## 5 Discussion
### Challenges
Gait is the task performed in the studied papers that is the easiest to apply in free-living conditions. However, the use of other activities such as reaction times, pronation-supination movements or TUG test can help to get a broader and more detailed overview of the disease for each PwPD. In addition, analysis of these less studied activities, or new ones yet to be discovered, could provide valuable information on more subtle aspects of disease progression. For example, reaction times may reveal alterations in processing speed in patients PwPD, while pronation-supination movements could provide clues about coordination and fine motor control. Therefore, all possible activities that have significant results for the follow-up and monitoring of PwPD should be further studied and promoted. **Monitoring settings:** Regarding the evaluation of therapy condition (ON/OFF), half of the studies were conducted in clinical, supervised settings. This raises questions about the applicability of the developed digital tools to unsupervised remote environments. When performing at-home monitoring, passive monitoring during activities of daily living lasted 1 h to 1 day [71; 82]. This represents rather a short period of time for fully evaluating the performance of automatic motor condition assessment. On the other hand, long-term monitoring of 2 weeks to 6 months [79; 80] provide robust performance estimate. However, this was achieved using active tests, involving patients to complete a scripted set of tasks. Ideally, passive monitoring over a long period of time would be desirable, so the evolution of biomarkers over time can be monitored without forcing patients to engage in sustained activities. **Report of the results:** A comprehensive reporting of performance metrics is essential to fully evaluate the potential of the prediction system and to fairly compare similar studies. Focusing on diagnosis, almost half of the studies reported classification performance in terms of AUC. This allows the diagnostic ability of DB-MS-PD to be assessed regardless of the selected classification threshold and serves as a summary of the overall model performance. However, AUC was not reported in the other half of the studies, where accuracy or the combination of sensitivity and specificity were preferred. With regard to regression metrics, the performance evaluation is rather heterogeneous across studies. A substantial number of studies reported the correlation coefficient alone or a single error measure (i.e., MAE, RMSE). Furthermore, test-retest reliability was assessed in a single study using the intra-class correlation coefficient. Finally, inter-rater variability was not studied. Establishing common metrics across all proposals would facilitate a meaningful comparison of their respective performances. Overall, the heterogeneity of the performance metrics hinders a fair comparison with similar work and does not allow a comprehensive evaluation of performance. Again, some guidelines are needed to suggest the minimum set of performance metrics to be reported. These may include at least the receiver operating characteristic-ROC curve andAUC value in classification problems and the correlation coefficient, the associated p-value and a measure of error (i.e., MAE, RMSE or MSE) for regression tasks. In the latter case, test-retest reliability and inter-rater variability are essential to assess the consistency of multiple measurements and multiple clinical raters, respectively [88; 89]. **Proposed Biomarkers:** Above all, the definition of biomarkers is heterogeneous in the evaluated studies, with no common report structure. Some studies used single features as DB-MS-PD and assessed their classification or regression performance. Other studies extracted multiple features from the data and combined them using an ML model. In this case, if adequately described, the aggregation of features and the ML model can represent a DB-MS-PD.