Simon A. Rogers

Movement competency (MC) development of high-school athletes can prepare them for the requirements of physical preparation training and the demands of sport. The aim of this study was to explore the physical effects of and athlete compliance to coach-led versus self-directed training approaches in this population. Thirty-nine high-school athletes (19 male, 14.5 ± 0.3 years old; 20 female, 14.6 ± 0.3 years) were allocated into two groups for a physical preparation program to improve MC. Groups were prescribed either (i) one face-to-face and one online (F2F, n = 18), or (ii) two online (OL, n = 21) sessions per week for 16-weeks. Before and after the intervention, the Athlete Introductory Movement Screen (AIMS) was used to assess MC alongside common physical capacity measures (triple-hop, star-excursion balance, medicine ball throw, 40 m sprint and countermovement jump). Dropout left 22 participants with pre-post physical scores. Compliance with online training was low and F2F session attendance moderate. Semi-structured interviews were conducted to assess participant perceptions following the intervention. Assessing individual responses, the F2F group had a higher proportion of positive responders to AIMS scores, yet capacity measures were inconclusive across groups. Face-to-face coaching when acquiring MCs as part of physical preparation, may provide greater positive perceptions towards training compared to self-directed online prescriptions, and thereby greater compliance.

This systematic review evaluates empirical evidence for intervention studies targeting delivery and improvement of movement competency (MC) within youth athletes. Previous research suggests that youth athletes lack adequate MCs required to meet the demands of training for sport. From the systematic search, studies were included if they involved healthy youth (8–19 years) involved in organised sport (i.e. athletes); used an assessment at two or more time points for assessing MC outcomes; and compared movement quality of a targeted intervention group to a control or comparison group. After removing duplicates, 799 unique articles across six databases were identified and screened, with six studies initially satisfying inclusion criteria. Additional records were identified through searching reference lists (n = 2), with eight articles included. Two authors evaluated and appraised studies for methodological quality using the PEDro assessment; with scores from 4 to 7 points. Five studies reported improvement in ratings of MC in experimental groups, compared to control or comparator groups. The Functional Movement Screen™ was the most common assessment used in five studies. Intensity, length and design of interventions varied, yet all studies revealed inclusion of squats and their derivatives to varying degrees of load from bodyweight to barbells. Future research should explore differences in training session exposures over time, alongside methodological improvements such as participant allocation to minimise baseline differences, blinding of assessors, and where possible athletes to some degree. Charting the course of MC development through multiple time points, along with reporting individual movement task scores, can aid practitioners in dedicating appropriate time to MC development in an athlete’s formative years.

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.

Background: 1,500 m running has long been a blue ribbon event of track championship racing. The eventual medalists employ common tactical behaviors such as a fast sustained pace from the start (gun-to-tape), or, slow initial laps that precede a precisely timed race kick. Before the kick, there are positional changes caused by surging, that can go uncharacterized. The inter-relationship of surge events, tactical positioning, and kick execution may have important implications for eventual medal winning outcomes and require further definition. Methods: In a randomized order, three middle-distance running experts were provided publically available video (YouTube) of 16 men's 1,500 m championship races across, European, World and Olympic championships. Each expert determined the occurrence of surges (defined as any point in the 1,500 m after the first 300 m where an athlete repositions by ≥3 places; or noticeably dictates a raise in the pace from the front) and the race kick. Following a second level verification of expert observations, tactical behaviors (quantity and distance marker within each race) mean distance from the finish were compared between fast (≤3:34.00, n = 5), medium (>3:34.00– ≤3:41.99, n = 7) and slow (≥3:42.00, n = 4) race categories. Results: Before the race kick, there were more surges in slow (5 ± 1.7, mean ±90% confidence limits) vs. fast races (1 ± 0.4, very large difference, very likely). The final surge before the race kick occurred earlier in fast (704 ± 133 m from the finish) vs. medium (427 ± 83 m, large difference, most likely), and slow races (370 ± 137 m, large difference, most likely). At initiation of the race kick in fast races, large positional differences were found between eventual gold (2 ± 1.2; likely) and silver (2.2 ± 1.6; likely) vs. bronze medalists (4.4 ± 1.2). In slow races, positional differences were unclear between eventual gold (4.3 ± 4.7), silver (4.8 ± 4.8) and bronze medalists (5.3 ± 1.5). Regardless of category, the race kick occurred on the last lap, with unclear differences between fast 244 ± 92 m medium 243 ± 56 m and slow 236 ± 142 m races. Conclusions: Presenting tactical behaviors by race categorization (slow, medium, fast race times), provides a novel understanding of the nuance of racing tactics. The present findings highlight the importance of considering within race athlete decision making across multiple-race scenarios during championship preparation.

