Different methods of training load quantification and their relationship to injury and illness in elite Australian football

Finding the Goldilocks Zone of Mechanical Loading: A Comprehensive Review of Mechanical Loading in the Prevention and Treatment of Knee Osteoarthritis

In this review, we discuss the interaction of mechanical factors influencing knee osteoarthritis (KOA) and post-traumatic osteoarthritis (PTOA) pathogenesis. Emphasizing the importance of mechanotransduction within inflammatory responses, we discuss its capacity for being utilized and harnessed within the context of prevention and rehabilitation of osteoarthritis (OA). Additionally, we introduce a discussion on the Goldilocks zone, which describes the necessity of maintaining a balance of adequate, but not excessive mechanical loading to maintain proper knee joint health. Expanding beyond these, we synthesize findings from current literature that explore the biomechanical loading of various rehabilitation exercises, in hopes of aiding future recommendations for physicians managing KOA and PTOA and athletic training staff strategically planning athlete loads to mitigate the risk of joint injury. The integration of these concepts provides a multifactorial analysis of the contributing factors of KOA and PTOA, in order to spur further research and illuminate the potential of utilizing the body's own physiological responses to mechanical stimuli in the management of OA.

Training stress, neuromuscular fatigue and well-being in volleyball: a systematic review

BACKGROUND

Volleyball, with its unique calendar structure, presents distinct challenges in training and competition scheduling. Like many team sports, volleyball features an unconventional schedule with brief off-season and pre-season phases, juxtaposed against an extensive in-season phase characterized by a high density of matches and training. This compact calendar necessitates careful management of training loads and recovery periods. The effectiveness of this management is a critical factor, influencing the overall performance and success of volleyball teams. In this review, we explore the associations between training stress measures, fatigue, and well-being assessments within this context, to better inform future research and practice.

METHODS

A systematic literature search was conducted in databases including PsycINFO, MEDLINE/PubMed, SPORTDiscus, Web of Science, and Scopus. Inclusion criteria were original research papers published in peer-reviewed journals involving volleyball athletes.

RESULTS

Of the 2535 studies identified, 31 were thoroughly analysed. From these 31 articles, 22 included professional athletes, seven included collegiate-level volleyball athletes, and two included young athletes. Nine studies had female volleyball players, while the remaining 22 had male volleyball athletes.

CONCLUSIONS

Internal training load should be collected daily after training sessions and matches with the session rating of perceived exertion method. External training load should also be measured daily according to the methods based on jump height, jump count, and kinetic energy. If force platforms are available, neuromuscular fatigue can be assessed weekly using the FT:CT ratio of a countermovement jump or, in cases where force platforms are not available, the average jump height can also be used. Finally, the Hooper Index has been shown to be a measure of overall wellness, fatigue, stress, muscle soreness, mood, and sleep quality in volleyball when used daily.

Training Load, Neuromuscular Fatigue, and Well-Being of Elite Male Volleyball Athletes During an In-Season Mesocycle

PURPOSE

Most high-intensity bouts of exercise in volleyball consist of jumping activities, which are responsible for inducing muscle damage, high levels of fatigue, and muscle soreness. Therefore, the aim of the current study was to quantify and analyze the training loads, neuromuscular fatigue, and perceptual well-being of a 5-week in-season mesocycle carried out by a professional male volleyball team.

METHODS

Fifteen volleyball players (age 28.51 [5.39] y; height 193.19 [9.87] cm; body mass 88.46 [13.18] kg) participated in this study. Internal training load assessed through the rating of perceived exertion, external training load (ETL; evaluated using an inertial motion unit), countermovement jump (CMJ) height and peak power, and wellness questionnaire responses were obtained from all athletes.

RESULTS

Results indicated a progressive decrease of the internal training load during the week and by the undulatory pattern of the ETL during the microcycles. Moreover, training monotony increased across the microcycles and was negatively associated with CMJ peak power (r = -.681, P < .05). Finally, sleep quality (ρ = -.747, P < .01) and fatigue (ρ = -.789, P < .01) were negatively associated with weekly ETL.

CONCLUSIONS

This study indicated that sleep quality and fatigue were negatively associated with weekly ETL. Therefore, decreases in weekly ETL might be needed to improve sleep quality and decrease fatigue in professional volleyball players. Plus, higher values of training monotony were associated with lower values of CMJ peak power. Consequently, avoiding training monotony might be important to improve jumping performance in professional volleyball athletes.

