The Influence of Playing Surface on the Loading Response to Soccer-Specific Activity

The Influence of the Playing Surface on Workload Response in Spanish Professional Male Soccer Players

This study aimed to quantify the influence of the playing surface on workload-related variables (i.e., external load, Rate of perceived exertion (RPE), and mental load) in training sessions with a Spanish professional soccer team. Twenty professional male players from the same soccer team were involved. A total of thirty training sessions related to the preseason period were included. All the players completed training sessions on three playing surfaces: natural turf of poor quality, natural turf of high quality, and third-generation artificial turf. Monitoring during sessions involved assessing internal load (i.e., RPE and mental load) via self-reported questionnaires, and external load using Global Positioning System devices. Linear mixed models showed that RPE was significantly higher on natural turf of high quality than on natural turf of poor quality (p < 0.001). Total distance, relative total distance, the number of accelerations, decelerations, and high metabolic load distance were significantly lower on third-generation artificial turf compared to natural turf of poor quality (p < 0.001) and high quality (p < 0.001). In addition, high-speed running, sprint running distances, and the number of sprints reached higher values on third-generation artificial turf compared to the other two playing surfaces. These findings highlight the need for coaches to consider the type of training surface in soccer to optimize training load planning and prevent injuries.

The Use of Wearable Sensors for Preventing, Assessing, and Informing Recovery from Sport-Related Musculoskeletal Injuries: A Systematic Scoping Review

Wearable technologies are often indicated as tools that can enable the in-field collection of quantitative biomechanical data, unobtrusively, for extended periods of time, and with few spatial limitations. Despite many claims about their potential for impact in the area of injury prevention and management, there seems to be little attention to grounding this potential in biomechanical research linking quantities from wearables to musculoskeletal injuries, and to assessing the readiness of these biomechanical approaches for being implemented in real practice. We performed a systematic scoping review to characterise and critically analyse the state of the art of research using wearable technologies to study musculoskeletal injuries in sport from a biomechanical perspective. A total of 4952 articles were retrieved from the Web of Science, Scopus, and PubMed databases; 165 were included. Multiple study features-such as research design, scope, experimental settings, and applied context-were summarised and assessed. We also proposed an injury-research readiness classification tool to gauge the maturity of biomechanical approaches using wearables. Five main conclusions emerged from this review, which we used as a springboard to propose guidelines and good practices for future research and dissemination in the field.

The influence of playing surface on external demands and physiological responses during a soccer match simulation

We investigated the effects of playing surfaces with different impact absorption characteristics on external demand and physiological responses. Fifteen participants completed a soccer match simulation on natural grass, synthetic turf and concrete surfaces. Accelerometry-derived PlayerLoad^TM per minute (PL·min^-1) and average net force (AvF_Net) were used to quantify external demands at the centre of mass (CoM), upper-back, mid-back and hip. Heart rate, oxygen uptake, energy expenditure and RPE quantified physiological responses. The concrete surface exhibited the least impact absorption, with peak decelerations ~3.5x synthetic turf and ~10x natural grass (p < 0.001). Despite this, there was no differences in external demand between surfaces (surface: p ≥ 0.194; η²_p≤0.092). Both AvF_Net and PL·min^-1 (location: p < 0.001; η²_p≥0.859) were higher at the hip (613(91)N; 12.5(1.2)arb.u), reduced at the mid-back (521(67)N; 8.8(0.7)arb.u) and upper-back (502(60)N; 8.8(0.7)arb.u) when compared to CoM (576(78)N; 10.7(1.0)arb.u). Although playing surface did not influence the external demands, heart rate or oxygen uptake (p > 0.05), energy expenditure was highest on natural grass compared to synthetic turf (P = 0.034) and RPE was highest on synthetic turf compared to concrete (p = 0.026). Different playing surfaces can alter physiological responses to soccer-specific activity even when the external demands are similar.

Reliability of wearable sensors to assess impact metrics during sport-specific tasks

There is little information on the reliability of inertial measurement units for capturing impact load metrics during sport-specific movements. The purpose of this study is to determine the reliability of the Blue Trident IMU sensors in measuring impact load, step count and cumulative bone stimulus during a series of soccer-related tasks. Ten healthy recreational soccer players (age: 27.9 ± 2.18; height: 1.77 ± 0.10 m; mass: 79.02 ± 13.07 kg) volunteered for a 3-visit study and performed 4 tasks. Bilateral impact load, total number of steps and cumulative bone stimulus during the tasks were collected. Data were sampled using a dual-g sensor. Intraclass correlation coefficients (ICC_3,1) with 95% confidence intervals assessed between-day reliability. Impact load (0.58-0.89) and cumulative bone stimulus (0.90-0.97) had good to excellent reliability across tasks. ICC values for right/left step count were good to excellent during acceleration-deceleration (0.728-0.837), change direction (0.734-0.955) and plant/cut manoeuvres (0.701-0.866) and fair to good during the ball kick (0.588-0.683). This suggests that wearable sensors can reliably measure the cumulative impact load during outdoor functional movements; however, kicking manoeuvres are less reliable. Measuring impact load in the field expands the ability to capture more ecologically valid data.