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The purpose of this study was to classify force-time metrics into distinct lower-body strength domains using a systematic data reduction analysis. A cross-sectional design was used, whereby competitive field sport athletes (F = 39, M = 96) completed a series of drop jumps, squat jumps, countermovement jumps (CMJs), loaded CMJs, and 2 isometric tasks on portable force platforms, resulting in a total of 285 force-time performance metrics. The metrics were split into 4 test "families" and each was entered into a sparse principal component analysis (sPCA) model. A single metric from each component of each family-specific sPCA were selected based on the loading, reliability, and simplicity of the metric and entered into a second sPCA that included metrics across all tests. The final sPCA revealed 7 principal components each containing 2 metrics and explained a total of 53% variance of the dataset. The final principal components were interpreted as 7 lower-body strength domains: (a) dynamic force, (b) dynamic timing, (c) early isometric, (d) maximal isometric, (e) countermovement velocity, (f) reactive output, and (g) reactive timing. The findings demonstrate that a total of 7 metrics from a drop jump, CMJ, and isometric test can be used to represent ~50% of variance in lower-body strength performance of field sport athletes. These results can help guide and simplify the lower-body strength diagnosis process in field sport athletes. [ABSTRACT FROM AUTHOR]

The National Institute of Health Stroke Scale (NIHSS) is used worldwide to classify stroke severity as 'mild', 'moderate', or 'severe' based on neurological impairment. Yet, stroke survivors argue that the classification of 'mild' does not represent the holistic experience and impact of stroke on their daily lives. In this observational cohort study, we aimed to identify different types of impairment profiles among stroke survivors classified as 'mild'. We used survivors of mild stroke' data from the START longitudinal stroke cohort (n = 73), with measures related to sensorimotor, cognition, depression, functional disability, physical activity, work, and social adjustment over 12 months. Given the multisource, multigranular, and unlabeled nature of the data, we utilized a structure-adapting, unsupervised machine learning approach, the growing self-organizing map (GSOM) algorithm, to generate distinct clinical profiles. These diverse impairment profiles revealed that survivors of mild stroke experience varying degrees of impairment and impact (cognitive, depression, physical activity, work/social adjustment) at different time points, despite the uniformity implied by their NIHSS-classified 'mild' stroke. This emphasizes the necessity of creating a holistic and more comprehensive representation of survivors of mild stroke' needs over the first year after stroke to improve rehabilitation and poststroke care. [ABSTRACT FROM AUTHOR]

Preparing athletes for competition requires the diagnosis and monitoring of relevant physical qualities (e.g., strength, power, speed, endurance characteristics). Decisions regarding test selection that attempt to measure these physical attributes are fundamental to the training process yet are complicated by the myriad of tests and measurements available. This article presents an evidenced based process to inform test measurement selection for the physical preparation of athletes. We describe a method for incorporating multiple layers of validity to link test measurement to competition outcome. This is followed by a framework by which to evaluate the suitability of test measurements based on contemporary validity theory that considers technical, decision-making, and organisational factors. Example applications of the framework are described to demonstrate its utility in different settings. The systems presented here will assist in distilling the range of measurements available into those most likely to have the greatest impact on competition performance. [ABSTRACT FROM AUTHOR]

This study aimed to measure the differences in commonly used summary acceleration metrics during elite Australian football games under three different data processing protocols (raw, custom-processed, manufacturer-processed). Estimates of distance, speed and acceleration were collected with a 10-Hz GNSS tracking technology device from fourteen matches of 38 elite Australian football players from one team. Raw and manufacturer-processed data were exported from respective proprietary software and two common summary acceleration metrics (number of efforts and distance within medium/high-intensity zone) were calculated for the three processing methods. To estimate the effect of the three different data processing methods on the summary metrics, linear mixed models were used. The main findings demonstrated that there were substantial differences between the three processing methods; the manufacturer-processed acceleration data had the lowest reported distance (up to 184 times lower) and efforts (up to 89 times lower), followed by the custom-processed distance (up to 3.3 times lower) and efforts (up to 4.3 times lower), where raw data had the highest reported distance and efforts. The results indicated that different processing methods changed the metric output and in turn alters the quantification of the demands of a sport (volume, intensity and frequency of the metrics). Coaches, practitioners and researchers need to understand that various processing methods alter the summary metrics of acceleration data. By being informed about how these metrics are affected by processing methods, they can better interpret the data available and effectively tailor their training programs to match the demands of competition. [ABSTRACT FROM AUTHOR]