Acute respiratory illness and return to sport: a systematic review and meta-analysis by a subgroup of the IOC consensus on 'acute respiratory illness in the athlete'

OBJECTIVE

To determine the days until return to sport (RTS) after acute respiratory illness (ARill), frequency of time loss after ARill resulting in >1 day lost from training/competition, and symptom duration (days) of ARill in athletes.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

PubMed, EBSCOhost, Web of Science, January 1990-July 2020.

ELIGIBILITY CRITERIA

Original research articles published in English on athletes/military recruits (15-65 years) with symptoms/diagnosis of an ARill and reporting any of the following: days until RTS after ARill, frequency (%) of time loss >1 day after ARill or symptom duration (days) of ARill.

RESULTS

767 articles were identified; 54 were included (n=31 065 athletes). 4 studies reported days until RTS (range: 0-8.5 days). Frequency (%) of time loss >1 day after ARill was 20.4% (95% CI 15.3% to 25.4%). The mean symptom duration for all ARill was 7.1 days (95% CI 6.2 to 8.0). Results were similar between subgroups: pathological classification (acute respiratory infection (ARinf) vs undiagnosed ARill), anatomical classification (upper vs general ARill) or diagnostic method of ARinf (symptoms, physical examination, special investigations identifying pathogens).

CONCLUSIONS

In 80% of ARill in athletes, no days were lost from training/competition. The mean duration of ARill symptoms in athletes was 7 days. Outcomes were not influenced by pathological or anatomical classification of ARill, or in ARinf diagnosed by various methods. Current data are limited, and future studies with standardised approaches to definitions, diagnostic methods and classifications of ARill are needed to obtain detailed clinical, laboratory and specific pathogen data to inform RTS.

PROSPERO REGISTRATION NUMBER

CRD42020160479.

Training load characteristics and injury and illness risk identification in elite youth ski racing: A prospective study

PURPOSE

The study aimed to investigate the role of training load characteristics and injury and illness risk in youth ski racing.

METHODS

The training load characteristics as well as traumatic injuries, overuse injuries, and illnesses of 91 elite youth ski racers (age = 12.1 ± 1.3 years, mean ± SD) were prospectively recorded over a period of 1 season by using a sport-specific online database. Multiple linear regression analyses were performed to monitor the influence of training load on injuries and illnesses. Differences in mean training load characteristics between preseason, in-season, and post-season were calculated using multivariate analyses of variance.

RESULTS

Differences were discovered in the number of weekly training sessions (p = 0.005) between pre-season (4.97 ± 1.57) and post-season (3.24 ± 0.71), in the mean training volume (p = 0.022) between in-season (865.8 ± 197.8 min) and post-season (497.0 ± 225.5 min) and in the mean weekly training intensity (Index) (p = 0.012) between in-season (11.7 ± 1.8) and post-season (8.9 ± 1.7). A total of 185 medical problems were reported (41 traumatic injuries, 12 overuse injuries, and 132 illnesses). The weekly training volume and training intensity was not a significant risk factor for injuries (p > 0.05). Training intensity was found to be a significant risk factor for illnesses in the same week (β = 0.348; p = 0.044; R² = 0.121) and training volume represents a risk factor for illnesses in the following week (β = 0.397; p = 0.027; R² = 0.157).

CONCLUSION

A higher training intensity and volume were associated with increased illnesses, but not with a higher risk of injury. Monitoring training and ensuring appropriate progression of training load between weeks may decrease incidents of illness in-season.

The Training-Performance Puzzle: How Can the Past Inform Future Training Directions?

Over the past 20 years, research on the training-load-injury relationship has grown exponentially. With the benefit of more data, our understanding of the training-performance puzzle has improved. What were we thinking 20 years ago, and how has our thinking changed over time? Although early investigators attributed overuse injuries to excessive training loads, it has become clear that rapid spikes in training load, above what an athlete is accustomed, explain (at least in part) a large proportion of injuries. In this respect, it appears that overuse injuries may arise from athletes being underprepared for the load they are about to perform. However, a question of interest to both athletic trainers (ATs) and researchers is why some athletes sustain injury at low training loads, while others can tolerate much greater training loads? A higher chronic training load and well-developed aerobic fitness and lower body strength appear to moderate the training-injury relationship and provide a protective effect against spikes in load. The training-performance puzzle is complex and dynamic-at any given time, multiple inputs to injury and performance exist. The challenge facing researchers is obtaining large enough longitudinal data sets to capture the time-varying nature of physiological and musculoskeletal capacities and training-load data to adequately inform injury-prevention efforts. The training-performance puzzle can be solved, but it will take collaboration between researchers and clinicians as well as an understanding that efficacy (ie, how training load affects performance and injury in an idealized or controlled setting) does not equate to effectiveness (ie, how training load affects performance and injury in the real-world setting, where many variables cannot be controlled).

