Telehealth Management of Parkinson's Disease Using Wearable Sensors: An Exploratory Study

Upper limb intention tremor assessment: opportunities and challenges in wearable technology

BACKGROUND

Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature.

METHODOLOGY & RESULTS

This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table.

CONCLUSIONS

Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.

Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol

INTRODUCTION

The clinical assessment of Parkinson's disease (PD) symptoms can present reliability issues and, with visits typically spaced apart 6 months, can hardly capture their frequent variability. Smartphones and smartwatches along with signal processing and machine learning can facilitate frequent, remote, reliable and objective assessments of PD from patients' homes.

AIM

To investigate the feasibility, compliance and user experience of passively and actively measuring symptoms from home environments using data from sensors embedded in smartphones and a wrist-wearable device.

METHODS AND ANALYSIS

In an ongoing clinical feasibility study, participants with a confirmed PD diagnosis are being recruited. Participants perform activity tests, including Timed Up and Go (TUG), tremor, finger tapping, drawing and vocalisation, once a week for 2 months using the Mobistudy smartphone app in their homes. Concurrently, participants wear the GENEActiv wrist device for 28 days to measure actigraphy continuously. In addition to using sensors, participants complete the Beck's Depression Inventory, Non-Motor Symptoms Questionnaire (NMSQuest) and Parkinson's Disease Questionnaire (PDQ-8) questionnaires at baseline, at 1 month and at the end of the study. Sleep disorders are assessed through the Parkinson's Disease Sleep Scale-2 questionnaire (weekly) and a custom sleep quality daily questionnaire. User experience questionnaires, Technology Acceptance Model and User Version of the Mobile Application Rating Scale, are delivered at 1 month. Clinical assessment (Movement Disorder Society-Unified Parkinson Disease Rating Scale (MDS-UPDRS)) is performed at enrollment and the 2-month follow-up visit. During visits, a TUG test is performed using the smartphone and the G-Walk motion sensor as reference device. Signal processing and machine learning techniques will be employed to analyse the data collected from Mobistudy app and the GENEActiv and correlate them with the MDS-UPDRS. Compliance and user aspects will be informing the long-term feasibility.

ETHICS AND DISSEMINATION

The study received ethical approval by the Swedish Ethical Review Authority (Etikprövningsmyndigheten), with application number 2022-02885-01. Results will be reported in peer-reviewed journals and conferences. Results will be shared with the study participants.

Overview on wearable sensors for the management of Parkinson's disease

Parkinson's disease (PD) is affecting about 1.2 million patients in Europe with a prevalence that is expected to have an exponential increment, in the next decades. This epidemiological evolution will be challenged by the low number of neurologists able to deliver expert care for PD. As PD is better recognized, there is an increasing demand from patients for rigorous control of their symptoms and for therapeutic education. In addition, the highly variable nature of symtoms between patients and the fluctuations within the same patient requires innovative tools to help doctors and patients monitor the disease in their usual living environment and adapt treatment in a more relevant way. Nowadays, there are various body-worn sensors (BWS) proposed to monitor parkinsonian clinical features, such as motor fluctuations, dyskinesia, tremor, bradykinesia, freezing of gait (FoG) or gait disturbances. BWS have been used as add-on tool for patients' management or research purpose. Here, we propose a practical anthology, summarizing the characteristics of the most used BWS for PD patients in Europe, focusing on their role as tools to improve treatment management. Consideration regarding the use of technology to monitor non-motor features is also included. BWS obviously offer new opportunities for improving management strategy in PD but their precise scope of use in daily routine care should be clarified.

Voice-Related Outcomes in Deep Brain Stimulation in Patients with Vocal Tremor: A Systematic Review and Meta-Analysis

OBJECTIVES

The effectiveness of deep brain stimulation (DBS) in treating vocal tremors is currently a subject of debate. To assess the efficacy of DBS therapy in adults with vocal tremors (VT), we analyzed its impact on voice tremor severity, voice-related quality of life, fundamental frequency, voice intensity, and emotional state.

METHODS

We conducted a systematic review with meta-analysis to investigate the impact of DBS therapy on voice tremor severity, voice-related quality of life, fundamental frequency, voice intensity, and emotional state in adults with vocal tremors (PROSPERO/CRD42023420272). The PubMed, Embase, Cochrane Library, Cochrane Central Register of Controlled Trials databases were searched up to September 20, 2022. Primary outcome measures included voice tremor severity and voice-related quality of life (V-RQOL), while fundamental frequency (F0) and voice intensity, along with emotional state, were selected as secondary outcome indicators. We employed the Cochrane Collaboration's tool for assessing bias risk in randomized trials. Meta-analysis (standardized difference of means and weighted mean differences) and heterogeneity analysis (I2) were performed.

