Abstract
o compare hurdle-resisted sprint training (EGH), sled-resisted training (EGS), and hurdles- only training (CG) on performance and kinematics using a smallest-effect-size-of-interest (SESOI) framework, fifteen U18 female hurdlers (16.3±1.3 years) were randomized to EGH, EGS, or CG (n= 5 each) for a 7-week intervention (7 microcycles). EGH used individualized resistance (10% velocity decrement), while EGS used fixed ~13% body- mass resistance. Outcomes included 30 m hurdle time (30 mH), Technique Index, and exploratory kinematics. Primary analysis used baseline-adjusted robust ANCOVA with permutation and linear mixed models (LMM) as sensitivity checks. A smallest-effect-size- of-interest (SESOI) of−0.066 s (~1.2%) was pre-specified. Adjusted ANCOVA favored EGH over CG (−0.19 s; 95% CI [−0.45, 0.06];p= 0.11). The point estimate exceeded the SESOI, though the CI captured both meaningful and trivial effects. Sensitivity analyses maintained this directional pattern, but LMM estimates varied in magnitude, suggesting model dependence. The EGH–EGS contrast was smaller and uncertain (−0.15 s;p= 0.10). Exploratory baseline-adjusted kinematic contrasts showed no clear differences at the first hurdle, but highlighted nominal differences in the EGH group at the second hurdle (greater take-off distance,p= 0.030) and third hurdle (shorter flight and landing times,p< 0.05), which
this directional pattern, but LMM estimates varied in magnitude, suggesting model dependence. The EGH–EGS contrast was smaller and uncertain (−0.15 s;p= 0.10). Exploratory baseline-adjusted kinematic contrasts showed no clear differences at the first hurdle, but highlighted nominal differences in the EGH group at the second hurdle (greater take-off distance,p= 0.030) and third hurdle (shorter flight and landing times,p< 0.05), which should be interpreted as hypothesis-generating. In this preliminary trial, the data are compatible with a range of effects from negligible to practically meaningful for hurdle- resisted sprint training relative to both control and sled-resisted conditions. All estimates are accompanied by wide compatibility intervals, precluding confirmatory conclusions. These findings establish protocol feasibility, provide estimation-based preliminary evidence with openly available individual-level data, and motivate adequately powered multi-center replication trials. Keywords:track and field; biomechanics; hurdle clearance; resistance training; speed; power; sport performance; development; adolescence 1. Introduction High hurdles, namely the men’s 110 m and women’s 100 m outdoor events, are sprint disciplines in which athletes must clear ten hurdles while preserving near-maximal sprint Appl. Sci.2026,16, 3989 https://doi.org/10.3390/app16083989
Appl. Sci.2026,16, 3989 2 of 25 velocity throughout the race. At the performance level, elite hurdling is characterized by (a) a high-velocity approach to the first hurdle [1,2], (b) minimal horizontal velocity loss during hurdle clearance [1,3], and (c) the rapid re-establishment of an efficient three- step rhythm between hurdles [2–5]. Accordingly, a practically important performance construct is the technical cost of hurdling relative to flat sprinting, often operationalized as a Technique Index (T index), defined as the time difference between hurdle and flat sprint performance over the same distance [6,7]. In the men’s 110 m hurdles, the event imposes additional sprint ‘constraints’ compared with flat sprinting because the short inter-hurdle distance limits the extent to which athletes can reach and sustain true maximal velocity between barriers; velocity is typically regulated through the hurdle units and can increase again on the run-in after the tenth hurdle. Moreover, men’s contemporary elite practice has increasingly shifted toward a seven-step approach to the first hurdle, reflecting an evolving acceleration solution specific to the men’s event [1–5]. While these features are informative for understanding sprint–hurdle constraints in general, the present study focuses on U18 female 100 m hurdles, where hurdle height and spacing differ and where the early hurdle units are still strongly determined by acceleration into H1 and the stabilization of a reproducible three-step rhythm. Gender-specific hurdle constraints (hurdle height and spacing) shape technique and kinematics, meaning that findings from men cannot be mechanically transferred to women without considering these event constraints [1,3]. Importantly, for U18 hurdlers the techni- cal model is further constrained by age-group hurdle specifications, because hurdle height increases across age groups and can alter key spatiotemporal demands around clearance and the early hurdle units [8]. In parallel, adolescent athletes are still influenced by growth and maturation processes that affect sprint mechanics and the expression of force and power, with meaningful inter-individual variability persisting through the mid-to-late teenage years [9]. During adolescence, sprint and jump performance trajectories diverge between gen- ders, an effect attributed to pubertal sexual dimorphism and its consequences for muscle mass and power development [9–11]. In addition,
