This study involved collegiate student-athletes, and institutional approval was obtained from the College of Physical Education at Dalian University for participant enrollment. Prior to enrollment, all prospective participants were briefed on the study protocol, including intervention procedures and testing schedules. Inclusion criteria were as follows: (a) National second-level basketball athlete in China; (b) at least 3 years of systematic basketball training since high school; (c) experience as a primary rotation player in provincial or higher-level competitions; (d) medically cleared and physically capable of completing all assessments; (e) right-hand dominance; (f) no neuromuscular or movement-related injuries within the last 6 months; and (g) normal vision, with no color blindness or color weakness.

The imbalance in the number of athletes across positions reflects the structural characteristics of basketball rosters. Although the a priori power calculation was based on the total sample size, smaller positional subsamples—particularly for centers—may reduce the precision of position-specific estimates. To account for the unbalanced positional structure and repeated measurements, linear mixed-effects models (LMM) were used, which provide robust inference under unequal group sizes and allow positional effects to be modeled while appropriately handling within-subject dependence (Verbeke, 2000).

A total of 64 male basketball players (aged 18–24 years) volunteered to participate and were randomly assigned to either the computerized agility training group (CAT; n = 32; Centers = 6; Forwards = 13; Guards = 13) or the rope ladder agility training group (RLT; n = 32; Centers = 6; Forwards = 13; Guards = 13). Group characteristics are provided in Table 1.

Written informed consent was obtained from all participants before enrollment. The study adhered to the Declaration of Helsinki and was approved by the institutional ethics committee (Approval No. DLUSPR20250506). All data were kept confidential and used exclusively for research purposes.

Rope ladder training (RLT) is a widely used basketball agility drill. Both CAT and RLT involve similar multi-directional footwork with overlapping patterns, stride frequency, and length, but they organize the same repertoire differently. Both methods are short, high-frequency, intermittent drills designed for rapid footwork and neural activation, but they employ distinct sensory stimuli. These drills simulate game actions such as defensive shuffling, help-defense rotations, and post-drive adjustments.

This study adopted a mixed (Group × Time) design to compare the effects of computerized agility training (CAT) and rope ladder training (RLT) on agility and basketball-specific performance. The trial was conducted in the basketball arena of the School of Physical Education, Dalian University, from May to July 2025. Each group completed a 4-week intervention, with three sessions per week and at least 24 h between sessions. Random allocation was used to reduce selection bias. To limit expectancy effects, participants were informed that both interventions were commonly used agility approaches and were not told the specific study hypotheses. All testing and training sessions were supervised by the principal investigator, and pre- and post-intervention assessments were administered at the same time of day to ensure consistency.

In the week prior to baseline testing, participants underwent a 1-week familiarization period (three sessions) to learn the testing procedures, intervention drills, and the use of the Firstbeat professional heart-rate monitor (Finland; sensor: MOVESENSE; model OP174; product ID 213830002517). This familiarization period was implemented to reduce learning and novelty-related effects associated with the computerized equipment and testing procedures. Agility exercises, basketball-specific drills, and test sequences were introduced under coach-led technical instruction, with oversight provided by a strength-and-conditioning specialist. The training plans are outlined in Tables 2, 3. Following familiarization, participants were randomly assigned to either the computerized agility training group (CAT, n = 32; centers = 6, forwards = 13, guards = 13) or the rope ladder training group (RLT, n = 32; centers = 6, forwards = 13, guards = 13) using the SPSS random number generator.

Both groups trained three times per week for 4 weeks (12 sessions in total). Each session lasted 45 min and targeted three agility components: change of direction, action transformation, and reaction and decision-making ability. A standardized session structure was implemented, consisting of a 10-min dynamic warm-up, a 30-min main training period, and a 5-min stretching and cool-down (Figure 1). Each session comprised three exercise blocks, with each block including 2–3 agility drills, 3–5 reactive drills, and 2–3 speed drills.

