Dr Thomas Sawczuk

The ability to accurately evaluate player and team performances in professional sport is particularly valuable. Doing so provides competitive advantages include extracting important information regarding the tactical strategies of future oppositions and producing player rating systems. A common method of evaluating player and team performances is via expected possession value (EPV) models. EPV models assign a value to every location and/or action on the pitch, which reflects the probability of points being scored within a given time period. EPV models have been produced in several sports, including football, basketball and ice hockey. However, there is limited research surrounding these models in rugby league. Rugby league has a unique set of rules, including a six tackle attacking set and five possible scoring options at the end of a possession. These two factors, alongside the poor data availability in the sport ensure that the majority of previous methods cannot be adapted for use in rugby league. Therefore the aim of this thesis was to develop new methodologies evaluating player and team performances in rugby league. In the first section of this thesis (studies 1 and 2), previous Markov models using zonal approaches were applied, adapted and extended in rugby league to provide insights into player and team performances. Six EPV models were produced with varying zone sizes using Markov Reward Processes. The Kullback-Leibler Divergence was used to evaluate the zone sizes which could reproduce future team attacking performances. The model was then extended to incorporate actions and context nodes using Markov Decision Processes. Novel methods of evaluating player and team performances were also produced. In the second section (studies 3 and 4), novel models producing smooth pitch surfaces were developed. The spatial trends of team attacking performances were evaluated using Kernel Density Estimation. Two novel Wasserstein distance metrics were used to provide valuable insights into team performances. A novel approach to the estimation of individual possession outcomes was also proposed using a Bayesian mixture model approach. The model used linear and bilinear interpolation techniques for its weights to produce a smooth pitch surface. Novel performance metrics evaluating player and team performances were also created. The research provides new methodologies for use within rugby league, providing zonal and smooth EPV models through which player and team performances can be evaluated. Professional experts were impressed with the results they provided and validated their use within the sport.

This study investigated: 1) monitoring post-match neuromuscular fatigue (NMF) status in rugby union players using two submaximal running tests (SRT); and 2) the sensitivity of each SRT to locomotor variables obtained during match-play. Twenty-three male rugby players (age: 21.0 ± 1.3 years; height: 185.2 ± 6.1 cm; body mass; 97.3 ± 10.3 kg) were monitored across one season (n = 71 player-match and 159 fatigue-players-testing observations). Two different SRTs (SRT-jog [5-minute shuttle run protocol] and SRT-stride [a repeat stride effort protocol]) were used to characterise post-match NMF, with measures taken two days prior to match day (baseline), on MD + 1, and MD + 2. Linear mixed models (±90% CIs) were used to explore differences between measures and match-play locomotor variables. SRT-jog presented a meaningful increase following MD + 1 (ES: 0.63 [0.37 to 0.89]). SRT-Stride showed small increases at MD + 1 (ES: 0.24 [0.02 to 0.49]) and MD + 2 (ES: 0.33 [0.07 to 0.59]) suggesting a potential impairment in running mechanics. SRT-stride was significantly associated with total distance (ES: 0.41 [−0.01 to 0.83]) and collisions (ES: 0.58 [0.18 to 0.99]). While both tests presented small to moderate changes post-match, only the SRT-stride was related to match locomotor variables. Therefore, the SRT-stride may be a more sensitive monitoring tool for monitoring NMF in rugby union players.

Despite the athlete monitoring cycle becoming increasingly popular within sport, very little evidence exists with regards to the relationships present between its measures or its relationship with illness incidence in youth athletes. The aim of this thesis was to evaluate the true predictive ability of an integrated athlete monitoring cycle model, incorporating measures of the training dose (training load), training recovery (sleep) and training response (wellness questionnaires (DWB and PRS), countermovement jumps and salivary IgA (s-IgA)), with regards to illness incidence in youth athletes. Study 1 outlined the reliability and usefulness of DWB (poor/marginal), PRS (poor/marginal) and CMJ (good/useful). Despite study 1’s findings, study 2 showed that CMJ was not suitable for use as a training response measure in youth athletes. Studies 3 and 4 supported the use of the sleep quality subscale as a training recovery measure rather than within the DWB training response measure (which was reduced to the four item DWBno-sleep). The overall DWBno-sleep score, fatigue, stress and mood were statistically related to the training recovery, whereas only muscle soreness was related to the training dose. Statistically, PRS was related to both the training dose and recovery. Despite the presence of these statistical relationships, only the effect of training load, including match exposure, on PRS was practically interpretable. Unfortunately, technical issues prevented the true predictive ability of an integrated athlete monitoring cycle model with regards to illness incidence being tested. However, study 5 showed that s-IgA measures could not accurately predict illness in youth athletes. Furthermore, analysis of the longitudinal trends of s-IgA, DWBno-sleep and PRS showed that the subjective fatigue/wellness measures were more responsive to qualitative events than objective measures of immune function. Overall, the results of this thesis provide support for the use of the integrated athlete monitoring cycle in youth athletes, particularly when subjective training response measures are included. However, future research needs to consider the true predictive ability of the proposed integrated athlete monitoring cycle model with regards to illness incidence.

Elite-level rugby union (RU) is a high-intensity contact sport that involves large training and match volumes across a season, which can lead to postmatch fatigue. Autonomic nervous system (ANS) regulation and perceived fatigue have been suggested to relate to measures of training and match load in RU. However, there have been no studies to assess specific ANS variables in elite RU during in-season microcycles. Player readiness during game-week microcycles was measured via heart rate variability (HRV) indices, direct current potential and self-reported well-being among 13, elite, male RU players. To enable comparison, data collection days were categorized in relation to their proximity to match day, ranging from match day minus 3 (MD - 3), to match day plus 3 (MD + 3). Differences between match days were evaluated using general linear models and Cohen's d effect sizes. There were significant differences between MD and MD + 1 for ANS indices (RMSSD p = 0.04, d = -0.66, 95% CI 0.11-1.20; the standard deviation of NN intervals p = 0.04, d = -0.66, 95% CI 0.12-1.20; total power p = 0.05, d = -0.65, 95% CI 0.11-1.20) and wellness measures (readiness p = 0.18; d = -2.33, 95% CI, 1.54-3.13; energy p = 0.02; d = -2.24, 95% CI 1.44-3.03; soreness p = 0.00; d = -2.42, 95% CI 1.63-3.23). Match day plus 3 effects were significantly greater than MD + 1 in several ANS responses, with wellness recovering at a slower time-course than ANS responses. Measures of HRV are dysregulated postmatch, but based on their rapid recovery thereafter, using HRV to assess readiness of elite-level players in RU across a weekly microcycle could be limited and requires further investigation.

This study aimed to introduce a novel Bayesian Mixture Model approach to the development of an EPV model in rugby league, which could produce a smooth pitch surface and estimate individual possession outcome probabilities. 99,966 observations from the 2021 Super League season were used. A set of 33 centres (30 in the field of play, 3 in the opposition try area) were located across the pitch. Each centre held the probability of five possession outcomes occurring (converted/unconverted try, penalty, drop goal and no points). Probabilities at each centre were interpolated to all locations on the pitch and estimated using a Bayesian approach. An EPV measure was derived from the possession outcome probabilities and their points value. The model produced a smooth pitch surface, which was able to provide different possession outcome probabilities and EPVs for every location on the pitch. Differences between team attacking and defensive plots were visualised and an actual vs expected player rating system was developed. The model provides significantly more flexibility than previous zonal approaches, allowing much more insightful results to be obtained. It could easily be adapted to other sports with similar data structures.

Background:Globally, sports are proactively aiming to reduce concussions and head acceleration events (HAEs) given the potential dose-response association with neurodegenerative diseases. No data exists regarding HAEs from training or commonly performed drills, which is important, as arguably the training environment is more modifiable than matches. Therefore, this study aimed to describe the HAEs during common training drills used in rugby league.Methodology:Fifteen male academy rugby league players from a professional Super League club participated. Players participated in three training sessions, with 7 standardised drills, designed in consultation with experienced coaches, completed in the same order in each session. Players wore a custom-fitted instrumented mouthguard (iMG) and each session was filmed. An iMG capture framework was developed andapplied to synchronise and process the iMG and video data to verify the HAEs occurring in a drill. The probability of a HAE being observed in a drill was estimated using binomial logistic regression and exceedance probabilities using ordinal mixed effectsregression.Results:1402 (93 ± 50 per player) drill observations were recorded, which resulted in approximately 133 observed HAEs (9 ± 8 per player). 130 HAEs were analysed further (wrestle = 48, tackler = 59, ball-carrier = 23). Standing wrestle had the highest overall probability of HAE occurrence of 41.3% (CI = 31.0 –52.3%) than the other drills (range: 0.67 –14.3%). HAE exposure was greater for tacklers than ball-carriers. Increasing the distance of the drill, e.g., tackle shield hit 1m (1.3% [0.5 –3.4]) vs 3m (9.0%[6.2 –12.8]), increased the probability of a HAE being observed. All drills were observed to have an exceedance probability of experiencing an HAE ≥25 g ~0.0% (CI = 0.0 –0.8%), except for standing wrestle 1.0% (CI = 0.2 -4.1%).Conclusion:For the first time the findings from this study offer insights into HAE exposure from various common training drills in rugby league. While the overall chance of high-magnitude HAEs was relatively low, the contextual and constraint-based variability in HAE exposure between drills demonstrates the need for practitioners to consider how manipulating constraints may affect HAE exposure and accumulation.

