Clinical Assessments in Parkinson's Disease: Scales and Monitoring

Combining features on vertical ground reaction force signal analysis for multiclass diagnosing neurodegenerative diseases

Neurodegenerative diseases (NDDs), which are caused by the degeneration of neurons and their functions, affect a significant part of the world's population. Although gait disorders are one of the critical and common markers to determine the presence of NDDs, diagnosing which NDD the patients have among a group of NDDs using gait data is still a significant challenge to be addressed. In this study, we addressed the multi-class classification of NDDs and aim to diagnose Parkinson's disease (PD), Amyotrophic lateral sclerosis disease (AD), and Huntington's disease (HD) from a group containing NDDs and healthy control subjects. We also examined the impact of disease-specific identified features derived from VGRF signals. Detrended Fluctuation Analysis (DFA), Dynamic Time Warping (DTW) and Autocorrelation (AC) were used for feature extraction on Vertical Ground Reaction Force (VGRF) signals. To compare the performance of the features, we employed Support Vector Machines, K-Nearest Neighbors, and Neural Networks as classifiers. In three-class problem addressing the classification of AD, PD and HD 93.3% accuracy rate was achieved, while in the four classes case, in which NDDs and HC groups were considered together, 93.5% accuracy rate was yielded. Considering the disease-specific impact of features, it is revealed that while DFA based features diagnose patients with AD with the highest accuracy, DTW has been shown to be more successful in diagnosing PD. AC based features provided the highest accuracy in diagnosing HD. Although gait disorder is common for NDDs, each disease may have its own distinctive gait rhythms; therefore, it is important to identify disease-specific patterns and parameters for the diagnosis of each disease. To increase the diagnostic accuracy, it is necessary to use a combination of features, which were effective for each disease diagnosis. Determining a limited number of disease-specific features would provide NDD diagnostic systems suitable to be deployed in edge-computing environments.

Old problems, new solutions: harnessing technology and innovation in Parkinson's disease-evidence and experiences from Thailand

The prevalence of Parkinson's disease (PD) is increasing rapidly worldwide, but there are notable inequalities in its distribution and in the availability of healthcare resources across different world regions. Low- and middle-income countries (LMICs), including Thailand, bear the highest burden of PD so there is an urgent need to develop effective solutions that can overcome the many regional challenges associated with delivering high-quality, and equitable care to a diverse population with limited resources. This article describes the evolution of healthcare delivery for PD in Thailand, as a case example of a LMIC. The discussions reflect the author's presentation at the Yoshikuni Mizuno Lectureship Award given during the 8th Asian and Oceanian Parkinson's Disease and Movement Disorders Congress in March 2023 for which he was the 2023 recipient. The specific challenges faced in Thailand are reviewed along with new solutions that have been implemented to improve the knowledge and skills of healthcare professionals nationally, the delivery of care, and the outcomes for PD patients. Technology and innovation have played an important role in this process with many new tools and devices being implemented in clinical practice. Without any realistic prospect of a curative therapy in the near future that could halt the current PD pandemic, it will be necessary to focus on preventative lifestyle strategies that can help reduce the risk of developing PD such as good nutrition (EAT), exercise (MOVE), good sleep hygiene (SLEEP), and minimizing environmental risks (PROTECT), which should be initiated and continued (REPEAT) as early as possible.

Evaluation of the Parkinson's Remote Interactive Monitoring System in a Clinical Setting: Usability Study

BACKGROUND

The fastest-growing neurological disorder is Parkinson disease (PD), a progressive neurodegenerative disease that affects 10 million people worldwide. PD is typically treated with levodopa, an oral pill taken to increase dopamine levels, and other dopaminergic agonists. As the disease advances, the efficacy of the drug diminishes, necessitating adjustments in treatment dosage according to the patient's symptoms and disease progression. Therefore, remote monitoring systems that can provide more detailed and accurate information on a patient's condition regularly are a valuable tool for clinicians and patients to manage their medication. The Parkinson's Remote Interactive Monitoring System (PRIMS), developed by PragmaClin Research Inc, was designed on the premise that it will be an easy-to-use digital system that can accurately capture motor and nonmotor symptoms of PD remotely.

OBJECTIVE

We performed a usability evaluation in a simulated clinical environment to assess the ease of use of the PRIMS and determine whether the product offers suitable functionality for users in a clinical setting.

METHODS

Participants were recruited from a user sign-up web-based database owned by PragmaClin Research Inc. A total of 11 participants were included in the study based on the following criteria: (1) being diagnosed with PD and (2) not being diagnosed with dementia or any other comorbidities that would make it difficult to complete the PRIMS assessment safely and independently. Patient users completed a questionnaire that is based on the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale. Interviews and field notes were analyzed for underlying themes and topics.

RESULTS

In total, 11 people with PD participated in the study (female individuals: n=5, 45%; male individuals: n=6, 55%; age: mean 66.7, SD 7.77 years). Thematic analysis of the observer's notes revealed 6 central usability issues associated with the PRIMS. These were the following: (1) the automated voice prompts are confusing, (2) the small camera is problematic, (3) the motor test exhibits excessive sensitivity to the participant's orientation and position in relation to the cameras, (4) the system poses mobility challenges, (5) navigating the system is difficult, and (6) the motor test exhibits inconsistencies and technical issues. Thematic analysis of qualitative interview responses revealed four central themes associated with participants' perspectives and opinions on the PRIMS, which were (1) admiration of purpose, (2) excessive system sensitivity, (3) video instructions preferred, and (4) written instructions disliked. The average system usability score was calculated to be 69.2 (SD 4.92), which failed to meet the acceptable system usability score of 70.

CONCLUSIONS

Although multiple areas of improvement were identified, most of the participants showed an affinity for the overarching objective of the PRIMS. This feedback is being used to upgrade the current PRIMS so that it aligns more with patients' needs.

Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson's Disease Using Machine Learning

Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson's disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments.

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review

BACKGROUND

The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.

OBJECTIVE

In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.

METHODS

A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.

RESULTS

Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.

CONCLUSIONS

This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.

Classification of mild Parkinson's disease: data augmentation of time-series gait data obtained via inertial measurement units

Data-augmentation methods have emerged as a viable approach for improving the state-of-the-art performances for classifying mild Parkinson's disease using deep learning with time-series data from an inertial measurement unit, considering the limited amount of training datasets available in the medical field. This study investigated effective data-augmentation methods to classify mild Parkinson's disease and healthy participants with deep learning using a time-series gait dataset recorded via a shank-worn inertial measurement unit. Four magnitude-domain-transformation and three time-domain-transformation data-augmentation methods, and four methods involving mixtures of the aforementioned methods were applied to a representative convolutional neural network for the classification, and their performances were compared. In terms of data-augmentation, compared with baseline classification accuracy without data-augmentation, the magnitude-domain transformation performed better than the time-domain transformation and mixed-data augmentation. In the magnitude-domain transformation, the rotation method significantly contributed to the best performance improvement, yielding accuracy and F1-score improvements of 5.5 and 5.9%, respectively. The augmented data could be varied while maintaining the features of the time-series data obtained via the sensor for detecting mild Parkinson's in gait; this data attribute may have caused the aforementioned trend. Notably, the selection of appropriate data extensions will help improve the classification performance for mild Parkinson's disease.

