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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Sports Sci Med Rehabil . 2026 Mar 29;18:186. doi: 10.1186/s13102-026-01670-4 Search in PMC Search in PubMed View in NLM Catalog Add to search Injury prevention relevance: markerless functional performance testing in athletes with chronic ankle instability to inform recurrent sprain risk screening Sinan Seyhan Sinan Seyhan 1 Faculty of Sport Sciences, Department of Coaching Education, Manisa Celal Bayar University, Manisa, Sehzadeler 45040 Türkiye Find articles by Sinan Seyhan 1, ✉ , Gorkem Acar Gorkem Acar 2 Faculty of Sport Sciences, Istanbul Gelisim University, Istanbul, 34000 Türkiye Find articles by Gorkem Acar 2 , Muhammed Fatih Bilici Muhammed Fatih Bilici 3 Coach Education Department, Mus Alparslan University, Mus, 49250 Türkiye Find articles by Muhammed Fatih Bilici 3 , Fatma Gozlukaya Girginer Fatma Gozlukaya Girginer 4 Faculty of Sports Sciences, Pamukkale University, Denizli, 20160 Türkiye Find articles by Fatma Gozlukaya Girginer 4 , Omer Faruk Bilici Omer Faruk Bilici 5 Department of Movement and Training, Marmara University, Istanbul, 34800 Türkiye Find articles by Omer Faruk Bilici 5 , Caglar Soylu Caglar Soylu 6 Department of Orthopedic Physiotherapy and Rehabilitation, Gulhane Faculty of Physiotherapy and Rehabilitation , University of Health Sciences, Ankara, Etlik 06010 Türkiye Find articles by Caglar Soylu 6, ✉ Author information Article notes Copyright and License information 1 Faculty of Sport Sciences, Department of Coaching Education, Manisa Celal Bayar University, Manisa, Sehzadeler 45040 Türkiye 2 Faculty of Sport Sciences, Istanbul Gelisim University, Istanbul, 34000 Türkiye 3 Coach Education Department, Mus Alparslan University, Mus, 49250 Türkiye 4 Faculty of Sports Sciences, Pamukkale University, Denizli, 20160 Türkiye 5 Department of Movement and Training, Marmara University, Istanbul, 34800 Türkiye 6 Department of Orthopedic Physiotherapy and Rehabilitation, Gulhane Faculty of Physiotherapy and Rehabilitation , University of Health Sciences, Ankara, Etlik 06010 Türkiye ✉ Corresponding author. Received 2026 Jan 18; Accepted 2026 Mar 25; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13069792 PMID: 41906131 Abstract Objective To compare functional performance between athletes with chronic ankle instability (CAI) and healthy controls using objective motion analysis, and to determine whether CAI-related deficits are more evident in agility and multidirectional hop tasks than in general vertical jump measures. Methods Thirty-two athletes participated in this cross-sectional study, including 16 athletes with CAI and 16 healthy controls. Functional performance was assessed using the DeepSport motion analysis system and included countermovement jump (CMJ), agility tests (5-10-5 and acceleration-deceleration-acceleration [ADA]), and multidirectional hop tasks. Subjective instability and injury history were evaluated using the Cumberland Ankle Instability Tool (CAIT) and clinical history variables. Between-group comparisons were performed using analysis of covariance adjusted for sex. Additional group × sex interaction and male-only sensitivity analyses were conducted to examine the potential effect of sex imbalance. An exploratory regression analysis within the CAI group examined the association between previous ankle sprain history and triple crossover hop performance. Results No significant between-group differences were observed for age, height, body mass, or body mass index (all p > 0.05), whereas CAIT score and injury-history variables differed significantly between groups (all p < 0.001). After adjustment for sex, CMJ height, CMJ power, and CMJ flight time did not differ significantly between groups. In contrast, athletes with CAI showed significantly poorer performance in the 5-10-5 agility test (F(1,29) = 14.62, p = 0.001), side jump test (F(1,29) = 35.80, p < 0.001), 6-m jump test (F(1,29) = 6.58, p = 0.016), single-leg hop test (F(1,29) = 5.12, p = 0.031), triple crossover hop distance (F(1,29) = 19.22, p < 0.001), and lateral hop distance (F(1,29) = 30.07, p < 0.001). ADA outcomes were numerically poorer in the CAI group but did not reach statistical significance after sex adjustment. Within the CAI group, a greater number of previous ankle sprains was associated with shorter triple crossover hop distance; however, because the overall regression model was not statistically significant (R² = 0.31, p = 0.087), this finding was considered exploratory and hypothesis-generating only. No significant group × sex interaction was detected for any primary outcome, and male-only sensitivity analyses showed a consistent pattern for the main agility and hopping variables. Conclusion Athletes with CAI demonstrated meaningful deficits in agility and multidirectional hop performance, whereas general vertical jump variables were less discriminative after sex adjustment. These findings suggest that multidirectional hop and agility tests may be more sensitive than CMJ-derived outcomes for identifying functional limitations in athletes with CAI. The exploratory association between sprain history and triple crossover hop performance requires confirmation in larger, adequately powered studies. Overall, the results support the inclusion of multiplanar hop and agility assessments in the functional evaluation and rehabilitation monitoring of athletes with CAI. Supplementary Information The online version contains supplementary material available at 10.1186/s13102-026-01670-4. Keywords: Chronic ankle instability, Physical functional performance, Performance tests, Ai motion analysis, Countermovement jump (CMJ) Introduction Lateral ankle sprain (LAS) represents one of the most common musculoskeletal injuries in sports, often leading to chronic ankle instability (CAI) if not adequately managed [ 1 – 3 ]. CAI is not merely a history of ankle sprain; rather, it refers to a persistent clinical condition characterized by recurrent sprains, repeated episodes of giving way, ongoing symptoms, and functional limitations that continue after the initial injury and reflect unresolved mechanical and sensorimotor deficits [ 4 , 5 ]. In this context, a clear distinction between a previous LAS and established CAI is