Effects of interval treadmill training on spatiotemporal parameters in children with cerebral palsy: a machine learning approach

Abstract

BackgroundChildren with cerebral palsy (CP) have reduced step length, reduced symmetry, and greater step width compared to their peers. Short-burst interval locomotor treadmill training (SBLTT) is a novel rehabilitation paradigm for children with CP that may improve spatiotemporal outcomes. However, for interventions like SBLTT, quantifying rehabilitation responses and optimizing therapy parameters remains challenging. Machine learning and causal modeling provide a platform to quantify step-by-step changes during gait training to understand mechanisms driving individual responses.Research questionWhat is the direct effect of SBLTT on step length, asymmetry, and step width in children with CP?MethodsWe recruited four children with spastic CP, ages 4-13. Each participant received 24 sessions of SBLTT over 8-12 weeks, with spatiotemporal outcomes monitored with an instrumented treadmill. We used Bayesian Additive Regression Trees (BART) to model the direct effect of therapy parameters on step length, step length asymmetry, and step width. Additionally, we generatedin silicodata for 150 virtual participants to quantify the quality of BART models to capture rehabilitation progression.ResultsAfter SBLTT, participants’ step lengths increased by 26 ± 13% (pre-post effect). Controlling for treadmill speed, time in session, limb, and treadmill incline with BART demonstrated that SBLTT directly increased step length for three participants (direct effect: 13.5 ± 4.5%), while one participant decreased step length (−11.6%). SBLTT had minimal effects on step length asymmetry and step width. Virtual datasets demonstrated that BART could accurately predict step length progression (R2= 0.73) and plateaus in progression (R2= 0.87), with better model fit for participants with less step-to-step variability.SignificanceTools such as BART can leverage step-by-step data collected during gait training to monitor progression and optimize rehabilitation protocols. This work can help personalize rehabilitation and understand the causal mechanisms driving individual responses.