Background: Head acceleration events (HAEs) are a source of concern across sport due to potential negative long-term brain health in athletes exposed to them. Tackle height is highlighted as a possible factor for risk mitigation in rugby codes. This study aimed to identify the probability of the ball-carrier and tackler receiving a HAE for a given tackle height and estimate the potential impact of changes in tackle height. Methodology: A prospective observational cohort study was conducted during the men’s elite rugby league Super League 2023 season (12 teams, 94 players, 702 player matches). HAEs recorded from instrumented mouthguards were linked to ball-carries and tackles confirmed via video. Events were then labelled by tackle height (i.e., contact on ball-carrier; head/neck, shoulder, upper torso, abdomen, shorts, upper leg and lower leg). Only initial collision HAEs were analysed. Ordinal mixed-effects regression models provided exceedance probabilities for peak linear acceleration (recorded, >10g, >25g, >40g, >55g and >70g) and peak angular acceleration (recorded, >1000rads/s2, >2000 rads/s2, >3000 rads/s2, >4000 rads/s2, and >5000 rads/s2). Differences in initial HAEs were simulated across a range of tackle height distributions using the probabilities and the total number of tackles across the season. Results: The probability of a ball-carrier and tackler recording an initial HAE were 13.4% and 24.2%. The greatest exceedance probabilities for the ball-carrier were initial impact to the head/neck: 35.5% recorded, 4.0% >25g, 13.6% >2000 rads/s2. For other impact locations, ball-carrier HAE probability was 20% at all tackle heights except impact to the ball-carriers head/neck (12.2%). The highest probability for the tackler was contact with the shorts (recorded; 30.9%, >25g; 3.0%, >2000 rads/s2; 11.7%). When 40% of tackles were redistributed from the shoulder to lower parts of the body evenly, the estimated number of HAEs reduced from 40,292 to 35,358. Conclusion: The probability of receiving a HAE for the tackler and ball-carrier differs by overall probability and tackle height. Consequently, simulating the redistribution of tackles below the line of the shoulder suggests there could be a lower number of initial HAE observed across a season.

Background:In collision sports, like rugby union, there is a growing interest in the long-term effects of head acceleration events (HAEs) on brain health. Current methods for understanding HAE exposure have focused on using “inferential variability” as opposed to “outcome variability”. This study aims to use simulation to evaluate outcome variability and provide expected HAE reference ranges in men’s and women’s rugby union across a micro-(weekly), meso-(monthly) and macro-(annual) cycle.Methodology:A prospective observational study was conducted in rugby union players from two professional men’s and two semi-professional women’s competitions. A total of 982 players were included across 132 training weeks and 365 matches. Generalised linear mixed models were used to estimate the count of HAEs, HAEs >25g and >2,000 rads/s2 across training contact types and match-play. Simulations of model estimates, accounting for player and weekly variation, were used to provide reference ranges of expected HAE counts, using current world rugby contact guidelines. Meso-cycles were simulated for players in three categories; high (30 matches), moderate (20 matches) and low (10 matches) match exposure.Results:For both sexes within a micro-and meso-cycle, the reference ranges between positions overlap despite differences in the median expected HAE exposures (e.g., >25g HAEs: male forwards 4 [0-10] vs. male backs 2 [0-8]). Where differences are present, forwards have greater expected HAE counts and variation (indicated by a wider distribution). Meso-cycles simulations identified a clear differentiation in distributions of expected HAEs between all match exposure levels. Generally, more matches playedresulted in higher reference ranges of HAEs, but some low match exposure simulations had a higher HAE count than some high match exposure simulations.Conclusion:The results show wide variability in “normal” weekly, monthly and annual HAE exposures. These reference ranges can be used by practitioners to identify individual players that are exposed to a large number of HAEs and serve as a baseline for future policy change regarding match and training exposure limits.

This study aimed to evaluate team attacking performances in rugby league via expected possession value (EPV) models. Location data from 59,233 plays in 180 Super League matches across the 2019 Super League season were used. Six EPV models were generated using arbitrary zone sizes (EPV-308 and EPV-77) or aggregated according to the total zone value generated during a match (EPV-37, EPV-19, EPV-13 and EPV-9). Attacking sets were considered as Markov Chains, allowing the value of each zone visited to be estimated based on the outcome of the possession. The Kullback-Leibler Divergence was used to evaluate the reproducibility of the value generated from each zone (the reward distribution) by teams between matches. Decreasing the number of zones improved the reproducibility of reward distributions between matches but reduced the variation in zone values. After six previous matches, the subsequent match's zones had been visited on 95% or more occasions for EPV-19 (95±4%), EPV-13 (100±0%) and EPV-9 (100±0%). The KL Divergence values were infinity (EPV-308), 0.52±0.05 (EPV-77), 0.37±0.03 (EPV-37), 0.20±0.02 (EPV-19), 0.13±0.02 (EPV-13) and 0.10±0.02 (EPV-9). This study supports the use of EPV-19 and EPV-13, but not EPV-9 (too little variation in zone values), to evaluate team attacking performance in rugby league.

OBJECTIVES: Concussion is a common injury in rugby union ('rugby') and yet its diagnosis is reliant on clinical judgment. Oculomotor testing could provide an objective measure to assist with concussion diagnosis. NeuroFlex® evaluates oculomotor function using a virtual-reality headset. This study examined differences in NeuroFlex® performance in clinician-diagnosed concussed and not concussed elite male rugby players over three seasons. METHODS: NeuroFlex® testing was completed alongside 140 head injury assessments (HIAs) in 122 players. The HIA is used for suspected concussion events. Of these 140 HIAs, 100 were eventually diagnosed as concussed, 38 were not concussed (2 were unclear) Eight of the 61 NeuroFlex® metrics were analysed as they were comparable at all time points. These eight metrics, from three oculomotor domains (vestibulo-ocular reflex, smooth pursuit and saccades), were tested for their ability to distinguish between concussed and not concussed players using mean difference / odds ratios and corresponding 95% confidence intervals (CI's). General and generalised linear mixed models, accounting for baseline test performance, were used to determine any meaningful differences in concussed and not concussed players. The diagnostic accuracy of these differences was provided by the area under the receiver operating curve (AUC). RESULTS: Only one of the eight metrics (number of saccades, smooth pursuit domain) had clear differences in performance between concussed and not concussed players at the HIA during the match (odds ratio: 0.76, 95%CI: 0.54-0.98) and after 48 hours (0.74, 95%CI: 0.52-0.96). However, the direction of this difference was contrary to clinical expectations (concussed performed better than not concussed) and the AUC for this outcome was also poor (0.52). CONCLUSION: NeuroFlex® was unable to distinguish between concussed and not concussed players in this elite male cohort. Future research could study other cohorts, later time points before return to play, and the tool's role in rehabilitation.

On-field spacing has been linked to successful performance in a number of sportsto date, there is limited research investigating this within rugby league. This study aims to (a) quantify the defensive dispersal during rugby league match-play and (b) identify if contextual factors are associated with the dispersal. Global Positioning System data were analysed from 47 European Super League matches (1598 player files). Defensive dispersal was calculated for 1959 defensive sets of rugby league. Linear mixed models were used to analyse the effects of contextual factors on the average defensive dispersal per set when accounting for team and fixture. On-field position and match half were found to significantly affect defensive dispersal. However, set length, play-the-ball length, and final score difference were found to have minimal impact on defensive dispersal. This study demonstrates that defensive dispersal in rugby league can be measured using GPS data and may be strongly influenced by on-field positioning. As such, it quantifies an important element of tactical preparation for rugby league teams.

This study investigated differences in external training load between microcycle lengths and its variation between microcycles, players, and head coaches. Commonly used external training load variables including total-, high-speed- (5-7 m∙s-1), and sprint-distance (> 7 m∙s-1) alongside combined high acceleration and deceleration distance (> 2 m∙s-2). Which were also expressed relative to time were collected using microtechnology within a repeated measures design from 54 male rugby league players from one Super League team over four seasons. 4337 individual observations across ninety-one separate microcycles and six individual microcycle lengths (5 to 10 day) were included. Linear mixed effects models established the differences in training load between microcycle-length and the variation between-microcycles, players and head coaches. The largest magnitude of difference in training load was seen when comparing 5-day with 9-day (ES = 0.31 to 0.53) and 10-day (ES = 0.19 to 0.66) microcycles. The greatest number of differences between microcycles were observed in high- (ES = 0.3 to 0.53) and sprint-speed (ES = 0.2 to 0.42) variables. Between-microcycle variability ranged between 11% to 35% dependent on training load variable. Training load also varied between players (5-65%) and head coaches (6-20%) with most variability existing within high-speed (19-43%) and sprinting (19-65%). Overall, differences in training load between microcycle lengths exist, likely due to manipulation of session duration. Furthermore, training load varies between microcycle, player and head coach.