essential, because the latter requires the presence of ongoing instability-related complaints rather than injury history alone [ 4 , 5 ]. This condition arises from a combination of mechanical and sensorimotor impairments, including ligamentous laxity, proprioceptive deficits, neuromuscular dysfunction, and altered postural control [ 6 , 7 ]. Recent epidemiological data indicate that CAI affects up to 74% of individuals with a history of LAS, with higher prevalence rates observed in high-demand sports such as basketball, soccer, and volleyball [ 8 – 10 ]. Notably, female athletes appear to be at elevated risk, with studies reporting prevalence rates exceeding 50% in sports involving rapid directional changes and jumping [ 11 ]. Furthermore, predictors of CAI in specific populations, such as soccer players, include prior sprain history, impaired balance, and delayed muscle activation times [ 12 ]. The biomechanical alterations associated with CAI are well-documented during dynamic activities like walking, running, and jumping. Individuals with CAI often exhibit increased ankle inversion angles, elevated plantar pressures, proximal joint compensations, knee adduction and internal rotation, and altered loading strategies, which contribute to recurrent injury and performance decrements [ 13 , 14 ]. These changes not only heighten the risk of further sprains but also impair overall athletic performance by reducing power output, agility, and stability [ 15 ]. For instance, fatigue exacerbates these deficits, leading to poorer dynamic balance and increased injury potential in CAI athletes compared to healthy controls [ 16 ]. Additionally, CAI has been linked to central nervous system adaptations, such as reduced corticomotor excitability in key ankle stabilizers like the peroneus longus and tibialis anterior muscles, further compounding neuromuscular mismatches [ 17 ]. The long-term consequences of CAI extend beyond physical impairments, significantly affecting quality of life and sports participation. Approximately 40% of individuals with recurrent sprains experience limitations in returning to pre-injury activity levels, with associated declines in self-reported function and increased fear of movement [ 16 ]. Childhood ankle injuries substantially elevate the risk of adult CAI, emphasizing the need for early intervention [ 18 ]. Etiological factors are multifactorial: mechanical elements include joint laxity and synovial changes, while functional deficits encompass proprioceptive loss, delayed reaction times, and muscle architectural alterations, including reduced pennation angles in peri-ankle muscles [ 19 ]. Emerging research also highlights associations with core stabilization deficits and abdominal wall dysfunction, suggesting that CAI may influence broader kinetic chain impairments [ 20 ]. Recent studies have explored the impact of fatigue on gait loading strategies in CAI, revealing compensatory mechanisms that may predispose athletes to overuse injuries [ 21 ]. Moreover, dual-task paradigms during landing tasks amplify injury risks in CAI populations, underscoring cognitive-motor interference [ 22 ]. Despite advances in understanding CAI pathophysiology, significant gaps remain in the literature, particularly regarding its specific effects on athletic performance metrics such as jumping and agility. While numerous studies have focused on static balance and proprioception [ 4 , 6 ], fewer have comprehensively examined dynamic functional performance in athletes, especially using a combination of hop-based, agility, and vertical jump tests [ 23 ]. Another important gap relates to how these performance deficits are quantified in applied sports settings. Conventional observational assessment and manually timed field tests are practical, but they may be limited in their ability to detect subtle movement asymmetries, temporal deviations, and compensatory strategies that are relevant in athletes with CAI. Marker-based three-dimensional motion analysis can provide detailed biomechanical information, yet its cost, laboratory dependence, and technical demands may restrict routine use in athletic environments. In contrast, artificial intelligence-assisted markerless motion analysis offers a clinically and practically relevant alternative by enabling rapid, non-invasive, and repeatable assessment of dynamic tasks under field-compatible conditions [ 24 ]. This methodological perspective is particularly important in CAI, because the condition is characterized by movement variability, altered joint coordination, and compensatory strategies that may not be fully captured by subjective reports or broad outcome scores alone [ 13 , 14 , 17 ]. AI-assisted systems can strengthen ecological validity by allowing performance evaluation during sport-relevant tasks while preserving objective quantification. Therefore, the use of the Deepsport program in the present study was not only a technological addition but also a necessary methodological choice to improve the precision of jump and agility assessment, to identify potential asymmetries during functional performance, and to better align measurement procedures with the multidirectional demands of volleyball, basketball, and soccer [ 2 , 3 , 24 ]. Furthermore, correlations between subjective tools like the Cumberland Ankle Instability Tool (CAIT) and objective performance metrics are inconsistently reported, with limited emphasis on team sport athletes who rely heavily on multidirectional movements [ 25 ]. Accordingly, the present study was designed around a clearly defined CAI framework and a multifaceted performance model. By combining self-reported ankle instability with objective jump and agility outcomes derived from AI-assisted markerless analysis, this study sought to examine whether persistent instability-related deficits are reflected in measurable athletic performance impairments. This study addresses these gaps by employing a multifaceted testing battery, including AI-enhanced assessments, to quantify CAI’s impact on jumping and agility in a matched cohort of athletes from volleyball, basketball, and soccer. By doing so, it fills a critical void in the literature, providing evidence on how CAI impairs sport-specific performance and highlighting the need for targeted rehabilitation beyond traditional