The successful completion of motor tasks requires effective control of multiple degrees of freedom (DOF), with adaptations occurring as a function of varying performance constraints. In this study we sought to compare the emergent coordination strategies employed in vertical jumping under different task constraints [countermovement jump (CMJ) with arm swing-CMJas and no arm swing-CMJnas]. In order to achieve this, principal component analysis (PCA) was conducted on joint moment waveform data from the hip, knee and ankle. This statistical approach has the advantage of analyzing the whole movement within a time series and reduces multidimensional datasets to lower dimensions for analysis. Both individual and group analyses were conducted. For individual analysis, PCA was conducted on combined hip, knee, and ankle joint moment data for each individual across both CMJnas (thirty-eight participants), and CMJas (twenty-two participants) conditions. PCA was also performed comparing all data from each individual across CMJnas and CMJas conditions. The results revealed a maximum of three principal components (PC) explained over 90% of the variance in the data sets for both conditions and within individual and group analyses. For individual analysis, no more than 2PCs were required for both conditions. For group analysis, CMJas required 3PCs to explain over 90% of the variance within the dataset and CMJnas only required 2PCs. Reconstruction of the original NJM waveforms from the PCA output demonstrates a greater loading of hip and knee joint moments to PC1, with PC2 showing a greater loading to ankle joint moment. The reduction in dimensions of the original data shows the proximal to distal extension pattern in the sagittal plane, typical of vertical jumping tasks, is governed by only 2 functional DOF, at both a group, and individual level, rather than the typically reported 3 mechanical DOF in some forms of jumping.

Purpose: A novel 4-task Athlete Introductory Movement Screen was developed and tested to provide an appropriate and reliable movement screening tool for youth sport practitioners. Methods: The overhead squat, lunge, push-up, and a prone brace with shoulder touches were selected based on previous assessments. A total of 28 mixed-sport junior athletes (18 boys and 10 girls; mean age = 15.7 [1.8] y) completed screening after viewing standardized demonstration videos. Athletes were filmed performing 8 repetitions of each task and assessed retrospectively by 2 independent raters using a 3-point scale. The primary rater reassessed the footage 3 weeks later. A subgroup (n = 11) repeated the screening 7 days later, and a further 8 athletes were reassessed 6 months later. Intraclass correlation coefficients (ICC), typical error (TE), coefficient of variation (CV%), and weighted kappa (k) were used in reliability analysis. Results: For the Athlete Introductory Movement Screen 4-task sum score, intrarater reliability was high (ICC = .97; CV = 2.8%), whereas interrater reliability was good (intraclass correlation coefficient = .88; CV = 5.6%). There was a range of agreement from fair to almost perfect (k = .31–.89) between raters across individual movements. A 7-day and 6-month test–retest held good reliability and acceptable CVs (≤ 10%) for sum scores. Conclusion: The 4-task Athlete Introductory Movement Screen appears to be a reliable tool for profiling emerging athletes. Reliability was strongest within the same rater; it was lower, yet acceptable, between 2 raters. Scores can provide an overview of appropriate movement competencies, helping practitioners assess training interventions in the athlete development pathway.

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Statistical errors are common in many biomedical fields. 1-5 We believe the nature and impact of these errors to be great enough in sports science and medicine to warrant special attention. 6-14 Poor methodological and statistical practices have led to calls for change in other fields, such as psychology. 15-18 We believe that a similar call to action is needed in sports science and medicine. Specifically, we see two pressing needs: (1) to increase collaboration between researchers and statisticians, and (2) to increase statistical training within the exercise science/medicine/physiotherapy (PT) discipline. Our call to action extends the work of those who have previously called for increased statistical collaboration in sports medicine and sports injury research.