Mateos-Angulo, AlvaroGalan-Mercant, AlejandroCuesta-Vargas, Antonio Ignacio2023-02-092023-02-092020-07Mateos-Angulo A, Galán-Mercant A, Cuesta-Vargas AI. Kinematic Mobile Drop Jump Analysis at Different Heights Based on a Smartphone Inertial Sensor. J Hum Kinet. 2020 Jul 21;73:57-651640-5544http://hdl.handle.net/10668/16077The purpose of this study was to describe the acceleration variables in a plyometric jump test using the inertial sensor built into an iPhone 4S® smartphone, and the jumping variables from a contact mat. A cross-sectional study was conducted involving 16 healthy young adults. Linear acceleration, flight time, contact time and jump height were measured in a drop jump test from 60 cm and from 30 cm. Greater acceleration values were found in the drop jump test from 60 cm; the same was observed for the values from the contact mat. Multiple regression analysis was performed for each drop jump test: jump height was used as the dependent variable, and the most relevant variables were used as predictor variables (weight and maximum angular velocity in the Y axis for analysis of the drop jump from 60 cm, and weight and maximum acceleration in the Z axis for the drop jump from 30 cm). We found a significant regression model for the drop jump test from 60 cm (R2 = 0.515, p " 0.001) and for the test from 30 cm (R2 = 0.460, p " 0.01). According to the results obtained in this study, the built-in iPhone 4S® inertial sensor is able to measure acceleration for healthy young adults performing a vertical drop jump test. The acceleration kinematic variables are higher in the drop jump test from 60 cm than from 30 cm.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/AccelerationBiomechanicsPhysical performancePlyometric trainingHumansCross-sectional studiesBiomechanical phenomenaSmartphoneRegression analysisYoung adultresearch article32774537open accessAdulto jovenAnálisis de regresiónEstudios transversalesFenómenos biomecánicosTeléfono inteligente10.2478/hukin-2019-0131PMC7386144https://www.sciendo.com/pdf/10.2478/hukin-2019-0131https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386144/pdf