This study investigated the effects of docosahexaenoic acid (DHA) supplementation on delayed onset muscle soreness (DOMS), physical function, and inflammation following eccentric exercise-induced muscle damage in physically trained male and female adults (training ≥ 5d/wk). Thirty-eight participants (12 Control, 26 DHA) completed a 12-week double-blind, placebo-controlled matched-pair trial. The Control group received high-oleic acid tablets. The DHA group received 715 mg/d of microencapsulated DHA tablets. Participants performed eccentric cycling at weeks 0 and 12, with assessments conducted pre-exercise, 0-h, 24-h, and 48-h post-exercise. The primary outcomes were DOMS (visual analogue scale) and the Omega-3 Index (O3I) (estimated by finger-stick dry blood spot). Secondary outcomes included neuromuscular function and inflammatory cytokines. O3I was not different between groups at week 0 but was elevated in the DHA group (∆2.43

INTRODUCTION:Effective and easily implementable methods to reduce the incidence and burden of injury during Army basic military training (BMT) are desirable. This study therefore investigated (1) the association between prior injury history and medical attention (MA) injury and (2) the association and accuracy of daily self-reported physical complaints on the incidence of MA injury, during Army BMT. MATERIALS AND METHODS:Recruits (n = 625, male = 524; female = 101; age: 22 ± 6 years [range: 17-55 years]) completed a 12-month prior injury history questionnaire during week 1 and throughout BMT reported physical complaints daily, using a modified Oslo Sports Trauma Research Centre Questionnaire on Health Problems (OSTRC-H). Medical attention injuries were recorded via physiotherapy reports. Cox proportional hazard regressions explored the association between prior injury and MA injury. Generalized linear mixed-effects models were used to model the association between OSTRC-H responses and an MA incident injury within 7 days. The predictive ability and accuracy of OSTRC-H responses were also assessed. RESULTS:Prior injury was not significantly associated with a greater risk of MA injury during BMT. Self-reported physical complaints effecting "participation" ("Full participation, but with injury/illness": OR = 2.23, 95% CI 1.97-2.52; "Reduced participation due to injury/illness": OR = 3.19, 95% CI 2.54-4.00), "severity" ("To a mild extent": OR = 2.19, 95% CI 1.91-2.51; "To a moderate extent": OR = 2.83, 95% CI 2.38-3.36; "To a severe extent": OR = 4.50, 95% CI 3.26-6.21), and "location" (OR = 2.19, 95% CI 1.96-2.45) were significantly associated with greater odds of MA incident injury within 7 days. Spine (OR = 4.39, 95% CI 3.07-6.30), upper extremity (OR = 2.45, 95% CI 1.76-3.40), and lower extremity (OR = 2.73, 95% CI 2.40-3.40) physical complaints were significantly associated with an MA incident injury to the corresponding general body region within 7 days. Using the presence of a physical complaint to indicate the occurrence of an MA incident injury within 7 days resulted in a high number of false positives and false negatives (area under the curve: 0.51-0.66). CONCLUSIONS:Independently, self-reported 12-month prior injury was not significantly associated with a greater risk of an MA injury during BMT. Daily self-reported physical complaints may however flag increased MA injury risk, which could help prevent more severe injuries.

Exercise triggers a proportional inflammatory response that is crucial for muscle fiber repair and adaptation. However, excessive or chronic inflammation can delay recovery and increase injury risk. Biomarkers such as creatine kinase and interleukins are commonly used to assess muscle damage and inflammation, respectively, but they have limitations in specificity and sensitivity. However, lipid mediators that signal all inflammatory events with distinct roles in pro-inflammatory, anti-inflammatory, and pro-resolving phases could provide a more complete understanding of the inflammatory response to exercise. In this review, we discuss the limitations of current biomarkers and the potential of lipid mediators to theoretically offer more precise insights into the inflammatory processes following exercise. Further research is needed to establish standardized protocols for measuring lipid mediators and to understand their temporal dynamics in relation to inflammation and recovery. This knowledge could lead to improved strategies for monitoring and enhancing recovery in athletes.