Gait Analysis in Neurorehabilitation: From Research to Clinical Practice

When brain damage occurs, gait and balance are often impaired. Evaluation of the gait cycle, therefore, has a pivotal role during the rehabilitation path of subjects who suffer from neurological disorders. Gait analysis can be performed through laboratory systems, non-wearable sensors (NWS), and/or wearable sensors (WS). Using these tools, physiotherapists and neurologists have more objective measures of motion function and can plan tailored and specific gait and balance training early to achieve better outcomes and improve patients' quality of life. However, most of these innovative tools are used for research purposes (especially the laboratory systems and NWS), although they deserve more attention in the rehabilitation field, considering their potential in improving clinical practice. In this narrative review, we aimed to summarize the most used gait analysis systems in neurological patients, shedding some light on their clinical value and implications for neurorehabilitation practice.

Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN

In this paper, we proposed a novel approach to diagnose and classify Parkinson's Disease (PD) using ensemble learning and 1D-PDCovNN, a novel deep learning technique. PD is a neurodegenerative disorder; early detection and correct classification are essential for better disease management. The primary aim of this study is to develop a robust approach to diagnosing and classifying PD using EEG signals. As the dataset, we have used the San Diego Resting State EEG dataset to evaluate our proposed method. The proposed method mainly consists of three stages. In the first stage, the Independent Component Analysis (ICA) method has been used as the pre-processing method to filter out the blink noises from the EEG signals. Also, the effect of the band showing motor cortex activity in the 7-30 Hz frequency band of EEG signals in diagnosing and classifying Parkinson's disease from EEG signals has been investigated. In the second stage, the Common Spatial Pattern (CSP) method has been used as the feature extraction to extract useful information from EEG signals. Finally, an ensemble learning approach, Dynamic Classifier Selection (DCS) in Modified Local Accuracy (MLA), has been employed in the third stage, consisting of seven different classifiers. As the classifier method, DCS in MLA, XGBoost, and 1D-PDCovNN classifier has been used to classify the EEG signals as the PD and healthy control (HC). We first used dynamic classifier selection to diagnose and classify Parkinson's disease (PD) from EEG signals, and promising results have been obtained. The performance of the proposed approach has been evaluated using the classification accuracy, F-1 score, kappa score, Jaccard score, ROC curve, recall, and precision values in the classification of PD with the proposed models. In the classification of PD, the combination of DCS in MLA achieved an accuracy of 99,31%. The results of this study demonstrate that the proposed approach can be used as a reliable tool for early diagnosis and classification of PD.

The Tale of DJ-1 (PARK7): A Swiss Army Knife in Biomedical and Psychological Research

DJ-1 (also known as PARK7) is a multifunctional enzyme in human beings that is highly conserved and that has also been discovered in diverse species (ranging from prokaryotes to eukaryotes). Its complex enzymatic and non-enzymatic activities (such as anti-oxidation, anti-glycation, and protein quality control), as well as its role as a transcriptional coactivator, enable DJ-1 to serve as an essential regulator in multiple cellular processes (e.g., epigenetic regulations) and make it a promising therapeutic target for diverse diseases (especially cancer and Parkinson's disease). Due to its nature as a Swiss army knife enzyme with various functions, DJ-1 has attracted a large amount of research interest, from different perspectives. In this review, we give a brief summary of the recent advances with respect to DJ-1 research in biomedicine and psychology, as well as the progress made in attempts to develop DJ-1 into a druggable target for therapy.

High levels of Bifidobacteriaceae are associated with the pathogenesis of Parkinson's disease

Background

The diagnosis of Parkinson's disease (PD) is complex and there are no biomarkers for early identification. Many studies have reported altered gut microbiota in patients with PD compared with healthy individuals. However, results from previous studies vary across countries.

Aims

The aim of this study was to identify gut microbiota biomarkers that could be used as a marker for the diagnosis of PD.

Methods

Firstly, the differential gut microbiota was obtained by meta-analysis, and then the results of meta-analysis were validated through metagenomic cohort. Finally, the ROC curve was drawn based on the metagenomic validation results.

Results

The meta-analysis showed a lower relative abundance of Prevotellaceae (p < 0.00001) and Lachnospiraceae (p = 0.002), and a higher of Ruminococcaceae (p < 0.00001), Christensenellaceae (p = 0.03), Bifidobacteriaceae (p < 0.00001), and Verrucomicrobiaceae (p = 0.02) in patients with PD. Only Bifidobacteriaceae was also at high levels in the validation cohort of the metagenome. Meanwhile, three species from the Bifidobacteriaceae, including Scardovia_inopinata (p = 0.022), Bifidobacterium_dentium (p = 0.005), and Scardovia_wiggsiae (p = 0.024) were also high. The ROC curve showed that the three species (71.2%) from Bifidobacteriaceae had good predictive efficiency for PD.

Conclusion

Elevated Bifidobacteriaceae may be associated with PD. Elevated three species from the Bifidobacteriaceae, including Scardovia_inopinata, Bifidobacterium_dentium and Scardovia_wiggsiae may provide new potential biomarkers for the diagnosis of PD.

The emerging role of circular RNAs in Parkinson's disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease and the most common movement disorder. It involves a gradual loss of dopaminergic neurons in the substantia nigra. Although many studies have been conducted, the underlying molecular pathways of PD remain largely unknown. Circular RNAs (circRNAs), a novel class of non-coding RNAs with a covalently closed loop structure, are common in the brain. They are stable, conserved molecules that are widely expressed in eukaryotes in tissue-, cell-, and development-specific patterns. Many circRNAs have recently been identified in nervous system diseases, and some circRNA expression profiles have been linked to PD. Given that recent research has indicated the essential roles of various circRNAs in the development and progression of neurodegenerative diseases, the identification of individual circRNAs may be a promising strategy for finding new treatment targets for PD. Moreover, the search for circRNAs with high specificity and sensitivity will open up new avenues for the early diagnosis and treatment of PD. Herein, we address the biogenesis, properties, and roles of circRNAs and review their potential utility as biomarkers and therapeutic targets in PD.

Digital phenotyping by wearable-driven artificial intelligence in older adults and people with Parkinson's disease: Protocol of the mixed method, cyclic ActiveAgeing study

BACKGROUND

Active ageing is described as the process of optimizing health, empowerment, and security to enhance the quality of life in the rapidly growing population of older adults. Meanwhile, multimorbidity and neurological disorders, such as Parkinson's disease (PD), lead to global public health and resource limitations. We introduce a novel user-centered paradigm of ageing based on wearable-driven artificial intelligence (AI) that may harness the autonomy and independence that accompany functional limitation or disability, and possibly elevate life expectancy in older adults and people with PD.

