A Viewpoint on Wearable Technology-Enabled Measurement of Wellbeing and Health-Related Quality of Life in Parkinson's Disease

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.

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.

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

Introduction

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

Methods

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

Results

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

Conclusion

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

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

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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

International Registered Report Identifier (IRRID)

RR2-10.1136/bmjopen-2020-041303

Parkinson's Disease and Wearable Technology: An Indian Perspective

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder. In India, an accurate number of PD patients remains uncertain owing to the unawareness of PD symptoms in the geriatric population and the large discrepancy between the number of PD patients and trained neurologists. Constructing additional neurological care centers along with using technology and integrating it into digital healthcare platforms will help reduce this burden. Use of technology in PD diagnosis and monitoring started in 1980s with invasive techniques performed in laboratories. Over the last five decades, PD technology has significantly evolved where now patients can track symptoms using their smartphones or wearable sensors. However, the use of such technology within the Indian population is non-existent primarily due to the cost of digital devices and limited technological capabilities of geriatric patients especially in rural areas. Other reasons include secure data transfers from patients to physicians and the general lack of awareness of wearables devices. Thus, creating a simple, cost-effective and inconspicuous wearable device would yield the highest compliance within the Indian PD patient population. Implementation of such technology will provide neurologists with wider outreach to patients in rural locations, remote monitoring and empirical data to titrate medication.

Wearables for Neurologic Conditions: Considerations for Our Patients and Research Limitations

Purpose of Review

In 2019, over 50 million Americans were expected to use wearables at least monthly. The technologies have varied capabilities, with many designed to monitor health conditions. We present a narrative review to raise awareness of wearable technologies that may be relevant to the field of neurology. We also discuss the implications of these wearables for our patients and briefly discuss issues related to researching new wearable technologies.

Recent Findings

There are a variety of wearables for neurologic conditions, e.g., stroke (for potential arrhythmia capture), epilepsy, Parkinson disease, and sleep. Research is being performed to capture the risk of neuropsychiatric relapse. However, data are limited and adherence to these wearables is often poorly studied.

Summary

The care of neurology patients may ultimately be improved with the use of wearable technologies. More research needs to examine efficacy and implementation strategies.

Critical Success Factors in Adoption of Wearable Technology Devices for Seniors in Thailand

The adoption of wearable devices has been proposed as a promising approach to improve the well-being of senior in Thai care services. This study aims to find the critical success factors (CSFs) in adopting wearable technology from the stakeholder perspective. We collected data from total 27 participants from three groups of stakeholders: formal caregivers, informal caregivers, and seniors. Using the grounded theory approach, we found four types of CSFs and its variety in according to devices and stakeholders’ viewpoints. Based on the findings, this paper also discusses how to develop wearable devices to satisfy stakeholder requirements and improve their wellbeing.

Protocol for PD SENSORS: Parkinson's Disease Symptom Evaluation in a Naturalistic Setting producing Outcome measuRes using SPHERE technology. An observational feasibility study of multi-modal multi-sensor technology to measure symptoms and activities of daily living in Parkinson's disease

INTRODUCTION

The impact of disease-modifying agents on disease progression in Parkinson's disease is largely assessed in clinical trials using clinical rating scales. These scales have drawbacks in terms of their ability to capture the fluctuating nature of symptoms while living in a naturalistic environment. The SPHERE (Sensor Platform for HEalthcare in a Residential Environment) project has designed a multi-sensor platform with multimodal devices designed to allow continuous, relatively inexpensive, unobtrusive sensing of motor, non-motor and activities of daily living metrics in a home or a home-like environment. The aim of this study is to evaluate how the SPHERE technology can measure aspects of Parkinson's disease.

METHODS AND ANALYSIS

This is a small-scale feasibility and acceptability study during which 12 pairs of participants (comprising a person with Parkinson's and a healthy control participant) will stay and live freely for 5 days in a home-like environment embedded with SPHERE technology including environmental, appliance monitoring, wrist-worn accelerometry and camera sensors. These data will be collected alongside clinical rating scales, participant diary entries and expert clinician annotations of colour video images. Machine learning will be used to look for a signal to discriminate between Parkinson's disease and control, and between Parkinson's disease symptoms 'on' and 'off' medications. Additional outcome measures including bradykinesia, activity level, sleep parameters and some activities of daily living will be explored. Acceptability of the technology will be evaluated qualitatively using semi-structured interviews.

