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Plyometrisk träning, dess effekt på spänst och snabbhet hos idrottare : En systematisk litteraturstudie / Plyometric training, its effect on jump-performance and speed in athletes : A systematic reviewEriksson, Adam, Helmerson, Filip January 2023 (has links)
Bakgrund: Idrott kan utföras i många olika former och av en bred population. I flera idrotter är det fördelaktigt att ha god spänst och/eller snabbhet. Flera olika fysiska faktorer har påverkan på snabbhet och spänst, däribland explosivitet och muskelstyrka. Plyometrisk träning (PT) är en träningsform där Stretch-shortening-cycle (SSC) utnyttjas. SSC beskrivs som en muskulärt förlängande (excentrisk) rörelse följt av en muskulärt förkortande (koncentrisk) rörelse som syftar till att träna explosivitet. Syfte: Analysera vilken effekt plyometrisk träning har på spänst och snabbhet hos idrottare, jämfört med ordinarie idrottsträning samt förekomst av skador Metod: En systematisk litteraturstudie som utgår från databaserna PubMed, PEDro, Cochrane Library och Cinahl. Studiernas kvalitet granskades med TESTEX och tillförlitligheten enligt GRADEstud. Resultat: Nio randomiserade kontrollerade studier inkluderades i litteraturöversikten. Studiekvalitén var mellan 8-12 poäng enligt TESTEX. Studiernas tillförlitlighet enligt GRADEstud var mycket låg (+). Studiernas resultat var varierande gällande signifikans för PT och dess effekt för spänst och snabbhet hos idrottare jämfört med kontrollgrupp. Ingen studie rapporterade skada i samband med PT. Konklusion: De studier som analyserades bedömdes ha, på grund av olikheter i intervention och resultat, en låg evidensgrad. PT har viss effekt på spänst och snabbhet hos idrottare jämfört med kontrollgrupp som utför ordinarie idrottsträning. Inga skador rapporterades i de inkluderade studierna. Fler homogena studier krävs för att klargöra PT´s effekt på spänst och snabbhet hos idrottare. / Background: Sports can be performed in many forms and is being performed by a wide population. In different sports it’s beneficial to have good jumping ability and to be fast. Many physical factors affect the ability to jump and sprint, where muscle strength and explosiveness are two of them. Plyometric training (PT) is a training form that utilizes the stretch-shortening-cycle (SSC). SSC is an eccentric muscle contraction followed by concentric contraction of the same muscle that aims to train muscle explosiveness. Objective: Evaluate the effect of plyometric training on jumping ability and sprinting within athletes compared with athletes only performing ordinary sport training. Method: A systematic review. The search was performed on the databases PubMed, PEDro, Cochrane Library and Cinahl. The included studies quality was examined with TESTEX and the reliability with GRADEstud. Results: Nine randomized controlled trials were included. The quality of the studies varied between 8 to 12 points. The reliability, examined with GRADEstud, was deemed very low (+). The effect of PT on jumping and sprinting for athletes compared with the control groups varied. None of the studies reported injuries associated with PT. Conclusion: The studies included had, due to differences in intervention and results, a low level of evidence. PT has a certain effect on jumping ability and sprinting for athletes compared with athletes only performing ordinary sport training. No injuries were reported by the studies included. More homogeneous studies are required to clarify the effect of PT on jump ability and sprint in athletes.