Identifying Contextual Influences on Training Load: An Example in Professional Rugby Union

ABSTRACT

Dalton-Barron, NE, McLaren, SJ, Black, CJ, Gray, M, Jones, B, and Roe, G. Identifying contextual influences on training load: an example in professional rugby union. J Strength Cond Res 35(2): 503-511, 2021-We aimed to investigate the contextual factors influencing training load (TL), as determined by session rating of perceived exertion (sRPE-TL), accumulated within a match-to-match microcycle in rugby union players. Session rating of perceived exertion-TL data were collected daily from 35 professional rugby union players from the same team in the English Championship over the course of an in-season period. Players were split by positional groups (backs and forwards) and sRPE-TL data were categorized as: field-based on-feet sRPE-TL (sRPEField-TL), gym-based sRPE-TL (sRPEGym-TL), and the total summation of both (sRPETotal-TL). Three 2-level linear mixed models were built for each dependent variable in each positional group, with magnitude-based inferences applied. Long between-match recovery cycles (≥7 days) resulted in very likely to almost certainly small to moderate increases in sRPE-TL for all modalities and positions (fixed effect [mean range] = 28.5%-42.0%), apart from sRPEField-TL for forwards. For backs, there was a very likely small decrease in sRPEField-TL as the season progressed (-16.7% per trimester). Losing the last league match was associated with very likely and almost certainly small decreases in sRPETotal-TL and sRPEGym-TL for backs (-20.7% and -36.4%, respectively). Losing the last match in any competition resulted in a very likely small increase in sRPEField-TL (21.2%) and a possibly small decrease sRPEGym-TL (-18.5%) for backs-with a likely smaller sRPEGym-TL for forwards (-33.4%). The strength of the upcoming opposition had no effect on sRPE-TL. Our findings highlight some of the multifactorial contextual factors that must be considered when planning and evaluating training microcycles.

The Goal Scale: A New Instrument to Measure the Perceived Exertion in Soccer (Indoor, Field, and Beach) Players

The rating of perceived exertion (RPE) can be used to monitor the exercise intensity during laboratory and specific tests, training sessions, and to estimate the internal training load of the athletes. The aim of the present study was to develop and validate a specific pictorial perceived exertion scale for soccer players (indoor, field, and beach soccer) called GOAL Scale. The pictorial GOAL Scale (six drawings; 1 “low exertion” to 6 “exhaustion”) was validated for twenty under-17 soccer players (16.4 ± 0.68 years; 175.4 ± 9 cm; 66.4 ± 7.7 kg; % fat mass 12.4 ± 3.3). In the validation phase, the athletes were evaluated in a progressive protocol involving stimuluses of 3 min with 1 min for the rest into the stages until the voluntary exhaustion in Maximal Cardiopulmonary Effort Test (MCET), and in the Yo Yo Intermittent Recovery Test – Level 1 (Yo-Yo). The RPE identified by the GOL Scale, by the Borg Scale 6 – 20 and by the Cavasini Scale, as well as the heart rate (HR), perceptual of the heart rate (%HR_max) and the blood lactate concentration ([La]) were immediately evaluated after each stage of both tests. Spearman’s correlation coefficient (p < 0.05) was used. Construct scale validity was examined by regressing GOAL Scale against Borg Scale 6 – 20 and Cavasini Scale and concurrent scale validity was investigated by regressing GOAL Scale against HR, beats/min and blood lactate concentration (mmol/L) during two progressive tests. There was a significant correlation values of the GOAL Scale with Borg Scale (r = 0.93; r = 0.88), Cavasini Scale (r = 0.91; r = 0.90), %HR_max (r = 0.91; r = 0,86), HR (r = 0.87; r = 0.83) and lactate (r = 0.68; r = 0.83) during tests (Maximal Incremental Cardiopulmonary Test and Yo-Yo test, respectively). The results evidenced concurrent and construct validity of the GOAL Scale across a wide range of exercise intensity. The absence of verbal anchors makes the use of this instrument to soccer, futsal and beach soccer athletes of different languages and different literacy levels possible.