ETHICS AND DISSEMINATION

Ethical approval has been given to commence this study; the results will be disseminated as widely as appropriate.

[Care of patients with Parkinson's disease in Germany]

In Germany various concepts for treating patients with Parkinson's disease (PD) are available, e.g. oral medication with levodopa or deep brain stimulation (DBS), depending on the stage and severity of symptoms and also multidisciplinary management up to intersectoral treatment approaches (e.g. complex PD treatment and integrative care concepts). Nevertheless, in the treatment of patients with PD a comprehensive provision of services and a nationwide standardized collation of treatment quality are so far lacking. This is particularly true for technically complicated procedures, which necessitate a high standard of expertise by the treating physician. Some of these challenges could be overcome by expanding digital approaches (e.g. teleneurological consultation and wearables) and by introducing quality assurance initiatives (e.g. comprehensive registries and certification programs).

Levodopa improves handwriting and instrumental tasks in previously treated patients with Parkinson's disease

Motor symptoms in patients with Parkinson's disease may be determined with instrumental tests and rating procedures. Their outcomes reflect the functioning and the impairment of the individual patient when patients are tested off and on dopamine substituting drugs. Objectives were to investigate whether the execution speed of a handwriting task, instrumentally assessed fine motor behavior, and rating scores improve after soluble levodopa application. 38 right-handed patients were taken off their regular drug therapy for at least 12 h before scoring, handwriting, and performance of instrumental devices before and 1 h after 100 mg levodopa intake. The outcomes of all performed procedures improved. The easy-to-perform handwriting task and the instrumental tests demand for fast and precise execution of movement sequences with considerable cognitive load in the domains' attention and concentration. These investigations may serve as additional tools for the testing of the dopaminergic response.

Long-term unsupervised mobility assessment in movement disorders

Mobile health technologies (wearable, portable, body-fixed sensors, or domestic-integrated devices) that quantify mobility in unsupervised, daily living environments are emerging as complementary clinical assessments. Data collected in these ecologically valid, patient-relevant settings can overcome limitations of conventional clinical assessments, as they capture fluctuating and rare events. These data could support clinical decision making and could also serve as outcomes in clinical trials. However, studies that directly compared assessments made in unsupervised and supervised (eg, in the laboratory or hospital) settings point to large disparities, even in the same parameters of mobility. These differences appear to be affected by psychological, physiological, cognitive, environmental, and technical factors, and by the types of mobilities and diagnoses assessed. To facilitate the successful adaptation of the unsupervised assessment of mobility into clinical practice and clinical trials, clinicians and researchers should consider these disparities and the multiple factors that contribute to them.

Systematic Review Looking at the Use of Technology to Measure Free-Living Symptom and Activity Outcomes in Parkinson's Disease in the Home or a Home-like Environment

BACKGROUND

The emergence of new technologies measuring outcomes in Parkinson's disease (PD) to complement the existing clinical rating scales has introduced the possibility of measurement occurring in patients' own homes whilst they freely live and carry out normal day-to-day activities.

OBJECTIVE

This systematic review seeks to provide an overview of what technology is being used to test which outcomes in PD from free-living participant activity in the setting of the home environment. Additionally, this review seeks to form an impression of the nature of validation and clinimetric testing carried out on the technological device(s) being used.

METHODS

Five databases (Medline, Embase, PsycInfo, Cochrane and Web of Science) were systematically searched for papers dating from 2000. Study eligibility criteria included: adults with a PD diagnosis; the use of technology; the setting of a home or home-like environment; outcomes measuring any motor and non-motor aspect relevant to PD, as well as activities of daily living; unrestricted/unscripted activities undertaken by participants.

RESULTS

65 studies were selected for data extraction. There were wide varieties of participant sample sizes (<10 up to hundreds) and study durations (<2 weeks up to a year). The metrics evaluated by technology, largely using inertial measurement units in wearable devices, included gait, tremor, physical activity, bradykinesia, dyskinesia and motor fluctuations, posture, falls, typing, sleep and activities of daily living.

CONCLUSIONS

Home-based free-living testing in PD is being conducted by multiple groups with diverse approaches, focussing mainly on motor symptoms and sleep.

