Serum neurofilament indicates accelerated neurodegeneration and predicts mortality in late-stage Parkinson's disease.

Different stages of Parkinson's disease (PD) are defined by clinical criteria, while late-stage PD is marked by the onset of morbidity milestones and rapid clinical deterioration. Based on neuropathological evidence, degeneration in the dopaminergic system occurs primarily in the early stage of PD, raising the question of what drives disease progression in late-stage PD. This study aimed to investigate whether late-stage PD is associated with increased neurodegeneration dynamics rather than functional decompensation using the blood-based biomarker serum neurofilament light chain (sNfL) as a proxy for the rate of neurodegeneration. The study included 118 patients with PD in the transition and late-stage (minimum disease duration 5 years, mean (SD) disease duration 15 (±7) years). The presence of clinical milestones (hallucinations, dementia, recurrent falls, and admission to a nursing home) and mortality were determined based on chart review. We found that sNfL was higher in patients who presented with at least one clinical milestone and increased with a higher number of milestones (Spearman's ρ = 0.66, p < 0.001). Above a cutoff value of 26.9 pg/ml, death was 13.6 times more likely during the follow-up period (95% CI: 3.53-52.3, p < 0.001), corresponding to a sensitivity of 85.0% and a specificity of 85.7% (AUC 0.91, 95% CI: 0.85-0.97). Similar values were obtained when using an age-adjusted cutoff percentile of 90% for sNfL. Our findings suggest that the rate of ongoing neurodegeneration is higher in advanced PD (as defined by the presence of morbidity milestones) than in earlier disease stages. A better understanding of the biological basis of stage-dependent neurodegeneration may facilitate the development of neuroprotective means.

The Gut Dysmotility Questionnaire for Parkinson's disease: Insights into development and pretest studies.

Objective: A total of 48% of patients with Parkinson's disease (PD) present symptoms of gastrointestinal dysfunction, particularly constipation. Furthermore, gastrointestinal tract (GIT)-related non-motor symptoms (NMSs) appear at all stages of PD, can be prodromal by many years and have a relevant impact on the quality of life. There is a lack of GIT-focused validated tools specific to PD to assess their occurrence, progress, and response to treatment. The aim of this study was to develop and evaluate a novel, disease- and symptom-specific, self-completed questionnaire, titled Gut Dysmotility Questionnaire (GDQ), for screening and monitoring gastrointestinal dysmotility of the lower GIT in patients with PD. Methods: In phase 1, a systematic literature review and multidisciplinary expert discussions were conducted. In phase 2, cognitive pretest studies comprising standard pretests, interviews, and evaluation questionnaires were performed in patients with PD (n = 21), age- and sex-matched healthy controls (HC) (n = 30), and neurologists (n = 11). Incorporating these results, a second round of cognitive pretests was performed investigating further patients with PD (n = 10), age- and sex-matched HC (n = 10), and neurologists (n = 5). The questionnaire was adapted resulting in the final GDQ, which underwent cross-cultural adaptation to the English language. Results: We report significantly higher GDQ total scores and higher scores in five out of eight domains indicating a higher prevalence of gastrointestinal dysmotility in patients with PD than in HC (p < 0.05). Cognitive pretesting improved the preliminary GDQ so that the final GDQ was rated as relevant (100/100%), comprehensive (100/90%), easy to understand concerning questions and answer options (100/90%), and of appropriate length (80/100%) by neurologists and patients with PD, respectively. The GDQ demonstrated excellent internal consistency (Cronbach's alpha value of 0.94). Evidence for good construct validity is given by moderate to high correlations of the GDQ total score and its domains by intercorrelations (r s = 0.67-0.91; p < 0.001) and with validated general NMS measures as well as with specific items that assess gastrointestinal symptoms. Interpretation: The GDQ is a novel, easy, and quick 18-item self-assessment questionnaire to screen for and monitor gastrointestinal dysmotility with a focus on constipation in patients with PD. It has shown high acceptance and efficacy as well as good construct validity in cognitive pretests.

