Patient diaries as a clinical endpoint in Parkinson's disease clinical trials

Diverse preferences, different solutions: Exploring remote monitoring preferences in Parkinson's disease through a discrete choice experiment

BackgroundRemote monitoring solutions (RMS) have the potential to improve Parkinson's disease (PD) management by enabling continuous symptom tracking and personalized care. Understanding patient preferences for RMS features is essential for successful implementation.ObjectiveThis study aimed to investigate the preferences of people with Parkinson's disease (PwP) for RMS features and identify preference heterogeneity across distinct patient subgroups.MethodsFrom November 2023 to February 2024, a discrete choice experiment (DCE) was conducted among PwP in Finland and Italy to elicit preferences for RMS attributes, including monitoring frequency, time spent filling questionnaires, home video recordings, and clinical benefits (delay in advanced symptom onset). Latent class analysis (LCA) was used to identify subgroups with distinct preference patterns, and adoption probabilities under varying RMS scenarios were estimated.ResultsA total of 411 PwP participated, revealing significant heterogeneity in RMS preferences. While clinical benefits, particularly delaying advanced symptom onset, were the most valued attribute overall, preferences diverged across subgroups. Some participants strongly preferred home video recordings, whereas others expressed aversion to this feature. A smaller subgroup exhibited reluctance toward RMS adoption, regardless of its benefits.ConclusionsPwP generally view RMS favorably, but preferences for specific features vary substantially across subgroups. Clinical benefits are a key driver of adoption, while home video recordings elicit both strong preference and aversion, highlighting the impracticality of a one-size-fits-all approach. Tailoring RMS to diverse patient needs, addressing concerns, and enhancing usability through customization are essential for successful implementation and widespread acceptance in PD management.

Evaluation of motor fluctuations in Parkinson's disease: electronic vs. conventional paper diaries

Background

Paper symptom diaries are a common tool for assessing motor fluctuations in Parkinson's disease (PD) patients, but there are concerns about inaccuracies in the assessment of motor fluctuation due to recall bias and poor compliance. We, therefore, developed an electronic diary with reminder and real-time recording functions.

Objectives and methods

To evaluate the effectiveness of the electronic diary, we compared compliance and motor fluctuation assessment with a paper diary. Nineteen PD patients were recruited and recorded paper diaries every 30 min from 8 am to 8 pm for 7 days, followed by 7 days of electronic diary recording using a smartphone and smartwatch. Prior to the recording period, the Parkinson's Disease Questionnaire (PDQ)-39 and the Movement Disorders Society-sponsored Unified Parkinson's Disease Rating Scale-Revised (MDS-UPDRS) 1, 2, 3, 4 were measured. Patients completed a patient questionnaire on the usability of the diaries after the recording period.

Results

Total reported time was significantly longer in paper diaries, but there was no significant difference in the number of entries (paper 115 [71-147] vs. electronic 109 [93-116], p = 0.77). There was a significant correlation between paper and electronic diaries with respect to motor status. ON time rate recorded in the electronic diary was significantly correlated with PDQ-39, MDS-UPDRS 1, 2, and 4, while MDS-UPDRS 1 was only correlated with ON time rate in the paper diary. The usability of our electronic diary was found to be satisfactory based on the results of patient questionnaire.

Conclusion

Electronic diaries are useful tools that more accurately reflect PD motor fluctuations.

Wearable-Enabled Algorithms for the Estimation of Parkinson's Symptoms Evaluated in a Continuous Home Monitoring Setting Using Inertial Sensors

Motor symptoms such as tremor and bradykinesia can develop concurrently in Parkinson's disease; thus, the ideal home monitoring system should be capable of tracking symptoms continuously despite background noise from daily activities. The goal of this study is to demonstrate the feasibility of detecting symptom episodes in a free-living scenario, providing a higher level of interpretability to aid AI-powered decision-making. Machine learning models trained on wearable sensor data from scripted activities performed by participants in the lab and clinician ratings of the video recordings of these tasks identified tremor, bradykinesia, and dyskinesia in the supervised lab environment with a balanced accuracy of 83%, 75%, and 81%, respectively, when compared to the clinician ratings. The performance of the same models when evaluated on data from subjects performing unscripted activities unsupervised in their own homes achieved a balanced accuracy of 63%, 63%, and 67%, respectively, in comparison to self-assessment patient diaries, further highlighting their limitations. The ankle-worn sensor was found to be advantageous for the detection of dyskinesias but did not show an added benefit for tremor and bradykinesia detection here.

[Commercial devices for monitoring symptoms in Parkinson's disease: benefits, limitations and trends]

Parkinson's disease (PD) is a neurodegenerative disorder that significantly affects patients' quality of life. Treatment of PD requires accurate assessment of motor and non-motor symptoms, which is often complicated by subjectivity in reporting symptoms, and the limited availability of neurologists. Commercial wearable devices, which monitor PD symptoms continuously and outside the clinical setting, have appeared to address these challenges. These devices include PKG™, Kinesia 360™, Kinesia U™, PDMonitor™ and STAT-ON™. These devices use advanced technologies, including accelerometers, gyroscopes and specific algorithms to provide objective data on motor symptoms, such as tremor, dyskinesia and bradykinesia. Despite their potential, the adoption of these devices has been limited, due to concerns about their accuracy, complexity of use and the lack of independent validation. The correlation between the measurements obtained from these devices and traditional clinical observations varies, and their usability and patient adherence are critical areas for improvement. Validation and usability studies with a sufficient number of patients, standardised protocols and integration with hospitals' IT systems are essential to optimise their usefulness and improve patient outcomes.

Paradigm shift in Parkinson's disease: using continuous telemonitoring to improve symptoms control. Results from a 2-years journey

Introduction

Conventional care in Parkinson's disease (PD) faces limitations due to the significant time and location commitments needed for regular assessments, lacking quantitative measurements. Telemonitoring offers clinicians an opportunity to evaluate patient symptomatology throughout the day during activities of daily living.

Methods

The progression of PD symptoms over a two-year period was investigated in patients undergoing traditional evaluation, supplemented by insights from ambulatory measurements. Physicians integrated a telemonitoring device, the PDMonitor®, into daily practice, using it for informed medication adjustments.

Results

Statistical analyses examining intra-subject changes for 17 subjects revealed a significant relative decrease of -43.9% in the device-reported percentage of time spent in "OFF" state (from 36.2 to 20.3%). Following the 24-month period, the majority of the subjects improved or exhibited stable symptom manifestation. In addition to positively impacting motor symptom control, telemonitoring was found to enhance patient satisfaction about their condition, medication effectiveness, and communication with physicians.

Discussion

Considering that motor function is significantly worsened over time in patients with PD, these findings suggest a positive impact of objective telemonitoring on symptoms control. Patient satisfaction regarding disease management through telemonitoring can potentially improve adherence to treatment plans. In conclusion, remote continuous monitoring paves the way for a paradigm shift in PD, focusing on actively managing and potentially improve symptoms control.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Unveiling the Unpredictable in Parkinson's Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life

BACKGROUND

Dyskinesias and freezing of gait are episodic disorders in Parkinson's disease, characterized by a fluctuating and unpredictable nature. This cross-sectional study aims to objectively monitor Parkinsonian patients experiencing dyskinesias and/or freezing of gait during activities of daily living and assess possible changes in spatiotemporal gait parameters.