are still influenced by growth and maturation processes that affect sprint mechanics and the expression of force and power, with meaningful inter-individual variability persisting through the mid-to-late teenage years [9]. During adolescence, sprint and jump performance trajectories diverge between gen- ders, an effect attributed to pubertal sexual dimorphism and its consequences for muscle mass and power development [9–11]. In addition, girls’ earlier maturation and typically smaller gains in strength- and power-related capacities across mid-adolescence highlight the practical importance of early and well-targeted strength/power development and individualized load progressions [12]. From a hurdling-specific perspective, women’s sprint hurdling is performed under different mechanical constraints than men’s (hurdle height and spacing), and world-class comparisons indicate that the women’s event is gen- erally less disruptive to horizontal velocity because of the lower hurdle height relative to stature [1,3,13]. For U18 female hurdlers, hurdle height is often further reduced at younger age groups (e.g., 0.762 m in U18 versus 0.838 m in senior women), which may shift the bal- ance toward “more sprinting and less barrier-induced deceleration” and, consequently, may alter how resisted sprint methods transfer to hurdle performance. Collectively, these sex- and age-related constraints provide a rationale for evaluating training transfer specifically within U18 female hurdling. In sprint hurdling, acceleration is especially important because it shapes the approach to the first hurdle and influences early-race velocity and rhythm. Evidence from sprint acceleration research indicates that performance is closely linked to the athlete’s abil- ity to produce and orient high mechanical power and to express an effective horizontal force–velocityprofile during the acceleration phase [14]. Moreover, start and initial acceler- ation characteristics are highly relevant in elite sprinters and high hurdlers, supporting the idea that acceleration capacity is a key physical determinant underpinning hurdle perfor- mance [15]. Consequently, minimizing the “technical gap” between flat sprint acceleration and hurdle acceleration is a primary training objective. Accordingly, coaches frequently https://doi.org/10.3390/app16083989
Appl. Sci.2026,16, 3989 3 of 25 employ sprint-specific methods that overload the acceleration phase, with training effects most meaningfully evaluated using short-distance outcomes that preserve event specificity (e.g., electronically timed 30 m sprint-hurdle performance). Resisted sprinting, where external resistance is added while the athlete sprints hor- izontally, has been widely used for this purpose. Systematic reviews and meta-analyses generally support small-to-moderate improvements in short-distance acceleration follow- ing resisted sprint training compared with unresisted sprinting, although outcomes depend on programming variables and load prescription [16,17]. Among resisted sprint methods, sled towing/pulling is one of the most common in applied settings and research. A major practical issue is how to prescribe “equivalent” resistance across athletes. Historically, loads have often been prescribed as a percentage of body mass (%BM). From an applied perspective, a ~10% body-mass sled load is often used as a practical light-load starting point in coaching and research; however, the same %BM can elicit different velocity decrements across athletes as individual differences in strength, technique, training history and gender can produce large variability in the actual velocity reduction caused by the same %BM [18]. This limitation is likely amplified in U18 female hurdlers, where maturation status and training age can differ markedly even within the same chronological age, making %BM pre- scriptions especially inconsistent. For that reason, approaches based on velocity decrement have been proposed to individualize loading more precisely [19–21]. This point may be even more important when working with U18 athletes, where between-athlete variability in load–velocity relationships can be substantial [21]. In addition, specific sprint training applied via resisted sprinting was found to be more efficient than other training modalities for provoking advanced adaptations in young athletes [22]. Evidence indicates that resisted sprint training can meaningfully improve short-distance acceleration, although training effects depend on how resistance is prescribed and progressed [17,23]. Despite the growing evidence base for resisted sprint training in flat sprinting, transfer- ring these methods to sprint hurdling is not straightforward. Specifically, sled towing cannot be practically implemented during sprint-hurdle drills that involve hurdle clearance and the early inter-hurdle rhythm, because the hurdles constrain the sled
