Dynamic postural control and physical stress: an approach to determining injury risk in real sporting conditions

Abstract

Dynamic postural control is one of the essential factors in situations where non-contact injuries mainly occur, i.e., landing, cutting, or stopping. Therefore, testing of dynamic postural control should be implemented in injury risk assessment. Moreover, non-contact injuries mainly occur under loaded conditions when the athlete is physically stressed. Therefore, risk factors and mechanisms of these injuries should also be regarded under loading conditions and not only when the athlete is recovered. Current studies examining the influence of physical load on risk factors, such as dynamic postural control, often use cycling protocols to stress the participants. Nevertheless, most types of sports require running as a central element and the induced internal load after cycling might not be the same after running. Therefore, the current study aimed to examine the influence of a running and a cycling protocol on dynamic postural control and to determine the potential injury risk under representative conditions. In total, 128 sport students (64 males and 64 females, age: 23.64 ± 2.44, height: 176.54 ± 8.96 cm, weight: 68.85 ± 10.98 kg) participated in the study. They were tested with the Y Balance Test before and after one loading protocol. A total of 64 participants completed a protocol on a cycle ergometer and the other 64 on a treadmill. A mixed ANOVA showed significant interactions of time and load type. Dynamic postural control was reduced immediately after cycling but did not change after running. These findings indicate a load type dependence of dynamic postural control that must be considered while assessing an athlete’s potential injury risk and they support the need for more representative designs.

Injury risk under real sporting conditions

Injury risk assessment

In the past few years, substantial work has been done on injury risk assessment and injury prevention. Different test batteries using strength, jump, or balance tests have been evaluated to assess an athlete’s potential injury risk, and different prevention programs have been developed. Nevertheless, the incidence of injury remains high, especially in sports games (Ekstrand, Spreco, Bengtsson, & Bahr, 2021). Therefore, there is still a need for appropriate diagnostic measures and empirically justified prevention programs to reduce the number of non-contact injuries. One of the main challenges in the currently used diagnostics is their restricted prognostic validity. Diagnostic methods need to be improved because the currently used diagnostic tests are not sufficient or fail to detect the potential deficits of an athlete and the deficits still occur despite (regular) diagnostic testing and preventive training (Bolt, Heuvelmans, Benjaminse, Robinson, & Gokeler, 2021; Verschueren et al., 2020).

In this context, in order to gain reliable information about the potential injury risk of an athlete, it is helpful to analyze the injury mechanisms and to identify possible risk factors. Most sports injuries occur without an opponent’s contact, mainly ligament ruptures (anterior cruciate ligament [ACL], ankle) or muscle injuries (Hewett, Ford, Hoogenboom, & Myer, 2010). Such non-contact injures mostly occur during dynamic actions, e.g., landing, cutting, or stopping, when the athlete must suddenly accelerate or decelerate or when the athlete must maintain stability in situations with the high(est) and often unilateral demands. Besides strength to compensate for the high demands that impact on the body, balance is a necessity in these situations (Güler et al., 2020). The athlete must maintain balance while transitioning from a dynamic to a static state, denoted as an athlete’s dynamic postural control (Johnston, Dolan, Reid, Coughlan, & Caulfield, 2018). “Dynamic balance involves maintaining an upright posture while (a) the center of gravity and base of support are moving and (b) the center of gravity is moving outside the base of support (for example, in walking)” (Yim-Chiplis & Talbot, 2000, p. 322). Hence, dynamic postural control seems to be important in sports, especially in regard to the large number of dynamic actions during competitions or matches (Whyte, Burke, White, & Moran, 2015). Additionally, impairment of dynamic postural control is associated with a higher risk for non-contact injuries (Butler, Lehr, Fink, Kiesel, & Plisky, 2013; Whyte et al., 2015; Wright, Lyons, & Navalta, 2013). Therefore, it might help assess an athlete’s dynamic postural control to obtain more insights into the potential injury risk (Plisky, Rauh, Kaminski, & Underwood, 2006). To this end, methods such as the Star Excursion Balance Test (SEBT) or the Y Balance Test (YBT) should be included in injury risk assessment (Plisky et al., 2009; Plisky et al., 2006).