METHODS

ActiveAgeing is a 4-year, multicentre, mixed method, cyclic study that combines digital phenotyping via commercial devices (Empatica E4, Fitbit Sense, and Oura Ring) with traditional evaluation (clinical assessment scales, in-depth interviews, and clinical consultations) and includes four types of participants: (1) people with PD and (2) their informal caregiver; (3) healthy older adults from the Helgetun living environment in Norway, and (4) people on the Helgetun waiting list. For the first study, each group will be represented by N = 15 participants to test the data acquisition and to determine the sample size for the second study. To suggest lifestyle changes, modules for human expert-based advice, machine-generated advice, and self-generated advice from accessible data visualization will be designed. Quantitative analysis of physiological data will rely on digital signal processing (DSP) and AI techniques. The clinical assessment scales are the Unified Parkinson's Disease Rating Scale (UPDRS), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Geriatric Anxiety Inventory (GAI), Apathy Evaluation Scale (AES), and the REM Sleep Behaviour Disorder Screening Questionnaire (RBDSQ). A qualitative inquiry will be carried out with individual and focus group interviews and analysed using a hermeneutic approach including narrative and thematic analysis techniques.

DISCUSSION

We hypothesise that digital phenotyping is feasible to explore the ageing process from clinical and lifestyle perspectives including older adults and people with PD. Data is used for clinical decision-making by symptom tracking, predicting symptom evolution, and discovering new outcome measures for clinical trials.

Scent-delivery devices as a digital healthcare tool for olfactory training: A pilot focus group study in Parkinson's disease patients

Parkinson's disease (PD) patients display a combination of motor and non-motor symptoms. The most common non-motor symptom is scent (olfactory) impairment, occurring at least four years prior to motor symptom onset. Recent and growing interest in digital healthcare technology used in PD has resulted in more technologies developed for motor rather than non-motor symptoms. Human-computer interaction (HCI), which uses computer technology to explore human activity and work, could be combined with digital healthcare technologies to better understand and support olfaction via scent training - leading to the development of a scent-delivery device (SDD). In this pilot study, three PD patients were invited to an online focus group to explore the association between PD and olfaction, understand HCI and sensory technologies and were demonstrated a new multichannel SDD with an associated mobile app. Participants had a preconceived link, a result of personal experience, between olfactory impairment and PD. Participants felt that healthcare professionals did not take olfactory dysfunction concerns seriously prior to PD diagnosis. Two were not comfortable with sharing scent loss experiences with others. Participants expected the multichannel SDD to be small, portable and easy-to-use, with customisable cartridges to deliver chosen scents and the mobile app to create a sense of community. None of the participants regularly performed scent training but would consider doing so if some scent function could be regained. Standardised digital SDDs for regular healthcare check-ups may facilitate improvement in olfactory senses in PD patients and potential earlier PD diagnosis, allowing earlier therapeutic and symptomatic PD management.

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review (Preprint).. [DOI: 10.2196/preprints.42950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.

OBJECTIVE

In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.

METHODS

A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.

RESULTS

Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.

CONCLUSIONS

This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.

CLINICALTRIAL

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Parkinson’s Disease Progression and Statins: Hydrophobicity Matters

Background: Recent randomized clinical trials using hydrophobic statins reported no influence on Parkinson’s disease (PD) clinical progression. Hydrophobicity is a key determinant for blood-brain barrier penetrance. Objective: Investigate a potential effect of statins on PD progression. Methods: Statin use was determined at baseline and subtyped according to hydrophobicity in 125 PD patients participating in the PD Biomarker Program (PDBP, 2012–2015) at our site. Clinical (N = 125) and susceptibility MRI (N = 86) data were obtained at baseline and 18-months. Movement Disorders Society-Unified PD Rating Scales were used to track progression of non-motor (MDS-UPDRS-I) and motor (MDS-UPDRS-II) symptoms, and rater-based scores (MDS-UPDRS-III) of patients in the “on” drug state. R2* values were used to capture pathological progression in the substantia nigra. Associations between statin use, its subtypes, and PD progression were evaluated with linear mixed effect regressions. Results: Compared to statin non-users, overall statin or lipophilic statin use did not significantly influence PD clinical or imaging progression. Hydrophilic statin users, however, demonstrated faster clinical progression of non-motor symptoms [MDS-UPDRS-I (β= 4.8, p = 0.010)] and nigral R2* (β= 3.7, p = 0.043). A similar trend was found for MDS-UPDRS-II (β= 3.9, p = 0.10), but an opposite trend was observed for rater-based MDS-UPDRS-III (β= –7.3, p = 0.10). Compared to lipophilic statin users, hydrophilic statin users also showed significantly faster clinical progression of non-motor symptoms [MDS-UPDRS-I (β= 5.0, p = 0.020)], but R2* did not reach statistical significance (β= 2.5, p = 0.24). Conclusion: This study suggests that hydrophilic, but not lipophilic, statins may be associated with faster PD progression. Future studies may have clinical and scientific implications.

Parkinson's disease severity clustering based on tapping activity on mobile device

In this study, we investigated the relationship between finger tapping tasks on the smartphone and the MDS-UPDRS I–II and PDQ-8 using the mPower dataset. mPower is a mobile application-based study for monitoring key indicators of PD progression and diagnosis. Currently, it is one of the largest, open access, mobile Parkinson’s Disease studies. Data from seven modules with a total of 8,320 participants who provided the data of at least one task were released to the public researcher. The modules comprise demographics, MDS-UPDRS I–II, PDQ-8, memory, tapping, voice, and walking. Finger-tapping is one of the tasks that easy to perform and has been analyzed for the quantitative measurement of PD. Therefore, participants who performed both the tapping activity and MDS-UPDRS I–II rating scale were selected for our analysis. Note that the MDS-UPDRS mPower Survey only contains parts of the original scale and has not been clinimetrically tested for validity and reliability. We obtained a total of 1851 samples that contained the tapping activity and MDS-UPDRS I–II for the analysis. Nine features were selected to represent tapping activity. K-mean was applied as an unsupervised clustering algorithm in our study. For determining the number of clusters, the elbow method, Sihouette score, and Davies–Bouldin index, were employed as supporting evaluation metrics. Based on these metrics and expert opinion, we decide that three clusters were appropriate for our study. The statistical analysis found that the tapping features could separate participants into three severity groups. Each group has different characteristics and could represent different PD severity based on the MDS-UPDRS I–II and PDQ-8 scores. Currently, the severity assessment of a movement disorder is based on clinical observation. Therefore, it is highly dependant on the skills and experiences of the trained movement disorder specialist who performs the procedure. We believe that any additional methods that could potentially assist with quantitative assessment of disease severity, without the need for a clinical visit would be beneficial to both the healthcare professionals and patients.

Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review

Parkinson's disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients' quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson's disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson's disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hundred and twelve of them have been selected in a systematic way and have been considered in the state-of-the-art systematic review presented in this paper. These studies propose various methods, applied on various sensory data to address different Parkinson's disease-related problems. The most widely deployed sensors, the most commonly addressed problems and the best performing algorithms are highlighted. Finally, some challenges are summarized along with some future considerations and opportunities that arise.

Insights into the multifaceted role of circular RNAs: implications for Parkinson's disease pathogenesis and diagnosis

Parkinson's disease (PD) is a complex, age-related, neurodegenerative disease whose etiology, pathology, and clinical manifestations remain incompletely understood. As a result, care focuses primarily on symptoms relief. Circular RNAs (circRNAs) are a large class of mostly noncoding RNAs that accumulate with aging in the brain and are increasingly shown to regulate all aspects of neuronal and glial development and function. They are generated by the spliceosome through the backsplicing of linear RNA. Although their biological role remains largely unknown, they have been shown to regulate transcription and splicing, act as decoys for microRNAs and RNA binding proteins, used as templates for translation, and serve as scaffolding platforms for signaling components. Considering that they are stable, diverse, and detectable in easily accessible biofluids, they are deemed promising biomarkers for diagnosing diseases. CircRNAs are differentially expressed in the brain of patients with PD, and growing evidence suggests that they regulate PD pathogenetic processes. Here, the biogenesis, expression, degradation, and detection of circRNAs, as well as their proposed functions, are reviewed. Thereafter, research linking circRNAs to PD-related processes, including aging, alpha-synuclein dysregulation, neuroinflammation, and oxidative stress is highlighted, followed by recent evidence for their use as prognostic and diagnostic biomarkers for PD.

A holistic approach to evaluating Parkinson's disease, using the Delphi method: a linear evaluation index

BACKGROUND

Parkinson's disease (PD) is a chronic disease that presents a multitude of symptoms, with symptoms of both motor and nonmotor nature. The Delphi method is widely used to create consensuses among experts in a field of knowledge.

OBJECTIVE

In order to reach a consensus on the values that should be assigned to the different motor and nonmotor manifestations of Parkinson's disease, a linear evaluation index (LEI) was created. Subsequently, the metric properties of this index were studied.

METHODS

120 consecutive patients with a Parkinson's diagnosis were chosen in accordance with the UKPDSBB criteria. The Delphi method was used to reach a consensus among experts regarding the values of each of the manifestations included. Subsequently, the following attributes were analyzed: quality and acceptability of the data; reliability, in terms of internal consistency, reliability index, Cronbach's alpha and standard error of measurement; and validity, in terms of convergent validity and validity for known groups.

RESULTS

Twenty-five experts participated. The importance factor did not differ between the first round and the second round (chi-square test). We analyzed the responses that assigned percentage values to the 10 dimensions of the LEI. Both in the first and in the second round, the values of the scattering coefficient Vr were always close to 0. The homogeneity index was 0.36; the corrected-item total correlation values ranged from 0.02 to 0.7; Cronbach's α was 0.69; and the SEM was 4.23 (55.1%).

CONCLUSIONS

The LEI was obtained through rigorous recommended methodology. The results showed adequate metric properties.

A Systematic Review of Parkinson's Disease Cluster Analysis Research

One way to understand the Parkinson’s disease (PD) population is to investigate the similarities and differences among patients through cluster analysis, which may lead to defined, patient subgroups for diagnosis, progression tracking and treatment planning. This paper provides a systematic review of PD patient clustering research, evaluating the variables included in clustering, the cluster methods applied, the resulting patient subgroups, and evaluation metrics. A search was conducted from 1999 to 2021 on the PubMed database, using various search terms including: Parkinson’s disease, cluster, and analysis. The majority of studies included a variety of clinical scale scores for clustering, of which many provide a numerical, but ordinal, categorical value. Even though the scale scores are ordinal, these were treated as numerical values with numerical and continuous values being the focus of the clustering, with limited attention to categorical variables, such as gender and family history, which may also provide useful insights into disease diagnosis, progression, and treatment. The results pointed to two to five patient clusters, with similarities among the age of onset and disease duration. The studies lacked the use of existing clustering evaluation metrics which points to a need for a thorough, analysis framework, and consensus on the appropriate variables to include in cluster analysis. Accurate cluster analysis may assist with determining if PD patients’ symptoms can be treated based on a subgroup of features, if personalized care is required, or if a mix of individualized and group-based care is the best approach.

Pedrosa D, Antonini A, Côté D, Fabbri M, Ferreira JJ, Růžička E, Grimes D, Mestre TA. Moving towards Integrated and Personalized Care in Parkinson's Disease: A Framework Proposal for Training Parkinson Nurses

Delivering healthcare to people living with Parkinson's disease (PD) may be challenging in face of differentiated care needs during a PD journey and a growing complexity. In this regard, integrative care models may foster flexible solutions on patients' care needs whereas Parkinson Nurses (PN) may be pivotal facilitators. However, at present hardly any training opportunities tailored to the care priorities of PD-patients are to be found for nurses. Following a conceptual approach, this article aims at setting a framework for training PN by reviewing existing literature on care priorities for PD. As a result, six prerequisites were formulated concerning a framework for training PN. The proposed training framework consist of three modules covering topics of PD: (i) comprehensive care, (ii) self-management support and (iii) health coaching. A fourth module on telemedicine may be added if applicable. The framework streamlines important theoretical concepts of professional PD management and may enable the development of novel, personalized care approaches.

An Investigation into the Use and Meaning of Parkinson's Disease Clinical Scale Scores

Parkinson's disease (PD) is the second most common, neurodegenerative disorder. It is a chronic, disabling, and progressive disease, and no treatment stops its progression. Rating scales are utilized to quantify PD progression and severity. The most conventional scale is the Unified Parkinson's Disease Rating Scale (UPDRS) and its modified version, Movement Disorder Society- (MDS-) UPDRS. An analytical investigation into the use and meaning of these clinical scale scores was conducted to determine if gaps exist in quantifying disease progression and severity. A series of discrepancies were identified including confusion among patients regarding the score meaning and misuse of the scores among clinicians and researchers to define disease progression. The scales are of an ordinal type and hence the resulting scores are ordinal, not providing a quantifiable progression nor severity level, but a categorical value and survey total. The knowledge that the scores are ordinal and the scales are subjective is mentioned in very limited publications, not the focus of these papers, but a brief introduction and a thoroughly researched, analytical investigation into the scales and scores have not been found. Therefore, the continuous misunderstanding and misuse of these scales and resulting scores warrant a comprehensive assessment and evaluation of these scales and scores to identify the gaps.

