Montalvo, S, Martinez, A, Arias, S, Lozano, A, Gonzalez, MP, Dietze-Hermosa, MS, Boyea, BL, and Dorgo, S. Smartwatches and commercial heart rate monitors: a concurrent validity analysis. J Strength Cond Res 37(9): 1802–1808, 2023—The purpose of this study was to explore the concurrent validity of 2 commercial smartwatches (Apple Watch Series 6 and 7) against a clinical criterion device (12-lead electrocardiogram [ECG]) and a field criterion device (Polar H-10) during exercise. Twenty-four male collegiate football players and 20 recreationally active young adults (10 men and 10 women) were recruited and participated in a treadmill-based exercise session. The testing protocol included 3 minutes of standing still (resting), then walking at low intensity, jogging at a moderate intensity, running at a high intensity, and postexercise recovery. The intraclass correlation (ICC_2,k), and Bland-Altman plot analyses showed a good validity of the Apple Watch Series 6 and Series 7 with increased error (bias) as jogging and running speed increased in the football and recreational athletes. The Apple Watch Series 6 and 7 are highly valid smartwatches at rest and different exercise intensities, with validity decreasing with increased running speed. Strength and conditioning professionals and athletes can confidently use the Apple Watch Series 6 and 7 when tracking heart rate; however, caution must be taken when running at moderate or higher speeds. The Polar H-10 can surrogate a clinical ECG for practical applications.

This study examined the relationship between broad jump (BJ), countermovement jump (CMJ) and light load countermovement jump (LL-CMJ) performance and sprint performance and Sprint Profile measures in athletes. Additionally, this study aimed to determine the predictive ability of jump measures on Sprint Profile components. Twenty-five athletes performed BJ, CMJ, LL-CMJ, 30-metre acceleration and 30-metre maximal speed fly-by sprints. Results revealed moderate to very large correlations between BJ, CMJ and LL-CMJ performance with acceleration sprint completion times (r = −0.423 to −0.807; p < 0.05), fly-by sprint completion times (r = −0.452 to −0.838; p < 0.05) and maximal sprint speed (r = 0.424 to 0.794; p < 0.05). Additionally, associations were observed with multiple jumping measures and components of the Sprint Profile (r = 0.431 to 0.777; p < 0.05) during acceleration sprints. Furthermore, the BJ distance was the best predictor of Sprint Profile components during acceleration sprints (R² = 0.57–0.76; p < 0.01) and maximal speed fly-by sprints (R² = 0.775; p < 0.001). The forces and the manner of force application during the BJ to propel the athlete forwards and upwards are similar to those necessary to exhibit superior sprint performance. This may be due to the rapid generation of forces and orientation of force application during both movements.

Erik, HT, Onn, SW, and Montalvo, S. Vertical jump height with artificial intelligence through a cell phone: a validity and reliability report. J Strength Cond Res 38(9): e529–e533, 2024—This study estimated the reliability and validity of an artificial intelligence (AI)-driven model in the My Jump 2 (My Jump Lab ) for estimating vertical jump height compared with the Force Platform (FP). The cross-sectional study involved 88 athletes (33 female and 55 male athletes), performing a total of 264 countermovement jumps with hands on hips. “Jump heights were simultaneously measured using the FP and the My Jump 2 app.” The FP estimated jump heights using the impulse-momentum method, whereas My Jump 2 used the flight-time method, with the latter using an AI feature for automated detection of jump take-off and landing. Results indicated high reliability for the AI model (intraclass correlation coefficient [ICC _1,3 ] = 0.980, coefficient of variation [CV] = 4.12) and FP (ICC _1,3 = 0.990, CV = 2.92). Validity assessment showed strong agreement between the AI model and FP (ICC _2,k = 0.973). This was also supported by the Bland-Altman analysis, and the ordinary least products regression revealed no significant systematic or proportional bias. The AI-driven model in My Jump 2 is highly reliable and valid for estimating jump height. Strength and conditioning professionals may use the AI-based mobile app for accurate jump height measurements, offering a practical and efficient alternative to traditional methods.

Montalvo, S, Gonzalez, MP, Dietze-Hermosa, MS, Martinez, A, Rodriguez, S, Gomez, M, Ibarra-Mejia, G, Tan, E, and Dorgo, S. Effects of different stretching modalities on the antagonist and agonist muscles on isokinetic strength and vertical jump performance in young men. J Strength Cond Res 39(2): 173–183, 2025—Exercise warm-up may include static or dynamic stretching, impacting performance differently. This study investigated the effects of various stretching protocols on isokinetic strength, muscular activity, and vertical jump performance. Sixteen subjects, divided evenly between trained and untrained groups, underwent 8 distinct stretching conditions in random order. Outcomes measured included isokinetic knee extension and flexion torque and power, muscular activity (vastus lateralis, vastus medialis, and biceps femoris), and jump performance (jump height and modified reactive strength index [RSImod]). Responses to the stretching conditions were analyzed using a mixed-methods approach. For isokinetic knee extension, dynamic stretching of both agonist and antagonist (DY-AG-ANT) and combined dynamic agonist with static antagonist stretching (DY-AG ST-ANT) produced significant improvements. Dynamic stretching of both agonist and antagonist increased peak torque by 12.72% and average torque by 30.80%, while DY-AG ST-ANT increased peak torque by 15.61% and average torque by 41.06%. Muscular activity also improved significantly; DY-AG ST-ANT increased EMG activity of the vastus lateralis by 29.43% and vastus medialis by 70.75%. Biceps femoris saw a 33.18% increase with DY-AG and a 22.15% increase with ST-AG. Countermovement jump height improved with DY-AG-ANT (12.6%) and static antagonist (ST-ANT) conditions (11.3%) (p < 0.05). Dynamic stretching of both agonist and antagonist also enhanced average power knee extension by 32.41%, while ST-AG DY-ANT improved it by 31.09% (p < 0.05). Dynamic stretching, especially when combined with static stretching, optimizes isokinetic strength, muscular activity, and jump height. Coaches should incorporate dynamic stretching, alone or with static antagonist stretching, to maximize performance.

This investigation aimed to dissect the kinematic differences in sprinting between high-speed treadmill and overground conditions, examining how these variations are influenced by the athlete's training status and biological sex. A total of 40 participants, 20 NCAA Division 1 sprinters and 20 recreational runners, performed a series of maximal sprints on a high-speed treadmill and on a standardized competition overground track. Sprinting kinematic variables such as stride length, stride frequency, contact time, and flight time were collected via photoelectric sensors. Maximal sprinting kinematics were analyzed by linear mixed-effects models, considering the impacts of sprinting environment (treadmill vs. overground), training level, and sex, with leg length as fixed factors and individual athletes as random effects. Statistical significance was set at a significance level of 0.05. The statistical analysis revealed that high-speed treadmill sprinting significantly affects all measured kinematic variables, leading to increased stride frequency and contact time. Elite sprinters demonstrated enhanced kinematic efficiency over recreational runners, characterized by increased stride length and frequency and reduced contact time. Sex-based kinematic distinctions also emerged, with male athletes exhibiting superior stride length and frequency compared to female athletes. Leg length significantly influenced stride frequency, and an interaction effect was observed for flight time between sprint type and athletic group. These findings elucidate the distinct biomechanical profiles across sprinting modalities and athlete demographics, emphasizing the need for sprint training customization. This study's insights offer a valuable reference for coaches and athletes to refine training and performance assessment in varied sprinting environments.