Illness and infection in elite full-contact football-code sports: A systematic review

OBJECTIVES

Full-contact football-code team sports offer a unique environment for illness risk. During training and match-play, players are exposed to high-intensity collisions which may result in skin-on-skin abrasions and transfer of bodily fluids. Understanding the incidence of all illnesses and infections and what impact they cause to time-loss from training and competition is important to improve athlete care within these sports. This review aimed to systematically report, quantify and compare the type, incidence, prevalence and count of illnesses across full-contact football-code team sports.

DESIGN/METHODS

A systematic search of Cochrane Library, MEDLINE, SPORTDiscus, PsycINFO and CINAHL electronic databases was performed from inception to October 2019; keywords relating to illness, athletes and epidemiology were used. Studies were excluded if they did not quantify illness or infection, involve elite athletes, investigate full-contact football-code sports or were review articles.

RESULTS

Twenty-eight studies met the eligibility criteria. Five different football-codes were reported: American football (n=10), Australian rules football (n=3), rugby league (n=2), rugby sevens (n=3) and rugby union (n=9). One multi-sport study included both American football and rugby union. Full-contact football-code athletes are most commonly affected by respiratory system illnesses. There is a distinct lack of consensus of illness monitoring methodology.

CONCLUSIONS

Full-contact football-code team sport athletes are most commonly affected by respiratory system illnesses. Due to various monitoring methodologies, illness incidence could only be compared between studies that used matching incidence exposure measures. High-quality illness surveillance data collection is an essential component to undertake effective and targeted illness prevention in athletes.

Injury, Illness, and Training Load in a Professional Contemporary Dance Company: A Prospective Study

CONTEXT

Professional dance is a demanding physical activity with high injury rates. Currently, no epidemiologic data exist regarding the incidence of injury and illness together with training load (TL) over a long period of time.

OBJECTIVE

To provide a detailed description of injury, illness, and TL occurring in professional contemporary dancers.

DESIGN

Descriptive epidemiology study.

SETTING

A single professional contemporary dance company during a 1-year period.

PATIENTS OR OTHER PARTICIPANTS

A total of 16 male and female professional contemporary dancers.

MAIN OUTCOME MEASURE(S)

Injury data consisted of medical-attention injury (Med-Inj) and time-loss injury (Time-Inj). Illness was measured using the Wisconsin Upper Respiratory Tract Infection Survey. Training load was collected for each dance session using the session rating of perceived exertion and classified into 3 groups based on individual and group percentiles: low, medium, or high.

RESULTS

Reported injuries totaled 79 (86.1% new, 6.3% reinjury, and 7.6% exacerbation). The Med-Inj incidence rate was 4.6 per 1000 hours (95% confidence interval [CI] = 3.8, 5.8), and the Time-Inj rate was 1.4 per 1000 hours (95% CI = 0.8, 2.1). The median time until injury for Med-Inj and Time-Inj was 3 months. The number of days dancers experienced illness symptoms was 39.9 ± 26.9 (range = 1-96), with an incidence rate of 9.1 per 1000 hours (95% CI = 7.7, 10.7). Mean weekly TL was 6685 ± 1605 (4641-10 391; arbitrary units). Inconsistent results were found for the incidence of injury and illness based on individual and group categorizations of TL.

CONCLUSIONS

Professional dancing is associated with high injury and illness rates. This is worrying from a health perspective and underlines the need for further studies to understand how to decrease the risk. The TL is higher than in other sport disciplines, but whether the high incidence of injuries and illnesses is related to high training demands needs additional investigation, possibly conducted as international, multicenter collaborative studies.