Kinematic and Clinical Outcomes to Evaluate the Efficacy of a Multidisciplinary Intervention on Functional Mobility in Parkinson's Disease

Introduction: Functional mobility (FM) is a concept that incorporates the capacity of a person to move independently and safely to accomplish tasks. It has been proposed as a Parkinson's disease (PD) functional and global health outcome. In this study, we aimed to identify which kinematic and clinical outcomes changes better predict FM changes when PD patients are submitted to a specialized multidisciplinary program. Methods: PD patients engaged in a pre-defined specialized multidisciplinary program were assessed at admission and discharge. Change from baseline was calculated for all kinematic and clinical outcomes, and Timed Up and Go (TUG) was defined as the primary outcome for FM. A stepwise multivariate linear regression was performed to identify which outcome measures better predict TUG changes. Results: Twenty-four patients were included in the study. The changes in TUG Cognitive test, supervised step length, and free-living (FL) step time asymmetry were identified as the best predictors of TUG changes. The supervised step length and FL step time asymmetry were able to detect a small to moderate effect of the intervention (d values ranging from -0.26 to 0.42). Conclusions: Our results support the use of kinematic outcome measures to evaluate the efficacy of multidisciplinary interventions on PD FM. The TUG Cognitive, step length, and FL step time asymmetry were identified as having the ability to predict TUG changes. More studies are needed to identify the minimal clinically important difference for step length and FL step time asymmetry in response to a multidisciplinary intervention for PD FM.

An artificial neural network approach to detect presence and severity of Parkinson's disease via gait parameters

Introduction

Gait deficits are debilitating in people with Parkinson’s disease (PwPD), which inevitably deteriorate over time. Gait analysis is a valuable method to assess disease-specific gait patterns and their relationship with the clinical features and progression of the disease.

Objectives

Our study aimed to i) develop an automated diagnostic algorithm based on machine-learning techniques (artificial neural networks [ANNs]) to classify the gait deficits of PwPD according to disease progression in the Hoehn and Yahr (H-Y) staging system, and ii) identify a minimum set of gait classifiers.

Methods

We evaluated 76 PwPD (H-Y stage 1–4) and 67 healthy controls (HCs) by computerized gait analysis. We computed the time-distance parameters and the ranges of angular motion (RoMs) of the hip, knee, ankle, trunk, and pelvis. Principal component analysis was used to define a subset of features including all gait variables. An ANN approach was used to identify gait deficits according to the H-Y stage.

Results

We identified a combination of a small number of features that distinguished PwPDs from HCs (one combination of two features: knee and trunk rotation RoMs) and identified the gait patterns between different H-Y stages (two combinations of four features: walking speed and hip, knee, and ankle RoMs; walking speed and hip, knee, and trunk rotation RoMs).

Conclusion

The ANN approach enabled automated diagnosis of gait deficits in several symptomatic stages of Parkinson’s disease. These results will inspire future studies to test the utility of gait classifiers for the evaluation of treatments that could modify disease progression.

Differentially Expressed Circular RNAs in Peripheral Blood Mononuclear Cells of Patients with Parkinson's Disease

Background

New noninvasive and affordable molecular approaches that will complement current practices and increase the accuracy of Parkinson's disease (PD) diagnosis are urgently needed. Circular RNAs (circRNAs) are stable noncoding RNAs that accumulate with aging in neurons and are increasingly shown to regulate all aspects of neuronal development and function.

Objectives

Τhe aims of this study were to identify differentially expressed circRNAs in blood mononuclear cells of patients with idiopathic PD and explore the competing endogenous RNA networks affected.

Methods

Eighty‐seven circRNAs were initially selected based on relatively high gene expression in the human brain. More than half of these were readily detectable in blood mononuclear cells using real‐time reverse transcription‐polymerase chain reaction. Comparative expression analysis was then performed in blood mononuclear cells from 60 control subjects and 60 idiopathic subjects with PD.

Results

Six circRNAs were significantly down‐regulated in patients with PD. The classifier that best distinguished PD consisted of four circRNAs with an area under the curve of 0.84. Cross‐linking immunoprecipitation‐sequencing data revealed that the RNA‐binding proteins bound by most of the deregulated circRNAs include the neurodegeneration‐associated FUS, TDP43, FMR1, and ATXN2. MicroRNAs predicted to be sequestered by most deregulated circRNAs have the Gene Ontology categories “protein modification” and “transcription factor activity” mostly enriched.

Conclusions

This is the first study that identifies specific circRNAs that may serve as diagnostic biomarkers for PD. Because they are highly expressed in the brain and are derived from genes with essential brain functions, they may also hint on the PD pathways affected. © 2021 Biomedical Research Foundation, Academy of Athens. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Building a Machine-Learning Framework to Remotely Assess Parkinson's Disease Using Smartphones

OBJECTIVE

Parkinson's disease (PD) is a neurodegenerative disorder that affects multiple neurological systems. Traditional PD assessment is conducted by a physician during infrequent clinic visits. Using smartphones, remote patient monitoring has the potential to obtain objective behavioral data semi-continuously, track disease fluctuations, and avoid rater dependency.

METHODS

Smartphones collect sensor data during various active tests and passive monitoring, including balance (postural instability), dexterity (skill in performing tasks using hands), gait (the pattern of walking), tremor (involuntary muscle contraction and relaxation), and voice. Some of the features extracted from smartphone data are potentially associated with specific PD symptoms identified by physicians. To leverage large-scale cross-modality smartphone features, we propose a machine-learning framework for performing automated disease assessment. The framework consists of a two-step feature selection procedure and a generic model based on the elastic-net regularization.

RESULTS

Using this framework, we map the PD-specific architecture of behaviors using data obtained from both PD participants and healthy controls (HCs). Utilizing these atlases of features, the framework shows promises to (a) discriminate PD participants from HCs, and (b) estimate the disease severity of individuals with PD.

SIGNIFICANCE

Data analysis results from 437 behavioral features obtained from 72 subjects (37 PD and 35 HC) sampled from 17 separate days during a period of up to six months suggest that this framework is potentially useful for the analysis of remotely collected smartphone sensor data in individuals with PD.

Parkinson's disease: current assessment methods and wearable devices for evaluation of movement disorder motor symptoms - a patient and healthcare professional perspective

BACKGROUND

Parkinson's disease is the second most common long-term chronic, progressive, neurodegenerative disease, affecting more than 10 million people worldwide. There has been a rising interest in wearable devices for evaluation of movement disorder diseases such as Parkinson's disease due to the limitations in current clinic assessment methods such as Unified Parkinson's Disease Rating Scale (UPDRS) and the Hoehn and Yahr (HY) scale. However, there are only a few commercial wearable devices available, which, in addition, have had very limited adoption and implementation. This inconsistency may be due to a lack of users' perspectives in terms of device design and implementation. This study aims to identify the perspectives of healthcare professionals and patients linked to current assessment methods and to identify preferences, and requirements of wearable devices.

METHODS

This was a qualitative study using semi-structured interviews followed by focus groups. Transcripts from sessions were analysed using an inductive thematic approach.

