u003e = 0.55 to 0.82) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.6.3 Kinematic asymmetry linked to running performance\u003c/h2\u003e \u003cp\u003eSignificant associations were reported between kinematic asymmetry and metabolic power (\u003cem\u003eβ\u003c/em\u003e = 0.10 to 0.80, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. More specifically, for every 10% increase in step time asymmetry and ground contact time asymmetry an increase of 3.5% and 7.8% in metabolic power was observed, respectively [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Similarly, ground contact time asymmetry was strongly and positively related to caloric unit cost (\u003cem\u003er\u003c/em\u003e = 0.81, p = 0.003) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. For every 1% increase in ground contact time asymmetry, the caloric unit cost increased by 0.0354 kcal · kg\u003csup\u003e− 1\u003c/sup\u003e km\u003csup\u003e− 1\u003c/sup\u003e. Symmetry in displacements of the body centre of mass during running was moderately and positively related to mechanical efficiency (\u003cem\u003er\u003c/em\u003e = 0.66, \u003cem\u003ep\u003c/em\u003e = 0.015) but not associated with metabolic cost (\u003cem\u003er\u003c/em\u003e = -0.00, \u003cem\u003ep\u003c/em\u003e = 0.995) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Kinematic asymmetry was not related to within-season personal records (\u003cem\u003eβ\u003c/em\u003e = -5.5 to 0.4, \u003cem\u003ep\u003c/em\u003e = 0.27 to 0.90) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], with the exception of asymmetry in peak ankle dorsiflexion (i.e., for every 1° increase in peak ankle dorsiflexion asymmetry, personal record times on distances of 8 km for male runners and 6 km for female runners decreased by 7.6 seconds). Support phase time asymmetry was negatively related to running velocity (\u003cem\u003eβ\u003c/em\u003e = -6.60, \u003cem\u003ep\u003c/em\u003e = 0.03) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.6.4 Kinetic asymmetry linked to running performance\u003c/h2\u003e \u003cp\u003eIt was reported that every 10% increase in peak braking ground reaction force asymmetry (\u003cem\u003eβ\u003c/em\u003e = 0.13, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), peak propulsive ground reaction force asymmetry (\u003cem\u003eβ\u003c/em\u003e = 0.20, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), stance average vertical ground reaction force (\u003cem\u003eβ\u003c/em\u003e = 0.35, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and leg stiffness asymmetry (\u003cem\u003eβ\u003c/em\u003e = 0.39, \u003cem\u003ep\u003c/em\u003e = 0.042), respectively elicits a 1.3%, 2.0%, 3.5% and 3.9% metabolic power increase [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In contrast, peak vertical ground reaction force asymmetry was not found to be significantly correlated with net metabolic power (\u003cem\u003ep\u003c/em\u003e = 0.469) and within-season personal records (\u003cem\u003eβ\u003c/em\u003e = -2.70, \u003cem\u003ep\u003c/em\u003e = 0.42) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Conversely, peak vertical ground reaction force asymmetry while running was reported to be significantly related to running velocity (i.e., 3 minutes at fixed velocity of 8, 9, 10, 11 and 12 km/h) (\u003cem\u003ep\u003c/em\u003e = 0.032; competitive runners: \u003cem\u003eβ\u003c/em\u003e = -0.5, recreational runners: \u003cem\u003eβ\u003c/em\u003e = 0.3, novice runners: \u003cem\u003eβ\u003c/em\u003e = 0.6) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In competitive and recreational runners, asymmetry in peak vertical ground reaction force and vertical load rate asymmetry showed a linear and U-shaped trend across velocities, respectively [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In novice runners, a lower asymmetry of time peak vertical ground reaction force was associated with increasing running velocity, whilst asymmetry in vertical load rate did not differ across velocities [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. As opposed to average loading rate and braking impulse asymmetry (\u003cem\u003eβ\u003c/em\u003e = 0.00, \u003cem\u003ep\u003c/em\u003e = 0.99), propulsive impulse asymmetry (\u003cem\u003eβ\u003c/em\u003e = 14.60, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) was positively associated with within-season personal records on distances of 8 km for male runners and 6 km for female runners [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Controlled for sex, for every 5% increase in propulsive impulse asymmetry, personal records times within the running season increased by 16 seconds [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the (significant positive and significant negative or no significant) associations in view of inter-limb asymmetry and running performance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of asymmetry\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e# of asymmetry outcome measures associated with running performance\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStudies\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunctional asymmetry\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificantly positive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificantly negative\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorphological asymmetry\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificantly positive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificantly negative\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKinematic asymmetry\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificantly positive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificantly negative\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKinetic asymmetry\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificantly positive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificantly negative\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificantly positive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificantly negative\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan additionalcitationids=\"CR47 CR48 CR49 CR50\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNote: Running performance includes actual running performance as well as running performance determinants. The outcome measures from the determinants metabolic power, running economy, metabolic cost and personal records were inversely interpreted given their negative relationship with running performance. Similarly, symmetry magnitudes were inversely construed as asymmetry magnitudes.