Purpose: Dozens of variables can be derived from the countermovement jump (CMJ). However, this does not guarantee an increase in useful information because many of the variables are highly correlated. Furthermore, practitioners should seek to find the simplest solution to performance testing and reporting challenges. The purpose of this investigation was to show how to apply dimensionality reduction to CMJ data with a view to offer practitioners solutions to aid applications in high-performance settings. Methods: The data were collected from 3 cohorts using 3 different devices. Dimensionality reduction was undertaken on the extracted variables by way of principal component analysis and maximum likelihood factor analysis. Results: Over 90% of the variance in each CMJ data set could be explained in 3 or 4 principal components. Similarly, 2 to 3 factors could successfully explain the CMJ. Conclusions: The application of dimensional reduction through principal component analysis and factor analysis allowed for the identification of key variables that strongly contributed to distinct aspects of jump performance. Practitioners and scientists can consider the information derived from these procedures in several ways to streamline the transfer of CMJ test information. [ABSTRACT FROM AUTHOR]

The aim of this study was to understand the association between impact and movement characteristics during whole game and peak 1- to 10-minute rolling windows in professional rugby union. Maximal impact (impacts·min-1) and corresponding running (m·min-1) characteristics as well as maximal running (m·min-1) and corresponding impact (impacts·min-1) characteristics were obtained for 160 athletes from 4 teams across the 2018 and 2019 Super Rugby seasons. A linear mixed-effects model reported a positive association between whole-game running and impacts, where greater impact characteristics corresponded with greater running characteristics. The average 1-minute peak running characteristics (150-160 m·min-1) typically occurred when no impacts occurred. The average 1-minute peak impact characteristics (4-6 impacts·min-1) corresponded with an average relative distance of 90-100 m·min-1. Worst case scenario observed impact characteristics as large as 15 impacts·min-1 with a corresponding relative distance of 140 m·min-1. When training for peak period characteristics, running may be completed in isolation; however, peak impacts often occur in conjunction with moderate to high running movements. Given running and impact characteristics can appear concurrently within game play, this highlights the need to train them accordingly. As such, when prescribing training drills to replicate the peak characteristics in rugby union, consideration should be taken for both running and impact characteristics. [ABSTRACT FROM AUTHOR]

The purpose of this study was to compare the peak periods (1- to 6-minute epochs) for three different training methods (game-based training – GBT; small-sided games – SSG; and conditioning training – CT) in elite male rugby union (RU) players. The peak movement (m·min−1) and impact (impact·min−1) characteristics of 42 players during in-season training were assessed. When comparing between training methods, SSG drills produced the greatest peak movement characteristics for all time epochs (1-minute average peak periods – SSG 195 m·min−1, GBT 160 m·min−1, and CT 144 m·min−1). The peak impact characteristics performed during training were 1–2 impact·min−1 for a 1-minute period and then decreased as the time period increased for all training methods. The greatest distribution of training time occurred at 30–39% (SSG and CT) and 40–49% (GBT) of peak movement intensity, with less than 5% of training performed at or above 80% peak intensity across all drill types. Findings from the current study show that the peak movement periods (m·min−1) in RU training from all three training methods match or exceed those which are previously reported in peak gameplay, yet their ability to replicate peak impact characteristics is questionable. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether preseason training plans for Australian football can be computer generated using current training-load guidelines to optimize injury-risk reduction and performance improvement. Methods: A constrained optimization problem was defined for daily total and sprint distance, using the preseason schedule of an elite Australian football team as a template. Maximizing total training volume and maximizing Banister-model-projected performance were both considered optimization objectives. Cumulative workload and acute:chronic workload-ratio constraints were placed on training programs to reflect current guidelines on relative and absolute training loads for injury-risk reduction. Optimization software was then used to generate preseason training plans. Results: The optimization framework was able to generate training plans that satisfied relative and absolute workload constraints. Increasing the off-season chronic training loads enabled the optimization algorithm to prescribe higher amounts of "safe" training and attain higher projected performance levels. Simulations showed that using a Banister-model objective led to plans that included a taper in training load prior to competition to minimize fatigue and maximize projected performance. In contrast, when the objective was to maximize total training volume, more frequent training was prescribed to accumulate as much load as possible. Conclusions: Feasible training plans that maximize projected performance and satisfy injury-risk constraints can be automatically generated by an optimization problem for Australian football. The optimization methods allow for individualized training-plan design and the ability to adapt to changing training objectives and different training-load metrics. [ABSTRACT FROM AUTHOR]

