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Background Olfactory dysfunction in coronavirus disease 2019 (COVID-19) is common during acute illness and appears to last longer than other symptoms. The aim of this study was to objectively investigate olfactory dysfunction in two cohorts of patients at two different stages: during acute illness and after a median recovery of 4 months. Methods Twenty-five acutely ill patients and 26 recovered subjects were investigated. Acute patients had a molecular diagnosis of COVID-19; recovered subjects had a positive antibody assay and a negative molecular test. A 33-item psychophysical olfactory identification test tailored for the Italian population was performed. Results Median time from symptoms onset to olfactory test was 33 days in acute patients and 122 days in recovered subjects. The former scored a significantly higher number of errors at psychophysical testing (median [IQR]: 8 [13] vs 3 [2], p < 0.001) and were more frequently hyposmic (64% vs 19%, p = 0.002). Recovered subjects reported a variable time to subjective olfactory recovery, from days up to 4 months. Participants included in the study reported no significant nasal symptoms at olfactory testing. Among recovered subject who reported olfactory loss during acute COVID-19, four (27%) were still hyposmic. Demographic and clinical characteristics did not show significant associations with olfactory dysfunction. Conclusion Moderate-to-severe hospitalized patients showed a high level and frequency of olfactory dysfunction compared to recovered subjects. In the latter group, subjects who reported persisting olfactory dysfunction showed abnormal scores on psychophysical testing, indicating that, at least in some subjects, persistent hyposmia may represent a long-term sequela of COVID-19.

Background Telemonitoring, a branch of telemedicine, involves the use of technological tools to remotely detect clinical data and evaluate patients. Telemonitoring of patients with Parkinson's disease (PD) should be performed using reliable and discriminant motor measures. Furthermore, the method of data collection and transmission, and the type of subjects suitable for telemonitoring must be well defined. Objective To analyze differences in patients with PD and healthy controls (HC) with the wearable inertial device SensHands-SensFeet (SH-SF), adopting a standardized acquisition mode, to verify if motor measures provided by SH-SF have a high discriminating capacity and high intraclass correlation coefficient (ICC). Methods Altogether, 64 patients with mild-to-moderate PD and 50 HC performed 14 standardized motor activities for assessing bradykinesia, postural and resting tremors, and gait parameters. SH-SF inertial devices were used to acquire movements and calculate objective motor measures of movement (total: 75). For each motor task, five or more biomechanical parameters were measured twice. The results were compared between patients with PD and HC. Results Fifty-eight objective motor measures significantly differed between patients with PD and HC; among these, 32 demonstrated relevant discrimination power (Cohen's d > 0.8). The test-retest reliability was excellent in patients with PD (median ICC = 0.85 right limbs, 0.91 left limbs) and HC (median ICC = 0.78 right limbs, 0.82 left limbs). Conclusion In a supervised environment, the SH-SF device provides motor measures with good results in terms of reliability and discriminant ability. The reliability of SH-SF measurements should be evaluated in an unsupervised home setting in future studies.

Parkinson’s disease (PD) is a progressive disorder of the central nervous system that causes motor dysfunctions in affected patients. Objective assessment of symptoms can support neurologists in fine evaluations, improving patients’ quality of care. Herein, this study aimed to develop data-driven models based on regression algorithms to investigate the potential of kinematic features to predict PD severity levels. Sixty-four patients with PD (PwPD) and 50 healthy subjects of control (HC) were asked to perform 13 motor tasks from the MDS-UPDRS III while wearing wearable inertial sensors. Simultaneously, the clinician provided the evaluation of the tasks based on the MDS-UPDRS scores. One hundred-ninety kinematic features were extracted from the inertial motor data. Data processing and statistical analysis identified a set of parameters able to distinguish between HC and PwPD. Then, multiple feature selection methods allowed selecting the best subset of parameters for obtaining the greatest accuracy when used as input for several predicting regression algorithms. The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). Therefore, this predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time. Electronic supplementary material The online version of this article (10.1007/s10439-020-02628-4) contains supplementary material, which is available to authorized users.