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e** PLEASE INSERT Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e ABOUT HERE **\u003c/p\u003e \u003cp\u003e** PLEASE INSERT Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e ABOUT HERE **\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"4. Discussion","content":"\u003cp\u003eThe main objective of this systematic review was to synthesize and evaluate the available literature regarding the associations between lower inter-limb asymmetry and endurance running performance in healthy populations. According to the risk of bias assessment, all included studies were of moderate to strong quality. To compare and evaluate the association of inter-limb asymmetry with running performance (and/or its determinants), it was necessary to differentiate between dimensions to quantify asymmetry. Therefore, this review addressed the link between functional, morphological, kinematic and kinetic inter-limb asymmetry with running performance, separately. It is important to note that the limited available literature on the topic alongside the high heterogeneity in terms of asymmetry assessments and running metrics, as well as the different mathematical equations for calculating asymmetry magnitude, made it difficult to compare studies and even impossible to conduct a meta-analysis. This discrepancy across test protocols and outcome measures resulted in inconsistent findings highlighting the task, metric, test occasion and individual specific nature of inter-limb asymmetry and its magnitude [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003e3.7 Functional asymmetry and running performance\u003c/h2\u003e\u003cp\u003eFunctional asymmetry was most commonly assessed using strength measures (e.g., isometric strength) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This is unsurprising since strength training-induced neuromuscular adaptations have been demonstrated to enhance running economy (i.e., 2–8%) as well as time trial performance and maximal sprint velocity in middle and long-distance runners [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e–\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Moreover, larger magnitudes of inter-limb strength asymmetry have also been associated with increased gait asymmetry (\u003cem\u003er\u003c/em\u003e = 0.44), indicating a transfer from functional assessments to sport-specific measures [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBlagrove et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] examined the relationship between the magnitude of isometric muscle strength asymmetry (i.e., quarter-squat, hip extension and hip abduction) and running performance as well as running economy in male and female competitive middle- and long-distance runners. In general, this study observed group mean asymmetry values ranging between 4.6–8.4%, resulting in negligible associations between inter-limb strength asymmetry and running economy (\u003cem\u003er\u003c/em\u003e = − 0.02 to 0.13) and running performance (\u003cem\u003er\u003c/em\u003e = − 0.26 to 0.13). However, a larger magnitude of inter-limb asymmetry of 10% was found in hip abduction torque asymmetry for the female endurance runners. This inter-limb asymmetry in abduction strength was significantly positively correlated (\u003cem\u003er\u003c/em\u003e = 0.85) with running economy (i.e., energy cost in kJ.kg\u003csup\u003e− 75\u003c/sup\u003e.km\u003csup\u003e− 1\u003c/sup\u003e), indicating the potential negative impact of larger inter-limb asymmetry magnitudes on running performance as higher energy costs are detrimental to endurance performances [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Similarly, Tabor et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] reported that reductions in the magnitude of the sum of muscle torque in knee and hip flexors and extensors were negatively correlated with running velocity in female middle-distance runners (\u003cem\u003eβ\u003c/em\u003e = -6.64). However, given the task-dependent nature of functional asymmetry [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], it may not be advisable to add together different strength measures to determine strength asymmetry. Therefore, this latter result should be interpreted with caution.\u003c/p\u003e\u003ch2\u003e3.8 Morphological asymmetry and running performance\u003c/h2\u003e\u003cp\u003ePrevious research documented a negative relationship between asymmetry in various traits (e.g., nostrils and ears) and running performance in middle-distance runners [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. However, the existing literature on the association between the magnitude of morphological asymmetry and (determinants of) running performance (e.g., metabolic cost) seems to be limited to only one study in untrained, occasional and skilled runners. A first important finding of this study conducted by Semanti at al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] was the moderate and positive correlation (\u003cem\u003er\u003c/em\u003e = 0.606) between anatomical asymmetry (i.e., side-to-side differences in volume of the lower limbs measured by magnetic resonance imaging) and dynamical asymmetry (i.e., body centre of mass displacements), indicating that runners with greater magnitudes of morphological asymmetry tend to exhibit more pronounced asymmetrical running patterns. Moreover, this latter study showed that training status moderated this relationship, as more experienced runners showed smaller magnitudes of dynamic asymmetry at higher running velocities compared to their untrained peers. However, this study did not report a significant correlation between anatomical asymmetry and metabolic cost. The authors speculated that certain physiological adaptations may compensate for the relatively small anatomical asymmetry magnitudes observed (i.e., 0.77 to 0.83%), regardless of training status [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven the scarcity of literature on the link between morphological inter-limb asymmetry and running performance, it is difficult to draw clear conclusions. However, it is important to note that none of the studies included in this review addressed leg length discrepancies. This is probably because leg length differences are typically reported as an absolute difference between the right and left lower limb, rather than as a relative asymmetry score (i.e., expressed as a percentage). As such, larger absolute leg length differences (\u0026gt; 2 cm) have been reported to increase energy expenditure during walking, to increase oxygen consumption during submaximal running and to impair running economy [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Moreover, and although this seems to be individual specific, absolute leg length differences have been positively associated with a more pronounced gait asymmetry (\u003cem\u003er\u003c/em\u003e = 0.29 to 0.51) [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In contrast, leg length differences