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

[The potential of wearable technology in knee arthroplasty]

BACKGROUND

Wearable technology has developed rapidly in recent years and offers promising possibilities for supporting and optimizing orthopaedic procedures, especially pre- and postoperatively. The continuous monitoring and precise analysis of movement patterns, as well as the individual adaptation of rehabilitation processes are just some of the potential benefits of wearable technology. The aim of this paper is to evaluate the potential of wearable technology in knee arthroplasty and to provide an overview of the evidence that is currently available.

MATERIAL AND METHODS

This overview is based on a literature search in Medline, Cochrane Library and Web of Science databases on the topic of wearables and knee arthroplasty.

RESULTS

Wearable technology enables precise and, above all, long-term and objective monitoring of knee joint movements and loads-regardless of the setting and environment in which the patient is located. So-called IMUs (inertial measurement units), which can record multidimensional directions of movement and speed, are most commonly used for movement analysis. Due to their small size and manageable costs, IMUs are suitable for movement monitoring in orthopaedics. In addition, continuous data acquisition through the corresponding development of algorithms allows early detection of complications and almost real-time adjustment of therapy. As wearables can also be used in the home setting, a combination with other telemedical and/or feedback applications is possible in the course of increasing ambulantization. Wearable technology has the potential to significantly improve pre- and post-operative care and rehabilitation in knee arthroplasty. Through the precise monitoring of movement patterns and the individual adjustment options, better or equivalent results could be achieved in the future compared to current standards. Despite the promising results so far, the current evidence is still limited and further clinical studies are needed to comprehensively assess the long-term effectiveness and cost-effectiveness of knee arthroplasty.

Optimal DaTQUANT Thresholds for Diagnostic Accuracy of Dementia with Lewy Bodies (DLB) and Parkinson's Disease (PD)

Background: Quantitative thresholds are helpful to define an abnormal DaT SPECT in patients with suspected nigrostriatal degenerative diseases (NSDD). The optimal DaTQUANT threshold for diagnostic accuracy of DaT SPECT across combined movement and cognitive disorder populations has been previously described. Methods: We established optimal DaTQUANT thresholds that enhance the discrimination between dementia with Lewy bodies (DLB) and non-DLB dementia types, as well as between Parkinsonian syndromes (PS) and conditions not characterized by nigrostriatal degeneration (non-PS). Results: Data from a total of 303 patients were used in this retrospective analysis. Posterior putamen of the more affected hemisphere (MAH) was shown to be an accurate single-variable predictor for both DLB and PS and was comparable to the most accurate multi-variable models. Conclusions: Automated quantification with DaTQUANT can accurately aid in the differentiation of DLB from non-DLB dementias and PS from non-PS. Optimal thresholds for assisting a diagnosis of DLB are striatal binding ratio (SBR) ≤ 0.65, z-score ≤ -2.36, and a percent deviation ≤ -0.54 for the posterior putamen of the MAH. Optimal posterior putamen thresholds for assisting a diagnosis of PS are SBR ≤ 0.92, z-score ≤ -1.53, and a percent deviation ≤ -0.33, which are similar to our previously reported posterior putamen threshold values using a blended patient pool from multiple study populations.

How can patients shape digital medicine? A rapid review of patient and public involvement and engagement in the development of digital health technologies for neurological conditions

INTRODUCTION

Patient and Public Involvement and Engagement (PPIE) involves working 'with' or 'by' patients and the public, rather than 'to,' 'about,' or 'for' them, and is integral to neurological and digital health research. This rapid review examined PPIE integration in the development and implementation of digital health technologies for neurological conditions.

METHODS

Key terms were input into six databases. Included articles were qualitative studies or PPIE activities involving patient perspectives in shaping digital health technologies for neurological conditions. Bias was evaluated using the NICE qualitative checklist, with reporting following PRISMA guidelines.

RESULTS

2,140 articles were identified, with 28 included. Of these, 25 were qualitative studies, and only three were focused PPIE activities. Patient involvement was mostly limited to one-off consultations during development.There was little evidence of PPIE during implementation, and minimal reporting on its impact.

CONCLUSIONS

PPIE has been inconsistently reported in this research area, highlighting the need for more guidance and best-practice examples This review used a UK-based definition of PPIE, which may have excluded relevant activities from other countries. Future reviews should broaden terminology to capture PPIE integration globally.

Assessing impaired bed mobility in patients with Parkinson's disease: a scoping review

BACKGROUND

Although most patients with Parkinson's disease (PD) experience difficulties in bed mobility, evidence on the suitability of the methods for assessing impaired bed mobility in PD are lacking.

OBJECTIVES

To identify objective methods for assessing impaired bed mobility in PD and to discuss their clinimetric properties and feasibility for use in clinical practice.

DATA SOURCES

PubMed, Web of Science, and Cochrane Library were searched between 1995 and 2022.

SELECTION CRITERIA

Studies were included if they described an objective assessment method for assessing impaired bed mobility in PD.

DATA EXTRACTION AND DATA SYNTHESIS

Characteristics of the identified measurement methods such as clinimetric properties and feasibility were extracted by two authors. The methodological quality of studies was evaluated using the Appraisal of studies tool.

RESULTS

Twenty-three studies were included and categorised into three assessment methods: sensor-based assessments (48%), rating scales (39%), and timed-tests (13%). The risk of bias was low for all but one study, which was medium.

LIMITATIONS

Despite applying wide selection criteria, a relatively small number of studies were identified in our results.