acceleration, although training effects depend on how resistance is prescribed and progressed [17,23]. Despite the growing evidence base for resisted sprint training in flat sprinting, transfer- ring these methods to sprint hurdling is not straightforward. Specifically, sled towing cannot be practically implemented during sprint-hurdle drills that involve hurdle clearance and the early inter-hurdle rhythm, because the hurdles constrain the sled system. At the same time, field-based mechanical resistance devices can provide quantifiable horizontal resistance and ac- ceptable reliability for resisted sprinting, offering a plausible hurdle-resisted alternative [24–26]. Despite the prevalence of intervention studies in sprint hurdlers, direct comparisons between hurdle-resisted sprinting and sled-resisted sprinting are limited, particularly when the pre- scription philosophies differ (performance-based velocitydecrement vs. fixed % body mass), making it difficult to attribute any between-method differences to the device/modality rather than to the loading strategy itself. This scarcity is particularly pronounced in U18 female hurdlers, where age-group hurdle specifications and maturation-related variability may significantly influence training responses [8]. In applied environments, resisted-load prescrip- tion is often standardized pragmatically within each modality (e.g., via performance-based decrement targets or nominal relative loads), prioritizing feasible field implementation over perfectly matched external-force profiles across methods. Furthermore, access to this specific athletic population is often limited, necessitating study designs that move beyond simple null-hypothesis significance testing toward estimation-based approaches (effect sizes and uncertainty) suitable for smaller sample sizes, thereby prioritizing effect magnitudes and their uncertainty over dichotomous “significant/non-significant” conclusions [27,28]. Accordingly, the present study adopts an estimation-first framework and uses planned methodological triangulation to examine whether inferences are consistent across complementary, small- sample-appropriate analytical perspectives. An additional applied consideration is the feasibility and reproducibility of interven- tion research in hurdling. Recruiting adequately sized groups of competitive hurdlers https://doi.org/10.3390/app16083989
Appl. Sci.2026,16, 3989 4 of 25 within a narrow age category is difficult, and this constraint is particularly evident in national-level samples. Consequently, the present study was designed as a field-based, coach-reproducible protocol using standard training frequency (three sessions per week), short-distance sprint/hurdle exposures, and readily implementable monitoring (electronic timing and 2D video). Nonetheless, wider replication will likely require multi-club or multi- center recruitment and harmonized load-prescription procedures (e.g., velocity-decrement targeting vs. % body mass) to ensure comparable functional intensity across settings. Therefore, the purpose of this preliminary randomized pre–post trial was to esti- mate the effects of hurdle-resisted sprint training (EGH) compared with sled-resisted sprint training (EGS) and control sprint-hurdle training (CG) on sprint-hurdling perfor- mance. In alignment with the planned statistical framework, the primary outcome was post-intervention 30 m sprint-hurdle time (30 mH), with the primary estimand defined as the baseline-adjusted between-group difference at post-test. Thea prioriprimary con- trast was EGH vs. CG, with EGH vs. EGS specified as a pre-specified key secondary contrast and EGS vs. CG considered secondary. The secondary performance outcome was T index(30 mH−30 mS) , reflecting hurdle-specific technical cost; 30 mS was measured to compute T indexand is reported descriptively [6,7]. Finally, exploratory analyses examined hurdle-clearance characteristics via a pre-specified set of spatiotemporal clearance variables across the early hurdle units (e.g., take-off and landing distances, hurdling distance, and support/flight timing measures over the first hurdles), treated as hypothesis-generating in line with the preliminary-report framework. Given the expected constraints of U18 hurdling recruitment, results were intended to provide estimation-based evidence (effect magnitudes and uncertainty) and to be interpreted relative to a pre-specified smallest-effect-size-of- interest (SESOI) rather than dichotomous “significant/non-significant” decisions [29,30]. The SESOI was defineda priorito anchor interpretation to practical relevance, and ro- bustness was evaluated via the planned triangulation framework and complementary nonparametric summaries of individual changes consistent with the analysis plan. 2. Materials and Methods 2.1. Study Design The exploratory study used a randomized, controlled, pre–post experimental design to examine the effects of resisted sprint training on sprint-hurdle acceleration performance and hurdle-running kinematics in trained young female hurdlers. The experimental time- line
