Accurate Monitoring of Parkinson's Disease Symptoms With a Wearable Device During COVID-19 Pandemic

Empowering the management of early-onset Parkinsons' disease: The role of technology

Early-onset Parkinson's disease (EOPD) is defined as PD with an age of onset after 21 years of age but before 50 years. It displays many important differences to late-onset PD in terms of its pathology, phenotype, presentation and disease course, all of which have consequences for achieving a definitive diagnosis, the choice of therapy and approach to management. Studies show that this younger population is keen to embrace digital technologies as part of PD care, being familiar with using digital tools in their daily lives. Although most of the literature relating to the use of technology in PD applies to the broad population, this review focuses on evidence and potential benefits of the use of digital technologies to support clinical management in EOPD as well as its value in empowering patients to achieve self-management and in improving their quality of life. Digital technologies also have important and increasing roles in providing telehealth, including rehabilitation strategies for motor and non-motor PD symptoms. EOPD is known to be associated with a higher risk of motor fluctuations, so technologies such as wearable sensors have a valuable role for monitoring symptoms, providing timely feedback, and informing treatment decisions. In addition, digital technologies allow easy provision and equitable access to education and networking opportunities that will enable patients to have a better understanding of their condition.

Devices for remote continuous monitoring of people with Parkinson's disease: a systematic review and cost-effectiveness analysis

Background

Parkinson's disease is a brain condition causing a progressive loss of co ordination and movement problems. Around 145,500 people have Parkinson's disease in the United Kingdom. Levodopa is the most prescribed treatment for managing motor symptoms in the early stages. Patients should be monitored by a specialist every 6-12 months for disease progression and treatment of adverse effects. Wearable devices may provide a novel approach to management by directly monitoring patients for bradykinesia, dyskinesia, tremor and other symptoms. They are intended to be used alongside clinical judgement.

Objectives

To determine the clinical and cost-effectiveness of five devices for monitoring Parkinson's disease: Personal KinetiGraph, Kinesia 360, KinesiaU, PDMonitor and STAT-ON.

Methods

We performed systematic reviews of all evidence on the five devices, outcomes included: diagnostic accuracy, impact on decision-making, clinical outcomes, patient and clinician opinions and economic outcomes. We searched MEDLINE and 12 other databases/trial registries to February 2022. Risk of bias was assessed. Narrative synthesis was used to summarise all identified evidence, as the evidence was insufficient for meta-analysis. One included trial provided individual-level data, which was re-analysed. A de novo decision-analytic model was developed to estimate the cost-effectiveness of Personal KinetiGraph and Kinesia 360 compared to standard of care in the UK NHS over a 5-year time horizon. The base-case analysis considered two alternative monitoring strategies: one-time use and routine use of the device.

Results

Fifty-seven studies of Personal KinetiGraph, 15 of STAT-ON, 3 of Kinesia 360, 1 of KinesiaU and 1 of PDMonitor were included. There was some evidence to suggest that Personal KinetiGraph can accurately measure bradykinesia and dyskinesia, leading to treatment modification in some patients, and a possible improvement in clinical outcomes when measured using the Unified Parkinson's Disease Rating Scale. The evidence for STAT-ON suggested it may be of value for diagnosing symptoms, but there is currently no evidence on its clinical impact. The evidence for Kinesia 360, KinesiaU and PDMonitor is insufficient to draw any conclusions on their value in clinical practice. The base-case results for Personal KinetiGraph compared to standard of care for one-time and routine use resulted in incremental cost-effectiveness ratios of £67,856 and £57,877 per quality-adjusted life-year gained, respectively, with a beneficial impact of the Personal KinetiGraph on Unified Parkinson's Disease Rating Scale domains III and IV. The incremental cost-effectiveness ratio results for Kinesia 360 compared to standard of care for one-time and routine use were £38,828 and £67,203 per quality-adjusted life-year gained, respectively.

Limitations

The evidence was limited in extent and often low quality. For all devices, except Personal KinetiGraph, there was little to no evidence on the clinical impact of the technology.

Conclusions

Personal KinetiGraph could reasonably be used in practice to monitor patient symptoms and modify treatment where required. There is too little evidence on STAT-ON, Kinesia 360, KinesiaU or PDMonitor to be confident that they are clinically useful. The cost-effectiveness of remote monitoring appears to be largely unfavourable with incremental cost-effectiveness ratios in excess of £30,000 per quality-adjusted life-year across a range of alternative assumptions. The main driver of cost-effectiveness was the durability of improvements in patient symptoms.

Study registration

This study is registered as PROSPERO CRD42022308597.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135437) and is published in full in Health Technology Assessment; Vol. 28, No. 30. See the NIHR Funding and Awards website for further award information.

The leading global health challenges in the artificial intelligence era

Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.

A Paradigm Shift in the Management of Patients with Parkinson's Disease

BACKGROUND

Technological evolution leads to the constant enhancement of monitoring systems and recording symptoms of diverse disorders.

SUMMARY

For Parkinson's disease, wearable devices empowered with machine learning analysis are the main modules for objective measurements. Software and hardware improvements have led to the development of reliable systems that can detect symptoms accurately and be implicated in the follow-up and treatment decisions.

KEY MESSAGES

Among many different devices developed so far, the most promising ones are those that can record symptoms from all extremities and the trunk, in the home environment during the activities of daily living, assess gait impairment accurately, and be suitable for a long-term follow-up of the patients. Such wearable systems pave the way for a paradigm shift in the management of patients with Parkinson's disease.

