Decision tree models for detection of physical activity intensity in ambulatory youth with cerebral palsy

Aims: Develop and test decision tree (DT) models to classify physical activity (PA) intensity from accelerometer output and Gross Motor Function Classification System (GMFCS) level in ambulatory youth with CP; and 2) compare the classification accuracy of the decision tree models to that achieved by previously published cut-points for youth with CP.

Methods: 51 youth with CP (GMFCS Levels I – III) completed a series of activity trials with increasing PA intensity while wearing a portable metabolic system and ActiGraph GT3X accelerometers. DT models were used to identify vertical axis and VM count thresholds corresponding to SED (< 1.5 METs), LPA (>1.5 and <3 METs) and MVPA (> 3 METs). Models were trained, tuned, and cross-validated using the ‘rpart’ and ‘caret’ packages within R.

Results: For the vertical axis (VC_DT) and VM decision trees (VM_DT), a single threshold differentiated SED from LPA, while the threshold for differentiating LPA from MVPA decreased as the level of impairment increased. The average cross-validation accuracy for the VC_DT was 81.1%, 76.7%, and 82.9% for GMFCS levels I, II, and III, respectively. The corresponding cross-validation accuracy for the VM_DT was 80.5%, 75.6%, and 84.2%, respectively. Within each GMFCS level, the decision tree models achieved better PA intensity recognition than previously published cut-points. The accuracy differential was greatest among GMFCS level III participants, in whom the previously published cutpoints misclassified 30-40% of the MVPA activity trials.

Conclusion: GMFCS-specific cut-points provide more accurate assessments of MVPA levels in youth with CP across the full spectrum of ambulatory ability.