Effects of consecutive domestic and international tournaments on heart rate variability in an elite rugby sevens team

Objectives: The purpose of this study was to evaluate heart rate variability and athlete self-report measures of recovery status (ASRM) in response to consecutive domestic and international tournaments among an elite rugby sevens team. Design: Retrospective. Methods: Olympic-level rugby sevens players (n = 10) recorded post-waking natural logarithm of the root mean square of successive differences (LnRMSSD) and ASRM (sleep quality, energy, soreness, recovery and mood) throughout a 1-week baseline period and daily thereafter throughout a domestic and subsequent international tournament, separated by five days. Linear mixed models and Hedge's effect sizes ± 95% confidence interval (ES ± 95% CI) were used to evaluate variation in LnRMSSD and ASRM relative to baseline. Results: Decrements in various ASRM were observed in response to both tournaments (ES = −0.80 ± 0.91 to −1.73 ± 1.03, p < 0.05) and international travel (ES = −1.03 ± 0.93 to −1.70 ± 1.02, p < 0.05) whereas decrements in LnRMSSD were only observed in response to the international tournament (ES = −0.89 ± 0.92 to −1.21 ± 0.96, p = 0.02–0.07). No clear differences in internal or external match-load parameters were observed between tournaments (ES = −0.35 ± 0.88 to 0.13 ± 0.88, p > 0.05). Conclusions: Greater decrements in cardiac-autonomic activity were observed in response to an international tournament relative to a domestic tournament, despite no difference in match-physical demands. Thus, factors separate from competition alone may impact players’ cardiac-autonomic response to an international tournament

Effects of consecutive domestic and international tournaments on heart rate variability in an elite rugby sevens team

Objectives The purpose of this study was to evaluate heart rate variability and athlete self-report measures of recovery status (ASRM) in response to consecutive domestic and international tournaments among an elite rugby sevens team. Design Retrospective. Methods Olympic-level rugby sevens players (n = 10) recorded post-waking natural logarithm of the root mean square of successive differences (LnRMSSD) and ASRM (sleep quality, energy, soreness, recovery and mood) throughout a 1-week baseline period and daily thereafter throughout a domestic and subsequent international tournament, separated by five days. Linear mixed models and Hedge’s effect sizes ± 95% confidence interval (ES ± 95% CI) were used to evaluate variation in LnRMSSD and ASRM relative to baseline. Results Decrements in various ASRM were observed in response to both tournaments (ES = −0.80 ± 0.91 to −1.73 ± 1.03, p \u3c 0.05) and international travel (ES = −1.03 ± 0.93 to −1.70 ± 1.02, p \u3c 0.05) whereas decrements in LnRMSSD were only observed in response to the international tournament (ES = −0.89 ± 0.92 to −1.21 ± 0.96, p = 0.02–0.07). No clear differences in internal or external match-load parameters were observed between tournaments (ES = −0.35 ± 0.88 to 0.13 ± 0.88, p \u3e 0.05). Conclusions Greater decrements in cardiac-autonomic activity were observed in response to an international tournament relative to a domestic tournament, despite no difference in match-physical demands. Thus, factors separate from competition alone may impact players’ cardiac-autonomic response to an international tournament

More than a Metric:How Training Load is Used in Elite Sport for Athlete Management

Training load monitoring is a core aspect of modern-day sport science practice. Collecting, cleaning, analysing, interpreting, and disseminating load data is usually undertaken with a view to improve player performance and/or manage injury risk. To target these outcomes, practitioners attempt to optimise load at different stages throughout the training process, like adjusting individual sessions, planning day-to-day, periodising the season, and managing athletes with a long-term view. With greater investment in training load monitoring comes greater expectations, as stakeholders count on practitioners to transform data into informed, meaningful decisions. In this editorial we highlight how training load monitoring has many potential applications and cannot be simply reduced to one metric and/or calculation. With experience across a variety of sporting backgrounds, this editorial details the challenges and contextual factors that must be considered when interpreting such data. It further demonstrates the need for those working with athletes to develop strong communication channels with all stakeholders in the decision-making process. Importantly, this editorial highlights the complexity associated with using training load for managing injury risk and explores the potential for framing training load with a performance and training progression mindset.</p

More than a Metric:How Training Load is Used in Elite Sport for Athlete Management

Training load monitoring is a core aspect of modern-day sport science practice. Collecting, cleaning, analysing, interpreting, and disseminating load data is usually undertaken with a view to improve player performance and/or manage injury risk. To target these outcomes, practitioners attempt to optimise load at different stages throughout the training process, like adjusting individual sessions, planning day-to-day, periodising the season, and managing athletes with a long-term view. With greater investment in training load monitoring comes greater expectations, as stakeholders count on practitioners to transform data into informed, meaningful decisions. In this editorial we highlight how training load monitoring has many potential applications and cannot be simply reduced to one metric and/or calculation. With experience across a variety of sporting backgrounds, this editorial details the challenges and contextual factors that must be considered when interpreting such data. It further demonstrates the need for those working with athletes to develop strong communication channels with all stakeholders in the decision-making process. Importantly, this editorial highlights the complexity associated with using training load for managing injury risk and explores the potential for framing training load with a performance and training progression mindset.</p

Legal Services for Veterans (LSV): Protocol for evaluating the grant-based LSV initiative supporting community organizations delivery of legal services to veterans.