RESULTS

It was noted that the well-known assessment process such as Unified Parkinson's Disease Rating Scale (UPDRS) was not used routinely in clinics since it is time consuming, subjective, inaccurate, infrequent and dependent on patients' memories. Participants suggested that objective assessment methods are needed to increase the chance of effective treatment. The participants' perspectives were positive toward using wearable devices, particularly if they were involved in early design stages. Patients emphasized that the devices should be comfortable, but they did not have any concerns regarding device visibility or data privacy transmitted over the internet when it comes to their health. In terms of wearing a monitor, the preferable part of the body for all participants was the wrist. Healthcare professionals stated a need for an economical solution that is easy to interpret. Some design aspects identified by patients included clasps, material choice, and form factor.

CONCLUSION

The study concluded that current assessment methods are limited. Patients' and healthcare professionals' involvement in wearable devices design process has a pivotal role in terms of ultimate user acceptance. This includes the provision of additional functions to the wearable device, such as fall detection and medication reminders, which could be attractive features for patients.

Validation of differentially expressed brain-enriched microRNAs in the plasma of PD patients

Objective

There is a pressing need to identify and validate, minimally invasive, molecular biomarkers that will complement current practices and increase the diagnostic accuracy in Parkinson’s disease (PD). Brain‐enriched miRNAs regulate all aspects of neuron development and function; importantly, they are secreted by neurons in amounts that can be readily detected in the plasma. Τhe aim of the present study was to validate a set of previously identified brain‐enriched miRNAs with diagnostic potential for idiopathic PD and recognize the molecular pathways affected by these deregulated miRNAs.

Methods

RT‐qPCR was performed in the plasma of 92 healthy controls and 108 idiopathic PD subjects. Statistical and in silico analyses were used to validate deregulated miRNAs and pathways in PD, respectively.

Results

miR‐22‐3p, miR‐124‐3p, miR‐136‐3p, miR‐154‐5p, and miR‐323a‐3p levels were found to be differentially expressed between healthy controls and PD patients. miR‐330‐5p, miR‐433‐3p, and miR‐495‐3p levels were overall higher in male subjects. Most of these miRNAs are clustered at Chr14q32 displaying CREB1, CEBPB, and MAZ transcription factor binding sites. Gene Ontology annotation analysis of deregulated miRNA targets revealed that “Protein modification,” “Transcription factor activity,” and “Cell death” terms were over‐represented. Kyoto Encyclopedia of Genes and Genome analysis revealed that “Long‐term depression,” “TGF‐beta signaling,” and “FoxO signaling” pathways were significantly affected.

Interpretation

We validated a panel of brain‐enriched miRNAs that can be used along with other measures for the detection of PD, revealed molecular pathways targeted by these deregulated miRNAs, and identified upstream transcription factors that may be directly implicated in PD pathogenesis.

Quantification of tremor severity with a mobile tremor pen

BACKGROUND

An objective evaluation of tremor severity is necessary to document the course of disease, the efficacy of treatment, or interventions in clinical trials. Most available objective quantification devices are complex, immobile, or not validated.

NEW METHOD

We used the TREMITAS-System that comprises a pen-shaped sensor for tremor quantification. The Power of Main Peak and the Total Power were used as surrogate markers for tremor amplitude. Tremor severity was assessed by the TREMITAS-System and relevant subscores of the MDS-UPDRS and TETRAS rating scales in 14 patients with Parkinson's disease (PD) and 16 patients with Essential tremor (ET) off and on therapy. We compared tremor amplitudes assessed during wearable and hand-held constellations.

RESULTS

We found significant correlations between tremor amplitudes captured by TREM and tremor severity assessed by the MDS-UPDRS in PD (r = 0.638-0.779) and the TETRAS in ET (r = 0.597-0. 704) off and on therapy. The TREMITAS-System captured the L-Dopa-induced improvement of tremor in PD patients (p = 0.027). Tremor amplitudes did not differ between the handheld and wearable constellation (p > 0.05).

COMPARISON WITH EXISTING METHODS

We confirm the results of previous studies using inertial based sensors that tremor severity and drug-induced changes of tremor severity can be quantified using inertial based sensors. The assessment of tremor amplitudes was not influenced by using a handheld or wearable constellation.

CONCLUSIONS

The TREMITAS-System can be used to quantify rest tremor in PD and postural tremor in ET and is capable of detecting clinically relevant changes in tremor in clinical and research settings.

New Sensor and Wearable Technologies to Aid in the Diagnosis and Treatment Monitoring of Parkinson's Disease

Parkinson's disease (PD) is a degenerative disorder of the brain characterized by the impairment of the nigrostriatal system. This impairment leads to specific motor manifestations (i.e., bradykinesia, tremor, and rigidity) that are assessed through clinical examination, scales, and patient-reported outcomes. New sensor-based and wearable technologies are progressively revolutionizing PD care by objectively measuring these manifestations and improving PD diagnosis and treatment monitoring. However, their use is still limited in clinical practice, perhaps because of the absence of external validation and standards for their continuous use at home. In the near future, these systems will progressively complement traditional tools and revolutionize the way we diagnose and monitor patients with PD.

Cell-free microRNAs in Parkinson's disease: potential biomarkers that provide new insights into disease pathogenesis

MicroRNAs (miRNAs) are master post-transcriptional regulators of gene expression and their specific footprints reflect disease conditions. Over the last few years, several primary reports have described the deregulation of cell-free miRNAs in Parkinson's disease (PD), however, results have been rather inconsistent due to preanalytical and analytical challenges. This study integrated the data across twenty-four reports to identify steadily deregulated miRNAs that may assist in the path towards biomarker development and molecular characterization of the underlying pathology. Stringent KEGG pathway analysis of the miRNA targets revealed FoxO, Prolactin, TNF, and ErbB signaling pathways as the most significantly enriched categories while Gene Ontology analysis revealed that the protein targets are mostly associated with transcription. Chromosomal location of the consistently deregulated miRNAs revealed that over a third of them were clustered at the same location at Chr14q32 suggesting that they are co-regulated by specific transcription factors. This genomic region is inherently unstable due to expanded TGG repeats and responsible for human abnormalities. Stringent analysis of transcription factor sites surrounding the deregulated miRNAs revealed that CREB1, CEBPB and MAZ sites existed in approximately half of the miRNAs, including all of the miRNAs located at Chr14q32. Additional studies are now needed to determine the biomarker potential of the consistently deregulated miRNAs in PD and the therapeutic implications of these bioinformatics insights.

Measurement Instruments to Assess Functional Mobility in Parkinson's Disease: A Systematic Review

BACKGROUND

Functional mobility (FM) is a person's ability to move to accomplish activities of daily living; it bridges the concepts of mobility and functional ability. There is frequently a loss of FM in Parkinson's disease (PD). Several instruments have been used to assess this concept in PD; however, there is no consensus on which are the most appropriate.

OBJECTIVE

We aimed to identify and critically appraise which measurement instruments have been used to assess FM.

