Acute:Chronic Workload Ratio: Conceptual Issues and Fundamental Pitfalls.

The number of studies examining associations between training load and injury has increased exponentially. As a result, many new measures of exposure and training-load-based prognostic factors have been created. The acute:chronic workload ratio (ACWR) is the most popular. However, when recommending the manipulation of a prognostic factor in order to alter the likelihood of an event, one assumes a causal effect. This introduces a series of additional conceptual and methodological considerations that are problematic and should be considered. Because no studies have even tried to estimate causal effects properly, manipulating ACWR in practical settings in order to change injury rates remains a conjecture and an overinterpretation of the available data. Furthermore, there are known issues with the use of ratio data and unrecognized assumptions that negatively affect the ACWR metric for use as a causal prognostic factor. ACWR use in practical settings can lead to inappropriate recommendations, because its causal relation to injury has not been established, it is an inaccurate metric (failing to normalize the numerator by the denominator even when uncoupled), it has a lack of background rationale to support its causal role, it is an ambiguous metric, and it is not consistently and unidirectionally related to injury risk. Conclusion: There is no evidence supporting the use of ACWR in training-load-management systems or for training recommendations aimed at reducing injury risk. The statistical properties of the ratio make the ACWR an inaccurate metric and complicate its interpretation for practical applications. In addition, it adds noise and creates statistical artifacts.

The Impact of 3 Different-Length Between-Matches Microcycles on Training Loads in Professional Rugby League Players.

PURPOSE: To examine the impact of varying between-matches microcycles on training characteristics (ie, intensity, duration, and load) in professional rugby league players and to report on match load related to these between-matches microcycles. METHODS: Training-load data were collected during a 26-wk competition period of an entire season. Training load was measured using the session rating of perceived exertion (session-RPE) method for every training session and match from 44 professional rugby league players from the same National Rugby League team. Using the category-ratio 10 RPE scale, the training intensity was divided into 3 zones (low <4 AU, moderate ≥ 4-≤ 7 AU, and high >7 AU). Three different-length between-matches recovery microcycles were used for analysis: 5-6 d, 7-8 d, and 9-10 d. RESULTS: A total of 3848 individual sessions were recorded. During the shorter-length between-matches microcycles (5-6 d), significantly lower training load was observed. No significant differences for subsequent match load or intensity were identified between the various match recovery periods. Overall, 16% of the training sessions were completed at the low-intensity zone, 61% at the moderate-intensity zone, and 23% at the high-intensity zone. CONCLUSIONS: The findings demonstrate that rugby league players undertake higher training load as the length of between-matches microcycles is increased. The majority of in-season training of professional rugby league players was at moderate intensity, and a polarized approach to training that has been reported in elite endurance athletes does not occur in professional rugby league.

Metabolic power demands of rugby league match play.

PURPOSE: To describe the metabolic demands of rugby league match play for positional groups and compare match distances obtained from high-speed-running classifications with those derived from high metabolic power. METHODS: Global positioning system (GPS) data were collected from 25 players from a team competing in the National Rugby League competition over 39 matches. Players were classified into positional groups (adjustables, outside backs, hit-up forwards, and wide-running forwards). The GPS devices provided instantaneous raw velocity data at 5 Hz, which were exported to a customized spreadsheet. The spreadsheet provided calculations for speed-based distances (eg, total distance; high-speed running, >14.4 km/h; and very-high-speed running, >18.1 km/h) and metabolic-power variables (eg, energy expenditure; average metabolic power; and high-power distance, >20 W/kg). RESULTS: The data show that speed-based distances and metabolic power varied between positional groups, although this was largely related to differences in time spent on field. The distance covered at high running speed was lower than that obtained from high-power thresholds for all positional groups; however, the difference between the 2 methods was greatest for hit-up forwards and adjustables. CONCLUSIONS: Positional differences existed for all metabolic parameters, although these are at least partially related to time spent on the field. Higher-speed running may underestimate the demands of match play when compared with high-power distance-although the degree of difference between the measures varied by position. The analysis of metabolic power may complement traditional speed-based classifications and improve our understanding of the demands of rugby league match play.

Factors affecting exercise intensity in rugby-specific small-sided games.

Small-sided games (SSGs) have been suggested as a method for concurrently training physical, technical and tactical capabilities of rugby union players. Therefore, it is important to understand how prescriptive variables such as player number and field size influence the training stimulus during rugby-specific SSGs. Twenty semiprofessional rugby union players participated in a series of SSGs of varying player numbers (4 vs. 4, 6 vs. 6, and 8 vs. 8) on small- (32 × 24 m) and large-sized fields (64 × 48 m). The physiological (blood lactate concentration and heart rate [HR]), perceptual (rating of perceived exertion [RPE]), and time-motion demands were assessed for each different SSG format. There were significant differences between the 4 vs. 4, 6 vs. 6, and 8 vs.8 SSG formats in mean speed (meters per minute), high-speed running (HSR) distance (meters), and RPE (all p < 0.05). Blood lactate was greater in 4 vs. 4 compared with that in 8 vs. 8 SSGs. The mean speed, HSR distance, number of sprints, peak speed, blood lactate concentration, and RPE were all significantly different between large- and small-field size (all p < 0.05). There were no significant difference between game formats (4 vs. 4, 6 vs. 6, and 8 vs. 8) or field size (small or large) for either percent HRmax or time spent >85% HRmax. These results show that SSGs with fewer players and larger field sizes elicit greater physiological and perceptual responses and time-motion demands. In contrast, the HR response was similar between all SSG formats, which may be attributable to high levels of individual variability in the HR response. This study provides new information about the influence of player number and field size on the training stimulus provided by rugby-specific SSGs.