Morbidity Milestones Demonstrate Long Disability-Free Survival in Parkinson's Disease Patients with Deep Brain Stimulation of the Subthalamic Nucleus.

Background: Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an effective treatment for Parkinson's disease (PD). The long-term benefit in PD patients with STN-DBS in comparison to medical treatment (MT) alone has not yet been demonstrated conclusively. Objectives: To judge the long-term outcome of patients with STN-DBS. Methods: To assess the evolution of PD symptoms and health-related quality of life (HRQoL) after deep brain stimulation (DBS) surgery, we conducted a cross-sectional analysis of 115 patients with STN-DBS with rater-based scales and self-reported questionnaires. In addition, we screened records of all our STN-DBS patients (2001-2019, n = 162 patients) for the onset of the morbidity milestones (falls, hallucinations, dementia, and nursing home placement) to assess disability-free life expectancy. Results: In the first year of STN-DBS, levodopa equivalent dose was reduced and motor function improved. Nonmotor symptoms and cognition remained stable. These effects were similar to previous studies. Morbidity milestones occurred 13 ± 7 years after diagnosis. Motor function, cognition, and HRQoL significantly worsened after the occurrence of any milestone, confirming the clinical relevance of these milestones. After onset of the first milestone, mean survival time was limited to 5 ± 0.8 years, which is comparable with patients with PD but without STN-DBS. Conclusions: On average, PD patients with STN-DBS live with their disease for a longer time, and morbidity milestones occur later in the disease course than in PD patients with MT. As judged by morbidity milestones, morbidity remains compressed into the final 5 years of life in PD patients with STN-DBS.

Gastrointestinal barriers to levodopa transport and absorption in Parkinson's disease.

Levodopa is the gold standard for the symptomatic treatment of Parkinson's disease (PD). There are well documented motor and non-motor fluctuations, however, that occur almost inevitably once levodopa is started after a variable period in people with PD. Whilst brain neurodegenerative processes play a part in the pathogenesis of these fluctuations, a range of barriers across the gastrointestinal (GI) tract can alter levodopa pharmacokinetics, ultimately contributing to non-optimal levodopa response and symptoms fluctuations. GI barriers to levodopa transport and absorption include dysphagia, delayed gastric emptying, constipation, Helicobacter pylori infection, small intestinal bacterial overgrowth and gut dysbiosis. In addition, a protein-rich diet and concomitant medication intake can further alter levodopa pharmacokinetics. This can result in unpredictable or sub-optimal levodopa response, 'delayed on' or 'no on' phenomena. In this narrative review, we provided an overview on the plethora of GI obstacles to levodopa transport and absorption in PD and their implications on levodopa pharmacokinetics and development of motor fluctuations. In addition, management strategies to address GI dysfunction in PD are highlighted, including use of non-oral therapies to bypass the GI tract.

The use of wearables for the diagnosis and treatment of Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, with increasing numbers of affected patients. Many patients lack adequate care due to insufficient specialist neurologists/geriatricians, and older patients experience difficulties traveling far distances to reach their treating physicians. A new option for these obstacles would be telemedicine and wearables. During the last decade, the development of wearable sensors has allowed for the continuous monitoring of bradykinesia and dyskinesia. Meanwhile, other systems can also detect tremors, freezing of gait, and gait problems. The most recently developed systems cover both sides of the body and include smartphone apps where the patients have to register their medication intake and well-being. In turn, the physicians receive advice on changing the patient's medication and recommendations for additional supportive therapies such as physiotherapy. The use of smartphone apps may also be adapted to detect PD symptoms such as bradykinesia, tremor, voice abnormalities, or changes in facial expression. Such tools can be used for the general population to detect PD early or for known PD patients to detect deterioration. It is noteworthy that most PD patients can use these digital tools. In modern times, wearable sensors and telemedicine open a new window of opportunity for patients with PD that are easy to use and accessible to most of the population.