Tredrea, MSJ, Middleton, KJ, Bourne, MN, Carey, DL, Scanlan, AT, and Dascombe, BJ. Load centralization does not affect the kinetic and kinematic output of countermovement jumps. J Strength Cond Res 36(4): 1084-1089, 2022-This study aimed to compare the kinetics, kinematics, and performance of countermovement jumps (CMJs) when completed with 2 different loading conditions (centralized or peripheral) across increasing loads. Seventeen subjects (12 men and 5 women) randomly completed 2 series of CMJs with increasing loads separated by a 30-minute rest period between conditions. Subjects were loaded with either a weighted vest (centralized) or straight barbell (peripheral). A randomized, counterbalanced crossover design was used with incremental loads of 10, 20, 30, 40, and 50% of body mass added to the vest or barbell. Measures of peak force, acceleration, velocity, and power were calculated across each subphase of the CMJs. No significant differences were observed in kinetic or kinematic variables between loading conditions. Within each condition there were significant reductions (p < 0.05) in peak concentric velocity and acceleration, as well as significant increases (p < 0.05) in peak force when the external load increased. Furthermore, braking and propulsive phase duration significantly increased (p < 0.05) and jump height significantly decreased (p < 0.05) as the external load increased. Countermovement jump performance was similar in both central and peripheral loading, whereas increasing load significantly affected jump height, force, velocity, and acceleration variables irrespective of load position. The training stimulus from an external load placed centrally or peripherally is similar regardless of where it is positioned; however, from a practical perspective, a weighted vest may provide a more mobile and safer alternative than a barbell. [ABSTRACT FROM AUTHOR]

High-speed running (HSR) loads have been linked with non-contact injury risks in team-sports. This study investigated whether player-specific speed zones, reflecting individual fitness characteristics, impact the associations between non-contact injury and acute and chronic HSR loads. Semi-professional soccer players from two clubs (n = 47) were tracked over two seasons using 10 Hz GPS (5552 observations). HSR distances were calculated arbitrarily (≥5.5 m·s−1), and in an individualised fashion based on the final speed of the 30–15 intermittent fitness test. Cumulative running loads were represented by exponentially weighted moving averages with 7-(acute) and 28-day (chronic) decay parameters. Physiotherapists collected non-contact, lower-limb, time-loss injury data (n = 101). Injury models using session type (training vs matches), coach, as well as arbitrary or individualised running loads were constructed via mixed-effect logistic regression. Session type had the largest effect on injury (training vs match OR = 0.28; 95%CI:0.17–0.44). Variations in individualised or arbitrary acute and chronic HSR loads within the mid-range of the observed data had negligible effects on predicted injury risk. However, the uncertainty of estimated effects at extreme values of acute and chronic HSR loads prevented any conclusive findings. Therefore, the efficacy of using customised speed thresholds in quantifying load for injury risk mitigation purposes remains unclear. [ABSTRACT FROM AUTHOR]