In football, the number of days without full participation in training/competition is often used as a surrogate measure for time-loss (TL) caused by injury. However, injury management and return-to-play processes frequently include modified participation, which to date has only been recorded through self-reports. This study aims to demonstrate the differentiation between 'full' (no participation in team football) and 'partial' (reduced/modified participation in team football) burden. Injury and exposure data were collected from 118 male elite footballers (U13-U18) over 3 consecutive seasons according to the Football Consensus Statement. TL injury burden was calculated separately as the number of total, 'full' and 'partial' days lost per 1000 h of exposure. Injury burden (137.2 days lost/1000 h, 95% CI 133.4-141.0) was comprised of 23% (31.9 days lost/1000 h, 95% CI 30.1-33.8) partial TL and 77% (105.3 days lost/1000 h, 95% CI 102.0-108.6) full TL burden. Injuries of moderate severity (8-28 days lost) showed 40% of partial TL. TL injury incidence rate (6.6 injuries/1000 h, 95% CI 5.8-7.5), the number of severe injuries (16%), and the distribution of TL and non-TL injuries (56% and 44%) were comparable to other reports in elite youth footballers. Almost one-quarter of the TL injury burden showed that injured players were still included in some team football activities, which, for injuries with TL >7 days, was likely related to the return to play process. Therefore, reporting on partial TL provides insight into the true impact of injury on participation levels.

INTRODUCTION:The injury definitions and surveillance methods commonly used in Army basic military training (BMT) research may underestimate the extent of injury. This study therefore aims to obtain a comprehensive understanding of injuries sustained during BMT by employing recording methods to capture all physical complaints. MATERIALS AND METHODS:Six hundred and forty-six recruits were assessed over the 12-week Australian Army BMT course. Throughout BMT injury, data were recorded via (1) physiotherapy reports following recruit consultation, (2) a member of the research team (third party) present at physical training sessions, and (3) recruit daily self-reports. RESULTS:Two hundred and thirty-five recruits had ≥1 incident injury recorded by physiotherapists, 365 recruits had ≥1 incident injury recorded by the third party, and 542 recruits reported ≥1 injury-related problems via the self-reported health questionnaire. Six hundred twenty-one, six hundred eighty-seven, and two thousand nine hundred sixty-four incident injuries were recorded from a total of 997 physiotherapy reports, 1,937 third-party reports, and 13,181 self-reported injury-related problems, respectively. The lower extremity was the most commonly injured general body region as indicated by all three recording methods. Overuse accounted for 79% and 76% of documented incident injuries from physiotherapists and the third party, respectively. CONCLUSIONS:This study highlights that injury recording methods impact injury reporting during BMT. The present findings suggest that traditional injury surveillance methods, which rely on medical encounters, underestimate the injury profile during BMT. Considering accurate injury surveillance is fundamental in the sequence of injury prevention, implementing additional injury recording methods during BMT may thus improve injury surveillance and better inform training modifications and injury prevention programs.

Purpose Many athletes are deficient in long chain omega-3 polyunsaturated fatty acids (LC n-3 PUFA). A consequent low Omega-3 Index (O3I) and high arachidonic acid/eicosapentaenoic acid (AA/EPA) ratio increase cardiovascular disease risk and inflammation. Algae oil is a plant-based, sustainable source of LC n-3 PUFA, suitable for vegans and vegetarians. Effects of algae oil supplementation on whole blood fatty acids among athletes has not been previously reported. This study evaluated the effects of 5 weeks of DHA-rich algae oil supplementation on the whole blood fatty acid profile, O3I and AA/EPA ratio of omnivorous Division I American College Football (ACF) players. Methods: Data, including a spot blood sample, were collected at baseline for all participants ( n = 47), then for a subset of players ( n = 22) following a 5-week control period (usual diet) and 5 weeks of algae oil supplementation (usual diet + 1575 mg docosahexaenoic acid (DHA) + eicosapentaenoic acid (EPA) 5 days/week; average 1125 mg/day). Results: Baseline O3I was 4.3% ± 0.1% and AA/EPA ratio was 45.6 ± 23.8. After 5 weeks of algae oil supplementation, the O3I was 6.1% ± 1.0% and the AA/EPA ratio was 25.1 ± 11.6. The O3I was significantly higher and the AA/EPA ratio was significantly lower ( P < 0.0001 for both) compared with both baseline and the end of the control period. The increase in O3I from baseline was correlated with calculated DHA + EPA dose per unit body mass ( R = 0.641, P = 0.001). Conclusions: Algae oil supplementation for 5 weeks improved both the low baseline O3I and high AA/EPA ratio among ACF players, with body mass specific dose effects.