Physical load and injury risk

Risk factors for non-contact injuries, such a dynamic postural control, are mainly tested when the athlete is in a recovered state. Nevertheless, most non-contact injuries occur under loaded conditions during matches, competitions, or sports training, when the athlete is physically stressed (Ekstrand et al., 2021). Attention should be paid that an applied external load, i.e., “the organization, quality, and quantity of exercise” (p. 270), leads to internal load, i.e., “the psychophysiological responses occurring during the execution of the exercise” (p. 270), (Impellizzeri, Marcora, & Coutts, 2019), causing, for example, an alteration of muscle activation patterns or a reduced level of voluntary muscle activation (Barber-Westin & Noyes, 2017; Santamaria & Webster, 2010). This, in turn, possibly affects risk factors and potentially increases the risk of injury. Therefore, investigating potential risk factors and injury mechanisms not only in recovered but also under loaded conditions is advisable so as to increase the prognostic validity of the diagnostic tests and to obtain more insight into an athlete’s potential injury risk under real sporting conditions (Bolt et al., 2021; Verschueren et al., 2020).

Several studies examined the influence of physical load¹ on potential risk factors. These studies show an influence of physical load on isometric strength of the legs (Verschueren et al., 2020) or hip and knee flexion angles (Benjaminse et al., 2008). No influence was shown on ground reaction forces (Barber-Westin & Noyes, 2017) or kinetic values (Santamaria & Webster, 2010). Regarding dynamic postural control, several studies showed a negative effect of different types of load—e.g., cycling (Heil, Schulte, & Büsch, 2020b; Johnston et al., 2018), running (Wright et al., 2013), a high-intensity, intermittent exercise protocol (HIIP) (Whyte et al., 2015), functional protocols (Sarshin, Mohammadi, Shahrabad, & Sedighi, 2011), or local loading protocols (Gribble, Robinson, Hertel, & Denegar, 2009)—on dynamic postural control. This might be due to the former prescribed load-induced physiological alterations that also affect systems involved in the maintenance of dynamic postural control, e.g., proprioception, vision, and the vestibular system (Güler et al., 2020). This might lead to neuromuscular impairment and a loss of sensorimotor control, resulting in a decrease of dynamic postural control (Steib, Zech, Hentschke, & Pfeifer, 2013; Taylor & Gandevia, 2008; Whyte et al., 2015; Zech, Steib, Hentschke, Eckhardt, & Pfeifer, 2012). Nevertheless, there are also differences between the results of the aforementioned studies, and diverging and conflicting results in other studies. Zech et al. (2012), e.g., found no effect of a running protocol and no effect of a local loading protocol on dynamic postural control of healthy handball players. Additionally, in a recent study by Verschueren, Tassignon, Verhagen, and Meeusen (2021), no effect of a Wingate anaerobic protocol on dynamic postural control measured with the YBT was found in contrast to the results obtained by Johnston et al. (2018).

Physical load is not equal to physical load

The aforementioned results show that physical load can change the potential injury risk of an athlete, but they also show that physical load is not equal to physical load and that the results are possibly influenced by the type of load and the applied protocol. The type of load, meaning the type of exercise implemented to physically stress the participants, varies a lot in the aforementioned studies and ranges from cycling or running to local loading protocols. Moreover, also in the same load type, different protocols with different durations or distances are used. Such differences in the load types and protocols possibly explain the ensuing differences in the results. Different load types and protocols probably lead to different resulting internal loads because different body systems and structures (vestibular, visual, proprioception, sensorimotor, muscular) are not stressed equally due to the varying external loads or load types (Impellizzeri et al., 2019).

This assumption is also supported in studies directly comparing the influence of two load types on the same parameter or risk factor. The results mainly show differences dependent on the load type and protocol, e.g., a greater decrement of dynamic postural control after running compared to cycling (James, Scheuermann, & Smith, 2010a; James, Scheuermann, & Smith, 2010b; Lepers, Bigard, Diard, Gouteyron, & Guezennec, 1997; Wright et al., 2013). Looking at this possible load-dependence of the changes, it seems logical to choose load types and protocols according to the demands of the sport that should be investigated to draw conclusions about the changes of potential injury risk under real sporting conditions (Benjaminse, Webster, Kimp, Meijer, & Gokeler, 2019; Bolt et al., 2021). In this context, cycling protocols seem to be easier to apply and to control in a laboratory setting. Nevertheless, besides cycling itself, most sports require running as a central element, e.g., in basketball, soccer, or track and field sports. Therefore, running protocols seem more suitable for reflecting the demands of sports and it might be better to include running protocols in injury risk assessment. Additionally, comparisons between different protocols might help detect differences in the resulting internal load and the use of different protocols can help to depict different aspects and situations of a sport that might occur during training, matches, or competitions.