CONCLUSION

Rating scales may be the most preferred for assessing impaired bed mobility in PD in clinical practice, until clinimetric validity are adequately demonstrated in the other assessment methods. CONTRIBUTION OF PAPER.

Attitudes Toward the Adoption of Remote Patient Monitoring and Artificial Intelligence in Parkinson's Disease Management: Perspectives of Patients and Neurologists

OBJECTIVE

Early detection of Parkinson's Disease (PD) progression remains a challenge. As remote patient monitoring solutions (RMS) and artificial intelligence (AI) technologies emerge as potential aids for PD management, there's a gap in understanding how end users view these technologies. This research explores patient and neurologist perspectives on AI-assisted RMS.

METHODS

Qualitative interviews and focus-groups were conducted with 27 persons with PD (PwPD) and six neurologists from Finland and Italy. The discussions covered traditional disease progression detection and the prospects of integrating AI and RMS. Sessions were recorded, transcribed, and underwent thematic analysis.

RESULTS

The study involved five individual interviews (four Italian participants and one Finnish) and six focus-groups (four Finnish and two Italian) with PwPD. Additionally, six neurologists (three from each country) were interviewed. Both cohorts voiced frustration with current monitoring methods due to their limited real-time detection capabilities. However, there was enthusiasm for AI-assisted RMS, contingent upon its value addition, user-friendliness, and preservation of the doctor-patient bond. While some PwPD had privacy and trust concerns, the anticipated advantages in symptom regulation seemed to outweigh these apprehensions.

DISCUSSION

The study reveals a willingness among PwPD and neurologists to integrate RMS and AI into PD management. Widespread adoption requires these technologies to provide tangible clinical benefits, remain user-friendly, and uphold trust within the physician-patient relationship.

CONCLUSION

This study offers insights into the potential drivers and barriers for adopting AI-assisted RMS in PD care. Recognizing these factors is pivotal for the successful integration of these digital health tools in PD management.

Young human alpha synuclein transgenic (BAC-SNCA) mice display sex- and gene-dose-dependent phenotypic disturbances

Parkinson's disease (PD) is a common neurodegenerative movement disorder, characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta and the accumulation of aggregated alpha synuclein (aSyn). The disease often presents with early prodromal non-motor symptoms and later motor symptoms. Diagnosing PD based purely on motor symptoms is often too late for successful intervention, as a significant neuronal loss has already occurred. Furthermore, the lower prevalence of PD in females is not well understood, highlighting the need for a better understanding of the interaction between sex and aSyn, the crucial protein for PD pathogenesis. Here, we conducted a comprehensive phenotyping study in 1- to 5-month-old mice overexpressing human aSyn gene (SNCA) in a bacterial artificial chromosome (BAC-SNCA). We demonstrate a SNCA gene-dose-dependent increase of human aSyn and phosphorylated aSyn, as well as a decrease in tyrosine hydroxylase expression in BAC-SNCA mice, with more pronounced effects in male mice. Phosphorylated aSyn was already found in the dorsal motor nucleus of the vagus nerve of 2-month-old mice. This was time-wise associated with significant gait altrations in BAC-SNCA mice as early as 1 and 3 months of age using CatWalk gait analysis. Furthermore, anxiety-related behavioral tests revealed an increase in anxiety levels in male BAC-SNCA mice. Finally, 5-month-old male BAC-SNCA mice exhibited a SNCA gene-dose-dependent elevation in energy expenditure in automated home-cage monitoring. For the first time, these findings describe early-onset, sex- and gene-dose-dependent, aSyn-mediated disturbances in BAC-SNCA mice, providing a model for sex-differences, early-onset neuropathology, and prodromal symptoms of PD.

Barriers and facilitators to health technology adoption by older adults with chronic diseases: an integrative systematic review

BACKGROUND

In recent years, healthcare systems have progressively adopted several technologies enhancing access to healthcare for older adults and support the delivery of efficient and effective care for this specific population. These technologies include both assistive technologies designed to maintain or improve the independence, social participation and functionality of older people at home, as well as health information technology developed to manage long-term conditions. Examples of such technologies include telehealth, wearable devices and mobile health. However, despite the great promise that health technology holds for promoting independent living among older people, its actual implementation remains challenging.

METHODS

This study aimed to conduct an integrative systematic review of the research evidence on the factors that facilitate or hinder the adoption of different types of technology by older individuals with chronic diseases. For this purpose, four electronic databases (PsycArticles, Scopus, Web of Science and PubMed) were queried to search for indexed published studies. The methodological quality of the selected papers has been assessed using the Mixed Methods Appraisal Tool (MMAT).

RESULTS

Twenty-nine articles were selected, including 6.213 adults aged 60 or older. The studies have been synthesised considering the types of technological interventions and chronic diseases, as well as the main barriers and facilitators in technology acceptance. The results revealed that the majority of the selected articles focused on comorbid conditions and the utilisation of telemedicine tools. With regard to hindering and facilitating factors, five main domains were identified: demographic and socioeconomic, health-related, dispositional, technology-related and social factors.

CONCLUSION

The study results have practical implications not only for technology developers but also for all the social actors involved in the design and implementation of healthcare technologies, including formal and informal caregivers and policy stakeholders. These actors could use this work to enhance their understanding of the utilisation of technology by the ageing population. This review emphasises the factors that facilitate technology adoption and identifies barriers that impede it, with the ultimate goal of promoting health and independent living.

