## 1 Introduction  
Agility is an essential component in most team and combat sports, as athletes must accelerate, decelerate, and change direction to position themselves optimally to execute sport-specific actions with speed and accuracy [1, 2]. These movements often involve three-dimensional acceleration and deceleration, requiring rapid changes of direction (COD) [3, 4, 5, 6, 7, 8, 9]. COD is defined as an athlete's ability to decelerate and accelerate quickly in a new direction [10], typically in a preplanned manner where the direction and timing of the movement are known in advance [11]. Unlike agility, which incorporates reactive decision-making in response to external stimulus, COD focuses on executing predetermined movement patterns without the need for cognitive processing or perception-action coupling [12]. Despite its importance, COD ability alone is insufficient for optimal athletic performance, as researchers have emphasized the need to integrate perceptual and decision-making components into agility training to enhance sport-specific transfer [1]. COD is not only fundamental for athletic performance but also critical for safe and effective participation in numerous sports [13; 14]. Consequently, research on training methods to develop COD has gained significant attention in recent years [7; 14; 15; 16; 17]. Physical abilities related to COD are commonly assessed using various tests, which can be conducted in laboratory or field settings [18]. While laboratory tests offer high reliability, they are often costly, time-consuming, and impractical for widespread use [19]. In contrast, field tests, such as the 505 test, \(t\)-test, Illinois agility test, and V-Cut test [20; 21; 22; 23], provide a more practical and accessible solution for measuring COD performance. These tests are characterized by their reliability, cost-effectiveness, and ease of administration, making them particularly suitable for use in school, health, and community settings [24; 25]. However, the accuracy and accessibility of measurement systems remain a significant challenge, limiting their applicability in diverse contexts. One of the key factors influencing COD performance is limb asymmetry, defined as differences in performance or ability between limbs [26]. Limb asymmetries are not only critical for performance but also serve as indicators of injury risk [27; 28; 29] and readiness for return to play [30; 31]. Accurate assessment of these asymmetries, alongside COD performance, is essential for optimizing training programs and reducing injury risk. However, existing measurement systems often fall short in providing accessible, cost-effective, and accurate solutions for assessing these parameters. Current methods for measuring sprint speed and COD performance include automatic timing systems, such as dual-beam photocells, post-processing photocells, laser guns, and high-speed video timing, which are considered the gold standard for accuracy and reliability [32; 33; 34; 35]. However, these systems are often prohibitively expensive and require specialized software, limiting their use to elite sports organizations or well-funded research laboratories [36; 37; 34; 38]. GPS technology, while useful for assessing top speeds over longer distances (30-40 m), lacks the precision required for shorter sprints and COD assessments [32]. Photocells, which convert light energy into electrical energy using infrared sensors, are widely used in sports research but are similarly constrained by high costs and technical requirements, making them inaccessible for many coaches, educators, and health professionals. To address these limitations, there is a growing need for portable, cost-effective, and user-friendly solutions that can accurately assess COD performance and limb asymmetries in diverse settings. Mobile applications, such as Fitnessmeter, have emerged as a potential alternative, offering the advantages of affordability, portability, and ease of use. Previous research has explored the reliability and validity of other mobile applications, such as COD.