In this context it must also be concerned that the resulting internal load is not only influenced by the load type or protocol but also by other factors, e.g., the task or procedures to measure a certain risk factor, sports-related factors, or individual characteristics that should be addressed and/or controlled (Fig. 1). Therefore, studies and injury risk assessment should gradually approximate real, i.e., representative, sports conditions by systematically varying and adapting factors shown in Fig. 1, e.g., the loading profiles, i.e., load types, duration etc., and tasks.

Fig. 1

Working model of factors influencing internal load

Aims and hypotheses

It was shown that in the context of injury prevention there is a need for appropriate diagnostic measures to assess an athlete’s potential injury risk. Thereby, the assessment of dynamic postural control should be one of the essential factors. Moreover, assessing an athlete’s potential injury risk under loaded conditions might provide additional insight and help to improve the prognostic validity of the diagnostics. In this context, the use of representative designs depicting the approximate real sport conditions is advisable. Nevertheless, although most sports require running as a central element, many studies still use cycling protocols to physically stress the participants, and studies using running in a systematic way are scarce. Therefore, the aim of the current study was to examine the influence of a running protocol on dynamic postural control and to compare it with a comparable widely used cycling protocol.

We hypothesized that there would be a decline of dynamic postural control after both types of load (H1). Moreover, we hypothesized that the impairments would be greater after running than after cycling (H2). Additionally, the recovery period after the load was regarded to examine how long the changes persist after the loading protocol. Accordingly, a successive increase of dynamic postural control during the recovery period was hypothesized (H3).

Methods

The study had a repeated-measures design with three pre-load and three post-load measurements and two examination groups (running vs. cycling). The study was conducted in accordance with the Declaration of Helsinki, and the local ethics committee approved the protocol. Furthermore, the study was preregistered at Open Science Framework (https://osf.io/9uwc5).

Participants

A sample size of n = 126 was determined a priori with a power estimation (F test: η²_p = 0.20, α = 0.01, 1 − β = 0.99) for a multivariate three-way mixed analysis of variance (MANOVA) using G*power software (vers. 3.1.9.7; Faul, Erdfelder, Lang, & Buchner, 2007). In total, 128 active and healthy people (64 males and 64 females, age: 23.64 ± 2.44, height: 176.54 ± 8.96 cm, weight: 68.85 ± 10.98 kg) with no history of musculoskeletal injury in the previous 6 months participated in the study (Table 1). The participants were randomly divided into two examination groups. Each group completed a comparable loading protocol. Group 1 completed the protocol on a bicycle ergometer and Group 2 on a treadmill.

Table 1 Characteristics of participants

Before testing, participants had to complete the Physical Activity Readiness Questionnaire (PAR‑Q; Warburton et al., 2011), and those answering “yes” to any question were excluded from participating. Additionally, participants were excluded if they were taking medication for a balance disorder or suffering from one of the following: balance disorder, vestibular or visual impairment, cardiovascular disease, previous reports of chest pain, neurological disease, or chronic ankle instability.

Procedures

Testing was performed in a laboratory setting. Each participant was tested in one 90-min session, including administration, anthropometric measurements, familiarization, and testing procedures. At first, participants were informed about the procedures and provided written informed consent to the following experiment. Additionally, personal data, sporting background, injury history, and the questions from the PAR‑Q (Warburton et al., 2011) were recorded in a questionnaire. The kicking leg and standing leg of a person were prompted. Individuals not fulfilling eligibility criteria were excluded from the study.

Anthropometric measurements (weight, height, leg length) were recorded using the Inbody270 (InBody Co., Seoul, South Korea), a stadiometer (Seca GmbH & CO. KG, Hamburg, Germany), and measuring tape. Leg length was measured while the participant was standing in front of a wall. Leg length was defined as the distance between the participant’s anterior–superior iliac spine and the most distal part of the medial malleolus (Gribble & Hertel, 2003; Plisky et al., 2009). Since both legs are involved in the sporting movements, and injuries can occur in both legs (Paterno et al., 2010), measurements were taken for both legs.