How Mobile Health Technology and Electronic Health Records Will Change Care of Patients with Parkinson's Disease

Care of patients with Parkinson’s disease (PD) will dramatically change in the upcoming years. The nationwide implementations of the patient-controlled electronic health record (EHR) and the technology-based home monitoring system will most probably be the cornerstones of this revolution. We speculate that, within the course of the next decade, EHRs will lead to a substantial empowerment of patients, and monitoring of motor and non-motor manifestations of PD will shift from the clinic to the home. As far as this can be foreseen, small, partly clothing-embedded and implanted sensor systems allowing passive (i.e., non-obtrusive) data collection will dominate the market. They will interoperate with the personal EHR and other potentially health-related electronic databases such as clinical warehouses and population health analytics platforms. Analysis software will be mainly built on artificial intelligence, and presentation of data will be intuitive. This scenario will eventually help both the patient and the medical professional by providing higher amounts of quality information about daily-relevant effects of disease and treatment, eventually allowing for a better and more personalized care.

Precision Medicine in Parkinson's Disease - Exploring Patient-Initiated Self-Tracking

Background:

Individually tailored healthcare, in the form of precision medicine, holds substantial potential for the future of medicine, especially for a complex disorder like Parkinson’s disease (PD). Patient self-tracking is an under-researched area in PD.

Objective:

This study aimed to explore patient-initiated self-tracking in PD and discuss it in the context of precision medicine.

Methods:

The first author used a smartphone app to capture finger-tapping data and also noted times for medication intakes.

Results:

Data were collected during four subsequent days. Only data from the first two days were complete enough to analyze, leading to the realization that the collection of data over a period of time can pose a significant burden to patients. From the first two days of data, a dip in finger function was observed around the time for the second medication dose of the day.

Conclusions:

Patient-initiated self-tracking enabled the first author to glean important insights about how her PD symptoms varied over the course of the day. Symptom tracking holds great potential in precision medicine and can, if shared in a clinical encounter, contribute to the learning of both patient and clinician. More work is needed to develop this field and extra focus needs to be given to balancing the burden of tracking for the patient against any expected benefit.

"You have to know why you're doing this": a mixed methods study of the benefits and burdens of self-tracking in Parkinson's disease

Background

This study explores opinions and experiences of people with Parkinson’s disease (PwP) in Sweden of using self-tracking. Parkinson’s disease (PD) is a neurodegenerative condition entailing varied and changing symptoms and side effects that can be a challenge to manage optimally. Patients’ self-tracking has demonstrated potential in other diseases, but we know little about PD self-tracking. The aim of this study was therefore to explore the opinions and experiences of PwP in Sweden of using self-tracking for PD.

Method

A mixed methods approach was used, combining qualitative data from seven interviews with quantitative data from a survey to formulate a model for self-tracking in PD. In total 280 PwP responded to the survey, 64% (n = 180) of which had experience from self-tracking.

Result

We propose a model for self-tracking in PD which share distinctive characteristics with the Plan-Do-Study-Act (PDSA) cycle for healthcare improvement. PwP think that tracking takes a lot of work and the right individual balance between burdens and benefits needs to be found. Some strategies have here been identified; to focus on positive aspects rather than negative, to find better solutions for their selfcare, and to increase the benefits through improved tools and increased use of self-tracking results in the dialogue with healthcare.

Conclusion

The main identified benefits are that self-tracking gives PwP a deeper understanding of their own specific manifestations of PD and contributes to a more effective decision making regarding their own selfcare. The process of self-tracking also enables PwP to be more active in communicating with healthcare. Tracking takes a lot of work and there is a need to find the right balance between burdens and benefits.

Electronic supplementary material

The online version of this article (10.1186/s12911-019-0896-7) contains supplementary material, which is available to authorized users.

Communication About OFF Periods in Parkinson's Disease: A Survey of Physicians, Patients, and Carepartners

Background: OFF periods impair quality of life in Parkinson's disease and are often amenable to treatment. Optimal treatment decisions rely on effective communication between physicians, patients and carepartners regarding this highly variable and complex phenomenon. Little is published in the literature about communication about OFF periods.

Methods: Informed by interviews with physicians, patients and carepartners we designed questionnaires for each group. We surveyed these parties using an online platform to investigate the frequency, content and ease of communication about OFF periods and barriers and facilitators of communication with physicians.

