Asymmetry Cloud Report and Mobile App Biofeedback Innovation

What You Need to Know About our NEW Asymmetry Features and How They Work | Lauren Green, Product Manager 

We are thrilled to unveil two innovative features in our software: Instantaneous Biofeedback and the Asymmetry Report. These enhancements provide users with real-time insights and detailed analysis, empowering them to optimize their performance through insight-driven training and rehab.

Imagine you're an athlete striving to perfect your balance and symmetry during a training session. With our new Instantaneous Biofeedback feature, you can now receive real-time visual feedback on your force distribution. This feature, integrated within the 'Free Run' test type, displays dynamic circles around the Center of Pressure markers. The size of these circles represents the distribution of total force across the Left and Right force plates. As you move, you can instantly see which circle is larger, indicating an asymmetry in your force application. This immediate visual cue allows you to make quick adjustments, helping you achieve better balance and enhance your performance on the spot. This is a great addition to the capture app that helps in rehab and training environments. 

On the other hand, the Asymmetry Report offers a comprehensive analysis of your performance over time. This new reporting feature within our cloud application provides a detailed breakdown of asymmetry percentages for selected metrics, displayed through dynamically filled horizontal bars. To create a report, you can select a mode (Unilateral or Bilateral test type), add between 1 and 6 metrics, customize asymmetry magnitude thresholds, and choose a timeframe of tests to include. The report can be filtered by the individual, showing a chart for each test within the selected period, or by team/group, displaying a chart for each athlete with average values. 

Each asymmetry metric in the report features a predominant horizontal bar that represents the magnitude of asymmetry, with left dominant values shown as positive and right dominant values as negative. Behind this bar, a shaded area indicates the average metric across the baseline timeframe, providing a clear comparison. The color of each metric bar changes dynamically based on its value: Green for Good, Yellow for Warning, and Red for Critical. While the default thresholds for warning and critical are set at 10% and 15% respectively, users have the flexibility to customize these thresholds in the report settings. 

By leveraging these new features, athletes, coaches, and clinicians can gain a deeper understanding of performance asymmetries and make data-driven decisions to enhance training outcomes. Whether you're fine-tuning your balance in real-time with Instantaneous Biofeedback or analyzing long-term trends with the Asymmetry Report, our software provides the tools you need to achieve your goals. 

Drawing Lines in The Sand for Asymmetry | Dr John McMahon, Director of Innovation

As Lauren wrote above, one of the innovative features of the new Asymmetry Cloud Reports is the ability for our force plate users to set lines in the sand for force asymmetry for each test metric. In other words, our users can set the percentage thresholds, or cut-offs, for flagging good, warning, and critical asymmetry scores. Now, many different asymmetry cut-offs are used in both the scientific literature and applied practice, so how might our users consider setting asymmetry thresholds for their athletes or patients? Well, there are several ways in which users might approach this, each with their pros and cons, which I’ve unpacked below to help users choose the option that best applies to the context within which they operate.  

The traditional and most common approach to asymmetry is to use a fixed percentage cut-off, beyond which is considered “significant” or, in other words, a potential problem worth exploring. Common fixed asymmetry cut-offs are included in Table 1 and typically range from 10-15%. These fixed asymmetry cut-offs are applied to all test metrics; therefore, they do not consider the test (i.e., task)- and metric-dependent nature of inter-limb asymmetry. Therefore, they risk being either too conservative or too sensitive for some test metrics/population groups by not accounting for specific asymmetry scores based on these contextual factors. Despite these fixed asymmetry cut-offs lacking consideration for specific population groups and test metrics, they are the easiest to apply and most reported approach in the sports science and medicine literature.  

A more novel approach to asymmetry cut-offs is for users to base them on normative data for their athletes/patients or equivalent population (Dos’Santos et al., 2021). This is achieved by understanding the mean and standard deviation (SD) of asymmetry scores for a given test metric and population group. Practitioners should ensure that they inspect the normality (normal distribution) of asymmetry scores for a population’s given test metric when applying cut-offs based on normative data. Suggested asymmetry cut-offs based on knowledge of the population mean and standard deviation are presented in Table 1 and typically range from 0.2 SD (sometimes referred to as the smallest worthwhile change [SWC]) to 1 SD. If a given test metric is not normally distributed for their population of interest, they could determine the median and interquartile range and apply the upper quartile score to represent the critical asymmetry cut-off value (Dos’Santos et al., 2021). 