Serum neurofilament indicates that DBS surgery can cause neuronal damage whereas stimulation itself does not.

Deep brain stimulation (DBS) is a potent symptomatic therapy for Parkinson's disease, but it is debated whether it causes or prevents neurodegeneration. We used serum neurofilament light chain (NFL) as a reporter for neuronal damage and found no difference between 92 patients with chronic STN-DBS and 57 patients on best medical treatment. Serum NFL transiently increased after DBS surgery whereas the initiation of STN stimulation did not affect NFL levels, suggesting that DBS surgery can be associated with neuronal damage whereas stimulation itself is not.

Parkinson's Disease Detection Based on Running Speech Data From Phone Calls.

OBJECTIVE: Parkinson's Disease (PD) is a progressive neurodegenerative disorder, manifesting with subtle early signs, which, often hinder timely and early diagnosis and treatment. The development of accessible, technology-based methods for longitudinal PD symptoms tracking in daily living, offers the potential for transforming disease assessment and accelerating diagnosis. METHODS: A privacy-aware method for classifying patients and healthy controls (HC), on the grounds of speech impairment present in PD, is proposed. Voice features from running speech signals were extracted from passively-captured recordings over voice calls. Language-aware training of multiple- and single-instance learning classifiers was employed to fuse and predict on voice features and demographic data from a multilingual cohort of 498 subjects (392/106 self-reported HC/PD patients). RESULTS: By means of leave-one-subject-out cross-validation, the best-performing models yielded 0.69/0.68/0.63/0.83 area under the Receiver Operating Characteristic curve (AUC) for the binary classification of PD patient vs. HC in sub-cohorts of English/Greek/German/Portuguese-speaking subjects, respectively. Out-of sample testing of the best performing models was conducted in an additional dataset, generated by 63 clinically-assessed subjects (24/39 HC/early PD patients). Testing has resulted in 0.84/0.93/0.83 AUC for the English/Greek/German-speaking sub-cohorts, respectively. CONCLUSIONS: The proposed approach outperforms other methods proposed for language-aware PD detection considering the ecological validity of the voice data. SIGNIFICANCE: This paper introduces for the first time a high-frequency, privacy-aware and unobtrusive PD screening tool based on analysis of voice samples captured during routine phone calls.

Predictors of short-term impulsive and compulsive behaviour after subthalamic stimulation in Parkinson disease.

BACKGROUND: The effects of subthalamic stimulation (subthalamic nucleus-deep brain stimulation, STN-DBS) on impulsive and compulsive behaviours (ICB) in Parkinson's disease (PD) are understudied. OBJECTIVE: To investigate clinical predictors of STN-DBS effects on ICB. METHODS: In this prospective, open-label, multicentre study in patients with PD undergoing bilateral STN-DBS, we assessed patients preoperatively and at 6-month follow-up postoperatively. Clinical scales included the Questionnaire for Impulsive-Compulsive Disorders in PD-Rating Scale (QUIP-RS), PD Questionnaire-8, Non-Motor Symptom Scale (NMSS), Unified PD Rating Scale in addition to levodopa-equivalent daily dose total (LEDD-total) and dopamine agonists (LEDD-DA). Changes at follow-up were analysed with Wilcoxon signed-rank test and corrected for multiple comparisons (Bonferroni method). We explored predictors of QUIP-RS changes using correlations and linear regressions. Finally, we dichotomised patients into 'QUIP-RS improvement or worsening' and analysed between-group differences. RESULTS: We included 55 patients aged 61.7 years±8.4 with 9.8 years±4.6 PD duration. QUIP-RS cut-offs and psychiatric assessments identified patients with preoperative ICB. In patients with ICB, QUIP-RS improved significantly. However, we observed considerable interindividual variability of clinically relevant QUIP-RS outcomes as 27.3% experienced worsening and 29.1% an improvement. In post hoc analyses, higher baseline QUIP-RS and lower baseline LEDD-DA were associated with greater QUIP-RS improvements. Additionally, the 'QUIP-RS worsening' group had more severe baseline impairment in the NMSS attention/memory domain. CONCLUSIONS: Our results show favourable ICB outcomes in patients with higher preoperative ICB severity and lower preoperative DA doses, and worse outcomes in patients with more severe baseline attention/memory deficits. These findings emphasise the need for comprehensive non-motor and motor symptoms assessments in patients undergoing STN-DBS. TRIAL REGISTRATION NUMBER: DRKS00006735.