The study aimed to illustrate how contact (from match‐event data) and head acceleration event (HAE) (from instrumented mouthguard [iMG]) data can be combined to inform match limits within rugby. Match‐event data from one rugby union and rugby league season, including all competitive matches involving players from the English Premiership and Super League, were used. Playing exposure was summarised as full game equivalents (FGE; total minutes played/80). Expected contact and HAE exposures at arbitrary thresholds were estimated using match‐event and iMG data. Generalised linear models were used to identify differences in contact and HAE exposure per FGE. For 30 FGEs, forwards had greater contact than backs in rugby union (n = 1272 vs. 618) and league (n = 1569 vs. 706). As HAE magnitude increased, the differences between positional groups decreased (e.g., rugby union; n = 34 and 22 HAE >40 g for forwards and backs playing 30 FGEs). Currently, only a relatively small proportion of rugby union (2.5%) and league (7.3%) players exceeded 25 FGEs. Estimating contact and HAEs per FGE allows policymakers to prospectively plan and model estimated overall and position‐specific loads over a season and longer term. Reducing FGE limits by a small amount would currently only affect contact and HAE exposure for a small proportion of players who complete the most minutes. This may be beneficial for this cohort but is not an effective HAE and contact exposure reduction strategy at a population level, which requires individual player management. Given the positional differences, FGE limits should exist to manage appropriate HAE and contact exposure.

ObjectiveTo assess whether calorific screening thresholds improved the agreement between objective consumer purchase data, from supermarket transaction records, and self-reported dietary intake, from a Food Frequency questionnaire (FFQ), for people living with (PLWOw/Obwith) and without (PLWOw/Obwithout) overweight/obesity.DesignParticipants were recruited across a one-year period (1st June 2020 – 31st May 2021). Six screening thresholds were employed, using the estimated number of calories purchased for the individual, to filter participant data. Bland-Altman analyses were compared between PLWOw/Obwith and PLWOw/Obwithout for energy, sugar, total fat, saturated fat, protein and sodium.SettingPartnered with a large UK retailer.ParticipantsParticipants (N=1788) were recruited via the retailer’s loyalty card customer database. Participants with completed FFQs, shared transaction records, height, weight and household composition data were included for analysis (N=642).ResultsAgreement was found between objective purchase data and self-reported dietary intake at ≥1000 Kcal/day (energy, sugar, total fat and saturated fat) and ≥1500 Kcal/day (protein and sodium). PLWOw/Obwith consumed greater energy (19%), sugar (36%), total fat (22%) and saturated fat (25%) than they were estimated to have purchased at the retailer. PLWOw/Obwithout only consumed greater sugar (19%). ConclusionsThe application of screening thresholds based on estimated individual calories purchased may provide a valuable preprocessing step within the analysis of consumer purchase data, allowing agreement to be found for absolute nutrient values. Differences in bias between PLWOw/Obwith and PLWOw/Obwithout show that insights into purchase and consumption patterns can be identified using consumer purchase data.

The Impact of Obesity when Validating Supermarket Transaction Records In Dietary Evaluation: FIO-STRIDE 1,2Thomas Sawczuk, 1Hannah C Greatwood, 1Mark S Gilthorpe, 3,4Michelle A Morris, 3,4Victoria Jenneson, 1Claire Griffiths, on behalf of the FIO-Food Team 1Obesity Institute, Leeds Beckett University, Leeds, LS6 3QT, UK; 2Carnegie Applied Rugby Research (CARR) Centre, Carnegie School of Sport, Leeds Beckett University, Leeds, LS6 3QT, UK; 3Leeds Institute for Data Analytics, Level 11 Worsley Building, Clarendon Way, University of Leeds, Leeds, LS2 9JT, UK; 4School of Food Science and Nutrition, University of Leeds, Willow Terrace Road, Leeds, LS2 9JT, UK. Background: Supermarket transaction data reflect dietary purchasing behaviours. FIO-STRIDE compares dietary purchase patterns (supermarket transactions) with consumption (Food Frequency Questionnaire (FFQ)) for people living with and without overweight/obesity. Methods: Participants (n=683) in England were recruited via a UK retailer’s loyalty card database within the Supermarket Transaction Records In Dietary Evaluation (STRIDE) study, where survey data were collected including: height, weight, and FFQ (Jenneson et al. 2023). Bland-Altman plots assessed agreement for absolute measures of: Kcal, sugar, fat, saturated fat, protein, and sodium among people living with and without overweight/obesity. Participant purchase data were filtered by estimated individual calorific consumption:≥1000 Kcal/day/person and ≥1500 Kcal/day/person. Results: People living with overweight/obesity consumed greater kcal, sugar, fat, and saturated fat than they purchased, whereas those living without consumed greater sugar. Agreements were observed among participants for their consumption of kcal, sugars, fat, and saturated fat after filtering for ≥1000 Kcal/day consumption, and for their consumption of protein and sodium after filtering for ≥1500 Kcal/day consumption. Conclusions: Agreement between food purchase and consumption records differed by weight status. The number of calories purchased should be considered as a filter to exclude inconsistent shoppers when using supermarket purchase data as a proxy for consumption.

Background: Persons experiencing food insecurity (FI) compared to lower levels are more likely to live with obesity and purchase foods of lower dietary quality. Communications within the retail environment have potential to influence behaviours, but little is known on how the messaging is perceived by this target audience. This qualitative paper explores the insights of people living with obesity (PLWO) and FI on two national campaigns targeted at: i) supporting customers with increased food prices; and ii) promoting the consumption of healthy sustainable meals. Methods: PLWO and FI (n=39) expressed their perceptions of purchasing healthier and more environmentally sustainable foods through four focus groups. Reflexive thematic analysis was used to generate themes. Results: Five themes and 12 subthemes were generated: (i) Do I have the resource? i.e. financial and/or time (ii) Do I know what it means? e.g. clarity of images (iii) Do I trust it? e.g. authentic images (iv) Do I want it? e.g. lack of appeal (v) Recommendations for future promotional communications. Conclusions: Findings provide insights for retailers on the need for upstream changes within the wider food system and the importance of tailored communications and messaging that supports PLWO and FI purchase healthier and more sustainable foods.

Abstract

Sawczuk, T, Jones, B, Scantlebury, S, and Till, K. Influence of perceptions of sleep on well-being in youth athletes. J Strength Cond Res 35(4): 1066–1073, 2021—To date, most research considering well-being questionnaires has only considered the training stress imposed on the athlete, without evaluating the questionnaire's relationship with a measure of recovery (e.g., sleep). This study aimed to assess the influence of sleep duration (Sduration), sleep quality (Squality), and sleep index (Sindex; Sduration × Squality) on well-being in youth athletes, while accounting for the known training stressors of training load and exposure to match play. Forty-eight youth athletes (age 17.3 ± 0.5 years) completed a daily questionnaire including well-being (DWBno-sleep; fatigue, muscle soreness, stress, and mood) measures, Perceived Recovery Status Scale (PRS), the previous day's training loads, Sduration, and Squality every day for 13 weeks. Linear mixed models assessed the impact of Sduration, Squality, and Sindex on DWBno-sleep, its individual subscales, and PRS. Sduration had a small effect on DWBno-sleep (d = 0.31; ±0.09), fatigue (d = 0.42; ±0.11), and PRS (d = 0.25; ±0.09). Squality had a small effect on DWBno-sleep (d = 0.47; ±0.08), fatigue (d = 0.53; ±0.11), stress (d = 0.35; ±0.07), mood (d = 0.41; ±0.09), and PRS (d = 0.37; ±0.08). Sindex had a small effect on DWBno-sleep (d = 0.44; ±0.08), fatigue (d = 0.55; ±0.11), stress (d = 0.29; ±0.07), mood (d = 0.37; ±0.09), and PRS (d = 0.36; ±0.09). The results indicate that an athlete's perceptions of sleep are associated with deviations in well-being measures and should be used as an input to the monitoring process rather than as part of the outcome well-being score. The sleep index is suggested as a potential input because it provides information on both the duration and quality of the sleep experienced.

This study assessed the influence of training load, exposure to match play and sleep duration on two daily wellbeing measures in youth athletes. Forty-eight youth athletes (age 17.3 ± 0.5 years) completed a daily wellbeing questionnaire (DWB), the Perceived Recovery Status scale (PRS), and provided details on the previous day’s training loads (TL) and self-reported sleep duration (sleep) every day for 13 weeks (n = 2727). Linear mixed models assessed the effect of TL, exposure to match play and sleep on DWB and PRS. An increase in TL had a most likely small effect on muscle soreness (d = −0.43;± 0.10) and PRS (d = −0.37;± 0.09). Match play had a likely small additive effect on muscle soreness (d = −0.26;± 0.09) and PRS (d = −0.25;± 0.08). An increase in sleep had a most likely moderate effect on sleep quality (d = 0.80;± 0.14); a most likely small effect on DWB (d = 0.45;± 0.09) and fatigue (d = 0.42;± 0.11); and a likely small effect on PRS (d = 0.25;± 0.09). All other effects were trivial or did not reach the pre-determined threshold for practical significance. The influence of sleep on multiple DWB subscales and the PRS suggests that practitioners should consider the recovery of an athlete alongside the training stress imposed when considering deviations in wellbeing measures.