METHODS

Seventy-one patients with Parkinson's disease (40 with dyskinesias and 33 with freezing of gait) were continuously monitored at home for a minimum of 5 days using a single wearable sensor. Dedicated machine-learning algorithms were used to categorize patients based on the occurrence of dyskinesias and freezing of gait. Additionally, specific spatiotemporal gait parameters were compared among patients with and without dyskinesias and/or freezing of gait.

RESULTS

The wearable sensor algorithms accurately classified patients with and without dyskinesias as well as those with and without freezing of gait based on the recorded dyskinesias and freezing of gait episodes. Standard spatiotemporal gait parameters did not differ significantly between patients with and without dyskinesias or freezing of gait. Both the time spent with dyskinesias and the number of freezing of gait episodes positively correlated with the disease severity and medication dosage.

CONCLUSIONS

A single inertial wearable sensor shows promise in monitoring complex, episodic movement patterns, such as dyskinesias and freezing of gait, during daily activities. This approach may help implement targeted therapeutic and preventive strategies for Parkinson's disease.

Home-Based Titration with Duodenal Infusion of Levodopa-Carbidopa Intestinal Gel in People with Parkinson's Disease: An Observational Feasibility Study

Background

Testing and titration of the right levodopa equivalent dose are usually performed during a hospital admission. However, optimal dose titration in people with Parkinson's disease (PwPs) may depend on home environment, emotional stress, and physical activity of everyday life.

Objective

Firstly, to evaluate the feasibility and safety of a home-based LCIG titration program and patients'/caregivers' satisfaction. Secondly, to identify barriers and facilitators for home-based titration.

Method

This study assesses the feasibility and safety of home-based titration of levodopa duodenal infusions with the use of self-reported evaluation questionnaires with open-ended questions included, registration of total time used, and number of contacts/visits. A telemedicine solution was used to remotely monitor the patients, adjust treatment, and provide support and guidance to patients and caregivers.

Results

Ten of 12 PwPs (5 females and 7 males) completed the total titration program. Eight of the 12 PwPs were dependent on help. These 8 PwPs also had a high burden of nonmotor symptoms (NMS). Cognitive impairments varied in severity (range 16-30). Time spent with home visits was on average 93.4 minutes (ranging from 35 to 180 minutes), and the length of the total titration (LCIG initiation to termination of titration) was on average 3.4 days with 2-5 (mean 3.2) contacts/visits with PD team members. The average score on the satisfaction evaluation questionnaires was lower in the caregiver group (mean 31.8) than the PwP outcome (mean 36.2).

Conclusions

Telehealth-assisted home-based titration programs are feasible due to the length of the titration period, number of contacts, and time spent in PwPs' private homes, are rated satisfactory and safe by PwPs and caregivers, and may be a substitute for in-hospital treatment. Clinical recommendations including facilitators and barriers from a patient/caregiver perspective are displayed. This trial is registered with NCT4196647.

Role of zonisamide in advanced Parkinson's disease: a randomized placebo-controlled study

BACKGROUND

Zonisamide (ZNS) has shown some efficacy in motor symptoms of PD; however, more evidence is lacking, and its effects on nonmotor symptoms (NMSs) and quality of life (QoL) remain to be investigated. This randomized double-blinded placebo-controlled crossover study investigated the effect of ZNS on motor and NMS symptoms and QoL in advanced PD.

METHODS

PD patients with Hoehn and Yahr stage ≥ 2 ("On" state) and at least 2 h off time daily were randomized to groups: ZNS 25 mg, ZNS 50 mg and placebo. Groups were assessed at baseline and at the 1- and 3-month follow-ups. The primary endpoint was the change in the total MDS-UPDRS III "On", while the secondary endpoint was the change in the total and parts I and IV MDS-UPDRS, Nonmotor Symptoms Scale and Parkinson's disease questionnaire-39 at the final assessment.

RESULTS

Sixty-nine patients were assessed for efficacy at the 1-month follow-up, and 58 patients were assessed at the 3-month follow-up. The primary endpoint showed significant improvement in the ZNS 25 mg group compared to the placebo group (p = 0.009). At the final assessment, the ZNS 25 mg group showed significant improvement of total and part VI MDS-UPDRS, bradykinesia, tremor and functional impact of fluctuations compared to placebo. There was no change in dyskinesia, NMSs, QoL or side effects except for sedation.

CONCLUSION

ZNS has a favourable effect on motor symptoms in patients with wearing off as adjunctive therapy with other dopaminergic drugs, with no exacerbation of dyskinesia and a limited impact on NMSs and QoL.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT04182399, in 24/11/2019.

Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST)

The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution. They can provide a comprehensive picture of psychological well-being, encompassing both psychological and physiological symptoms. This study explores how using advanced digital technologies, i.e., automatic speech recognition and natural language processing, can efficiently capture patient insights in oncology settings. We introduce the MRAST framework, a simplified way to collect, structure, and understand patient data using questionnaires and diary recordings. The framework was validated in a prospective study with 81 colorectal and 85 breast cancer survivors, of whom 37 were male and 129 were female. Overall, the patients evaluated the solution as well made; they found it easy to use and integrate into their daily routine. The majority (75.3%) of the cancer survivors participating in the study were willing to engage in health monitoring activities using digital wearable devices daily for an extended period. Throughout the study, there was a noticeable increase in the number of participants who perceived the system as having excellent usability. Despite some negative feedback, 44.44% of patients still rated the app's usability as above satisfactory (i.e., 7.9 on 1-10 scale) and the experience with diary recording as above satisfactory (i.e., 7.0 on 1-10 scale). Overall, these findings also underscore the significance of user testing and continuous improvement in enhancing the usability and user acceptance of solutions like the MRAST framework. Overall, the automated extraction of information from diaries represents a pivotal step toward a more patient-centered approach, where healthcare decisions are based on real-world experiences and tailored to individual needs. The potential usefulness of such data is enormous, as it enables better measurement of everyday experiences and opens new avenues for patient-centered care.

Home-based monitoring of persons with advanced Parkinson's disease using smartwatch-smartphone technology

Movement deterioration is the hallmark of Parkinson's disease (PD), characterized by levodopa-induced motor-fluctuations (i.e., symptoms' variability related to the medication cycle) in advanced stages. However, motor symptoms are typically too sporadically and/or subjectively assessed, ultimately preventing the effective monitoring of their progression, and thus leading to suboptimal treatment/therapeutic choices. Smartwatches (SW) enable a quantitative-oriented approach to motor-symptoms evaluation, namely home-based monitoring (HBM) using an embedded inertial measurement unit. Studies validated such approach against in-clinic evaluations. In this work, we aimed at delineating personalized motor-fluctuations' profiles, thus capturing individual differences. 21 advanced PD patients with motor fluctuations were monitored for 2 weeks using a SW and a smartphone-dedicated app (Intel Pharma Analytics Platform). The SW continuously collected passive data (tremor, dyskinesia, level of activity using dedicated algorithms) and active data, i.e., time-up-and-go, finger tapping, hand tremor and hand rotation carried out daily, once in OFF and once in ON levodopa periods. We observed overall high compliance with the protocol. Furthermore, we observed striking differences among the individual patterns of symptoms' levodopa-related variations across the HBM, allowing to divide our participants among four data-driven, motor-fluctuations' profiles. This highlights the potential of HBM using SW technology for revolutionizing clinical practices.