Background: Identifying key variables that predict sleep quality in youth athletes allows practitioners to monitor the most parsimonious set of variables that can improve athlete buy-in and compliance for athlete self-report measurement. Translating these findings into a decision-making tool could facilitate practitioner willingness to monitor sleep in athletes.Hypothesis: Key predictor variables, identified by feature reduction techniques, will lead to higher predictive accuracy in determining youth athletes with poor sleep quality.Study Design: Cross-sectional study.Level Of Evidence: Level 3.Methods: A group (N = 115) of elite youth athletes completed questionnaires consisting of the Pittsburgh Sleep Quality Index and questions on sport participation, training, sleep environment, and sleep hygiene habits. A least absolute shrinkage and selection operator regression model was used for feature reduction and to select factors to train a feature-reduced sleep quality classification model. These were compared with a classification model utilizing the full feature set.Results: Sport type, training before 8 am, training hours per week, presleep computer usage, presleep texting or calling, prebedtime reading, and during-sleep time checks on digital devices were identified as variables of greatest influence on sleep quality and used for the reduced feature set modeling. The reduced feature set model performed better (area under the curve, 0.80; sensitivity, 0.57; specificity, 0.80) than the full feature set models in classifying youth athlete sleep quality.Conclusion: The findings of our study highlight that sleep quality of elite youth athletes is best predicted by specific sport participation, training, and sleep hygiene habits.Clinical Relevance: Education and interventions around the training and sleep hygiene factors that were identified to most influence the sleep quality of youth athletes could be prioritized to optimize their sleep characteristics. The developed sleep quality nomogram may be useful as a decision-making tool to improve sleep monitoring practice among practitioners. [ABSTRACT FROM AUTHOR]

Diatoms are photosynthetic algae with a siliceous exoskeleton. Diatoms are utilized by a wide array of industries for applications such as filtration and pest control. Unsubstantiated claims have also propelled their societal reach to trendy oral and topical uses. This case highlights a rare case of an oral granuloma secondary to diatoms. An 80‐year‐old woman presented with a mobile, firm, asymptomatic submucosal mass on her lower left mandibular vestibular mucosa. Histopathology showed a non‐caseating granulomatous reaction to diatoms. Her only verified contact with a diatomaceous earth product was a dental impression using alginate after upper front teeth trauma 5 months before. Although there have been several cases of allergic contact dermatitis attributed to diatoms, there are no reported cases of diatom‐induced granuloma formation found in the literature. There are, however, ample data on granulomas initiated by silica. Given the silica‐based composition of diatoms, and the broad use of diatoms in industry and alternative medicine, it is unclear why diatom‐induced granulomas are not more widely described. This report may alert clinicians to the existence of diatom granulomas and incline them to tailor their history to cover questions about possible exposure when evaluating patients presenting with a localized oral lesion. [ABSTRACT FROM AUTHOR]

Aims: (1) To investigate whether a daily acute:chronic workload ratio informs injury risk in Australian football players; (2) to identify which combination of workload variable, acute and chronic time window best explains injury likelihood.Methods: Workload and injury data were collected from 53 athletes over 2 seasons in a professional Australian football club. Acute:chronic workload ratios were calculated daily for each athlete, and modelled against non-contact injury likelihood using a quadratic relationship. 6 workload variables, 8 acute time windows (2-9 days) and 7 chronic time windows (14-35 days) were considered (336 combinations). Each parameter combination was compared for injury likelihood fit (using R2).Results: The ratio of moderate speed running workload (18-24 km/h) in the previous 3 days (acute time window) compared with the previous 21 days (chronic time window) best explained the injury likelihood in matches (R2=0.79) and in the immediate 2 or 5 days following matches (R2=0.76-0.82). The 3:21 acute:chronic workload ratio discriminated between high-risk and low-risk athletes (relative risk=1.98-2.43). Using the previous 6 days to calculate the acute workload time window yielded similar results. The choice of acute time window significantly influenced model performance and appeared to reflect the competition and training schedule.Conclusions: Daily workload ratios can inform injury risk in Australian football. Clinicians and conditioning coaches should consider the sport-specific schedule of competition and training when choosing acute and chronic time windows. For Australian football, the ratio of moderate speed running in a 3-day or 6-day acute time window and a 21-day chronic time window best explained injury risk. [ABSTRACT FROM AUTHOR]