Examination of Coach and Player Perceptions of Recovery and Exertion

Kraft, JA, Laurent, ML, Green, JM, Helm, J, Roberts, C, and Holt, S. Examination of coach and player perceptions of recovery and exertion. J Strength Cond Res 34(5): 1383-1391, 2020-Monitoring training and recovery are essential for exercise programming. Athletes can validly assess training load (TL) via the session rating of perceived exertion (SRPE) technique. However, it is unclear if coaches can successfully use this model. This study compared coach and athlete perceptions of effort and recovery, and it evaluated the efficacy of perceptually based TL monitoring. Participants included 56 athletes (Women's volleyball, soccer, and basketball and Men's basketball) and their coaches (n = 4). Perceived recovery was estimated via the Perceived Recovery Status scale. Scores of TL were calculated using the Edward's heart rate (HR) method and by multiplying SRPE by duration. Coaches provided an intended SRPE (SRPE-CI) before practice. Also, SRPE was independently estimated by coaches (SRPE-CO) and athletes (SRPE-A) ∼15-20 minutes after practice. Paired t-tests and Pearson's correlations were applied to make comparisons (α ≤ 0.05). Values of SRPE-CI, SRPE-CO, SRPE-A TLs were strongly correlated with Edwards' HR-based TLs (R = 0.74, 0.73, and 0.76, respectively). However, SRPE-CI (5.5 ± 1.9) and SRPE-CO (5.0 ± 1.9) was higher than SRPE-A (4.5 ± 1.9). Coaches estimated recovery higher than athletes (7.1 ± 1.3 vs. 5.8 ± 1.6). Estimates of TL strongly correlated with Edwards' TL regardless of information source (coach or athlete) or time point (SRPE-CI TL or SRPE-CO TL). Results suggest that coaches' perceptions validly indicated TL. Coaches' perceptions provide parallel information (correlated strongly with Edwards TL) but not identical information (demonstrated by differences in SRPE) as athlete perceptions. Differences in perceived recovery indicate that coaches overestimate recovery when compared with athletes' perceptions.

Acute Illness in the Athlete

Athletes are susceptible to many acute illnesses that can interfere with their ability to train and compete as well as potentially affecting teammates and coaching staff. A solid understanding of the preventive measures, diagnosis, and management of such diseases is paramount in the care of an athletic population.

Monitoring Training Loads and Perceived Stress in Young Elite University Athletes

With increased professionalism in sport there has been a greater interest in the scientific approach to training and recovery of athletes. Applying appropriate training loads along with adequate recovery, is essential in gaining maximal adaptation in athletes, while minimizing harm such as overreaching, overtraining, injury and illness. Although appropriate physical stress is essential, stress for many athletes may come from areas other than training. Stress from may arise from social or environmental pressure, and for many athletes who combine elite athletic training with university study, academic workloads create significant stress which adds to the constant pressure to perform athletically. This research aimed to determine if subjective stressors were associated with counterproductive training adaptations in university athletes. Moreover, it aimed to elucidate if, and when, such stressors are most harmful (i.e., certain times of the academic year or sports training season). We monitored subjective (mood state, energy levels, academic stress, sleep quality/quantity, muscle soreness, training load) and objective (injury and illness) markers in 182 young (18–22 years) elite athletes over a 4-year period using a commercially available software package. Athletes combined full-time university study with elite sport and training obligations. Results suggest athletes were relatively un-stressed with high levels of energy at the beginning of each university semester, however, energy levels deteriorated along with sleep parameters toward the examination periods of the year. A logistical regression indicated decreased levels of perceived mood (0.89, 0.85–0.94, Odds Ratio and 95% confidence limits), sleep duration (0.94, 0.91–0.97) and increased academic stress (0.91, 0.88–0.94) and energy levels (1.07, 1.01–1.14) were able to predict injury in these athletes. Examination periods coincided with the highest stress levels and increased likelihood of illness. Additionally, a sudden and high increase in training workload during the preseason was associated with an elevated incidence of injury and illness (r = 0.63). In conclusion, young elite athletes undertaking full-time university study alongside their training and competition loads were vulnerable to increased levels of stress at certain periods of the year (pre-season and examination time). Monitoring and understanding these stressors may assist coaches and support staff in managing overall stress in these athletes.

Association Between Preseason Training and Performance in Elite Australian Football

PURPOSE

To examine the association between preseason training variables and subsequent in-season performance in an elite Australian football team.

METHODS

Data from 41 elite male Australian footballers (mean [SD] age = 23.4 [3.1] y, height =188.4 [7.1] cm, and mass = 86.7 [7.9] kg) were collected from 1 Australian Football League (AFL) club. Preseason training data (external load, internal load, fitness testing, and session participation) were collected across the 17-wk preseason phase (6 and 11 wk post-Christmas). Champion Data© Player Rank (CDPR), coaches' ratings, and round 1 selection were used as in-season performance measures. CDPR and coaches' ratings were examined over the entire season, first half of the season, and the first 4 games. Both Pearson and partial (controlling for AFL age) correlations were calculated to assess if any associations existed between preseason training variables and in-season performance measures. A median split was also employed to differentiate between higher- and lower-performing players for each performance measure.