METHODS

A systematic review was conducted using the databases CENTRAL, MEDLINE, Embase, and PEDro from their inception to January 2019 to identify all observational and experimental studies conducted in PD or atypical parkinsonism that included an FM assessment. Two reviewers independently screened citations, extracted data, and assessed clinimetric properties.

RESULTS

We included 95 studies that assessed FM in PD. Fifty-five (57.9%) studies mentioned FM in the article, and 39 (41.1%) specified the measurement tools used to evaluate FM. FM was the primary outcome in 12 (12.6%) studies. The Timed Up and Go test was the most frequently used measurement tool. Only one study presented a definition of FM. Several overlapping terms were used, the most common being mobility.

CONCLUSION

Several studies reported the use of FM measurement tools in PD, though with frequent misconceptions, an inadequate context of use, or suboptimal assessment. We propose the establishment of the concept of FM applied to PD, followed by the adequate clinimetric validation of existing measurement tools to provide a comprehensive and reliable evaluation of FM in PD.

Existing and emerging applications for the neuromodulation of nerve activity through targeted delivery of electric stimuli

The effective treatment of many diseases requires the use of multiple treatment strategies among which neuromodulation is playing an increasingly important role. Neuromodulation devices that act to normalize or modulate nerve activity through the targeted delivery of electrical stimuli will be the focus of this review. These devices encompass deep brain stimulators, vagus nerve stimulators, spinal cord simulators and sacral nerve stimulators. Already neuromodulation has proven successful in the treatment of a broad range of conditions from Parkinson's disease to chronic pain and urinary incontinence. Many of these approaches seek to exploit the activities of the autonomic nervous system, which influences organ function through the release of neurotransmitters and associated signalling cascades. This review will outline existing and emerging applications for each of these neuromodulation devices, proposed mechanisms of action and clinical studies evaluating both their safety and therapeutic efficacy.

Subthalamic deep brain stimulation aggravates speech problems in Parkinson's disease: Objective and subjective analysis of the influence of stimulation frequency and electrode contact location

BACKGROUND

Speech disorders, including stuttering and hypophonia, have been reported in patients with Parkinson's disease (PD) after subthalamic deep brain stimulation (STN-DBS).

OBJECTIVE

To evaluate the effect of stimulation frequency or electrode contact location on speech disorders in PD patients with STN-DBS.

METHOD

In this case-controlled study, we enrolled 50 PD patients with, and 100 PD patients without STN-DBS to compare their vocal intensities, measured by a sound pressure meter, and perceptual speech ratings, obtained from the speech sections of the United Parkinson's Disease Rating Scale (UPDRS) and subjective ratings regarding the impediment of functional communication by stuttering. For patients with STN-DBS, comparisons were made between high-frequency (HFS; 130 Hz), low-frequency (LFS; 80 Hz), and off-stimulation. We also evaluated the effect of electrode contact locations on speech function.

RESULTS

Patients with STN-DBS had decreased vocal intensities and UPDRS scores compared to those without (p < 0.05). Vocal intensity was significantly lower during HFS than during LFS and off-stimulation (both, p < 0.05). Stuttering impeded STN-DBS patients' communication to greater extent than for those without (p < 0.001). Vocal intensity was lower when active contacts were in the dorsal zone compared to those in the ventral zone (p < 0.05). Only STN-DBS treatment was a predictive factor for low vocal intensity (OR = 9.53, p = 0.04).

CONCLUSION

High-frequency STN-DBS with dorsal zone contacts can aggravate certain speech problems in PD patients. Therefore, it is important to balance between motor control and speech impairments in these patients.

IMU-Based Classification of Parkinson's Disease From Gait: A Sensitivity Analysis on Sensor Location and Feature Selection

Inertial measurement units (IMUs) have a long-lasting popularity in a variety of industrial applications from navigation systems to guidance and robotics. Their use in clinical practice is now becoming more common, thanks to miniaturization and the ability to integrate on-board computational and decision-support features. IMU-based gait analysis is a paradigm of this evolving process, and in this study its use for the assessment of Parkinson's disease (PD) is comprehensively analyzed. Data coming from 25 individuals with different levels of PD symptoms severity and an equal number of age-matched healthy individuals were included into a set of 6 different machine learning (ML) techniques, processing 18 different configurations of gait parameters taken from 8 IMU sensors. Classification accuracy was calculated for each configuration and ML technique, adding two meta-classifiers based on the results obtained from all individual techniques through majority of voting, with two different weighting schemes. Average classification accuracy ranged between 63% and 80% among classifiers and increased up to 96% for one meta-classifier configuration. Configurations based on a statistical preselection process showed the highest average classification accuracy. When reducing the number of sensors, features based on the joint range of motion were more accurate than those based on spatio-temporal parameters. In particular, best results were obtained with the knee range of motion, calculated with four IMUs, placed bilaterally. The obtained findings provide data-driven evidence on which combination of sensor configurations and classification methods to be used during IMU-based gait analysis to grade the severity level of PD.

Rating Scales for Movement Disorders With Sleep Disturbances: A Narrative Review

Introduction: In recent years, a wide variety of rating scales and questionnaires for movement disorders have been developed and published, making reviews on their contents, and attributes convenient for the potential users. Sleep disorders are frequently present in movement disorders, and some movement disorders are accompanied by specific sleep difficulties. Aim: The aim of this study is to perform a narrative review of the most frequently used rating scales for movement disorders with sleep problems, with special attention to those recommended by the International Parkinson and Movement Disorders Society. Methods: Online databases (PubMed, SCOPUS, Web of Science, Google Scholar), related references from papers and websites and personal files were searched for information on comprehensive or global rating scales which assessed sleep disturbances in the following movement disorders: akathisia, chorea, dystonia, essential tremor, myoclonus, multiple system atrophy, Parkinson's disease, progressive supranuclear palsy, and tics and Tourette syndrome. For each rating scale, its objective and characteristics, as well as a summary of its psychometric properties and recommendations of use are described. Results: From 22 rating scales identified for the selected movement disorders, only 5 included specific questions on sleep problems. Movement Disorders Society-Unified Parkinson's Disease Rating scale (MDS-UPDRS), Non-Motor Symptoms Scale and Questionnaire (NMSS and NMSQuest), Scales for Outcomes in Parkinson's Disease (SCOPA)-Autonomic and Progressive Supranuclear Palsy Rating Scale (PSPRS) were the only rating scales that included items for assessing sleep disturbances. Conclusions: Despite sleep problems are frequent in movement disorders, very few of the rating scales addresses these specific symptoms. This may contribute to an infra diagnosis and mistreatment of the sleep problems in patients with movement disorders.

Exploring Bedroom Usability and Accessibility in Parkinson's Disease (PD): The Utility of a PD Home Safety Questionnaire and Implications for Adaptations

Background

Although bedrooms are identified as a major location for accidents among Parkinson’s disease (PD) patients, there are no studies that specifically evaluate the bedroom environments of PD patients.