RESULTS

Our search identified 1186 studies, of which nine studies involving 61 patients met the inclusion criteria. The severity of voice tremor (SMD = -1.08; 95% CI: -1.80 to 0.35; P = 0.02) and V-RQOL (SMD = -1.39; 95% CI: -2.68 to -0.09; P = 0.04) in patients with vocal tremor significantly improved after DBS "on". Subgroup analyses revealed that the stimulation site may contribute to high heterogeneity. Specifically, Vim DBS showed significant improvement in voice tremor severity (SMD = -0.97; 95% CI: -1.84 to -0.09; I2 = 51.01%), while STN DBS did not demonstrate a clear benefit in addressing vocal tremor. There was no significant difference between DBS "on" and DBS "off" in terms of F0, voice intensity, or emotional status.

CONCLUSION

DBS therapy is effective in enhancing voice quality and voice-related quality of life in patients with vocal tremors. Notably, Vim DBS demonstrates a significant improvement in voice tremor severity, particularly in VT patients with ET and SD.

Analytical and clinical validity of wearable, multi-sensor technology for assessment of motor function in patients with Parkinson's disease in Japan

Continuous, objective monitoring of motor signs and symptoms may help improve tracking of disease progression and treatment response in Parkinson's disease (PD). This study assessed the analytical and clinical validity of multi-sensor smartwatch measurements in hospitalized and home-based settings (96 patients with PD; mean wear time 19 h/day) using a twice-daily virtual motor examination (VME) at times representing medication OFF/ON states. Digital measurement performance was better during inpatient clinical assessments for composite V-scores than single-sensor-derived features for bradykinesia (Spearman |r|= 0.63, reliability = 0.72), tremor (|r|= 0.41, reliability = 0.65), and overall motor features (|r|= 0.70, reliability = 0.67). Composite levodopa effect sizes during hospitalization were 0.51-1.44 for clinical assessments and 0.56-1.37 for VMEs. Reliability of digital measurements during home-based VMEs was 0.62-0.80 for scores derived from weekly averages and 0.24-0.66 for daily measurements. These results show that unsupervised digital measurements of motor features with wrist-worn sensors are sensitive to medication state and are reliable in naturalistic settings.Trial Registration: Japan Pharmaceutical Information Center Clinical Trials Information (JAPIC-CTI): JapicCTI-194825; Registered June 25, 2019.

Decoding degeneration: the implementation of machine learning for clinical detection of neurodegenerative disorders

Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis, treatment, and tracking of complex conditions, including neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. While no definitive methods of diagnosis or treatment exist for either disease, researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers. Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment. However, such techniques require further development aimed at improving transparency, adaptability, and reproducibility. In this review, we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer's and Parkinson's diseases.

Objective quantification of the severity of postural tremor based on kinematic parameters: A multi-sensory fusion study

BACKGROUND

Current clinical assessments of essential tremor (ET) are primarily based on expert consultation combined with reviewing patient complaints, physician expertise, and diagnostic experience. Thus, traditional evaluation methods often lead to biased diagnostic results. There is a clinical demand for a method that can objectively quantify the severity of the patient's disease.

METHODS

This study aims to develop an artificial intelligence-aided diagnosis method based on multi-sensory fusion wearables. The experiment relies on a rigorous clinical trial paradigm to collect multi-modal fusion of signals from 98 ET patients. At the same time, three clinicians scored independently, and the consensus score obtained was used as the ground truth for the machine learning models.

RESULTS

Sixty kinematic parameters were extracted from the signals recorded by the nine-axis inertial measurement unit (IMU). The results showed that most of the features obtained by IMU could effectively characterize the severity of the tremors. The accuracy of the optimal model for three tasks classifying five severity levels was 97.71%, 97.54%, and 97.72%, respectively.

CONCLUSIONS

This paper reports the first attempt to combine multiple feature selection and machine learning algorithms for fine-grained automatic quantification of postural tremor in ET patients. The promising results showed the potential of the proposed approach to quantify the severity of ET objectively.

Remote Programming in Patients With Parkinson's Disease After Deep Brain Stimulation: Safe, Effective, and Economical

Objective

The purpose of this study was to evaluate the safety, efficiency, and cost expenditure of remote programming in patients with Parkinson's disease (PD) after deep brain stimulation (DBS).

Methods

A total of 74 patients who underwent DBS at the Department of Neurosurgery, Zhongnan Hospital of Wuhan University between June 2018 and June 2020 were enrolled in this study. There were 27 patients in the remote programming group and 47 patients in the outpatient programming group. Clinical data, programming efficiency, adverse events, expenditure, and satisfaction were compared between the two groups.

Results

A total of 36 times of remote programming were performed on the 27 patients in the remote programming group, and four had mild adverse events during programming, and the adverse events disappeared within 1 week. The satisfaction questionnaire showed that 97.3% of the patients were satisfied with the surgical effect. The patients in the remote programming group (88.9%) were more likely to receive long-term programming after DBS than the patients in the outpatient programming group (74.5%). The Parkinsonism symptoms improved in both programming groups. The majority (18/27) of patients in the remote programming group lived away from the programming center, while the majority (27/47) of patients in the outpatient programming group lived in Wuhan, where the programming center was located (P = 0.046). The cost per patient per programming was US$ 43.5 in the remote programming group and $59.5 (56–82.7) in the outpatient programming group (P < 0.001). The median time cost for each visit was 30 min (25–30) in the remote programming group and 150 min (135–270.0) in the outpatient programming group (P < 0.001).

