Wearable sensing and mobile devices: the future of post-concussion monitoring?

Abstract

function; however, at this time scientific evidence to support these views is limited [8]. To establish the short-and long-term effects that concussion/PCS may have, clinicians and researchers need to focus on developing 'multi-modal' assessment models that elucidate the broad-spectrum of deficits which may inhibit an individual's physical activity capacity. These models can be optimized by adopting a 'multi-point' assessment window, to account for the variability and heterogeneity of the recovery process. Thus, we contend that there is a need to move to a new paradigm in concussion assessment and evaluation – the 'multi-point–multi-modal' model. Doing so would incorporate deployment of a battery of assessments that are not only specific to the stage of recovery, but are capable of sufficiently challenging the various domains of physical activity capacity to determine if an individ-ual is ready to progress to the next stage in the return to learn/sport/performance process. Following thor-ough curricula review and extensive consultations with researchers and practitioners in a variety of healthcare contexts involved in the management of concussion, we have based the 'multi-point–multi-modal' framework around an individual's capacity for physical activity. This capacity is underpinned by three competencies: • Physical (related to physiological and motor p erformance measures); • Cognitive (related to concentration, memory and cognitive load); and • Behavioral (related to daily-living physical activity behaviors). As we realize the effect that concussion has on these domains, there is a challenge related to how best to mea-sure them. The digital revolution of the 21st century has resulted in an exponential growth in the amount of technology available to the general population. Recent advances in smartphone and wearable sensor technologies have provided researchers and clinicians with the capacity to bring a completely new paradigm to the management of various patient populations [9]. The smartphone can serve as a mobile sensing, data processing, computing and feedback device that is carried by the patient and serves as a vital link between the patient and clinical stakehold-ers on a 24/7 basis. It brings the laboratory and clinical expertise into the patient's daily life, and affords the clini-cal team the opportunity to understand how the patient is progressing in the physical, cognitive and behavioral domains proposed in the multi-point–multi-modal assessment model presented in this Editorial. For instance, in the initial 72-hours post injury, such technology could be utilized to capture detailed measures derived from traditionally used assessments related to an individual's symptoms, behaviors and cognitive/motor function. Likewise, as the individual progresses past this acute stage, such resources may provide a means to deliver standardized time-relevant assessments for each of these domains. For instance, in the physical domain, sensors such as accelerometers, gyroscopes and magnetometers provide an accessible means to obtain micro-level detail surrounding a per-son's balance during traditional static measures such as the balance error scoring system [10,11]. In addition, the use of such sensor technology during more dynamic assessments such as the Y Balance Test may capture vital information about an individual's balance during more challenging sport specific tasks [12]. The objec-tive quantification of both static and dynamic balance tasks using sensor technology may provide a means to objectively evaluate the function and integration of the motor control subsystems as the individual progresses through the different stages of recovery [13]. In the behavioral domain, biophysical sensors pro-vide the means to acquire longitudinal data regarding an individual's ongoing behaviors during daily life (e.g., sleep, patterns of ambulatory activity and heart rate variability). These physical competencies and behavioral data could be leveraged to provide macro-level informa-tion surrounding a person's true activity levels, and micro-level physiological information relating to auto-nomic function. When combined with self-reported information such as symptomatology, a contextually rich dataset is derived which may help clinicians and researchers determine if someone is truly recovering, and if not, what factors may be contributing to their development of PCS. In the cognitive domain, the traditional assessments currently included in the sports concussion assess-ment tool could be delivered via mobile platforms in the acute stage of concussion, covering the domains of orientation, immediate memory and concentration [2]. Randomization algorithms could be applied through cloud computing to account for the learning effect of such cognitive assessments, providing appropriate variations to the current assessments to ensure accu-rate results. As the recovery progresses, more advanced evaluation of key neurophysiological domains such as verbal memory, visual memory, visuomotor speed and reaction time, as is commonly assessed in computer-ized neurophysiological testing [4] could be delivered using mobile platforms. It is important to highlight that this editorial sim-ply presents a concept: that mobile technology, if lever-aged appropriately, could provide both researchers and clinicians with the means to capture large amounts of