Rotigotine Transdermal Patch for Motor and Non-motor Parkinson's Disease: A Review of 12 Years' Clinical Experience.

Motor and non-motor symptoms (NMS) have a substantial effect on the health-related quality of life (QoL) of patients with Parkinson's disease (PD). Transdermal therapy has emerged as a time-tested practical treatment option, and the rotigotine patch has been used worldwide as an alternative to conventional oral treatment for PD. The efficacy of rotigotine on motor aspects of PD, as well as its safety and tolerability profile, are well-established, whereas its effects on a wide range of NMS have been described and studied but are not widely appreciated. In this review, we present our overall experience with rotigotine and its tolerability and make recommendations for its use in PD and restless legs syndrome, with a specific focus on NMS, underpinned by level 1-4 evidence. We believe that the effective use of the rotigotine transdermal patch can address motor symptoms and a wide range of NMS, improving health-related QoL for patients with PD. More specifically, the positive effects of rotigotine on non-motor fluctuations are also relevant. We also discuss the additional advantages of the transdermal application of rotigotine when oral therapy cannot be used, for instance in acute medical emergencies or nil-by-mouth or pre/post-surgical scenarios. We highlight evidence to support the use of rotigotine in selected cases (in addition to general use for motor benefit) in the context of personalised medicine.

Assessment of real life eating difficulties in Parkinson's disease patients by measuring plate to mouth movement elongation with inertial sensors.

Parkinson's disease (PD) is a neurodegenerative disorder with both motor and non-motor symptoms. Despite the progressive nature of PD, early diagnosis, tracking the disease's natural history and measuring the drug response are factors that play a major role in determining the quality of life of the affected individual. Apart from the common motor symptoms, i.e., tremor at rest, rigidity and bradykinesia, studies suggest that PD is associated with disturbances in eating behavior and energy intake. Specifically, PD is associated with drug-induced impulsive eating disorders such as binge eating, appetite-related non-motor issues such as weight loss and/or gain as well as dysphagia-factors that correlate with difficulties in completing day-to-day eating-related tasks. In this work we introduce Plate-to-Mouth (PtM), an indicator that relates with the time spent for the hand operating the utensil to transfer a quantity of food from the plate into the mouth during the course of a meal. We propose a two-step approach towards the objective calculation of PtM. Initially, we use the 3D acceleration and orientation velocity signals from an off-the-shelf smartwatch to detect the bite moments and upwards wrist micromovements that occur during a meal session. Afterwards, we process the upwards hand micromovements that appear prior to every detected bite during the meal in order to estimate the bite's PtM duration. Finally, we use a density-based scheme to estimate the PtM durations distribution and form the in-meal eating behavior profile of the subject. In the results section, we provide validation for every step of the process independently, as well as showcase our findings using a total of three datasets, one collected in a controlled clinical setting using standardized meals (with a total of 28 meal sessions from 7 Healthy Controls (HC) and 21 PD patients) and two collected in-the-wild under free living conditions (37 meals from 4 HC/10 PD patients and 629 meals from 3 HC/3 PD patients, respectively). Experimental results reveal an Area Under the Curve (AUC) of 0.748 for the clinical dataset and 0.775/1.000 for the in-the-wild datasets towards the classification of in-meal eating behavior profiles to the PD or HC group. This is the first work that attempts to use wearable Inertial Measurement Unit (IMU) sensor data, collected both in clinical and in-the-wild settings, towards the extraction of an objective eating behavior indicator for PD.

Emotional well-being and pain could be a greater determinant of quality of life compared to motor severity in cervical dystonia.