Conclusion

Remote programming is safe and effective after DBS in patients with Parkinson's disease. Moreover, it reduces expenditure and time costs for patients and achieves high satisfaction, particularly for patients living far from programming centers.

Participant and Research Team Perspectives on the Conduct of a Remote Therapeutic COVID-19 Clinical Trial: A Mixed Methods Approach

Background:

Responding to the need to investigate potential treatments of COVID-19, a research team employed a telehealth platform to determine whether niclosamide, an oral anthelmintic drug that had shown antiviral activity, reduced SARS-CoV-2 shedding and duration of symptoms in patients with mild-to-moderate symptoms of COVID-19. To encourage compliance with patient self-quarantine, this randomized placebo-controlled clinical trial was conducted utilizing a remote telehealth design to complete all study visits, monitor symptoms, and coordinate participant self-collected specimens.

Methods:

A mixed methods approach employing surveys and interviews of trial participants and interviews of research team members was used to collect their experiences with and perspectives on the acceptability of the remote clinical trial design and delivery.

Results:

Of the 67 eligible trial participants invited to take part in a study to evaluate the telehealth platform, 46% (n = 31) completed a post-participation survey. While 97% (n = 30) of respondents had not previously participated in a clinical trial, 77% (n = 24) reported they would consider taking part in a future remote research study. The majority of respondents were moderately or very comfortable (93%) with using the technology.

Conclusions:

The COVID-19 crisis was a call to action to expand understanding of the conduct of remote clinical trials, including the experiences of research participants. Our findings showed that this approach can be both effective for the conduct of research and positive for participants. Further research on the use of telehealth research platforms seems warranted in rural, underserved populations, and remote trials of prevention, screening, and treatment.

Predicting Severity of Huntington's Disease With Wearable Sensors

The Unified Huntington's Disease Rating Scale (UHDRS) is the primary clinical assessment tool for rating motor function in patients with Huntington's disease (HD). However, the UHDRS and similar rating scales (e.g., UPDRS) are both subjective and limited to in-office assessments that must be administered by a trained and experienced rater. An objective, automated method of quantifying disease severity would facilitate superior patient care and could be used to better track severity over time. We conducted the present study to evaluate the feasibility of using wearable sensors, coupled with machine learning algorithms, to rate motor function in patients with HD. Fourteen participants with symptomatic HD and 14 healthy controls participated in the study. Each participant wore five adhesive biometric sensors applied to the trunk and each limb while completing brief walking, sitting, and standing tasks during a single office visit. A two-stage machine learning method was employed to classify participants by HD status and to predict UHDRS motor subscores. Linear discriminant analysis correctly classified all participants' HD status except for one control subject with abnormal gait (96.4% accuracy, 92.9% sensitivity, and 100% specificity in leave-one-out cross-validation). Two regression models accurately predicted individual UHDRS subscores for gait, and dystonia within a 10% margin of error. Our regression models also predicted a composite UHDRS score–a sum of left and right arm rigidity, total chorea, total dystonia, bradykinesia, gait, and tandem gait subscores–with an average error below 15%. Machine learning classifiers trained on brief in-office datasets discriminated between controls and participants with HD, and could accurately predict selected motor UHDRS subscores. Our results could enable the future use of biosensors for objective HD assessment in the clinic or remotely and could inform future studies for the use of this technology as a potential endpoint in clinical trials.

Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor impairments such as tremor, bradykinesia, dyskinesia, and gait abnormalities. Current protocols assess PD symptoms during clinic visits and can be subjective. Patient diaries can help clinicians evaluate at-home symptoms, but can be incomplete or inaccurate. Therefore, researchers have developed in-home automated methods to monitor PD symptoms to enable data-driven PD diagnosis and management. We queried the US National Library of Medicine PubMed database to analyze the progression of the technologies and computational/machine learning methods used to monitor common motor PD symptoms. A sub-set of roughly 12,000 papers was reviewed that best characterized the machine learning and technology timelines that manifested from reviewing the literature. The technology used to monitor PD motor symptoms has advanced significantly in the past five decades. Early monitoring began with in-lab devices such as needle-based EMG, transitioned to in-lab accelerometers/gyroscopes, then to wearable accelerometers/gyroscopes, and finally to phone and mobile & web application-based in-home monitoring. Significant progress has also been made with respect to the use of machine learning algorithms to classify PD patients. Using data from different devices (e.g., video cameras, phone-based accelerometers), researchers have designed neural network and non-neural network-based machine learning algorithms to categorize PD patients across tremor, gait, bradykinesia, and dyskinesia. The five-decade co-evolution of technology and computational techniques used to monitor PD motor symptoms has driven significant progress that is enabling the shift from in-lab/clinic to in-home monitoring of PD symptoms.