RESULTS

Preseason training activities appeared to have almost no association with performance measured across the entire season and the first half of the season. However, many preseason training variables were significantly linked with performance measured across the first 4 games. Preseason training variables that were measured post-Christmas were the most strongly associated with in-season performance measures. Specifically, total on-field session rating of perceived exertion post-Christmas, a measurement of internal load, displayed the greatest association with performance.

CONCLUSION

Late preseason training (especially on-field match-specific training) is associated with better performance in the early season.

A Narrative Review of the Physical Demands and Injury Incidence in American Football: Application of Current Knowledge and Practices in Workload Management

The sport of American football (AmF) exposes athletes to high-velocity movements and frequent collisions during competition and training, placing them at risk of contact and non-contact injury. Due to the combative nature of the game, the majority of injuries are caused by player contact; however, a significant number are also non-contact soft-tissue injuries. The literature suggests that this mechanism of injury can be prevented through workload monitoring and management. The recent introduction of microtechnology into AmF allows practitioners and coaches to quantify the external workload of training and competition to further understand the demands of the sport. Significant workload differences exist between positions during training and competition; coupling this with large differences in anthropometric and physical characteristics between and within positions suggests that the training response and physiological adaptations will be highly individual. Effective athlete monitoring and management allows practitioners and coaches to identify how athletes are coping with the prescribed training load and, subsequently, if they are prepared for competition. Several evidence-based principles exist that can be adapted and applied to AmF and could decrease the risk of injury and optimise athletic performance.

Getting the most out of intensive longitudinal data: a methodological review of workload-injury studies

OBJECTIVES

To systematically identify and qualitatively review the statistical approaches used in prospective cohort studies of team sports that reported intensive longitudinal data (ILD) (>20 observations per athlete) and examined the relationship between athletic workloads and injuries. Since longitudinal research can be improved by aligning the (1) theoretical model, (2) temporal design and (3) statistical approach, we reviewed the statistical approaches used in these studies to evaluate how closely they aligned these three components.

DESIGN

Methodological review.

METHODS

After finding 6 systematic reviews and 1 consensus statement in our systematic search, we extracted 34 original prospective cohort studies of team sports that reported ILD (>20 observations per athlete) and examined the relationship between athletic workloads and injuries. Using Professor Linda Collins' three-part framework of aligning the theoretical model, temporal design and statistical approach, we qualitatively assessed how well the statistical approaches aligned with the intensive longitudinal nature of the data, and with the underlying theoretical model. Finally, we discussed the implications of each statistical approach and provide recommendations for future research.

RESULTS

Statistical methods such as correlations, t-tests and simple linear/logistic regression were commonly used. However, these methods did not adequately address the (1) themes of theoretical models underlying workloads and injury, nor the (2) temporal design challenges (ILD). Although time-to-event analyses (eg, Cox proportional hazards and frailty models) and multilevel modelling are better-suited for ILD, these were used in fewer than a 10% of the studies (n=3).

CONCLUSIONS

Rapidly accelerating availability of ILD is the norm in many fields of healthcare delivery and thus health research. These data present an opportunity to better address research questions, especially when appropriate statistical analyses are chosen.

The influence of sleep and training load on illness in nationally competitive male Australian Football athletes: A cohort study over one season

OBJECTIVES

To determine the incidence of illness, and identify the relationship between sleep, training load and illness in nationally competitive Australian football athletes. Second, to assess multivariate effect between training load and/or sleep variables.

DESIGN

Cohort study.

METHODS

Retrospective analyses of prospectively collected cohort data were conducted on forty-four male athletes over a 46-week season. The primary outcome was illness incidence, recorded daily by medical doctors. Independent variables were acute, chronic and acute:chronic ratios of: sleep quality, sleep quantity, internal training load and external training load defined as: total running distance, high speed running distance and sprint distance. Generalised estimating equations using Poisson (count) models were fit to examine both univariate and multivariate associations between independent variables and illness incidence.

RESULTS

67 incidences of illness were recorded, with an incidence rate of 11 illnesses per 1000 running hours. Univariate analysis showed acute and chronic sleep hours and quality, as well as acute sprint and total running distance to be significantly associated with illness. Multivariate analysis identified that only acute sleep quantity was significantly, negatively associated with illness incidence (OR 0.49, CI 0.25-0.94) once all univariate significant variables were controlled for. There was no relationship between external training load and illness when sleep metrics were controlled for.

