In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson’s disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired are pre-processed by advanced knowledge processing methods, integrated by fusion algorithms to allow health professionals to remotely monitor the overall status of the patients, adjust medication schedules and personalize treatment. The information collected by the sensors (accelerometers and gyroscopes) is processed by several classifiers. As a result, it is possible to evaluate and quantify the PD motor symptoms related to end of dose deterioration (tremor, bradykinesia, freezing of gait (FoG)) as well as those related to over-dose concentration (Levodopa-induced dyskinesia (LID)). Based on this information, together with information derived from tests performed with a virtual reality glove and information about the medication and food intake, a patient specific profile can be built. In addition, the patient specific profile with his evaluation during the last week and last month, is compared to understand whether his status is stable, improving or worsening. Based on that, the system analyses whether a medication change is needed—always under medical supervision—and in this case, information about the medication change proposal is sent to the patient. The performance of the system has been evaluated in real life conditions, the accuracy and acceptability of the system by the PD patients and healthcare professionals has been tested, and a comparison with the standard routine clinical evaluation done by the PD patients’ physician has been carried out. The PERFORM system is used by the PD patients and in a simple and safe non-invasive way for long-term record of their motor status, thus offering to the clinician a precise, long-term and objective view of patient’s motor status and drug/food intake. Thus, with the PERFORM system the clinician can remotely receive precise information for the PD patient’s status on previous days and define the optimal therapeutical treatment.

Introduction: The aim was to investigate the frequency of neurological adverse events in patients with rheumatoid arthritis (RA) and spondylarthropathies (SpA) treated with tumor necrosis factor (TNF) α antagonists. Methods: Seventy-seven patients eligible for anti-TNFα therapy were evaluated. There were 36 patients with RA, 41 with SpA [24 psoriatic arthritis (PsA) and 17 with ankylosing spondylitis (AS)]. All patients had a complete physical and neurological examination. Brain and cervical spine magnetic resonance imaging (MRI) and neurophysiological tests were performed in all patients before the initiation of anti-TNFα therapy and after a mean of 18 months or when clinical symptoms and signs indicated a neurological disease. Exclusion criteria included hypertension, diabetes mellitus, dyslipidemia, heart arrhythmias, atherothrombotic events, vitamin B12 and iron deficiency, head and neck trauma and neurological surgeries. Results: Two patients did not receive anti-TNFα therapy because brain MRIs at baseline revealed lesions compatible with demyelinating diseases. Thus, 75 patients received anti-TNFα (38 infliximab, 19 adalimumab and 18 etanercept). Three patients developed neurological adverse events. A 35-year-old man with PsA after 8 months of infliximab therapy presented with paresis of the left facial nerve and brain MRI showed demyelinating lesions. Infliximab was discontinued and he was treated with pulses of corticosteroids recovering completely after two months. The second patient was a 45-year-old woman with RA who after 6 months of adalimumab therapy presented with optic neuritis. The third patient was a 50-year-old woman with AS, whom after 25 months of infliximab therapy, presented with tingling and numbness of the lower extremities and neurophysiological tests revealed peripheral neuropathy. In both patients anti-TNF were discontinued and they improved without treatment after 2 months. The rest of our patients showed no symptoms and MRIs showed no abnormalities. The estimated rate of neurological adverse events in patients treated with anti-TNF therapy is 4% (3/75). Conclusions: Neurological adverse events after anti-TNFα therapy were observed in our patient. Brain MRI and neurophysiological tests are essential tools to discriminate neurological diseases.

Freezing of Gait (FoG) is a common symptom in Parkinson’s Disease (PD) occurring with significant variability and severity and is associated with increased risk of falls. FoG detection in everyday life is not trivial, particularly in patients manifesting the symptom only in specific conditions. Various wearable devices have been proposed to detect PD symptoms, primarily based on inertial sensors. We here report the results of the validation of a novel system based on a pair of pressure insoles equipped with a 3D accelerometer to detect FoG episodes. Twenty PD patients attended a motor assessment protocol organized into eight multiple video recorded sessions, both in clinical and ecological settings and both in the ON and OFF state. We compared the FoG episodes detected using the processed data gathered from the insoles with those tagged by a clinician on video recordings. The algorithm correctly detected 90% of the episodes. The false positive rate was 6% and the false negative rate 4%. The algorithm reliably detects freezing of gait in clinical settings while performing ecological tasks. This result is promising for freezing of gait detection in everyday life via wearable instrumented insoles that can be integrated into a more complex system for comprehensive motor symptom monitoring in PD.