Results: Fifty movement disorder neurologists, 50 general neurologists, 442 patients and 97 carepartners participated. A free-flowing dialogue is the mainstay of communication according to all parties. Motor aspects of OFF periods are discussed more frequently than non-motor aspects (90 vs. <50% according to both general neurologists and movement disorder neurologists). The most common physician-reported barriers to communication are patient cognitive impairment, patient difficulty recognizing OFF periods and poor patient understanding of OFF periods' relationship to medication timing. The barriers most commonly cited as major by patients were that they perceived OFF periods to be part of the disease (i.e., not a clinical aspect that could be improved by a physician), variability of symptoms, and difficulty in describing symptoms. The most commonly described facilitator (by physicians) was the input of a caregiver. Positively viewed but less commonly used facilitators included pre-visit questionnaires or diaries, digital apps and wearable devices to monitor fluctuations. The majority of patients and carepartners identified a free-flowing dialogue with their physicians and having an agenda as helpful facilitators of communication about OFF periods which they already use. The majority of both groups felt that keeping a diary and pre-visit questionnaires were potentially helpful facilitators that were not currently in use.

Conclusions: Perceived barriers and facilitators to communication about OFF periods are different between health care providers and receivers of health care. Modifiable barriers and facilitators that could be implemented were identified by both groups. Future research should develop and test strategies based on this input to optimize communication and thus clinical care for this common and debilitating problem.

A roadmap for implementation of patient-centered digital outcome measures in Parkinson's disease obtained using mobile health technologies

Obtaining reliable longitudinal information about everyday functioning from individuals with Parkinson's disease (PD) in natural environments is critical for clinical care and research. Despite advances in mobile health technologies, the implementation of digital outcome measures is hindered by a lack of consensus on the type and scope of measures, the most appropriate approach for data capture (eg, in clinic or at home), and the extraction of timely information that meets the needs of patients, clinicians, caregivers, and health care regulators. The Movement Disorder Society Task Force on Technology proposes the following objectives to facilitate the adoption of mobile health technologies: (1) identification of patient-centered and clinically relevant digital outcomes; (2) selection criteria for device combinations that offer an acceptable benefit-to-burden ratio to patients and that deliver reliable, clinically relevant insights; (3) development of an accessible, scalable, and secure platform for data integration and data analytics; and (4) agreement on a pathway for approval by regulators, adoption into e-health systems and implementation by health care organizations. We have developed a tentative roadmap that addresses these needs by providing the following deliverables: (1) results and interpretation of an online survey to define patient-relevant endpoints, (2) agreement on the selection criteria for use of device combinations, (3) an example of an open-source platform for integrating mobile health technology output, and (4) recommendations for assessing readiness for deployment of promising devices and algorithms suitable for regulatory approval. This concrete implementation guidance, harmonizing the collaborative endeavor among stakeholders, can improve assessments of individuals with PD, tailor symptomatic therapy, and enhance health care outcomes. © 2019 International Parkinson and Movement Disorder Society.

An update on adaptive deep brain stimulation in Parkinson's disease

Advancing conventional open‐loop DBS as a therapy for PD is crucial for overcoming important issues such as the delicate balance between beneficial and adverse effects and limited battery longevity that are currently associated with treatment. Closed‐loop or adaptive DBS aims to overcome these limitations by real‐time adjustment of stimulation parameters based on continuous feedback input signals that are representative of the patient's clinical state. The focus of this update is to discuss the most recent developments regarding potential input signals and possible stimulation parameter modulation for adaptive DBS in PD. Potential input signals for adaptive DBS include basal ganglia local field potentials, cortical recordings (electrocorticography), wearable sensors, and eHealth and mHealth devices. Furthermore, adaptive DBS can be applied with different approaches of stimulation parameter modulation, the feasibility of which can be adapted depending on specific PD phenotypes. Implementation of technological developments like machine learning show potential in the design of such approaches; however, energy consumption deserves further attention. Furthermore, we discuss future considerations regarding the clinical implementation of adaptive DBS in PD. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes

Background

Few studies assessing the correlation between patient-reported outcomes and patient-generated health data from wearable devices exist.

Objective

The aim of this study was to determine the direction and magnitude of associations between patient-generated health data (from the Fitbit Charge HR) and patient-reported outcomes for sleep patterns and physical activity in patients with type 2 diabetes mellitus (T2DM).

