## 1 Introduction  
In sports science research and field-based performance testing, concepts of validity and reliability are fundamental to evaluating the quality of measurement tools [1]. Validity refers to the degree to which an instrument measures what it is intended to measure, encompassing subcategories such as concurrent validity (comparison with a gold standard), construct validity (theoretical consistency) and criterion validity (correlation with a criterion measure) [1, 2, 3]. Reliability, on the other hand, concerns the consistency and repeatability of a measurement across time and conditions. It includes intra-rater reliability (consistency of measurements by the same evaluator), inter-rater reliability (consistency across different evaluators) and test-retest reliability (stability of results across repeated trials) [1; 2; 3]. In method comparison studies, [PERSON] and [PERSON] (1986) highlighted the limitations of correlation coefficients for assessing agreement among measurement tools, proposing instead a graphical method (Bland-Altman plot) and complementary calculations that more accurately evaluate how closely two methods agree, rather than simply how strongly they are associated [4; 5]. Thus, establishing both validity and reliability is essential when introducing or adopting performance assessment tools in both research and applied sports settings, particularly when evaluating explosive actions such as vertical jumping [6]. A substantial amount of research has either investigated the accuracy and reliability of different wearable devices (including MyJump) on jump performance or used them for jumping ability evaluation [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. A survey of the recent literature indicates that [PERSON] et al. [7] examined the validity and reliability of the MyJump 3 app and Enode Sensor against a force plate to measure vertical jump height in professional male basketball players. Their findings demonstrated that both tools showed excellent reliability (ICC \(\geq\) 0.91) and strong agreement with force plate measurements, although both slightly overestimated jump height. However, it is important to note that force plates serve as gold standards for capturing jump height and other kinetic variables, such as force and power, which are particularly useful for detailed biomechanical analysis. Notably, the MyJump 3 app demonstrated closer agreement and stronger predictive validity (R\({}^{2}\) = 0.973) than the Enode Sensor, further supporting its use as a practical and cost-effective alternative for field-based performance monitoring; however, the authors emphasize the need for further research to explore its effectiveness across more diverse populations and real-world conditions [7]. [PERSON] et al. [11] assessed the validity and reliability of the MyJump 2 app for evaluating countermovement jump (CMJ) performance on sand surfaces in elite female beach volleyball players. The app demonstrated strong agreement with force platform measurements for jump height and flight time (ICC = 0.85), with Bland-Altman analysis showing narrow limits of agreement. However, peak power showed only moderate agreement (ICC = 0.64), indicating that while the app is valid for assessing jump height and flight time on sand, caution is warranted when interpreting power metrics. A recent advancement in mobile-based assessment tools is the updated version of MyJump 2, the MyJump Lab app, which incorporates artificial intelligence to estimate vertical jump metrics such as height, force, velocity and power. [PERSON] and [PERSON] [10] demonstrated that the app exhibits high validity and reliability compared to force platforms (r > 0.91; \(\alpha\) > 0.93; CV < 6%) under both loaded and unloaded countermovement jump (CMJ) conditions, highlighting the app's practical potential as a cost-effective alternative for comprehensive jump performance analysis in field settings. The study by [PERSON] et al.