smaller than 1 cm do not appear to be significantly associated with running economy [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. These results support the notion that the magnitude of morphological asymmetry between the lower limbs could affect running performance [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003e3.9 Kinematic asymmetry and running performance\u003c/h2\u003e\u003cp\u003eDespite the wide range of kinematic asymmetry magnitudes observed (i.e., 3–54%), the narrative review by Carpes et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] concluded in 2010 that the available studies failed to establish significant relationships between kinematic asymmetry and running performance. Due to the recently growing interest on the topic, several studies attempted to investigate the association between kinematic inter-limb asymmetry and determinants of running performance as well as personal records. For instance, two studies included in the current systematic review indicated that inter-limb asymmetry in ground contact times (i.e., the average time each foot spends in contact with the ground while running) was correlated with impaired running economy (\u003cem\u003er\u003c/em\u003e = 0.808) and metabolic power (i.e., energy cost, based on O\u003csub\u003e2\u003c/sub\u003e consumption and CO\u003csub\u003e2\u003c/sub\u003e production [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]) (\u003cem\u003eβ\u003c/em\u003e = 0.78) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. As discussed in a recent review by Moore et al. [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], there is still debate on whether short or long contact times are favourable in view of running performance. Whereas short ground contact times are suggested to impose a higher metabolic cost due to the need for faster force production [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], longer ground contact times are suggested to increase the metabolic cost during the increased deceleration, resulting in a lengthened braking phase [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. However, the findings in our review indicate that inter-limb asymmetry in ground contact times has a negative impact on energy depletion and running economy, potentially impairing running performance. Similarly, step time asymmetry (i.e., including both the ground contact time and the subsequent aerial time) was significantly positively correlated (\u003cem\u003eβ\u003c/em\u003e = 0.35) with metabolic power [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This result is consistent with previous research in which larger asymmetric step times were associated with increased metabolic power in walking [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Beck et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] attributed these findings to reduced mechanical energy conservation in asymmetric step times, resulting in increased muscle mechanical work per step and an increased metabolic rate.\u003c/p\u003e\u003cp\u003eStudies investigating the association of asymmetry in trajectories of body centre of mass and (determinants of) running performance revealed equivocal results. Whilst asymmetry in body centre of mass displacements was moderately negatively related to mechanical efficiency (\u003cem\u003er\u003c/em\u003e = -0.66), no significant association was found with metabolic cost [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Differences in duration of the running protocol have been postulated as a possible explanation for these discrepancies. Melo et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] argued that longer distance protocols (e.g., 10 km) are more suitable for detecting kinematic asymmetry, which may not be evident in shorter running bouts. Moreover, variations in running experience [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], running intensity [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] and muscle fatigue [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] could also explain these differences in findings.\u003c/p\u003e\u003cp\u003ePrevious research showed that peak ankle dorsiflexion (i.e., maximal ankle dorsiflexion angle during stance phase) later in stance was positively related to running economy and thus possibly affecting running performance [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. In the study by Stiffler-Joachim et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], inter-limb asymmetry in peak ankle dorsiflexion was the only kinematic variable that was significantly and negatively correlated with within-season personal records (\u003cem\u003eβ\u003c/em\u003e = -6.1, \u003cem\u003eCI\u003c/em\u003e: [-12.9, 0.7]. Every 1° increase in peak ankle dorsiflexion asymmetry was related to a 7.6 s decrease in the best running time on 8km for male and 6 km for female distance runners. Whilst the underlying mechanism for this finding is unclear, it should be noted that the magnitude for peak dorsiflexion was quantified as the absolute value of the inter-limb differences and not as a percentage. Lastly, Tabor et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] demonstrated that swing phase asymmetry can impair running velocity in intermediate and advanced middle-distance runners. Given that a shorter support phase has been related to increased running velocity [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], it seems plausible that asymmetry in the extension of the swing phase could impair running velocity [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003e3.10 Kinetic asymmetry and running performance\u003c/h2\u003e\u003cp\u003eStiffler-Joachim et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] documented varying kinetic asymmetry percentages in National Collegiate Athletic Association (NCAA) Division I runners, ranging from 3% for peak vertical ground reaction force up to 20% for average vertical loading rate. Propulsive impulse asymmetry has been reported to be related to impaired race performance in distance running [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Given that metabolic cost during running is to a large extent determined by propulsive impulse [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], practitioners should not only consider to improve runners’ overall propulsive impulse but also aim to minimize side-to-side differences in this respect. In contrast, average vertical loading rate, braking impulse and peak vertical loading rate were not found to be significant predictors for race performance [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. This could potentially be attributed to the fact that these associations were investigated in elite runners, who exhibited low overall asymmetry scores for these particular metrics.