Wearable-Sensor-Based Weakly Supervised Parkinson's Disease Assessment with Data Augmentation

Parkinson's disease (PD) is the second most prevalent dementia in the world. Wearable technology has been useful in the computer-aided diagnosis and long-term monitoring of PD in recent years. The fundamental issue remains how to assess the severity of PD using wearable devices in an efficient and accurate manner. However, in the real-world free-living environment, there are two difficult issues, poor annotation and class imbalance, both of which could potentially impede the automatic assessment of PD. To address these challenges, we propose a novel framework for assessing the severity of PD patient's in a free-living environment. Specifically, we use clustering methods to learn latent categories from the same activities, while latent Dirichlet allocation (LDA) topic models are utilized to capture latent features from multiple activities. Then, to mitigate the impact of data imbalance, we augment bag-level data while retaining key instance prototypes. To comprehensively demonstrate the efficacy of our proposed framework, we collected a dataset containing wearable-sensor signals from 83 individuals in real-life free-living conditions. The experimental results show that our framework achieves an astounding 73.48% accuracy in the fine-grained (normal, mild, moderate, severe) classification of PD severity based on hand movements. Overall, this study contributes to more accurate PD self-diagnosis in the wild, allowing doctors to provide remote drug intervention guidance.

Optimizing classification of diseases through language model analysis of symptoms

This paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored the use of two Medical Concept Normalization-Bidirectional Encoder Representations from Transformers (MCN-BERT) models and a Bidirectional Long Short-Term Memory (BiLSTM) model, each optimized with a different hyperparameter optimization method, to predict diseases from symptom descriptions. In this paper, we utilized two distinct dataset called Dataset-1, and Dataset-2. Dataset-1 consists of 1,200 data points, with each point representing a unique combination of disease labels and symptom descriptions. While, Dataset-2 is designed to identify Adverse Drug Reactions (ADRs) from Twitter data, comprising 23,516 rows categorized as ADR (1) or Non-ADR (0) tweets. The results indicate that the MCN-BERT model optimized with AdamP achieved 99.58% accuracy for Dataset-1 and 96.15% accuracy for Dataset-2. The MCN-BERT model optimized with AdamW performed well with 98.33% accuracy for Dataset-1 and 95.15% for Dataset-2, while the BiLSTM model optimized with Hyperopt achieved 97.08% accuracy for Dataset-1 and 94.15% for Dataset-2. Our findings suggest that language models and deep learning techniques have promise for supporting earlier detection and more prompt treatment of diseases, as well as expanding remote diagnostic capabilities. The MCN-BERT and BiLSTM models demonstrated robust performance in accurately predicting diseases from symptoms, indicating the potential for further related research.

Alleviating Stress in Parkinson's Disease: Symptomatic Treatment, Disease Modification, or Both?

Psychological stress, a state of mental strain caused by mentally or physically threatening situations, plays a significant role in Parkinson's disease (PD). Motor symptoms worsen during acute stress and common non-motor symptoms in PD, such as anxiety and depression, are linked to chronic stress. Although evidence in humans is lacking, animal models of PD suggest that chronic stress can accelerate dopaminergic cell death. This suggests that stress-reducing interventions have not only symptomatic, but perhaps also disease-modifying effects. Our objective was to identify the most promising strategies for stress-reduction in PD and to analyze their potential value for disease-modification. An unstructured literature search was performed, primarily focusing on papers published between 2020-2023. Several large clinical trials have tested the efficacy of aerobic exercise and mindfulness-based interventions on PD symptoms. The evidence is promising, but not definitive yet: some exercise trials found a reduction in stress-related symptoms, whereas others did not or did not report it. In the majority of trials, biological measures of stress and of disease progression are missing. Furthermore, follow-up periods were generally too short to measure disease-modifying effects. Hence, mechanisms underlying the intervention effects remain largely unclear. These effects may consist of attenuating progressive neurodegeneration (measured with MRI-markers of substantia nigra integrity or cortical thickness), or a strengthening of compensatory cerebral mechanisms (measured with functional neuroimaging), or both. Lifestyle interventions are effective for alleviating stress-related symptoms in PD. They hold potential for exerting disease-modifying effects, but new evidence in humans is necessary to fulfill that promise.

A multimodal dataset of real world mobility activities in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterised by motor symptoms such as gait dysfunction and postural instability. Technological tools to continuously monitor outcomes could capture the hour-by-hour symptom fluctuations of PD. Development of such tools is hampered by the lack of labelled datasets from home settings. To this end, we propose REMAP (REal-world Mobility Activities in Parkinson's disease), a human rater-labelled dataset collected in a home-like setting. It includes people with and without PD doing sit-to-stand transitions and turns in gait. These discrete activities are captured from periods of free-living (unobserved, unstructured) and during clinical assessments. The PD participants withheld their dopaminergic medications for a time (causing increased symptoms), so their activities are labelled as being "on" or "off" medications. Accelerometry from wrist-worn wearables and skeleton pose video data is included. We present an open dataset, where the data is coarsened to reduce re-identifiability, and a controlled dataset available on application which contains more refined data. A use-case for the data to estimate sit-to-stand speed and duration is illustrated.

The effectiveness and safety of botulinum toxin injections for the treatment of sialorrhea with Parkinson's disease: a systematic review and meta-analysis

BACKGROUND

Botulinum toxin (BoNT) injection is an important adjunctive method to treat sialorrhea. The purpose of this systematic review was to analyze the effect and safety of BoNT injections in the intervention of sialorrhea with Parkinson's disease (PD).

