Practical application of the ACWR.

In line with the consensus statement on monitoring athlete workloads (Bourdon, et al., 2017), coach and player education was carried out at the start of pre-season. This highlighted the key metrics used, the basic principles of the ACWR and its role in the prescription of workload for performance enhancement and injury prevention. Coach support for monitoring the ACWR was augmented due to the use of this PhD research using club data to set workload thresholds.

At the start of each week, a presentation to coaches, sport science and medical practitioners summarised the workload completed in the previous week and recommended workload targets for the preceding week. Based on coach feedback, a report was designed, detailing the basic workload completed in each training session or match. Each morning, before training, there was a multi- disciplinary team meeting involving the relevant key stakeholders to discuss

training content based on the technical, tactical and physical workload aims. Modifications in workload were made throughout the week to ensure, where possible, planned workloads were adhered to. For example, four days before the game, training was typically intensive, involving small pitch sizes and short high intensity drills. An example focus of an intensive session is quick movement of the ball (technical) and pressing the opponent (tactical). These sessions accumulated many accelerations and decelerations because of the small areas and high player contacts. If the session outputs for these actions were greater than planned, the drills would be adapted on the following day, to reduce further accumulation beyond weekly recommendations. Three days before the game was usually extensive, with larger pitch sizes, and physical outputs closer to game activity. To reduce accelerations and decelerations based on the previous day, drills were modified by increasing pitch area per player, or shortening total session duration.

By applying the findings and learnings from Chapters 4 & 5, a training programme was implemented during the 2017-18 season which aimed to progressively increase workloads (avoiding acute workloading spikes) to achieve moderate-high chronic exposure (Figure 9). Training at consistently high chronic workloads may result in stress-related injuries (Drew, Raysmith, & Charlton, 2017) and therefore a workload ‘ceiling’ was set. The players may have two games within a week maximum, interspersed with light training. Thus, the equivalent of three games worth of work a week was used as the workload ceiling, as this was considered worst-case scenario. In addition to the progressive increases in workload, workload was periodised in three-week blocks (based on

the accumulated workload results of Chapter 4), involving a de-load week, a maintenance week and an overload week (Figure 9). The ACWR thresholds for each of these weeks were at ~0.85 for a de-load, ~1.0-1.2 for a moderate week and ~1.4-1.7 for a high week, based on the results of Chapter 5. Players who did not regularly play in matches or were returning from injury, were provided with top-up conditioning across the required metrics to maintain sufficient acute and chronic workloads.

Figure 9. Graphical representation of the progressive increase of a player’s chronic high speed running over the course of 8 weeks. The player begins with a chronic workload of 1700 m representing a 2-match workload and is trained with the goal of progressively increasing his chronic workload to the equivalent of 3 matches per week. The periodisation model used a 3-weekly cycle including a maintenance week with an acute:chronic workload ratio (ACWR) of ~1.0-1.2, an overload week (~1.4-1.7), followed by a de-load (~0.85). Adapted from “Recommendations for hamstring injury prevention in elite football: translating research into practice” by M. Buckthorpe et al., 2018, British Journal of Sports

Medicine, doi: 10.1136/bjsports-2018-099616.

6.03.6 Statistical analyses.

Independent samples T-tests were used to compare ACWR and RR (based on Chapter 5 results) between injured and non-injured players during the 2017-18 season. RR was calculated in Chapter 5 to determine the magnitude of the injury risk above and below given workloads or ratios (MedCalc Software,

118% 144% 88% 115% 164% 86% 108% 142% 0.00 0.43 0.85 1.28 1.70 2.13 0. 1000. 2000. 3000. 4000. 5000. 1 2 3 4 5 6 7 8 A CWR H SR (m ) Weeks

Ostend, Belgium). When an RR was greater than 1.00, an increased risk of injury was reported (ie, RR=1.50 is indicative of a 50% increased risk) and vice versa. For an RR to be significant, 95% CIs did not contain the null RR of 1.00. One Way ANOVA tests were used to examine differences in workload and injury incidence between the current season and the previous three seasons. Data were analysed using IBM SPSS Statistics V.25.0 and reported as means ± standard deviations. Significance was accepted at p<0.05. Magnitude based inferences were also used to determine the practical meaningfulness of the differences between groups and seasons. These were reported as Cohen’s effect sizes (d) with d ± 95% CI described as < 0.2 trivial, 0.2–0.6 small, 0.6–1.2 moderate, 1.2–2.0 large, 2.0–4.0 very large (Hopkins, 2002).The likelihoods that the true values of the effect represented meaningful differences were assigned the following qualitative terms: <75% trivial, >75% likely, 95% very likely, >99.5% almost certainly that the effect size exceeded 0.20 (Batterham & Hopkins, 2006). The magnitudes of differences between groups were considered practically meaningful when the likelihood was ≥75% (Batterham & Hopkins, 2006). An effect where there was >5% chance of the change being positive or negative was deemed as unclear.

6.04 Results

6.04.1 Injury incidence.

During the 2017-18 season there were 42 injuries (7.0 injuries/1,000h) (Appendix C) compared to (13.3/1000h) across the previous 3 seasons (2014-15 season, 17.6/1000h; 2015-16 season, 10.2/1000h; 2016-17 season,

12.4/1000h). The posterior thigh was the most common site of injury (1.8/1000h), all of which were non-contact muscle injuries. The ankle was the most common site of contact injury (0.7/1000hours), which were either ligament sprains or synovitis/effusions. The injury incidence in competition was almost six times that of training (23.4/1000hours vs 3.6/1000hours). Despite a lower exposure to competition, 83% of contact injuries and 43% of non-contact injuries occurred in matches. The total number of days missed through injury was 545 (13.0±19.0 [mean±SD] days per injury). There were no statistically significant differences in non-contact injury incidence between the 2017-18 season (3.3/1000h) and the previous three seasons (2014-15; 7.6/1000h, 2015-16; 4.6/1000h, 2016-17; 4.5/1000h). However, magnitude-based inferences revealed a moderate effect that is likely to be positive (F(3,82)= 1.56, p=0.20, d=1.6±2.4, 81%, likely +ve).

6.04.2 ACWR and RR between injured and non-injured players.