Background: Mobile health, predominantly wearable technology and mobile apps, have been considered in Parkinson disease to provide valuable ecological data between face-to-face visits and improve monitoring of motor symptoms remotely. Objective: We explored the feasibility of using a technology-based mHealth platform comprising a smartphone in combination with a smartwatch and a pair of smart insoles, described in this study as the PD_manager system, to collect clinically meaningful data. We also explored outcomes and disease-related factors that are important determinants to establish feasibility. Finally, we further validated a tremor evaluation method with data collected while patients performed their daily activities. Methods: PD_manager trial was an open-label parallel group randomized study.The mHealth platform consists of a wristband, a pair of sensor insoles, a smartphone (with dedicated mobile Android apps) and a knowledge platform serving as the cloud backend. Compliance was assessed with statistical analysis and the factors affecting it using appropriate regression analysis. The correlation of the scores of our previous algorithm for tremor evaluation and the respective Unified Parkinson’s Disease Rating Scale estimations by clinicians were explored. Results: Of the 75 study participants, 65 (87%) completed the protocol. They used the PD_manager system for a median 11.57 (SD 3.15) days. Regression analysis suggests that the main factor associated with high use was caregivers’ burden. Motor Aspects of Experiences of Daily Living and patients’ self-rated health status also influence the system’s use. Our algorithm provided clinically meaningful data for the detection and evaluation of tremor. Conclusions: We found that PD patients, regardless of their demographics and disease characteristics, used the system for 11 to 14 days. The study further supports that mHealth can be an effective tool for the ecologically valid, passive, unobtrusive monitoring and evaluation of symptoms. Future studies will be required to demonstrate that an mHealth platform can improve disease management and care. Trial Registration: ISRCTN Registry ISRCTN17396879; http://www.isrctn.com/ISRCTN17396879 International Registered Report Identifier (IRRID): RR2-10.1186/s13063-018-2767-4

Parkinson’s disease (PD) has become the second most common neurodegenerative condition following Alzheimer’s disease (AD), exhibiting high prevalence and incident rates. Current care strategies for PD patients include brief appointments, which are sparsely allocated, at outpatient clinics, where, in the best case scenario, expert neurologists evaluate disease progression using established rating scales and patient-reported questionnaires, which have interpretability issues and are subject to recall bias. In this context, artificial-intelligence-driven telehealth solutions, such as wearable devices, have the potential to improve patient care and support physicians to manage PD more effectively by monitoring patients in their familiar environment in an objective manner. In this study, we evaluate the validity of in-office clinical assessment using the MDS-UPDRS rating scale compared to home monitoring. Elaborating the results for 20 patients with Parkinson’s disease, we observed moderate to strong correlations for most symptoms (bradykinesia, rest tremor, gait impairment, and freezing of gait), as well as for fluctuating conditions (dyskinesia and OFF). In addition, we identified for the first time the existence of an index capable of remotely measuring patients’ quality of life. In summary, an in-office examination is only partially representative of most PD symptoms and cannot accurately capture daytime fluctuations and patients’ quality of life.

Parkinson’s disease therapy is still focused on the use of l-3,4-dihydroxyphenylalanine (levodopa or l-dopa) for the symptomatic treatment of the main clinical features of the disease, despite intensive pharmacological research in the last few decades. However, regardless of its effectiveness, the long-term use of levodopa causes, in combination with disease progression, the development of motor complications termed levodopa-induced dyskinesias (LIDs). LIDs are the result of profound modifications in the functional organization of the basal ganglia circuitry, possibly related to the chronic and pulsatile stimulation of striatal dopaminergic receptors by levodopa. Hence, for decades the key feature of a potentially effective agent against LIDs has been its ability to ensure more continuous dopaminergic stimulation in the brain. The growing knowledge regarding the pathophysiology of LIDs and the increasing evidence on involvement of nondopaminergic systems raises the possibility of more promising therapeutic approaches in the future. In the current review, we focus on novel therapies for LIDs in Parkinson’s disease, based mainly on agents that interfere with glutamatergic, serotonergic, adenosine, adrenergic, and cholinergic neurotransmission that are currently in testing or clinical development.