Past, Present, and Future of Deep Brain Stimulation: Hardware, Software, Imaging, Physiology and Novel Approaches

Deep brain stimulation (DBS) has advanced treatment options for a variety of neurologic and neuropsychiatric conditions. As the technology for DBS continues to progress, treatment efficacy will continue to improve and disease indications will expand. Hardware advances such as longer-lasting batteries will reduce the frequency of battery replacement and segmented leads will facilitate improvements in the effectiveness of stimulation and have the potential to minimize stimulation side effects. Targeting advances such as specialized imaging sequences and “connectomics” will facilitate improved accuracy for lead positioning and trajectory planning. Software advances such as closed-loop stimulation and remote programming will enable DBS to be a more personalized and accessible technology. The future of DBS continues to be promising and holds the potential to further improve quality of life. In this review we will address the past, present and future of DBS.

The influence of wearables on healthcare outcomes in chronic disease: a systematic review (Preprint)

Background

Chronic diseases contribute to high rates of disability and mortality. Patient engagement in chronic disease self-management is an essential component of chronic disease models of health care. Wearables provide patient-centered health data in real time, which can help inform self-management decision-making. Despite the perceived benefits of wearables in improving chronic disease self-management, their influence on health care outcomes remains poorly understood.

Objective

This review aimed to examine the influence of wearables on health care outcomes in individuals with chronic diseases through a systematic review of the literature.

Methods

A narrative systematic review was conducted by searching 6 databases for randomized and observational studies published between January 1, 2016, and July 1, 2021, that included the use of a wearable intervention in a chronic disease group to assess its impact on a predefined outcome measure. These outcomes were defined as any influence on the patient or clinician experience, cost-effectiveness, or health care outcomes as a result of the wearable intervention. Data from the included studies were extracted based on 6 key themes, which formed the basis for a narrative qualitative synthesis. All outcomes were mapped against each component of the Quadruple Aim of health care. The guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement were followed in this study.

Results

A total of 30 articles were included; studies reported 2446 participants (mean age: range 10.1-74.4 years), and the influence of 14 types of wearables on 18 chronic diseases was presented. The most studied chronic diseases were type 2 diabetes (4/30, 13%), Parkinson disease (3/30, 10%), and chronic lower back pain (3/30, 10%). The results were mixed when assessing the impact on a predefined primary outcome, with 50% (15/30) of studies finding a positive influence on the studied outcome and 50% (15/30) demonstrating a nil effect. There was a positive effect of 3D virtual reality systems on chronic pain in 7% (2/30) of studies that evaluated 2 distinct chronic pain syndromes. Mixed results were observed in influencing exercise capacity; weight; and biomarkers of disease, such as hemoglobin A_1c, in diabetes. In total, 155 outcomes were studied. Most (139/155, 89.7%) addressed the health care outcomes component. This included pain (11/155, 7.5%), quality of life (7/155, 4.8%), and physical function (5/155, 3.4%). Approximately 7.7% (12/155) of outcome measures represented the patient experience component, with 1.3% (2/155) addressing the clinician experience and cost.

Conclusions

Given their popularity and capability, wearables may play an integral role in chronic disease management. However, further research is required to generate a strong evidence base for safe and effective implementation.

Trial Registration

PROSPERO International Prospective Register of Systematic Reviews CRD42021244562; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=244562

Real-World Evidence for a Smartwatch-Based Parkinson's Motor Assessment App for Patients Undergoing Therapy Changes

Introduction

Parkinson's disease (PD) is poorly quantified by patients outside the clinic, and paper diaries have problems with subjective descriptions and bias. Wearable sensor platforms; however, can accurately quantify symptoms such as tremor, dyskinesia, and bradykinesia. Commercially available smartwatches are equipped with accelerometers and gyroscopes that can measure motion for objective evaluation. We sought to evaluate the clinical utility of a prescription smartwatch-based monitoring system for PD utilizing periodic task-based motor assessment.

Methods

Sixteen patients with PD used a smartphone- and smartwatch-based monitoring system to objectively assess motor symptoms for 1 week prior to instituting a doctor recommended change in therapy and for 4 weeks after the change. After 5 weeks the participants returned to the clinic to discuss their results with their doctor, who made therapy recommendations based on the reports and his clinical judgment. Symptom scores were synchronized with the medication diary and the temporal effects of therapy on weekly and hourly timescales were calculated.

Results

Thirteen participants successfully completed the study and averaged 4.9 assessments per day for 3 days per week during the study. The doctor instructed 8 participants to continue their new regimens and 5 to revert to their previous regimens. The smartwatch-based assessments successfully captured intraday fluctuations and short- and long-term responses to therapies, including detecting significant improvements (p < 0.05) in at least one symptom in 7 participants.