balance training. Its originality lies not simply in comparing athletes with and without CAI, but in integrating subjective instability status with objective, field-relevant, AI-assisted performance analysis to capture deficits that may be missed by conventional approaches. This research was conducted to bridge the disconnect between perceived instability and measurable deficits, ultimately guiding more effective, evidence-based interventions to reduce recurrence and enhance return-to-sport outcomes. CAI significantly reduces jump height and agility in athletes by altering ankle inversion proprioceptive acuity during landing [ 26 ] and provoking neuromuscular adaptations at the knee–ankle chain [ 27 ]. In female CAI participants, diminished hip abductor torque is strongly coupled with lower functional performance and perceived instability, indicating proximal–distal neuromuscular interference constraining explosive and multi-directional tasks [ 28 ]. Within this framework, the rationale for using DeepSport AI-based performance analysis was to obtain standardized, objective, and real-time measurements from functional tasks that closely resemble sport participation demands. This approach supports the evaluation of performance impairments in a manner that is more accessible than laboratory-based motion capture and potentially more sensitive than purely observational assessment [ 24 ]. The originality of this study lies in advancing beyond simply confirming that athletes with CAI perform worse than healthy controls, by being the first to integrate field-ecological jumping and agility tests with an AI-assisted vision algorithm (DeepSport AI-based performance analysis), enabling real-time quantification of limb asymmetries. DeepSport-derived metrics reveal with high resolution how sensorimotor deafferentation and mechanical deficits, including disrupted ankle inversion proprioceptive acuity during landing, interact with compensatory reorganization of the hip–knee–ankle torque chain, ultimately constraining both vertical/lateral jump power and change-of-direction agility. By clarifying these mechanistic relationships, this approach adds a novel, translational perspective that expands rehabilitation targets beyond conventional balance training toward kinetic-chain moment symmetry and task-specific neuromuscular precision [ 24 ]. The objective of this study was to examine the performance implications in individuals with CAI compared to healthy controls. More specifically, the study aimed to determine whether athletes with a clearly defined profile of chronic ankle instability demonstrate impaired jumping and agility performance relative to matched healthy athletes when assessed using an AI-assisted markerless analysis system. We hypothesized that athletes with CAI would exhibit significant impairments in jumping, reduced countermovement jump height and power, and agility parameters, increased times in 5-10-5 and ADA tests, when compared to athletes without CAI. Materials and methods Participants A total of 32 subjects were recruited for this study: 16 adult athletes afflicted with chronic ankle instability (CAI group) and 16 age-matched healthy athletes without a history of lateral ankle sprains (LAS; control group, CG). Age and gender matching were performed to eliminate potential influences on muscle mechanical properties. The inclusion criteria for the CAI group were aligned with the guidelines established by the International Ankle Consortium (IAC), which required: (1) a documented history of at least one significant LAS occurring at least 12 months before participation, with the sprain involving inflammatory symptoms (pain, swelling, etc.) and disruption of desired physical activity; (2) at least two instances of “giving way” (perceived instability) within the past six months; (3) regular participation in sports for a minimum of five years, with at least 3 h per week of training; and (4) self-reported instability with a Cumberland Ankle Instability Tool (CAIT) score of ≤ 24 (Gribble et al., 2013). For the control group, inclusion criteria included: (1) no history of ankle sprains or instability; (2) matched age, sex, height, weight, and sport participation level to the CAI group; (3) regular sports participation for at least five years; and (4) CAIT score ≥ 25 to confirm no perceived instability. Exclusion criteria for both groups encompassed: (1) a history of lower extremity fractures, surgeries, or neurological disorders; (2) any musculoskeletal injuries (e.g., knee, hip, or back issues) occurring within the previous three months that could affect performance; (3) vestibular or balance disorders unrelated to ankle issues; (4) use of orthotics or ankle braces during testing; (5) acute inflammation or pain in the ankle at the time of testing; and (6) any cardiovascular or systemic conditions that could impair physical performance. The CAI and CG groups exhibited comparable anthropometric parameters and weekly training characteristics. The sample size calculation was conducted to ensure adequate statistical power for intergroup comparisons, utilizing G*Power software (Germany). The calculation was based on an expected large effect size (Cohen’s d = 0.789) for primary outcomes like jump height, derived from pilot data and prior literature on CAI performance deficits. The alpha level was set at P = 0.05, and the analysis was performed with 80% power for independent t-tests. The study’s final sample size was determined to be 32 athletes, with 16 athletes from each group [ 25 ]. All procedures performed in this study involving human participants were reviewed and approved by the Ethics Committee of Çankırı Karatekin University (Approval/Application Code: 821c0699143e44f4). The study was conducted in full accordance with the ethical principles outlined in the Declaration of Helsinki and its later amendments. Prior to participation, all eligible participants were provided with detailed verbal and written information regarding the study objectives, procedures, potential risks, and benefits. Written informed consent was obtained from all participants before their inclusion in the study. Following the provision of informed consent, participants completed a standardized questionnaire including clinical and educational information and