## 5 Discussion  
This article provides an updated review of the existing literature on digital biomarkers for motor symptoms assessment in PD. This disorder represents the second most common neurodegenerative disease in the world [3], and new digital technologies promise to significantly support the approach to the diagnosis, prognosis and monitoring of PD. In this review, a total of 22 articles were selected and thoroughly analyzed to provide an summary of current DB-MS-PD in PD. As the term biomarker is very broad and the use of wearable devices can be extended to different locations, a large heterogeneity was found among the studies examined. Nevertheless, it was possible to identify trends and patterns in the definition of the experimental protocols, the number of participants, the number and type of devices and sensors used, the location of the devices on the human body, the objectives pursued and the types of DB-MS-PD proposed. Overall, the results indicate that wearable devices have the potential to be used to define DB-MS-PD. These can provide measurable and objective evaluations in clinical or hospital settings. Furthermore, the indicators collected through wearable devices could contribute to the development of remote and continuous patient monitoring systems to follow the evolution of different symptoms, especially in unsupervised settings such as patient's home. The vast majority of studies included in this review employed commercial IMUs, mostly embedding at least accelerometer and gyroscope, or the touchscreen of smartphones and tablets. These sensors allowed assessment and monitoring of a wide spectrum of motor symptoms in PD for diagnosis, prognosis, and monitoring purposes. Importantly, more than half studies used a single device embedding multiple sensors. Among these, IMUs seem to represent the most promising solution (11 of 22 studies). IMUs are widespread and used on various applications due to their versatility, accuracy, and relatively compact form factor, thus can be used for data collection and analysis without requiring additional dedicated hardware. They feature built-in sensors like an accelerometer, gyroscope, and magnetometer for comprehensive motor symptom evaluation. The results are promising, with high diagnostic accuracy in discriminating PwPD from HC and high correlation with clinical scores. However, smartphones (7 of 22 studies) are used on a daily basis, and are equipped with a large number of built-in sensors such as accelerometer, gyroscope, magnetometer, touchscreen, camera, and microphone. Smartphones were mostly used in the execution of scripted active tasks (e.g., finger tapping, memory, walking). None of the studies evaluated the potential of smartphones for long term, continuous monitoring in unsupervised environments. While representing technology which subjects are familiar with, the dimension and weight of smartphones are not comparable to the small and tiny inertial modules that can be even embedded in smart-clothes or devices. Passive continuous monitoring through IMUs or smartphones (i.e., in the pocket) was not addressed and needs further evaluation. Gait and finger tapping represent the most frequent activities in the studies analyzed. Other tasks and activities such as TUG test, balance task, and reaction times were poorly addressed and thus do not allow to obtain robust conclusions. As previously discussed, gait and finger tapping provided similar results in terms of discrimination capability of PwPD from HC and for therapy condition assessment. These tasks were analysed both in supervised clinical settings and in unsupervised home environments. Despite similar results, gait has the potential to provide a continuous passive evaluation of disease severity, the presence of motor symptoms, and the effect of therapy. On the other hand, finger tapping represents an active task and should be performed several times a day to accurately estimate motor fluctuations. In general, this review emphasizes the potential of DB-MS-PD for PD diagnosis, prognosis, and monitoring.