METHODS

We searched PubMed, Web Of Science (WOS), Scopus, Cochrane CENTRAL, and Embase from inception until April 2022. Randomized controlled trials or randomized crossover trials comparing BoNT with placebo in sialorrhea with PD were eligible. PRISMA guidelines were used to carry out the meta-analysis. The Drooling Severity Frequency Scale (DSFS) score and the number of adverse events (AEs) were the primary and secondary outcomes, respectively. Standardized mean differences (SMDs) and risk differences (RDs) are used to express continuous and categorical outcomes, respectively. Heterogeneity among these studies was evaluated using I2 tests. We used the GRADE tool to assess the certainty of evidence (COE).

RESULTS

Eight articles involving 259 patients compared BoNT injections with a placebo for PD with sialorrhea. This meta-analysis showed a significant reduction in DSFS scores between BoNT injections and placebo (SMD=-0.98; 95% CI, -1.27 to 0.70, p<0.001; COE: high). This meta-analysis showed a significant difference in AEs between BoNT injections and placebo (RD=0.15; 95% CI, 0.05 to 0.24, p=0.002; COE: low).

CONCLUSIONS

The pooled results suggest that BoNT injections have some effect on DSFS scores with sialorrhea caused by PD. There are also mild adverse events, which generally recover within a week or so. The results indicate that BoNT injection is one of the treatments for sialorrhea caused by PD, but we need to pay attention to adverse events. In addition, the follow-up time was extended to observe oral hygiene, ulceration or dental caries, and digestive function.

TRIAL REGISTRATION

Our review protocol was registered on PROSPERO (42021288334).

Molecular understanding of ER-MT communication dysfunction during neurodegeneration

Biological researchers are seeing organelles in a new light. These cellular entities have been believed to be singular and distinctive structures that performed specialized purposes for a very long time. But in recentpast years, scientists have learned that organelles become dynamic and make physical contact. Additionally, Biological processes are regulated by organelles interactions and its alteration play an important role in cell malfunctioning and several pathologies, including neurodegenerative diseases. Mitochondrial-ER contact sites (MERCS) have received considerable attention in the domain of cell homeostasis and dysfunction, specifically in the area of neurodegeneration. This is largely due to the significant role of this subcellular compartment in a diverse array of vital cellular functions, including Ca2+ homeostasis, transport, bioenergetics and turnover, mitochondrial dynamics, apoptotic signaling, ER stress, and inflammation. A significant number of disease-associated proteins were found to physically interact with the ER-Mitochondria (ER-MT) interface, causing structural and/or functional alterations in this compartment. In this review, we summarize current knowledge about the structure and functions of the ER-MT contact sites, as well as the possible repercussions of their alteration in notable neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and fronto-temporal dementia. The constraints and complexities in defining the nature and origin of the highlighted defects in ER-MT communication, as well as their concise contribution to the neurodegenerative process, are illustrated in particular. The possibility of using MERCS as a potential drug target to prevent neuronal damage and ultimately neurodegeneration is the topic of our final discussion.

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.

Toward objective monitoring of Parkinson's disease motor symptoms using a wearable device: wearability and performance evaluation of PDMonitor®

Parkinson's disease (PD) is characterized by a variety of motor and non-motor symptoms. As disease progresses, fluctuations in the response to levodopa treatment may develop, along with emergence of freezing of gait (FoG) and levodopa induced dyskinesia (LiD). The optimal management of the motor symptoms and their complications, depends, principally, on the consistent detection of their course, leading to improved treatment decisions. During the last few years, wearable devices have started to be used in the clinical practice for monitoring patients' PD-related motor symptoms, during their daily activities. This work describes the results of 2 multi-site clinical studies (PDNST001 and PDNST002) designed to validate the performance and the wearability of a new wearable monitoring device, the PDMonitor^®, in the detection of PD-related motor symptoms. For the studies, 65 patients with Parkinson's disease and 28 healthy individuals (controls) were recruited. Specifically, during the Phase I of the first study, participants used the monitoring device for 2-6 h in a clinic while neurologists assessed the exhibited parkinsonian symptoms every half hour using the Unified Parkinson's Disease Rating Scale (UPDRS) Part III, as well as the Abnormal Involuntary Movement Scale (AIMS) for dyskinesia severity assessment. The goal of Phase I was data gathering. On the other hand, during the Phase II of the first study, as well as during the second study (PDNST002), day-to-day variability was evaluated, with patients in the former and with control subjects in the latter. In both cases, the device was used for a number of days, with the subjects being unsupervised and free to perform any kind of daily activities. The monitoring device produced estimations of the severity of the majority of PD-related motor symptoms and their fluctuations. Statistical analysis demonstrated that the accuracy in the detection of symptoms and the correlation between their severity and the expert evaluations were high. As a result, the studies confirmed the effectiveness of the system as a continuous telemonitoring solution, easy to be used to facilitate decision-making for the treatment of patients with Parkinson's disease.

Measurement instruments to assess basic functional mobility in Parkinson's Disease: A systematic review of clinimetric properties and feasibility for use in clinical practice

Taniguchi S, Yamamoto A. Measurement instruments to assess basic functional mobility in Parkinson's Disease: A systematic review of clinimetric properties and feasibility for use in clinical practice. Jpn J Compr Rehabil Sci 2023; 14: 16-25.

