Model evaluation

For the strength and sensation LA models, the primary evaluation metric was R² which represents the variance in the outcome explained by the selected input features. When comparing the explained variance between models, it is important to account for the number of features included in the model, as models with more features likely have greater explained variance. Therefore, the adjusted R², which applies a correction to R² for the number of features in the model, was used as the primary evaluation metric for the supplemental analysis for strength and sensation when comparisons were made between models containing only covariates or with covariates and LA [52]. Statistical significance of the change in the strength and sensation linear regression models when LA features were added to covariates was assessed using the change in the F-statistic and p < 0.05. For both the primary and supplemental analyses, other evaluation metrics included mean absolute error, mean squared error, and root mean squared error. Cohen’s ² was used to evaluate effect size from the adjusted R² with 0.02, 0.15, and 0.35 indicative of small, medium, and large effects, respectively [53].

The overall classification accuracy (OCA), precision, recall, and F1-score were used to describe the spasticity model performance. OCA represents the percentage of participants who were correctly classified. Precision represents the accuracy of the true classifications (i.e., positive predictive value) while recall represents the fraction of the correctly identified positive classifications (i.e., true positive rate). The F1-score is the weighted average of precision and recall [54, 55]. The log-likelihood ratio was used to assess for statistically significant change from the addition of LA to the covariates logistic regression models in the supplemental analysis.