The normative data approach to drawing lines in the sand for inter-limb asymmetry is also not without its limitations. The first potential barrier is gathering normative data in the first place for each test metric of interest. While normative data creation can be limited by athlete/patient group sizes that practitioners have access to, this can be overcome through consistent data collection over time (adding new athletes/patients) and/or by collaborating with other users of our force plates who operate in an equivalent context. Due to the unique complexity of clinical pathologies, this approach can also be difficult to apply with patients unless careful consideration is given to clustering to create a normative data pool comprised of similar patients’ data.  

An example of applying each asymmetry cut-off option discussed in this section is presented in Figure 1 and Table 2 to illustrate the impact of the user’s choice on the lines that they would draw in the sand to flag warning and critical asymmetry scores. Whatever approach to determining asymmetry cut-offs works best for our users’ unique contexts should ideally be informed by an understanding of what options are possible and their potential benefits and limitations (hopefully this blog section has somewhat helped with that), balanced against what they think will likely work best for their athletes/patients. Being too conservative will place fewer athletes/patients in the warning and critical categories whereas being too sensitive will cause the opposite so I think that finding the sweet spot for a given use case is certainly worth the time investment.  

Clinical Applications of Our New Asymmetry Features | Josh Segal, Clinical Lead 

The new Asymmetry Report and Biofeedback Features were made with the clinician in mind, to help with informed decision-making around the relevancy of an asymmetry as well as taking the next step by utilizing the force plates as part of physiotherapy treatment.  As we know, many of our patients present with weight-bearing asymmetries acutely post-surgically, or secondary to a chronic condition.  Abujaber et al. (2017) found that visual biofeedback elicited an improvement in weight-bearing asymmetry during a sit-to-stand task in patients following hip replacement. Chan and Sigward (2019) found that patients following ACL-R have weight-bearing distribution symmetry deficits during squatting up to three months post-op, which improved with visual biofeedback. We’re excited to offer solutions to continue to push the physio space forward through the ability to track and treat these potentially meaningful deficits.  

One of the goals of the asymmetry cloud report is to give the clinician more time with the patient by being able to quickly highlight possible areas of improvement and flag areas of concern. This should help drive patient buy-in to the established plan of care as well as create substantiated and meaningful goals. By understanding the level of asymmetry during a task it should make clear the physiotherapy intervention needed to elicit the desired change. This feature is also very helpful if baseline data was previously collected, as it gives a reference point for when it may be appropriate to return to activity. To speak to John’s earlier point, many of our patients will not have baseline data, and that is why we have included fixed asymmetry cut-offs that the clinician can change/tailor to a specific population based on your experience. However, taking the time to explore trends in your various patient populations will not only allow for better data interpretation but also an explanation to the patient of the value of your treatment plan. I would like to emphasize though, that it is important to understand what is causing the desired change to asymmetry, as an uninvolved limb producing less force, impulse, etc. will also decrease the asymmetry between the two sides. Therefore, understanding how the asymmetry score was achieved by also checking the individual limb data that formed it is an important part of the data interpretation process.  

References 

Abujaber S, Pozzi F, Zeni J Jr. (2017). Influence of weight bearing visual feedback on movement symmetry during sit to stand task. Clinical Biomechanics, 47:110-116. 

Chan MS, Sigward SM. (2019). Loading Behaviors Do Not Match Loading Abilities Postanterior Cruciate Ligament Reconstruction. Medicine and Science in Sports and Exercise, 51(8):1626-1634. 

Dos’Santos, Thomas, Thomas, Christopher and Jones, Paul A (2021) Assessing Interlimb Asym- metries: Are We Heading in the Right Direction? Strength and Conditioning Journal, 43(3), 91-100.