Throughout training, participants wore Firstbeat devices for real-time heart-rate monitoring to support adherence to the prescribed intensity and to ensure that training was broadly maintained within the target zone (70%–89% HRmax) during the main training period. Real-time monitoring focused on HR-related indicators (e.g., current HR/%HRmax and time accumulated within the target zone) for session supervision purposes (Bogdány et al., 2016; Conte et al., 2025). Based on real-time feedback, coaches adjusted work–rest timing when necessary to maintain the intended intensity while preserving movement quality. The exercise selection was basketball-specific: Blocks 1–2 emphasized non-ball movement tasks, whereas Block 3 incorporated ball-handling (dribble-based) drills. All training actions were finalized following expert consultation and are detailed in Tables 2, 3.

Four domains were assessed: change of direction, action transformation, reaction and decision-making, and basketball-specific performance. All tests were administered before and after the intervention, scheduled at the same time and on the same day of the week to minimize diurnal variation. For all tests, the best performance at each time point was retained for analysis.

Action transformation ability: Footwork speed test: number of pad contacts (foot taps); CBST: completion time.

Blinded statistical analyses were performed by a third researcher, unaware of experimental group assignments. We employed linear mixed-effects models (LMM) as the primary analytical approach to account for repeated measurements within participants and to accommodate unbalanced data structures and incomplete cases under a missing-at-random assumption (Bates et al., 2015). Compared with paired t-tests or repeated-measures ANOVA, LMM provide a flexible framework for longitudinal sport-performance data by explicitly modeling within-participant dependence and inter-individual heterogeneity while retaining all available observations for inference (Bates et al., 2015; Pinheiro and Bates, 2000). For outcomes assessed with two attempts, the best performance at each time point was retained prior to modeling (highest value for repetition- or accuracy-based outcomes; lowest value for time-based outcomes).

A priori sample size estimation was performed using G*Power software (version 3.1.9.7; Franz Faul, University of Kiel, Germany) (Ruxton and Neuhäuser, 2010), following established recommendations for power analysis in sports science (Beck, 2013; Atkinson and Nevill, 1998). Since G*Power does not directly support LMM, the design was approximated using a mixed-design ANOVA (repeated-measures, within-between interaction F-test) (Cook et al., 2002; Hopkins, 2000; West et al., 2022; Singer, 2003; Faul et al., 2007). A small-to-moderate effect size (Cohen’s f = 0.20) was specified based on conservative estimates reported in prior work (Batterham and Hopkins, 2006; Murphy et al., 2023; Mesquida et al., 2022). Based on these parameters (Figure 6), the minimum required sample size was 54 participants (Lakens, 2013). To enhance methodological rigor, improve statistical robustness, and reduce the risk of Type II error, the target sample size was increased to 64 (Ioannidis, 2005; Kaur and Stoltzfus, 2017). The final sample comprised 64 athletes (Centers = 12; Forwards = 26; Guards = 26).

For each outcome, the test metric was specified as the dependent variable. Fixed effects included Group (CAT vs. RLT), Position (center, forward, guard), and Time (pre, post), along with all two-way and three-way interactions. A random intercept for participant was included to account for within-participant dependence (Carey and Wang, 2001). Models were fitted using restricted maximum likelihood (REML) with the lme4 package. Degrees of freedom and p-values for fixed effects were obtained using Satterthwaite’s approximation as implemented in lmerTest (Bates et al., 2015). Post hoc simple-effects and pairwise comparisons were performed using model-estimated marginal means (emmeans), with Bonferroni adjustment for multiple testing.

Model assumptions were evaluated using residual diagnostics (Q–Q plots, residual histograms, and residuals-versus-fitted plots) to assess normality and homoscedasticity. Statistical significance was set at p < 0.05. Effect sizes were expressed as Cohen’s f derived from marginal R ² values for fixed effects (R ²_m), with thresholds of 0.10 (small), 0.25 (medium), and 0.40 (large) (Cohen, 1988; Middel and Van Sonderen, 2002). All analyses were conducted in R (version 4.4.1; R Core Team, 2024).