Conclusions

The smartwatch-based app successfully captured temporal trends in symptom scores following application of new therapy on hourly, daily, and weekly timescales. These results suggest that validated smartwatch-based PD monitoring can provide clinically relevant information and may reduce the need for traditional office visits for therapy adjustment.

Technology for Measuring and Monitoring Treatment Compliance Remotely

Medication non-adherence during clinical trials is an ongoing challenge that can result in insufficient safety and efficacy data. For patients with Parkinson's disease and other neurological disorders, symptomatology such as forgetfulness compounds traditional obstacles to adherence. Today, sponsors and clinical study sites can call upon various technology tools that improve adherence by monitoring and confirming dosage in near real-time. These tools have the potential to improve the quality of data gleaned from these studies.

Validation of Inertial Sensing-Based Wearable Device for Tremor and Bradykinesia Quantification

Neurologists judge the severity of Parkinsonian motor symptoms according to clinical scales, and their judgments exist inconsistent because of differences in clinical experience. Correspondingly, inertial sensing-based wearable devices (ISWDs) produce objective and standardized quantifications. However, ISWDs indirectly quantify symptoms by parametric modeling of angular velocities and linear accelerations nd trained by the judgments of several neurologists through supervised learning algorithms. Hence, the ISWD outputs are biased along with the scores provided by neurologists. To investigate the effectiveness ISWDs for Parkinsonian symptoms quantification, technical verification and clinical validation of both tremor and bradykinesia quantification methods were carried out. A total of 45 Parkinson's disease patients and 30 healthy controls performed the tremor and finger-tapping tasks, which were tracked simultaneously by an ISWD and a 6-axis high-precision electromagnetic tracking system (EMTS). The Unified Parkinson's Disease Rating Scale (UPDRS) prescribed parameters obtained from the EMTS, which directly provides linear and rotational displacements, were compared with the scores provided by both the ISWD and seven neurologists. EMTS-based parameters were regarded as the ground truth and were employed to train several common machine learning (ML) algorithms, i.e., support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF) algorithms. Inconsistency among the scores provided by the neurologists was proven. Besides, the quantification performance (sensitivity, specificity, and accuracy) of the ISWD employed with ML algorithms were better than that of the neurologists. Furthermore, EMTS can be utilized to both modify the quantification algorithms of ISWDs and improve the assessment skills of young neurologists.

What the Tech? The Management of Neurological Dysfunction Through the Use of Digital Technology

Worldwide, it is estimated that millions of individuals suffer from a neurological disorder which can be the result of head injuries, ischaemic events such as a stroke, or neurodegenerative disorders such as Parkinson's disease (PD) and multiple sclerosis (MS). Problems with mobility and hemiparesis are common for these patients, making daily life, social factors and independence heavily affected. Current therapies aimed at improving such conditions are often tedious in nature, with patients often losing vital motivation and positive outlook towards their rehabilitation. The interest in the use of digital technology in neuro-rehabilitation has skyrocketed in the past decade. To gain insight, a systematic review of the literature in the field was conducting following the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines for three categories: stroke, Parkinson's disease and multiple sclerosis. It was found that the majority of the literature (84%) was in favour of the use of digital technologies in the management of neurological dysfunction; with some papers taking a "neutral" or "against" standpoint. It was found that the use of technologies such as virtual reality (VR), robotics, wearable sensors and telehealth was highly accepted by patients, helped to improve function, reduced anxiety and make therapy more accessible to patients living in more remote areas. The most successful therapies were those that used a combination of conventional therapies and new digital technologies.

Feasibility and Utility of mHealth for the Remote Monitoring of Parkinson Disease: Ancillary Study of the PD_manager Randomized Controlled Trial

Background

Mobile health, predominantly wearable technology and mobile apps, have been considered in Parkinson disease to provide valuable ecological data between face-to-face visits and improve monitoring of motor symptoms remotely.

Objective

We explored the feasibility of using a technology-based mHealth platform comprising a smartphone in combination with a smartwatch and a pair of smart insoles, described in this study as the PD_manager system, to collect clinically meaningful data. We also explored outcomes and disease-related factors that are important determinants to establish feasibility. Finally, we further validated a tremor evaluation method with data collected while patients performed their daily activities.

Methods

PD_manager trial was an open-label parallel group randomized study.The mHealth platform consists of a wristband, a pair of sensor insoles, a smartphone (with dedicated mobile Android apps) and a knowledge platform serving as the cloud backend. Compliance was assessed with statistical analysis and the factors affecting it using appropriate regression analysis. The correlation of the scores of our previous algorithm for tremor evaluation and the respective Unified Parkinson’s Disease Rating Scale estimations by clinicians were explored.

Results

Of the 75 study participants, 65 (87%) completed the protocol. They used the PD_manager system for a median 11.57 (SD 3.15) days. Regression analysis suggests that the main factor associated with high use was caregivers’ burden. Motor Aspects of Experiences of Daily Living and patients’ self-rated health status also influence the system’s use. Our algorithm provided clinically meaningful data for the detection and evaluation of tremor.