Non-motor symptoms (NMS) occur in patients with cervical dystonia (CD) but with variable frequencies and impact on health-related quality of life (HRQoL). To define non-motor and motor profiles and their respective impact on HRQoL in CD patients using the newly validated Dystonia Non-Motor Symptoms Questionnaire (DNMSQuest). In an observational prospective multicentre case-control study, we enrolled 61 patients with CD and 61 age- and sex-matched healthy controls (HC) comparing demographic data, motor and non-motor symptoms and HRQoL measurements. 95% CD patients reported at least one NMS. Mean total NMS score was significantly higher in CD patients (5.62 ± 3.33) than in HC (1.74 ± 1.52; p < 0.001). Pain, insomnia and stigma were the most prevalent NMS and HRQoL was significantly impaired in CD patients compared to HC. There was strong correlation of NMS burden with HRQoL (CDQ-24: r = 0.72, EQ-5D: r = - 0.59; p < 0.001) in CD patients. Regression analysis between HRQoL and NMS suggested that emotional well-being (standardized beta = - 0.352) and pain (standardized beta = - 0.291) had a major impact on HRQoL while, in contrast motor severity had no significant impact in this model. Most NMS with the exception of pain, stigma and ADL did not correlate with motor severity. NMS are highly prevalent in CD patients and occur independent of age, sex, disease duration, duration of botulinum neurotoxin therapy and socio-economic status. Specific NMS such as emotional well-being and pain have a major impact on HRQoL and are more relevant than motor severity.

Unobtrusive detection of Parkinson's disease from multi-modal and in-the-wild sensor data using deep learning techniques.

Parkinson's Disease (PD) is the second most common neurodegenerative disorder, affecting more than 1% of the population above 60 years old with both motor and non-motor symptoms of escalating severity as it progresses. Since it cannot be cured, treatment options focus on the improvement of PD symptoms. In fact, evidence suggests that early PD intervention has the potential to slow down symptom progression and improve the general quality of life in the long term. However, the initial motor symptoms are usually very subtle and, as a result, patients seek medical assistance only when their condition has substantially deteriorated; thus, missing the opportunity for an improved clinical outcome. This situation highlights the need for accessible tools that can screen for early motor PD symptoms and alert individuals to act accordingly. Here we show that PD and its motor symptoms can unobtrusively be detected from the combination of accelerometer and touchscreen typing data that are passively captured during natural user-smartphone interaction. To this end, we introduce a deep learning framework that analyses such data to simultaneously predict tremor, fine-motor impairment and PD. In a validation dataset from 22 clinically-assessed subjects (8 Healthy Controls (HC)/14 PD patients with a total data contribution of 18.305 accelerometer and 2.922 typing sessions), the proposed approach achieved 0.86/0.93 sensitivity/specificity for the binary classification task of HC versus PD. Additional validation on data from 157 subjects (131 HC/26 PD with a total contribution of 76.528 accelerometer and 18.069 typing sessions) with self-reported health status (HC or PD), resulted in area under curve of 0.87, with sensitivity/specificity of 0.92/0.69 and 0.60/0.92 at the operating points of highest sensitivity or specificity, respectively. Our findings suggest that the proposed method can be used as a stepping stone towards the development of an accessible PD screening tool that will passively monitor the subject-smartphone interaction for signs of PD and which could be used to reduce the critical gap between disease onset and start of treatment.

Using IMU Sensors to Assess Motor Degradation of PD Patients by Modeling In-meal Plate-to-Mouth Movement Elongation.