The Combined Basketball Skill Test was employed to assess players’ technical execution and decision-making ability under time pressure. Performance time, defined as the sum of completion time and penalty time, reflects the integrated efficiency of speed and accuracy in basketball-specific skill execution.

Our findings are consistent with contemporary research on agility development, indicating that QuickBoard-based computerized agility training (CAT) leads to substantial improvements in reaction time, decision accuracy, and change-of-direction performance, with gains exceeding those typically reported following traditional rope ladder training (RLT) (Jorgensen et al., 2013; Galpin et al., 2008; Engeroff et al., 2018; Engeroff et al., 2021). Although studies derived QuickBoard specifically in basketball populations remain limited, the present results extend previous findings reported by Collins (2014) and Engeroff et al. (2019), who demonstrated that stimulus-driven agility training enhances visuomotor integration by coupling visual information with rapid motor responses, thereby facilitating more effective agility adaptations across sport contexts.

In contrast, improvements associated with RLT appear to be largely confined to rhythm, step frequency, and basic coordination, with relatively limited transfer to reaction-time and decision-making performance in open-skill contexts (Afonso et al., 2020). Prior studies have similarly reported non-significant or small effects of ladder-based drills on agility and dribble-related dynamic tasks (Padrón-Cabo et al., 2020), consistent with our observation that the RLT group showed minimal changes in reaction time and decision accuracy. Collectively, these findings underscore a key limitation of RLT: its fixed, preprogrammed movement patterns do not replicate the random, time-pressured stimuli characteristic of competitive play. Although recent dual-task ladder protocols can elicit modest improvements in cognition–action coupling (Castillo de Lima et al., 2023), their effects appear weaker than those fully stimulus-driven paradigms. In our study, the randomized visual cues embedded in CAT likely facilitated neural and perceptual–cognitive adaptations that support faster perceptual–decision responses, offering a plausible explanation for CAT’s superior effects.

The superior CBST performance observed in the CAT group is consistent with the broader advantages of open-skill agility training, which provides higher ecological validity and cognitive load and is thought to preferentially engage neural systems involved in perceptual–motor integration and decision-making under time pressure. Such training enhances visuomotor coupling and decision-making under game-realistic conditions, whereas these mechanisms are comparatively under-recruited during ladder drills that rely on fixed and preplanned movement sequences.

Overall, this study provides, to our knowledge, the first head-to-head comparison of CAT and RLT in a basketball-specific context. While both interventions produced measurable improvements, CAT yielded superior enhancements in both the magnitude and breadth of performance outcomes. These findings support the view that stimulus–response–driven training models offer greater efficacy than repetitive footwork paradigms and emphasize that agility development in basketball should integrate the full input–perception–decision–action chain within ecologically valid, game-representative environments.

The superior performance gains observed following computerized agility training (CAT), compared with rope ladder training (RLT), are likely attributable to differences in perceptual–cognitive demands and neuro-mechanical engagement. CAT emphasizes visuomotor coupling and stimulus-driven decision-making, which more closely reflect the requirements of reactive agility in open-skill sports such as basketball (Hassan et al., 2022; Wang et al., 2024; Paul et al., 2016; Zhang et al., 2025; Li et al., 2024; Silvestri et al., 2023). In contrast, RLT primarily targets rhythmic coordination and step-frequency regulation, resulting mainly in improvements in foundational movement coordination rather than decision-dependent agility (Afonso et al., 2020; Paul et al., 2016).

Delivered via the QuickBoard platform, CAT exposes athletes to unpredictable visual stimuli that require continuous perception–action coupling and rapid response selection (Wang et al., 2024; Zhang et al., 2025; Steff et al., 2024; Hassan, 2025). Such training is proposed to preferentially engage higher-order perceptual–motor control systems, thereby facilitating sensorimotor plasticity and more efficient movement adaptation under time pressure (Paul et al., 2016; Zhang et al., 2025; Li et al., 2024; Panchuk et al., 2018; Gou and Li, 2023; Mangine et al., 2014; Lucia et al., 2023). By comparison, RLT relies on repetitive, preprogrammed movement patterns that impose relatively low perceptual–cognitive demands and are more strongly associated with automatized motor control processes (Sun et al., 2025; Afonso et al., 2020). Consequently, CAT more effectively promotes reactive agility and basketball-specific performance outcomes that depend on rapid decision-making and movement adjustment.