Objective

To systematically review the evaluation of clinimetric properties and feasibility of the "Modified Parkinson Activity Scale (M-PAS)" and the "Lindop Parkinson's Disease Mobility Assessment (LPA)," which are Parkinson's Disease (PD)-specific measurement instruments to assess basic functional mobility, and to discuss their considerations for use in clinical practice.

Methods

A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A risk of bias assessment was also performed.

Results

Eleven studies were included: five studies used M-PAS (45%), five studies used LPA (45%), and one study used M-PAS and LPA (13%). The risk of bias was low for all evaluated studies.

Conclusion

M-PAS and LPA showed adequate reliability, validity, and responsiveness in detecting intervention changes. M-PAS has more detailed qualitative scoring options, a lack of ceiling effect, and can be used by a non-expert in PD.In contrast, the drawback of M-PAS is that it is time-consuming to apply in everyday clinical practice. On the other hand, LPA with greater simplicity may lead to lower burdens for both patients and raters in situations with strict time limitations. Further research is required to identify new resources.

Quantification of Movement Error from Spiral Drawing Test

Parkinson's disease is a neurodegenerative disease that often comes with symptoms such as muscle stiffness, slowness of movement, and tremors at rest. Since this disease negatively influences the quality of life in patients, an early and accurate diagnosis is important for slowing the progression of the disease and providing effective treatment to patients. One of the quick and easy methods for diagnosing is the spiral drawing test and the differences between the target spiral picture and the drawing by patients can be used as an indicator of movement error. Simply, the average distance between paired samples of the target spiral and the drawing can be easily calculated and used as the level of movement error. However, finding the correct pair of samples between the target spiral and the drawing is relatively difficult, and the accurate algorithm to quantify the movement error has not been thoroughly studied. In this study, we propose algorithms applicable to the spiral drawing test, that ultimately can be used to measure the level of movement error in Parkinson's disease patients. They are equivalent inter-point distance (ED), shortest distance (SD), varying inter-point distance (VD), and equivalent angle (EA). To evaluate the performance and sensitivity of the methods, we collected data from simulation and experiments with healthy subjects and evaluated the four methods. As a result, in normal (good drawing) and severe symptom (poor drawing) conditions, the calculated errors were 3.67/5.48 from ED, 0.11/1.21 from SD, 0.38/1.46 from VD and 0.01/0.02 from EA, meaning that ED, SD, and VD measure movement error with high noise while EA is sensitive to even small symptom levels. Similarly, in the experiment data, only EA shows the linear increase of error distance to changing symptom levels from 1 to 3. In summary, we found that EA is the most effective algorithm in finding the correct pair of samples between the spiral and the drawing, and consequently yields low errors and high sensitivity to symptom levels.

Successful implementation of technology in the management of Parkinson's disease: Barriers and facilitators

Background

Parkinson's disease (PD) is a progressive neurodegenerative disease with a fast increasing prevalence. Several pharmacological and non-pharmacological interventions are available to alleviate symptoms. Technology can be used to improve the efficiency, accessibility and feasibility of these treatments. Although many technologies are available, only few are actually implemented in daily clinical practice.

Aim

Here, we study the barriers and facilitators, as experienced by patients, caregivers and/or healthcare providers, to successful implement technology for PD management.

Methods

We performed a systematic literature search in the PubMed and Embase databases until June 2022. Two independent raters screened the titles, abstracts and full texts on: 1) people with PD; 2) using technology for disease management; 3) qualitative research methods providing patients', caregivers and/or healthcare providers' perspective, and; 4) full text available in English or Dutch. Case studies, reviews and conference abstracts were excluded.

Results

We found 5420 unique articles of which 34 were included in this study. Five categories were made: cueing (n = 3), exergaming (n = 3), remote monitoring using wearable sensors (n = 10), telerehabilitation (n = 8) and remote consultation (n = 10). The main barriers reported across categories were unfamiliarity with technology, high costs, technical issues and (motor) symptoms hampering the use of some technologies. Facilitators included good usability, experiencing beneficial effects and feeling safe whilst using the technology.

Conclusion

Although only few articles presented a qualitative evaluation of technologies, we found some important barriers and facilitators that may help to bridge the gap between the fast developing technological world and actual implementation in day-to-day living with PD.

Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson's Disease Severity

Introduction

Technology holds the potential to track disease progression and response to neuroprotective therapies in Parkinson's disease (PD). The sit-to-stand (STS) transition is a frequently occurring event which is important to people with PD. The aim of this study was to demonstrate an automatic approach to quantify STS duration and speed using a real-world free-living dataset and look at clinical correlations of the outcomes, including whether STS parameters change when someone withholds PD medications.

Methods

Eighty-five hours of video data were collected from 24 participants staying in pairs for 5-day periods in a naturalistic setting. Skeleton joints were extracted from the video data; the head trajectory was estimated and used to estimate the STS parameters of duration and speed.

Results

3.14 STS transitions were seen per hour per person on average. Significant correlations were seen between automatic and manual STS duration (Pearson rho - 0.419, p = 0.042) and between automatic STS speed and manual STS duration (Pearson rho - 0.780, p < 0.001). Significant and strong correlations were seen between the gold-standard clinical rating scale scores and both STS duration and STS speed; these correlations were not seen in the STS transitions when the participants were carrying something in their hand(s). Significant differences were seen at the cohort level between control and PD participants' ON medications' STS duration (U = 6,263, p = 0.018) and speed (U = 9,965, p < 0.001). At an individual level, only two participants with PD became significantly slower to STS when they were OFF medications; withholding medications did not significantly change STS duration at an individual level in any participant.