To get familiar with the YBT and to minimize potential learning effects (Gribble, Hertel, & Plisky, 2012), each participant performed four practice rounds of the YBT before testing. Each round lasted about 60 s and consisted of one trial on each leg. The starting leg varied between the participants. In each group, one half of the subjects started the round standing on their preferred kicking leg, while the other half started the measurements standing on their preferred standing leg. After a short resting period, testing started with three YBT rounds at baseline, with a time of 10 min rest between the rounds, 20 min pre-load (pre01), 10 min pre-load (pre02), and immediately pre-load (pre03). Afterward, participants completed one of the two protocols. Directly after the loading protocol, participants went back on the YBT. Post-load measurements were identical to the pre-load measurements. All participants performed three rounds of the YBT again, with 10 min rest in between, immediately post-load (post01), 10 min post-load (post02), and 20 min post-load (post03). The design and procedures were adapted and expanded from the study by Johnston et al. (2018).

Instruments

Y Balance Test.

To assess dynamic postural control, the YBT (functionalmovement.com, Danville, VA, USA) was used (Fig. 2). It is derived from the SEBT and proved to be a reliable tool for assessing dynamic postural control (Plisky et al., 2009). It represents the demands of sporting situations and provides information about an athlete’s potential injury risk with the composite score obtained (Boden, Sheehan, Torg, & Hewett, 2010). The YBT trials were conducted according to the guidelines by Gribble et al. (2012). During a YBT trial, the participant stands barefoot on a platform with the leg whose dynamic postural control should be examined. The goal was to maintain balance on one leg while sliding a block as far as possible in each of the three directions, anterior (ANT), posteromedial (PM), and posterolateral (PL) with the other leg and returning to bilateral stance afterward. The reach distance (cm) in each direction was recorded. Then the participants switched sides, and the same procedure was conducted standing on the other leg. A trial was considered invalid if one of the formerly published criteria by Plisky et al. (2009) was fulfilled. In such a case, participants had to start over with the current trial.

Fig. 2

Y Balance Test. (Adapted from Heil et al., 2020b)

The reach distance in each direction was measured and normalized to leg length using the following equation (Plisky et al., 2009):

$$\text{Normalized reach distance }\left(\mathrm{NRD}\right)\left(\mathrm{\% }\right)=\frac{\text{reach distance }(\mathrm{cm})}{\text{leg length }(\mathrm{cm})}\times 100$$

(1)

Moreover, a normalized composite score (CS) was computed for each leg (Plisky et al., 2009):

$$\text{Composite score}\,\left(\mathrm{\% }\right)=\frac{\text{reach distance ANT }(\mathrm{cm})+\text{reach distance PM }(\mathrm{cm})+\text{reach distance PL }(\mathrm{cm})}{\text{leg length}\,\left(\mathrm{cm}\right)\times 3}\times 100$$

(2)

External load

Cycling.

The participants of Group 1 completed a modified version of the Wingate Anaerobic Test (Carey & Richardson, 2003) on a bicycle ergometer (Cyclus 2, RBM elektronik-automation GmbH, Leipzig, Germany). The protocol started with a 5-min warm-up (male: 90 RPM, female: 60 RPM). After a transition phase of 30 s (50–60 RPM), it merged into the loading protocol, during which the participants had to accelerate and maintain their maximal effort for 60 s. The ergometer’s resistance was set at 7.5% of the participant’s weight based on Johnston et al. (2018). Heart rate was measured during the protocol with a Polar® sensor (Polar Electro Oy, Kempele, Finland).

Running.

The participants of Group 2 completed a loading protocol on a treadmill (PPS 55med‑I, WOODWAY GmbH, Weil am Rhein, Germany). After a 5-min warm-up (8 km/h), the participants completed an incremental test to determine their individual maximum velocity. Starting with 8 km/h, the velocity was increased 2 km/h every 20 s until the participant could not run at a certain speed. The final feasible velocity was set as the participant’s maximum load. After a 5-min rest, the participants performed the running protocol. The loading protocol was based on a protocol by Schnabel and Kindermann (1983). The treadmill had a 7.5% slope and was accelerated to the participant’s prior determined individual maximum load within 10 s. Then the participants ran at their maximum load until volitional exhaustion. This should lead to an average duration of about 60 s, comparable to the modified Wingate Anaerobic Test in cycling. Heart rate was measured with a Polar® sensor (Polar Electro Oy, Kempele, Finland) during running.

Internal load

Both protocols were aimed at exhausting the participants in 60 s maximum. In a pilot study it was ensured that both protocols are appropriate to fulfill this prerequisite.

