Dynamic and Kinematic differences in slackliners, non- slackliner athletes and sedentary individuals during four balance tests, a cross-sectional study

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While previous studies have documented improvements in postural control following slackline training, little is known about how motor strategies differ between slackliners, athletes from other sports, and sedentary individuals during non-slackline balance tasks. The purpose of this study was to characterize differences in dynamic and kinematic motor strategies among professional slackliners, non-slackliner athletes, and sedentary individuals during a set of balance tasks. Thirty-two adults, including professional slackliners, athletes from other sports, and sedentary individuals, performed four balance tasks of increasing complexity. Dynamic and kinematic data were collected using a force-platform–based system combined with marker-less three-dimensional motion analysis. Group differences in center-of-pressure displacement and joint kinematics were analyzed using non-parametric statistical tests. Sedentary individuals exhibited greater trunk extension and larger antero-posterior center-of-pressure displacement compared with both slackliners and athletes. Slackliners demonstrated reduced postural sway and greater shoulder abduction and hip flexion across multiple tasks. In more demanding balance conditions, slackliners showed distinct upper- and lower-limb kinematic strategies compared with the other groups, particularly at the shoulder, hip, and knee joints. Individuals with different motor backgrounds adopt distinct motor control strategies during balance tasks. Long-term slackline practice is associated with joint-specific kinematic adaptations involving the shoulders, hips, and knees, which may partially extend beyond slackline-specific contexts. Key Terms : Slacklining; Dynamic balance; Postural control; Kinematic analysis; Motor strategies. Health sciences/Anatomy Health sciences/Health care Biological sciences/Neuroscience Biological sciences/Physiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Balance is a prerequisite for human motor function, supporting posture and coordination during both static and dynamic activities. It is particularly crucial in sports and rehabilitation, where individuals must rapidly adjust motor control to preserve stability under variable conditions (Hof, 2007 ; Pollock et al., 2000 ). Within this framework, slacklining has emerged as an innovative and highly demanding balance training modality (Gabel et al., 2021 ; Volery et al., 2017 ). It involves maintaining equilibrium on a narrow, tensioned webbing anchored between two fixed points, generating continuous multidirectional perturbations that challenge the neuromuscular system to perform rapid, coordinated adjustments (Buckingham, 2025 .; Gabel et al., 2021 ). Slacklining elicits substantial task-specific adaptations in human kinematics, characterized by refined postural control, improved interjoint coordination, and altered muscle activation patterns during balance tasks (Donath et al., 2017 ; Giboin et al., 2018 ; Santos et al., 2017 ; Volery et al., 2017 ). At the muscular level, slackline training leads to a reduction in corrective joint actions and an increase in anticipatory activation of stabilizing muscles; Pfusterschmied et al. ( 2013 ) demonstrated decreased reliance on reactive knee adjustments and enhanced preparatory recruitment of the rectus femoris, suggesting greater functional knee stability and feedforward neuromuscular control. Likewise, Donath et al. ( 2017 ) reported attenuated electromyographic activity in both lower-limb and trunk muscles during slackline standing, indicating improved efficiency of postural control mechanisms in trained individuals. These adaptations are accompanied by smoother and slower kinematic trajectories, including reduced velocity and frequency of center-of-mass and joint movements (Serrien et al., 2017 ). Importantly, the benefits of slackline practice are highly task-specific. Current literature consistently demonstrates that improvements in slackline performance do not generalize to untrained static or dynamic balance tasks (Donath et al., 2017 ; Giboin et al., 2018 ). Slackline training, therefore, should be understood as a specialized modality that enhances dynamic and context-dependent balance control rather than as a universal tool for broad postural enhancement. Supporting this specificity, neuroimaging and electrophysiological evidence reveal neuroplastic adaptations at cortical, subcortical and spinal levels following slackline training, reflecting a finely tuned reorganization of the motor system that is unique to this task (Giboin et al., 2018 ). Despite the growing body of literature describing these neuromechanical adaptations, little is known about how dynamic and kinematic strategies differ among populations with distinct motor backgrounds. So, we investigated the differences in motor patterns among three different populations (professional slackliners, athletes and sedentary individuals). Accordingly, the present study aims to characterize the dynamic and kinematic strategies adopted by the study populations during balance tasks, providing insights into the specific dynamic and kinematic adaptations that distinguish expert balance performers from non-specialists. Methods Setting This cross-sectional study was conducted at academic and clinical research institutions in Italy. The study was approved by a local institutional ethics committee (protocol No. 414, May 30, 2025), and data were collected between June and September 2025. All participants provided written informed consent prior to participation. This study adhered to the STROBE checklist (Cuschieri, 2019 ) to enhance the quality and transparency of reporting. Participants In this study, slackline athletes, athletes from other disciplines without slackline experience and sedentary controls, were enrolled and screened for eligibility according to population-specific criteria. Volunteer slackliners were recruited from local Slackline amateur sport associations ( Slackline Toscana ASD , 2026.), non-slackliner athletes were recruited among university students enrolled in sport-related academic programs. Sedentary individuals were recruited among researchers and clinicians from participating institutions. All participants provided written informed consent prior to participation. The respective inclusion and exclusion criteria for each population are outlined below: Slackliners Inclusion criteria: age over 18 years; engaged in slacklining for more than 1 year; training frequency approximately 2 times per week. Exclusion criteria: orthopedic conditions or other health issues that could influence participants’ motor performance. Athletes from other sports Inclusion criteria: age over 18 years; engaged in their sport for more than 1 year; training frequency at least 2 times per week. Exclusion criteria: orthopedic conditions or other health issues that could influence participants’ motor performance; previous intensive slackline practice (defined as having practiced slacklining for 2 months, approximately 2 times per week). Sedentary individuals Inclusion criteria: age over 18 years; individuals not regularly engaged in any sports activities. Exclusion criteria: orthopedic conditions or other health issues that could influence participants’ motor performance. Data Acquisition Demographic and anthropometric Age, sex, weight, height, type of sport practiced, average frequency of sports practice over the past two months variables were collected during the eligibility screening in a dedicated Excel database: A series of biomarkers were extracted, such as shoulder abduction and knee flexion, that in the execution of the tests may reveal population-specific motor control patterns. Dynamic and kinematic data Dynamic and kinematic data were obtained using the D-Wall system (TecnoBody S.p.A, Dalmine BG, Italy) an assessment and rehabilitation system designed to improve movement quality through auditory and visual feedback support. D-Wall performs dynamics registration, via force platforms, and markerless three-dimensional analysis by means of depth camera and proprietary software. Parameters extracted included sway amplitude, CoP (center of pressure) displacement proxies, and joint angular variability during the tasks. [Insert Fig. 1 here] Outcomes The primary outcome was the difference in kinematic and dynamic parameters among the study populations, recorded using the D-Wall (Fig. 1 ) system during the balance tasks included in the protocol. The D-Wall system provides measurements of the CoP displacement along both the