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.

How should we be using biomarkers in trials of disease modification in Parkinson's disease?

The recent validation of the α-synuclein seed amplification assay as a biomarker with high sensitivity and specificity for the diagnosis of Parkinson's disease has formed the backbone for a proposed staging system for incorporation in Parkinson's disease clinical studies and trials. The routine use of this biomarker should greatly aid in the accuracy of diagnosis during recruitment of Parkinson's disease patients into trials (as distinct from patients with non-Parkinson's disease parkinsonism or non-Parkinson's disease tremors). There remain, however, further challenges in the pursuit of biomarkers for clinical trials of disease modifying agents in Parkinson's disease, namely: optimizing the distinction between different α-synucleinopathies; the selection of subgroups most likely to benefit from a candidate disease modifying agent; a sensitive means of confirming target engagement; and the early prediction of longer-term clinical benefit. For example, levels of CSF proteins such as the lysosomal enzyme β-glucocerebrosidase may assist in prognostication or allow enrichment of appropriate patients into disease modifying trials of agents with this enzyme as the target; the presence of coexisting Alzheimer's disease-like pathology (detectable through CSF levels of amyloid-β42 and tau) can predict subsequent cognitive decline; imaging techniques such as free-water or neuromelanin MRI may objectively track decline in Parkinson's disease even in its later stages. The exploitation of additional biomarkers to the α-synuclein seed amplification assay will, therefore, greatly add to our ability to plan trials and assess the disease modifying properties of interventions. The choice of which biomarker(s) to use in the context of disease modifying clinical trials will depend on the intervention, the stage (at risk, premotor, motor, complex) of the population recruited and the aims of the trial. The progress already made lends hope that panels of fluid biomarkers in tandem with structural or functional imaging may provide sensitive and objective methods of confirming that an intervention is modifying a key pathophysiological process of Parkinson's disease. However, correlation with clinical progression does not necessarily equate to causation, and the ongoing validation of quantitative biomarkers will depend on insightful clinical-genetic-pathophysiological comparisons incorporating longitudinal biomarker changes from those at genetic risk with evidence of onset of the pathophysiology and those at each stage of manifest clinical Parkinson's disease.

Application of single wrist-wearable accelerometry for objective motor diary assessment in fluctuating Parkinson's disease

Advanced Parkinson's disease (PD) is characterized by motor fluctuations including unpredictable oscillations remarkably impairing quality of life. Effective management and development of novel therapies for these response fluctuations largely depend on clinical rating instruments such as the widely-used PD home diary, which are associated with biases and errors. Recent advancements in digital health technologies provide user-friendly wearables that can be tailored for continuous monitoring of motor fluctuations. Their criterion validity under real-world conditions using clinical examination as the gold standard remains to be determined. We prospectively examined this validity of a wearable accelerometer-based digital Parkinson's Motor Diary (adPMD) using the Parkinson's Kinetigraph (PKG®) in an alternative application by converting its continuous data into one of the three motor categories of the PD home diary (Off, On and Dyskinetic state). Sixty-three out of 91 eligible participants with fluctuating PD (46% men, average age 66) had predefined sufficient adPMD datasets (>70% of half-hour periods) from 2 consecutive days. 92% of per-protocol assessments were completed. adPMD monitoring of daily times in motor states showed moderate validity for Off and Dyskinetic state (ICC = 0.43-0.51), while inter-rating methods agreements on half-hour-level can be characterized as poor (median Cohen's κ = 0.13-0.21). Individualization of adPMD thresholds for transferring accelerometer data into diary categories improved temporal agreements up to moderate level for Dyskinetic state detection (median Cohen's κ = 0.25-0.41). Here we report that adPMD real-world-monitoring captures daily times in Off and Dyskinetic state in advanced PD with moderate validities, while temporal agreement of adPMD and clinical observer diary data is limited.

Holter STAT-ON™ against other tools for detecting MF in advanced Parkinson's disease: an observational study

Background

Different screening tools to identify advanced Parkinson's disease (APD) have emerged in recent years. Among them, wearable medical devices, such as STAT-ON™, have been proposed to help to objectively detect APD.

Objectives

To analyze the correlation between STAT-ON™ reports and other assessment tools to identify APD and to assess the accuracy of screening tools in APD patients, using the STAT-ON™ as the gold standard.

Methods

In this retrospective, observational study, data from the University Hospital Complex of Pontevedra database on 44 patients with potential APD who wore STAT-ON™ were extracted. Data were collected according to different sources of tools for identifying APD: (1) STAT-ON™, (2) information provided by the patient, (3) questionnaire for advanced Parkinson's disease (CDEPA), (4) 5-2-1 Criteria, and (5) Making Informed Decisions to Aid Timely Management of Parkinson's Disease (MANAGE-PD). Considering STAT-ON™ recordings as a reference, the sensitivity, specificity, and positive and negative predictive values for each tool were calculated. The kappa index assessed the degree of agreement between the gold standard and the other instruments.

Results

Although no statistically significant association was found between STAT-ON™ recordings and any screening methods evaluated, the CDEPA questionnaire demonstrated the highest sensitivity and VPN values to detect patients with APD candidates for second-line therapy (SLT). According to the correlation analyses, MANAGE-PD demonstrated the highest degree of concordance with STAT-ON™ recordings to identify the SLT indication and to predict the SLT decision.

Conclusion

STAT-ON™ device may be a helpful tool to detect APD and to guide treatment decisions.

Objective measurement versus clinician-based assessment for Parkinson's disease

INTRODUCTION

Although clinician-based assessment through standardized clinical rating scales is currently the gold standard for quantifying motor impairment in Parkinson's disease (PD), it is not without limitations, including intra- and inter-rater variability and a degree of approximation. There is increasing evidence supporting the use of objective motion analyses to complement clinician-based assessment. Objective measurement tools hold significant potential for improving the accuracy of clinical and research-based evaluations of patients.

AREAS COVERED

The authors provide several examples from the literature demonstrating how different motion measurement tools, including optoelectronics, contactless and wearable systems allow for both the objective quantification and monitoring of key motor symptoms (such as bradykinesia, rigidity, tremor, and gait disturbances), and the identification of motor fluctuations in PD patients. Furthermore, they discuss how, from a clinician's perspective, objective measurements can help in various stages of PD management.

EXPERT OPINION

In our opinion, sufficient evidence supports the assertion that objective monitoring systems enable accurate evaluation of motor symptoms and complications in PD. A range of devices can be utilized not only to support diagnosis but also to monitor motor symptom during the disease progression and can become relevant in the therapeutic decision-making process.