Methods

This was a pilot study conducted with adults diagnosed with T2DM (n=86). All participants wore a Fitbit Charge HR for 14 consecutive days and completed internet-based surveys at 3 time points: day 1, day 7, and day 14. Patient-generated health data included minutes asleep and number of steps taken. Questionnaires assessed the number of days of exercise and nights of sleep problems per week. Means and SDs were calculated for all data, and Pearson correlations were used to examine associations between patient-reported outcomes and patient-generated health data. All respondents provided informed consent before participating.

Results

The participants were predominantly middle-aged (mean 54.3, SD 13.3 years), white (80/86, 93%), and female (50/86, 58%). Use of oral T2DM medication correlated with the number of mean steps taken (r=.35, P=.001), whereas being unaware of the glycated hemoglobin level correlated with the number of minutes asleep (r=−.24, P=.04). On the basis of the Fitbit data, participants walked an average of 4955 steps and slept 6.7 hours per day. They self-reported an average of 2.0 days of exercise and 2.3 nights of sleep problems per week. The association between the number of days exercised and steps walked was strong (r=.60, P<.001), whereas the association between the number of troubled sleep nights and minutes asleep was weaker (r=.28, P=.02).

Conclusions

Fitbit and patient-reported data were positively associated for physical activity as well as sleep, with the former more strongly correlated than the latter. As extensive patient monitoring can guide clinical decisions regarding T2DM therapy, passive, objective data collection through wearables could potentially enhance patient care, resulting in better patient-reported outcomes.

Effect of Fear of Falling on Turning Performance in Parkinson's Disease in the Lab and at Home

Background: Parkinson’s disease (PD) is a neurodegenerative movement disorder associated with gait and balance problems and a substantially increased risk of falling. Falls occur often during complex movements, such as turns. Both fear of falling (FOF) and previous falls are relevant risk factors for future falls. Based on recent studies indicating that lab-based and home assessment of similar movements show different results, we hypothesized that FOF and a positive fall history would influence the quantitative turning parameters differently in the laboratory and home.

Methods: Fifty-five PD patients (43 underwent a standardized lab assessment; 40 were assessed over a mean of 12 days at home with approximately 10,000 turns per participant; and 28 contributed to both assessments) were classified regarding FOF and previous falls as “vigorous” (no FOF, negative fall history), “anxious” (FOF, negative fall history), “stoic” (no FOF, positive fall history) and “aware” (FOF, positive fall history). During the assessments, each participant wore a sensor on the lower back.

Results: In the lab assessment, FOF was associated with a longer turning duration and lowered maximum and middle angular velocities of turns. In the home evaluations, a lack of FOF was associated with lowered maximum and average angular velocities of turns. Positive falls history was not significantly associated with turning parameters, neither in the lab nor in the home.

Conclusion: FOF but not a positive fall history influences turning metrics in PD patients in both supervised and unsupervised environments, and this association is different between lab and home assessments. Our findings underline the relevance of comprehensive assessments including home-based data collection strategies for fall risk evaluation.

Wearables in epilepsy and Parkinson's disease-A focus group study

OBJECTIVES

Wearable sensors that measure movement and physiological variables are attractive for clinical evaluation of neurological diseases such as epilepsy and Parkinson's disease (PD). The aim of this study was to explore perceptions regarding the use of wearable technology in disease monitoring and management as reported by individuals with epilepsy and Parkinson's disease as well as health professionals working with these patient groups.

MATERIALS AND METHODS

Six patient groups (n=25) and two groups with health professionals (n=15) participated in this qualitative, descriptive study with focus group interviews. A manifest qualitative content analysis was used.

RESULTS

Four categories and nine subcategories emerged from the analysis. Participants saw possible benefits for improved treatment effect and valued this benefit more than possible inconvenience of wearing the sensors. Discrete design and simplicity were considered as facilitators for improved usability. They emphasized the importance of interactive information between patients and health professionals. However, they were concerned about unclear information and inconclusive recordings and some fears about personal integrity were at odds with the expectations on interactivity.

CONCLUSIONS

Patients need to feel well informed and find an added value in using wearables. Wearables need to be user-friendly, have an attractive design, and show clinical efficacy in improving disease management. Variations in perceptions regarding integrity, benefits, and effectiveness of monitoring indicate possible conflicts of expectations among participants. The engagement of end users, patients, and health professionals, in the design and implementation process, is crucial for the development of wearable devices that enhance and facilitate neurological rehabilitation practice.

