Für den aus 8 Items bestehenden Deutschen Motorik-Test 6-18 ( Bös et al., 2009 ) konnte bislang weder ein eindimensionales noch zweidimensionales (Koordination/Kondition) Strukturmodell bestätigt werden. Anhand probabilistischer (Mixed-Rasch-Modell) und klassischer (konfirmatorische Faktorenanalyse) Testmodelle wird geprüft, ob von Bös et al. (2009) empfohlene Normkategorien oder eine alternative Kategorisierung empirische Validität besitzen. Im Kreuzvalidierungsdesign werden für 9 – 10-Jährige mittels zweier Stichproben (N 1 = 1495, N 2 = 1357; 49,2 % weiblich) Analysen mit den Norm-Quintilen und Z-normierten Norm-Leistungsklassen sowie Standardnoten nach T-Transformation der empirischen Daten (eT-Kategorien) durchgeführt. Die Konstruktvalidität bezüglich der beiden publizierten Normkategorisierungen kann nicht nachgewiesen werden. Demgegenüber ergibt die Analyse der eT-Kategorisierung eine Ein-Klassen-Lösung des Mixed-Rasch-Modells, wobei theoriekonform „Rumpfbeuge“ und aufgrund lokaler Modellverletzungen „Balancieren rückwärts“ auszuschließen sind. Die faktorielle Validität dieses eindimensionalen Modells wird bestätigt. Für 9 – 10-Jährige kann demnach Homogenität bezüglich eines eindimensionalen Motorik-Index (mit sechs Items) angenommen werden. Dieser Motorik- resp. Fitness-Index kann aus den sechs homogenen Items gebildet werden und ist für sportmotorische Testungen valide interpretierbar.
Objective The aim of this study is to measure the motor development and tracking of physical fitness (PF) components of primary school children of Trier in Germany. Methods Two longitudinal cohorts, of 1768 children (915 f, 853 m) aged 5–11, were measured. In longitudinal cohort 1, a total of 116 female and 137 male participants aged 6.80 ± 0.42 years at baseline were measured four times from grade 1 to grade 4 (response: 40.4%). Participants of longitudinal cohort 2 (166 f, 149 m; 6.70 ± 0.36 years at baseline, response: 42.6%) were examined three times from grade 1 to grade 3 with the German Motor Test 6–18 (DMT 6–18). Results Physical fitness increased significantly over time in all test tasks except flexibility. Gender-specific differences were found in 20 m sprint, 6-minute run, balancing backwards, jumping sideways, and stand and reach. 74.4% of PF stability coefficients were moderate (r = 0.30 to 0.60). Stability of PF declined with increased time frames. Tracking was lower in girls than in boys. Flexibility showed the highest stability among PF variables (r > 0.50). BMI showed the overall highest stability coefficient with r > 0.7. Conclusions Gender-specific differences of PF were obvious but cannot always be secured statistically in primary school. Tracking was only moderate. Variability in the timing and speed of the adolescent growth spurt and sexual maturation influence stability of PF. Results from longitudinal cohort 2 largely confirm those from longitudinal cohort 1.
Devices that combine HR and ACC data yield an accurate classification of MVPA in preschoolers but perform less well for classifying SB. These differences underscore the need to match evaluation methods with the objectives of future PA interventions.
The aim of the current study was to explore relative age’s influence on physical and motor tests among fourth grade children (9 to 10 years) from Germany. Data from 1218 children (49% female) who had performed the German Motor Ability Test (Bös et al., 2009) were analysed. The test battery, which was comprised of physical and motor tests, included 20 m sprint, balance backwards, jumping sideways, stand and reach, push-ups, sit-ups, standing broad jump, and six-minute run. Analyses of variance only revealed statistically significant effects for height, weight, and 20 m sprint time ( p < .01) among boys, with relatively older boys performing better than relatively younger boys. For the girls, the only significant difference between quartiles was for height ( p < .01), with the oldest quartiles being taller than the younger quartiles. These results may have implications for statistical vs. practical significance, sampling, and how youth are evaluated in physical education classes.
Two different computational approaches were used to predict Olympic distance triathlon race time of German male elite triathletes. Anthropometric measurements and two treadmill running tests to collect physiological variables were repeatedly conducted on eleven male elite triathletes between 2008 and 2012. After race time normalization, exploratory factor analysis (EFA), as a mathematical preselection method, followed by multiple linear regression (MLR) and dominance paired comparison (DPC), as a preselection method considering professional expertise, followed by nonlinear artificial neural network (ANN) were conducted to predict overall race time. Both computational approaches yielded two prediction models. MLR provided R² = 0.41 in case of anthropometric variables (predictive: pelvis width and shoulder width) and R² = 0.67 in case of physiological variables (predictive: maximum respiratory rate, running pace at 3-mmol·L -1 blood lactate and maximum blood lactate). ANNs using the five most important variables after DPC yielded R² = 0.43 in case of anthropometric variables and R² = 0.86 in case of physiological variables. The advantage of ANNs over MLRs was the possibility to take non-linear relationships into account. Overall, race time of male elite triathletes could be well predicted without interfering with individual training programs and season calendars.
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