\u003c/p\u003e\u003cp\u003eRegarding the relationship between kinetic inter-limb asymmetry and determinants of running performance, results indicated that stance average vertical ground reaction force, peak propulsive ground reaction force and leg stiffness asymmetry were positively associated with metabolic power in recreational runners [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This suggests that more pronounced kinetic inter-limb asymmetry could eventually increase energy expenditure while running and thus potentially have an adverse effect on running performance. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In contrast, peak ground reaction force asymmetry was not found to be significantly associated with metabolic power, demonstrating the variable nature of asymmetry [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This variability in asymmetry metrics and their associations with running performance was further emphasized in the study conducted by Mo et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The latter study indicated that the association between inter-limb kinematic asymmetry and running performance highly depends on the velocity of the running test, the running experience of the participants and the parameter of interest assessed. Consistent with previous research, the magnitude of asymmetry not only varied considerably within kinetic variables, but also appeared to be more pronounced compared to kinematic variables [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003e3.11 Limitations and strengths\u003c/h2\u003e\u003cp\u003eAlthough this is the first systematic review to provide a holistic view on the available evidence concerning lower inter-limb asymmetry and the association with (determinants of) running performance in endurance runners, this research effort is not without limitations.\u003c/p\u003e\u003cp\u003eFirst, it is difficult to draw a definitive conclusion due to the high heterogeneity among the included studies. More specifically, the heterogeneity was evident in a variety of dimensions, (e.g., functional, morphological, kinematic or kinetic), assessments, equations and metrics being used to assess and express inter-limb asymmetry, as well as in a diversity of population characteristics (e.g., sex, age and/or training status of participants. Furthermore, caution is warranted when interpreting these results because of the scarcity of eligible studies, making comparisons between study results difficult and less robust. Moreover, only a quarter of the studies documenting Pearson’s or Spearman’s rank order correlations also reported an assessment of normality on their raw data. Since falsely (i.e., with non-normal data) using a Pearson’s correlation coefficient highly increases type I error rates, justification for the use of parametric statistics by means of normality tests (e.g., Shapiro-Wilk test) is essential [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Lastly, only one study reported the reliability of the asymmetry metric of interest. Given the inherently high variably nature of inter-limb asymmetry [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], a good test-rest as well as inter-rater reliability is necessary to ensure the quality of the data.\u003c/p\u003e\u003ch2\u003e3.12 Directions for future research\u003c/h2\u003e\u003cp\u003eBy analogy with the work of Afonso et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], the synopsis of literature on the topic presented in the current systematic review is important to identify a research agenda highlighting some key areas for future research on inter-limb asymmetry in endurance runners (see Fig.\u0026nbsp;2 for an illustrative overview):\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn literature, inter-limb asymmetry is often not reported using sport-specific and field-based assessments in endurance runners. Whilst functional asymmetry is generally measured using maximal (isometric) strength, (repeated) hop tests are presumed to have a greater ecological validity for assessing inter-limb asymmetry in runners due to their ability to measure various facets related to the stretch and shortening cycle [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Notably, storing and returning mechanical energy in the process of elastic energy utilization plays a key role in the metabolic energy-saving mechanism, and consequently running economy [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. In this regard, leg stiffness (i.e., resistance to deformation of the limb) and reactive strength (i.e., the ability to effectively use the stretch and shortening cycle as well as the energy produced by the muscle-tendon complex) have been proposed to be important neuromuscular factors contributing to the elastic energy utilization [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Given that these factors can be measured using (repeated) hop tests and rebound jump protocols, moderate to large correlations between a countermovement jump and running economy have been previously reported [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Hence, future research should consider investigating the effect of functional inter-limb asymmetry in leg stiffness and reactive strength using unilateral (repeated) hop tests. Moreover, the use of sport-specific, valid and reliable field-based assessments of functional asymmetry is needed to enhance the ecological validity and applicability among practitioners.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDisparities in asymmetry outcomes and magnitudes between different types of runners underscore the necessity for practitioners to account for inter-individual differences. Variables such as type of running (e.g., track versus road running), training status of runners (e.g., trained versus untrained) and injury history of runners (i.e., injured versus non-injured) should be considered when assessing lower inter-limb asymmetries [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan additionalcitationids=\"CR82\" citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e–\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Although larger magnitudes of inter-limb asymmetry are expected in novice endurance runners than in elite endurance runners [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], the results of the present review indicate that – based on the included studies – 68% of research has been conducted in competitive runners and only 22% in recreational runners and 10% in novice runners. Therefore, addressing a more diverse range of running populations in terms of training status, age and sex, while acknowledging the high inter- and intra-variability, is warranted.