Parkinson's disease (PD) is the second most common age-related neurodegenerative disorder after Alzheimer's disease, associated, among others, with motor symptoms such as resting tremor, rigidity and bradykinesia. At the same time, early diagnosis of PD is hindered by a high misdiagnosis rate and the subjective nature of the diagnosis process itself. Recent developments in mobile and wearable devices, such as smartphones and smartwatches, have allowed the automated detection and objective measurement of PD symptoms. In this paper we investigate the hypothesis that PD motor symptom degradation can be assessed by studying the in-meal behavior and modeling the food intake process. To achieve this, we use the inertial data from a commercial smartwatch to investigate the in-meal eating behavior of healthy controls and PD patients. In addition, we define and provide a methodology for calculating Plate-to-Mouth (PtM), an indicator that relates with the average time that the hand spends transferring food from the plate towards the mouth during the course of a meal. The presented experimental results, using our collected dataset of 28 participants (7 healthy controls and 21 PD patients), support our hypothesis. Results initially point out that PD patients have a higher PtM value than the healthy controls. Finally, using PtM we achieve a precision/recall/F1 of 0.882/0.714/0.789 towards classifying the meals from the PD patients and healthy controls.

Smartwatch-based Activity Analysis During Sleep for Early Parkinson's Disease Detection.

Parkinson's Disease (PD) is the second most common neurodegenerative disorder with the non-motor symptoms preceding the motor impairment that is needed for clinical diagnosis. In the current study, an angle-based analysis that processes activity data during sleep from a smartwatch for quantification of sleep quality, when applied on controls and PD patients, is proposed. Initially, changes in their arm angle due to activity are captured from the smartwatch triaxial accelerometry data and used for the estimation of the corresponding binary state (awake/sleep). Then, sleep metrics (i.e., sleep efficiency index, total sleep time, sleep fragmentation index, sleep onset latency, and wake after sleep onset) are computed and used for the discrimination between controls and PD patients. A process of validation of the proposed approach when compared with the PSG-based ground truth in an in-the-clinic setting, resulted in comparable state estimation. Moreover, data from 15 early PD patients and 11 healthy controls were used as a test set, including 1,376 valid sleep recordings in-the-wild setting. The univariate analysis of the extracted sleep metrics achieved up to 0.77 AUC in early PD patients vs. healthy controls classification and exhibited a statistically significant correlation (up to 0.46) with the clinical PD Sleep Scale 2 counterpart Items. The findings of the proposed method show the potentiality to capture non-motor behavior from users' nocturnal activity to detect PD in the early stage.

Parcellation of the Subthalamic Nucleus in Parkinson's Disease: A Retrospective Analysis of Atlas- and Diffusion-Based Methods.

BACKGROUND: Deep brain stimulation (DBS) is an established method of treatment for Parkinson's disease (PD). A stimulation sweet spot at the interface between the motor and associative clusters of the subthalamic nucleus (STN) has recently been postulated. The aim of this study was to analyze the available clustering methods for the STN and their correlation to outcome. METHODS: This is a retrospective analysis of a group of 20 patients implanted with a DBS device for PD. Atlas-based and diffusion tractography-based parcellation of the STN was performed. The distances of the electrode to the obtained clusters were compared to each other and to outcome parameters, which included levodopa equivalent dose (LED) reduction, Unified Parkinson's Disease Rating Scale (UPDRS)-III scores, and reduction in scores for items 32 and 36 of the UPDRS-IV. RESULTS: The implanted electrodes were located nearest to the motor clusters of the STN. The following significant associations with postoperative LED reduction were found: (1) distance of the electrode to the motor cluster in the Accolla and DISTAL atlases (p < 0.01) and (2) distance of the electrode to the supplementary motor area cluster (p = 0.02). There was no association with either the UPDRS-III or the UPDRS-IV score. CONCLUSIONS: The results of this study suggest the possibility that atlas-based clustering, as well as diffusion tractography-based parcellation, can be useful in estimating the stimulation target ("sweet spot") for STN-DBS in PD patients. Atlas-based as well as diffusion-based clustering might become a useful tool in DBS trajectory planning.

Lower Energy Intake among Advanced vs. Early Parkinson's Disease Patients and Healthy Controls in a Clinical Lunch Setting: A Cross-Sectional Study.