x- and y-axes, together with time-based joint angle data across multiple planes of motion for different body segments. These outputs allowed for a detailed quantitative assessment of postural control and joint kinematics throughout the balance tasks. From the raw D-Wall data, a series of features was systematically analyzed across all tests. For each variable derived from the D-Wall, the investigated parameters included the median, interquartile range (IQR), mean, standard deviation (SD), range, as well as the 5th (Q05) and 95th (Q95) percentiles. Experimental Procedures Participants completed four dynamic balance tests, during which the D-Wall system continuously recorded both kinematic and dynamic parameters. Balance tests Each participant performed four different balance tasks using the D-Wall system. The tests were: Bipodal standing on bump stimulating disc: participants performed three trials of 35 seconds each, standing as still as possible, feet together, arms by sides. Tandem standing on bump stimulating disc: participants performed three trials of 35 seconds each, standing with trunk as still as possible. Participants had to keep feet in tandem and could choose the preferred pattern for legs and arms. Tandem standing with lateral space exploration: participants were positioned in the center of the force platform with feet in tandem, and three times had to explore the right and left lateral space with hands as far as possible. Pistol squat: the participants, positioned at the center of the force platform, were instructed to perform a single-leg squat without losing balance. They were asked to squat as deeply as possible and return to the upright position without any time limits or other constraints. This test was repeated three times on the right leg and three times on the left leg. For each trial, the first and last 5 seconds were discarded to ensure that data were analyzed exclusively while the participant was performing the task, thereby avoiding signal acquisition during steady-state periods. Processing and statistical analysis The pre-processing stage begins with structured data ingestion and harmonization of the available recordings. Each file name is parsed to identify the participant identifier, test number, and repetition index, enabling the correct association of trials to individual participants and experimental conditions. Data from all available participants are retrieved from the hierarchical folder structure and imported into a unified data frame. Force plate and CoP signals (retained in the mediolateral and anteroposterior directions, respectively x and y) were resampled from their original frequency of 40 Hz to a common target frequency of 30 Hz using linear interpolation, to match kinematic variables sampling rate. The recorded time series were further refined by removing the first five seconds of each trial to eliminate potential transient effects. All repetitions were then trimmed to a uniform duration defined by the shortest valid trial to guarantee temporal comparability across participants and repetitions. Data are averaged between the three repetitions for each test, to obtain a mean representative time-series of the participant-test pair. In addition to these time-domain adjustments, a normalization procedure is applied to the force cell data using individual body weight, obtained from the demographic dataset provided separately. This step ensures that inter-individual differences in body mass do not bias the comparison of force-related outcomes across experimental groups. Then, starting from the cleaned and averaged time-series, scalar features were computed to be then compared among groups. The statistical descriptors included were the median and IQR and Q05 and Q95. Furthermore, for the CoP data, the sway area was quantified by fitting a covariance ellipse to the two-dimensional CoP trajectory, with its area representing the dispersion of postural sway. Group differences for all metrics were assessed using the Kruskal-Wallis test. Whenever a significant overall group effect was detected (significance level ɑ = 0.05), post-hoc pairwise comparisons were performed using Mann-Whitney U tests with Bonferroni correction to control for multiple testing. The results are reported with corresponding effect statistics and adjusted significance levels. All processing and statistical analysis was performed using Python custom code and openly-available libraries (code and data are available at the following GitHub link: LINK). Results Participants 32 participants were enrolled in the study: 11 slackliners, 10 non-slackliners athletes and 11 sedentary. All participants met the inclusion criteria and completed the study (flow chart in Fig. 2 ). Demographic and anthropometric data are shown in Table 1 . Table 1 Median and IQR values are reported (in brackets) for continuous variables while absolute and relative counts (in parenthesis) are reported for categorical variables. Age, years Cohort (N = 31) Slackliners (N = 11) Athletes (N = 10) Sedentary (N = 10) 23 [5.5] 24 [7] 23.5 [8] 22 [1.75] Sex, F 16 (51.6) 3 (27.2) 5 (50.0) 8 (80.0) Height, cm 172 [10.5] 175 [11] 172.5 [12.25] 168.5 [8.25] Weight, cm 65 [18] 71 [10.5] 65.5 [7] 59.5 [9.5] Dominance, Left 3 (9.7) 2 (18.1) 1 (10.0) 0 (0.0) No. of weekly training 2 [2] 2 [0] 2 [1.5] 0 [0] [Insert Fig. 2 here] [Insert Table 1 here] Motor patterns in dynamic balance tests differ across groups Test 1 – Reduced trunk extension in slackliners and athletes In test 1 (Supplementary Table 1) ANOVA revealed a significant group effect on APT for both Q95 (F = 6.35, p = 0.042; Fig. 3 A) and Q05 (F = 6.09, p = 0.048; Fig. 3 B). Sedentary participants showed greater trunk extension than slackliners and athletes, although post-hoc differences did not survive Bonferroni correction. Median APT (Q95): sedentary − 1.60 [2.25], slackliners − 0.12 [1.46], athletes 0.07 [1.30]. Median APT (Q05): sedentary − 4.11 mm [2.24], slackliners − 2.36 mm [1.52], athletes − 2.14 mm [1.70]. No group differences were found in CoP xy coordinates (Fig. 3 C). Test 2 – Reduced sway and greater shoulder/hip involvement in slackliners Slackliners showed greater shoulder abduction than sedentary participants (p < 0.05), though athlete comparisons did not survive correction (Fig. 3 D–E). A strong group effect emerged for CoP y-displacement variability (F = 14.04, p < 0.001; Fig. 3 F): sedentary participants swayed more (17.86 [3.55]) than slackliners (12.32 [3.49], p = 0.013) and athletes (14.36 [1.42], p = 0.001). Right shoulder abduction (Q05) was lower in sedentary (42.18° [29.18]) vs slackliners (67.82° [20.37]; p = 0.010), with no corrected differences vs athletes. Right hip flexion was higher in slackliners (5.67° [14.84]) vs sedentary and athletes (− 8°), approaching significance (p = 0.051). Left knee showed no group effect (p = 0.33) (Fig. 3 G–H). CoP y-position differed at group level (p = 0.045), with sedentary more forward (29.2 mm [24.5]) vs athletes (6.9 mm [12.9]) and slackliners (10.3 mm [16.2]), though not after correction; no x-axis differences (Fig. 3 I). [Insert Fig. 3 here] Test 3 – Reduced trunk extension and greater shoulder mobility in slackliners Sedentary participants had lower APT Q05 (− 3.22° [2.49]) than slackliners (− 1.63° [1.44]; p = 0.037) and athletes (− 0.75° [1.62]; p = 0.009). Slackliners showed greater shoulder abduction (right 83.73° [28.05], left 86.00° [26.78]) vs both sedentary and athletes (p ≤ 0.027). Shoulder flexion–extension Q05 was also higher in slackliners vs both groups (p ≤ 0.019) (Fig. 4 A–D). [Insert Fig. 4 here] Test 4 – Greater lower-limb flexion and shoulder abduction in slackliners Left side: Athletes showed higher APT than sedentary (p = 0.008; Fig. 5 A). Hip and knee flexion were greater in athletes vs sedentary (p = 0.011; p = 0.038), with slackliners showing intermediate/closer-to-athlete values (Fig. 5 B–C). Shoulder abduction was higher in slackliners vs athletes (p = 0.018; p = 0.003; Fig. 5 D–G). Right side: APT was higher in athletes vs slackliners and sedentary (p ≤ 0.038; Fig. 5 H). Hip flexion was higher in slackliners vs sedentary (p = 0.024), similar to athletes (Fig. 5 L). Knee flexion showed no significant difference (p = 0.07; Fig. 5 M). Shoulder abduction remained higher in slackliners vs athletes (p = 0.018; Fig. 5 N–Q). [Insert Fig. 5 here] Discussion The aim of this study was to determine whether distinct populations adopt different motor control strategies and to what extent these differences emerge in dynamic and kinematic parameters during balance tasks. Our results indicate that the groups exhibited distinct motor strategies, with key dynamic differences observed in CoP displacement along the y-axis, often