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe direction of asymmetry is highly variable between tasks and between test occasions [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. For instance, a distance runner may favour his right limb on a first test occasion whilst favouring his left limb on a second test occasion. Given that asymmetry is a ratio metric, reporting kappa values is highly recommended to assess differences in the direction of asymmetry between different tasks and/or test occasions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSeveral factors relating to test protocols, such as running velocity, test intensity or fatigue, will likely induce intra-individual differences in asymmetry [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. In addition, also environmental factors such as the running underground, air humidity and ambient temperature may possibly lead to different asymmetry magnitudes and/or running performances. This accentuates the need for a standardized approach under stable conditions when evaluating inter-limb asymmetry.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRecognizing the highly variable nature of inter-limb asymmetry, researchers are urged to report the reliability of their tests and related outcome measures (e.g., test-retest or inter-rater reliability) to mitigate the impact of fluctuations on asymmetry due to test errors. A standardized approach for expressing asymmetry magnitude across studies is also needed, preferably using “stronger” and “weaker” limb instead of “right” and left “limb” [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e** PLEASE INSERT FIGURE 2 ABOUT HERE **\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, the limited literature on inter-limb asymmetry in endurance runners displayed a high heterogeneity regarding study samples, study methods, assessments and metrics used, making direct comparisons extremely difficult. With the exception of one study demonstrating a positive association between peak ankle dorsiflexion asymmetry and running performance, the majority of findings either suggest inter-limb asymmetries to be negatively associated or not affect running performance or its determinants in healthy populations. However, more research across diverse running populations is needed to confirm these assertions and to establish critical thresholds within this respect. Practitioners should be mindful of the task, test occasion and metric specificity as well as the inter- and intra-individual variability when monitoring inter-limb asymmetry.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCMJ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecountermovement jump\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDJ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edrop jump\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eJoachim D\u0026rsquo;Hondt, Laurent Chapelle, Chris Bishop, Dirk Aerenhouts, Kevin de Pauw, Peter Clarys and Eva D\u0026rsquo;Hondt have no conflicts of\u0026nbsp;interest relevant to this review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eNo funding was received for this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eAll authors contributed to the development of the present systematic review. The initial design of the search strategy was performed by J.D. and revised by L.C., C.B., D.A., K.D.P., P.C. and E.D.. The screening process as well as the data-analysis and risk of bias assessment was conducted by J.D. and L.C.. In case of divergency, D.A. was involved. The first version of the manuscript was drafted by J.D. and revised and edited by all authors. All authors read and approved the final version of the manuscript prior to submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNone to disclosure.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcCartney G, Hepper P. Development of lateralized behaviour in the human fetus from 12 to 27 weeks' gestation. Dev Med Child Neurol. 1999;41(2):83\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapelle L, Bishop C, D'Hondt J, D'Hondt E, Clarys P. Morphological and functional asymmetry in elite youth tennis players compared to sex- and age-matched controls. J Sports Sci. 2022;40(14):1618\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD'Hondt J, Chapelle L, Van Droogenbroeck L, Aerenhouts D, Clarys P, D'Hondt E. Bioelectrical impedance analysis as a means of quantifying upper and lower limb asymmetry in youth elite tennis players: An explorative study. Eur J Sport Sci. 2022;22(9):1343\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBishop C, Turner A, Read P. Effects of inter-limb asymmetries on physical and sports performance: a systematic review. J Sports Sci. 2018;36(10):1135\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagahara R, Gleadhill S. Asymmetries of kinematics and kinetics in female and male sprinting. J Sports Med Phys Fitness. 2023;63(8):891\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapelle L, Bishop C, Clarys P, D'Hondt E. No Relationship between Lean Mass and Functional Asymmetry in High-Level Female Tennis Players. Int J Env Res Pub He. 2021;18(22).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVirgile A, Bishop C. A Narrative Review of Limb Dominance: Task Specificity and the Importance of Fitness Testing. J Strength Cond Res. 2021;35(3):846\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStiffler-Joachim MR, Lukes DH, Kliethermes SA, Heiderscheit BC. Lower Extremity Kinematic and Kinetic Asymmetries during Running. Med Sci Sports Exerc. 2021;53(5):945\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaramanidis K, Arampatzis A, Bruggemann GP. Symmetry and reproducibility of kinematic parameters during various running techniques. Med Sci Sport Exer. 2003;35(6):1009\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBissas A, Walker J, Paradisis GP, Hanley B, Tucker CB, Jongerius N, et al. Asymmetry in sprinting: An insight into sub-10 and sub-11 s men and women sprinters. Scand J Med Sci Sports. 2022;32(1):69\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDos'Santos T, Thomas C, Jones PA. Assessing Interlimb Asymmetries: Are We Heading in the Right Direction? Strength Cond J. 2021;43(3):91\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapelle L, Bishop C, D'Hondt J, Rommers N, D'Hondt E, Clarys P. Development of Upper and Lower Extremity Functional Asymmetries in Male and Female Elite Youth Tennis Players: A Longitudinal Study. 2023:[unpublished observations].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfonso J, Pena J, Sa M, Virgile A, Garcia-de-Alcaraz A, Bishop C. Why Sports Should Embrace Bilateral Asymmetry: A Narrative Review. Symmetry-Basel. 2022;14(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelme M, Tee J, Emmonds S, Low C. Does lower-limb asymmetry increase injury risk in sport? A systematic review. Phys Ther Sport. 