The four-week intervention adopted in the present study aligns with commonly used short mesocycles in the agility-training literature and may be sufficient to elicit short-term improvements in relevant performance outcomes. Previous investigations using computerized agility platforms have reported improvements in foot-speed performance, reaction time, and postural control following four-week programs delivered three times per week with session durations comparable to those used here (Engeroff et al., 2019; Collins, 2014), suggesting that short-term, high-frequency agility training can yield measurable adaptations on test-based indicators.

With respect to rope ladder training, prior research has suggested an effective training window of approximately 4–8 weeks, with shorter interventions often yielding limited effects and longer programs potentially exhibiting diminishing returns due to skill saturation and accumulated fatigue (Afonso et al., 2020). Consistent with this view, Padrón-Cabo et al. (2020) reported minimal changes in agility outcomes following 6 weeks of isolated ladder-based drills, indicating that repetitive footwork training may require integration with reactive or decision-based elements to sustain performance gains over time.

Regarding training intensity, both CAT and RLT were prescribed at a moderate-to-vigorous intensity (70%–89% HRmax), which is generally considered sufficient to stimulate neuromuscular and perceptual demands while preserving movement quality (Afonso et al., 2020). By standardizing training frequency, planned session structure, and the intended intensity prescription, and by monitoring heart rate in real time to promote adherence to the target zone (70%–89% HRmax), we aimed to reduce confounding by training load. Nevertheless, heart-rate–derived internal-load indices were not exported and statistically summarized, and external training load was not comprehensively quantified; therefore, residual load-related confounding cannot be fully excluded. Neuroadaptive responses to agility-oriented training are often observed within the first 3 weeks and tend to stabilize thereafter (Engeroff et al., 2019). Accordingly, the present four-week mesocycle was designed to capture the early phase of adaptation while minimizing fatigue-related interference.

Overall, the present findings indicate that a 4-week program can elicit meaningful short-term improvements under standardized frequency, session structure, and intended intensity conditions; nevertheless, longer interventions and match-derived outcomes are required to determine whether these gains are optimized, retained, and translated to competitive match performance.

Our data reveal position-dependent improvements in agility outcomes (guards, forwards, and centers), reflecting the combined influence of role-specific functional demands and baseline agility (Pradana et al., 2020; Liu, 2023). Guards showed smaller gains, consistent with a ceiling effect due to their already superior agility and reaction capabilities. Their regular exposure to high-frequency decision-making and frequent change-of-direction tasks likely maintains their sensorimotor coordination at a high level (Jin et al., 2023). Forwards showed moderate improvements, particularly in postural control and decision speed, likely reflecting their hybrid functional role requiring both explosiveness and stability. Centers demonstrated the largest gains, especially in reaction time and change-of-direction performance, aligning with previous reports indicating greater adaptive potential among athletes with lower baseline agility (Afonso et al., 2020).

Taken together, these position-specific responses support the task-specificity principle of agility training and highlight the importance of considering positional roles when interpreting training effects. Rather than adopting uniform training prescriptions, agility development programs may benefit from adjusting the emphasis of perceptual, cognitive, and postural demands according to positional requirements.

Taken together, the present findings indicate that agility training yields the greatest performance benefits when improvements in speed and coordination are functionally integrated with perceptual–cognitive demands. The superior outcomes observed following computerized agility training, particularly in tasks such as the Combined Basketball Skill Test, suggest that enhancements in reaction speed and change-of-direction ability are more likely to translate into effective basketball-specific performance when training emphasizes perception–action coupling under time pressure. These results highlight the importance of designing agility interventions that extend beyond isolated footwork speed and instead promote the coordinated execution of decision-making and movement within game-representative contexts.