subsequently underwent all planned assessment and measurement procedures. Data collecting The side jump test was used to assess lateral hopping performance under repeated single-limb loading conditions. For this test, participants stood on one leg beside a marked line on the floor and were instructed to jump laterally over the line and back again as quickly as possible while maintaining the prescribed movement pattern. A total of 10 side-to-side repetitions was completed for each limb. The primary outcome variable was total completion time (s), with lower values indicating better performance. The test was performed separately for the right and left limbs, and three trials were completed per side, with the best (lowest) time retained for analysis [ 29 , 30 ]. The 6-meter timed hop test was used to assess unilateral forward hopping performance over a fixed distance. Participants started behind a clearly marked starting line and were instructed to hop forward on the same leg as fast as possible until the 6-meter line was crossed. Timing began at movement initiation and stopped when the participant completed the 6-meter distance. The recorded variable was completion time in seconds, and lower times reflected better performance. Each limb was tested separately, three trials were performed for each side, and the best time was used for statistical analysis [ 31 , 32 ]. The single-leg hop test was used to assess unilateral forward hopping performance over a fixed distance. Participants performed the test on one leg over a 6-meter course, and the analyzed variable was completion time (s). They were instructed to hop forward as quickly as possible while maintaining single-leg support throughout the task. The test was performed separately for both limbs, three trials were completed per leg, and the best time was retained for analysis [ 31 , 32 ]. The triple crossover hop test was included to assess multi-hop forward performance with a crossover component. Participants began on one leg and performed three consecutive forward hops while crossing over a central line in an alternating pattern. In the present study, the line width was standardized at 15 cm, and participants were instructed to maintain balance and continue the hopping sequence without interruption. The primary outcome variable was total hop distance (m) measured from the starting position to the final landing point. Three trials were performed for each limb, and the best distance was used for analysis [ 31 , 32 ]. The lateral hop test was used to assess unilateral lateral hopping performance in the frontal plane. Because the reviewer requested clarification regarding the analyzed parameter, this is now stated explicitly: the primary variable for this test was total distance (m). Participants performed three consecutive lateral hops on one leg over a pre-defined lateral course marked with cones placed approximately 30 cm apart. The distance from the initial starting point to the final landing point after the third hop was measured in meters. Each limb was tested separately, three trials were completed per side, and the best distance value was retained for analysis [ 33 ]. The 5-10-5 test (pro-agility shuttle) was used to assess change-of-direction speed. Participants started from the center line in a three-point stance, sprinted 5 m to one side and touched the line, then changed direction and sprinted 10 m to the far line, touched it, and finally reversed direction again to sprint 5 m back through the starting line. The primary analyzed variable was total completion time (s). Two trials were performed, and the best time was used for analysis [ 34 , 35 ]. The acceleration-deceleration-acceleration (ADA) test was used to assess short-distance linear speed performance involving a braking phase and re-acceleration. Participants accelerated maximally over the first 10 m, decelerated to a complete stop within the next 5 m, and then immediately re-accelerated over a further 10 m. In response to the reviewer’s concern, the analyzed variables are stated explicitly: total time (s) for the full ADA task and stop time (s) for the deceleration/braking phase were recorded separately using the timing system. These variables were selected to capture both overall performance and braking-control performance within the test [ 36 ]. Each participant performed two trials of the ADA test. To ensure consistency with the analysis approach used in the other performance tests, the best performance was retained for statistical analysis. Specifically, the lowest total completion time and the lowest stopping time values obtained across trials were used as the representative outcomes for each participant. The countermovement jump (CMJ) was used to assess lower-extremity explosive performance. Participants started from an upright bilateral stance, performed a rapid downward countermovement to a self-selected depth, and then immediately executed a maximal vertical jump. To address the reviewer’s request for clarification, the analyzed CMJ variables in the present study were jump height (cm), flight time (s), and estimated power output (W). Participants completed three maximal CMJ trials, and the best trial was used for analysis. Measurements were obtained using a validated optical or AI-assisted motion-analysis system, and the test was performed with the arms free to allow natural jumping mechanics (Fig. 1 ) [ 37 , 38 ]. Fig. 1. Open in a new tab CMJ analysis performed using the DeepSort system Procedures The test protocol was structured to apply standardized rest periods between sequential functional performance assessments, ensuring maximal neuromuscular output and effort in each task. Thereafter, side jump, 6-m timed single-leg hop, and single-leg crossover hop tests were executed in front of validated optical or AI-assisted camera-based motion analysis systems to evaluate field-ecological speed, lateral explosiveness, and dynamic postural stabilization, while preserving movement quality through inter-test recovery. Given the high sensitivity of the Countermovement Jump (CMJ) to accumulated fatigue, CMJ trials were positioned in the final phase of the performance battery and reported exclusively from technically-accepted, maximal-effort jumps achieved after sufficient recovery. The absence of