Introduction Parkinson's disease (PD) is a common neurodegenerative disorder characterized by disabling motor and non-motor symptoms. For example, idiopathic hyposmia (IH), which is a reduced olfactory sensitivity, is typical in >95% of PD patients and is a preclinical marker for the pathology. Methods In this work, a wearable inertial device, named SensHand V1, was used to acquire motion data from the upper limbs during the performance of six tasks selected by MDS-UPDRS III. Three groups of people were enrolled, including 30 healthy subjects, 30 IH people, and 30 PD patients. Forty-eight parameters per side were computed by spatiotemporal and frequency data analysis. A feature array was selected as the most significant to discriminate among the different classes both in two-group and three-group classification. Multiple analyses were performed comparing three supervised learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes, on three different datasets. Results Excellent results were obtained for healthy vs. patients classification (F-Measure 0.95 for RF and 0.97 for SVM), and good results were achieved by including subjects with hyposmia as a separate group (0.79 accuracy, 0.80 precision with RF) within a three-group classification. Overall, RF classifiers were the best approach for this application. Conclusion The system is suitable to support an objective PD diagnosis. Further, combining motion analysis with a validated olfactory screening test, a two-step non-invasive, low-cost procedure can be defined to appropriately analyze people at risk for PD development, helping clinicians to identify also subtle changes in motor performance that characterize PD onset.

Millions of people worldwide are affected by Parkinson's disease (PD), which significantly worsens their quality of life. Currently, the diagnosis is based on assessment of motor symptoms, but interest toward non-motor symptoms is increasing, as well. Among them, idiopathic hyposmia (IH) is associated with an increased risk of developing PD in healthy adults. In this work, a wearable inertial device, named SensFoot V2, was used to acquire motor data from 30 healthy subjects, 30 people with IH, and 30 PD patients while performing tasks from the MDS-UPDRS III for lower limb assessment. The most significant and non-correlated extracted parameters were selected in a feature array that can identify differences between the three groups of people. A comparative classification analysis was performed by applying three supervised machine learning algorithms. The system resulted able to distinguish between healthy and patients (specificity and recall equal to 0.967), and the people with IH can be identified as a separate class within a three-group classification (accuracy equal to 0.78). Thus, the system could support the clinician in objective assessment of PD. Further, identification of IH together with changes in motor parameters could be a non-invasive two-step approach to investigate the early onset of PD.

Parkinson's disease is a common neurodegenerative pathology that significantly influences quality of life (QoL) of people affected. The increasing interest and development in telemedicine services and internet of things technologies aim to implement automated smart systems for remote assistance of patients. The wide variability of Parkinson's disease in the clinical expression, as well as in the symptom progression, seems to address the patients' care toward a personalized therapy.This review addresses automated systems based on wearable/portable devices for the remote treatment and management of Parkinson's disease. The idea is to obtain an overview of the telehealth and automated systems currently developed to address the impairments due to the pathology to allow clinicians to improve the quality of care for Parkinson's disease with benefits for patients in QoL.The research was conducted within three databases: IEEE XploreAccurate exclusion criteria and selection strategy were applied to screen the 173 articles found.Ultimately, 55 articles were fully evaluated and included in this review. Divided into three categories, they were automated systems actually tested at home, implemented mobile applications for Parkinson's disease assessment, or described a telehealth system architecture.This review would provide an exhaustive overview of wearable systems for the remote management and automated assessment of Parkinson's disease, taking into account the reliability and acceptability of the implemented technologies.

Objectives Preclinical diagnosis of Parkinson's disease (PD) is nowadays a topic of interest as the neuropathological process could begin years before the appearance of motor symptoms. Several symptoms, among them hyposmia, could precede motor features in PD. In the preclinical phase of PD, a subclinical reduction in motor skills is highly likely. In this pilot study, we investigate a step-by-step method to achieve preclinical PD diagnosis. Material and methods We used the IOIT (Italian Olfactory Identification Test) to screen a population of healthy subjects. We identified 20 subjects with idiopathic hyposmia. Hyposmic subjects underwent an evaluation of motor skills, at baseline and after 1 year, using motion analysis sensors previously created by us. Results One subject showed significant worsening in motor measurements. In this subject, we further conducted a dopaminergic challenge test monitored with the same sensors and, finally, he underwent [123I]-FP/CIT (DaTscan) SPECT brain imaging. The results show that he is probably affected by preclinical PD. Conclusions Our pilot study suggests that the combined use of an olfactory test and motor sensors for motion analysis could be useful for a screening of healthy subjects to identify those at a high risk of developing PD.