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

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

The Use of Liquid Sinemet in Routine Clinical Practice of Advanced Parkinson's Disease: A Comparison of Available Options

BACKGROUND

Tablet formulations of Parkinson's disease (PD) medications may become ineffective at managing motor fluctuations in advanced PD. The liquid formulation, levodopa carbidopa ascorbic acid solution, or LCAS, is an effective and inexpensive treatment for motor fluctuations however it remains underutilized.

OBJECTIVE

We compared the efficacy of LCAS with tablet formulations and Duodopa jejunal infusion through routine inpatient management using hourly functional status measures, the Timed Up and Go Test (TUG). The TUG differentiates between 'off' and 'on' states and quantifies motor fluctuations.

METHODS

Experienced nurses used the TUG times and functional observations recorded hourly throughout the waking day to optimize the LCAS hourly dose and the Duodopa flow rate over several days. When patients were stabilized on each of the interventions, the TUG measures were then recorded to compare the outcomes of the interventions.

RESULTS

Twenty-six participants had TUG times recorded while on one or more of the formulations: 19 had TUG times recorded on tablets, 23 on LCAS and 10 on Duodopa. TUG times on LCAS and Duodopa were significantly faster compared to tablets (p <  0.0001, p = 0.001 respectively). Severity of dyskinesia was not significantly different between formulations (p = 0.35). Daily dose for the three formulations and the hourly doses for LCAS and Duodopa did not differ significantly (p = 0.37, p = 0.19 respectively).

CONCLUSION

This report demonstrated the efficacy of LCAS for improving motor complications and its equivalency with Duodopa jejunal infusion.

Feasibility of a wearable inertial sensor to assess motor complications and treatment in Parkinson's disease

BACKGROUND

Wearable sensors-based systems have emerged as a potential tool to continuously monitor Parkinson's Disease (PD) motor features in free-living environments.

OBJECTIVES

To analyse the responsivity of wearable inertial sensor (WIS) measures (On/Off-Time, dyskinesia, freezing of gait (FoG) and gait parameters) after treatment adjustments. We also aim to study the ability of the sensor in the detection of MF, dyskinesia, FoG and the percentage of Off-Time, under ambulatory conditions of use.

METHODS

We conducted an observational, open-label study. PD patients wore a validated WIS (STAT-ONTM) for one week (before treatment), and one week, three months after therapeutic changes. The patients were analyzed into two groups according to whether treatment changes had been indicated or not.

RESULTS

Thirty-nine PD patients were included in the study (PD duration 8 ± 3.5 years). Treatment changes were made in 29 patients (85%). When comparing the two groups (treatment intervention vs no intervention), the WIS detected significant changes in the mean percentage of Off-Time (p = 0.007), the mean percentage of On-Time (p = 0.002), the number of steps (p = 0.008) and the gait fluidity (p = 0.004). The mean percentage of Off-Time among the patients who decreased their Off-Time (79% of patients) was -7.54 ± 5.26. The mean percentage of On-Time among the patients that increased their On-Time (59% of patients) was 8.9 ± 6.46. The Spearman correlation between the mean fluidity of the stride and the UPDRS-III- Factor I was 0.6 (p = <0.001). The system detected motor fluctuations (MF) in thirty-seven patients (95%), whilst dyskinesia and FoG were detected in fifteen (41%), and nine PD patients (23%), respectively. However, the kappa agreement analysis between the UPDRS-IV/clinical interview and the sensor was 0.089 for MF, 0.318 for dyskinesia and 0.481 for FoG.

CONCLUSIONS

It's feasible to use this sensor for monitoring PD treatment under ambulatory conditions. This system could serve as a complementary tool to assess PD motor complications and treatment adjustments, although more studies are required.

Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients

The use of wearable sensors in movement disorder patients such as Parkinson's disease (PD) and normal pressure hydrocephalus (NPH) is becoming more widespread, but most studies are limited to characterizing general aspects of mobility using smartphones. There is a need to accurately identify specific activities at home in order to properly evaluate gait and balance at home, where most falls occur. We developed an activity recognition algorithm to classify multiple daily living activities including high fall risk activities such as sit to stand transfers, turns and near-falls using data from 5 inertial sensors placed on the chest, upper-legs and lower-legs of the subjects. The algorithm is then verified with ground truth by collecting video footage of our patients wearing the sensors at home. Our activity recognition algorithm showed >95% sensitivity in detection of activities. Extracted features from our home monitoring system showed significantly better correlation (~69%) with prospectively measured fall frequency of our subjects compared to the standard clinical tests (~30%) or other quantitative gait metrics used in past studies when attempting to predict future falls over 1 year of prospective follow-up. Although detecting near-falls at home is difficult, our proposed model suggests that near-fall frequency is the most predictive criterion in fall detection through correlation analysis and fitting regression models.

Therapeutic education for empowerment and engagement in patients with Parkinson's disease: A non-pharmacological, interventional, multicentric, randomized controlled trial

Background

In 1997 the European Parkinson's Disease Associations launched the Charter for People with Parkinson's disease that stated the right of patients to be informed and trained on the disease, its course, and treatments available. To date, few data analyzed the effectiveness of education program on motor and non-motor symptoms of PD.

Objective

The aim of this study was to evaluate the efficacy of an education program as it was a pharmacological treatment, thus choosing as the primary endpoint the change in daily OFF hours, the most widely used outcome in pharmaceutical clinical trials on PD patients with motor fluctuations. Secondary outcomes were change in motor and non-motor symptoms, quality of life and social functioning. The long-term efficacy of the education therapy was also evaluated by analyzing data collected at 12- and 24-weeks follow-up outpatient visits.

Methods

One hundred and twenty advanced patients and their caregivers were assigned to the intervention or control group in a single-blind, multicentric, prospective, randomized study evaluating an education program structured in individual and group sessions over a 6-weeks period.At the end of study, the intervention group showed a significant reduction in daily OFF hours compared to control patients (-1.07 ± 0.78 vs. 0.09 ± 0.35, p < 0.0001) and a significant improvement was also reported in most secondary outcomes. Patients retained significant medication adherence and daily OFF hours reduction at 12- and 24-weeks follow-up.

Conclusion

The results obtained demonstrated that education programs may translate in a notable improvement in motor fluctuations and non-motor symptoms in advanced PD patients.Clinical Trial Registration:Clinicaltrials.gov, identifier NCT04378127.

Need for personalized monitoring of Parkinson's disease: the perspectives of patients and specialized healthcare providers

Background

Digital tools such as wearable sensors may help to monitor Parkinson's disease (PD) in daily life. To optimally achieve the expected benefits, such as personized care and improved self-management, it is essential to understand the perspective of both patients and the healthcare providers.

Objectives

We identified the motivations for and barriers against monitoring PD symptoms among PD patients and healthcare providers. We also investigated which aspects of PD were considered most important to monitor in daily life, and which benefits and limitations of wearable sensors were expected.