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Precision i Rörelse : Horisontell Hoppmätning med IMU och MagnetometerAbuawad, Ismail January 2024 (has links)
Detta examensarbete har genomförts med syftet att utveckla Inno-x företagets system, som är avsett för vardagsidrottare för att mäta neuromuskulära aktiviteter i underkroppen med hjälp av modern teknologi. Systemet omfattar en tröghetsmätningsenhet (IMU) med accelerometer, gyroskop och en EMG-sensor (elektromyografi). Denna konfiguration möjliggör noggrann övervakning av neuromuskulära aktiviteter genom analys av svar på träning. Studiens mål var att identifiera en effektiv sensor för mätning av horisontella hoppavstånd och att utveckla en algoritm som sedan ska integreras i företagets produkt. Produkten kommer att använda magnetometer och IMU för att tolka mänskliga rörelser och för att förbättra noggrannheten i företagets mätningssystem. Processen inkluderar förbättring av mätningarnas noggrannhet, integration av teknik med biomekaniska principer, utvärdering av kalibreringstekniker för magnetometeravläsningar, kombination av sensorer för rörelseanalys och genomförande av utvärdering med olika åldersgrupper som består av 10 deltagare för att bedöma systemets effektivitet. Även om ingen av metoderna helt uppnådde den önskade noggrannheten inom ±5 cm, visade alla metoder god prestanda för olika tillämpningar. Detta antyder att implementeringen av en kalibrerad magnetometer potentiellt kan förbättra systemets noggrannhet vid bestämning av horisontella hoppavstånd, dock endast med en liten marginal, eftersom studien visade att med kalibrerade magnetometer RMSE (Root Mean Square Error) ökat med 0.99 cm. Ytterligare forskning rekommenderas för att undersöka nya sätt att kalibrera sensorer och integrera dem för mer precisa avläsningar. Dock bör det beaktas att magnetometeravläsningar påverkas av miljöfaktorer. Dessutom är det viktigt att skapa ett användarvänligt gränssnitt som gör det möjligt för idrottare att enkelt spåra och analysera sina prestandadata. / This thesis has been conducted with the objective of developing the Inno-X company's system, which is intended for everyday athletes to measure neuromuscular activities in the lower body using modern technology. The system includes an Inertial Measurement Unit (IMU) with an accelerometer, gyroscope, and an Electromyography (EMG) sensor. This configuration enables accurate monitoring of neuromuscular activities through the analysis of responses to training. The study's goal was to identify an effective sensor for measuring horizontal jump distances and to develop an algorithm that would then be integrated into the company's product. The product will use a magnetometer and IMU to interpret human movements and to improve the accuracy of the company's measurement system. The process includes improving the accuracy of measurements, integrating technology with biomechanical principles, evaluating calibration techniques for magnetometer readings, combining sensors for motion analysis, and conducting evaluations with different age groups consisting of 10 participants to assess the system's effectiveness. Although none of the methods fully achieved the desired accuracy within ±5 cm, all methods showed good performance for various applications. This suggests that the implementation of a calibrated magnetometer could potentially improve the system's accuracy in determining horizontal jump distances, albeit only by a small margin, as the study showed that with calibrated magnetometers, the Root Mean Square Error (RMSE) increased by 0.99 cm. Further research is recommended to explore new ways to calibrate sensors and integrate them for more precise readings. However, it should be considered that magnetometer readings are affected by environmental factors. Additionally, it is important to create a user-friendly interface that enables athletes to easily track and analyze their performance data.
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Selected anthropometric, physical and motor performance predictors of lower body explosive power in adolescents : the PAHL study / Koert Nicolaas van der WaltVan der Walt, Koert Nicolaas January 2014 (has links)
Lower body explosive power (LBEP) forms a critical component in any individual and team sport performance and it is therefore essential to develop a means of predicting LBEP in adolescents for early identification of future talent in various sporting codes. LBEP is frequently used by athletes during matches or competitions where explosive movements such as jumping, agility running and sprinting are required for successful performance. These movements are usually found in individual sports such as long jump and high jump as well as in team sports such as basketball, volleyball and soccer. To date not much literature is available on LBEP, especially with regard to LBEP prediction models. Furthermore, studies on adolescents are scarce and a LBEP prediction model has not yet been developed for a South African adolescent population. It is against this background that the objectives of this study were firstly, to develop a LBEP prediction model from various physical and motor performance components among a cohort of adolescents living in the Tlokwe local municipality of Dr Kenneth Kaunda district in the North-West Province, South Africa; and secondly, to develop a LBEP prediction model from several anthropometric measurements among a cohort of male and female adolescents living in the Tlokwe local municipality of Dr Kenneth Kaunda district in the North-West Province, South Africa. Two hundred and fourteen (15.8±0.68 years) 15-year-old adolescents (126 females, 88 males) from 6 surrounding schools within the Tlokwe local municipality of Dr Kenneth Kaunda district in the North-West Province of South Africa were purposefully selected from pre-acquired class lists took part in the study. Data was collected by means of various questionnaires as well as anthropometrical, physical and motor performance tests. For representation of LBEP a principal component factor analysis was done and the results indicated that the vertical jump test (VJT) was the best indicator of LBEP in the cohort of adolescents.