2021;49:204\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarpes FP, Mota CB, Faria IE. On the bilateral asymmetry during running and cycling - A review considering leg preference. Phys Ther Sport. 2010;11(4):136\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhilipp NM, Garver MJ, Crawford DA, Davis DW, Hair JN. Interlimb asymmetry in collegiate American football players: Effects on combine-related performance. J Hum Sport Exerc. 2022;17(3):708\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadruga-Parera M, Bishop C, Read P, Lake J, Brazier J, Romero-Rodriguez D. Jumping-based Asymmetries are Negatively Associated with Jump, Change of Direction, and Repeated Sprint Performance, but not Linear Speed, in Adolescent Handball Athletes. J Hum Kinet. 2020;71:47\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFort-Vanmeerhaeghe A, Bishop C, Busc\u0026agrave; B, Aguilera-Castells J, Vicens-Bordas J, Gonzalo-Skok O. Inter-limb asymmetries are associated with decrements in physical performance in youth elite team sports athletes. PLoS ONE. 2020;15(3):e0229440.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBishop C, Read P, McCubbine J, Turner A. Vertical and Horizontal Asymmetries Are Related to Slower Sprinting and Jump Performance in Elite Youth Female Soccer Players. J Strength Cond Res. 2021;35(1):56\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBishop C, Turner A, Maloney S, Lake J, Loturco I, Bromley T et al. Drop Jump Asymmetry is Associated with Reduced Sprint and Change-of-Direction Speed Performance in Adult Female Soccer Players. Sports (Basel). 2019;7(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBishop C, Brashill C, Abbott W, Read P, Lake J, Turner A. Jumping Asymmetries Are Associated With Speed, Change of Direction Speed, and Jump Performance in Elite Academy Soccer Players. J Strength Cond Res. 2021;35(7):1841\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBishop C, Read P, Bromley T, Brazier J, Jarvis P, Chavda S, et al. The Association Between Interlimb Asymmetry and Athletic Performance Tasks: A Season-Long Study in Elite Academy Soccer Players. J Strength Conditioning Res. 2022;36(3):787\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoturco I, Pereira LA, Kobal R, Abad CCC, Rosseti M, Carpes FP, et al. Do asymmetry scores influence speed and power performance in elite female soccer players? Biol Sport. 2019;36(3):209\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLockie RG, Callaghan SJ, Berry SP, Cooke ER, Jordan CA, Luczo TM, et al. Relationship between unilateral jumping ability and asymmetry on multidirectional speed in team-sport athletes. J Strength Cond Res. 2014;28(12):3557\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichailidis Y, Pirounakis V, Savvakis C, Margonis K, Metaxas T. The influence of unilateral jumping asymmetry on acceleration and speed performance, in U10 and U15 groups of youth soccer players. Trends in Sport Sciences. 2019;26(4):145\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHart NH, Nimphius S, Spiteri T, Newton RU. Leg Strength and Lean Mass Symmetry Influences Kicking Performance in Australian Football. J Sport Sci Med. 2014;13(1):157\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRauter S, Simenko J. Morphological Asymmetries Profile and the Difference between Low- and High-Performing Road Cyclists Using 3D Scanning. Biology (Basel). 2021;10(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrivers R, Fink B, Russell M, McCarty K, James B, Palestis BG. Lower body symmetry and running performance in elite Jamaican track and field athletes. PLoS ONE. 2014;9(11):e113106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBini RR, Hume PA. Relationship between pedal force asymmetry and performance in cycling time trial. J Sports Med Phys Fitness. 2015;55:892\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRannama I, Port K, Bazanov B, Pedak K. Sprint cycling performance and asymmetry. J Hum Sport Exerc. 2015;10:247\u0026ndash;S58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox KT, Pearson LT, Hicks KM. The effect of lower inter-limb asymmetries on athletic performance: A systematic review and meta-analysis. PLoS ONE. 2023;18(6):e0286942.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuan Y, Bredin SSD, Taunton J, Jiang Q, Wu N, Warburton DER. Association between Inter-Limb Asymmetries in Lower-Limb Functional Performance and Sport Injury: A Systematic Review of Prospective Cohort Studies. J Clin Med. 2022;11(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYanci J, Los Arcos A. Muscle strength and leg asymmetries in elite runners and cyclists. Int SportMed J. 2014;15(3):285\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZifchock RA, Davis I, Hamill J. Kinetic asymmetry in female runners with and without retrospective tibial stress fractures. J Biomech. 2006;39(15):2792\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlagrove RC, Bishop C, Howatson G, Hayes PR. Inter-limb strength asymmetry in adolescent distance runners: Test-retest reliability and relationships with performance and running economy. J Sports Sci. 2021;39(3):312\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrivers R, Palestis BG, Manning JT. The symmetry of children's knees is linked to their adult sprinting speed and their willingness to sprint in a long-term Jamaican study. PLoS ONE. 2013;8(8):e72244.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyers RW, Oliver JL, Hughes MG, Lloyd RS, Cronin JB. Asymmetry During Maximal Sprint Performance in 11- to 16-Year-Old Boys. Pediatr Exerc Sci. 2017;29(1):94\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMackala K, Michalski R, Coh M. Asymmetry of Step Length in Relationship to Leg Strength in 200 meters Sprint of different Performance Levels. J Hum Kinetics. 2010;25:101\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDebaere S, Delecluse C, Aerenhouts D, Hagman F, Jonkers I. From block clearance to sprint running: characteristics underlying an effective transition. J Sports Sci. 2013;31(2):137\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePage MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021;372:n160.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOuzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDowns SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Commun H. 1998;52(6):377\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJarvis P, Turner A, Read P, Bishop C. Reactive Strength Index and its Associations with Measures of Physical and Sports Performance: A Systematic Review with Meta-Analysis. Sports Med. 2022;52(2):301\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox JL, Stanton R, Sargent C, Wintour SA, Scanlan AT. The Association Between Training Load and Performance in Team Sports: A Systematic Review. Sports Med. 2018;48(12):2743\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeminati E, Nardello F, Zamparo P, Ardig\u0026ograve; LP, Faccioli N, Minetti AE. Anatomically asymmetrical runners move more asymmetrically at the same metabolic cost. PLoS ONE. 2013;8(9):e74134.