Conclusion

We demonstrate a novel approach to automatically quantify and ecologically validate two STS parameters which correlate with gold-standard clinical tools measuring disease severity in PD.

Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson's Disease: An Integrated Approach Based on Azure Kinect

Motor impairments are among the most relevant, evident, and disabling symptoms of Parkinson’s disease that adversely affect quality of life, resulting in limited autonomy, independence, and safety. Recent studies have demonstrated the benefits of physiotherapy and rehabilitation programs specifically targeted to the needs of Parkinsonian patients in supporting drug treatments and improving motor control and coordination. However, due to the expected increase in patients in the coming years, traditional rehabilitation pathways in healthcare facilities could become unsustainable. Consequently, new strategies are needed, in which technologies play a key role in enabling more frequent, comprehensive, and out-of-hospital follow-up. The paper proposes a vision-based solution using the new Azure Kinect DK sensor to implement an integrated approach for remote assessment, monitoring, and rehabilitation of Parkinsonian patients, exploiting non-invasive 3D tracking of body movements to objectively and automatically characterize both standard evaluative motor tasks and virtual exergames. An experimental test involving 20 parkinsonian subjects and 15 healthy controls was organized. Preliminary results show the system’s ability to quantify specific and statistically significant (p < 0.05) features of motor performance, easily monitor changes as the disease progresses over time, and at the same time permit the use of exergames in virtual reality both for training and as a support for motor condition assessment (for example, detecting an average reduction in arm swing asymmetry of about 14% after arm training). The main innovation relies precisely on the integration of evaluative and rehabilitative aspects, which could be used as a closed loop to design new protocols for remote management of patients tailored to their actual conditions.

Hands-feet wireless devices: Test-retest reliability and discriminant validity of motor measures in Parkinson's disease telemonitoring

BACKGROUND

Telemonitoring, a branch of telemedicine, involves the use of technological tools to remotely detect clinical data and evaluate patients. Telemonitoring of patients with Parkinson's disease (PD) should be performed using reliable and discriminant motor measures. Furthermore, the method of data collection and transmission, and the type of subjects suitable for telemonitoring must be well defined.

OBJECTIVE

To analyze differences in patients with PD and healthy controls (HC) with the wearable inertial device SensHands-SensFeet (SH-SF), adopting a standardized acquisition mode, to verify if motor measures provided by SH-SF have a high discriminating capacity and high intraclass correlation coefficient (ICC).

METHODS

Altogether, 64 patients with mild-to-moderate PD and 50 HC performed 14 standardized motor activities for assessing bradykinesia, postural and resting tremors, and gait parameters. SH-SF inertial devices were used to acquire movements and calculate objective motor measures of movement (total: 75). For each motor task, five or more biomechanical parameters were measured twice. The results were compared between patients with PD and HC.

RESULTS

Fifty-eight objective motor measures significantly differed between patients with PD and HC; among these, 32 demonstrated relevant discrimination power (Cohen's d > 0.8). The test-retest reliability was excellent in patients with PD (median ICC = 0.85 right limbs, 0.91 left limbs) and HC (median ICC = 0.78 right limbs, 0.82 left limbs).

CONCLUSION

In a supervised environment, the SH-SF device provides motor measures with good results in terms of reliability and discriminant ability. The reliability of SH-SF measurements should be evaluated in an unsupervised home setting in future studies.

Closing the loop for patients with Parkinson disease: where are we?

Although levodopa remains the most efficacious symptomatic therapy for Parkinson disease (PD), management of levodopa treatment during the advanced stages of the disease is extremely challenging. This difficulty is a result of levodopa's short half-life, a progressive narrowing of the therapeutic window, and major inter-patient and intra-patient variations in the dose-response relationship. Therefore, a suitable alternative to repeated oral administration of levodopa is being sought. Recent research efforts have focused on the development of novel levodopa delivery strategies and wearable physical sensors that track symptoms and disease progression. However, the need for methods to monitor the levels of levodopa present in the body in real time has been overlooked. Advances in chemical sensor technology mean that the development of wearable and mobile biosensors for continuous or frequent levodopa measurements is now possible. Such levodopa monitoring could help to deliver personalized and timely medication dosing to alleviate treatment-related fluctuations in the symptoms of PD. Therefore, with the aim of optimizing therapeutic management of PD and improving the quality of life of patients, we share our vision of a future closed-loop autonomous wearable 'sense-and-act' system. This system consists of a network of physical and chemical sensors coupled with a levodopa delivery device and is guided by effective big data fusion algorithms and machine learning methods.