Methods

Online questionnaires were completed by 434 PD patients and 166 healthcare providers who were specialized in PD care (86 physiotherapists, 55 nurses, and 25 neurologists). To gain further understanding in the main findings, we subsequently conducted homogeneous focus groups with patients (n = 14), physiotherapists (n = 5), and nurses (n = 6), as well as individual interviews with neurologists (n = 5).

Results

One third of the patients had monitored their PD symptoms in the past year, most commonly using a paper diary. Key motivations were: (1) discuss findings with healthcare providers, (2) obtain insight in the effect of medication and other treatments, and (3) follow the progression of the disease. Key barriers were: (1) not wanting to focus too much on having PD, (2) symptoms being relatively stable, and (3) lacking an easy-to-use tool. Prioritized symptoms of interest differed between patients and healthcare providers; patients gave a higher priority to fatigue, problems with fine motor movements and tremor, whereas professionals more frequently prioritized balance, freezing and hallucinations. Although both patients and healthcare providers were generally positive about the potential of wearable sensors for monitoring PD symptoms, the expected benefits and limitations varied considerably between groups and within the patient group.

Conclusion

This study provides detailed information about the perspectives of patients, physiotherapists, nurses and neurologists on the merits of monitoring PD in daily life. The identified priorities differed considerably between patients and professionals, and this information is critical when defining the development and research agenda for the coming years. We also noted considerable differences in priorities between individual patients, highlighting the need for personalized disease monitoring.

Monitoring gait at home with radio waves in Parkinson's disease: A marker of severity, progression, and medication response

Parkinson's disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people's bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients' daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.

Detecting motor symptom fluctuations in Parkinson's disease with generative adversarial networks

Parkinson's disease is a neurodegenerative disorder characterized by several motor symptoms that develop gradually: tremor, bradykinesia, limb rigidity, and gait and balance problems. While there is no cure, levodopa therapy has been shown to mitigate symptoms. A patient on levodopa experiences cycles in the severity of their symptoms, characterized by an ON state-when the drug is active-and an OFF state-when symptoms worsen as the drug wears off. The longitudinal progression of the disease is monitored using episodic assessments performed by trained physicians in the clinic, such as the Unified Parkinson's Disease Rating Scale (UPDRS). Lately, there has been an effort in the field to develop continuous, objective measures of motor symptoms based on wearable sensors and other remote monitoring devices. In this work, we present an effort towards such a solution that uses a single wearable inertial sensor to automatically assess the postural instability and gait disorder (PIGD) of a Parkinson's disease patient. Sensor data was collected from two independent studies of subjects performing the UPDRS test and then used to train and validate a convolutional neural network model. Given the typical limited size of such studies we also employed the use of generative adversarial networks to improve the performance of deep-learning models that usually require larger amounts of data for training. We show that for a 2-min walk test, our method's predicted PIGD scores can be used to identify a patient's ON/OFF states better than a physician evaluated on the same criteria. This result paves the way for more reliable, continuous tracking of Parkinson's disease symptoms.

A Systematic Survey of Research Trends in Technology Usage for Parkinson's Disease

Parkinson's disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist's subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018-2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community's growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications.

Objective Observer vs. Patient Motor State Assessments Using the PD Home Diary in Advanced Parkinson's Disease

Background

The Parkinson Disease (PD) Home Diary (HD) is a commonly used clinical outcome measure, but it has not been extensively compared to direct assessments by experienced observers.

Objective

Validation of patient-reported HD by investigating the agreement between motor state assessments by patients and observers.

Methods

This observational study included patients with PD and motor fluctuations. Observers were physicians or research nurses. Patients completed a screening visit, one day of diary ratings at home, and then two days of ratings on-site during which patients and observers simultaneously judged the participants' motor state.

Results

Observers and 40 patients completed 1,288 pairs of half-hourly blinded motor state assessments. There were significant differences between observer and patient ratings (P &lt; 0.001) and the temporal agreement was poor (Cohen's κ = 0.358). The agreement between patient and observer ratings was 71.1% for observed “On without dyskinesia”, 57.3% for observed “Off”, and 49.4% for observed “On with dyskinesia”. Daily times spent in the three motor states as aggregated diary data showed fair to excellent reliability with intraclass coefficient values ranging from 0.45 to 0.52 for “On” and 0.77 for “Off”.

Conclusion

There were significant differences between observer and patient ratings. Patients and observers generally agreed on when the patients was in the “On” state (with or without dyskinesia). Patient ratings on the hour level seem to be influenced by other aspects of the patients' experience than the observed motor state, but assessment of daily time spent in the different motor state provides reasonable reliability.

Comparison of the Results of a Parkinson's Holter Monitor With Patient Diaries, in Real Conditions of Use: A Sub-analysis of the MoMoPa-EC Clinical Trial

Background

For specialists in charge of Parkinson's disease (PD), one of the most time-consuming tasks of the consultations is the assessment of symptoms and motor fluctuations. This task is complex and is usually based on the information provided by the patients themselves, which in most cases is complex and biased. In recent times, different tools have appeared on the market that allow automatic ambulatory monitoring. The MoMoPa-EC clinical trial (NCT04176302) investigates the effect of one of these tools—Sense4Care's STAT-ON—can have on routine clinical practice. In this sub-analysis the agreement between the Hauser diaries and the STAT-ON sensor is analyzed.

Methods

Eighty four patients from MoMoPa-EC cohort were included in this sub-analysis. The intraclass correlation coefficient was calculated between the patient diary entries and the sensor data.

Results

The intraclass correlation coefficient of both methods was 0.57 (95% CI: 0.3–0.73) for the OFF time (%), 0.48 (95% CI: 0.17–0.68) for the time in ON (%), and 0.65 (95% CI%: 0.44–0.78) for the time with dyskinesias (%). Furthermore, the Spearman correlations with the UPDRS scale have been analyzed for different parameters of the two methods. The maximum correlation found was −0.63 (p < 0.001) between Mean Fluidity (one of the variables offered by the STAT-dON) and factor 1 of the UPDRS.

Conclusion

This sub-analysis shows a moderate concordance between the two tools, it is clearly appreciated that the correlation between the different UPDRS indices is better with the STAT-ON than with the Hauser diary.

Trial Registration

https://clinicaltrials.gov/show/NCT04176302 (NCT04176302).

A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ONTM

In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ONTM, a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ONTM since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ONTM are presented.

Body-Worn Sensors for Parkinson’s disease: A qualitative approach with patients and healthcare professionals

Body-Worn Sensors (BWS) provide reliable objective and continuous assessment of Parkinson’s disease (PD) motor symptoms, but their implementation in clinical routine has not yet become widespread. Users’ perceptions of BWS have not been explored. This study intended to evaluate the usability, user experience (UX), patients’ perceptions of BWS, and health professionals’ (HP) opinions on BWS monitoring. A qualitative analysis was performed from semi-structured interviews conducted with 22 patients and 9 HP experts in PD. Patients completed two interviews before and after the BWS one-week experiment, and they answered two questionnaires assessing the usability and UX. Patients rated the three BWS usability with high scores (SUS median [range]: 87.5 [72.5–100]). The UX across all dimensions of their interaction with the BWS was positive. During interviews, all patients and HP expressed interest in BWS monitoring. Patients’ hopes and expectations increased the more they learned about BWS. They manifested enthusiasm to wear BWS, which they imagined could improve their PD symptoms. HP highlighted needs for logistical support in the implementation of BWS in their practice. Both patients and HP suggested possible uses of BWS monitoring in clinical practice, for treatment adjustments for example, or for research purposes. Patients and HP shared ideas about the use of BWS monitoring, although patients may be more likely to integrate BWS into their disease follow-up compared to HP in their practice. This study highlights gaps that need to be fulfilled to facilitate BWS adoption and promote their potential.

