The purpose of this investigation was to analyse the concurrent validity and reliability of an iPhone app (called: My Jump) for measuring vertical jump performance. Twenty recreationally active healthy men (age: 22.1 ± 3.6 years) completed five maximal countermovement jumps, which were evaluated using a force platform (time in the air method) and a specially designed iPhone app. My jump was developed to calculate the jump height from flight time using the high-speed video recording facility on the iPhone 5 s. Jump heights of the 100 jumps measured, for both devices, were compared using the intraclass correlation coefficient, Pearson product moment correlation coefficient (r), Cronbach's alpha (α), coefficient of variation and Bland-Altman plots. There was almost perfect agreement between the force platform and My Jump for the countermovement jump height (intraclass correlation coefficient = 0.997, P < 0.001; Bland-Altman bias = 1.1 ± 0.5 cm, P < 0.001). In comparison with the force platform, My Jump showed good validity for the CMJ height (r = 0.995, P < 0.001). The results of the present study showed that CMJ height can be easily, accurately and reliably evaluated using a specially developed iPhone 5 s app.

Enacted measures to control the spread of COVID-19 disease such as compulsory confinement may influence health behaviors. The present study investigated changes in physical activity (PA) levels during the first days of confinement. Using an online survey, the Spanish population (n = 2042, 54% women, age 35.9 (SD 13.6) years) replied to questions concerning sociodemographic characteristics as well as PA behavior before and during the first week of enacted isolation. Physical activity vital sign (PAVS) short form was used to estimate weekly minutes of PA before and during the isolation period. Statistical analysis used the following tests: Mc Nemar Chi-squared tests, independent and paired samples t-test, and effect size (Cohen’s d). During the first week of confinement, participants reduced their weekly PA levels by 20% (~45.2 weekly minutes (95% CI: 37.4−53.0)). This led to a decrease from 60.6% to 48.9% (difference: 11.7%) (p < 0.0001) in the number of participants meeting the recommended World Health Organization (WHO) PA levels. Subgroups including men, participants aged 43 or over, and those not holding a university degree had the greatest reductions in both weekly minutes of PA and adherence to guidelines. The PA levels of the Spanish population generally declined during the first days of COVID-19 confinement.

The purpose of this study was to assess validity and reliability of sprint performance outcomes measured with an iPhone application (named: MySprint) and existing field methods (i.e. timing photocells and radar gun). To do this, 12 highly trained male sprinters performed 6 maximal 40-m sprints during a single session which were simultaneously timed using 7 pairs of timing photocells, a radar gun and a newly developed iPhone app based on high-speed video recording. Several split times as well as mechanical outputs computed from the model proposed by Samozino et al. [(2015). A simple method for measuring power, force, velocity properties, and mechanical effectiveness in sprint running. Scandinavian Journal of Medicine & Science in Sports. https://doi.org/10.1111/sms.12490] were then measured by each system, and values were compared for validity and reliability purposes. First, there was an almost perfect correlation between the values of time for each split of the 40-m sprint measured with MySprint and the timing photocells (r = 0.989-0.999, standard error of estimate = 0.007-0.015 s, intraclass correlation coefficient (ICC) = 1.0). Second, almost perfect associations were observed for the maximal theoretical horizontal force (F), the maximal theoretical velocity (V), the maximal power (P) and the mechanical effectiveness (DRF - decrease in the ratio of force over acceleration) measured with the app and the radar gun (r = 0.974-0.999, ICC = 0.987-1.00). Finally, when analysing the performance outputs of the six different sprints of each athlete, almost identical levels of reliability were observed as revealed by the coefficient of variation (MySprint: CV = 0.027-0.14%; reference systems: CV = 0.028-0.11%). Results on the present study showed that sprint performance can be evaluated in a valid and reliable way using a novel iPhone app.

The purpose of this study was to analyze the validity, reliability, and accuracy of new wearable and smartphone-based technology for the measurement of barbell velocity in resistance training exercises. To do this, 10 highly trained powerlifters (age = 26.1 ± 3.9 years) performed 11 repetitions with loads ranging 50–100% of the 1-Repetition maximum in the bench-press, full-squat, and hip-thrust exercises while barbell velocity was simultaneously measured using a linear transducer (LT), two Beast wearable devices (one placed on the subjects' wrist –BW–, and the other one directly attached to the barbell –BB–) and the iOS PowerLift app. Results showed a high correlation between the LT and BW (r = 0.94–0.98, SEE = 0.04–0.07 m•s−1), BB (r = 0.97–0.98, SEE = 0.04–0.05 m•s−1), and the PowerLift app (r = 0.97–0.98, SEE = 0.03–0.05 m•s−1) for the measurement of barbell velocity in the three exercises. Paired samples T-test revealed systematic biases between the LT and BW, BB and the app in the hip-thrust, between the LT and BW in the full-squat and between the LT and BB in the bench-press exercise (p < 0.001). Moreover, the analysis of the linear regression on the Bland-Altman plots showed that the differences between the LT and BW (R2 = 0.004–0.03), BB (R2 = 0.007–0.01), and the app (R2 = 0.001–0.03) were similar across the whole range of velocities analyzed. Finally, the reliability of the BW (ICC = 0.910–0.988), BB (ICC = 0.922–0.990), and the app (ICC = 0.928–0.989) for the measurement of the two repetitions performed with each load were almost the same than that observed with the LT (ICC = 0.937–0.990). Both the Beast wearable device and the PowerLift app were highly valid, reliable, and accurate for the measurement of barbell velocity in the bench-press, full-squat, and hip-thrust exercises. These results could have potential practical applications for strength and conditioning coaches who wish to measure barbell velocity during resistance training.

Balsalobre-Fernández, C, Kuzdub, M, Poveda-Ortiz, P, and Campo-Vecino, Jd. Validity and reliability of the PUSH wearable device to measure movement velocity during the back squat exercise. J Strength Cond Res 30(7): 1968-1974, 2016-The purpose of this study was to analyze the validity and reliability of a wearable device to measure movement velocity during the back squat exercise. To do this, 10 recreationally active healthy men (age = 23.4 ± 5.2 years; back squat 1 repetition maximum [1RM] = 83 ± 8.2 kg) performed 3 repetitions of the back squat exercise with 5 different loads ranging from 25 to 85% 1RM on a Smith Machine. Movement velocity for each of the total 150 repetitions was simultaneously recorded using the T-Force linear transducer (LT) and the PUSH wearable band. Results showed a high correlation between the LT and the wearable device mean (r = 0.85; standard error of estimate [SEE] = 0.08 m·s) and peak velocity (r = 0.91, SEE = 0.1 m·s). Moreover, there was a very high agreement between these 2 devices for the measurement of mean (intraclass correlation coefficient [ICC] = 0.907) and peak velocity (ICC = 0.944), although a systematic bias between devices was observed (PUSH peak velocity being -0.07 ± 0.1 m·s lower, p ≤ 0.05). When measuring the 3 repetitions with each load, both devices displayed almost equal reliability (Test-retest reliability: LT [r = 0.98], PUSH [r = 0.956]; ICC: LT [ICC = 0.989], PUSH [ICC = 0.981]; coefficient of variation [CV]: LT [CV = 4.2%], PUSH [CV = 5.0%]). Finally, individual load-velocity relationships measured with both the LT (R = 0.96) and the PUSH wearable device (R = 0.94) showed similar, very high coefficients of determination. In conclusion, these results support the use of an affordable wearable device to track velocity during back squat training. Wearable devices, such as the one in this study, could have valuable practical applications for strength and conditioning coaches.