Conclusions

We found that PD patients, regardless of their demographics and disease characteristics, used the system for 11 to 14 days. The study further supports that mHealth can be an effective tool for the ecologically valid, passive, unobtrusive monitoring and evaluation of symptoms. Future studies will be required to demonstrate that an mHealth platform can improve disease management and care.

Trial Registration

ISRCTN Registry ISRCTN17396879; http://www.isrctn.com/ISRCTN17396879

International Registered Report Identifier (IRRID)

RR2-10.1186/s13063-018-2767-4

mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson's disease

Accurately monitoring motor and non-motor symptoms as well as complications in people with Parkinson's disease (PD) is a major challenge, both during clinical management and when conducting clinical trials investigating new treatments. A variety of strategies have been relied upon including questionnaires, motor diaries, and the serial administration of structured clinical exams like part III of the MDS-UPDRS. To evaluate the potential use of mobile and wearable technologies in clinical trials of new pharmacotherapies targeting PD symptoms, we carried out a project (project BlueSky) encompassing four clinical studies, in which 60 healthy volunteers (aged 23-69; 33 females) and 95 people with PD (aged 42-80; 37 females; years since diagnosis 1-24 years; Hoehn and Yahr 1-3) participated and were monitored in either a laboratory environment, a simulated apartment, or at home and in the community. In this paper, we investigated (i) the utility and reliability of self-reports for describing motor fluctuations; (ii) the agreement between participants and clinical raters on the presence of motor complications; (iii) the ability of video raters to accurately assess motor symptoms, and (iv) the dynamics of tremor, dyskinesia, and bradykinesia as they evolve over the medication cycle. Future papers will explore methods for estimating symptom severity based on sensor data. We found that 38% of participants who were asked to complete an electronic motor diary at home missed ~25% of total possible entries and otherwise made entries with an average delay of >4 h. During clinical evaluations by PD specialists, self-reports of dyskinesia were marked by ~35% false negatives and 15% false positives. Compared with live evaluation, the video evaluation of part III of the MDS-UPDRS significantly underestimated the subtle features of tremor and extremity bradykinesia, suggesting that these aspects of the disease may be underappreciated during remote assessments. On the other hand, live and video raters agreed on aspects of postural instability and gait. Our results highlight the significant opportunity for objective, high-resolution, continuous monitoring afforded by wearable technology to improve upon the monitoring of PD symptoms.

New directions in clinical trials for frontotemporal lobar degeneration: Methods and outcome measures

INTRODUCTION

Frontotemporal lobar degeneration (FTLD) is the most common form of dementia for those under 60 years of age. Increasing numbers of therapeutics targeting FTLD syndromes are being developed.

METHODS

In March 2018, the Association for Frontotemporal Degeneration convened the Frontotemporal Degeneration Study Group meeting in Washington, DC, to discuss advances in the clinical science of FTLD.

RESULTS

Challenges exist for conducting clinical trials in FTLD. Two of the greatest challenges are (1) the heterogeneity of FTLD syndromes leading to difficulties in efficiently measuring treatment effects and (2) the rarity of FTLD disorders leading to recruitment challenges.

DISCUSSION

New personalized endpoints that are clinically meaningful to individuals and their families should be developed. Personalized approaches to analyzing MRI data, development of new fluid biomarkers and wearable technologies will help to improve the power to detect treatment effects in FTLD clinical trials and enable new, clinical trial designs, possibly leveraged from the experience of oncology trials. A computational visualization and analysis platform that can support novel analyses of combined clinical, genetic, imaging, biomarker data with other novel modalities will be critical to the success of these endeavors.

Deep Phenotyping of Parkinson's Disease

Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.

Multimodal Wearable Sensors to Measure Gait and Voice

Background

Traditional measurement systems utilized in clinical trials are limited because they are episodic and thus cannot capture the day-to-day fluctuations and longitudinal changes that frequently affect patients across different therapeutic areas.

Objectives

The aim of this study was to collect and evaluate data from multiple devices, including wearable sensors, and compare them to standard lab-based instruments across multiple domains of daily tasks.

Methods

Healthy volunteers aged 18-65 years were recruited for a 1-h study to collect and assess data from wearable sensors. They performed walking tasks on a gait mat while instrumented with a watch, phone, and sensor insoles as well as several speech tasks on multiple recording devices.

Results

Step count and temporal gait metrics derived from a single lumbar accelerometer are highly precise; spatial gait metrics are consistently 20% shorter than gait mat measurements. The insole's algorithm only captures about 72% of steps but does have precision in measuring temporal gait metrics. Mobile device voice recordings provide similar results to traditional recorders for average signal pitch and sufficient signal-to-noise ratio for analysis when hand-held. Lossless compression techniques are advised for signal processing.