Impact of Actual Use Behavior of Healthcare Wearable Devices on Quality of Life: A Cross-Sectional Survey of People with Dementia and Their Caregivers in Ghana

The increasing prevalence of dementia has necessitated the introduction and use of healthcare wearable devices to augment the healthcare services delivered to people with dementia (PWDs). Unfortunately, evidence on user feedback and the real impact of the actual use of these devices on the quality of life of PWDs has not been fully explored, especially in the context of developing countries. This study, therefore, seeks to evaluate the impact of actual use behavior (AUB) of healthcare wearable devices on the quality of life (QoL) of PWDs in Ghana by using constructs from the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model and the SF-36 quality of life instrument. Data were collected from people with dementia and, in some cases, caregivers (acting as proxies) in three administrative regions of Ghana using structured questionnaires. Of the 420 questionnaires distributed, 356 were returned, giving a response rate of (356/420, 84.8%). Three hundred and twenty (320) responses were used for further analysis, employing the Partial Least Squares based on Structural Equation Modeling. The results showed a significant positive relationship between AUB and QoL (p < 0.001). Other predictors such as behavioral intention, facilitating conditions, effort expectancy and social influence were also found to significantly impact the actual use behavior of healthcare wearable devices among PWDs. The study also revealed that 4% (14) of patients rated their reported health transition associated with the use of healthcare wearable devices as excellent, 42.5% (136) as very good, and 39.4% (126) as good, respectively at the time of the study compared to a year ago. This study provided empirical evidence of the positive impact of the actual use of healthcare wearable devices on the quality of life of PWDs and further highlighted the factors that drive actual use behavior among PWDs in Ghana. Based on the findings of this study, stakeholders need to create the necessary conditions that will facilitate the adoption and use of healthcare wearable devices, as this will help improve the quality of life, reduce the burden of the disease on caregivers, and enable independent living of PWDs.

Validation and Reliability of the Japanese Version of the Modified Parkinson Activity Scale (M-PAS)

Objective:

The Modified Parkinson Activity Scale (M-PAS) is used to identify the most important activity limitations in patients with Parkinson’s disease. We developed a Japanese version of the M-PAS and evaluated its reliability and validity.

Methods:

Twenty-five patients with Parkinson’s disease (median age 71 years old, range 58–83) were enrolled, and two raters used the Japanese version of M-PAS to assess the subjects. The inter-rater reliability was evaluated using Cohen’s weighted kappa coefficient for the total score and three domain scores; systematic error was investigated using Bland-Altman analysis. Concurrent validity of the Japanese M-PAS was measured using Spearman’s rank correlation coefficients.

Results:

Cohen’s kappa coefficients for the total score and the three domain scores were in the range 0.81–0.98, and 95% confidence intervals included zero for each item, suggesting excellent agreement and no systematic errors. The scores of the Japanese version of M-PAS were significantly correlated with the scores of the Movement Disorder Society–Unified Parkinson’s Disease Rating Scale Part II (Spearman’s rho=–0.56, P <0.01) and Part III (Spearman’s rho=–0.32, P <0.01). The percentage of patients with the highest and the lowest scores in the Japanese version of M-PAS suggested no ceiling or floor effects.

Conclusion:

The Japanese version of M-PAS showed excellent inter-rater reliability and good concurrent validity without ceiling or floor effects.

Acceptability of an In-Home Multimodal Sensor Platform in Parkinson’s Disease: A Qualitative Study (Preprint)

Background

Parkinson disease (PD) symptoms are complex, gradually progressive, and fluctuate hour by hour. Home-based technological sensors are being investigated to measure symptoms and track disease progression. A smart home sensor platform, with cameras and wearable devices, could be a useful tool to use to get a fuller picture of what someone’s symptoms are like. High-resolution video can capture the ground truth of symptoms and activities. There is a paucity of information about the acceptability of such sensors in PD.

Objective

The primary objective of our study was to explore the acceptability of living with a multimodal sensor platform in a naturalistic setting in PD. Two subobjectives are to identify any suggested limitations and to explore the sensors’ impact on participant behaviors.

Methods

A qualitative study was conducted with an inductive approach using semistructured interviews with a cohort of PD and control participants who lived freely for several days in a home-like environment while continuously being sensed.

Results

This study of 24 participants (12 with PD) found that it is broadly acceptable to use multimodal sensors including wrist-worn wearables, cameras, and other ambient sensors passively in free-living in PD. The sensor that was found to be the least acceptable was the wearable device. Suggested limitations on the platform for home deployment included camera-free time and space. Behavior changes were noted by the study participants, which may have related to being passively sensed. Recording high-resolution video in the home setting for limited periods of time was felt to be acceptable to all participants.

Conclusions

The results broaden the knowledge of what types of sensors are acceptable for use in research in PD and what potential limitations on these sensors should be considered in future work. The participants’ reported behavior change in this study should inform future similar research design to take this factor into account. Collaborative research study design, involving people living with PD at every stage, is important to ensure that the technology is acceptable and that the data outcomes produced are ecologically valid and accurate.

International Registered Report Identifier (IRRID)

RR2-10.1136/bmjopen-2020-041303

The Views and Needs of People With Parkinson Disease Regarding Wearable Devices for Disease Monitoring: Mixed Methods Exploration

BACKGROUND

Wearable devices can diagnose, monitor, and manage neurological disorders such as Parkinson disease. With a growing number of wearable devices, it is no longer a case of whether a wearable device can measure Parkinson disease motor symptoms, but rather which features suit the user. Concurrent with continued device development, it is important to generate insights on the nuanced needs of the user in the modern era of wearable device capabilities.

OBJECTIVE

This study aims to understand the views and needs of people with Parkinson disease regarding wearable devices for disease monitoring and management.

METHODS

This study used a mixed method parallel design, wherein survey and focus groups were concurrently conducted with people living with Parkinson disease in Munster, Ireland. Surveys and focus group schedules were developed with input from people with Parkinson disease. The survey included questions about technology use, wearable device knowledge, and Likert items about potential device features and capabilities. The focus group participants were purposively sampled for variation in age (all were aged >50 years) and sex. The discussions concerned user priorities, perceived benefits of wearable devices, and preferred features. Simple descriptive statistics represented the survey data. The focus groups analyzed common themes using a qualitative thematic approach. The survey and focus group analyses occurred separately, and results were evaluated using a narrative approach.