The quantification of internal (i.e., the physical stress imposed on the athlete) and external (i.e., distance covered) training load is viewed as essential to determine whether an athlete is adapting to a training programme, whilst minimising the risk of injury and overreaching. Although research has established correlations between internal measures of training load (i.e., session rating of perceived exertion [s-RPE] vs. summated heart rate zone method; Borrensen & Lambert, 2008, International Journal of Sports physiologi and performance, 3, 16-30) limited research exists comparing internal and external methods in team sports. The aim of this study was to establish the accuracy of s-RPE to quantify internal and external training load in adolescent rugby and hockey. Following institutional ethics approval, 22 youth sport (rugby & hockey) athletes were monitored across 125 training sessions (64 rugby & 61 hockey). External training load was monitored using a microtechnology unit to determine total distance and PlayerLoad, whilst internal loads were monitored using heart rate (summated heart rate zones) and s-RPE. Pearson correlation coefficients and 90% confidence intervals were calculated. Fishers r to z transformation compared the correlations between rugby and hockey. For summated HR zones and s-RPE, a large correlation (r=0.58, 90% CI: 0.43-0.70) was found for rugby with a very large correlation (r=0.75 90% CI: 0.64 to 0.83) for hockey. In rugby, large correlations were found between s-RPE and PlayerLoad (r= 0.64, 90% CI: 0.50 to 0.75), and total distance (r= 0.66 90% CI: 0.52 to 0.76). In hockey, large and moderate correlations were found between s-RPE and PlayerLoad (r= 0.55 90% CI: 0.39 to 0.69) and total distance (r= 0.42, 90% CI: 0.23 to 0.58) respectively. No significant differences were found between the correlations of internal and external measures between sports. The large and moderate correlations found between measures of total distance & PlayerLoad to s-RPE appear to support the theory that the individuals internal load is influenced by the external load they are exposed to highlighting the need for future research within this area. Furthermore, the large correlations found between s-RPE and the summated heart rate zones method highlights the potential for s-RPE to be used as an efficient technique in quantifying internal training load within adolescent rugby and hockey athletes. This suggests coaches can confidently monitor the internal training load of their athletes using s-RPE methods when HR technology is not available.

Purpose: To assess the relationships between training load, sleep duration and three daily wellbeing, recovery and fatigue measures in youth athletes. Methods: Fifty-two youth athletes completed three maximal countermovement jumps (CMJ), a daily wellbeing questionnaire (DWB), the Perceived Recovery Status scale (PRS), and provided details on their previous day's training loads (training) and self-reported sleep duration (sleep) on four weekdays over a seven week period. Partial correlations, linear mixed models and magnitude-based inferences were used to assess the relationships between the predictor variables (training; sleep) and the dependent variables (CMJ; DWB; PRS). Results: There was no relationship between CMJ and training (r=-0.09; ±0.06) or sleep (r=0.01; ±0.06). The DWB was correlated with sleep (r=0.28; ±0.05, small), but not training (r=-0.05; ±0.06). The PRS was correlated with training (r=-0.23; ±0.05, small), but not sleep (r=0.12; ±0.06). The DWB was sensitive to low sleep(d=-0.33; ±0.11) relative to moderate, PRS was sensitive to high (d=-0.36; ±0.11) and low (d=0.29; ±0.17) training relative to moderate. Conclusions: The PRS is a simple tool to monitor the training response, but DWB may provide a greater understanding of the athlete's overall wellbeing. The CMJ was not associated with the training or sleep response in this population.

The monitoring of training load is important to ensure athletes are adapting optimally to a training stimulus. Before quanti ca- tion of training load can take place, coaches must be con dent that the tools available are accurate. We aimed to quantify the within-participant correlation between the session rating of perceived exertion (s-RPE) and summated heart rate zone (sHRz) methods of monitoring internal training load. Training load (s-RPE and heart rate) data were collected for rugby, soc- cer and eld hockey eld-based training sessions over a 14- week in-season period. A total of 397 sessions were monitored (rugby n = 170, soccer n = 114 and eld hockey n = 113). With- in-subject correlations between s-RPE and sHRz were quanti- ed for each sport using a general linear model. Large correla- tions between s-RPE and the sHRz method were found for rugby (r = 0.68; 95 % CI 0.59–0.75) and eld hockey (r = 0.60; 95 % CI 0.47–0.71) with a very large correlation found for soccer (r = 0.72; 95 % CI 0.62–0.80). No signi cant di erences were found between the correlations for each sport. The very large and large correlations found between s-RPE and the sHRz meth- ods support the use of s-RPE in quantifying internal training load in youth sport.

Sex, chronological age, and maturity potentially impact multidimensional health-related characteristics (i.e. motor competence, physical fitness, psychosocial, physical activity), which adds to the challenges of reversing current youth health-related concerns. Previous research fails to optimally assess such characteristics and consider sex, age, and maturity among youth. Therefore, the aims were to 1) present the multidimensional health-related characteristics of 9–14-year-olds from the UK, 2) examine sex differences, and 3) account for the effect of age and maturity on such characteristics. Eighty-one girls (mean age = 12.8 ± 1.2 years) and 136 boys (mean age = 13.1 ± 1.2 years) were purposively sampled and assessed across each of the four health-related domains. Multiple ANCOVA analyses examined sex differences among characteristics while accounting for chronological age. Pearson’s correlations were used to evaluate the associations between maturity and multidimensional health-related characteristics. Multidimensional health-related characteristics were lower than similar populations and highly variable. Boys outperformed girls on most physical measures (ES = −0.76 to 0.76), elicited greater self-determined motivation (ES = 0.36), greater perceived competence (ES = 0.54), and engaged in more vigorous physical activity (ES = 0.78). Small age effects were present across some characteristics (e.g. isometric mid-thigh pull). Associations between maturity and multidimensional health-related characteristics were different for boys and girls (e.g. maturity offset positively associated with motor competence scores in girls only). Results suggest that multidimensional health-related characteristics of 9- to 14-year-olds are a concern, and are impacted by sex, age, and maturity. Identifying methods to improve multidimensional health-related characteristics which considers sex, age, and maturity are required. Assessing multidimensional health-related characteristics across youth is recommended to inform and measure interventions.

Abstract

The purpose of this study was to investigate the neuromuscular and perceptual fatigue responses of elite rugby players during the inaugural Under-18 (U18) Six Nations Festival. One hundred and thirty-three male players from five national squads (73 forwards, 60 backs) were examined during the competition. Each national squad was involved in three matches separated by 96 h each. Over the competition, players completed a daily questionnaire to monitor perceived well-being (WB) and performed daily countermovement jumps (CMJ) to assess neuromuscular function (NMF). Reductions in WB were substantial 24 h after the first and second match in forwards (d=0.77±0.21, p<0.0001; d=0.84±0.22, p< 0.001) and backs (d=0.89±0.22, p <0.0001; d=0.58±0.23, p<0.0001) but reached complete recovery in time for the subsequent match. Reductions in CMJ height were substantial 24 h after the first and second match for forwards (d=0.31±0.15, p=0.001; d=0.25±0.17, p=0.0205) and backs (d=0.40±0.17, p=0.0001; d=0.28±0.17, p=0.0062) and recovered at 48 h after match-play. Average WB and CMJ height attained complete recovery within matchday cycles in the investigated international competition. The findings of this study can be useful for practitioners and governing bodies involved with fixture scheduling and training prescription during competitive periods.

Background Head acceleration events (HAEs) are an increasing concern in collision sports owing to potential negative health outcomes. Objectives The objective of this study is to describe the probabilities of HAEs in tackles of differing heights and body positions in elite men’s and women’s rugby union. Methods Instrumented mouthguards (iMGs) were worn in men’s (n = 24 teams, 508 players, 782 observations) and women’s (n = 26 teams, 350 players, 1080 observations) rugby union matches. Tackle height (i.e. point of contact on ball-carrier) and body positions of tacklers and ball-carriers were labelled for all tackles in which a player wore an iMG. HAEs from the initial impact were identified. Mean player, tackler and ball-carrier exceedance probabilities for various peak linear and angular acceleration thresholds were estimated from ordinal mixed-effects models. Results Contact with ball-carriers’ head/neck resulted in the highest mean HAE probabilities for both sexes. The probability of an HAE to the ball-carrier decreased as tackle height lowered. The highest probability for the tackler was initial contact to the ball-carriers upper leg. Body position influenced the probability of HAEs, with falling/diving ball-carriers resulting in higher mean probabilities. When a player, regardless of role, was bent-at-waist, elevated HAE probabilities were observed in men’s competitions. Women’s data demonstrated similar probabilities of an HAE for all body positions. Conclusions Initial contact to the ball-carrier’s head/neck had the highest chance of an HAE, whilst role-specific differences are apparent for different tackle heights and body positions. Future player-welfare strategies targeting contact events should therefore consider HAE mechanisms along with current literature.