Conclusions

Gait metrics from a single lumbar accelerometer sensor are in reasonable concordance with standard measurements, with some variation between devices and across individual metrics. Finally, participants in this study were familiar with mobile devices and had high acceptance of potential future continuous wear for clinical trials.

Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements

Tremor is one of the main symptoms of Parkinson's Disease (PD) that reduces the quality of life. Tremor is measured as part of the Unified Parkinson Disease Rating Scale (UPDRS) part III. However, the assessment is based on onsite physical examinations and does not fully represent the patients' tremor experience in their day-to-day life. Our objective in this paper was to develop algorithms that, combined with wearable sensors, can estimate total Parkinsonian tremor as the patients performed a variety of free body movements. We developed two methods: an ensemble model based on gradient tree boosting and a deep learning model based on long short-term memory (LSTM) networks. The developed methods were assessed on gyroscope sensor data from 24 PD subjects. Our analysis demonstrated that the method based on gradient tree boosting provided a high correlation (r = 0.96 using held-out testing and r = 0.93 using subject-based, leave-one-out cross-validation) between the estimated and clinically assessed tremor subscores in comparison to the LSTM-based method with a moderate correlation (r = 0.84 using held-out testing and r = 0.77 using subject-based, leave-one-out cross-validation). These results indicate that our approach holds great promise in providing a full spectrum of the patients' tremor from continuous monitoring of the subjects' movement in their natural environment.

Feasibility and utility of a clinician dashboard from wearable and mobile application Parkinson's disease data

Mobile and wearable device-captured data have the potential to inform Parkinson’s disease (PD) care. The objective of the Clinician Input Study was to assess the feasibility and clinical utility of data obtained using a mobile health technology from PD patients. In this observational, exploratory study, PD participants wore a smartwatch and used the Fox Wearable Companion mobile phone app to stream movement data and report symptom severity and medication intake for 6 months. Data were analyzed using the Intel® Pharma Analytics Platform. Clinicians reviewed participants’ data in a dashboard during in-office visits at 2 weeks, 1, 3, and 6 months. Clinicians provided feedback in focus groups leading to dashboard updates. Between June and August 2017, 51 PD patients were recruited at four US sites, and 39 (76%) completed the 6-month study. Patients streamed 83,432 h of movement data from their smartwatches (91% of expected). Reporting of symptoms and medication intake using the app was lower than expected, 44% and 60%, respectively, but did not differ according to baseline characteristics. Clinicians’ feedback resulted in ten updates to the dashboard during the study period. Clinicians reported that medications and patient reported outcomes were generally discernable in the dashboard and complementary to clinical assessments. Movement, symptoms, and medication intake data were feasibly translated from the app into a clinician dashboard but there was substantial attrition rate over 6 months. Further enhancements are needed to ensure long-term patient adherence to portable technologies and optimal digital data transfer to clinicians caring for PD patients.

Telemedicine in Neurological Disorders: Opportunities and Challenges

Introduction: Telemedicine represents an emerging model for the assessment and management of various neurological disorders. Methods: We sought to discuss opportunities and challenges for the integration of telemedicine in the management of common and uncommon neurological disorders by reviewing and appraising studies that evaluate telemedicine as a means to facilitate the access to care, deliver highly specialized visits, diagnostic consultations, rehabilitation, and remote monitoring of neurological disorders. Results: Opportunities for telemedicine in neurological disorders include the replacement of or complement to in-office evaluations, decreased time between follow-up visits, reduction in disparities in access to healthcare, and promotion of education and training through interactions between primary care physicians and tertiary referral centers. Critical challenges include the integration of the systems for data monitoring with an easy-to-use, secure, and cost-effective platform that is both widely adopted by patients and healthcare systems and embraced by international scientific societies. Conclusions: Multiple applications may spawn from a model based on digitalized healthcare services. Integrated efforts from multiple stakeholders will be required to develop an interoperable software platform capable of providing not only a holistic approach to care but also one that reduces disparities in the access to care.

Smartwatch for the analysis of rest tremor in patients with Parkinson's disease

Wearable technology used in Parkinson's disease (PD) research has become an increasing focus of interest in this field. Our group assessed the feasibility, clinical correlation, reliability, and acceptance of smartwatches in order to quantify arm resting tremors in PD patients. An Android application on a smartwatch was used to obtain raw data from the smartwatch's gyroscopes. Twenty-two PD patients were consecutively recruited and followed for 1 year. Arm rest tremors were video filmed and scored by two independent raters using the motor subscale of the Unified Parkinson's Disease Rating Scale (UPDRS-III). The tremor intensity parameter was defined by the root mean square of the angular speed measured by the smartwatch at the wrist. Sixty-four smartwatch evaluations were completed. The Spearman coefficient among the mean of the resting tremor (UPDRS-III) scores and smartwatch measurements for tremor intensity was 0.81 (p < .001); smartwatch reliability to quantify tremors was checked by intraclass reliability coefficient with a resting tremor = 0.89, minimum detectable change = 59.03%. Good acceptance of the system was shown. Smartwatch use for PD tremor analysis is possible, reliable, well-correlated with clinical scores, and well-accepted by patients for clinical follow-up. The results from these experiments suggest that this commodity hardware has the potential to quantify PD patients' tremors objectively in a consulting-room.