fatigue-controlled, camera-integrated kinematic and agility performance profiling in existing CAI literature supports the scientific novelty of this study and emphasizes the need to re-examine the mechanistic coupling between perceived ankle instability and objective explosive neuromuscular asymmetry using AI-driven spatiotemporal motion quantification approaches. Following a five-minute passive rest period, the Deepsport program (Deepsport, Istanbul, Türkiye), an artificial intelligence algorithm, was implemented. High-resolution, markerless motion analytics were employed to assess the effects of chronic ankle instability (CAI) on sport-specific functional performance. The study introduces innovation by integrating field-ecological performance testing with an AI-driven kinematic assessment pipeline delivered by DeepSport AI-Vision Engine, which automates anatomical landmark detection (heel, ankle, knee, hip, trunk, shoulder) through spatiotemporal neural image-processing networks, reducing user-dependent analysis error. All discrete jumping and agility outcomes were extracted automatically using DeepSport modules including Countermovement Jump (CMJ) and Squat Jump (SJ) tests as well as change-of-direction speed and agility tests, specifically the 5-10-5 agility assessment and the Athletic Directional Agility (ADA) task battery, capturing micro-temporal limb asymmetries relevant to vertical power and multidirectional directional control. The testing protocol was executed under natural indoor court conditions using an iPad Pro camera mounted 2 m from the athlete at 1.2 m height, where participants completed arms-akimbo CMJ, 5-10-5, and ADA tests while DeepSport was used to generate automated kinematic outputs and kinetic proxy metrics in the present study. Its selection was informed by our previous validation study, in which the system demonstrated acceptable validity and excellent test-retest reliability for vertical jump assessment in elite basketball players when compared with OptoJump [ 24 ]. In that study, DeepSport data acquisition was performed using an iPad Pro mounted on a tripod positioned 2 m from the participant at a height of 1.2 m, and the system recorded movement at 60 Hz. Jump variables were extracted automatically using AI-based pose estimation algorithms. The validation study was conducted in a standardized indoor basketball court environment, and the same testing area and equipment setup were used throughout data collection to support measurement consistency. Reliability was evaluated using intraclass correlation coefficient (ICC), coefficient of variation (CV), standard error of measurement (SEM), and smallest detectable change (SDC). DeepSport showed ICC values of 0.90 for CMJ height, 0.91 for CMJ anaerobic power, 0.90 for SJ height, and 0.90 for SJ anaerobic power, with corresponding CV values ranging from 2.64% to 4.95%, indicating good-to-excellent measurement consistency. SEM values ranged from 0.059 to 0.083, and SDC values ranged from 0.163 to 0.230. However, the specific software/algorithm version was not reported in the original validation paper and should therefore be specified separately if available from the developer or internal system records [ 24 ]. Statistical analysis All statistical analyses were performed using IBM SPSS Statistics version 26 (IBM Corp., Armonk, NY, USA) and employed standard parametric and non-parametric procedures appropriate for continuous and categorical data. Descriptive statistics were computed for all variables and are presented as mean ± standard deviation (SD). Group differences in age, height, body mass, and body mass index (BMI) were examined using independent-samples t-tests, whereas sex distribution was compared using a chi-square test. These analyses confirmed that the groups were comparable on key demographic and anthropometric characteristics ( p > 0.05), thereby supporting the validity of subsequent inferential comparisons. Given the imbalance in sex distribution between groups, all performance outcomes were analyzed using analysis of covariance (ANCOVA), with sex entered as a covariate to control for its potential confounding effects. Separate ANCOVA models were conducted for each outcome variable, including countermovement jump (CMJ) height, CMJ power, CMJ flight time, 5-10-5 agility time, ADA completion and stopping times, side jump time, 6-m jump time, hops jump time, and multidirectional hop distances (triple crossover and lateral hop). For each model, adjusted means, F-statistics, p-values, and effect sizes (partial η²) were reported. Effect size interpretation followed conventional thresholds: small (0.01), medium (0.06), and large (0.14). Model diagnostics were examined to verify ANCOVA assumptions, including normality of residuals, homogeneity of variance, linearity between the covariate and dependent variables, and homogeneity of regression slopes. No violations that would impact interpretation were detected; however, in cases where assumptions were borderline yet acceptable, results were interpreted with appropriate caution. Exploratory analyses were conducted within the CAI group to evaluate the relationship between injury history and functional performance. A multiple linear regression model was used to determine whether the number of previous ankle sprains predicted triple crossover hop distance after accounting for sex. Regression coefficients (β), 95% confidence intervals, model R² values, and significance levels were reported. Model adequacy and multicollinearity diagnostics (tolerance and VIF) indicated no violations of linear regression assumptions. For all analyses, statistical significance was set at p < 0.05, and two-tailed tests were used. Effect sizes and confidence intervals were emphasized to support interpretation of clinical and practical significance beyond mere statistical significance. Results No significant between-group differences were observed for age, height, body mass, or body mass index (BMI) (all p > 0.05; Table 1 ). In contrast, CAIT score, number of ankle sprains, episodes of giving way, time since first lateral ankle sprain (LAS), and time since most recent LAS differed significantly between groups (all p < 0.001; Table 1 ). Table 1. Descriptive characteristics of athletes with chronic ankle instability (CAI) and healthy