The primary goal of this study was to investigate computerized assessment methods to classify motor dysfunctioning of patients with Parkinson’s disease on the clinical scale. In this proposed system, machine learning–based computerized assessment methods were introduced to assess the motor performance of patients with Parkinson’s disease. Biomechanical parameters were acquired from six exercises through wearable inertial sensors: SensFoot V2 and SensHand V1. All patients were evaluated via neurologist by means of the clinical scale. The average rating was calculated from all exercise ratings given by clinicians to estimate overall rating for each patient. Patients were divided in two groups: slight–mild patients with Parkinson’s disease and moderate–severe patients with Parkinson’s disease according to average rating (“0: slight and mild” and “1: moderate and severe”). Feature selection methods were used for the selection of significant features. Selected features were trained in support vector machine, logistic regression, and neural network to classify the two groups of patients. The highest classification accuracy obtained by support vector machine classifier was 79.66%, with 0.8790 area under the curve. A 76.2% classification accuracy was obtained with 0.7832 area under the curve through logistic regression. A 83.10% classification accuracy was obtained by neural network classifier, with 0.889 area under the curve. Strong distinguishability of the models between the two groups directs the high possibility of motor impairment classification through biomechanical parameters in patients with Parkinson’s disease based on the clinical scale.

Objective: Hypomimia is a common and early symptom of Parkinson's disease (PD), which reduces the ability of PD patients to manifest emotions. Currently, it is visually evaluated by the neurologist during neurological examinations for PD diagnosis, as described in task 3.2 of the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Since such an evaluation is semi-quantitative and affected by inter-variability, this paper aims to measure the physiological parameters related to eye blink and facial expressions extracted from a vertical electro-oculogram (VEOG) and facial surface electromyography (fsEMG) to differentiate PD patients from healthy control subjects (HCs). Approach: The spontaneous eye blink rate-minute (sEBR), its maximum amplitude (BMP), and facial cutaneous muscle activity were measured in 24 PD patients and 24 HCs while the subjects looked at a visual-tester composed of three main parts: static vision, dynamic vision and reading silently. Specificity and sensitivity for each parameter were calculated. Main results: The VEOG and the fsEMG allowed the identification of some parameters related to eye blink and facial expressions (i.e. sEBR, BMP, frontal and peribuccal muscular activities), being able to distinguish between PD patients and HCs with high sensitivity and specificity. Significance: The demonstration that the combination of parameters related to eye blink and facial expressions can discriminate (with high accuracy) between PD patients versus HCs, thus resulting in a useful tool to support the neurologist in objective assessment of hypomimia for improving PD diagnosis.

The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies.

Nowadays, the increasing old population 65+ as well as the pace imposed by work activities lead to a high number of people that have particular injuries for limbs. In addition to persistent or temporary disabilities related to accidental injuries we must take into account that part of the population suffers from motor deficits of the hands due to stroke or diseases of various clinical nature. The most recurrent technological solutions to measure the rehabilitation or skill motor performance of the hand are glove-based devices, able to faithfully capture the movements of the hand and fingers. This paper presents a system for hand motion analysis based on 9-axis complete inertial modules and dedicated microcontroller which are fixed on fingers and forearm. The technological solution presented is able to track the patients' hand motions in real-time and then to send data through wireless communication reducing the clutter and the disadvantages of a glove equipped with sensors through a different technological structure. The device proposed has been tested in the study of Parkinson's disease.