This study aimed to quantify and compare mean head acceleration event (HAE) incidence within and between men's and women's rugby union competitions; quantify the incidence of HAEs during all contact‐events and describe individual player incidence. Players competing during the 2022/2023 season in women's (337 players; Premiership Women's Rugby, Farah Palmer Cup) and men's (371 players; Premiership Rugby, Currie Cup and Super Rugby) competitions wore instrumented mouthguards (iMGs). Mean HAE incidences using peak linear (PLA) and peak angular acceleration (PAA) were quantified by sex, positional groups and individual players per competition and for contact‐events across a range of magnitude thresholds. Within positional groups, there was high between‐player variability, with some players experiencing up to a 3‐fold greater mean HAE incidence than their positional average. Per full‐game equivalent (FGE), men had significantly higher HAE incidences in most positional groups and HAE magnitude thresholds compared to women ranging from approximately 0.11–3.44 HAEs per FGE. Incidence of HAEs (PLA > 25 g) per FGE was lowest in scrums (0.00–0.04/FGE) and highest for tackles and ball carries (0.21–1.97/FGE) in both women and men, whereas mauling was a frequent source of HAEs for men's back row (0.95/FGE). No significant differences were observed between competitions for most positional groups and HAE magnitude thresholds in both men and women. Per FGE, HAE incidences were similar within, but significant differences were apparent between men's and women's players. The scrum had the lowest HAE incidence of all contact‐events. Individual players can show large variation from the mean, emphasising the importance of HAE mitigation strategies that include individual player monitoring and management processes.

This study quantified and compared the movement characteristics of elite domestic and international netball match-play, including fifteen individual players who compete at both levels. Microtechnology data were collected across 75 matches in a league-wide study from players (n = 113) competing in the Netball Superleague (elite domestic) and from international players (n = 23) in 22 international matches. Players were categorised according to the seven playing positions. Accelerometer-derived variables were analysed per whole-match and per quarter, for both absolute (i.e., volume) and relative to duration (i.e., intensity [per minute]) values. The median playing duration ranged across positions from 23.6 to 42.4 minutes at international and 31.6 to 48.1 minutes at domestic level. International matches were greater than elite domestic competition for relative variables across all positions. Moderate to large effect sizes (1.00–1.50) were found between playing levels for PlayerLoadTM per minute (AU·min-1). Significant decreases in both absolute and relative variables were observed across quarters for both competition levels. The movement characteristics are position dependent, with greater absolute characteristics at domestic level across whole-match analysis, but greater relative characteristics at international level. These findings provide practitioners with information to guide training prescription, return-to-play protocols, and transitioning athletes between levels of competition.

Participation in women’s rugby league has been growing since the foundation of the English women’s rugby league Super League in 2017. However, the evidence base to inform women’s rugby league remains sparse. This study provides the largest quantification of anthropometric and physical qualities of women’s rugby league players to date, identifying differences between positions (forwards & backs) and playing standard (Women’s Super League [WSL] vs. International). The height, weight, body composition, lower body strength, jump height, speed and aerobic capacity of 207 players were quantified during the pre-season period. Linear mixed models and effects sizes were used to determine differences between positions and standards. Forwards were significantly (p < 0.05) heavier (forwards: 82.5 ± 14.8kg; backs: 67.7 ± 9.2kg) and have a greater body fat % (forwards: 37.7 ± 6.9%; backs: 30.4 ± 6.3%) than backs. Backs had significantly greater lower body power measured via jump height (forwards: 23.5 ± 4.4cm; backs: 27.6 ± 4.9cm), speed over 10m (forwards: 2.12 ± 0.14s; backs: 1.98 ± 0.11s), 20m (forwards: 3.71 ± 0.27s; backs: 3.46 ± 0.20s), 30m (forwards: 5.29 ± 0.41s; backs: 4.90 ± 0.33s), 40m (forwards: 6.91 ± 0.61s; backs: 6.33 ± 0.46s) and aerobic capacity (forwards: 453.4 ± 258.8m; backs: 665.0 ± 298.2m) than forwards. Additionally, international players were found to have greater anthropometric and physical qualities in comparison to their WSL counterparts. This study adds to the limited evidence base surrounding the anthropometric and physical qualities of elite women’s rugby league players. Comparative values for anthropometric and physical qualities are provided which practitioners may use to evaluate the strengths and weaknesses of players, informing training programs to prepare players for the demands of women’s rugby league.

Understanding the most demanding passages of European Super League competition can optimise training prescription. We established positional and match half differences in peak relative distances (m·min-1) across durations, and the number of collisions, high-speed- and very-high-speed-distance completed in the peak 10 min period. Moving-averages (10 s, 30 s, 1 min, 5 min, 10 min) of instantaneous speed (m·s-1) were calculated from 25 professional rugby league players during 25 matches via microtechnology. Maximal m·min-1 was taken for each duration for each half. Concurrently, collisions (n), high-speed- (5 to 7 m·s-1; m) and very-high-speed-distance (> 7 m·s-1; m) were coded during each peak 10 min. Mixed-effects models determined differences between positions and halves. Aside from peak 10 s, trivial differences were observed in peak m·min-1 between positions or halves across durations. During peak 10 min periods, adjustables, full- and outside-backs ran more at high-speed and very-high-speed whilst middle- and edge-forwards completed more collisions. Peak m·min-1 is similar between positional groups across a range of durations and are maintained between halves of the match. Practitioners should consider that whilst the overall peak locomotor "intensity" is similar, how they achieve this differs between positions with forwards also exposed to additional collision bouts.

To alleviate issues arising from the over/under prescription of training load, coaches must ensure that desired athlete responses to training are being achieved. The present study aimed to assess the level of agreement between the coach intended (pre-session) and observed (post-session) rating of perceived exertion (RPE), with athlete RPE during different training intensities (easy, moderate, hard). Coach intended RPE was taken prior to all field based training sessions over an 8 week in-season period. Following training, all coaches and athletes, whom were participants in hockey, netball, rugby and soccer were asked to provide an RPE measure for the completed session. Sessions were then classified based on the coaches intended RPE, with a total of 28, 125 and 66 easy, moderate and hard training sessions collected respectively. A univariate analysis of variance was used to calculate within-participant correlations between coach intended/observed RPE and athlete RPE. Moderate correlations were found between coach intended and athlete RPE for sessions intended to be moderate and hard whilst a small correlation was found for sessions intended to be easy. The level of agreement between coach and athlete RPE improved following training with coaches altering their RPE to align with those of the athlete. Despite this, moderate and small differences between coach observed and athlete RPE persisted for sessions intended to be easy and moderate respectively. Coaches should therefore incorporate strategies to monitor training load to increase the accuracy of training periodisation and reduce potential over/under prescription of training.

Objectives The aim of this study was to describe the incidence and magnitude of head acceleration events (HAEs) during elite men’s and women’s rugby union training for different contact training levels and drill types. Method Data were collected during the 2022–23 and 2023–24 seasons from 203 men and 125 women from 13 clubs using instrumented mouthguards (iMGs) during in-season training. One author reviewed the training videos to identify the contact level and drill type. HAE incidence was calculated per player minute. Results For men’s forwards and backs, only 4.7% and 5.8% of HAEs were ≥ 25 g and ≥ 1.5 Krad/s2, and 3.4% and 4.4% for women’s forwards and backs, respectively. The incidence of ≥ 5 g and ≥ 0.4 Krad/s2 was highest during full-contact training for men’s forwards (0.20/min) and backs (0.16/min) and women’s forwards (0.10/min). HAE incidence was 2–3 times higher during repetition-based compared with game-based training drills for men’s forwards (0.25/min vs 0.09/min) and backs (0.22/min vs 0.09/min) and women’s forwards (0.09/min vs 0.04/min) and backs (0.08/min vs 0.03/min). HAE incidences were halved when repetition-based training drills used pads compared with no pads for men’s forwards (0.21/min vs 0.44/min) and backs (0.17/min vs 0.30/min), and women’s forwards (0.06/min vs 0.14/min) and backs (0.06/min vs 0.10/min). Conclusion The average HAE incidence (~ 13–20% of weekly HAEs) and magnitude during an in-season training week is very low compared with matches. Opportunities to materially reduce HAE exposure in training are likely more limited than previously assumed. Future research on HAE load and injury, and understanding players’ specific weekly training exposure, may inform effective individual player management.

This study aimed to quantify contact-events and associated head acceleration event (HAE) probabilities in semi-elite women's rugby union. Instrumented mouthguards (iMGs) were worn by players competing in the 2023 Farah Palmer Cup season (13 teams, 217 players) during 441 player-matches. Maximum peak linear acceleration (PLA) and peak angular acceleration (PAA) per-event were used as estimates of in vivo HAE (HAEmax), linked to video analysis-derived contact-events and analysed using mixed-effects regression. Back-rows had the highest number of contact-events per full-match (44.1 [41.2 to 47.1]). No differences were apparent between front-five and centres, or between half-backs and outside-backs. The probability of higher HAEmax occurring was greatest in ball-carries, followed by tackles, defensive rucks and attacking rucks. Probability profiles were similar between positions but the difference in contact-events for each position influenced HAEmax exposure. Overall, most HAEmax were relatively low. For example, the probability of a back-row experiencing a PLA HAEmax ≥25g was 0.045 (0.037-0.054) for ball carries (1 in every 22 carries), translating to 1 in every 2.3 full games. This study presents the first in-depth analysis of contact-events and associated HAEmax in semi-elite women's rugby union. The HAEmax profiles during contact-events can help inform both policy and research into injury mitigation strategies.