accompanied by group-specific patterns or trends in APT. Other key kinematic differences involved greater shoulder abduction and hip flexion in slackliners compared to controls across multiple tests. Test 1 findings were limited to APT but supported by CoP centroid data, while the similar pattern in Test 2 suggests underlying dynamic contributions to these kinematic differences. Test 2 also revealed additional significant markers at the shoulder and hip. In this test, shoulder abduction differed between slackliners and sedentary participants, while athletes showed median values comparable to slackliners. In contrast, in Test 4 slackliners exhibited the greatest abduction and athletes the lowest. In the same test, shoulders’ flexion kinematics showed the greater discriminative power across groups, with slackliners again reaching the highest values and athletes the lowest. These examples show that even within the same group, motor strategies vary by task, reflecting the highly specialized skills with specific training. The present findings are consistent with previous research demonstrating that slackliners develop discipline-specific motor patterns characterized by refined joint coordination. Mildren and colleagues ( 2018 ) conducted a one-week intensive intervention study with a similarly limited sample size of 10 participants per group. Despite the small sample and short training period, their main findings revealed significant learning-related changes (Mildren et al., 2018 ). Pfusterschmied et al. ( 2013 ) conducted a four-week slackline training program in a small cohort (12 slackliners, 12 controls). Using 3D kinematics during single-leg stance on stable and perturbed surfaces, they reported reduced medio-lateral CoG velocity and decreased frontal-plane hip and knee ROM and velocity, indicating improved stability and reactive control. Unlike Mildren’s one-week protocol focused on interjoint coordination in tandem stance, Pfusterschmied’s approach used external perturbations, eliciting adaptations mainly at proximal joints. The greater active shoulders and hips’ ROM in slackliners observed in this study is consistent with findings by Santos et al. ( 2017 ), Volery et al. ( 2017 ), and Donath et al. ( 2017 ), all supporting multi-joint adaptations following slackline training. Although methodological heterogeneity precludes direct comparison, a common pattern emerges: a reorganization of motor strategies involving proximal body segments according to the individual background. Santos et al. (2016) reported reduced postural sway and mediolateral deviations and enhanced hip–trunk stability; Volery et al. ( 2017 ) described improved sensorimotor control driven by proximal coordination; and Donath et al. (2013) showed reduced lower-limb activation alongside substantial balance gains, reflecting more efficient, integrated control of the hips. Although previous literature suggests that skills acquired in task-specific contexts do not automatically transfer to other settings (Donath et al., 2017 ; Giboin et al., 2018 ), our results reveal multiple dynamic and kinematic differences between populations during balance tasks that simulate slacklining but are performed overground. This apparent discrepancy with the literature may be explained by differences in the study populations and the methodological approaches adopted. This study protocol incorporated both static and dynamic balance tasks and analyzed upper- and lower-limb joints across all three planes of movement, thereby providing a more comprehensive characterization of motor patterns in individuals practicing this discipline compared with previous literature. Within this broader analytical framework, the observed findings align with the theoretical foundations of motor learning in dynamic systems-based models of motor control. This type of approach, in fact, views skill acquisition as an emergent process shaped by the interaction between the individual, the environment and the task. Instead of pre-planned movement patterns, motor actions arise from dynamic interactions governed by environmental and task constraints (Bernstein, 1996 ), while practice leads to the formation of general motor structures rather than fixed movements (Levin & Demers, 2021 ). This perspective emphasizes perception–action coupling, where learning strengthens the coordination between sensory input and motor output in context. Skill mastery involves effectively managing the body's degrees of freedom through exploration and adaptation, using available constraints to find optimal movement solutions (Levin & Demers, 2021 ). Motor synergy learning occurs in two main stages. Initially, an appropriate control trajectory is identified and refined to ensure consistent performance despite external forces. With repetition, synergies strengthen within elemental variables, enabling compensation for perturbations. This stage is characterized by reduced task-relevant variability (VORT), while variability within the uncontrolled manifold (VUCM) remains beneficial for flexibility and adaptation. The second stage refines higher-order synergies that stabilize lower-level control. Strong synergies at higher levels often coincide with weaker ones at lower levels due to variability distribution across the hierarchy (Latash, 2010 ). Once the control trajectory is stabilized and VORT minimized, further practice optimizes constraints such as efficiency or fatigue, narrowing allowable variability, reducing VUCM, and weakening performance-level synergies while more specific coordination emerges at the muscle level (Latash, 2010 ). From this perspective, slacklining promotes the development of energy-efficient synergies. This explains task-specific dynamic and kinematic patterns compared to other athletes and sedentary individuals. Future studies should integrate motion capture with neurophysiological tools (e.g., EEG, EMG) to map the multi-level control hierarchy and assess whether these adaptations transfer to sport-specific or rehabilitative contexts. This study is limited by its cross-sectional design, precluding causal inference between motor strategies and training background. Group comparisons were also influenced by demographic and anthropometric differences, with sedentary participants and athletes predominantly female and athletes generally shorter and lighter. Additionally, the moderate sample size may have masked near-significant patterns, potentially obscuring subtle dynamic and kinematic differences. In conclusion, this study supports that prolonged task-specific training fosters distinctive motor strategies characterized by efficient postural adjustments and integrated joint synergies. Consistent with evidence on neuromechanical adaptations based on motor background, our findings also reveal inter-group kinematic differences beyond the slackline context, suggesting partially transferable components of postural expertise. Abbreviations CoP Center of Pressure IQR interquartile range Q05 fifth percentile Q95 ninety-fifth percentile Declarations Ethics approval and consent to participate The study was approved by a local institutional ethics committee of the “University of Florence” (protocol No. 414, May 30, 2025) in accordance with the Declaration of Helsinki. Consent for publication Not Applicable Availability of data and materials Not Applicable Funding This research was conducted without external financial support and was carried out using institutional resources only. Competing interests The authors declare that they have no competing interests. Authors' contributions SD: conceptualization, methodology, data curation, visualization, writing – original draft, project administration MP: Investigation, data curation, writing PL: data curation, formal analysis, visualization, writing – review & editing TC: Investigation, data curation, writing FM: Investigation, data curation, writing MLD: Investigation, data curation, writing DL: writing – review & editing, validation AM: writing – review & editing, validation FC: methodology, supervision, writing – review & editing Acknowledgements We gratefully acknowledge all participants in this study, whose willingness and cooperation made the data collection for this research possible. Data Availability The datasets generated and/or analysed during the current study are available in the Slackline Dataset repository (Doronzio, Stefano), Zenodo, https://doi.org/10.5281/zenodo.19812691. References Bernstein, N. A. Change in Movement and Skill: Learning, Retention, and Transfer. 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Sport . 16 (6), 562–566. https://doi.org/10.1016/j.jsams.2012.12.006 (2013). Pollock, A. S., Durward, B. R., Rowe, P. J. & Paul, J. P. What is balance? Clin. Rehabil. 