Olfactory function can be rapidly evaluated by means of standardized olfactory tests. Multiple-choice smell identification tests can be conditioned by cultural background. To investigate a new tool for detecting olfactory deficit in Italian subjects we developed a multiple-choice identification test prepared with odorants belonging to the Italian culture.The Italian Olfactory Identification Test (IOIT) was developed with 33 microencapsulated odorants with intensity of odors and headspace Gas Chromatography being tested. Test-retest reliability of the IOIT was evaluated. The IOIT was administered to 511 controls and 133 Parkinson's patients.In healthy subjects the number of IOIT errors increased with age for both females (p0.0001) and males (p0.0001), while in the Parkinson's disease group the number of IOIT errors was significantly greater where compared to healthy subjects (p0.0001 in all age groups). The reference limits applied to all age groups revealed an IOIT sensitivity of 93% and a specificity of 99%. The test-retest reliability was excellent.The IOIT is highly reliable, disposable, easy to administer, not fragile, and has a long shelf-life. All these features make the IOIT suitable for clinical use as well as for population screening and to discriminate Parkinson's patients from healthy subjects.

To evaluate the prevalence of epilepsy syndromes in a district in Northwest Tuscany (Vecchiano, 9,952 inhabitants) we identified all suspected cases (probable epilepsy) from several information sources: files of local doctors, prescriptions of antiepileptic drugs (AEDs), hospital files from the center for epilepsy at the University of Pisa, medical files from university and hospital departments in Pisa, and files of the school doctors and social workers in the district of Vecchiano. All persons suspected of having epilepsy were examined; 51 cases of epilepsy were found, i.e., a prevalence rate of 5.1 in 1,000. During a previous study performed in the same district with the "house-to-house" method, a lower rate was found. Our study confirms that use of different epidemiologic investigational methods can produce different results.

Growth factors are believed to be involved in the mitotic regulation of the animal olfactory epithelium (OE). We investigated mucus covering the human OE area to see if it contained the insulin-like growth factor-I (IGF-I) and its binding proteins (IGFBPs) and to examine their behaviour in neurodegenerative diseases. Thirty patients with idiopathic late onset cerebellar ataxia (ILOCA), Parkinson's disease, and amyotrophic lateral sclerosis (ALS) and 30 age- and sex-matched healthy subjects were studied. In 10 controls, we also analyzed the mucus of the respiratory mucosa of the nose and tears. We detected IGF-I in the mucus covering the OE and Western ligand blot analysis (WLB) showed IGFBPs with an apparent Mr of 41, 500/38,500, 34,000 and 24,000, which were immunoprecipitated by specific antisera to IGFBP-3, -2 and -4, respectively. Their levels were higher than those observed in the respiratory mucosa of the nose or in tears. Mucus of the OE of the patients contained significantly reduced levels of IGF-I in comparison with those of controls. The intensity of all the IGFBPs-related bands were reduced in the ILOCA, while the remaining patients had a loss in the amounts of IGFBP-3. Plasma IGF-I and IGFBPs levels were similar in patients and controls. In conclusion, our data show that mucus covering the human OE contains IGF-I and IGFBPs, suggesting that these factors have a role in the activity of the OE. The amounts are reduced in the patients' mucus, possibly reflecting a dysfunction of the OE itself.

A 19 kDa soluble protein was purified from human nasal mucus. Its N-terminal amino-acid sequence appeared to be identical to that of a lipocalin synthesised both in lachrymal glands and in von Ebner's glands (VEG) of circumvallate papillae. In order to verify whether this protein was synthesised in the nasal cavity or was the result of tear contamination, we adopted an immunohistochemical approach. Polyclonal antibodies, raised against a primate VEG protein, were used on sections of human nasal mucosa obtained from surgery. The results clearly indicate that the protein is synthesised in sero-mucous glands underlying the respiratory ciliated epithelium. Although ligand-binding experiments with some odorant molecules have given negative results, we cannot exclude a role of odorant solubiliser and carrier for this protein.

In an open-label trial, we evaluated the efficacy of clozapine on abnormal involuntary movements in five patients with Huntington's chorea. We administered clozapine at increasing doses of 25, 50, and 150 mg/d for 3 weeks. Subjective self-evaluation of all patients reported reduction of chorea and improvement of daily living activities. At the end of the trial, all patients requested to continue with clozapine. Objective evaluation with the Abnormal Involuntary Movements Scale demonstrated in all patients moderate-to-marked reduction of abnormal involuntary movements without any significant side effects; the improvement was dose-dependent and markedly decreased 1 week after drug withdrawal.