Efficacy of wearable devices for describing fatigue-related movement patterns in running and neurological disease
Date
2024-01-09
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Abstract
Wearable technology allows for research to take place in more applied settings and generate more data than ever before, giving researchers the opportunity to collect enough data to construct individualized models in a variety of settings. These techniques can help tie subjective feelings of fatigue to objective physiological and biomechanical observations, driving better understanding into this psychosomatic connection. In Chapter 3, good-to-excellent reliability was shown for a variety of statistical features derived from the acceleration waveform of a low-back IMU worn while running during both non-fatigued and fatigued conditions. However, this utilized a group-based analysis and did not include reliability metrics for individuals or quantify within-subject variability and was performed on a treadmill, limiting generalizability. Due to these limitations, Chapter 4 aimed to classify group and individual changes in biomechanics with fatigue in both laboratory and overground environments, finding that classification accuracy was lower for the group-based models (57.0 – 61.5%) than the individualized models (68.2 – 68.9%), and variable importance rankings differed between models and participants. We concluded that using an individualized approach to measure fatigue-related biomechanics in running could lead to better understanding of how these may impact performance or injury. Furthermore, we hypothesized that this approach could similarly be used to investigate fatigue in other sports science and clinical applications, such as neurological disease. Thus, in Chapter 5, we reviewed the evidence for the relationship between gait and fatigue in neurological disease and found no obvious transdiagnostic relationships between gait/mobility and fatigue in neurological diseases, and instead indicated that these relationships were more likely to be condition- and subject-specific. Based on these conclusions, Chapter 6 investigated the association between activity/sleep metrics and fatigue/symptom severity in myasthenia gravis, employing similar methods to Chapter 4. When analyzing the individual models, it was clear that there are often individual-level associations between movement and fatigue/symptom severity, highlighting the importance of analyzing within individuals to determine potentially relevant outcomes to the patient. Overall, these investigations demonstrate how individualized approaches may be superior to group-based analyses when made possible using wearable devices and big data approaches, with implications for injury prevention, performance enhancement, and improving patient care.
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Keywords
wearable technology, individualized modeling, running, biomechanics, neurological disease
Citation
Dimmick, H. L. (2024). Efficacy of wearable devices for describing fatigue-related movement patterns in running and neurological disease (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.