Background: The United Kingdom faces complex economic and structural challenges that have disrupted food pricing, contributing to widespread food insecurity. These fluctuations diminish the affordability and accessibility of healthy, nutrient-dense foods among vulnerable groups. In high-income countries, food insecurity is associated with higher levels of obesity, and in the UK specifically, the cost of living crisis, where the cost of food has increased quicker than wages, is likely to have exacerbated existing dietary inequalities. This qualitative paper explores insights of people living with obesity and food insecurity, in an ethnically diverse community, to develop further understanding on their food shopping experiences.Methods: A secondary analysis of qualitative data from four focus groups (8–11 participants per group; 92% female) was undertaken with participants who self-reported as living with obesity and food insecurity (n=39) and were attempting to reduce their weight. Results: Three themes and eight subthemes were generated using deductive and reflexive thematic analysis: (1) the Conscious Consumer, reflects the preparation and planning participants undertook by participants to maximise their limited resources. Subthemes include advanced meal planning, and price-comparison shopping. Despite these efforts, participants frequently encountered barriers to being able to purchase nutritionally balanced foods. (2) the Restricted Consumer highlights how structural and systemic limitations, including time pressures due to work or caregiving responsibilities, further constrained participants’ food purchasing choices. and (3) Mitigating the rising cost of food, describes the actions required to manage the challenges in purchasing foods with rising costs. Subthemes include substituting affordable, less-healthy products for costlier fresh produce and bulk buying of staple items. Conclusions: Findings challenge societal beliefs that people living on low incomes need to budget more carefully to afford a healthy diet. People living with obesity and food insecurity often report experiencing cognitive dissonance. In this context, participants faced difficult and emotive trade-offs, as they recognised the suboptimal nutritional value of their food purchases but felt compelled by necessity to buy unhealthier food that matched their budget. Findings provide further insights to support healthy, sustainable food purchasing, as part of transforming the UK food system.

Background: People experiencing food insecurity (FI) are more likely to live with obesity and purchase foods of lower dietary quality. Retail campaigns have the potential to influence food purchasing behaviours. Still, little is known about how the retailers’ messaging is perceived by people living with obesity (PLWO) and FI. This qualitative paper explores the insights of PLWO and FI on two national online and in-store campaigns targeted at i) supporting customers with increased food prices, and ii) promoting the consumption of healthier and more environmentally sustainable meals. Methods: Participants who self-reported as living with obesity and FI (n=39) expressed their perceptions of campaign images, from one retailer, through four in-person focus groups. Findings from the focus groups were then presented to the retail partner in an online participatory workshop. Themes were generated using reflexive thematic analysis.Results: Five themes and 12 subthemes were generated from the focus groups: (i) ‘Do I have the resources needed?’ Finances and, or time influenced participants’ food purchasing. (ii) ‘Do I know what it means?’ Participants did not always understand the images presented. (iii) ‘Do I trust it?’ Participants questioned whether the prices or images in the campaigns were authentic. (iv) ‘Do I want it?’ Participants questioned whether the food presented in the images appealed to them. (v) ‘Recommendations for future promotional communications’. Participants outlined how they wanted messaging to apply to them by using ethnically diverse food images that are suitable for a range of health conditions. From the retail partner participatory workshop we identified one theme and three subthemes. (i) ‘It is a conundrum’, the diverse needs of subgroups for national campaigns make it challenging for retailers to communicate healthy sustainable food promotions.Conclusions: These findings provide insights for retailers on the need for tailored communications, that reflect the requirements of different customers, to support PLWO and FI to purchase healthier and more sustainable foods. Acknowledging and addressing the inherent complexity of promoting healthier and more environmentally sustainable food is vital to making meaningful improvements to the food environment.

The original article has been updated to add the missing Electronic Supplemental Material.

Purpose Head acceleration events (HAEs) are a growing concern in contact sports, prompting two rugby governing bodies to mandate instrumented mouthguards (iMGs). This has resulted in an influx of data imposing financial and time constraints. This study presents two computational methods that leverage a dataset of video-coded match events: cross-correlation synchronisation aligns iMG data to a video recording, by providing playback timestamps for each HAE, enabling analysts to locate them in video footage; and post-synchronisation event matching identifies the coded match event (e.g. tackles and ball carries) from a video analysis dataset for each HAE, this process is important for calculating the probability of match events resulting in HAEs. Given the professional context of iMGs in rugby, utilising commercial sources of coded match event datasets may expedite iMG analysis. Methods Accuracy and validity of the methods were assessed via video verification during 60 rugby matches. The accuracy of cross-correlation synchronisation was determined by calculating synchronisation error, whilst the validity of post-synchronisation event matching was evaluated using diagnostic accuracy measures (e.g. positive predictive value [PPV] and sensitivity). Results Cross-correlation synchronisation yielded mean synchronisation errors of 0.61–0.71 s, with all matches synchronised within 3 s’ error. Post-synchronisation event matching achieved PPVs of 0.90–0.95 and sensitivity of 0.99–1.00 for identifying correct match events for SAEs. Conclusion Both methods achieved high accuracy and validity with the data sources used in this study. Implementation depends on the availability of a dataset of video-coded match events; however, integrating commercially available video-coded datasets offers the potential to expedite iMG analysis, improve feedback timeliness, and augment research analysis.

This study aimed to identify and compare the training frequency and intensity (via session rating of perceived exertion load (sRPE load)) of representative and non-representative late adolescent athletes. Thirty-six team sport athletes completed a web-based questionnaire daily over an 8-month period, reporting their training/match activities from the previous day. Athletes were categorised as representative (academy/county/international) or non-representative (club/school) depending on the highest level of their sport they participated. Mean weekly frequencies and sRPE load of different training/match activities were quantified for each athlete across five school terms. Mann-Whitney U tests established the significance of differences and effect sizes between playing standards for mean weekly frequencies and mean sRPE load. Within-athlete weekly sRPE loads were highly variable for both playing standards however representative level athletes participated in significantly more activity outside of school compared to non-representative athletes during November to December (effect size; 0.43 – club technical training; 0.36 – club matches), January to February (effect size; 0.78 – club technical training; 0.75 – club matches) and February to March (effect size; 0.63 – club technical training; 0.44 – club matches). Therefore, club and school coaches must ensure that all elements of representative athlete's training schedules are coordinated and flexible to promote positive adaptions to training such as skill & physical development and prevent maladaptive responses such as overuse injury and non-functional overreaching. A cooperative and malleable training schedule between club/school coaches and the athlete will allow the athlete to perform on multiple fronts whilst also being able to meet the demands of additional stressors such as schoolwork.

The development of a youth team sport athlete is a complex process. This paper outlines challenges which may restrict the optimal balance between training and recovery and provide solutions to help practitioners overcome these challenges. To facilitate positive youth athletic development, training aims must be aligned between stakeholders to synchronise periods of intensified training and recovery. Within- and between-athlete variations in weekly training load must be managed and practitioners should attempt to ensure the intended load of training equals the load perceived by the athlete. Furthermore, practitioners should be cognizant of the athletes’ non-sport related stressors to enable both academic and sporting pursuits. Whilst each of these challenges adds intricacy, they may be overcome through collaboration, monitoring and if necessary, the modification of the athletes’ training load.

Purpose: To evaluate the relative importance and predictive ability of salivary immunoglobulin A (s-IgA) measures with regards to upper respiratory illness (URI) in youth athletes. Methods: Over a 38-week period, 22 youth athletes (age = 16.8 [0.5] y) provided daily symptoms of URI and 15 fortnightly passive drool saliva samples, from which s-IgA concentration and secretion rate were measured. Kernel-smoothed bootstrapping generated a balanced data set with simulated data points. The random forest algorithm was used to evaluate the relative importance (RI) and predictive ability of s-IgA concentration and secretion rate with regards to URI symptoms present on the day of saliva sampling (URIday), within 2 weeks of sampling (URI2wk), and within 4 weeks of sampling (URI4wk). Results: The percentage deviation from average healthy s-IgA concentration was the most important feature for URIday (median RI 1.74, interquartile range 1.41–2.07). The average healthy s-IgA secretion rate was the most important feature for URI4wk (median RI 0.94, interquartile range 0.79–1.13). No feature was clearly more important than any other when URI symptoms were identified within 2 weeks of sampling. The values for median area under the curve were 0.68, 0.63, and 0.65 for URIday, URI2wk, and URI4wk, respectively. Conclusions: The RI values suggest that the percentage deviation from average healthy s-IgA concentration may be used to evaluate the short-term risk of URI, while the average healthy s-IgA secretion rate may be used to evaluate the long-term risk. However, the results show that neither s-IgA concentration nor secretion rate can be used to accurately predict URI onset within a 4-week window in youth athletes.