controls. Data are mean ± SD unless otherwise stated. p -values are from independent-samples t-tests (continuous variables) or chi-square tests (sex) Variable CAI ( n = 16) Control ( n = 16) p -value Sex, n (female / male) 9 / 7 3 / 13 0.068 Age, years 20.62 ± 1.36 21.75 ± 3.07 0.19 Height, cm 176.62 ± 10.05 182.62 ± 7.82 0.07 Body mass, kg 72.88 ± 17.44 77.06 ± 15.51 0.48 Body mass index, kg·m⁻² 23.16 ± 3.84 22.94 ± 3.37 0.87 CAIT score (0–30) 19.81 ± 2.01 28.62 ± 1.45 < 0.001 Number of ankle sprains 11.50 ± 2.56 0.00 ± 0.00 < 0.001 Episodes of “giving way” 3.69 ± 0.95 0.00 ± 0.00 < 0.001 Time since first sprain, years 9.12 ± 1.02 0.00 ± 0.00 < 0.001 Time since last sprain, months 4.94 ± 1.34 0.00 ± 0.00 < 0.001 Open in a new tab Data are presented as mean ± SD unless otherwise stated. P -values were derived from independent-samples t-tests for continuous variables and chi-square test for sex distribution. CAI chronic ankle instability, CAIT Cumberland Ankle Instability Tool, LAS lateral ankle sprain, BMI body mass index, SD standard deviation The CAI group consisted of 9 females and 7 males, whereas the control group consisted of 3 females and 13 males. Although this difference in sex distribution did not reach statistical significance ( p = 0.068), sex was included as a covariate in subsequent ANCOVA analyses. In the CAI group, all included athletes had involvement of the right lower extremity. In the control group, the dominant lower extremity was right in 10 athletes and left in 6 athletes. Athletes in both groups participated in volleyball, basketball, and soccer. Jump performance Unadjusted values suggested lower CMJ performance in the CAI group than in controls; however, after adjustment for sex, no statistically significant between-group differences were identified for CMJ height, CMJ power, or CMJ flight time (Table 2 ). CMJ height was lower in the CAI group (32.25 ± 8.25 cm) than in the control group (41.62 ± 13.05 cm), but this difference did not reach significance after adjustment (F(1,29) = 3.41, p = 0.075, partial η² = 0.105). Likewise, no significant group effects were observed for CMJ power (F(1,29) = 0.10, p = 0.756, partial η² = 0.003) or flight time (F(1,29) = 0.61, p = 0.440, partial η² = 0.021). Figure 2 shows the distribution of CMJ height values across groups. Table 2. Jumping, agility and multidirectional hop performance in athletes with chronic ankle instability (CAI) and healthy controls. Values are mean ± SD. p -values and partial η² are from ANCOVA controlling for sex Outcome CAI ( n = 16) Control ( n = 16) F(1,29) p -value partial η² CMJ jump height (cm) 32.25 ± 8.25 41.62 ± 13.05 3.41 0.075 0.105 CMJ power (W) 427,558.31 ± 114,028.02 470,272.50 ± 191,556.96 0.10 0.756 0.003 CMJ flight time (ms) 50.62 ± 12.82 57.12 ± 15.49 0.61 0.440 0.021 5-10-5 agility test time (s) 5.47 ± 1.17 4.17 ± 1.08 14.62 0.001 0.335 ADA completion time (ms) 1158.94 ± 244.54 1025.44 ± 169.37 3.49 0.072 0.107 ADA stopping time (s) 1.28 ± 0.31 8.11 ± 20.68 0.95 0.337 0.032 Side jump test time (s) 5.33 ± 0.71 3.97 ± 0.42 35.80 < 0.001 0.552 6-m jump test time (s) 2.75 ± 0.57 2.24 ± 0.38 6.58 0.016 0.185 Hops jump test time (s) 3.95 ± 0.25 3.68 ± 0.30 5.12 0.031 0.150 Triple crossover hop distance (m) 3.52 ± 0.90 5.02 ± 0.80 19.22 < 0.001 0.399 Lateral hop distance (m) 3.84 ± 0.67 5.00 ± 0.56 30.07 < 0.001 0.509 Open in a new tab Values are presented as mean ± SD. Group comparisons were performed using analysis of covariance (ANCOVA) with sex entered as a covariate. CAI chronic ankle instability, CMJ countermovement jump, ADA acceleration-deceleration-acceleration, SD standard deviation, ANCOVA analysis of covariance, η² eta squared Fig. 2. Open in a new tab CMJ height by group (CAI vs. control) Agility and hopping performance After adjustment for sex, athletes with CAI demonstrated significantly poorer performance in several agility and hopping outcomes compared with controls (Table 2 ). In the 5-10-5 agility test, the CAI group recorded longer completion times than the control group (5.47 ± 1.17 s vs. 4.17 ± 1.08 s), with a significant group effect (F(1,29) = 14.62, p = 0.001, partial η² = 0.335). Figure 3 shows the distribution of 5-10-5 agility test times across groups. Fig. 3. Open in a new tab 5-10-5 agility test time by group (CAI vs control) Similarly, the CAI group performed worse in the side jump test (5.33 ± 0.71 s vs. 3.97 ± 0.42 s; F(1,29) = 35.80, p < 0.001, partial η² = 0.552) and the 6-m jump test (2.75 ± 0.57 s vs. 2.24 ± 0.38 s; F(1,29) = 6.58, p = 0.016, partial η² = 0.185). Performance in the single-leg hop test was also poorer in the CAI group, with longer completion times than in controls (3.95 ± 0.25 s vs. 3.68 ± 0.30 s; F(1,29) = 5.12, p = 0.031, partial η² = 0.150). For multidirectional hop distance measures, the CAI group showed significantly lower values than controls. Triple crossover hop distance was shorter in athletes with CAI (3.52 ± 0.90 m) than in controls (5.02 ± 0.80 m), with a significant group effect (F(1,29) = 19.22, p < 0.001, partial η² = 0.399). Lateral hop distance was also lower in the CAI group (3.84 ± 0.67 m vs. 5.00 ± 0.56 m; F(1,29) = 30.07, p < 0.001, partial η² = 0.509). Figure 4 shows the distribution of triple crossover hop distance values across groups. Fig. 4. Open in a new tab Triple crossover hop distance by group (CAI vs. control) Acceleration–deceleration–acceleration (ADA) performance ADA performance was numerically poorer in the CAI group, but these differences were not statistically significant after adjustment for sex. ADA completion time was longer in the CAI group (1158.94 ± 244.54 ms) than in the control group (1025.44 ± 169.37 ms), although the group effect did not reach significance (F(1,29) = 3.49, p = 0.072, partial η² = 0.107). ADA stopping time also did not differ significantly between groups (F(1,29) = 0.95, p = 0.337, partial η² = 0.032) (Table 2 ). Exploratory regression analysis Within the CAI group, an exploratory multiple linear regression analysis was conducted to examine whether the number of previous ankle sprains predicted triple crossover hop distance after controlling for sex. The model explained 31% of the variance in triple crossover hop performance; however, the overall model was not statistically significant (R² = 0.31, p = 0.087). The number of