Statistical analyses

The data were analyzed using SPSS (version 28.0, IBM Corporation, Armonk, NY, USA). First, the normal distribution of the data was checked using the Shapiro–Wilk test. Reliability of the data was assessed using different values: The intraclass correlation coefficient (ICC 3, 1) with absolute agreement (Shrout & Fleiss, 1979) was calculated across the three baseline measurements of the YBT to determine the reliability of the normalized reach distances. The values were interpreted according to Koo and Li (2016) as > 0.9 = excellent, 0.75–0.9 = good, 0.5–0.75 = moderate, and < 0.5 = poor. Within-session reliability was assessed using the coefficient of variation (CV) calculated as CV = (SD / M) × 100. All CV values < 10% were acceptable according to Cormack, Newton, McGuigan, and Doyle (2008). Moreover, the standard error of measurement (SEM) was calculated as SD × √1 − ICC to assess the degree of variation between the repeated measures. Additionally, the minimal detectable change (MDC₉₀) was calculated according to Schmitt and Di Fabio (2004) representing the value that can be considered as real change beyond measurement error with 90% confidence.

Due to given multicollinearity between the three reach directions and the composite score (Tabachnick & Fidell, 2013), instead of a multivariate three-way mixed ANOVA, a univariate three-way mixed ANOVA (load type × leg × time) with time and leg as within-subject and load type as between-subject factor, as well as with a Greenhouse–Geisser correction, if necessary, was conducted to compare the normalized reach distances in each direction and the composite score between the two groups and between both legs. The significance level was set at p < 0.01. Additional contrast analyses for repeated measures were conducted to specify the differences between adjacent points of time. For the mixed ANOVA conducted, η²_p is stated as effect size. Additionally, 90% confidence intervals (CIs) for the η²_p were calculated using an SPSS syntax by Wuensch (2016). Additionally, effect sizes for repeated measures (Cohen’s d_z) and 95% CIs for the adjacent points of time were also calculated with the syntax by Wuensch (2012).

Results

The participants of the cycling group achieved a heart rate of 184 ± 10 bpm and in the running group a heart rate of 184 ± 8 bpm at the end of the protocol. All participants were subjectively exhausted and unable to continue.

All data of the CS were normally distributed according to West et al. (1995) due to a given skewness of < 2 and a kurtosis of < 7. Excellent reliability was given for the three baseline values, (ICC = 0.91–0.96). The SEM values ranged between 0.34 and 0.78, and the CV values were between 2.25 and 3.17 (supplementary Table S1). Therefore, only the final pre-load measurement (pre03) was used for the repeated measures analysis. A three-way mixed ANOVA (load type × leg × time) revealed a significant interaction for load type × time (F_{3, 378} = 10.99, p < 0.001, η²_p = 0.08, 90% CI [0.04, 0.12]). No significant interactions were found for load type × leg (F_{1, 126} = 0.04, p = 0.85, η²_p < 0.01, 1 − β = 0.05), leg × time (F_{3, 378} = 0.39, p = 0.71, η²_p < 0.01, 1 − β = 0.12) and load type × leg × time (F_{3, 378} = 1.16, p = 0.32, η²_p < 0.01, 1 − β = 0.27). The CS values are provided in Fig. 3 and supplementary Table S2. Furthermore, a significant main effect of time (F_{3, 378} = 26.68, p < 0.001, η²_p = 0.18, 90% CI [0.12, 0.23]) was found but no differences between the legs (F_{1, 126} = 0.59, p = 0.44, η²_p < 0.01, 1 − β = 0.12). Among participants, a significant difference was found between the two load types (F_{1, 126} = 15.31, p < 0.001, η²_p = 0.11; 90% CI [0.04, 0.20]). A difference between the groups was already apparent for the pre-test values. Therefore, the pre-test values were considered as a covariate in an additional ANCOVA (time × load type). The results show a significant interaction of time and load type for the kicking (F_{2, 250} = 12.09, p < 0.001, η²_p = 0.09, 90% CI [0.04, 0.14]) and the standing leg (F_{2, 250} = 5.19, p = 0.01, η²_p = 0.04, 90% CI [0.01, 0.08]) supporting the results of the ANOVA already conducted.