Objectives To describe the incidence, location, mechanism and burden of injury in community male adolescent rugby. Methods A prospective cohort injury surveillance study using sports trainers to record 'any physical complaint' over three seasons (2018/2019/2021) in 979 U13-U17 community male rugby union players. Results One hundred and fifty-two time-loss injuries (27.6/1000 hours) with an associated burden of 2313 days (419.7 days/1000 hours), 169 non-time loss medical attention (30.1/1000 hours) and 813 physical complaints (147.5/1000 hours) were recorded from 5511.7 exposure hours (matches 3932.5 hours, training 1579.2 hours). Time-loss injury incidence was highest in U16 (45/1000 hours) and lowest in U17 (16.6/1000 hours), with U17 significantly lower than U16 and U15 age-grades (p < 0.05). Injury burden was greatest in U13 (561.4 days/1000 hours), and significantly higher than U15 and U17 (p < 0.05). Collectively, injury incidence was greatest for the head/neck (11.8/1000 hours), bruise/contusions were most common (8.7/1000 hours) and concussion (4.5/1000 hours) accounted for the greatest injury burden (102 days/1000 hours). Being tackled was the most observed injury mechanism (10.0/1000 hours). Forwards had significantly higher incidence in mild injury (p < 0.01). The total burden (p < 0.001) associated with mild (p < 0.001) and moderate injuries (p < 0.001) was significantly higher in forwards, as was the burden of being tackled (p < 0.001), collisions (p < 0.001), trunk (p < 0.001) and lower limb (p < 0.01) injury locations. In contrast, ruck-related injury burden was greater in backs (p < 0.001). Conclusion This study showed age-grade and positional differences in incidence and burden of injury in community adolescent rugby union. The rate of non-time loss relative to time-loss injury and muscle strain injury in U13-U14s suggests further research into injury risk and maturation in rugby is needed.

INTRODUCTION:Subjective measures may offer practitioners a relatively simple method to monitor recruit responses to basic military training (BMT). Yet, a lack of agreement between subjective and objective measures may presents a problem to practitioners wishing to implement subjective monitoring strategies. This study therefore aims to examine associations between subjective and objective measures of workload and sleep in Australian Army recruits. MATERIALS AND METHODS:Thirty recruits provided daily rating of perceived exertion (RPE) and differential RPE (d-RPE) for breathlessness and leg muscle exertion each evening. Daily internal workloads determined via heart rate monitors were expressed as Edwards training impulse (TRIMP) and average heart rate. External workloads were determined via global positioning system (PlayerLoadTM) and activity monitors (step count). Subjective sleep quality and duration was monitored in 29 different recruits via a customized questionnaire. Activity monitors assessed objective sleep measures. Linear mixed-models assessed associations between objective and subjective measures. Akaike Information Criterion assessed if the inclusion of d-RPE measures resulted in a more parsimonious model. Mean bias, typical error of the estimate (TEE) and within-subject repeated measures correlations examined agreement between subjective and objective sleep duration. RESULTS:Conditional R2 for associations between objective and subjective workloads ranged from 0.18 to 0.78, P < 0.01, with strong associations between subjective measures of workload and TRIMP (0.65-0.78), average heart rate (0.57-0.73), and PlayerLoadTM (0.54-0.68). Including d-RPE lowered Akaike Information Criterion. The slope estimate between objective and subjective measures of sleep quality was not significant. A trivial relationship (r = 0.12; CI -0.03, 0.27) was observed between objective and subjective sleep duration with subjective measures overestimating (mean bias 25 min) sleep duration (TEE 41 min). CONCLUSIONS:Daily RPE offers a proxy measure of internal workload in Australian Army recruits; however, the current subjective sleep questionnaire should not be considered a proxy measure of objective sleep measures.

Adolescent elite-level footballers are exposed to unique physical and psychological stressors which may increase injury risk, with fluctuating injury prevalence and burden. This study investigates the patterns of injury incidence and burden from 2017 to 2020 within combined pre-, start-of-, mid- and end-of-season and school-holiday phases in U13-U18 Australian male academy players. Injury incidence rate and burden were calculated for medical attention (MA), full and partial time-loss (TL) and non-time-loss (non-TL) injuries. Injury rate ratios (IRR) for injury incidences were assessed using Generalised Linear Mixed Models, and 99% confidence intervals for injury burden differences between phases. MA and non-TL injury incidence rates were higher during pre-season (IRR 1.65, p = 0.01; IRR 2.08, p = 0.02, respectively), and mid-season showed a higher non-TL incidence rate (IRR 2.15, p = 0.02) and burden (69 days with injury/1000 hrs, CI 47-103) compared to end-of-season (25 days with injury/1000 hrs, CI 15-45). MA injury rates and partial TL injury burden were higher during school compared to holiday periods (IRR 0.6, p = 0.04; 61 partial days lost/1000 hrs, CI 35-104; 13 partial days lost/1000 hrs, CI 8-23). Season phase and return-to-school may increase injury risks for elite academy footballers, and considering these phases may assist in developing injury prevention systems.