Unintentional weight loss has been observed among Parkinson's disease (PD) patients. Changes in energy intake (EI) and eating behavior, potentially caused by fine motor dysfunction and eating-related symptoms, might contribute to this. The primary aim of this study was to investigate differences in objectively measured EI between groups of healthy controls (HC), early (ESPD) and advanced stage PD patients (ASPD) during a standardized lunch in a clinical setting. The secondary aim was to identify clinical features and eating behavior abnormalities that explain EI differences. All participants (n = 23 HC, n = 20 ESPD, and n = 21 ASPD) went through clinical evaluations and were eating a standardized meal (200 g sausages, 400 g potato salad, 200 g apple purée and 500 mL water) in front of two video cameras. Participants ate freely, and the food was weighed pre- and post-meal to calculate EI (kcal). Multiple linear regression was used to explain group differences in EI. ASPD had a significantly lower EI vs. HC (-162 kcal, p < 0.05) and vs. ESPD (-203 kcal, p < 0.01) when controlling for sex. The number of spoonfuls, eating problems, dysphagia and upper extremity tremor could explain most (86%) of the lower EI vs. HC, while the first three could explain ~50% vs. ESPD. Food component intake analysis revealed significantly lower potato salad and sausage intakes among ASPD vs. both HC and ESPD, while water intake was lower vs. HC. EI is an important clinical target for PD patients with an increased risk of weight loss. Our results suggest that interventions targeting upper extremity tremor, spoonfuls, dysphagia and eating problems might be clinically useful in the prevention of unintentional weight loss in PD. Since EI was lower in ASPD, EI might be a useful marker of disease progression in PD.

Screening of Parkinsonian subtle fine-motor impairment from touchscreen typing via deep learning.

Fine-motor impairment (FMI) is progressively expressed in early Parkinson's Disease (PD) patients and is now known to be evident in the immediate prodromal stage of the condition. The clinical techniques for detecting FMI may not be robust enough and here, we show that the subtle FMI of early PD patients can be effectively estimated from the analysis of natural smartphone touchscreen typing via deep learning networks, trained in stages of initialization and fine-tuning. In a validation dataset of 36,000 typing sessions from 39 subjects (17 healthy/22 PD patients with medically validated UPDRS Part III single-item scores), the proposed approach achieved values of area under the receiver operating characteristic curve (AUC) of 0.89 (95% confidence interval: 0.80-0.96) with sensitivity/specificity: 0.90/0.83. The derived estimations result in statistically significant ([Formula: see text]) correlation of 0.66/0.73/0.58 with the clinical standard UPDRS Part III items 22/23/31, respectively. Further validation analysis on 9 de novo PD patients vs. 17 healthy controls classification resulted in AUC of 0.97 (0.93-1.00) with 0.93/0.90. For 253 remote study participants, with self-reported health status providing 252.000 typing sessions via a touchscreen typing data acquisition mobile app (iPrognosis), the proposed approach predicted 0.79 AUC (0.66-0.91) with 0.76/0.71. Remote and unobtrusive screening of subtle FMI via natural smartphone usage, may assist in consolidating early and accurate diagnosis of PD.

Assistive HCI-Serious Games Co-design Insights: The Case Study of i-PROGNOSIS Personalized Game Suite for Parkinson's Disease.

