## 4 Results
### Endpoints  
In 11 studies (50%), different motor tasks were evaluated. The distribution of the most relevant tasks in studies addressing multiple tasks is shown in Figure 13.  
Figure 11: Distribution of the aims addressed in the selected studies. Figure 12: Biomarker extraction methods. ML and DL models used. In 7 studies (32%), finger tapping was associated to different tasks, including balance, gait, rest tremor, postural tremor, pronation-supination, leg agility and reaction time. In 6 of these studies, finger tapping performed better than the other tasks for PD diagnosis, ON/OFF detection and PD progression. Gait gave better results than other activities in 2 cases (18%) for PD detection and progression. The 3 remaining articles (27%) studies another tasks. In a single study [75], resting and postural tremor provided better results than the other tasks (including finger tapping) for the diagnosis of PD. A single study [73] (which included neither finger tapping nor gait) found that toe tapping and TUG tasks give better results than leg agility and hand movements for PD progression estimation. Finally, a single study [81] concluded that resting tremor and bradykinesia achieve agreement with clinical assessment of symptom severity and treatment-related changes in motor states. In half of the studies, DB-MS-PD were extracted in the laboratory/clinic, whereas in the other half, remote home monitoring was used. In the laboratory-based studies, mostly supervised active tasks (participants were asked to perform different tasks) were used, whereas in one study both active and passive monitoring were used. When data were recorded at home (11 studies), active tasks were used in 6 and passive monitoring (extraction of DB-MS-PD during daily life) in the other 5. Focusing on the tapping task (Figure 14), of the 7 studies in which tapping was the most relevant task, most found that, in 5 cases each (71% of the cases), statistical metrics calculated on the intervals between taps (i.e., mean value, variability, percentiles) or on the spatial accuracy (i.e., two-dimensional distance between tap position and target point) were the most significant DB-MS-PD. This is followed by total taps (N = 4, 57%) and the number of on-target taps (N = 2, 29%). Other characteristics include tap duration and fatigue. The latter was calculated as the difference in tap speed between the first and last tap. These features were highly relevant for the diagnosis of PD, the estimation of motor fluctuations, the assessment of disease progression and the prediction of clinical motor scores. Regarding to the gait task (Figure 15), of the 11 studies where gait was the primary task, most (N = 7, 63%) found gait speed and its variability to be the one of most significant DB-MS-PD in both supervised and unsupervised contexts. This is followed by average step length and their variability (N = 4, 36%), stride length (N = 3, 27%), step time (N = 3, 27%), swing time (N = 2, 18%), stance time (N = 2, 18%), and cadence (N = 2, 18%). Other characteristics include total power, center of pressure, force variations, power spectrals and entropy. These features were found to be highly relevant for the diagnosis of PD, the estimation of motor fluctuations and the assessment of disease progression. Figure 13: Most relevant tasks in studies addressing multiple tasks. Overall, the supervised and unsupervised finger tapping task (assessed through position and acceleration analysis on a smartphone or tablet) and the supervised and unsupervised gait task (assessed through inertial or force sensors) represented the most explored tasks. When comparing the results obtained from different studies, it is evident that these two tasks provide similar performance for PD diagnosis and treatment response assessment, as shown in Table 2. On the other hand, gait was also explored for severity estimation, while the finger tapping task for predicting the clinical motor score.