14 (4), 402–406. https://doi.org/10.1191/0269215500cr342oa (2000). Santos, L. et al. Effects of supervised slackline training on postural instability, freezing of gait, and falls efficacy in people with Parkinson’s disease. Disabil. Rehabil. 39 (16), 1573–1580. https://doi.org/10.1080/09638288.2016.1207104 (2017). Serrien, B. et al. Changes in balance coordination and transfer to an unlearned balance task after slackline training: A self-organizing map analysis. Exp. Brain Res. 235 (11), 3427–3436. https://doi.org/10.1007/s00221-017-5072-7 (2017). Slackline Toscana, A. S. D. & Retrieved November 3, from (2025). https://www.slacklinetoscana.it/ The effects of supervised Slackline Training on postural balance in judoists—Medicina dello Sport (n.d.). Retrieved November 3, 2025, from (2014). https://www.minervamedica.it/en/journals/medicina-dello-sport/article.php?cod=R26Y2014N04A0539 Volery, S. et al. Traditional balance and slackline training are associated with task-specific adaptations as assessed with sensorimotor tests. Eur. J. Sport Sci. 17 (7), 838–846. https://doi.org/10.1080/17461391.2017.1317833 (2017). Additional Declarations No competing interests reported. Supplementary Files Test1statisticalsummary.xlsx Test4Rightstatisticalsummary.xlsx Test2statisticalsummary.xlsx Test4Leftstatisticalsummary.xlsx Test3statisticalsummary.xlsx RawData.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 18 May, 2026 Reviewers invited by journal 04 May, 2026 Editor assigned by journal 04 May, 2026 Editor invited by journal 29 Apr, 2026 Submission checks completed at journal 28 Apr, 2026 First submitted to journal 28 Apr, 2026 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. 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Del","lastName":"Vicario","suffix":""},{"id":637605233,"identity":"47417c3e-27d1-46d2-82b6-16862d622c3e","order_by":6,"name":"Diego Longo","email":"","orcid":"","institution":"University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Diego","middleName":"","lastName":"Longo","suffix":""},{"id":637605235,"identity":"e88b9d0f-ad58-4836-a656-ffba728792a0","order_by":7,"name":"Andrea Mannini","email":"","orcid":"","institution":"Don Carlo Gnocchi Foundation","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Mannini","suffix":""},{"id":637605238,"identity":"c748d468-fb2f-48b7-aea8-ec6b0591bc96","order_by":8,"name":"Francesca Cecchi","email":"","orcid":"","institution":"Don Carlo Gnocchi Foundation","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"Cecchi","suffix":""}],"badges":[],"createdAt":"2026-04-17 12:25:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9449108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9449108/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109093867,"identity":"76ae7830-e3a2-41de-afbb-4670497df36a","added_by":"auto","created_at":"2026-05-12 13:48:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":158049,"visible":true,"origin":"","legend":"\u003cp\u003eD-Wall – TecnoBody.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9449108/v1/1f9c86ef203800cc7b9ac5e8.png"},{"id":109093942,"identity":"d8afba46-46cd-437c-8eb6-3227362a9224","added_by":"auto","created_at":"2026-05-12 13:48:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17126,"visible":true,"origin":"","legend":"\u003cp\u003eParticipants flow chart.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9449108/v1/c65778885f311afd71241792.png"},{"id":109093932,"identity":"efa871ca-3d92-449d-b5b7-091dc6137dee","added_by":"auto","created_at":"2026-05-12 13:48:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128464,"visible":true,"origin":"","legend":"\u003cp\u003eshows the box plot and the centroid plot for some key features analyzed in test 1 and 2.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9449108/v1/b314c8391485ca29633c5d4f.png"},{"id":109093986,"identity":"cab4974d-f77d-4fcd-876d-8eb94655cc70","added_by":"auto","created_at":"2026-05-12 13:49:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":162656,"visible":true,"origin":"","legend":"\u003cp\u003eshows the box plot and the centroid plot for the key features analyzed in test 3.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9449108/v1/4d996ad5d6c35d2a57ccdbb3.png"},{"id":109093795,"identity":"98c6ecf4-d39a-4a95-a2ae-c9d318af6852","added_by":"auto","created_at":"2026-05-12 13:48:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":200758,"visible":true,"origin":"","legend":"\u003cp\u003eshows the box plot and the centroid plot for some key features analyzed in test 4.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9449108/v1/74b4b1a5ecce653b80f93eb3.png"},{"id":109095438,"identity":"2b3433a7-b4c1-473a-b9d1-2f4acfa9707c","added_by":"auto","created_at":"2026-05-12 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13:48:51","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27574,"visible":true,"origin":"","legend":"","description":"","filename":"Test4Rightstatisticalsummary.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9449108/v1/29c42599eb6fff2dd66e0ecb.xlsx"},{"id":109093964,"identity":"49cfdac4-eaaf-457a-9c7f-b67f0064ed25","added_by":"auto","created_at":"2026-05-12 13:49:07","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":26237,"visible":true,"origin":"","legend":"","description":"","filename":"Test2statisticalsummary.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9449108/v1/3d663dd9a89884b5ad7ddd18.xlsx"},{"id":109093962,"identity":"32240c1b-9543-4ae3-aa52-a8032c51a5b4","added_by":"auto","created_at":"2026-05-12 13:49:02","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":28167,"visible":true,"origin":"","legend":"","description":"","filename":"Test4Leftstatisticalsummary.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9449108/v1/8790f465a9a1b30ee60d3b16.xlsx"},{"id":109093915,"identity":"a0354452-0ec3-4368-af4b-ac82beccf8c5","added_by":"auto","created_at":"2026-05-12 13:48:51","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":26329,"visible":true,"origin":"","legend":"","description":"","filename":"Test3statisticalsummary.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9449108/v1/bca806bf5027d857c8dda99e.xlsx"},{"id":109093793,"identity":"a9f8dbff-c99c-4acd-ad04-7682340425c3","added_by":"auto","created_at":"2026-05-12 13:48:23","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":39667,"visible":true,"origin":"","legend":"","description":"","filename":"RawData.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9449108/v1/dc8a32453c34d571ad4a51db.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic and Kinematic differences in slackliners, non- slackliner athletes and sedentary individuals during four balance tests, a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBalance is a prerequisite for human motor function, supporting posture and coordination during both static and dynamic activities. It is particularly crucial in sports and rehabilitation, where individuals must rapidly adjust motor control to preserve stability under variable conditions (Hof, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Pollock et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Within this framework, slacklining has emerged as an innovative and highly demanding balance training modality (Gabel et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Volery et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It involves maintaining equilibrium on a narrow, tensioned webbing anchored between two fixed points, generating continuous multidirectional perturbations that challenge the neuromuscular system to perform rapid, coordinated adjustments (Buckingham, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e.; Gabel et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Slacklining elicits substantial task-specific adaptations in human kinematics, characterized by refined postural control, improved interjoint coordination, and altered muscle activation patterns during balance tasks (Donath et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Giboin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Santos et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Volery et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the muscular level, slackline training leads to a reduction in corrective joint actions and an increase in anticipatory activation of stabilizing muscles; Pfusterschmied et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) demonstrated decreased reliance on reactive knee adjustments and enhanced preparatory recruitment of the rectus femoris, suggesting greater functional knee stability and feedforward neuromuscular control. Likewise, Donath et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported attenuated electromyographic activity in both lower-limb and trunk muscles during slackline standing, indicating improved efficiency of postural control mechanisms in trained individuals. These adaptations are accompanied by smoother and slower kinematic trajectories, including reduced velocity and frequency of center-of-mass and joint movements (Serrien et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, the benefits of slackline practice are highly task-specific. Current literature consistently demonstrates that improvements in slackline performance do not generalize to untrained static or dynamic balance tasks (Donath et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Giboin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Slackline training, therefore, should be understood as a specialized modality that enhances dynamic and context-dependent balance control rather than as a universal tool for broad postural enhancement. Supporting this specificity, neuroimaging and electrophysiological evidence reveal neuroplastic adaptations at cortical, subcortical and spinal levels following slackline training, reflecting a finely tuned reorganization of the motor system that is unique to this task (Giboin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the growing body of literature describing these neuromechanical adaptations, little is known about how dynamic and kinematic strategies differ among populations with distinct motor backgrounds. So, we investigated the differences in motor patterns among three different populations (professional slackliners, athletes and sedentary individuals). Accordingly, the present study aims to characterize the dynamic and kinematic strategies adopted by the study populations during balance tasks, providing insights into the specific dynamic and kinematic adaptations that distinguish expert balance performers from non-specialists.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSetting\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted at academic and clinical research institutions in Italy. The study was approved by a local institutional ethics committee (protocol No. 414, May 30, 2025), and data were collected between June and September 2025. All participants provided written informed consent prior to participation. This study adhered to the STROBE checklist (Cuschieri, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to enhance the quality and transparency of reporting.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eIn this study, slackline athletes, athletes from other disciplines without slackline experience and sedentary controls, were enrolled and screened for eligibility according to population-specific criteria. Volunteer slackliners were recruited from local Slackline amateur sport associations (\u003cem\u003eSlackline Toscana ASD\u003c/em\u003e, 2026.), non-slackliner athletes were recruited among university students enrolled in sport-related academic programs. Sedentary individuals were recruited among researchers and clinicians from participating institutions. All participants provided written informed consent prior to participation.\u003c/p\u003e \u003cp\u003eThe respective inclusion and exclusion criteria for each population are outlined below:\u003c/p\u003e \u003cp\u003e \u003cem\u003eSlackliners\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eInclusion criteria: age over 18 years; engaged in slacklining for more than 1 year; training frequency approximately 2 times per week.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExclusion criteria: orthopedic conditions or other health issues that could influence participants\u0026rsquo; motor performance.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAthletes from other sports\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eInclusion criteria: age over 18 years; engaged in their sport for more than 1 year; training frequency at least 2 times per week.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExclusion criteria: orthopedic conditions or other health issues that could influence participants\u0026rsquo; motor performance; previous intensive slackline practice (defined as having practiced slacklining for 2 months, approximately 2 times per week).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSedentary individuals\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eInclusion criteria: age over 18 years; individuals not regularly engaged in any sports activities.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExclusion criteria: orthopedic conditions or other health issues that could influence participants\u0026rsquo; motor performance.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eData Acquisition\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and anthropometric\u003c/h2\u003e \u003cp\u003eAge, sex, weight, height, type of sport practiced, average frequency of sports practice over the past two months variables were collected during the eligibility screening in a dedicated Excel database:\u003c/p\u003e \u003cp\u003eA series of biomarkers were extracted, such as shoulder abduction and knee flexion, that in the execution of the tests may reveal population-specific motor control patterns.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDynamic and kinematic data\u003c/h3\u003e\n\u003cp\u003eDynamic and kinematic data were obtained using the D-Wall system (TecnoBody S.p.A, Dalmine BG, Italy) an assessment and rehabilitation system designed to improve movement quality through auditory and visual feedback support. D-Wall performs dynamics registration, via force platforms, and markerless three-dimensional analysis by means of depth camera and proprietary software. Parameters extracted included sway amplitude, CoP (center of pressure) displacement proxies, and joint angular variability during the tasks.\u003c/p\u003e \u003cp\u003e \u003cem\u003e[Insert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003ehere]\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe primary outcome was the difference in kinematic and dynamic parameters among the study populations, recorded using the D-Wall (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) system during the balance tasks included in the protocol. The D-Wall system provides measurements of the CoP displacement along both the x- and y-axes, together with time-based joint angle data across multiple planes of motion for different body segments. These outputs allowed for a detailed quantitative assessment of postural control and joint kinematics throughout the balance tasks. From the raw D-Wall data, a series of features was systematically analyzed across all tests. For each variable derived from the D-Wall, the investigated parameters included the median, interquartile range (IQR), mean, standard deviation (SD), range, as well as the 5th (Q05) and 95th (Q95) percentiles.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental Procedures\u003c/h3\u003e\n\u003cp\u003eParticipants completed four dynamic balance tests, during which the D-Wall system continuously recorded both kinematic and dynamic parameters.\u003c/p\u003e\n\u003ch3\u003eBalance tests\u003c/h3\u003e\n\u003cp\u003eEach participant performed four different balance tasks using the D-Wall system. The tests were:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eBipodal standing on bump stimulating disc: participants performed three trials of 35 seconds each, standing as still as possible, feet together, arms by sides.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTandem standing on bump stimulating disc: participants performed three trials of 35 seconds each, standing with trunk as still as possible. Participants had to keep feet in tandem and could choose the preferred pattern for legs and arms.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTandem standing with lateral space exploration: participants were positioned in the center of the force platform with feet in tandem, and three times had to explore the right and left lateral space with hands as far as possible.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePistol squat: the participants, positioned at the center of the force platform, were instructed to perform a single-leg squat without losing balance. They were asked to squat as deeply as possible and return to the upright position without any time limits or other constraints. This test was repeated three times on the right leg and three times on the left leg.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e For each trial, the first and last 5 seconds were discarded to ensure that data were analyzed exclusively while the participant was performing the task, thereby avoiding signal acquisition during steady-state periods.