Fig. 3

Mean values and 95% confidence interval of the normalized composite scores for both load types (cycling and running) and both legs (kicking leg and standing leg) at the four points of time (pre03 pre-load, post01 0 min post-load, post02 10 min post-load, post 3 20 min post load)

Contrast analysis of the interaction between time and load type (Table 2) showed significant differences between running and cycling for the composite score between pre03 and post01 (contrast 1) The composite score significantly decreased from pre03 to post01 in both legs of the cycling group (kicking leg: −4.13%; F_{1, 63} = 28.44, p < 0.001, η²_p = 0.31, 90% CI [0.16, 0.44]; standing leg: −4.21%, F_{1, 63} = 31.92, p < 0.001, η²_p = 0.34, 90% CI [0.18, 0.46]). No significant results were found for the standing leg (−1.08%, F_{1, 63} = 5.47, p = 0.02, η²_p = 0.08, 1 − β = 0.63) and the kicking leg (−0.5%, F_{1, 63} = 1.65, p = 0.20, η²_p = 0.03, 1 − β = 0.25) of the running group. There was also a significant difference between the two load types between post01 and post02 (contrast 2). No interactions were found between post02 and post03 (contrast 3) and between pre03 and post03 (contrast 4). The results of the contrast analysis for the separated groups are provided in supplementary Table S3.

Table 2 Results of the contrast analysis for the interaction between load type and time

Discussion

The study aimed to investigate potential injury risk in a representative design that reflects aspects of real sporting conditions. Therefore, the influence of physical load on dynamic postural control was investigated due to its importance in many sporting situations, such as landing or cutting, and its association with injury risk. Since cycling protocols are often used to physically stress the athletes, although most sports require running as a central element, the influence of a widely used cycling and a comparable running protocol was assessed.

It was hypothesized that dynamic postural control would decrease after both types of load. Moreover, according to the results of former studies comparing the influence of cycling and running on dynamic postural control, it was hypothesized that the decrease would be greater after running than after cycling (Lepers et al., 1997; Nardone, Tarantola, Giordano, & Schieppati, 1997; Wright et al., 2013).

Nevertheless, the results of the current study do not confirm this hypothesis. A significant interaction between time and load type was found. Dynamic postural control in both legs was detrimentally affected after cycling but not after running. This might be explained by differences in the resulting internal load. Cycling primarily affects the lower body and the muscular system, causing a more local and muscular internal load. By contrast, running is more challenging for the whole body, with more influence on the vestibular, proprioceptive, and visual systems, causing a global internal load (Bijker, De Groot, & Hollander, 2002; Nardone et al., 1997). Nevertheless, since the latter are the systems primarily involved in dynamic postural control (Fusco et al., 2020), the effect was expected to be greater after running than after cycling (Paillard, 2012; Wright et al., 2013). However, muscular internal loads induced during cycling seem to be more detrimental to dynamic postural control than the changes due to running. Therefore, the factors involved in maintaining dynamic postural control need to be regarded and discussed in more detail.

In the current study, dynamic postural control was only regarded as an overall construct. However, dynamic postural control is determined by different factors, e.g., anthropometric characteristics, sex, strength, or mobility (Fusco et al., 2020). Therefore, it is possible that the implemented protocols might not have such a detrimental effect on the systems involved in dynamic postural control but rather on one of the determining factors. Since cycling preliminarily challenges the lower body, the muscles and strength of the legs might be more affected due to the cycling protocol. This, in turn, potentially induces greater changes in dynamic postural control after cycling than after running.

This point might explain the results of the current study, but it does not explain the opposing results that contrast other studies by Lepers et al. (1997), Nardone et al. (1997), and Wright et al. (2013). The significantly different loading protocols with different duration or distances might explain these differences. These different external loads might result in different internal loads and, in addition, to different changes in dynamic postural control. Moreover, the studies used different tasks/procedures to measure dynamic postural control. Wright et al. (2013) measured dynamic postural control with a dynamic balance test on a Biodex System, Lepers et al. (1997) used a sensory organization test, and Nardone et al. (1997) used quiet standing in different visual conditions. The current study investigated dynamic postural control with the YBT. Although all these tests measure dynamic postural control, they might challenge different types of dynamic postural control. Tests such as the YBT evaluate “actions taken in preparation for a potential destabilizing event” (Yim-Chiplis & Talbot, 2000, p. 322), i.e., anticipatory postural control. Whereas tests such as the dynamic balance test challenge reactive postural control, meaning the “response to an external disturbance in stability” (Yim-Chiplis & Talbot, 2000, p. 322). Both balance types probably have different demands. Hence, the running protocol implemented did not change anticipatory postural control but might influence reactive postural control. Therefore, comparisons between different load types and comparisons of the same load type/protocol on both types of dynamic postural control are needed in order to get a comprehensive picture of an athlete’s dynamic postural control. Additionally, reactive dynamic postural control seems to be closer to the demands of sports because injuries mainly occur in situations with unanticipated movements.

