Pain in Multiple System Atrophy a Systematic Review and Meta-Analysis.

Background: Individuals with multiple system atrophy (MSA) often complain about pain, nonetheless this remains a poorly investigated non-motor feature of MSA. Objectives: Here, we aimed at assessing the prevalence, characteristics, and risk factors for pain in individuals with MSA. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines, we systematically screened the PubMED, Cochrane, and Web of Science databases for papers published in English until September 30, 2022, combining the following keywords: "pain," "multiple system atrophy," "MSA," "olivopontocerebellar atrophy," "OPCA," "striatonigral degeneration," "SND," "Shy Drager," and "atypical parkinsonism." Results: The search identified 700 records. Sixteen studies provided information on pain prevalence in cohorts of MSA individuals and were included in a qualitative assessment based on the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool. Thirteen studies (11 cross-sectional, two longitudinal) scored ≥14 points on QUADAS assessment and were included in a quantitative analysis, pooling data from 1236 MSA individuals. The resulting pooled prevalence of pain in MSA was 67% (95% confidence intervals [CI] = 57%-75%), and significantly higher in individuals with MSA of parkinsonian rather than cerebellar type (76% [95% CI = 63%-87%] vs. 45% [95% CI = 33%-57%], P = 0.001). Pain assessment tools and collected information were highly heterogeneous across studies. Two studies reported pain treatment strategies and found that only every second person with MSA complaining about pain had received targeted treatment. Conclusions: We found that pain is a frequent, but still under-recognized and undertreated feature of MSA. Further research is needed to improve pain detection and treatment in MSA.

Clinical subtypes of Parkinson's disease.

The clinical heterogeneity of Parkinson's disease (PD) may point at the existence of subtypes. Because subtypes likely reflect distinct underlying etiologies, their identification may facilitate future genetic and pharmacotherapeutic studies. Aim of this study was to identify subtypes by a data-driven approach applied to a broad spectrum of motor and nonmotor features of PD. Data of motor and nonmotor PD symptoms were collected in 802 patients in two different European prevalent cohorts. A model-based cluster analysis was conducted on baseline data of 344 patients of a Dutch cohort (PROPARK). Reproducibility of these results was tested in data of the second annual assessment of the same cohort and validated in an independent Spanish cohort (ELEP) of 357 patients. The subtypes were subsequently characterized on clinical and demographic variables. Four similar PD subtypes were identified in two different populations and are largely characterized by differences in the severity of nondopaminergic features and motor complications: Subtype 1 was mildly affected in all domains, Subtype 2 was predominantly characterized by severe motor complications, Subtype 3 was affected mainly on nondopaminergic domains without prominent motor complications, while Subtype 4 was severely affected on all domains. The subtypes had largely similar mean disease durations (nonsignificant differences between three clusters) but showed considerable differences with respect to their association with demographic and clinical variables. In prevalent disease, PD subtypes are largely characterized by the severity of nondopaminergic features and motor complications and likely reflect complex interactions between disease mechanisms, treatment, aging, and gender.

Wearing-off scales in Parkinson's disease: critique and recommendations.

Wearing-off occurs in the majority of patients with Parkinson's disease after a few years of dopaminergic therapy. Because a variety of scales have been used to estimate wearing-off, the Movement Disorder Society commissioned a task force to assess their clinimetric properties. A systematic review was conducted to identify wearing-off scales that have either been validated or used in Parkinson's patients. A scale was designated "Recommended" if it had been used in clinical studies beyond the group that developed it, if it had been specifically used in Parkinson's disease reports, and if clinimetric studies had established that it is valid, reliable, and sensitive. "Suggested" scales met 2 of the above criteria, and those meeting 1 were "Listed." We identified 3 diagnostic and 4 severity rating scales for wearing-off quantification. Two questionnaires met the criteria to be Recommended for diagnostic screening (questionnaires for 19 and 9 items), and 1 was Suggested (questionnaire for 32 items). Only the patient diaries were Recommended to assess wearing-off severity, with the caveat of relatively limited knowledge of validity. Among the other severity assessment tools, the Unified Parkinson Disease Rating Scale version 3 and the version revised from the Movement Disorders Society were classified as Suggested, whereas the Treatment Response Scale was Listed.

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.

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.

Multiple-Instance Learning for In-The-Wild Parkinsonian Tremor Detection.

Parkinson's Disease (PD) is a neurodegenerative disorder that manifests through slowly progressing symptoms, such as tremor, voice degradation and bradykinesia. Automated detection of such symptoms has recently received much attention by the research community, owing to the clinical benefits associated with the early diagnosis of the disease. Unfortunately, most of the approaches proposed so far, operate under a strictly laboratory setting, thus limiting their potential applicability in real world conditions. In this work, we present a method for automatically detecting tremorous episodes related to PD, based on acceleration signals. We propose to address the problem at hand, as a case of Multiple-Instance Learning, wherein a subject is represented as an unordered bag of signal segments and a single, expert-provided, ground-truth. We employ a deep learning approach that combines feature learning and a learnable pooling stage and is trainable end-to-end. Results on a newly introduced dataset of accelerometer signals collected in-the-wild confirm the validity of the proposed approach.

Early Parkinson's Disease Detection via Touchscreen Typing Analysis using Convolutional Neural Networks.

Parkinson's Disease (PD) is the second most common neurodegenerative disorder worldwide, causing both motor and non-motor symptoms. In the early stages, symptoms are mild and patients may ignore their existence. As a result, they do not undergo any related clinical examination; hence delaying their PD diagnosis. In an effort to remedy such delay, analysis of data passively captured from user's interaction with consumer technologies has been recently explored towards remote screening of early PD motor signs. In the current study, a smartphone-based method analyzing subjects' finger interaction with the smartphone screen is developed for the quantification of fine-motor skills decline in early PD using Convolutional Neural Networks. Experimental results from the analysis of keystroke typing in-the-clinic data from 18 early PD patients and 15 healthy controls have shown a classification performance of 0.89 Area Under the Curve (AUC) with 0.79/0.79 sensitivity/specificity, respectively. Evaluation of the generalization ability of the proposed approach was made by its application on typing data arising from a separate self-reported cohort of 27 PD patients' and 84 healthy controls' daily usage with their personal smartphones (data in-the-wild), achieving 0.79 AUC with 0.74/0.78 sensitivity/specificity, respectively. The results show the potentiality of the proposed approach to process keystroke dynamics arising from users' natural typing activity to detect PD, which contributes to the development of digital tools for remote pathological symptom screening.