Molecular Mechanisms and Therapeutic Strategies for Levodopa-Induced Dyskinesia in Parkinson's Disease: A Perspective Through Preclinical and Clinical Evidence

Parkinson's disease (PD) is the second leading neurodegenerative disease that is characterized by severe locomotor abnormalities. Levodopa (L-DOPA) treatment has been considered a mainstay for the management of PD; however, its prolonged treatment is often associated with abnormal involuntary movements and results in L-DOPA-induced dyskinesia (LID). Although LID is encountered after chronic administration of L-DOPA, the appearance of dyskinesia after weeks or months of the L-DOPA treatment has complicated our understanding of its pathogenesis. Pathophysiology of LID is mainly associated with alteration of direct and indirect pathways of the cortico-basal ganglia-thalamic loop, which regulates normal fine motor movements. Hypersensitivity of dopamine receptors has been involved in the development of LID; moreover, these symptoms are worsened by concurrent non-dopaminergic innervations including glutamatergic, serotonergic, and peptidergic neurotransmission. The present study is focused on discussing the recent updates in molecular mechanisms and therapeutic approaches for the effective management of LID in PD patients.

The impact of wearable electronics in assessing the effectiveness of levodopa treatment in Parkinsons disease

OBJECTIVE

In order to evaluate Parkinson disease patients response to therapeutic interventions, sources of information are mainly patient reports and clinicians assessment of motor functions. However, these sources can suffer from patients subjectivity and from inter/intra raters score variability. Our work aimed at determining the impact of wearable electronics and data analysis in objectifying the effectiveness of levodopa treatment.

METHODS

Seven motor tasks performed by thirty-six patients were measured by wearable electronics and related data were analyzed. This was at the time of therapy initiation (T0), and repeated after six (T1) and 12 months (T2). Wearable electronics consisted of inertial measurement units each equipped with 3-axis accelerometer and 3-axis gyroscope, while data analysis of ANOVA and Pearson correlation algorithms, in addition to a support vector machine (SVM) classification.

RESULTS

According to our findings, levodopa-based therapy alters the patients conditions in general, ameliorating something (e.g. bradykinesia), leaving unchanged others (e.g. tremor), but with poor correlation to the levodopa dose.

CONCLUSION

A technology-based approach can objectively assess levodopa-based therapy effectiveness.

SIGNIFICANCE

Novel devices can improve the accuracy of the assessment of motor function, by integrating the clinical evaluation and patient reports.

Assessing inertial measurement unit locations for freezing of gait detection and patient preference

Background

Freezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference.

Methods

Sixteen people with Parkinson’s disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson’s disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events.

Results

The best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86) for the best technical set and minimal IMU set, respectively.

Conclusions

Several IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the development and adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-00992-x.

Cost-effectiveness analysis of the Parkinson's KinetiGraph and clinical assessment in the management of Parkinson's disease

AIMS

The Parkinson's KinetiGraph (PKG) is a wrist-worn movement recording system that collates continuous, objective, data during daily activities in people with Parkinson's disease (PD) providing a report for clinicians. This study explores the cost-effectiveness of adding the PKG to routine PD assessments.

METHODS

A de novo Markov model of three health states: uncontrolled, controlled and death compared PKG plus routine assessment by a Movement Disease Specialist (MDS) versus routine assessment. Uncontrolled and controlled states were based on the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS) II and III scores. The transition between health states was dependent on improvement in MDS-UPDRS II and III, and transition to death state on all cause-mortality and PD-specific relative mortality risk. Markov cycle length was yearly beyond year 1 and lifetime horizon 22 years.

LIMITATIONS

PKG evidence incorporated in this analysis is based on findings from one clinical trial. Health state utilities were mapped and the probability of patients progressing from uncontrolled to controlled health state at the second visit and beyond was derived from a bootstrap method which assumed a normal distribution for MDS-UPDRS.

RESULTS

The addition of the PKG to usual PD assessments is a cost-effective intervention. PKG plus routine assessment is associated with lower total costs compared to routine assessment (£141,950 versus £159,312) and improved quality-adjusted life years (7.88 versus 7.61), resulting in an incremental cost-effectiveness ratio of -£64,978.99 and a net monetary benefit of £22,706.37 using a £20,000 threshold. Results were robust across sensitivity and scenario analyses.

CONCLUSIONS

Management of PD involves monitoring and evaluation of symptoms to assess disease progression and ensure appropriate treatment choices. Adding the PKG to clinical assessment in routine care allows for improved and objective identification of PD motor symptoms which can be used in clinical decision making to improve patient outcomes.

Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer

Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.

Adaptive, personalized closed-loop therapy for Parkinson's disease: biochemical, neurophysiological, and wearable sensing systems

INTRODUCTION

Motor complication management is one of the main unmet needs in Parkinson's disease patients.

AREAS COVERED

Among the most promising emerging approaches for handling motor complications in Parkinson's disease, adaptive deep brain stimulation strategies operating in closed-loop have emerged as pivotal to deliver sustained, near-to-physiological inputs to dysfunctional basal ganglia-cortical circuits over time. Existing sensing systems that can provide feedback signals to close the loop include biochemical-, neurophysiological- or wearable-sensors. Biochemical sensing allows to directly monitor the pharmacokinetic and pharmacodynamic of antiparkinsonian drugs and metabolites. Neurophysiological sensing relies on neurotechnologies to sense cortical or subcortical brain activity and extract real-time correlates of symptom intensity or symptom control during DBS. A more direct representation of the symptom state, particularly the phenomenological differentiation and quantification of motor symptoms, can be realized via wearable sensor technology.

EXPERT OPINION

Biochemical, neurophysiologic, and wearable-based biomarkers are promising technological tools that either individually or in combination could guide adaptive therapy for Parkinson's disease motor symptoms in the future.

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.

Comparison of the Parkinson's KinetiGraph to off/on levodopa response testing: Single center experience

BACKGROUND AND OBJECTIVE

Levodopa off/on testing is frequently performed to assess medication response in patients with Parkinson's disease (PD) as an aid in determining best medical management or potential surgical candidacy. The Parkinson's Kinetigraph (PKG) is a wearable device which generates tremor, bradykinesia (BKS) and dyskinesia (DKS) scores representing motor symptoms over a six-day period. In this study, we compared off/on testing with PKG motor scores.