Moreover, there is also a difference in the involvement of postural control between the different load types. During cycling, postural control is required less due to the sitting position. Whereas during running, posture needs to be controlled continuously to keep the body in an upright position and to avoid falling. Therefore, the systems involved in dynamic postural control, i.e., the vestibular, proprioceptive, and visual systems, are more challenged during running and are already activated. Hence it might be easier to execute a postural task afterward. During cycling, the systems are less activated, and they also need to adapt to the change of position from sitting to standing, which makes it possibly harder to control postural control directly after cycling.

Since anticipatory dynamic postural control is associated with an athlete’s injury risk (Johnston et al., 2018; Whyte et al., 2015), this also enables a comprehensive assessment of potential injury risk in loaded conditions. Regarding the results of the current study in terms of injury risk, it is shown that the participants’ potential injury risk is increased after cycling for about 20 min. By contrast, running does not increase the potential injury risk. Nevertheless, predicting the likelihood of injuries using the YBT has recently been doubted (Luedke, Geisthardt, & Rauh, 2020; Plisky, Schwartkopf-Phifer, Huebner, Garner, & Bullock, 2021). This is another point supporting the need to use different (balance) measures in the injury assessment and prevention.

Moreover, the current study had some limitations that must be considered.

• 1. Both groups consisted of different participants. Therefore, no intra-individual comparison could be conducted. Further studies regarding the influence of different load types on the same athlete might reduce the number of additional factors. In this context, it could also be helpful to test athletes from one sport only in order to reduce the number of potential sports conditional factors.

• 2. Additional factors such as strength and flexibility were not controlled. Therefore, it could not be investigated whether a reduction of strength or flexibility probably induces changes in anticipatory dynamic postural control. Hence, the potential influence of load on these factors and their potential influence on anticipatory dynamic postural control need to be concerned.

• 3. The protocol consisted of only one high-intensity bout for both load types. Nevertheless, during sports, clearly more bouts are present (Whyte et al., 2015). Therefore, this should also be depicted during the protocols. Therewith, changes that might accumulate during these bouts and that are only present during the progression of a load might be detected.

• 4. Both protocols did not optimally reflect the sporting demands in, e.g., sports games. Loading protocols closer to the demands of sports are needed for more representative testing of dynamic postural control. In this context, running protocols seem to be the better choice because most sports require running as a central demand. Considering these points in future studies may help improve understanding of the influence of load on dynamic postural control and its association with injury risk. Additionally, because side differences are another factor possibly enhancing the likelihood for non-contact injuries, it could also help to compare the values between the sides (Bishop, Turner, & Read, 2018; Dos’Santos, Thomas, & Jones, 2020; Heil, Loffing, & Büsch, 2020a).

Altogether, it was shown that physical load influences anticipatory dynamic postural control. Nevertheless, the influence differs between different load types and different load types might stress the systems differently. This, in turn, could lead to different changes of anticipatory dynamic postural control. Therefore, protocols and representative designs depicting certain aspects of the sporting demands should be used to obtain more practical insights into the potential injury risk under real sporting conditions (Benjaminse et al., 2019). Moreover, different testing methods should be used to capture all aspects of dynamic postural control (anticipatory and reactive) to assess an athlete’s potential injury risk appropriately. In this context, influencing factors, such as strength of different muscle groups (abs, gluteus, hamstrings etc.) and flexibility, need to be controlled to determine (a) the potential influence of load on these factors and (b) their potential influence on dynamic postural control.

Conclusion

The current study showed a decrease in anticipatory dynamic postural control after cycling but not after running. This indicates a dependence of anticipatory dynamic postural control and the potential injury risk on the implemented load type. Therefore, during injury risk assessment, load types and protocols should be designed following the demands of a certain sport to obtain better insights. Additionally, a combination of different tests challenging different aspects of dynamic postural control could help obtain a more comprehensive approach. In this context, studies should gradually approximate the sporting demands by systematically varying and adapting certain factors (Fig. 1), such as loading profiles and tasks/procedures.