The Design and Application of an Appropriate Parkinson's Disease Animal Model in Regenerative Medicine

OBJECTIVES

Aging as an inevitable and complex physiological process occurs through a progressive decrease in the potential of tissue regeneration. Given the increasing global outbreak of aging and age-related disorders, it is important to control this phenomenon. Parkinson's disease (one of the age-related neurodegenerative and progressive disorders) resulted from predominant dopaminergic neurons deficiency. Usual Parkinson's disease treatments just can lead to symptomatically relieving. Recently, cell therapy and regenerative medicine a great promise in the treatment of several types of disorders including Parkinson's disease. Herein, before starting clinical trials, preclinical studies should be performed to answer some fundamental questions about the safety and efficacy of various treatments. Additionally, developing a well-designed and approved study is required to provide an appropriate animal model with strongly reliable validation methods. Hereupon, this review will discuss about the design and application of an appropriate Parkinson's disease animal model in regenerative medicine.

EVIDENCE ACQUISITION

In order to conduct the present review, numbers of Parkinson's disease preclinical studies, as well as literatures related to the animal modeling, were considered.

RESULTS

Appropriate animal models which approved by related authorize committees should have a high similarity to humans from anatomical, physiological, behavioral, and genetic characteristics view of point.

CONCLUSION

It is concluded that animal studies before starting clinical trials have an important role in answering the crucial questions about the various treatments safety and efficacy. Therein, it is recommended that all of animal modeling stages be assessed by animal ethics and welfare guidelines and also evaluated by different validation tests. However, it is better to find some alternatives to replacement, refinement, and, reduction of animals. Nowadays, some novel technologies such as using imaging methods have been introduced.

Application of telehealth intervention in Parkinson's disease: A systematic review and meta-analysis

INTRODUCTION

Telehealth intervention has been proposed as a sustainable and innovative intervention approach to Parkinson's disease (PD) patients, but there are still conflicting results in the literature about its effect. This study aimed to evaluate the efficacy of telehealth intervention for PD patients.

METHODS

PubMed, EMBASE, CENTRAL and China National Knowledge Infrastructure (CNKI) were searched from the inception to June 2018 for randomized controlled trials (RCTs) and cohort studies, without language restrictions. When feasible, data were statistically pooled for meta-analysis using Review Manager 5.3. Otherwise, narrative summaries were used.

RESULTS

Twenty-one studies were included. With respect to PD severity, compared with usual care, telehealth intervention was beneficial in lowering motor impairment of PD patients significantly (mean difference (MD) = -2.27, 95% confidence interval (95% CI) -4.25 to -0.29, p = 0.02), rather than mental status (MD = -0.98, 95% CI -2.61 to 0.65, p = 0.24), activities of daily living (MD = -1.51, 95% CI -4.91 to 1.89, p = 0.38) and motor complications (MD = -0.36, 95% CI -1.31 to 0.59, p = 0.46). Telehealth intervention did not lead to significant reduction in quality of life (standardized mean difference (SMD) = 0.04, 95% CI -0.20 to 0.28, p = 0.76), depression (SMD = -0.12, 95% CI -0.37 to 0.13, p = 0.34), cognition (MD = 0.37, 95% CI -0.34 to 1.09, p = 0.31) and balance (MD = 0.09, 95% CI -2.49 to 2.66, p = 0.95).

DISCUSSION

Telehealth intervention is an effective option for individuals with PD to improve their motor impairment. Further well-designed studies are warranted to confirm our findings.

Can composite digital monitoring biomarkers come of age? A framework for utilization

INTRODUCTION

The application of digital monitoring biomarkers in health, wellness and disease management is reviewed. Harnessing the near limitless capacity of these approaches in the managed healthcare continuum will benefit from a systems-based architecture which presents data quality, quantity, and ease of capture within a decision-making dashboard.

METHODS

A framework was developed which stratifies key components and advances the concept of contextualized biomarkers. The framework codifies how direct, indirect, composite, and contextualized composite data can drive innovation for the application of digital biomarkers in healthcare.

RESULTS

The de novo framework implies consideration of physiological, behavioral, and environmental factors in the context of biomarker capture and analysis. Application in disease and wellness is highlighted, and incorporation in clinical feedback loops and closed-loop systems is illustrated.

CONCLUSIONS

The study of contextualized biomarkers has the potential to offer rich and insightful data for clinical decision making. Moreover, advancement of the field will benefit from innovation at the intersection of medicine, engineering, and science. Technological developments in this dynamic field will thus fuel its logical evolution guided by inputs from patients, physicians, healthcare providers, end-payors, actuarists, medical device manufacturers, and drug companies.