Workload a-WEAR-ness: Monitoring Workload in Team Sports With Wearable Technology. A Scoping Review

Abstract

Objectives To (1) identify the wearable devices and associated metrics used to monitor workload and assess injury risk, (2) describe the situations in which workload was monitored using wearable technology (including sports, purpose of the analysis, location and duration of monitoring, and athlete characteristics), and (3) evaluate the quality of evidence that workload monitoring can inform injury prevention. Design Scoping review. Literature Search We searched the CINAHL, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Embase, HealthSTAR, MEDLINE, PsycINFO, SPORTDiscus, and Web of Science databases. Study Selection Criteria We included all studies that used wearable devices (eg, heart rate monitor, inertial measurement units, global positioning system) to monitor athlete workload in a team sport setting. Data Synthesis We provided visualizations that represented the workload metrics reported, sensors used, sports investigated, athlete characteristics, and the duration of monitoring. Results The 407 included studies focused on team ball sports (67% soccer, rugby, or Australian football), male athletes (81% of studies), elite or professional level of competition (74% of studies), and young adults (69% of studies included athletes aged between 20 and 28 years). Thirty-six studies of 7 sports investigated the association between workload measured with wearable devices and injury. Conclusion Distance-based metrics derived from global positioning system units were common for monitoring workload and are frequently used to assess injury risk. Workload monitoring studies have focused on specific populations (eg, elite male soccer players in Europe and elite male rugby and Australian football players in Oceania). Different injury definitions and reported workload metrics and poor study quality impeded conclusions regarding the relationship between workload and injury. J Orthop Sports Phys Ther 2020;50(10):549–563. doi:10.2519/jospt.2020.9753

Magnitude, Frequency, and Accumulation: Workload Among Injured and Uninjured Youth Basketball Players

“…These findings are consistent with previous studies that identified an increased risk of injury was associated with both low chronic workloads and high acute relative to chronic workloads (Hulin et al, 2016a,b;Colby et al, 2017;Stares et al, 2018;Bowen et al, 2020). Several review articles have summarized other studies that also demonstrated an association between high ACWR and injury (Drew and Finch, 2016;Jones et al, 2017;Eckard et al, 2018;Benson et al, 2020a;Griffin et al, 2020). It should be noted that in most cases, the original studies on the workload-injury relationship investigated many combinations of workload metrics and methods for accumulating workload, resulting in the identification of multiple significant associations (Benson et al, 2020a).…”

“…Several review articles have summarized other studies that also demonstrated an association between high ACWR and injury (Drew and Finch, 2016;Jones et al, 2017;Eckard et al, 2018;Benson et al, 2020a;Griffin et al, 2020). It should be noted that in most cases, the original studies on the workload-injury relationship investigated many combinations of workload metrics and methods for accumulating workload, resulting in the identification of multiple significant associations (Benson et al, 2020a). In contrast to the results of this study, significant associations between workload and injury have also been reported when the chronic workload is high (Colby et al, 2014(Colby et al, , 2018Bowen et al, 2017;Malone et al, 2017;Esmaeili et al, 2018;Jaspers et al, 2018), and embedded in the studies that investigated many combinations of workload and accumulation methods are reports of no association between ACWR and injury (Impellizzeri et al, 2020c).…”

“…However, workload-injury models have been criticized for lacking a biological basis both in the metrics recorded and how workload is accumulated over time to represent adaptations in the body due to fitness and fatigue (Impellizzeri et al, 2020a;Kalkhoven et al, 2020;Wang et al, 2020). Furthermore, previous investigations have used multiple similar metrics (e.g., total distance, high-speed distance) and redundant accumulation methods [e.g., acute-chronic workload ratio (ACWR), 2-and 3week cumulative workloads] to generate many highly correlated workload variables, which may contribute to the inconclusive evidence regarding the relationship between workload and injury (Benson et al, 2020a). For example, several systematic reviews have identified a relationship between the ACWR and injury (Drew and Finch, 2016;Jones et al, 2017;Eckard et al, 2018;Griffin et al, 2020), but these summaries may be subject to selective reporting bias (Impellizzeri et al, 2020c).…”

Wearable Technologies in Field Hockey Competitions: A Scoping Review

“…Despite the imprecision of GPS units, they are widely used to monitor the movement of players [ 29 ], while heart rate monitors inform the coaches about levels of exertion [ 18 ] when players are in play. This allows coaches to monitor and influence decisions, such as whether to take a player out to prevent extreme fatigue or injuries when they are performing near maximal exertion.…”

Physiological Monitoring Detected Changes During Women's Soccer Anterior Cruciate Ligament Injury

“…The increased ability to collect EB data is having a large impact in the field of sports medicine when it comes to monitoring and forecasting workload and cardiovascular health and assessment of injury risk of athletes on various team sports around the world. However, wearable technology is still limited in terms of injury prediction due to different injury definitions and reported workload metrics, poor study quality, and the lack of accurate and multivariate probabilistic models due to an incomplete understanding of the determinants of an injury [10][11][12].…”