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

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

Evaluating Gait Impairment in Parkinson's Disease from Instrumented Insole and IMU Sensor Data

Parkinson's disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients' mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait features, independently, from the two aforementioned systems. Subsequently, subsets comprised of the extracted features, were used by machine learning algorithms for gait impairment assessment. The results indicated that insole gait kinematic features were highly correlated with those extracted from IMU-based devices. Moreover, both had the capacity to train accurate machine learning models for the detection of PD gait impairment.

Clinical Evaluation in Parkinson's Disease: Is the Golden Standard Shiny Enough?

Parkinson's disease (PD) has become the second most common neurodegenerative condition following Alzheimer's disease (AD), exhibiting high prevalence and incident rates. Current care strategies for PD patients include brief appointments, which are sparsely allocated, at outpatient clinics, where, in the best case scenario, expert neurologists evaluate disease progression using established rating scales and patient-reported questionnaires, which have interpretability issues and are subject to recall bias. In this context, artificial-intelligence-driven telehealth solutions, such as wearable devices, have the potential to improve patient care and support physicians to manage PD more effectively by monitoring patients in their familiar environment in an objective manner. In this study, we evaluate the validity of in-office clinical assessment using the MDS-UPDRS rating scale compared to home monitoring. Elaborating the results for 20 patients with Parkinson's disease, we observed moderate to strong correlations for most symptoms (bradykinesia, rest tremor, gait impairment, and freezing of gait), as well as for fluctuating conditions (dyskinesia and OFF). In addition, we identified for the first time the existence of an index capable of remotely measuring patients' quality of life. In summary, an in-office examination is only partially representative of most PD symptoms and cannot accurately capture daytime fluctuations and patients' quality of life.

Barriers to home exercise for patients with Parkinson's disease: a qualitative study

OBJECTIVE

This study aimed to explore the barriers to home exercise for patients with Parkinson's disease (PwPDs) and to provide guidelines for healthcare providers to build and implement home exercise strategies for PwPDs.

DESIGN

A qualitative descriptive method was used. Semistructured interviews were conducted and thematic analysis was employed.

SETTING

The study was conducted at the Department of Neurology at a grade 3 Class A general hospital in China.

PARTICIPANTS

A total of 24 participants were interviewed, including 10 PwPDs, 7 caregivers, 4 nurses, 1head nurse, and 2 Parkinson's clinicians.

RESULTS

Five themes were identified in this analysis. (1) Psychosomatic stress and low activity; (2) Lack of early rehabilitation authorisation; (3) Poor 'flow' state of home exercise; (4) iInaccessibility of continued service; (5) Sociocultural impact on family coping.

CONCLUSION

PwPDs, caregivers and specialised medical staff raised the challenges faced by patients' home exercises from different perspectives. We can improve services and integrate resources through the management of multi-disciplinary, early rehabilitation authorisation, exercise experience, continuous service mode, and family coping strategies under different cultures to gradually adjust the home exercise behaviour of PwPDs.

Artificial intelligence in elderly healthcare: A scoping review

The ageing population has led to a surge in the adoption of artificial intelligence (AI) technologies in elderly healthcare worldwide. However, in the advancement of AI technologies, there is currently a lack of clarity about the types and roles of AI technologies in elderly healthcare. This scoping review aimed to provide a comprehensive overview of AI technologies in elderly healthcare by exploring the types of AI technologies employed, and identifying their roles in elderly healthcare based on existing studies. A total of 10 databases were searched for this review, from January 1 2000 to July 31 2022. Based on the inclusion criteria, 105 studies were included. The AI devices utilized in elderly healthcare were summarised as robots, exoskeleton devices, intelligent homes, AI-enabled health smart applications and wearables, voice-activated devices, and virtual reality. Five roles of AI technologies were identified: rehabilitation therapists, emotional supporters, social facilitators, supervisors, and cognitive promoters. Results showed that the impact of AI technologies on elderly healthcare is promising and that AI technologies are capable of satisfying the unmet care needs of older adults and demonstrating great potential in its further development in this area. More well-designed randomised controlled trials are needed in the future to validate the roles of AI technologies in elderly healthcare.

Movement disorders in coronavirus disease 2019 times: impact on care in movement disorders and Parkinson disease

PURPOSE OF REVIEW

The purpose of this review is to outline the impact of the COVID-19 pandemic on movement disorder holistic care, particularly in the care of people with Parkinson disease (PWP).

RECENT FINDINGS

As the pandemic unfolds, a flurry of literature was published regarding the impact of COVID-19 on people with Parkinson disease including the direct impact of infection, availability of ambulatory care, loss of community-based team care, and acceptability of telemedicine.

SUMMARY

COVID-19 has impacted the care of PWP in numerous ways. Recognizing infection in PWP poses challenges. Specific long-term complications, including emerging reports of long COVID syndrome is a growing concern. Caregivers and PWP have also been impacted by COVID-19 social isolation restrictions, with radical changes to the structure of social networks and support systems globally. In a matter of weeks, the global community saw an incredible uptake in telemedicine, which brought with benefits and pitfalls. As PWP adapted to virtual platforms and the changing architecture of care delivery, the pandemic amplified many preexisting inequities amongst populations and countries, exposing a new 'digital divide'.