This study investigated the seasonal change in physical performance of 113 (Under 10: U10 (n=20), U12 (n=30), U14 (n=31) and U16 (n=32)) elite youth female soccer players. Players completed testing pre-, mid- and post-season, including speed (10 and 30m sprint), change of direction (CoD; 505 test), power (Countermovement jump, CMJ), strength (isometric midthigh pull) and aerobic capacity (YoYo Intermittent Recovery Test Level 1; YYIRL1).

Youth athletes frequently participate in multiple sports or for multiple teams within the same sport. To optimise player development and minimise undesirable training outcomes (e.g., overuse injuries), practitioners must be cognizant of an athlete's training load within and outside of their practice. The present study aimed to establish the validity of a 24-hour (s-RPE24) and 72-hour (s-RPE72) recall of session rating of perceived exertion (s-RPE) against the criterion measure of s-RPE collected 30 minutes' post training (s-RPE30). Thirty-eight adolescent athletes provided a s-RPE30 following the first field based training session of the week. Approximately 24 hours later subjects were asked to recall the intensity and duration of the previous days training. The following week subjects once again provided a s-RPE30 measure post training before recalling the intensity and duration of the session approximately 72 hours later. A nearly perfect correlation (0.98 [0.97 - 0.99]) was found between s-RPE30 and s-RPE24, with a small typical error of estimate (TEE; 8.3% [6.9 - 10.5]) and trivial mean bias (-1.1% [-2.8 - 0.6]). Despite a large correlation between s-RPE30 and s-RPE72 (0.73 [0.59 - 0.82]) and a trivial mean bias (-0.2% [-6.8 - 6.8]) there was a large typical error of estimate (TEE; 35.3% [29.6 - 43.9]). s-RPE24 provides a valid measure of retrospectively quantifying s-RPE, however the large error associated with s-RPE72 suggests it is not a suitable method for monitoring training load in youth athletes.

This study aimed to (1) compare individual player match action characteristics between scholarship, academy, and senior (European Super League, ESL) levels of the rugby league player pathway, and (2) compare match actions between players that have progressed to play ESL and those that did not. Data was collected on 147 players from 95 senior, 69 academy, and 23 scholarship matches over three seasons. Matches were filmed via 2 angles and 26 match action characteristics (e.g., carry, missed tackle) were coded. Linear mixed models identified forty-eight significant differences in match action characteristics when accounting for playing position between playing levels. Over seventy percent of the differences were defensive match actions, indicating there are higher defensive match demands in the ESL when compared to academy and scholarship match play. Seven and eleven match actions characteristics were identified at scholarship and academy levels that differentiated between players who had progressed to play in the ESL and those who had not. All but one of these characteristics were attacking match actions, indicating a player’s attacking qualities are important in their progression to the ESL. These results have implications for both talent identification and long-term athlete development in rugby league.

The aim of this study was to investigate the difference in head acceleration event (HAE) incidence between training and match‐play in women's and men's players competing at the highest level of domestic rugby union globally. Players from Women's (Premiership Women's Rugby, Farah Palmer Cup) and Men's (Premiership Rugby, Currie Cup) rugby union competitions wore instrumented mouthguards during matches and training sessions during the 2022/2023 seasons. Peak linear (PLA) and angular (PAA) acceleration were calculated from each HAE and included within generalized linear mixed‐effects models. The incidence of HAEs was significantly greater in match‐play compared to training for all magnitude thresholds in both forwards and backs, despite players spending approximately 1.75–2.5 times more time in training. For all HAEs (PLA > 5 g and PAA > 400 rad/s2), incidence rate ratios (IRRs) for match versus training ranged from 2.80 (95% CI: 2.38–3.30; men's forwards) to 4.00 (3.31–4.84; women's forwards). At higher magnitude thresholds (PLA > 25 g; PAA > 2000 rad/s2), IRRs ranged from 3.64 (2.02–6.55; PAA > 2000 rad/s2 in men's backs) to 11.70 (6.50–21.08; PAA > 2000 rad/s2 in women's forwards). Similar trends were observed in each competition. Players experienced significantly more HAEs during match‐play than training, particularly at higher magnitude thresholds. Where feasible, HAE mitigation strategies may have more scope for HAE reduction if targeted at match‐play, particularly where higher magnitude HAEs are the primary concern. However, the number of HAEs associated with different training drills requires exploration to understand if HAEs can be reduced in training, alongside optimizing match performance (e.g., enhancing contact technique).

This two-part study evaluated the inter- and intra-unit reliability of Catapult Vector S7 microtechnology units in an indoor court-sport setting. In part-one, 27 female netball players completed a controlled movement series on two separate occasions to assess the inter- and intra-unit reliability of inertial movement analysis (IMA) variables (acceleration, deceleration, changes of direction and jumps). In part-two, 13 female netball players participated in 10 netball training sessions to assess the inter-unit reliability of IMA and PlayerLoadTM variables. Participants wore two microtechnology units placed side-by-side. Reliability was assessed using intraclass correlation coefficient (ICC), coefficient of variation (CV) and typical error (TE). Total IMA events showed good inter-unit reliability during the movement series (ICC, 1.00; CV, 3.7%) and training sessions (ICC, 0.99; CV, 4.5%). Inter-unit (ICC, 0.97; CV, 4.7%) and intra-unit (ICC, 0.97; CV, 4.3%) reliability for total IMA jump count was good in the movement series, with moderate CV (7.7%) during training. Reliability decreased when IMA counts were categorised by intensity and movement type. PlayerLoadTM (ICC, 1.00; CV, 1.5%) and associated variables revealed good inter-reliability, except peak PlayerLoadTM (moderate) and PlayerLoadSLOW (moderate). Counts of IMA variables, when considered as total and low-medium counts, and PlayerLoad variables are reliable for monitoring indoor court-sports players.

Rugby league has a relatively high injury risk, with the tackle having the greatest injury propensity. The number of tackles players engage in, prior to injurious tackles may influence injury risk, which has yet to be investigated. Therefore, this study investigated if rugby league players are involved in more tackles (as either tackler or ball carrier) (i) in the 10 minutes, or (ii) 1-min periods prior to an injurious tackle-event, (iii) differences for ball carriers vs. tacklers, and (iv) forwards vs. backs. Video analysis was utilised to quantify the number and rate of tackles in the 10-min periods prior to 61 tackle-related injuries. One thousand two hundred and eighty 10-min periods where players were not injured, were used as matched-controls. Generalized mixed linear models were used to analyse mean total and rate for tackles. Injured players were involved in significantly fewer tackles during the 10-min period, yet significantly more tackles during the final minute prior to the injurious tackle-event, compared to non-injured players. There were no differences between ball carriers vs. tacklers during the 10-min period. Both injured position groups were involved in significantly more tackles in the final minute. Additional match data sources are needed to further inform injury preventive strategies of tackle events.

Interpreting the physical qualities of youth athletes is complex due to the effects of growth, maturation and development. This study aimed to evaluate the effect of position, chronological age, relative age and maturation on the physical qualities of elite male academy rugby union players. 1,424 participants (n=2,381 observations) from nine Rugby Football Union Regional Academies prospectively completed a physical testing battery at three time points, across three playing seasons. Anthropometrics, body composition, muscular power, muscular strength, speed, aerobic capacity and running momentum were assessed. Positional differences were identified for all physical qualities. The largest effect sizes were observed for the associations between chronological age (d=0.65 to 0.73) and maturation (d=-0.77 to -0.69) and body mass related variables (i.e. body mass and running momentum). Relative strength, maximum velocity and aerobic capacity were the only models to include two fixed effects with all other models including at least three fixed effects (i.e. position and a combination of chronological age, relative age and maturation). These findings suggest a multidimensional approach considering position, chronological age, relative age and maturation is required to effectively assess the physical qualities of male age grade rugby union players. Therefore practitioners should use regression equations rather than traditional descriptive statistic tables to provide individualised normative comparisons thus enhancing the application of testing results for talent identification and player development.

Identifying the external training load variables which influence subjective internal response will help reduce the mismatch between coach-intended and athlete-perceived training intensity. Therefore, this study aimed to reduce external training load measures into distinct principal components (PCs), plot internal training response (quantified via session Rating of Perceived Exertion [sRPE]) against the identified PCs and investigate how the prescription of PCs influences subjective internal training response. Twenty-nine school to international level youth athletes wore microtechnology units for field-based training sessions. SRPE was collected post-session and assigned to the microtechnology unit data for the corresponding training session. 198 rugby union, 145 field hockey and 142 soccer observations were analysed. The external training variables were reduced to two PCs for each sport cumulatively explaining 91%, 96% and 91% of sRPE variance in rugby union, field hockey and soccer, respectively. However, when internal response was plotted against the PCs, the lack of separation between low-, moderate- and high-intensity training sessions precluded further analysis as the prescription of the PCs do not appear to distinguish subjective session intensity. A coach may therefore wish to consider the multitude of physiological, psychological and environmental factors which influence sRPE alongside external training load prescription.