Human-Computer Interaction (HCI) and games set a new domain in understanding people's motivations in gaming, behavioral implications of game play, game adaptation to player preferences and needs for increased engaging experiences in the context of HCI serious games (HCI-SGs). When the latter relate with people's health status, they can become a part of their daily life as assistive health status monitoring/enhancement systems. Co-designing HCI-SGs can be seen as a combination of art and science that involves a meticulous collaborative process. The design elements in assistive HCI-SGs for Parkinson's Disease (PD) patients, in particular, are explored in the present work. Within this context, the Game-Based Learning (GBL) design framework is adopted here and its main game-design parameters are explored for the Exergames, Dietarygames, Emotional games, Handwriting games, and Voice games design, drawn from the PD-related i-PROGNOSIS Personalized Game Suite (PGS) (www.i-prognosis.eu) holistic approach. Two main data sources were involved in the study. In particular, the first one includes qualitative data from semi-structured interviews, involving 10 PD patients and four clinicians in the co-creation process of the game design, whereas the second one relates with data from an online questionnaire addressed by 104 participants spanning the whole related spectrum, i.e., PD patients, physicians, software/game developers. Linear regression analysis was employed to identify an adapted GBL framework with the most significant game-design parameters, which efficiently predict the transferability of the PGS beneficial effect to real-life, addressing functional PD symptoms. The findings of this work can assist HCI-SG designers for designing PD-related HCI-SGs, as the most significant game-design factors were identified, in terms of adding value to the role of HCI-SGs in increasing PD patients' quality of life, optimizing the interaction with personalized HCI-SGs and, hence, fostering a collaborative human-computer symbiosis.

Detecting Parkinsonian Tremor From IMU Data Collected in-the-Wild Using Deep Multiple-Instance Learning.

Parkinson's Disease (PD) is a slowly evolving neurological disease that affects about [Formula: see text] of the population above 60 years old, causing symptoms that are subtle at first, but whose intensity increases as the disease progresses. Automated detection of these symptoms could offer clues as to the early onset of the disease, thus improving the expected clinical outcomes of the patients via appropriately targeted interventions. This potential has led many researchers to develop methods that use widely available sensors to measure and quantify the presence of PD symptoms such as tremor, rigidity and braykinesia. However, most of these approaches operate under controlled settings, such as in lab or at home, thus limiting their applicability under free-living conditions. In this work, we present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device. We propose a Multiple-Instance Learning approach, wherein a subject is represented as an unordered bag of accelerometer signal segments and a single, expert-provided, tremor annotation. Our method combines deep feature learning with a learnable pooling stage that is able to identify key instances within the subject bag, while still being trainable end-to-end. We validate our algorithm on a newly introduced dataset of 45 subjects, containing accelerometer signals collected entirely in-the-wild. The good classification performance obtained in the conducted experiments suggests that the proposed method can efficiently navigate the noisy environment of in-the-wild recordings.

Restless legs syndrome - the under-recognised non-motor burden: a questionnaire-based cohort study.

Objectives: Non-motor symptoms (NMS) range from neuropsychiatric to pain and are an important but underexplored feature of restless legs syndrome (RLS). There are currently no tools available which enable the holistic assessment of NMS in RLS in clinical practice. The primary aim of this study was to systematically assess NMS prevalence and burden in patients with RLS using the NMS Questionnaire (NMSQuest) validated for Parkinson's disease. Methods: Patients with idiopathic RLS according to the criteria of the international RLS study group (IRLSSG) were included. Patients underwent a physical examination and clinical interview as well as completed the NMS Questionnaire and the international restless legs syndrome study group (IRLSSG) rating scale. Results: Seventy-four patients with primary RLS were included (mean age 64.6 ± 14.4 years, 62.2% female, mean disease duration 23.5 ± 17.8 years, mean Levodopa equivalent daily dose 63.3 ± 67.4 mg). On average patients reported an IRLSSG rating scale score of 24.8 ± 8.2 (maximum 40) and NMSQuest score of 9.9 ± 5.0 (maximum 30). Patients reported a minimum of two NMS with the majority (39.2%) reporting a moderate NMS burden, followed by severe (28.4%) and very severe (17.6%) burden. The most frequent NMS were insomnia (89.2%) followed by nocturia (70.3%), feeling sad (59.5%), forgetfulness (54.1%), urgency (47.3%), feeling anxious (43.2%), unexplained pain (41.9%), difficulty concentrating (40.5%) and dizziness (40.5%). There were no significant differences in NMSQuest total scores according to disease duration and gender (p = 0.739, p = 0.849). Conclusion: In conclusion, this study is one of the first to address NMS in RLS systematically and the data underlines the need to holistically assess NMS in RLS in order to deliver true value-based healthcare for these patients.