Objective

To examine the physical bedroom environment of patients with PD by generating a home safety questionnaire to rate bedroom accessibility and usability specifically for PD patients, and piloting it in a small set of PD patients, to identify environmental barriers and recommend adaptations to reduce accident risks.

Methods

Questionnaire development was based on the concept of Personal (P)-Environmental (E) fit. The P component covers five clinical domains that contribute to a patients’ current state of health, including PD-related motor symptoms, PD-related non-motor symptoms, gait and balance impairments, comorbidities, and limitations on specific activities. The E component focuses on both indoor (bedroom, bathroom, living room, stairs, and kitchen), and outdoor (outdoor area and entrance) areas within a home where PD patients commonly get injured. Total score for the whole questionnaire is 171. A higher score indicates more P-E problems.

Results

Comprehension of questions was tested for content validity with an item-objective congruence index of above 0.6 for all items. High internal consistency (reliability) was confirmed by Cronbach’s alpha coefficient of 0.828 (r). The pilot in five PD patients gave a mean total score of 48.2 ± 7.29 with a mean score on personal and environmental components of 16.8 ± 5.12 and 31.4 ± 4.51, respectively.

Conclusion

This PD home safety questionnaire is a valid and reliable instrument for examining P-E problems by a multidisciplinary team during their home visits. More studies, involving a large number of PD patients, are needed to establish its utility as a screening instrument in PD patients to assess for home adaptations.

Levodopa/carbidopa/entacapone versus levodopa/dopa-decarboxyiase inhibitor for the treatment of Parkinson's disease: systematic review, meta-analysis, and economic evaluation

Aims

To review the evidence for efficacy, safety, and cost-effectiveness of levodopa/carbidopa/entacapone (LCE) compared with levodopa/dopa-decarboxyiase inhibitor (DDCI) for Parkinson’s disease (PD).

Methods

PubMed, Embase, the Cochrane Library, and Chinese databases WangFang Data, Chinese Sci-tech Journals Database and China National Knowledge Infrastructure, as well as ClinicalTrials.gov, were searched for randomized controlled trials with “levodopa/carbidopa/entacapone” as keywords. The search period was from inception to August 2017. We conducted meta-analyses to synthesize the evidence quantitatively.

Results

A total of 5,693 records were obtained. We included seven randomized controlled trials and one cost-effectiveness study after the screening process. Compared with levodopa–DDCI, LCE improved patient Unified Parkinson’s Disease Rating Scale (UPDRS) II score (mean difference [MD] −1.17, 95% CI −1.64 to −0.71), UPDRS III score (MD −1.55, 95% CI −2.29 to −0.81), and Schwab and England daily activity rating (MD 2.05, 95% CI 0.85–3.26). There was no statistically significant difference in the risk of serious adverse events (AEs) or discontinuation due to AEs in patients with LCE, and the risk of total AEs was higher in the LCE group (risk ratio [RR] 1.33, 95% CI 1.05–1.70). The incremental cost-effectiveness ratio of LCE was £3,105 per quality-adjusted life-year (QALY) gained in the UK.

Conclusion

LCE can improve PD patients’ motor symptoms and daily living functioning when compared with levodopa/DDCI.

Advanced Parkinson's or "complex phase" Parkinson's disease? Re-evaluation is needed

Holistic management of Parkinson's disease, now recognised as a combined motor and nonmotor disorder, remains a key unmet need. Such management needs relatively accurate definition of the various stages of Parkinson's from early untreated to late palliative as each stage calls for personalised therapies. Management also needs to have a robust knowledge of the progression pattern and clinical heterogeneity of the presentation of Parkinson's which may manifest in a motor dominant or nonmotor dominant manner. The "advanced" stages of Parkinson's disease qualify for advanced treatments such as with continuous infusion or stereotactic surgery yet the concept of "advanced Parkinson's disease" (APD) remains controversial in spite of growing knowledge of the natural history of the motor syndrome of PD. Advanced PD is currently largely defined on the basis of consensus opinion and thus with several caveats. Nonmotor aspects of PD may also reflect advancing course of the disorder, so far not reflected in usual scale based assessments which are largely focussed on motor symptoms. In this paper, we discuss the problems with current definitions of "advanced" PD and also propose the term "complex phase" Parkinson's disease as an alternative which takes into account a multimodal symptoms and biomarker based approach in addition to patient preference.

Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales

Background

Our group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson’s (On and Off state) based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson’s Disease Rating Scale part-III (UPDRS-III).

Method

Seventy-five patients suffering from Parkinson’s disease were asked to walk both in the Off and the On state while wearing the inertial sensor on the waist. Additionally, all patients were administered the motor section of the UPDRS in both motor phases. Tests were conducted at the patient’s home. Convergence between the algorithm and the scale was evaluated by using the Spearman’s correlation coefficient.

Results

Correlation with the UPDRS-III was moderate (rho −0.56; p < 0.001). Correlation between the algorithm outputs and the gait item in the UPDRS-III was good (rho −0.73; p < 0.001). The factorial analysis of the UPDRS-III has repeatedly shown that several of its items can be clustered under the so-called Factor 1: “axial function, balance, and gait.” The correlation between the algorithm outputs and this factor of the UPDRS-III was −0.67 (p < 0.01).

Conclusion

The correlation achieved by the algorithm with the UPDRS-III scale suggests that this algorithm might be a useful tool for monitoring patients with Parkinson’s disease and motor fluctuations.

Impaired bed mobility: quantitative torque analysis with axial inertial sensors

Difficulty in turning in bed is rated as the most troublesome night-time symptom among Parkinson’s disease (PD) patients. Aim: To develop a practical objective method for home assessment of a patient’s ability to turn in bed. Methods: Nocturnal parameters and torque of self-turning in bed from 17 PD couples were assessed and compared using a wearable axial sensor for two nights in their homes. Results: The torque of axial rotation which indicates the ability of PD patients to turn in bed was significantly less than their spouses (p < 0.001). Significant correlations were observed between the torque of turning in bed and total unified Parkinson’s Disease Rating Scale score (r = 0.71; p = 0.001), and total Nocturnal Akinesia Dystonia and Cramp score (r = 0.634; p = 0.006). Conclusion: Our study confirms a decreased ability in turning in PD.

Measurement of Nonmotor Symptoms in Clinical Practice

Nonmotor symptoms constitute a prominent part of Parkinson's disease manifestations. They are present since the first phases of the disease, increase their number and severity with disease progression, and importantly impact on patients' health and quality of life, caregivers' burden, and social resources. Research on Parkinson's disease has traditionally focused on the motor aspects of the disease, but an increasing interest in the nonmotor manifestations has risen in the past decade. The availability of assessment instruments for detecting and measuring these symptoms has allowed understanding of their importance and course over time, as well as estimation of therapeutic effects on them. In this chapter, a review of the basic characteristics of nonmotor symptom assessments used in clinical practice and research are presented.