Objectives Describe head acceleration events (HAEs) experienced by professional male rugby union players during tackle, ball‐carry, and ruck events using instrumented mouthguards (iMGs). Design Prospective observational cohort. Methods Players competing in the 2023 Currie Cup (141 players) and Super Rugby (66 players) seasons wore iMGs. The iMG‐recorded peak linear acceleration (PLA) and peak angular acceleration (PAA) were used as in vivo HAE approximations and linked to contact‐event data captured using video analysis. Using the maximum PLA and PAA per contact event (HAEmax), ordinal mixed‐effects regression models estimated the probabilities of HAEmax magnitude ranges occurring, while accounting for the multilevel data structure. Results As HAEmax magnitude increased the probability of occurrence decreased. The probability of a HAEmax ≥15g was 0.461 (0.435–0.488) (approximately 1 in every 2) and ≥45g was 0.031 (0.025–0.037) (1 in every 32) during ball carries. The probability of a HAEmax >15g was 0.381 (0.360–0.404) (1 in every 3) and >45g 0.019 (0.015–0.023) (1 in every 53) during tackles. The probability of higher magnitude HAEmax occurring was greatest during ball carries, followed by tackles, defensive rucks and attacking rucks, with some ruck types having similar profiles to tackles and ball carries. No clear differences between positions were observed. Conclusion Higher magnitude HAEmax were relatively infrequent in professional men's rugby union players. Contact events appear different, but no differences were found between positions. The occurrence of HAEmax was associated with roles players performed within contact events, not their actual playing position. Defending rucks may warrant greater consideration in injury prevention research.

Abstract

The provision of instantaneous visual kinematic feedback has been shown to improve physical performance and psychological traits. However, this research has only investigated changes across a single set of exercise in adolescent males. Therefore, the aim of this study was to assess the effects of visual kinematic feedback on kinematic outputs during multiple sets of the jump squat in adolescent female athletes. In addition, motivation and competitiveness were also assessed. Eleven adolescent female athletes volunteered to take part in this study. In a randomised-crossover study design, subjects either were or were not provided peak concentric velocity using visual feedback during three sets of six repetitions of the jump squat. A linear position transducer measured peak concentric velocity of each repetition across the three sets, while motivation and competitiveness were measured before and after exercise. Magnitude-based inferences were used to assess changes between conditions, with mean peak concentric velocity (mean ±90%CI: 0.23 ±0.04m·s-1; ES ±90%CI: 2.73 ±0.44; percent ±90%CI: 10.3 ±1.8) and power (mean ±90%CI: 330 ±53W; ES ±90%CI: 2.87 ±0.52; percent ±90%CI: 16.5 ±3.2) almost certainly greater when feedback was provided. Furthermore, motivation almost certainly improved (ES ±90%CI: 2.81 ±0.63) when feedback was provided, while competitiveness was almost certainly greater (ES ±90%CI: 4.88 ±0.58) following the provision of kinematic feedback. Findings from this study demonstrate that providing adolescent female athletes visual kinematic information while completing plyometric exercise is beneficial for performance and can enhance psychological responses across multiple sets. Consequently, practitioners are advised to utilise kinematic feedback during training to enhance training quality and improve motivation and competitiveness.

Background: Growing evidence highlights that elite rugby union players experience poor sleep quality and quantity which can be detrimental for performance. Objectives: This study aimed to i) compare objective sleep measures of rugby union players between age categories over a one week period, and ii) compare self-reported measures of sleep to wristwatch actigraphy as the criterion. Methods: Two hundred and fifty-three nights of sleep were recorded from 38 players representing four different age groups (i.e. under 16, under 18, senior academy, elite senior) in a professional rugby union club in the United Kingdom (UK). Linear mixed models and magnitude-based decisions were used for analysis. Results: The analysis of sleep schedules showed that U16 players went to bed and woke up later than their older counterparts (small differences). In general, players obtained seven hours of sleep per night, with trivial or unclear differences between age groups. The validity analysis highlighted a large relationship between objective and subjective sleep measures for bedtime (r = 0.56 [0.48 to 0.63]), and get up time (r = 0.70 [0.63 to 0.75]). A large standardised typical error (1.50 [1.23 to 1.88]) was observed for total sleep time. Conclusion: This study highlights that differences exist in sleep schedules between rugby union players in different age categories that should be considered when planning training. Additionally, self-reported measures overestimated sleep parameters. Coaches should consider these results to optimise sleep habits of their players and should be careful with self-reported sleep measures.

Purpose: Collision sports are characterised by frequent high intensity collisions that induce substantial muscle damage, potentially increasing the energetic cost of recovery. Therefore, this study investigated the energetic cost of collision-based activity for the first time across any sport. Methods: Using a randomised crossover design, six professional young male rugby league players completed two different five-day pre-season training microcycles. Players completed either a collision (COLL; 20 competitive one-on-one collisions) or non-collision (nCOLL; matched for kinematic demands, excluding collisions) training session on the first day of each microcycle, exactly seven days apart. All remaining training sessions were matched and did not involve any collision-based activity. Total energy expenditure was measured using doubly labelled water, the literature gold standard. Results: Collisions resulted in a very likely higher (4.96 ± 0.97 MJ; ES = 0.30 ±0.07; p=0.0021) total energy expenditure across the five-day COLL training microcycle (95.07 ± 16.66 MJ) compared with the nCOLL training microcycle (90.34 ± 16.97 MJ). The COLL training session also resulted in a very likely higher (200 ± 102 AU; ES = 1.43 ±0.74; p=0.007) session rating of perceived exertion and a very likely greater (-14.6 ± 3.3%; ES = -1.60 ±0.51; p=0.002) decrease in wellbeing 24h later. Conclusions: A single collision training session considerably increased total energy expenditure. This may explain the large energy expenditures of collision sport athletes, which appear to exceed kinematic training and match demands. These findings suggest fuelling professional collision-sport athletes appropriately for the "muscle damage caused” alongside the kinematic “work required”. Key words: Nutrition, Recovery, Contact, Rugby

Participation in women’s rugby league has been growing since the foundation of the English women’s rugby league Super League in 2017. However, the evidence base to inform women’s rugby league remains sparse. This study provides the largest quantification of anthropometric and physical qualities of women’s rugby league players to date, identifying differences between positions (forwards & backs) and playing level (Women’s Super League [WSL] vs. International). The height, weight, body composition, lower body strength, jump height, speed and aerobic capacity of 207 players were quantified during the pre-season period. Linear mixed models and effects sizes were used to determine differences between positions and levels. Forwards were significantly (p < 0.05) heavier (forwards: 82.5 ± 14.8kg; backs: 67.7 ± 9.2kg) and have a greater body fat % (forwards: 37.7 ± 6.9%; backs: 30.4 ± 6.3%) than backs. Backs had significantly greater lower body power measured via jump height (forwards: 23.5 ± 4.4cm; backs: 27.6 ± 4.9cm), speed over 10m (forwards: 2.12 ± 0.14s; backs: 1.98 ± 0.11s), 20m (forwards: 3.71 ± 0.27s; backs: 3.46 ± 0.20s), 30m (forwards: 5.29 ± 0.41s; backs: 4.90 ± 0.33s), 40m (forwards: 6.91 ± 0.61s; backs: 6.33 ± 0.46s) and aerobic capacity (forwards: 453.4 ± 258.8m; backs: 665.0 ± 298.2m) than forwards. Additionally, international players were found to have greater anthropometric and physical qualities in comparison to their WSL counterparts. This study adds to the limited evidence base surrounding the anthropometric and physical qualities of elite women’s rugby league players. Comparative values for anthropometric and physical qualities are provided which practitioners may use to evaluate the strengths and weaknesses of players, informing training programs to prepare players for the demands of women’s rugby league.

© 2017 Thomas Sawczuk, Ben Jones, Sean Scantlebury, Jonathan Weakley, Dale Read, Nessan Costello, Joshua David Darrall-Jones, Keith Stokes, and Kevin Till This study aimed to evaluate the between-day reliability and usefulness of a fitness testing battery in a group of youth sport athletes. Fifty-nine youth sport athletes (age = 17.3 ± 0.7 years) undertook a fitness testing battery including the isometric mid-thigh pull, counter-movement jump, 5–40 m sprint splits, and the 5–0-5 change of direction test on two occasions separated by 7 days. Usefulness was assessed by comparing the reliability (typical error) to the smallest worthwhile change. The typical error was 5.5% for isometric mid-thigh pull and 3.8% for counter-movement jump. The typical error values were 2.7, 2.5, 2.2, 2.2, and 1.8% for the 5, 10, 20, 30, and 40 m sprint splits, and 4.1% (left) and 5.4% (right) for the 5–0-5 tests. The smallest worthwhile change ranged from 1.1 to 6.1%. All tests were identified as having “good” or “acceptable” reliability. The isometric mid-thigh pull and counter-movement jump had “good” usefulness, all other tests had “marginal” usefulness.