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eProcessing and statistical analysis\u003c/h2\u003e \u003cp\u003eThe pre-processing stage begins with structured data ingestion and harmonization of the available recordings. Each file name is parsed to identify the participant identifier, test number, and repetition index, enabling the correct association of trials to individual participants and experimental conditions. Data from all available participants are retrieved from the hierarchical folder structure and imported into a unified data frame.\u003c/p\u003e \u003cp\u003eForce plate and CoP signals (retained in the mediolateral and anteroposterior directions, respectively x and y) were resampled from their original frequency of 40 Hz to a common target frequency of 30 Hz using linear interpolation, to match kinematic variables sampling rate. The recorded time series were further refined by removing the first five seconds of each trial to eliminate potential transient effects. All repetitions were then trimmed to a uniform duration defined by the shortest valid trial to guarantee temporal comparability across participants and repetitions. Data are averaged between the three repetitions for each test, to obtain a mean representative time-series of the participant-test pair. In addition to these time-domain adjustments, a normalization procedure is applied to the force cell data using individual body weight, obtained from the demographic dataset provided separately. This step ensures that inter-individual differences in body mass do not bias the comparison of force-related outcomes across experimental groups.\u003c/p\u003e \u003cp\u003eThen, starting from the cleaned and averaged time-series, scalar features were computed to be then compared among groups. The statistical descriptors included were the median and IQR and Q05 and Q95. Furthermore, for the CoP data, the sway area was quantified by fitting a covariance ellipse to the two-dimensional CoP trajectory, with its area representing the dispersion of postural sway.\u003c/p\u003e \u003cp\u003eGroup differences for all metrics were assessed using the Kruskal-Wallis test. Whenever a significant overall group effect was detected (significance level ɑ = 0.05), post-hoc pairwise comparisons were performed using Mann-Whitney U tests with Bonferroni correction to control for multiple testing. The results are reported with corresponding effect statistics and adjusted significance levels.\u003c/p\u003e \u003cp\u003eAll processing and statistical analysis was performed using Python custom code and openly-available libraries (code and data are available at the following GitHub link: LINK).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e32 participants were enrolled in the study: 11 slackliners, 10 non-slackliners athletes and 11 sedentary. All participants met the inclusion criteria and completed the study (flow chart in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Demographic and anthropometric data are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\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\u003eMedian and IQR values are reported (in brackets) for continuous variables while absolute and relative counts (in parenthesis) are reported for categorical variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlackliners\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAthletes\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSedentary\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 [5.5]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 [7]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.5 [8]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 [1.75]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (80.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172 [10.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175 [11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e172.5 [12.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e168.5 [8.25]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 [18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 [10.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.5 [7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.5 [9.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominance, Left\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of weekly training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 [2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 [0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 [1.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 [0]\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 \u003cem\u003e[Insert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003ehere]\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003e[Insert\u003c/em\u003e Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003ehere]\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMotor patterns in dynamic balance tests differ across groups\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eTest 1 \u0026ndash; Reduced trunk extension in slackliners and athletes\u003c/h2\u003e \u003cp\u003eIn test 1 (Supplementary Table\u0026nbsp;1) ANOVA revealed a significant group effect on APT for both Q95 (F\u0026thinsp;=\u0026thinsp;6.35, p\u0026thinsp;=\u0026thinsp;0.042; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and Q05 (F\u0026thinsp;=\u0026thinsp;6.09, p\u0026thinsp;=\u0026thinsp;0.048; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Sedentary participants showed greater trunk extension than slackliners and athletes, although post-hoc differences did not survive Bonferroni correction. Median APT (Q95): sedentary\u0026thinsp;\u0026minus;\u0026thinsp;1.60 [2.25], slackliners\u0026thinsp;\u0026minus;\u0026thinsp;0.12 [1.46], athletes 0.07 [1.30]. Median APT (Q05): sedentary\u0026thinsp;\u0026minus;\u0026thinsp;4.11 mm [2.24], slackliners\u0026thinsp;\u0026minus;\u0026thinsp;2.36 mm [1.52], athletes\u0026thinsp;\u0026minus;\u0026thinsp;2.14 mm [1.70]. No group differences were found in CoP xy coordinates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTest 2 \u0026ndash; Reduced sway and greater shoulder/hip involvement in slackliners\u003c/h2\u003e \u003cp\u003eSlackliners showed greater shoulder abduction than sedentary participants (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), though athlete comparisons did not survive correction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u0026ndash;E). A strong group effect emerged for CoP y-displacement variability (F\u0026thinsp;=\u0026thinsp;14.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF): sedentary participants swayed more (17.86 [3.55]) than slackliners (12.32 [3.49], p\u0026thinsp;=\u0026thinsp;0.013) and athletes (14.36 [1.42], p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eRight shoulder abduction (Q05) was lower in sedentary (42.18\u0026deg; [29.18]) vs slackliners (67.82\u0026deg; [20.37]; p\u0026thinsp;=\u0026thinsp;0.010), with no corrected differences vs athletes. Right hip flexion was higher in slackliners (5.67\u0026deg; [14.84]) vs sedentary and athletes (\u0026minus;\u0026thinsp;8\u0026deg;), approaching significance (p\u0026thinsp;=\u0026thinsp;0.051). Left knee showed no group effect (p\u0026thinsp;=\u0026thinsp;0.33) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG\u0026ndash;H).\u003c/p\u003e \u003cp\u003eCoP y-position differed at group level (p\u0026thinsp;=\u0026thinsp;0.045), with sedentary more forward (29.2 mm [24.5]) vs athletes (6.9 mm [12.9]) and slackliners (10.3 mm [16.2]), though not after correction; no x-axis differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003e \u003cem\u003e[Insert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003ehere]\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTest 3 \u0026ndash; Reduced trunk extension and greater shoulder mobility in slackliners\u003c/h2\u003e \u003cp\u003eSedentary participants had lower APT Q05 (\u0026minus;\u0026thinsp;3.22\u0026deg; [2.49]) than slackliners (\u0026minus;\u0026thinsp;1.63\u0026deg; [1.44]; p\u0026thinsp;=\u0026thinsp;0.037) and athletes (\u0026minus;\u0026thinsp;0.75\u0026deg; [1.62]; p\u0026thinsp;=\u0026thinsp;0.009). Slackliners showed greater shoulder abduction (right 83.73\u0026deg; [28.05], left 86.00\u0026deg; [26.78]) vs both sedentary and athletes (p\u0026thinsp;\u0026le;\u0026thinsp;0.027). Shoulder flexion\u0026ndash;extension Q05 was also higher in slackliners vs both groups (p\u0026thinsp;\u0026le;\u0026thinsp;0.019) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e[Insert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cem\u003ehere]\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eTest 4 \u0026ndash; Greater lower-limb flexion and shoulder abduction in slackliners\u003c/h2\u003e \u003cp\u003eLeft side: Athletes showed higher APT than sedentary (p\u0026thinsp;=\u0026thinsp;0.008; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Hip and knee flexion were greater in athletes vs sedentary (p\u0026thinsp;=\u0026thinsp;0.011; p\u0026thinsp;=\u0026thinsp;0.038), with slackliners showing intermediate/closer-to-athlete values (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u0026ndash;C). Shoulder abduction was higher in slackliners vs athletes (p\u0026thinsp;=\u0026thinsp;0.018; p\u0026thinsp;=\u0026thinsp;0.003; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u0026ndash;G).