METHODS

Patients were enrolled as part of an observational study: Assessing the Longitudinal Signs in PD, a three-year study evaluating clinical and biomarker evolution in patients with PD taking levodopa. Patients underwent off/on testing at baseline and 6-month visits. A greater than 30% improvement between off and on MDS-Unified Parkinson's Disease Rating Scale scores was considered a robust response. After each visit, patients wore the PKG for 6 days. A bradykinesia score (BKS) greater than 26 and dyskinesia score (DKS) greater than 9 were considered poorly controlled bradykinesia and dyskinesia, respectively.

RESULTS

The median BKS at the baseline and 6-month visits were 27.15 and 27.55, respectively, despite a robust median off/on improvement at both visits. In addition, 10/18 (66%) and 7/13 (53.8%) patients with robust off/on improvement at the baseline and 6-month visits, respectively, demonstrated a BKS > 26 or DKS > 9.

CONCLUSION

A robust off/on response during a clinic visit does not necessarily reflect adequately controlled motor symptoms. The PKG, by virtue of its continuous recording of motor movements, may provide additional clinically relevant data on motor symptoms which may be useful for prospective observational studies.

Multicentre, randomised, single-blind, parallel group trial to compare the effectiveness of a Holter for Parkinson's symptoms against other clinical monitoring methods: study protocol

INTRODUCTION

In recent years, multiple studies have aimed to develop and validate portable technological devices capable of monitoring the motor complications of Parkinson's disease patients (Parkinson's Holter). The effectiveness of these monitoring devices for improving clinical control is not known.

METHODS AND ANALYSIS

This is a single-blind, cluster-randomised controlled clinical trial. Neurologists from Spanish health centres will be randomly assigned to one of three study arms (1:1:1): (a) therapeutic adjustment using information from a Parkinson's Holter that will be worn by their patients for 7 days, (b) therapeutic adjustment using information from a diary of motor fluctuations that will be completed by their patients for 7 days and (c) therapeutic adjustment using clinical information collected during consultation. It is expected that 162 consecutive patients will be included over a period of 6 months.The primary outcome is the efficiency of the Parkinson's Holter compared with traditional clinical practice in terms of Off time reduction with respect to the baseline (recorded through a diary of motor fluctuations, which will be completed by all patients). As secondary outcomes, changes in variables related to other motor complications (dyskinesia and freezing of gait), quality of life, autonomy in activities of daily living, adherence to the monitoring system and number of doctor-patient contacts will be analysed. The noninferiority of the Parkinson's Holter against the diary of motor fluctuations in terms of Off time reduction will be studied as the exploratory objective.Ethics and dissemination approval for this study has been obtained from the Hospital Universitari de Bellvitge Ethics Committee. The results of this study will inform the practical utility of the objective information provided by a Parkinson's Holter and, therefore, the convenience of adopting this technology in clinical practice and in future clinical trials. We expect public dissemination of the results in 2022.

TRIAL REGISTRATION

NCT04176302; https://clinicaltrials.gov/show/NCT04176302.

Parkinson's Disease: Personalized Pathway of Care for Device-Aided Therapies (DAT) and the Role of Continuous Objective Monitoring (COM) Using Wearable Sensors

Parkinson’s disease (PD) is a chronic, progressive neurological disorder and the second most common neurodegenerative condition. Advanced PD is complicated by erratic gastric absorption, delayed gastric emptying in turn causing medication overload, and hence the emergence of motor and non-motor fluctuations and dyskinesia, which is initially predictable and then becomes unpredictable. As the patient progresses to the advanced stage, advanced Parkinson’s disease (APD) is characterized by refractory motor and non motor fluctuations, unpredictable OFF periods, and troublesome dyskinesias. The management of APD is a complex affair. There is growing recognition that GI dysfunction is common in PD, with virtually the entire GI system (the upper and lower GI tracts) causing problems from dribbling to defecation. The management of PD should focus on personalized care addressing both motor and non-motor symptoms, ideally including not only dopamine replacement but also associated non-dopaminergic circuits, particularly focusing on noradrenergic, serotonergic, and cholinergic therapies bypassing the gastrointestinal tract (GIT) by infusion or device-aided therapies (DAT), including levodopa–carbidopa intestinal gel infusion, apomorphine subcutaneous infusion, and deep brain stimulation, which are available in many countries for the management of the advanced stage of Parkinson’s disease (APD). The PKG (KinetiGrap) can be used as a continuous objective monitoring (COM) aid, as a screening tool to help to identify advanced PD (APD) patients suitable for DAT, and can thus improve clinical outcomes.

A Long-Term, Real-Life Parkinson Monitoring Database Combining Unscripted Objective and Subjective Recordings

Accurate real-life monitoring of motor and non-motor symptoms is a challenge in Parkinson’s disease (PD). The unobtrusive capturing of symptoms and their naturalistic fluctuations within or between days can improve evaluation and titration of therapy. First-generation commercial PD motion sensors are promising to augment clinical decision-making in general neurological consultation, but concerns remain regarding their short-term validity, and long-term real-life usability. In addition, tools monitoring real-life subjective experiences of motor and non-motor symptoms are lacking. The dataset presented in this paper constitutes a combination of objective kinematic data and subjective experiential data, recorded parallel to each other in a naturalistic, long-term real-life setting. The objective data consists of accelerometer and gyroscope data, and the subjective data consists of data from ecological momentary assessments. Twenty PD patients were monitored without daily life restrictions for fourteen consecutive days. The two types of data can be used to address hypotheses on naturalistic motor and/or non-motor symptomatology in PD.

Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson's disease

Longitudinal, remote monitoring of motor symptoms in Parkinson's disease (PD) could enable more precise treatment decisions. We developed the Motor fluctuations Monitor for Parkinson's Disease (MM4PD), an ambulatory monitoring system that used smartwatch inertial sensors to continuously track fluctuations in resting tremor and dyskinesia. We designed and validated MM4PD in 343 participants with PD, including a longitudinal study of up to 6 months in a 225-subject cohort. MM4PD measurements correlated to clinical evaluations of tremor severity (ρ = 0.80) and mapped to expert ratings of dyskinesia presence (P < 0.001) during in-clinic tasks. MM4PD captured symptom changes in response to treatment that matched the clinician's expectations in 94% of evaluated subjects. In the remaining 6% of cases, symptom data from MM4PD identified opportunities to make improvements in pharmacologic strategy. These results demonstrate the promise of MM4PD as a tool to support patient-clinician communication, medication titration, and clinical trial design.

Continuous Subcutaneous Levodopa Delivery for Parkinson's Disease: A Randomized Study

Background:

ND0612 is a continuous, subcutaneous levodopa/carbidopa delivery system in development for patients with Parkinson’s disease (PD) experiencing motor fluctuations

Objective:

Evaluate the efficacy and safety of two ND0612 dosing regimens in patients with PD.

Methods:

This was a 28-day open-label study (NCT02577523) in PD patients with ≥2.5 hours/day of OFF time despite optimized treatment. Patients were randomized to treatment with either a 24-hour infusion (levodopa/carbidopa dose of 720/90 mg) or a 14-hour ‘waking-day’ infusion (levodopa/carbidopa dose of 538/68 mg plus a morning oral dose of 150/15 mg). Supplemental oral doses of levodopa were permitted for patients in both groups if required. In-clinic assessments of OFF time (primary endpoint) and ON time with or without dyskinesia were determined by a blinded rater over 8 hours (normalized to 16 hours).