previous ankle sprains showed a negative regression coefficient, indicating that a greater number of prior sprains was associated with shorter triple crossover hop distance (β = − 0.19 m per sprain, 95% CI − 0.37 to − 0.02, p = 0.031), whereas sex was not a significant predictor ( p = 0.77). Because this analysis was based on a small sample and the overall regression model did not reach significance, the result should be interpreted as exploratory and hypothesis-generating only. Figure 5 presents this association graphically within the CAI group. Fig. 5. Open in a new tab Triple crossover hop distance versus number of previous ankle sprains in the CAI group Because sex distribution differed between groups, additional analyses were performed to examine whether sex modified the association between group status and performance. Across all outcomes, no statistically significant group × sex interaction was detected (all p > 0.05), indicating that the direction and magnitude of the group effect did not significantly differ by sex in the present sample (Table 3 ). To further assess the robustness of the findings, a male-only sensitivity analysis was conducted. In this analysis, the pattern of results remained consistent for the main agility and hopping outcomes, with significant between-group differences observed for the 5-10-5 agility test (F = 11.53, p = 0.003), side jump test (F = 38.43, p < 0.001), 6-m jump test (F = 5.12, p = 0.036), single-leg hop test (F = 5.76, p = 0.027), triple crossover hop distance (F = 19.41, p < 0.001), and lateral hop distance (F = 35.69, p < 0.001). In contrast, CMJ outcomes and ADA variables remained non-significant in the male-only analysis (all p > 0.05). These additional analyses suggest that the primary between-group differences in hopping and agility performance were not solely attributable to the imbalance in sex distribution. Table 3. Group × sex interaction analyses and male-only sensitivity analyses for primary outcomes Outcome Group × sex interaction F p partial η² Male-only F p partial η² CMJ jump height (cm) 0.815 0.374 0.028 3.222 0.089 0.152 CMJ flight time (ms) 0.624 0.436 0.022 1.000 0.331 0.053 CMJ power (W) 0.015 0.903 0.001 0.023 0.880 0.001 5-10-5 agility test time (s) 1.395 0.247 0.047 11.530 0.003 0.390 ADA completion time (ms) 0.489 0.490 0.017 1.227 0.283 0.064 ADA stopping time (s) 0.531 0.472 0.019 0.944 0.344 0.050 Side jump test time (s) 0.654 0.426 0.023 38.426 < 0.001 0.681 6-m jump test time (s) 0.000 0.987 0.000 5.117 0.036 0.221 Single-leg hop test time (s) 0.804 0.378 0.028 5.759 0.027 0.242 Triple crossover hop distance (m) 1.134 0.296 0.039 19.410 < 0.001 0.519 Lateral hop distance (m) 0.265 0.611 0.009 35.690 < 0.001 0.665 Open in a new tab CMJ countermovement jump, ADA acceleration-deceleration-acceleration, η² eta squared, CAI chronic ankle instability Discussion The main finding of this study is that athletes with chronic ankle instability (CAI) showed clear impairments in agility and multidirectional hop performance compared with healthy controls, whereas countermovement jump (CMJ) outcomes and acceleration–deceleration–acceleration (ADA) variables did not differ significantly after adjustment for sex. Because age, height, body mass, and BMI were similar between groups, these between-group differences are more likely to reflect CAI-related functional limitations than general anthropometric differences. In addition, markedly lower CAIT scores and greater sprain history and giving-way episodes in the CAI group support that the sample represented a clinically relevant CAI profile consistent with accepted diagnostic frameworks [ 1 ]. A second important finding is that the clearest between-group differences emerged in tasks with greater directional and coordinative demand. Athletes with CAI performed worse in the 5-10-5 agility test, side jump test, 6-m timed hop test, single-leg hop test, triple crossover hop distance, and lateral hop distance, with moderate-to-large effect sizes. In contrast, CMJ height showed only a non-significant trend toward lower performance, and CMJ flight time and power were not different between groups after sex adjustment. These findings suggest that CAI-related deficits may be more readily detected during tasks that require rapid change of direction, repeated single-limb loading, and multiplanar movement control than during a bilateral vertical jump task. This interpretation is consistent with earlier work showing that individuals with CAI demonstrate greater functional difficulty in agility and hop-based tasks than in simpler or less directionally demanding assessments [ 39 – 41 ]. The non-significant ADA findings further suggest that not all high-speed tasks are equally sensitive to CAI-related deficits, and that test selection may influence the ability to detect performance limitations in this population [ 40 , 42 ]. The present results can be interpreted within a focused sensorimotor framework without extending beyond the variables measured in this study. Previous research has reported that CAI is associated with altered movement organization, impaired dynamic control, and reduced efficiency during landing, hopping, and cutting tasks [ 43 – 46 ]. Other studies have also described deficits in peri-ankle muscle function, proximal compensatory strategies, and changes in neuromuscular control that may affect performance during high-demand functional tasks [ 19 , 28 , 45 , 47 – 51 ]. Although these structural and neurophysiological variables were not directly measured here, the current pattern of findings is compatible with the view that CAI has greater functional consequences during multiplanar, unilateral, and agility-based tasks than during bilateral jump performance. For this reason, the present discussion is best centered on the practical implication that multidirectional hop and agility tests appear to provide more discriminative functional information than CMJ-derived variables in athletes with CAI. The exploratory regression analysis within the CAI group should be interpreted cautiously. Although a greater number of previous ankle sprains was associated with shorter triple crossover hop distance, the overall regression model did not reach statistical significance. Accordingly, this finding should be considered hypothesis-generating