###### Abstract  
Parkinson's disease (PD) is the second most common neurodegenerative disorder, entailing several motor-related symptoms that contribute to a reduced quality of life in affected subjects. Recent advances in wearable technologies and computing resources have shown great potential for the assessment of PD-related symptoms. However, the potential applications (e.g., early diagnosis, prognosis and monitoring) and key features of digital biomarkers for motor symptoms of PD (DB-MS-PD) have not been comprehensively studied. This study aims to provide a state-of-the-art review of current digital biomarker definitions for PD, focusing on the use of wearable devices. This review systematically examines research articles from 2012 to 2024, focusing on key features and recent technologies in PD research. A total of 22 studies were included and thoroughly analyzed. Results indicate that DB-MS-PD can accurately distinguish patients with PD (PwPD) from healthy controls (HC), assess disease severity or treatment response, and detect motor symptoms. Large sample sizes, proper validation, non-invasive devices, and ecological monitoring make DB-MS-PD promising for improving PD management. Challenges include sample and method heterogeneity and lack of public datasets. Future studies can leverage evidence of the current literature to provide more effective and ready-to-use digital tools for monitoring PD. [PERSON]'s disease; digital biomarkers; motor symptoms; wearables; body-worn sensors; machine learning +
Footnote †: journal: applied sciences  
0000-0001-6660-3300]L.S. PD include motor symptoms such as tremor, bradykinesia, rigidity, or postural instability; and non-motor symptoms such as as loss of smell, constipation, fatigue, anxiety, depression, and REM sleep behavior disorder, which can occur years before the diagnosis [5; 6; 7]. These symptoms vary from patient to patient, as well as over the course of the disease [8], gradually reducing patients' quality of life [9]. The diagnosis of PD is complex, as there is no pathognonomic sign or biomarker to confirm the disease [2]. Instead, the diagnosis is based on a combination of medical history, clinical examination and the presence of specific motor and non-motor symptoms [10]. The diagnosis process involves an evaluation by neurologists, and may be difficult especially in the early stages of the disease, when symptoms may be mild or non-specific. Therefore, it is important to monitor symptoms regularly to appropriately adjust the treatment plan [11]. Currently, there is no cure that can stop or reverse the disease progression. Instead, several symptomatic therapies are available, which can guarantee a good control of the disease for several years. The most common options are drug treatment or, in severe cases, surgical interventions (HIFU, intracranial implants or duodenal pumps, among others) [12]. The most widely used drug is Levodopa, a metabolic precursor of dopamine [13], which is effective in controlling symptoms during the first years of treatment. However, after several years of Levodopa treatment, motor complications such as dyskinesias (i.e., involuntary movements) and motor fluctuations may occur, and the patient may switch between periods of adequate and poor control of the disease [14]. At present, the most commonly used scale for evaluating PD progression is the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) [15]. This scale is designed to evaluate both motor and non-motor symptoms during a clinical examination, using a series of questions and guided exercises performed by the patient. The fluctuating nature of PD symptoms during the day [16] and the infrequent clinical consultations (6-9 months apart [17]) make it difficult to accurately assess the disease. Furthermore, the correct application and interpretation of this scale depend on the doctor's experience.