Purpose : To assess objective strain and subjective muscle soreness in “Bigs” (offensive and defensive line), “Combos” (tight ends, quarterbacks, line backers, and running backs), and “Skills” (wide receivers and defensive backs) in American college football players during off-season, fall camp, and in-season phases. Methods : Twenty-three male players were assessed once weekly (3-wk off-season, 4-wk fall camp, and 3-wk in-season) for hydroperoxides (free oxygen radical test [FORT]), antioxidant capacity (free oxygen radical defense test [FORD]), oxidative stress index (OSI), countermovement-jump flight time, Reactive Strength Index (RSI) modified, and subjective soreness. Linear mixed models analyzed the effect of a 2-within-subject-SD change between predictor and dependent variables. Results : Compared to fall camp and in-season phases, off-season FORT ( P ≤ .001 and <.001), FORD ( P ≤ .001 and <.001), OSI ( P ≤ .001 and <.001), flight time ( P ≤ .001 and <.001), RSI modified ( P ≤ .001 and <.001), and soreness ( P ≤ .001 and <.001) were higher for “Bigs,” whereas FORT ( P ≤ .001 and <.001) and OSI ( P = .02 and <.001) were lower for “Combos.” FORT was higher for “Bigs” compared to “Combos” in all phases ( P ≤ .001, .02, and .01). FORD was higher for “Skills” compared with “Bigs” in off-season ( P = .02) and “Combos” in-season ( P = .01). OSI was higher for “Bigs” compared with “Combos” ( P ≤ .001) and “Skills” ( P = .01) during off-season and to “Combos” in-season ( P ≤ .001). Flight time was higher for “Skills” in fall camp compared with “Bigs” ( P = .04) and to “Combos” in-season ( P = .01). RSI modified was higher for “Skills” during off-season compared with “Bigs” ( P = .02) and “Combos” during fall camp ( P = .03), and in-season ( P = .03). Conclusion : Off-season American college football training resulted in higher objective strain and subjective muscle soreness in “Bigs” compared with fall camp and during in-season compared with “Combos” and “Skills” players.

Objectives: To investigate: (i) the chronicity and phasic variability of sleep patterns and restriction in recruits during basic military training (BMT); and (ii) identify subjective sleep quality in young adult recruits prior to entry into BMT.Design: Prospective observational study.Methods: Sleep was monitored using wrist-worn actigraphy in Army recruits (n = 57, 18-43 y) throughout 12 weeks of BMT. The Pittsburgh Sleep Quality Index (PSQI) was completed in the first week of training to provide a subjective estimate of pre-BMT sleep patterns. A mixed-effects model was used to compare week-to-week and training phase (Orientation, Development, Field, Drill) differences for rates of sub-optimal sleep (6-7 h), sleep restriction (<= 6 h), and actigraphy recorded sleep measures.Results: Sleep duration was 06:24 +/- 00:18h (mean +/- SD) during BMT with all recruits experiencing sub-optimal sleep and 42% (n = 24) were sleep restricted for >= 2 consecutive weeks. During Field, sleep duration (06:06 +/- 00:36h) and efficiency (71 +/- 6%; p < 0.01) were reduced by 15-18 min (minimum maximum) and 7-8% respectively; whereas, sleep latency (30 +/- 15 min), wake after sleep onset (121 +/- 23 min), sleep fragmentation index (41 +/- 4%) and average awakening length (6.5 +/- 1.6 min) were greater than non-Field phases (p < 0.01) by 16-18 min, 28-33 min, 8-10% and 2.5-3 min respectively. Pre-BMT global PSQI score was 5 +/- 3, sleep duration and efficiency were 7.4 +/- 1.3 h and 88 +/- 9% respectively. Sleep schedule was highly variable at pre-BMT (bedtime: 22:34 +/- 7:46 h; wake time: 6:59 +/- 1:42 h) unlike BMT (2200-0600 h).Conclusions: The chronicity of sub-optimal sleep and sleep restriction is substantial during BMT and increased training demands exacerbate sleep disruption. Exploration of sleep strategies (e.g. napping, night-time routine) are required to mitigate sleep-associated performance detriments and maladaptive outcomes during BMT.(c) 2022 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

K. GreenK. NewellS. DownieP. StapleyJ. SteeleT. MitchellJ. SampsonP. ElseH. GroellerB. MeyerM. BrownD. McGheeX.F. Huang

To support the understanding, preparation, and long-term development of youth rugby players, the accurate measurement of their physical qualities is vital. This chapter summarises how anthropometry, body composition, strength, power, speed, agility and change-of-direction, aerobic and anaerobic capacity, and athletic movement skills are measured within youth rugby players and discusses the accuracy and reliability of these methods. Furthermore, the implications of using these different testing methods within research are considered. Due to the large discrepancies in testing outcomes between rugby players of similar ages, this chapter will provide recommendations for accurate and reproducible testing of youth rugby players. Additionally, future research directions are provided that will enhance the understanding of youth rugby player development.