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabor P, Mastalerz A, Iwańska D, Grabowska O. Asymmetry Indices in Female Runners as Predictors of Running Velocity. Pol J Sport Tourism. 2019;26(3):3\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeck ON, Azua EN, Grabowski AM. Step time asymmetry increases metabolic energy expenditure during running. Eur J Appl Physiol. 2018;118(10):2147\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoubert DP, Guerra NA, Jones EJ, Knowles EG, Piper AD. Ground Contact Time Imbalances Strongly Related to Impaired Running Economy. Int J Exerc Sci. 2020;13(4):427\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStiffler-Joachim MR, Kliethermes SA, Martin JA, Tanaka CS, Benkert R, Heiderscheit BC. Longitudinal Changes in Running Gait Asymmetries and Their Relationship to Personal Record Race Times in Collegiate Cross Country Runners. Symmetry-Basel. 2021;13(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelo CC, Carpes FP, Vieira TM, Mendes TT, de Paula LV, Chagas MH, et al. Correlation between running asymmetry, mechanical efficiency, and performance during a 10 km run. J Biomech. 2020;109:109913.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMo S, Lau FOY, Lok AKY, Chan ZYS, Zhang JH, Shum G, et al. Bilateral asymmetry of running gait in competitive, recreational and novice runners at different speeds. Hum Mov Sci. 2020;71:102600.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrockway JM. Derivation of Formulas Used to Calculate Energy-Expenditure in Man. Hum Nutr-Clin Nutr. 1987;41c(6):463\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuglielmo LGA, Greco CC, Denadai BS. Effects of Strength Training on Running Economy. Int J Sports Med. 2009;30(1):27\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStoren O, Helgerud J, Stoa EM, Hoff J. Maximal strength training improves running economy in distance runners. Med Sci Sport Exer. 2008;40(6):1087\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlagrove RC, Howatson G, Hayes PR. Effects of Strength Training on the Physiological Determinants of Middle- and Long-Distance Running Performance: A Systematic Review. Sports Med. 2018;48(5):1117\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaroche DP, Cook SB, Mackala K. Strength asymmetry increases gait asymmetry and variability in older women. Med Sci Sports Exerc. 2012;44(11):2172\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConley DL, Krahenbuhl GS. Running economy and distance running performance of highly trained athletes. Med Sci Sports Exerc. 1980;12(5):357\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManning JT, Pickup LJ. Symmetry and performance in middle distance runners. / Symetrie et performance chez des coureurs de demi-fond. Int J Sports Med. 1998;19(3):205\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelacerda FG, McCrory ML. A case report: effect of a leg length differential on oxygen consumption. J Orthop Sports Phys Ther. 1981;3(1):17\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGurney B, Mermier C, Robergs R, Gibson A, Rivero D. Effects of limb-length discrepancy on gait economy and lower-extremity muscle activity in older adults. J Bone Joint Surg Am. 2001;83(6):907\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaufman KR, Miller LS, Sutherland DH. Gait asymmetry in patients with limb-length inequality. J Pediatr Orthop. 1996 Mar-Apr;16(2):144\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeeley MK, Umberger BR, Clasey JL, Shapiro R. The relation between mild leg-length inequality and able-bodied gait asymmetry. J Sport Sci Med. 2010;9(4):572\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerttunen JR, Anttila E, Sodergard J, Merikanto J, Komi PV. Gait asymmetry in patients with limb length discrepancy. Scand J Med Sci Sports. 2004;14(1):49\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore IS. Is There an Economical Running Technique? A Review of Modifiable Biomechanical Factors Affecting Running Economy. Sports Med. 2016;46(6):793\u0026ndash;807.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts TJ, Kram R, Weyand PG, Taylor CR. Energetics of bipedal running I. Metabolic cost of generating force. J Exp Biol. 1998;201(19):2745\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKram R, Taylor CR. Energetics of Running - a New Perspective. Nature. 1990;346(6281):265\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNummela A, Ker\u0026auml;nen T, Mikkelsson LO. Factors related to top running speed and economy. Int J Sports Med. 2007;28(8):655\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllis RG, Howard KC, Kram R. The metabolic and mechanical costs of step time asymmetry in walking. Proc Biol Sci. 2013;280(1756):20122784.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCavanagh PR, Pollock ML, Landa J. A biomechanical comparison of elite and good distance runners. Ann N Y Acad Sci. 1977;301:328\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JB, Sutter KJ, Askew CD, Burkett BJ. Identifying symmetry in running gait using a single inertial sensor. J Sci Med Sport. 2010;13(5):559\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArampatzis A, Br\u0026uuml;ggemann GP, Metzler V. The effect of speed on leg stiffness and joint kinetics in human running. J Biomech. 1999;32(12):1349\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMizrahi J, Verbitsky O, Isakov E, Daily D. Effect of fatigue on leg kinematics and impact acceleration in long distance running. Hum Mov Sci. 2000;19(2):139\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore IS, Jones AM, Dixon SJ. Mechanisms for Improved Running Economy in Beginner Runners. Med Sci Sport Exer. 2012;44(9):1756\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrughelli M, Cronin J, Chaouachi A. Effects of Running Velocity on Running Kinetics and Kinematics. J Strength Conditioning Res. 2011;25(4):933\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFurlong LM, Egginton NL. Kinetic Asymmetry during Running at Preferred and Nonpreferred Speeds. Med Sci Sports Exerc. 2018;50(6):1241\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBishara AJ, Hittner JB. Testing the significance of a correlation with nonnormal data: comparison of Pearson, Spearman, transformation, and resampling approaches. Psychol Methods. 2012;17(3):399\u0026ndash;417.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarkstrom JL, Olsson CJ. Countermovement jump peak force relative to body weight and jump height as predictors for sprint running performances: (in)homogeneity of track and field athletes? J Strength Cond Res. 2013;27(4):944\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasaki K, Neptune RR. Muscle mechanical work and elastic energy utilization during walking and running near the preferred gait transition speed. Gait Posture. 