Sprint performance and mechanical outputs computed with an iPhone app: Comparison with existing reference methods.

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Abstract

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 (F0), the maximal theoretical velocity (V0), the maximal power (Pmax) 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.

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The validity and reliability of an iPhone app for measuring vertical jump performance.

Carlos Balsalobre-Fernández, Mark Glaister, Richard Anthony Lockey (2015)

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.

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Sprint Running Performance Monitoring: Methodological and Practical Considerations.

Thomas Haugen, Martin Buchheit (2016)

The aim of this review is to investigate methodological concerns associated with sprint performance monitoring, more specifically the influence and magnitude of varying external conditions, technology and monitoring methodologies not directly related to human physiology. The combination of different starting procedures and triggering devices can cause up to very large time differences, which may be many times greater than performance changes caused by years of conditioning. Wind, altitude, temperature, barometric pressure and humidity can all combine to yield moderate time differences over short sprints. Sprint performance can also be affected by the athlete's clothing, principally by its weight rather than its aerodynamic properties. On level surfaces, the track compliance must change dramatically before performance changes larger than typical variation can be detected. An optimal shoe bending stiffness can enhance performance by a small margin. Fully automatic timing systems, dual-beamed photocells, laser guns and high-speed video are the most accurate tools for sprint performance monitoring. Manual timing and single-beamed photocells should be avoided over short sprint distances (10-20 m) because of large absolute errors. The validity of today's global positioning systems (GPS) technology is satisfactory for long distances (>30 m) and maximal velocity in team sports, but multiple observations are still needed as reliability is questionable. Based on different approaches used to estimate the smallest worthwhile performance change and the typical error of sprint measures, we have provided an assessment of the usefulness of speed evaluation from 5 to 40 m. Finally, we provide statistical guidelines to accurately assess changes in individual performance; i.e. considering both the smallest worthwhile change in performance and the typical error of measurement, which can be reduced while repeating the number of trials.

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Relationships between ground reaction force impulse and kinematics of sprint-running acceleration.

Joseph Hunter, Robert N. Marshall, Peter J McNair (2005)

The literature contains some hypotheses regarding the most favorable ground reaction force (GRF) for sprint running and how it might be achieved. This study tested the relevance of these hypotheses to the acceleration phase of a sprint, using GRF impulse as the GRF variable of interest. Thirty-six athletes performed maximal-effort sprints from which video and GRF data were collected at the 16-m mark. Associations between GRF impulse (expressed relative to body mass) and various kinematic measures were explored with simple and multiple linear regressions and paired t-tests. The regression results showed that relative propulsive impulse accounted for 57% of variance in sprint velocity. Relative braking impulse accounted for only 7% of variance in sprint velocity. In addition, the faster athletes tended to produce only moderate magnitudes of relative vertical impulse. Paired t-tests revealed that lower magnitudes of relative braking impulse were associated with a smaller touchdown distance (p < 0.01) and a more active touchdown (p < 0.001). Also, greater magnitudes of relative propulsive impulse were associated with a high mean hip extension velocity of the stance limb (p < 0.05). In conclusion, it is likely that high magnitudes of propulsion are required to achieve high acceleration. Although there was a weak trend for faster athletes to produce lower magnitudes of braking, the possibility of braking having some advantages could not be ruled out. Further research is required to see if braking, propulsive, and vertical impulses can be modified with specific training. This will also provide insight into how a change in one GRF component might affect the others.