Youth rugby players are often organised into (bi)annual-age groups to create equal competition and development opportunities for all players. However, the variability in kinanthropometry (i.e., the size, shape, proportion, composition and maturation) that exists between players of a similar chronological age can affect injury risk, physical performance, and talent identification. This chapter aims to review the research on the kinanthropometry of youth rugby players and presents a range of practical implications for coaches, sport scientists and practitioners working with young rugby players to consider in relation to kinanthropometry and grouping strategies within youth rugby development programmes. These practical implications include understanding and assessing growth and maturity, considerations for training and competition, talent identification and development strategies, and stakeholder communication.

Background:Although the 11+ is known to reduce injuries and improve performance in adolescent footballers, its duration presents a notable barrier to implementation. Hence, this study investigated injury and performance outcomes when 65 elite male academy footballers either performed Part 2 3x/week at training (TG) or at home (HG). Methods:Time to stabilisation (TTS), eccentric hamstring strength (EH-S) and countermovement jump height (CMJ-H) were collected 4 times during the 2019 football season. Linear mixed models were used to evaluate main and interaction effects of group and time. Bonferroni post-hoc tests were used to account for multiple comparisons. Differences in time loss and medical attention injuries were determined using a two-tailed Z test for a comparison of rates. Results:Relative to baseline, EH-S (HG 4.3 kg, 95% CI 3 to 5.7, p < 0.001; TG 5.5 kg, 95% CI 4.3 to 6.6, p < 0.001) and CMJ-H (HG 3.5 cm, 95% CI 2.2 to 4.7, p < 0.001; TG 3.2 cm, 95% CI 2.2 to 4.3, p < 0.001) increased, with no difference between groups observed at the end of the season. All injury outcomes were similar. Conclusion: Rescheduling Part 2 did not affect performance or increased injury risks in academy footballers.

Military personnel are required to complete physically demanding tasks when performing work and training, which may be quantified through the physical stress imposed (external load) or the resultant physiological strain (internal load). The aim of this narrative review is to provide an overview of the techniques used to monitor work and training load in military settings, summarise key findings, and discuss important practical, analytical, and conceptual considerations. Most investigations have focused upon measuring external and internal load in military training environments; however, limited data exist in operational settings. Accelerometry has been the primary tool used to estimate external load, with heart rate commonly used to quantify internal load. Supplemental to heart rate, psychophysiological and biochemical measures have also been investigated to elucidate aspects of internal load. Broadly, investigations have revealed that military training requires personnel to perform relatively large volumes of physical activity (e.g. averaging ∼15,000 steps·day-1) of typically low-moderate intensity activity (<6 MET), although considerable temporal and inter-individual variability is observed from these gross mean estimates. There are limitations associated with these measures and, at best, estimates of external and internal load can only be inferred. These limitations are particularly pertinent for military tasks such as load carriage and manual material handling, which often involve complex activities performed individually or in teams, in a range of operational environments, with multiple layers of protection, over a protracted duration. Comprehensively quantifying external and internal loads during these functional activities poses substantial practical and analytical challenges.

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

This study aimed to examine differences in sleep and perceived wellness between a group of adolescent academy football (soccer) players from sport high schools (SHS) and regular high schools (RHS) during different phases of the year, with a secondary focus on their school physical activity (s-PA) levels. Data were collected from 51 adolescent football players from one youth Australian academy in two blocks of two weeks (four weeks total). Subjective sleep quantity and quality, wellness and s-PA were assessed through validated daily questionnaires and weekly surveys. MANOVAs and ANOVAs for repeated measures were conducted to assess sleep and wellness variables across different weeks (school vs. holidays, early vs. late season) and between groups (SHS vs. RHS). No differences in sleep or muscle pain were found between players at SHS and RHS (p > 0.05). No effect of week (school vs. holidays, early season vs. late season) on sleep quality or wellness was found, nor did hours of s-PA affect sleep duration (all p > 0.05). Total sleep time was within recommended guidelines and significantly longer sleep times were experienced during the holidays compared to school term (p = 0.002). Overall, adolescent academy football players reported sleep quantities within recommended ranges and had greater sleep volumes during the holidays rather than during school. School type (SHS vs. RHS) and hours of s-PA had no effect on the players sleep within our cohort. Additionally, it appears perceptual wellness in this population is unaffected by time of season, or school compared to holidays.