With regard to the anthropometrical related LBEP prediction model, the forward stepwise regression analysis yielded a correlation coefficient of R2 = 0.69. The following variables contributed significantly (p≤0.001) to the anthropometrical LBEP prediction model: stature (57%), muscle mass percentage (10%) and maturity age (3%). The LBEP prediction model that was developed equated to LBEP (vertical jump) = -136.30 + 0.84(stature) + 0.7(muscle mass percentage) + 4.6(maturity age). Variables other than the variables that formed part of the study could explain the further 31% variance in the LBEP of the adolescents. The physical and motor performance LBEP prediction model indicated that gender (39%) and 10 m speed (7%) contributed significantly (p ≤ 0.001) to the overall prediction of the LBEP of the adolescents. The LBEP prediction model delivered a stepwise forward regression analysis coefficient of R2=0.458 and a prediction formula LBEP = 68.21 + 9.82 (gender) – 18.33(10 m speed). The remaining 56% of the variance in the results could be explained by other factors than the variables considered in the study. In conclusion, to the best of the researchers’ knowledge, this is the first study which has made an attempt at developing LBEP prediction models from the anthropometrical, physical and motor performance components of a cohort of adolescents of South Africa. The prediction models developed in the study will assist teachers sport scientists and sporting coaches who have limited resources available, to measure and calculate LBEP in adolescents, with the means to do so in South Africa. Further high quality studies are necessary to further improve and develop such prediction models for various age groups of adolescents in the greater South Africa. / MSc (Sport Science), North-West University, Potchefstroom Campus, 2014
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Selected anthropometric, physical and motor performance predictors of lower body explosive power in adolescents : the PAHL study / Koert Nicolaas van der WaltVan der Walt, Koert Nicolaas January 2014 (has links)
Lower body explosive power (LBEP) forms a critical component in any individual and team sport performance and it is therefore essential to develop a means of predicting LBEP in adolescents for early identification of future talent in various sporting codes. LBEP is frequently used by athletes during matches or competitions where explosive movements such as jumping, agility running and sprinting are required for successful performance. These movements are usually found in individual sports such as long jump and high jump as well as in team sports such as basketball, volleyball and soccer. To date not much literature is available on LBEP, especially with regard to LBEP prediction models. Furthermore, studies on adolescents are scarce and a LBEP prediction model has not yet been developed for a South African adolescent population. It is against this background that the objectives of this study were firstly, to develop a LBEP prediction model from various physical and motor performance components among a cohort of adolescents living in the Tlokwe local municipality of Dr Kenneth Kaunda district in the North-West Province, South Africa; and secondly, to develop a LBEP prediction model from several anthropometric measurements among a cohort of male and female adolescents living in the Tlokwe local municipality of Dr Kenneth Kaunda district in the North-West Province, South Africa. Two hundred and fourteen (15.8±0.68 years) 15-year-old adolescents (126 females, 88 males) from 6 surrounding schools within the Tlokwe local municipality of Dr Kenneth Kaunda district in the North-West Province of South Africa were purposefully selected from pre-acquired class lists took part in the study. Data was collected by means of various questionnaires as well as anthropometrical, physical and motor performance tests. For representation of LBEP a principal component factor analysis was done and the results indicated that the vertical jump test (VJT) was the best indicator of LBEP in the cohort of adolescents.
With regard to the anthropometrical related LBEP prediction model, the forward stepwise regression analysis yielded a correlation coefficient of R2 = 0.69. The following variables contributed significantly (p≤0.001) to the anthropometrical LBEP prediction model: stature (57%), muscle mass percentage (10%) and maturity age (3%). The LBEP prediction model that was developed equated to LBEP (vertical jump) = -136.30 + 0.84(stature) + 0.7(muscle mass percentage) + 4.6(maturity age). Variables other than the variables that formed part of the study could explain the further 31% variance in the LBEP of the adolescents. The physical and motor performance LBEP prediction model indicated that gender (39%) and 10 m speed (7%) contributed significantly (p ≤ 0.001) to the overall prediction of the LBEP of the adolescents. The LBEP prediction model delivered a stepwise forward regression analysis coefficient of R2=0.458 and a prediction formula LBEP = 68.21 + 9.82 (gender) – 18.33(10 m speed). The remaining 56% of the variance in the results could be explained by other factors than the variables considered in the study. In conclusion, to the best of the researchers’ knowledge, this is the first study which has made an attempt at developing LBEP prediction models from the anthropometrical, physical and motor performance components of a cohort of adolescents of South Africa. The prediction models developed in the study will assist teachers sport scientists and sporting coaches who have limited resources available, to measure and calculate LBEP in adolescents, with the means to do so in South Africa. Further high quality studies are necessary to further improve and develop such prediction models for various age groups of adolescents in the greater South Africa. / MSc (Sport Science), North-West University, Potchefstroom Campus, 2014
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