2006;23(3):383\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVogt M, Hoppeler HH. Eccentric exercise: mechanisms and effects when used as training regime or training adjunct. J Appl Physiol. 2014;116(11):1446\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi F, Newton RU, Shi Y, Sutton D, Ding HY. Correlation of Eccentric Strength, Reactive Strength, and Leg Stiffness With Running Economy in Well-Trained Distance Runners. J Strength Conditioning Res. 2021;35(6):1491\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eExell T, Irwin G, Gittoes M, Kerwin D. Strength and performance asymmetry during maximal velocity sprint running. Scand J Med Sci Sports. 2017;27(11):1273\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshimura K, Sakurai S. Asymmetry in Determinants of Running Speed During Curved Sprinting. J Appl Biomech. 2016;32(4):394\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBredeweg SW, Buist I, Kluitenberg B. Differences in kinetic asymmetry between injured and noninjured novice runners: a prospective cohort study. Gait Posture. 2013;38(4):847\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBishop C, Lake J, Loturco I, Papadopoulos K, Turner A, Read P. Interlimb Asymmetries: The Need for an Individual Approach to Data Analysis. J Strength Cond Res. 2021;35(3):695\u0026ndash;701.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBishop C, Read P, Chavda S, Jarvis P, Brazier J, Bromley T, et al. Magnitude or Direction? Seasonal Variation of Interlimb Asymmetry in Elite Academy Soccer Players. J Strength Cond Res. 2022;36(4):1031\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeil J, Loffing F, B\u0026uuml;sch D. The Influence of Exercise-Induced Fatigue on Inter-Limb Asymmetries: a Systematic Review. Sports Med-Open. 2020;6(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBishop C, Read P, Chavda S, Turner A. Asymmetries of the Lower Limb: The Calculation Conundrum in Strength Training and Conditioning. Strength Cond J. 2016;38(6):27\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sports-medicine-open","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"smoa","sideBox":"Learn more about [Sports Medicine-Open](http://sportsmedicine-open.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/smoa/default.aspx","title":"Sports Medicine-Open","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Side-to-side differences, bilateral difference, between-limb, Functional asymmetry, Morphological asymmetry, Kinematic asymmetry, Kinetic asymmetry, Biomechanics, Running economy, Distance running","lastPublishedDoi":"10.21203/rs.3.rs-3787566/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3787566/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003eː The presence of inter-limb asymmetry in the human body has traditionally been perceived to be detrimental for athletic performance. However, a systematic review addressing and comprehensively assessing the association of asymmetry between the lower limbs and endurance running performance is currently lacking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: The main purpose of this systematic review was to examine the relationship between lower inter-limb asymmetry and running performance in healthy endurance runners. The secondary objective was to identify possible avenues for further research in this area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003eː Pubmed, Web of Science and SPORTDiscus were systematically searched for studies investigating the relationship between lower inter-limb asymmetry and (determinants of) running performance in healthy and injury-free endurance runners. The quality of studies eligible for inclusion was assessed using the Downs and Black Quality Index Tool.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003eː Out of 4817 articles screened, 8 studies were included in this review. The quality score of the included research varied between 5/10 and 9/10. Except from one finding demonstrating a positive association between peak ankle dorsiflexion asymmetry and running performance, all other lower inter-limb asymmetry outcome measures were either negatively (N = 16) or not significantly (N = 30) associated with running performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003eː A high heterogeneity across study methods and outcomes was apparent, making it difficult to draw a straightforward conclusion. Despite one study showing a positive relationship, the results demonstrate that some, but not all, metrics of functional, morphological, kinematic and kinetic inter-limb asymmetry are negatively or not associated with running performance. Thus, a more extensive high-quality body of research is essential to determine whether and to what extent asymmetry between the lower limbs could affect endurance running performance as well as to establish potential trade-off values for practitioners in developing training programs.\u003c/p\u003e","manuscriptTitle":"Association between inter-limb asymmetry and endurance running performance in healthy populations: A systematic review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-10 18:58:21","doi":"10.21203/rs.3.rs-3787566/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-03-11T19:56:21+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-08T07:22:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-04T22:51:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sports Medicine-Open","date":"2024-01-04T08:52:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sports-medicine-open","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"smoa","sideBox":"Learn more about [Sports Medicine-Open](http://sportsmedicine-open.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/smoa/default.aspx","title":"Sports Medicine-Open","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"58179a96-9d9a-44d2-b998-d557c85d255e","owner":[],"postedDate":"January 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T16:03:34+00:00","versionOfRecord":{"articleIdentity":"rs-3787566","link":"https://doi.org/10.1186/s40798-024-00790-w","journal":{"identity":"sports-medicine-open","isVorOnly":false,"title":"Sports Medicine-Open"},"publishedOn":"2024-11-26 15:57:48","publishedOnDateReadable":"November 26th, 2024"},"versionCreatedAt":"2024-01-10 18:58:21","video":"","vorDoi":"10.1186/s40798-024-00790-w","vorDoiUrl":"https://doi.org/10.1186/s40798-024-00790-w","workflowStages":[]},"version":"v1","identity":"rs-3787566","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3787566","identity":"rs-3787566","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}