MasterclassInnovations and pitfalls in the use of wearable devices in the prevention and rehabilitation of running related injuries
Introduction
Running-related injuries (RRI) have a complex and multifactorial etiology. Of potential factors, aberrant biomechanics and training load errors have long been considered to play a role in the development of RRI (Novacheck, 1998). To date, endurance running biomechanics have largely been studied in laboratory settings with the use of expensive and complicated instrumentation, most notably 3-dimensional motion capture. Recently, wearable technologies have improved substantially in quality and cost, providing the means of moving instrumented running analysis into the clinic. Even more intriguing, wearable devices enable analysis of running biomechanics in the field (“in-field”) in a runner's normal training environment. In-field assessments permit the evaluation of a runner's biomechanics under various conditions, such as different surfaces, across shoe types, gradients and environmental conditions. Furthermore, running biomechanics can now be evaluated during different training and racing scenarios. For instance, 3-dimensional biomechanics in 3 runners were recently sampled during a competitive marathon using an array of inertial measurement unit (IMU's) sensors attached to the trunk, pelvis and lower extremities (Reenalda, Maartens, Homan, & Buurke, 2016). Compared with the first 8km of the marathon, the runners altered their strike pattern, demonstrated less peak knee flexion and increased vertical acceleration of their centers of mass in the last 6km of the event (Reenalda et al., 2016).
Wearable technologies are commonplace in many team sports, notably rugby, cricket, soccer, and Australian rules football, to guide the prescription of training loads in an attempt to maximize athlete performance and reduce injury risk (Cummins et al., 2013, Gabbett, 2016, Murray et al., 2016). Aside from analyzing running accelerations, velocities and distance, the quantification of in-field joint biomechanics e.g., knee kinematics, is challenging due to the highly variable and random movement patterns inherent to team sports. Comparatively, endurance running is characterized by a highly repetitive and predictable movement pattern with low inter-stride variability. Thus, the development of algorithms to recognize movement patterns and quantify joint and tissue loads in runners is potentially less challenging for wearable manufacturers. For a host of reasons though, adoption of wearable devices to evaluate biomechanics and training loads in endurance runners is not yet as widespread as in team sports. For instance, interpretation of wearable data can be time and skill intensive. Furthermore, adoption of wearable devices has largely been driven by coaching staff and strength and conditioning personnel, neither of which are available to most endurance runners. Recent advances in wearable devices and analytics can now provide metrics that are informative to runners and clinicians at an affordable price point. Thus, it is expected that wearable devices will rapidly become a valuable tool in the clinic and in the field.
While wearable devices are often thought to be a recent phenomenon, wireless heart rate monitors have been commercially available and used by runners since 1982 with the advent of the POLAR Sport Tester PE2000 (Polar, 2017). Inexpensive, high quality accelerometers, IMU's, instrumented insoles and global positioning systems are now readily available to the clinician seeking to add instrumented running analysis or load monitoring to RRI prevention and rehabilitation programs (Shull, Jirattigalachote, Hunt, Cutkosky, & Delp, 2014) (Cummins et al., 2013, Gabbett, 2016). With an ever-increasing number of products entering the market, the array of choices of wearable devices can be daunting for clinicians and runners alike. This Master Class will provide a basic overview of the need to quantify biomechanical loading in runners and the technology, best practices, clinical applications and potential pitfalls related to using wearable devices in the evaluation and treatment of runners.
Section snippets
Why the need to quantify biomechanics and loading patterns in runners?
Across sports, there is a growing body of evidence to suggest that poor management of training loads is a major risk factor for injury (Soligard et al., 2016). While training load errors are often cited as the leading factor in the development of RRI, an evidence-based definition of “training error” has yet to be established for endurance runners (Nielsen, Buist, Sorensen, Lind, & Rasmussen, 2012). Available guidelines e.g., the so-called “10% rule”, focus on running volume, defined as distance
Overview of wearable devices
Wearable devices may have three main components: 1) the wearable sensor(s) and associated firmware; 2) software that is housed either on a mobile device, such as a running watch, or computer; and/or 3) a web-based server maintained by the manufacturer. Wearable sensors may be skin-mounted or embedded in a runner's clothing or shoes. The wearable housing may contain a single sensor, often an accelerometer, or may include multiple sensors such as global positioning system technology and an IMU.
What makes a wearable device valuable?
Commercially available wearable devices provide a multitude of analyses that can overwhelm the clinician and runner with data. Clinicians are strongly cautioned to only use analytics that relate directly to a runner's injury, while ensuring that the device has appropriate criterion validity. Adapted from Ringuet-Riot's summary of applications of technology to high performance sport (Hahn, 2011, Riot et al., 2014), wearable devices should be evaluated for their potential to:
- 1)
Determine movement
Wearables and big data: predicting running performance and the epidemiology of running injuries
RRI is the primary reason (42%) why registered runners fail to start a marathon (Clough, Dutch, Maughan, & Shepherd, 1987). While RRI epidemiology studies of a few hundred participants were the norm in the past (Buist et al., 2010, Nielsen et al., 2013a, Taunton et al., 2003), research using wearable devices may soon be able to analyze the data of thousands of runners training for a major event. A major benefit to modern wearable devices is that they enable mass data collections that do not
Use of wearables in returning the injured runner back to running
The high rate of recurrence of many common RRI's strongly suggests that runners are particularly susceptible to re-injury during the return to running phase of rehabilitation. For instance, greater than 50% of individuals with patellofemoral pain report recurring symptoms after seeking care (Lankhorst et al., 2016). Similarly, a previous history of a tibial stress injury is associated with a 6-fold greater risk of developing a subsequent tibial stress injury (Tenforde, Sayres, McCurdy, Sainani,
Future directions
In the near future, rehabilitation and prevention efforts for RRI will undergo considerable advancements due to appropriate integration of wearables in clinical practice. Massive, large scale running epidemiology studies will shed light on the role of training loads and specific biomechanics in the development of RRI's. The large sample sizes made possible by wearables will also provide the statistical power necessary to provide insight on training load and biomechanical factors that may
Conclusion
Training errors and running biomechanics are commonly suggested as primary factors in the development of RRI's. Yet, an evidence-based definition of a “training error” is presently not available. Previously, training loads and running biomechanics have been quantified via patient recall and via clinic- and laboratory-based biomechanical analysis, respectively. Wearable devices are rapidly becoming a feasible means to quantify biomechanics and training loads in runners. While the quantification
Funding
None declared.
Conflict of interest
The author has no conflicts of interest, including financial or endorsement relationships with manufacturers of wearable devices.
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