CONCLUSIONS

In a cohort of Australian football athletes, whose load was well monitored, reduced sleep quantity was associated with increased incidence of illness within the next 7 days. Monitoring sleep parameters may assist in identifying individuals at risk of illness.

Training Load and Fatigue Marker Associations with Injury and Illness: A Systematic Review of Longitudinal Studies

Background

Coaches, sport scientists, clinicians and medical personnel face a constant challenge to prescribe sufficient training load to produce training adaption while minimising fatigue, performance inhibition and risk of injury/illness.

Objective

The aim of this review was to investigate the relationship between injury and illness and longitudinal training load and fatigue markers in sporting populations.

Methods

Systematic searches of the Web of Science and PubMed online databases to August 2015 were conducted for articles reporting relationships between training load/fatigue measures and injury/illness in athlete populations.

Results

From the initial 5943 articles identified, 2863 duplicates were removed, followed by a further 2833 articles from title and abstract selection. Manual searching of the reference lists of the remaining 247 articles, together with use of the Google Scholar ‘cited by’ tool, yielded 205 extra articles deemed worthy of assessment. Sixty-eight studies were subsequently selected for inclusion in this study, of which 45 investigated injury only, 17 investigated illness only, and 6 investigated both injury and illness. This systematic review highlighted a number of key findings, including disparity within the literature regarding the use of various terminologies such as training load, fatigue, injury and illness. Athletes are at an increased risk of injury/illness at key stages in their training and competition, including periods of training load intensification and periods of accumulated training loads.

Conclusions

Further investigation of individual athlete characteristics is required due to their impact on internal training load and, therefore, susceptibility to injury/illness.

Relationships Between Training Load Indicators and Training Outcomes in Professional Soccer

BACKGROUND

In professional senior soccer, training load monitoring is used to ensure an optimal workload to maximize physical fitness and prevent injury or illness. However, to date, different training load indicators are used without a clear link to training outcomes.

OBJECTIVE

The aim of this systematic review was to identify the state of knowledge with respect to the relationship between training load indicators and training outcomes in terms of physical fitness, injury, and illness.

METHODS

A systematic search was conducted in four electronic databases (CINAHL, PubMed, SPORTDiscus, and Web of Science). Training load was defined as the amount of stress over a minimum of two training sessions or matches, quantified in either external (e.g., duration, distance covered) or internal load (e.g., heart rate [HR]), to obtain a training outcome over time.

RESULTS

A total of 6492 records were retrieved, of which 3304 were duplicates. After screening the titles, abstracts and full texts, we identified 12 full-text articles that matched our inclusion criteria. One of these articles was identified through additional sources. All of these articles used correlations to examine the relationship between load indicators and training outcomes. For pre-season, training time spent at high intensity (i.e., >90 % of maximal HR) was linked to positive changes in aerobic fitness. Exposure time in terms of accumulated training, match or combined training, and match time showed both positive and negative relationships with changes in fitness over a season. Muscular perceived exertion may indicate negative changes in physical fitness. Additionally, it appeared that training at high intensity may involve a higher injury risk. Detailed external load indicators, using electronic performance and tracking systems, are relatively unexamined. In addition, most research focused on the relationship between training load indicators and changes in physical fitness, but less on injury and illness.

CONCLUSION

HR indicators showed relationships with positive changes in physical fitness during pre-season. In addition, exposure time appeared to be related to positive and negative changes in physical fitness. Despite the availability of more detailed training load indicators nowadays, the evidence about the usefulness in relation to training outcomes is rare. Future research should implement continuous monitoring of training load, combined with the individual characteristics, to further examine their relationship with physical fitness, injury, and illness.

Effects of Whey, Soy or Leucine Supplementation with 12 Weeks of Resistance Training on Strength, Body Composition, and Skeletal Muscle and Adipose Tissue Histological Attributes in College-Aged Males