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRight side: APT was higher in athletes vs slackliners and sedentary (p\u0026thinsp;\u0026le;\u0026thinsp;0.038; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). Hip flexion was higher in slackliners vs sedentary (p\u0026thinsp;=\u0026thinsp;0.024), similar to athletes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eL). Knee flexion showed no significant difference (p\u0026thinsp;=\u0026thinsp;0.07; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eM). Shoulder abduction remained higher in slackliners vs athletes (p\u0026thinsp;=\u0026thinsp;0.018; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eN\u0026ndash;Q).\u003c/p\u003e \u003cp\u003e \u003cem\u003e[Insert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cem\u003ehere]\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this study was to determine whether distinct populations adopt different motor control strategies and to what extent these differences emerge in dynamic and kinematic parameters during balance tasks. Our results indicate that the groups exhibited distinct motor strategies, with key dynamic differences observed in CoP displacement along the y-axis, often accompanied by group-specific patterns or trends in APT. Other key kinematic differences involved greater shoulder abduction and hip flexion in slackliners compared to controls across multiple tests. Test 1 findings were limited to APT but supported by CoP centroid data, while the similar pattern in Test 2 suggests underlying dynamic contributions to these kinematic differences. Test 2 also revealed additional significant markers at the shoulder and hip. In this test, shoulder abduction differed between slackliners and sedentary participants, while athletes showed median values comparable to slackliners. In contrast, in Test 4 slackliners exhibited the greatest abduction and athletes the lowest. In the same test, shoulders\u0026rsquo; flexion kinematics showed the greater discriminative power across groups, with slackliners again reaching the highest values and athletes the lowest. These examples show that even within the same group, motor strategies vary by task, reflecting the highly specialized skills with specific training. The present findings are consistent with previous research demonstrating that slackliners develop discipline-specific motor patterns characterized by refined joint coordination. Mildren and colleagues (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) conducted a one-week intensive intervention study with a similarly limited sample size of 10 participants per group. Despite the small sample and short training period, their main findings revealed significant learning-related changes (Mildren et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePfusterschmied et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) conducted a four-week slackline training program in a small cohort (12 slackliners, 12 controls). Using 3D kinematics during single-leg stance on stable and perturbed surfaces, they reported reduced medio-lateral CoG velocity and decreased frontal-plane hip and knee ROM and velocity, indicating improved stability and reactive control. Unlike Mildren\u0026rsquo;s one-week protocol focused on interjoint coordination in tandem stance, Pfusterschmied\u0026rsquo;s approach used external perturbations, eliciting adaptations mainly at proximal joints. The greater active shoulders and hips\u0026rsquo; ROM in slackliners observed in this study is consistent with findings by Santos et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Volery et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Donath et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), all supporting multi-joint adaptations following slackline training. Although methodological heterogeneity precludes direct comparison, a common pattern emerges: a reorganization of motor strategies involving proximal body segments according to the individual background. Santos et al. (2016) reported reduced postural sway and mediolateral deviations and enhanced hip\u0026ndash;trunk stability; Volery et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) described improved sensorimotor control driven by proximal coordination; and Donath et al. (2013) showed reduced lower-limb activation alongside substantial balance gains, reflecting more efficient, integrated control of the hips.\u003c/p\u003e \u003cp\u003eAlthough previous literature suggests that skills acquired in task-specific contexts do not automatically transfer to other settings (Donath et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Giboin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), our results reveal multiple dynamic and kinematic differences between populations during balance tasks that simulate slacklining but are performed overground. This apparent discrepancy with the literature may be explained by differences in the study populations and the methodological approaches adopted. This study protocol incorporated both static and dynamic balance tasks and analyzed upper- and lower-limb joints across all three planes of movement, thereby providing a more comprehensive characterization of motor patterns in individuals practicing this discipline compared with previous literature.\u003c/p\u003e \u003cp\u003eWithin this broader analytical framework, the observed findings align with the theoretical foundations of motor learning in dynamic systems-based models of motor control. This type of approach, in fact, views skill acquisition as an emergent process shaped by the interaction between the individual, the environment and the task. Instead of pre-planned movement patterns, motor actions arise from dynamic interactions governed by environmental and task constraints (Bernstein, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), while practice leads to the formation of general motor structures rather than fixed movements (Levin \u0026amp; Demers, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This perspective emphasizes perception\u0026ndash;action coupling, where learning strengthens the coordination between sensory input and motor output in context. Skill mastery involves effectively managing the body's degrees of freedom through exploration and adaptation, using available constraints to find optimal movement solutions (Levin \u0026amp; Demers, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMotor synergy learning occurs in two main stages. Initially, an appropriate control trajectory is identified and refined to ensure consistent performance despite external forces. With repetition, synergies strengthen within elemental variables, enabling compensation for perturbations. This stage is characterized by reduced task-relevant variability (VORT), while variability within the uncontrolled manifold (VUCM) remains beneficial for flexibility and adaptation. The second stage refines higher-order synergies that stabilize lower-level control. Strong synergies at higher levels often coincide with weaker ones at lower levels due to variability distribution across the hierarchy (Latash, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Once the control trajectory is stabilized and VORT minimized, further practice optimizes constraints such as efficiency or fatigue, narrowing allowable variability, reducing VUCM, and weakening performance-level synergies while more specific coordination emerges at the muscle level (Latash, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). From this perspective, slacklining promotes the development of energy-efficient synergies. This explains task-specific dynamic and kinematic patterns compared to other athletes and sedentary individuals. Future studies should integrate motion capture with neurophysiological tools (e.g., EEG, EMG) to map the multi-level control hierarchy and assess whether these adaptations transfer to sport-specific or rehabilitative contexts.