was evaluated via the planned triangulation framework and complementary nonparametric summaries of individual changes consistent with the analysis plan. 2. Materials and Methods 2.1. Study Design The exploratory study used a randomized, controlled, pre–post experimental design to examine the effects of resisted sprint training on sprint-hurdle acceleration performance and hurdle-running kinematics in trained young female hurdlers. The experimental time- line consisted of three phases: (1) an initial evaluation (pre-test), (2) a seven-week training intervention, and (3) a reassessment (post-test). Following baseline testing, participants were assigned to an identification code and randomly allocated in a 1:1:1 ratio to one of three groups: (a) sprint-hurdle training combined with mechanically applied horizon- tal resistance (EGH), (b) sprint training combined with weighted sled towing (EGS), or (c) a control group performing regular sprint and sprint-hurdle training without external resistance (CG). Group allocation was generated using a computer-based blocked random- ization list (Sealed Envelope Ltd., London, UK) by an independent researcher not involved in testing or training supervision. Although allocation was randomized, assessors were not blinded to group assignment. 2.2. Participants Fifteen (n= 15) healthy female track-and-field hurdlers (age: 16.3±1.3 years; height: 167.9±4.4 cm ; body mass: 59.2±6.1 kg; body mass index: 20.9±1.6 kg·m −2 ) volunteered to participate. Athletes were of medium-to-high competitive level, regularly competing in national-level U18 championships, with 2–6 years of athletics experience and limited prior exposure to resisted sprinting (~1 year); none had previously completed https://doi.org/10.3390/app16083989
Appl. Sci.2026,16, 3989 5 of 25 a structured block of resisted sprint-hurdle training. Testing and the intervention were conducted during the pre-competition period. Recruitment was based on feasibility from the available pool of eligible athletes at the national level (≈30 nationwide) during the pre-competition period; this constraint is common in applied research with competitive/elite athletes, where recruiting large samples is often impractical [27]. Athletes were eligible if they: (i) were female sprint hurdlers around the target age group, (ii) were currently training and competing, (iii) were free from injury at study entry, and (iv) could attend the planned training schedule. Participants were instructed to avoid additional training outside the prescribed program that could influence performance during the testing and intervention period. Adherence and protocol deviations were defineda priori. Adherence was defined as attendance at the supervised intervention sessions, recorded by the investigators. A participant was considered adherent if she completed≥85% of the planned sessions (≥15 of 18 sessions). In the event that participants exhibited protocol deviations, such as completing less than 85% of sessions, undertaking additional high-intensity sprint or hurdle training outside the prescribed program during the intervention period, or sustaining an injury or illness that impeded the completion of training or post-testing, they were excluded from the final analyses. The study was approved by the Ethics Committee of the School of Physical Edu- cation and Sports Science, National and Kapodistrian University of Athens (approval number: 1433/21-11-2022), and was conducted in accordance with the Declaration of Helsinki. Parental/guardian signed informed consent was collected for all participants. 2.3. Procedure and Instrumentation Basic anthropometric measurements such as body height (cm), body mass (kg), and body mass index (kg·m −2 ) were collected [31]. Body height was measured to the nearest 0.01 m, and body mass was measured to the nearest 0.1 kg using a SECA 769 scale and a SECA 220 stadiometer (Seca GmbH & Co. KG, Hamburg, Germany) during a preliminary session. Body composition was estimated based on the sum of four skinfolds (biceps, triceps, subscapular, and supra-iliac) [32] using pre-calibrated callipers (Holtain Ltd., Crosswell, Crymych, Pembrokeshire, UK). Both the initial