BACKGROUND: 1.8 million Veterans are estimated to need legal services, such as for housing eviction prevention, discharge upgrades, and state and federal Veterans benefits. While having ones legal needs met is known to improve ones health and its social determinants, many Veterans legal needs remain unmet. Public Law 116-315 enacted in 2021 authorizes VA to fund legal services for Veterans (LSV) by awarding grants to legal service providers including nonprofit organizations and law schools legal assistance programs. This congressionally mandated LSV initiative will award grants to about 75 competitively selected entities providing legal services. This paper describes the protocol for evaluating the initiative. The evaluation will fulfill congressional reporting requirements, and inform continued implementation and sustainment of LSV over time. METHODS: Our protocol calls for a prospective, mixed-methods observational study with a repeated measures design, aligning to the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) and Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) frameworks. In 2023, competitively selected legal services-providing organizations will be awarded grants to implement LSV. The primary outcome will be the number of Veterans served by LSV in the 12 months after the awarding of the grant. The evaluation has three Aims. Aim 1 will focus on measuring primary and secondary LSV implementation outcomes aligned to RE-AIM. Aim 2 will apply the mixed quantitative-qualitative Matrixed Multiple Case Study method to identify patterns in implementation barriers, enablers, and other i-PARIHS-aligned factors that relate to observed outcomes. Aim 3 involves a mixed-methods economic evaluation to understand the costs and benefits of LSV implementation. DISCUSSION: The LSV initiative is a new program that VA is implementing to help Veterans who need legal assistance. To optimize ongoing and future implementation of this program, it is important to rigorously evaluate LSVs outcomes, barriers and enablers, and costs and benefits. We have outlined the protocol for such an evaluation, which will lead to recommending strategies and resource allocation for VAs LSV implementation

Modelling the HRV response to training loads in elite rugby Sevens players

A systems modelling approach can be used to describe and optimise responses to training stimuli within individuals. However, the requirement for regular maximal performance testing has precluded the widespread implementation of such modelling approaches in team-sport settings. Heart rate variability (HRV) can be used to measure an athlete’s adaptation to training load, without disrupting the training process. As such, the aim of the current study was to assess whether chronic HRV responses, as a representative marker of training adaptation, could be predicted from the training loads undertaken by elite Rugby Sevens players. Eight international male players were followed prospectively throughout an eight-week pre-season period, with HRV and training loads (session-RPE [sRPE] and high-speed distance [HSD]) recorded daily. The Banister model was used to estimate vagally-mediated chronic HRV responses to training loads over the first four weeks (tuning dataset); these estimates were then used to predict chronic HRV responses in the subsequent four-week period (validation dataset). Across the tuning dataset, high correlations were observed between modelled and recorded HRV for both sRPE (r = 0.66 ± 0.32) and HSD measures (r = 0.69 ± 0.12). Across the sRPE validation dataset, seven of the eight athletes met the criterion for validity (typical error r\u3e0.30), compared to one athlete in the HSD validation dataset. The sRPE validation data produced likely lower mean bias values, and most likely higher Pearson correlations, compared to the HSD validation dataset. These data suggest that a systems theory approach can be used to accurately model chronic HRV responses to internal training loads within elite Rugby Sevens players, which may be useful for optimising the training process on an individual basis

A concerted appeal for international cooperation in preclinical stroke research

Special report.-- et al.Peer reviewe

Six years of demography data for 11 reef coral species

Scleractinian corals are colonial animals with a range of life history strategies, making up diverse species assemblages that define coral reefs. We tagged and tracked approximately 30 colonies from each of 11 species during seven trips spanning six years (2009-2015) in order to measure their vital rates and competitive interactions on the reef crest at Trimodal Reef, Lizard Island, Australia. Pairs of species were chosen from five growth forms where one species of the pair was locally rare (R) and the other common (C). The sampled growth forms were massive [Goniastrea pectinata (R) and G. retiformis (C)], digitate [Acropora humilis (R) and A. cf. digitifera (C)], corymbose [A. millepora (R) and A. nasuta (C)], tabular [A. cytherea (R) and A. hyacinthus (C)] and arborescent [A. robusta (R) and A. intermedia (C)]. An extra corymbose species with intermediate abundance, A. spathulata was included when it became apparent that A. millepora was too rare on the reef crest, making the 11 species in total. The tagged colonies were visited each year in the weeks prior to spawning. During visits, two or more observers each took 2-3 photographs of each tagged colony from directly above and on the horizontal plane with a scale plate to track planar area. Dead or missing colonies were recorded and new colonies tagged in order to maintain approximately 30 colonies per species throughout the six years of the study. In addition to tracking tagged corals, 30 fragments were collected from neighboring untagged colonies of each species for counting numbers of eggs per polyp (fecundity); and fragments of untagged colonies were brought into the laboratory where spawned eggs were collected for biomass and energy measurements. We also conducted surveys at the study site to generate size structure data for each species in several of the years. Each tagged colony photograph was digitized by at least two people. Therefore, we could examine sources of error in planar area for both photographers and outliners. Competitive interactions were recorded for a subset of species by measuring the margins of tagged colony outlines interacting with neighboring corals. The study was abruptly ended by Tropical Cyclone Nathan (Category 4) that killed all but nine of the over 300 tagged colonies in early 2015. Nonetheless, these data will be of use to other researchers interested in coral demography and coexistence, functional ecology, and parametrizing population, community and ecosystem models. The data set is not copyright restricted, and users should cite this paper when using the data.Publisher PDFPeer reviewe

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p