rather than confirmatory, and it does not by itself establish a cumulative injury mechanism. Nevertheless, the direction of the association is clinically plausible and suggests that recurrent sprain burden may be relevant to multidirectional performance loss in some athletes with CAI. This issue should now be tested directly in larger and adequately powered samples. In addition, because sex distribution differed between groups, we performed supplementary group × sex interaction and male-only sensitivity analyses. No significant interaction was detected for any primary outcome, and the main agility and hopping findings remained consistent in the male-only analysis. These additional analyses reduce, but do not eliminate, concern that the primary findings were driven by sex imbalance. The clinical implications of the present findings are relatively specific. For functional profiling in athletes with CAI, the 5-10-5 agility test, side jump test, 6-m timed hop test, single-leg hop test, triple crossover hop test, and lateral hop test appear to be more informative than CMJ variables. These results suggest that rehabilitation and return-to-sport monitoring in CAI should not rely solely on general jump metrics or subjective instability scores, but should also include multiplanar hop and agility tasks that challenge rapid force transfer, directional control, and single-limb performance. Future studies should move beyond broad calls for larger prospective work and instead examine three specific questions: first, whether recurrent sprain history prospectively predicts decline in triple crossover and lateral hop performance; second, whether rehabilitation programs targeting agility and multiplanar hopping produce greater improvements than conventional approaches; and third, whether these functional deficits differ by sex in larger, more balanced cohorts [ 28 , 39 , 49 ]. Conclusion This study showed that athletes with chronic ankle instability exhibit meaningful deficits in agility and multidirectional hop performance, whereas CMJ outcomes and ADA variables were not significantly different after sex adjustment. The most consistent between-group differences were observed in the 5-10-5 agility test, side jump test, 6-m timed hop test, single-leg hop test, triple crossover hop distance, and lateral hop distance, suggesting that these measures may be more sensitive than general vertical jump variables for identifying functional limitations in CAI. The exploratory association between greater sprain history and shorter triple crossover hop distance should be interpreted cautiously because the overall regression model was not statistically significant. Additional analyses indicated that the main findings were not clearly modified by sex, although the sample remained small and sex-imbalanced. Overall, the present results support the use of multidirectional agility and hop-based assessments in the functional evaluation of athletes with CAI and provide targeted directions for future research focused on recurrent sprain burden, rehabilitation responsiveness, and sex-specific effects. Supplementary Information Supplementary Material 1. (13.5KB, xlsx) Acknowledgements No. Generative AI statement The author(s) declare that no Generative AI was used in the creation of this manuscript. Abbreviations CAI Chronic Ankle Instability CAIT Cumberland Ankle Instability Tool CMJ Countermovement Jump PL Peroneus Longus TA Tibialis Anterior IAC International Ankle Consortium LAS Lateral Ankle Sprain ADA Agility and Dynamic Assessment BMI Body Mass Index Authors’ contributions SS: Formal Analysis, Investigation, Writing – original draft, Data curation, Methodology, Supervision, Visualization. GA: Writing – original draft, Writing – review and editing, Investigation, Methodology. MFB: Visualization, Methodology, Writing – original draft, Writing – review and editing. FGG: Investigation, Methodology, Supervision, Writing – original draft. OFB: Visualization, Methodology, Writing – original draft, Writing – review and editing. CS: Writing – original draft, Writing – review and editing, Investigation, Methodology, Supervision, Writing – original draft. Funding The author(s) declare that they have received no financial support for the research and/or publication of this article. Data availability The datasets generated and/or analyzed during the current study are not publicly available due to ethical restrictions related to participant confidentiality and the inclusion of sensitive clinical data. However, the data are available from the corresponding authors upon reasonable request, subject to approval by the Ethics Committee of Çankırı Karatekin University. Declarations Ethics approval and consent to participate All procedures performed in this study involving human participants were reviewed and approved by the Ethics Committee of Çankırı Karatekin University (Approval/Application Code: 821c0699143e44f4). The study was conducted in full accordance with the ethical principles outlined in the Declaration of Helsinki and its later amendments. Prior to participation, all eligible participants were provided with detailed verbal and written information regarding the study objectives, procedures, potential risks, and benefits. Written informed consent was obtained from all participants before their inclusion in the study. Following the provision of informed consent, participants completed a standardized questionnaire including clinical and educational information and subsequently underwent all planned assessment and measurement procedures. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Contributor Information Sinan Seyhan, Email: [email protected]. Caglar Soylu, Email: [email protected]. 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[ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary Material 1. (13.5KB, xlsx) Data Availability Statement The datasets generated and/or analyzed during the current study are not publicly available due to ethical restrictions related to participant confidentiality and the inclusion of sensitive clinical data. However, the data are available from the corresponding authors upon reasonable request, subject to approval by the Ethics Committee of Çankırı Karatekin University. 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