and body mass was measured to the nearest 0.1 kg using a SECA 769 scale and a SECA 220 stadiometer (Seca GmbH & Co. KG, Hamburg, Germany) during a preliminary session. Body composition was estimated based on the sum of four skinfolds (biceps, triceps, subscapular, and supra-iliac) [32] using pre-calibrated callipers (Holtain Ltd., Crosswell, Crymych, Pembrokeshire, UK). Both the initial assessment (pre-test) and reassessment (post-test) were conducted on the same synthetic indoor track (Recortan M99, APT Corp., Harmony, PA, USA) at approximately the same time of day to minimize diurnal variability [33] and environmental influences (e.g., ambient temperature). Participants wore the same spike shoes in all testing sessions. Post-testing was scheduled 48 h (two days) after the final training session of the intervention to allow recovery from maximal-intensity work [34]. During this 48 h period, training and life-style was kept as in a typical training microcycle. Maximum 30 mS and 30 mH trials were performed from a block start and timed electronically using photocell timing gates (Microgate Srl, Bolzano, Italy), with times recorded to the nearest 0.01 s. The 30 mH test consisted of a 30 m run with three hurdles (height: 0.762 m) in accordance with World Athletics’ rules Technical Rule 22 [35]. The first hurdle was placed 13 m from the start line, and the second and third hurdles were placed at 8.5 m intervals thereafter (i.e., at 21.5 m and 30.0 m from the start). Either side of the track lane was marked with black and white markers placed at 1 m intervals lying parallel to the track lane long axis. Three high-definition digital video cameras (SONY HDR—SR10, Sony Corporation, Minato, Tokyo, Japan), operating at 100 frames·s −1 (resolution 1920×1080 pixels), were positioned at 20 m from the runway with their optical axis perpendicular to the 1st, 2nd and 3rd hurdle respectively, and recorded the hurdle clearance stride for each hurdle. For spatial calibration, two pairs of iron calibration rods (length: 3.0 m; diameter: 0.025 m) were used to provide a two- https://doi.org/10.3390/app16083989
the runway with their optical axis perpendicular to the 1st, 2nd and 3rd hurdle respectively, and recorded the hurdle clearance stride for each hurdle. For spatial calibration, two pairs of iron calibration rods (length: 3.0 m; diameter: 0.025 m) were used to provide a two- https://doi.org/10.3390/app16083989
Appl. Sci.2026,16, 3989 6 of 25 dimensional (2D) scale reference for each stationary camera [36]. For each hurdle, the first rod was placed 3.5 m before the hurdle and the second rod 2.0 m after the hurdle, yielding a calibrated field of view of 5.5 m along the x-axis and 3.0 m along the y-axis. Hurdle clearance time (HCT; s) was determined by frame-by-frame analysis using the Kinovea version 2023.1.1 software (©Joan Charmant and contributors, France), with take-off defined as the last frame of ground contact before the hurdle and landing as the first frame of ground contact after clearance; HCT was computed as the number of frames between events divided by the sampling frequency. T index, an indicator of hurdle-specific technical cost, was calculated as the difference between hurdle and flat sprint performance over the same distance: T index= 30 mH−30 mS [6,7]. The following hurdle technique parameters were quantified from the calibrated 2D video recordings. Temporal variables (take-off support time, flight time, landing support time) were quantified for the first, second, and third hurdles; spatial distance variables (take- off distance, landing distance, hurdling clearance distance) were quantified for the second hurdle. For each hurdle, event timing was identified frame-by-frame (toe-off before the hurdle; first ground contact after the hurdle; toe-off after landing), and temporal variables were computed as the number of frames between events divided by 100. • Hurdling clearance distance (HCD; m): Horizontal distance covered from take-off (toe-off) before the hurdle to the first touchdown after the hurdle as HCD = toe touchdown point after hurdle−toe-off point before hurdle. • Take-off distance (TOD; m): Horizontal distance from the vertical plane of the hurdle to the athlete’s take-off point for clearance, measured at the last frame of ground contact of the take-off foot as TOD = hurdle−toe-off point before hurdle. • Landing distance (LD; m): Horizontal distance from the vertical plane of the hurdle to the first ground-contact point after clearance, measured at the first frame of landing contact as LD = toe touchdown point after hurdle−hurdle. • Take-off support time (TST; s): Duration of the final ground-contact
Description
This study evaluates the effects of different sprint training methods on U18 female hurdlers.