Turn Around Freezing: Community-Living Turning Behavior in People with Parkinson's Disease

Difficulty in turning while walking is common among patients with Parkinson’s disease (PD). This difficulty often leads to significant disability, falls, and loss of function; moreover, turning is a common trigger for freezing of gait (FoG). We hypothesized that the quantity and quality of turning mobility while walking during daily life would be different among subjects with PD with and without FoG. Here, we investigated, for the first time, the turning quality during daily life as it relates to FoG in people with PD using a single inertial sensor. Ninety-four subjects with PD (among whom 25 had FoG) wore an inertial sensor attached by a belt on the lower back during normal daily activity consecutively for 3 days. An algorithm identified periods of walking and calculated the number and quality metrics of turning. Quality, but not the quantity, of turning at home was different in freezers compared to the non-freezers. The number of turns (19.3 ± 9.2/30 min in freezers, 22.4 ± 12.9/30 min non-freezers; p = 0.194) was similar in the two groups. Some aspects of quality of turns, specifically mean jerkiness, mean and variability of medio-lateral jerkiness were significantly higher (p < 0.05) in the freezers, compared to non-freezers. Interestingly, subjects with FoG showed specific turning differences in the turns with larger angles compared to those without FoG. These findings suggest that turning during daily activities among patients with PD is impaired in subjects with FoG, compared to subject without freezing. As such, clinical decision-making and rehabilitation assessment may benefit from measuring the quality of turning mobility during daily activities in PD.

Classification of advanced stages of Parkinson's disease: translation into stratified treatments

Advanced stages of Parkinson's disease (advPD) still impose a challenge in terms of classification and related stage-adapted treatment recommendations. Previous concepts that define advPD by certain milestones of motor disability apparently fall short in addressing the increasingly recognized complexity of motor and non-motor symptoms and do not allow to account for the clinical heterogeneity that require more personalized approaches. Therefore, deep phenotyping approaches are required to characterize the broad-scaled, continuous and multidimensional spectrum of disease-related motor and non-motor symptoms and their progression under real-life conditions. This will also facilitate the reasoning for clinical care and therapeutic decisions, as neurologists currently have to refer to clinical trials that provide guidance on a group level; however, this does not always account for the individual needs of patients. Here, we provide an overview on different classifications for advPD that translate into critical phenotypic patterns requiring the differential therapeutic adjustments. New concepts refer to precision medicine approaches also in PD and first studies on genetic stratification for therapeutic outcomes provide a potential for more objective treatment recommendations. We define novel treatment targets that align with this concept and make use of emerging device-based assessments of real-life information on PD symptoms. As these approaches require empowerment of patients and integration into treatment decisions, we present communication strategies and decision support based on new technologies to adjust treatment of advPD according to patient demands and safety.

No relevant association of kinematic gait parameters with Health-related Quality of Life in Parkinson's disease

BACKGROUND

Health-related Quality of Life (HrQoL) is probably the most important outcome parameter for the evaluation and management of chronic diseases. As this parameter is subjective and prone to bias, there is an urgent need to identify objective surrogate markers. Gait velocity has been shown to be associated with HrQoL in numerous chronic diseases, such as Parkinson's disease (PD). With the development and wide availability of simple-to-use wearable sensors and sophisticated gait algorithms, kinematic gait parameters may soon be implemented in clinical routine management. However, the association of such kinematic gait parameters with HrQoL in PD has not been assessed to date.

METHODS

Kinematic gait parameters from a 20-meter walk from 43 PD patients were extracted using a validated wearable sensor system. They were compared with the Visual Analogue Scale of the Euro-Qol-5D (EQ-5D VAS) by performing a multiple regression analysis, with the International Classification of Functioning, Disability and Health (ICF) model as a framework.

RESULTS

Use of assistive gait equipment, but no kinematic gait parameter, was significantly associated with HrQoL.

CONCLUSION

The widely accepted concept of a positive association between gait velocity and HrQoL may, at least in PD, be driven by relatively independent parameters, such as assistive gait equipment.

New methods for the assessment of Parkinson's disease (2005 to 2015): A systematic review

BACKGROUND

The past decade has witnessed a highly dynamic and growing expansion of novel methods aimed at improving the assessment of Parkinson's disease with technology (NAM-PD) in laboratory, clinical, and home environments. However, the current state of NAM-PD regarding their maturity, feasibility, and usefulness in assessing the main PD features has not been systematically evaluated.