We sought to determine the effects of L-leucine (LEU) or different protein supplements standardized to LEU (~3.0 g/serving) on changes in body composition, strength, and histological attributes in skeletal muscle and adipose tissue. Seventy-five untrained, college-aged males (mean ± standard error of the mean (SE); age = 21 ± 1 years, body mass = 79.2 ± 0.3 kg) were randomly assigned to an isocaloric, lipid-, and organoleptically-matched maltodextrin placebo (PLA, n = 15), LEU (n = 14), whey protein concentrate (WPC, n = 17), whey protein hydrolysate (WPH, n = 14), or soy protein concentrate (SPC, n = 15) group. Participants performed whole-body resistance training three days per week for 12 weeks while consuming supplements twice daily. Skeletal muscle and subcutaneous (SQ) fat biopsies were obtained at baseline (T1) and ~72 h following the last day of training (T39). Tissue samples were analyzed for changes in type I and II fiber cross sectional area (CSA), non-fiber specific satellite cell count, and SQ adipocyte CSA. On average, all supplement groups including PLA exhibited similar training volumes and experienced statistically similar increases in total body skeletal muscle mass determined by dual X-ray absorptiometry (+2.2 kg; time p = 0.024) and type I and II fiber CSA increases (+394 μm² and +927 μm²; time p < 0.001 and 0.024, respectively). Notably, all groups reported increasing Calorie intakes ~600–800 kcal/day from T1 to T39 (time p < 0.001), and all groups consumed at least 1.1 g/kg/day of protein at T1 and 1.3 g/kg/day at T39. There was a training, but no supplementation, effect regarding the reduction in SQ adipocyte CSA (−210 μm²; time p = 0.001). Interestingly, satellite cell counts within the WPC (p < 0.05) and WPH (p < 0.05) groups were greater at T39 relative to T1. In summary, LEU or protein supplementation (standardized to LEU content) does not provide added benefit in increasing whole-body skeletal muscle mass or strength above PLA following 3 months of training in previously untrained college-aged males that increase Calorie intakes with resistance training and consume above the recommended daily intake of protein throughout training. However, whey protein supplementation increases skeletal muscle satellite cell number in this population, and this phenomena may promote more favorable training adaptations over more prolonged periods.

How much is too much? (Part 2) International Olympic Committee consensus statement on load in sport and risk of illness

The modern-day athlete participating in elite sports is exposed to high training loads and increasingly saturated competition calendar. Emerging evidence indicates that inappropriate load management is a significant risk factor for acute illness and the overtraining syndrome. The IOC convened an expert group to review the scientific evidence for the relationship of load—including rapid changes in training and competition load, competition calendar congestion, psychological load and travel—and health outcomes in sport. This paper summarises the results linking load to risk of illness and overtraining in athletes, and provides athletes, coaches and support staff with practical guidelines for appropriate load management to reduce the risk of illness and overtraining in sport. These include guidelines for prescription of training and competition load, as well as for monitoring of training, competition and psychological load, athlete well-being and illness. In the process, urgent research priorities were identified.

How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury

Athletes participating in elite sports are exposed to high training loads and increasingly saturated competition calendars. Emerging evidence indicates that poor load management is a major risk factor for injury. The International Olympic Committee convened an expert group to review the scientific evidence for the relationship of load (defined broadly to include rapid changes in training and competition load, competition calendar congestion, psychological load and travel) and health outcomes in sport. We summarise the results linking load to risk of injury in athletes, and provide athletes, coaches and support staff with practical guidelines to manage load in sport. This consensus statement includes guidelines for (1) prescription of training and competition load, as well as for (2) monitoring of training, competition and psychological load, athlete well-being and injury. In the process, we identified research priorities.

How do training and competition workloads relate to injury? The workload-injury aetiology model

Injury aetiology models that have evolved over the previous two decades highlight a number of factors which contribute to the causal mechanisms for athletic injuries. These models highlight the pathway to injury, including (1) internal risk factors (eg, age, neuromuscular control) which predispose athletes to injury, (2) exposure to external risk factors (eg, playing surface, equipment), and finally (3) an inciting event, wherein biomechanical breakdown and injury occurs. The most recent aetiological model proposed in 2007 was the first to detail the dynamic nature of injury risk, whereby participation may or may not result in injury, and participation itself alters injury risk through adaptation. However, although training and competition workloads are strongly associated with injury, existing aetiology models neither include them nor provide an explanation for how workloads alter injury risk. Therefore, we propose an updated injury aetiology model which includes the effects of workloads. Within this model, internal risk factors are differentiated into modifiable and non-modifiable factors, and workloads contribute to injury in three ways: (1) exposure to external risk factors and potential inciting events, (2) fatigue, or negative physiological effects, and (3) fitness, or positive physiological adaptations. Exposure is determined solely by total load, while positive and negative adaptations are controlled both by total workloads, as well as changes in load (eg, the acute:chronic workload ratio). Finally, we describe how this model explains the load-injury relationships for total workloads, acute:chronic workload ratios and the training load-injury paradox.