Results:

A total of 38 patients were randomized and 33 (87%) completed the study. Compared to baseline, OFF time for the overall population was reduced by a least squares (LS) mean[95% CI] of 2.0[– 3.3, – 0.7] hours (p = 0.003). ON time with no/mild dyskinesia (no troublesome dyskinesia) was increased from baseline by a LS mean of 3.3[2.0, 4.6] hours (p < 0.0001), and ON time with moderate/severe dyskinesia was reduced by a LS mean of 1.2[– 1.8, – 0.5] hours (p≤0.001). Reduction in OFF time was larger in the 24-hour group (– 2.8[– 4.6, – 0.9] hours; p = 0.004) than in the 14-hour group (– 1.3[– 3.1, 0.5] hours; p = 0.16). Complete resolution of OFF time was observed in 42% (n = 8) of patients in the 24-hour group. Infusion site reactions were the most common adverse event.

Conclusion:

This study demonstrates the feasibility and safety of continuous subcutaneous delivery of levodopa as a treatment for PD and provides preliminary evidence of efficacy.

Comparing Objective and Subjective Measures of Parkinson's Disease Using the Parkinson's KinetiGraph

Background: Parkinson's disease (PD) is a neurodegenerative disease that can lead to impaired motor function and execution of activities of daily living (ADL). Since clinicians typically can only observe patients' symptoms during visits, prescribed medication schedules may not reflect the full range of symptoms experienced throughout the day. Therefore, objective tools are needed to provide comprehensive symptom data to optimize treatment. One such tool is the Parkinson's KinetiGraph^® (PKG), a wearable sensor that measures motor symptoms of Parkinson's disease.

Objective: To build a mathematical model to determine if PKG data measuring Parkinson's patients' motor symptoms can predict patients' ADL impairment.

Methods: Thirty-four patients with PD wore the PKG device for 6 days while performing their ADL. Patients' PKG scores for bradykinesia and dyskinesia, as well as their responses to a questionnaire asking if their ADL-level had been impacted by various motor symptoms, were used to build a multiple regression model predicting the patients' Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part II scores.

Results: Calculation of bradykinesia score response to medication showed that using a dosage response time of 30 min yielded a greater bradykinesia response than when the response time was set to 40, 50, 60, 70, 80, or 90 min. The overall multiple regression model predicting MDS-UPDRS part II score was significant (R² = 0.546, p < 0.001).

Conclusion: The PKG's ability to provide motor symptom data that correlates with clinical measures of ADL impairment suggests that it has strong potential as a tool for the assessment and management of Parkinson's disease motor symptoms.

Technology-Enabled Care: Integrating Multidisciplinary Care in Parkinson's Disease Through Digital Technology

Parkinson's disease (PD) management requires the involvement of movement disorders experts, other medical specialists, and allied health professionals. Traditionally, multispecialty care has been implemented in the form of a multidisciplinary center, with an inconsistent clinical benefit and health economic impact. With the current capabilities of digital technologies, multispecialty care can be reshaped to reach a broader community of people with PD in their home and community. Digital technologies have the potential to connect patients with the care team beyond the traditional sparse clinical visit, fostering care continuity and accessibility. For example, video conferencing systems can enable the remote delivery of multispecialty care. With big data analyses, wearable and non-wearable technologies using artificial intelligence can enable the remote assessment of patients' conditions in their natural home environment, promoting a more comprehensive clinical evaluation and empowering patients to monitor their disease. These advances have been defined as technology-enabled care (TEC). We present examples of TEC under development and describe the potential challenges to achieve a full integration of technology to address complex care needs in PD.

Associations Between Fluctuations in Daytime Sleepiness and Motor and Non-Motor Symptoms in Parkinson's Disease

Background

Non‐motor fluctuations are a major concern in Parkinson's disease (PD), and they have been categorized into neuropsychiatric, autonomic and sensory fluctuations. However, this categorization does not include sleep and sleep‐related features, and the association between daytime sleepiness and other motor and/or non‐motor fluctuations in PD remains to be elucidated.

Objective

To investigate the relationship between daytime sleepiness and other non‐motor and motor fluctuations in people with PD.

Methods

A three‐day home diary recording daytime sleepiness, mood, anxiety, and motor symptoms was used along with the Karolinska Sleepiness Scale (KSS) and 6 days of accelerometer (Parkinson's KinetiGraph™; PKG™) registration to detect motor fluctuations among people with a DaTSCAN verified clinical PD diagnosis (32 men; mean PD duration, 8.2 years). Participants were categorized as motor fluctuators or non‐fluctuators according to the UPDRS part IV and/or the presence of motor and non‐motor fluctuations.

Results

Fifty‐two people with PD participated. Daytime sleepiness correlated significantly with motor symptoms, mood and anxiety among those classified as motor fluctuators (n = 28). Motor fluctuators showed stronger correlations between the individual mean level of all diary variables (daytime sleepiness, anxiety, mood and motor symptoms) when compared to the non‐fluctuators (n = 24). Stronger positive within‐individual correlations were found among fluctuators in comparison to non‐fluctuators. In general, PKG data did not correlate with diary data.

Conclusion

Episodes of daytime sleepiness, as reported by home diaries, were associated with other self‐reported non‐motor and motor fluctuations, but were not supported by PKG data.

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

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

Recommendations for Developing Support Tools With People Suffering From Chronic Obstructive Pulmonary Disease: Co-Design and Pilot Testing of a Mobile Health Prototype

BACKGROUND

Gaps exist between developers, commissioners, and end users in terms of the perceived desirability of different features and functionalities of mobile apps.

OBJECTIVE

The objective of this study was to co-design a prototype mobile app for people with chronic obstructive pulmonary disease (COPD). We present lessons learned and recommendations from working on a large project with various stakeholders to develop a mobile app for patients with COPD.

METHODS

We adopted a user-centered, participatory approach to app development. Following a series of focus groups and interviews to capture requirements, we developed a prototype app designed to enable daily symptom recording (experience sampling). The prototype was tested in a usability study applying the think aloud protocol with people with COPD. It was then released via the Android app store, and experience sampling data and event data were captured to gather further usability data.

RESULTS

A total of 5 people with COPD participated in the pilot study. Identified themes include familiarity with technology, appropriate levels for feeding back information, and usability issues such as manual dexterity. Moreover, 37 participants used the app over a 4-month period (median age 47 years). The symptoms most correlated to perceived well-being were tiredness (r=0.61; P<.001) and breathlessness (r=0.59; P<.001).

CONCLUSIONS

Design implications for COPD apps include the need for clearly labeled features (rather than relying on colors or symbols that require experience using smartphones), providing weather information, and using the same terminology as health care professionals (rather than simply lay terms). Target users, researchers, and developers should be involved at every stage of app development, using an iterative approach to build a prototype app, which should then be tested in controlled settings as well as in the wild (ie, when deployed and used in real-world settings) over longer periods.