A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ONTM

Abstract

In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ONTM, a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ONTM since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ONTM are presented.

Toward objective monitoring of Parkinson's disease motor symptoms using a wearable device: wearability and performance evaluation of PDMonitor®

“…Other systems previously reported to detect parkinsonian symptoms in PD ( 36 , 73 , 74 ), do provide useful information to physicians leading to improved therapeutic decisions and patient outcomes ( 41 , 75 ). However, PDMonitor ® has the main advantage of evaluating all motor symptoms and their complications, including gait, freezing of gait and postural instability.…”

Feasibility of a wearable inertial sensor to assess motor complications and treatment in Parkinson’s disease

“…These barriers could be lack of awareness of the symptom among patients, cognitive impairment, reluctance to discuss symptoms, caregiver absence, lack of time’s physician to evaluate all the symptoms or physician’s lack of appreciation of the Off periods [ 55 ]. In this regard, the use of wearable medical devices can be crucial and potentially help the clinicians in the detection of MF [ 56 ]. Future studies with larger samples using specific WO scales and exploring the ability of STAT-ON TM of capturing initial MF are mandatory.…”

A Paradigm Shift in the Management of Patients with Parkinson’s Disease

“…This will probably be used more frequently in the future because it offers a more precise base for the decision about the need for invasive therapy and the suitable type of invasive therapy, offering also a valuable support to decision‐makers in state and private insurances. At the same time, the follow-up of a patient in advanced therapy will be more effective with objective monitoring, leading to clear decisions about adjusting a treatment to get the optimal effect of it, or stopping it if the right result cannot be attained, oftentimes leading to a change from secondary to primary delivery of care [7, 55]. Furthermore, there is currently some confusion, regarding these choices and, sooner or later, the medical authorities or insurance companies will require the doctors to provide an objective validation about the stratification of patients to one of these invasive and expensive treatments [56, 57].…”

“…Despite significant technological advancements and the incorporation of new features in monitoring devices, wearables still face certain limitations. One major pitfall lies in their accuracy in symptom detection, as there is considerable variability in sensitivity and specificity, ranging from 0.39 to 0.97, across different devices and study designs [9,55,[58][59][60]. It is not surprising that objective measurements do not always align perfectly with subjective symptom estimations, but the correlation between supervised and unsupervised motor function assessments is also not flawless [61].…”