Purpose: Anaerobic speed reserve (ASR), defined as the speed range from velocity associated with maximal oxygen uptake (vVO₂max) to maximal sprint speed, has recently been shown to be an important tool for middle-distance coaches to meet event surge demands and inform on the complexity of athlete profiles. To enable field application of ASR, the relationship between gun-to-tape 1500-m average speed (1500_v) and the vVO₂max for the determination of lower landmark of the ASR was assessed in elite middle-distance runners. Methods: A total of 8 national and 4 international middle-distance runners completed a laboratory-measured vVO₂max assessment within 6 wk of a nonchampionship 1500-m gun-to-tape race. ASR was calculated using both laboratory-derived vVO₂max (ASR-LAB) and 1500_v (ASR-1500_v), with maximal sprint speed measured using radar technology. Results: 1500_v was on average +2.06 ± 1.03 km/h faster than vVO₂max (moderate effect, very likely). ASR-LAB and ASR-1500_v mean differences were −2.1 ± 1.5 km/h (large effect, very likely). 1500_v showed an extremely large relationship with vVO₂max, r = .90 ± .12 (most likely). Using this relationship, a linear-regression vVO₂max-estimation equation was derived as vVO₂max (km/h) = (1500_v [km/h] − 14.921)/0.4266. Conclusions: A moderate difference was evident between 1500_v and vVO₂max in elite middle-distance runners. The present regression equation should be applied for an accurate field prediction of vVO₂max from 1500-m gun-to-tape races. These findings have strong practical implications for coaches lacking access to a sports physiology laboratory who seek to monitor and profile middle-distance runners.

This study aimed to assess the validity and reliability of jump assessments using the MyJump2 application. Eleven junior athletes (15 ± 1.4 years) performed five countermovement (CMJ) and drop jumps (DJ) measured simultaneously by a force platform and MyJump2. Additionally, intra- and inter-day reliability was assessed over two sessions, 7 days apart. Extremely high agreement between MyJump2 and the force platform (intra-class correlation coefficient, ICC ≥ 0.99) and the intra- and inter-operator agreement (ICC = 0.98–0.99) confirmed the validity and reliability of MyJump2. Mean typical errors (coefficient of variation percentage, CV%) within the first and second sessions were 4.9% and 4.5% respectively for CMJs, and 8.0% to 11.8% for DJ outcomes. CMJ height held acceptable inter-day reliability (CV < 10%; ICC > 0.8), while DJ did not. Results supported MyJump2 to be a valid and reliable tool for assessing jumps; however, with variability in DJs in this cohort, appropriate caution should be taken if including in a junior assessment battery.

Purpose: To examine relationships between methods of lower-limb stiffness and their associations with running economy (RE) and maximal velocity (v_max) in middle-distance (MD) runners. Methods: Eleven highly trained male MD runners performed a series of mechanical and physiological tests to determine maximal overground sprint speed, RE, and . Achilles tendon stiffness (k_T) was estimated using ultrasonography during maximal isometric ankle plantar flexion. Global stiffness qualities were evaluated using a spring-mass model, providing measures of leg (k_leg) and vertical stiffness (k_vert) during running and jumping, respectively. Results: Very large (r = −.70) and large (r = −.60) negative relationships existed between RE and k_T and k_vert, during plantar flexion and unilateral jumps, respectively. There were large (r = .63) and extremely large (r = −.92) associations between k_vert and k_T and k_leg during sprinting, respectively. Runners’ v_max had large positive associations between k_T (r = .52) and k_leg (r = .59) during sprinting. Conclusions: In well-trained MD athletes, greater stiffness appears linked to faster and more economical running. Although k_T had the strongest relationship with RE, k_leg while sprinting and k_vert in maximal unilateral jumps may be more practical measures of stiffness. Agreement between global stiffness assessments and k_T highlights the energy contribution of the Achilles tendon to running efficiency and velocity. Further research incorporating these assessment tools could help establish more comprehensive mechanical and metabolic athlete profiles and further our understanding of training adaptations, especially stiffness modification, longitudinally.