RESULTS

Overall, 32 surveys were completed by individuals with Parkinson disease. Four semistructured focus groups were held with 24 people with Parkinson disease. Overall, the participants were positive about wearable devices and their perceived benefits in the management of symptoms, especially those of motor dexterity. Wearable devices should demonstrate clinical usefulness and be user-friendly and comfortable. Participants tended to see wearable devices mainly in providing data for health care professionals rather than providing feedback for themselves, although this was also important. Barriers to use included poor hand function, average technology confidence, and potential costs. It was felt that wearable device design that considered the user would ensure better compliance and adoption.

CONCLUSIONS

Wearable devices that allow remote monitoring and assessment could improve health care access for patients living remotely or are unable to travel. COVID-19 has increased the use of remotely delivered health care; therefore, future integration of technology with health care will be crucial. Wearable device designers should be aware of the variability in Parkinson disease symptoms and the unique needs of users. Special consideration should be given to Parkinson disease-related health barriers and the users' confidence with technology. In this context, a user-centered design approach that includes people with Parkinson disease in the design of technology will likely be rewarded with improved user engagement and the adoption of and compliance with wearable devices, potentially leading to more accurate disease management, including self-management.

The impact of clinical scales in Parkinson’s disease: a systematic review

Background

Parkinson’s disease is one of the non-curable diseases and occurs by the prominent loss of neurotransmitter (dopamine) in substantia nigra pars compacta (SNpc). The main cause behind this is not yet identified and even its diagnosis is very intricate phase due to non-identified onset symptoms. Despite the fact that PD has been extensively researched over the decades, and various algorithms and strategies for early recognition and avoiding misdiagnosis have been published. The objective of this article is to focus on the current scenario and to explore the involvement of various clinical diagnostic scales in the detection of PD.

Method

An exhaustive literature review is conducted to synthesize the earlier work in this area, and the articles were searched using different keywords like Parkinson disease, motor/non-motor, treatment, diagnosis, scales, PPMI, etc., in all repositories such as Google scholar, Scopus, Elsevier, PubMed and many more. From the year 2017 to 2021, a total of 451 publications were scanned, but only 24 studies were chosen for a review process.

Findings

Mostly as clinical tools, UPDRS and HY scales are commonly used and even there are many other scales which can be helpful in detection of symptoms such as depression, anxiety, sleepiness, apathy, smell, anhedonia, fatigue, pain, etc., that affect the QoL of pateint. The recognition of non-motor manifests is typically very difficult than motor signs.

Conclusion

This study can give the beneficial research paths at an early stage diagnosis by focusing on frequent inspection of daily activities, interactions, and routine, which may also give a plethora of information on status changes, directing self-reformation, and clinical therapy.

Screening of Parkinson's Disease Using Geometric Features Extracted from Spiral Drawings

Conventional means of Parkinson’s Disease (PD) screening rely on qualitative tests typically administered by trained neurologists. Tablet technologies that enable data collection during handwriting and drawing tasks may provide low-cost, portable, and instantaneous quantitative methods for high-throughput PD screening. However, past efforts to use data from tablet-based drawing processes to distinguish between PD and control populations have demonstrated only moderate classification ability. Focusing on digitized drawings of Archimedean spirals, the present study utilized data from the open-access ParkinsonHW dataset to improve existing PD drawing diagnostic pipelines. Random forest classifiers were constructed using previously documented features and highly-predictive, newly-proposed features that leverage the many unique mathematical characteristics of the Archimedean spiral. This approach yielded an AUC of 0.999 on the particular dataset we tested on, and more importantly identified interpretable features with good promise for generalization across diverse patient cohorts. It demonstrated the potency of mathematical relationships inherent to the drawing shape and the usefulness of sparse feature sets and simple models, which further enhance interpretability, in the face of limited sample size. The results of this study also inform suggestions for future drawing task design and data analytics (feature extraction, shape selection, task diversity, drawing templates, and data sharing).

Boxing vs Sensory Exercise for Parkinson's Disease: A Double-Blinded Randomized Controlled Trial

Background. Exercise is increasingly becoming recognized as an important adjunct to medications in the clinical management of Parkinson's disease (PD). Boxing and sensory exercise have shown immediate benefits, but whether they continue beyond program completion is unknown. This study aimed to investigate the effects of boxing and sensory training on motor symptoms of PD, and whether these benefits remain upon completion of the intervention. Methods. In this 20-week double-blinded randomized controlled trial, 40 participants with idiopathic PD were randomized into 2 treatment groups, (n = 20) boxing or (n = 20) sensory exercise. Participants completed 10 weeks of intervention. Motor symptoms were assessed at (week 0, 10, and 20) using the Unified Parkinson's Disease Rating Scale (UPDRS-III). Data were analyzed using SPSS, and repeated-measures ANOVA was conducted. Results. A significant interaction effect between groups and time were observed F(1, 39) = 4.566, P = .036, where the sensory group improved in comparison to the boxing group. Post hoc analysis revealed that in comparison to boxing, the effects of exercise did not wear off at washout (week 20) P < .006. Conclusion. Future rehabilitation research should incorporate similar measures to explore whether effects of exercise wear off post intervention.