\u003c/p\u003e \u003cp\u003eThis study is limited by its cross-sectional design, precluding causal inference between motor strategies and training background. Group comparisons were also influenced by demographic and anthropometric differences, with sedentary participants and athletes predominantly female and athletes generally shorter and lighter. Additionally, the moderate sample size may have masked near-significant patterns, potentially obscuring subtle dynamic and kinematic differences.\u003c/p\u003e \u003cp\u003eIn conclusion, this study supports that prolonged task-specific training fosters distinctive motor strategies characterized by efficient postural adjustments and integrated joint synergies. Consistent with evidence on neuromechanical adaptations based on motor background, our findings also reveal inter-group kinematic differences beyond the slackline context, suggesting partially transferable components of postural expertise.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCoP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCenter of Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQ05\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efifth percentile\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQ95\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eninety-fifth percentile\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by a local institutional ethics committee of the “University of Florence” (protocol No. 414, May 30, 2025) in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted without external financial support and was carried out using institutional resources only.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSD: conceptualization, methodology, data curation, visualization, writing – original draft, \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eproject administration\u003c/p\u003e\n\u003cp\u003eMP: Investigation, data curation, writing\u003c/p\u003e\n\u003cp\u003ePL: data curation, formal analysis, visualization, writing – review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eTC: Investigation, data curation, writing\u003c/p\u003e\n\u003cp\u003eFM: Investigation, data curation, writing\u003c/p\u003e\n\u003cp\u003eMLD: Investigation, data curation, writing\u003c/p\u003e\n\u003cp\u003eDL: writing – review \u0026amp; editing, validation\u003c/p\u003e\n\u003cp\u003eAM: writing – review \u0026amp; editing, validation\u003c/p\u003e\n\u003cp\u003eFC: methodology, supervision, writing – review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge all participants in this study, whose willingness and cooperation\u003c/p\u003e\n\u003cp\u003emade the data collection for this research possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The datasets generated and/or analysed during the current study are available in the \u003cem\u003eSlackline Dataset\u003c/em\u003e repository (Doronzio, Stefano), Zenodo, https://doi.org/10.5281/zenodo.19812691.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBernstein, N. 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Brain Res.\u003c/em\u003e \u003cb\u003e235\u003c/b\u003e (11), 3427\u0026ndash;3436. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00221-017-5072-7\u003c/span\u003e\u003cspan address=\"10.1007/s00221-017-5072-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlackline Toscana, A. S. D. \u0026amp; Retrieved November 3, from (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.slacklinetoscana.it/\u003c/span\u003e\u003cspan address=\"https://www.slacklinetoscana.it/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eThe effects of supervised Slackline Training on postural balance in judoists\u0026mdash;Medicina dello Sport\u003c/em\u003e (n.d.). Retrieved November 3, 2025, from (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.minervamedica.it/en/journals/medicina-dello-sport/article.php?cod=R26Y2014N04A0539\u003c/span\u003e\u003cspan address=\"https://www.minervamedica.it/en/journals/medicina-dello-sport/article.php?cod=R26Y2014N04A0539\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolery, S. et al. Traditional balance and slackline training are associated with task-specific adaptations as assessed with sensorimotor tests. \u003cem\u003eEur. J. Sport Sci.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (7), 838\u0026ndash;846. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17461391.2017.1317833\u003c/span\u003e\u003cspan address=\"10.1080/17461391.2017.1317833\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9449108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9449108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSlacklining is a highly demanding dynamic balance discipline that induces task-specific neuromotor and kinematic adaptations. While previous studies have documented improvements in postural control following slackline training, little is known about how motor strategies differ between slackliners, athletes from other sports, and sedentary individuals during non-slackline balance tasks. The purpose of this study was to characterize differences in dynamic and kinematic motor strategies among professional slackliners, non-slackliner athletes, and sedentary individuals during a set of balance tasks. Thirty-two adults, including professional slackliners, athletes from other sports, and sedentary individuals, performed four balance tasks of increasing complexity. Dynamic and kinematic data were collected using a force-platform\u0026ndash;based system combined with marker-less three-dimensional motion analysis. Group differences in center-of-pressure displacement and joint kinematics were analyzed using non-parametric statistical tests. Sedentary individuals exhibited greater trunk extension and larger antero-posterior center-of-pressure displacement compared with both slackliners and athletes. Slackliners demonstrated reduced postural sway and greater shoulder abduction and hip flexion across multiple tasks. In more demanding balance conditions, slackliners showed distinct upper- and lower-limb kinematic strategies compared with the other groups, particularly at the shoulder, hip, and knee joints. Individuals with different motor backgrounds adopt distinct motor control strategies during balance tasks. Long-term slackline practice is associated with joint-specific kinematic adaptations involving the shoulders, hips, and knees, which may partially extend beyond slackline-specific contexts.\u003c/p\u003e \u003cp\u003e \u003cb\u003eKey Terms\u003c/b\u003e: Slacklining; Dynamic balance; Postural control; Kinematic analysis; Motor strategies.\u003c/p\u003e","manuscriptTitle":"Dynamic and Kinematic differences in slackliners, non- slackliner athletes and sedentary individuals during four balance tests, a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 13:25:59","doi":"10.21203/rs.3.rs-9449108/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"108409767958104098313718443477755863671","date":"2026-05-18T16:29:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184892171795612799077785213545899321813","date":"2026-05-18T12:40:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294500465821584327071375661543458333247","date":"2026-05-18T11:50:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-04T14:00:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T13:58:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-29T12:22:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-28T09:29:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-28T08:32:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b016aa3-005e-457f-a9d9-902c12f4cd13","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"108409767958104098313718443477755863671","date":"2026-05-18T16:29:20+00:00","index":172,"fulltext":""},{"type":"reviewerAgreed","content":"184892171795612799077785213545899321813","date":"2026-05-18T12:40:22+00:00","index":170,"fulltext":""},{"type":"reviewerAgreed","content":"294500465821584327071375661543458333247","date":"2026-05-18T11:50:57+00:00","index":169,"fulltext":""},{"type":"reviewersInvited","content":"91","date":"2026-05-04T14:00:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T13:58:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-29T12:22:03+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67864307,"name":"Health sciences/Anatomy"},{"id":67864308,"name":"Health sciences/Health care"},{"id":67864309,"name":"Biological sciences/Neuroscience"},{"id":67864310,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2026-05-12T13:25:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 13:25:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9449108","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9449108","identity":"rs-9449108","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}