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
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:nwu/oai:dspace.nwu.ac.za:10394/12207 |
| Date | January 2014 |
| Creators | Van der Walt, Koert Nicolaas |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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