METHODS

A systematic review of articles published in the field from 2005 to 2015 was performed. Of 9,503 publications identified in PubMed and the Web of Science, 848 full papers were evaluated, and 588 original articles were assessed to evaluate the technological, demographic, clinimetric, and technology transfer readiness parameters of NAM-PD.

RESULTS

Of the studies, 65% included fewer than 30 patients, < 50% employed a standard methodology to validate diagnostic tests, 8% confirmed their results in a different dataset, and 87% occurred in a clinic or lab. The axial features domain was the most frequently studied, followed by bradykinesia. Rigidity and nonmotor domains were rarely investigated. Only 6% of the systems reached a technology level that justified the hope of being included in clinical assessments in a useful time period.

CONCLUSIONS

This systematic evaluation provides an overview of the current options for quantitative assessment of PD and what can be expected in the near future. There is a particular need for standardized and collaborative studies to confirm the results of preliminary initiatives, assess domains that are currently underinvestigated, and better validate the existing and upcoming NAM-PD. © 2016 International Parkinson and Movement Disorder Society.

A clinical view on the development of technology-based tools in managing Parkinson's disease

Recently, quantitative, objective, and easy-to-use technology-based tools that can assess PD features over long time periods have been developed and generate clinically relevant and comparable patient information. Herein, we present a clinician's view on technological developments that have the potential to revolutionize clinical management concepts in PD. According to prominent examples in clinical medicine (e.g., blood glycosylated hemoglobin and blood pressure), we argue that the consideration of technology-based assessment in the clinical management of PD must be based on specific assumptions: (1) It provides a valid and accurate parameter of a clinically relevant feature of the disease; (2) there is confirmed evidence that the parameter has an ecologically relevant effect on the specific clinical application; (3) a target range can be defined wherein the parameter reflects the adequate treatment response; and (4) implementation is simple to allow repetitive use. Currently, there are no technology-based tools available that fulfil all these assumptions; however, assessments of akinesia, dyskinesia, motor fluctuations, physical inactivity, gait impairment, and postural instability seem relatively close to the specifications described. An iterative process of integration is recommended to bring technology-based tools into clinical practice. © 2016 International Parkinson and Movement Disorder Society.

Quantitative Timed-Up-and-Go Parameters in Relation to Cognitive Parameters and Health-Related Quality of Life in Mild-to-Moderate Parkinson's Disease

Introduction

The instrumented-Timed-Up-and-Go test (iTUG) provides detailed information about the following movement patterns: sit-to-walk (siwa), straight walking, turning and walk-to-sit (wasi). We were interested in the relative contributions of respective iTUG sub-phases to specific clinical deficits most relevant for daily life in Parkinson’s disease (PD). More specifically, we investigated which condition–fast speed (FS) or convenient speed (CS)–differentiates best between mild- to moderate-stage PD patients and controls, which parameters of the iTUG sub-phases are significantly different between PD patients and controls, and how the iTUG parameters associate with cognitive parameters (with particular focus on cognitive flexibility and working memory) and Health-Related-Quality of Life (HRQoL).

Methods

Twenty-eight PD participants (65.1±7.1 years, H&Y stage 1–3, medication OFF state) and 20 controls (66.1±7.5 years) performed an iTUG (DynaPort^®, McRoberts BV, The Netherlands) under CS and FS conditions. The PD Questionnaire 39 (PDQ-39) was employed to assess HRQoL. General cognitive and executive functions were assessed using the Montreal Cognitive Assessment and the Trail Making Test.

Results

The total iTUG duration and sub-phases durations under FS condition differentiated PD patients slightly better from controls, compared to the CS condition. The following sub-phases were responsible for the observed longer total duration PD patients needed to perform the iTUG: siwa, turn and wasi. None of the iTUG parameters correlated relevantly with general cognitive function. Turning duration and wasi maximum flexion velocity correlated strongest with executive function. Walking back duration correlated strongest with HRQoL.

Discussion

This study confirms that mild- to moderate-stage PD patients need more time to perform the iTUG than controls, and adds the following aspects to current literature: FS may be more powerful than CS to delineate subtle movement deficits in mild- to moderate-stage PD patients; correlation levels of intra-individual siwa and wasi parameters may be interesting surrogate markers for the level of automaticity of performed movements; and sub-phases and kinematic parameters of the iTUG may have the potential to reflect executive functioning and HRQoL aspects of PD patients.