Abstract
Dynamical systems theory suggests that studying the complexity of biological signals could lead to a single gait metric that reliably predicts risk of running-related injury (RRI). The purposes of this pilot study were to examine center of mass (COM) acceleration complexity at baseline, prior to RRI, and the change between timepoints between collegiate runners who developed RRI during a competitive season and those who remained uninjured, and to determine if complexity at these timepoints was associated with increased odds of RRI. Twenty-two collegiate runners from the same cross-country team wore a waist-mounted triaxial accelerometer (100 Hz) during easy-intensity runs throughout the competitive season. RRIs requiring medical attention were reported via an online survey. Control entropy was used to estimate the complexity of the resultant COM acceleration recorded during each run. Associations between complexity and RRI were assessed using a frequency-matching strategy where uninjured participants were paired with injured participants using complexity from the most time-proximal run prior to RRI. Seven runners sustained an RRI. No significant differences were observed between injured and uninjured groups for baseline complexity (p = 0.364, d = 0.405), pre-injury complexity (p = 0.258, d = 0.581), or change from baseline to pre-injury (p = 0.101, d = 0.963). There were no statistically significant associations found between complexity and RRI risk. Although no significant associations were found, the median effect from the models indicated that an increase in baseline complexity, pre-injury complexity, and change in complexity from baseline each corresponded to an increased odds of sustaining an RRI [baseline: odds ratio (OR) = 1.560, 95% CI = 0.587–4.143, p = 0.372; pre-injury: OR = 1.926, 95% CI: 0.689–5.382, p = 0.211; change from baseline: OR = 1.119; 95% CI: 0.839–1.491, p = 0.445). Despite non-significance and wide confidence intervals that included both positive and negative associations, the point estimates for >98% of the 10,000 frequency-case–control-matched model fits indicated that matching strategy did not influence the directionality of the association estimates between complexity and RRI risk (i.e., odds ratio >1.0). This pilot study demonstrates initial feasibility that additional research may support COM acceleration complexity as a useful single-metric monitoring system for RRI risk during real-world training. Follow-up work should assess longitudinal associations between gait complexity and running-related injury in larger cohorts.
Highlights
Human running is a complex movement that arises from the interaction and coordination of multiple codependent subsystems, including the respiratory, cardiovascular, nervous, and musculoskeletal systems
A dynamical systems approach offers an elegant solution to these individual determinants of related injuries (RRI)— regardless of the specific risk factor leading to RRI within an individual, all factors contribute in some way to deteriorations in the overall health of the dynamical system and should alter the complexity of biological signals generated by the system
The purposes of this proof-of-concept study were (a) to compare center of mass (COM) acceleration complexity at baseline, the run before a reported RRI (“pre-injury”), and the change in complexity from baseline to pre-injury between collegiate runners who developed an RRI during a competitive season and those who remained uninjured and (b) to determine if complexity at these timepoints or its change during a competitive season was associated with increased odds of sustaining an RRI
Summary
Human running is a complex movement that arises from the interaction and coordination of multiple codependent subsystems, including the respiratory, cardiovascular, nervous, and musculoskeletal systems. This coordination and codependence between systems makes human movement a complex dynamical systems (Glazier et al, 2003). A dynamical systems approach offers an elegant solution to these individual determinants of RRI— regardless of the specific risk factor leading to RRI within an individual, all factors contribute in some way to deteriorations in the overall health of the dynamical system and should alter the complexity of biological signals generated by the system (i.e., the runner). As such, examining changes in the biological signals produced by the system—such as the acceleration of the body’s center of mass— may lead to a method that can identify critical changes in running gait prior to RRI onset
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