Night-time sleep in Parkinson's disease - the potential use of Parkinson's KinetiGraph: a prospective comparative study

Parkinson's Kinetigraph for Wearable Sensor Detection of Clinically Unrecognized Early-Morning Akinesia in Parkinson's Disease: A Case Report-Based Observation

Early-morning off periods, causing early-morning akinesia, can lead to significant motor and nonmotor morbidity in levodopa-treated fluctuating Parkinson's disease (PD) cases. Despite validated bedside scales in clinical practice, such early-morning off periods may remain undetected unless specific wearable technologies, such as the Parkinson's KinetiGraph™ (PKG) watch, are used. We report five PD cases for whom the PKG detected early-morning off periods that were initially clinically undetected and as such, untreated. These five cases serve as exemplars of this clinical gap in care. Post-PKG assessment, clinicians were alerted and targeted therapies helped abolish the early-morning off periods.

Setting the clinical context to non-motor symptoms reflected by Park-pain, Park-sleep, and Park-autonomic subtypes of Parkinson's disease

Non-motor symptoms (NMS) of Parkinson's disease (PD) are well described in both clinical practice and the literature, enabling their management and enhancing our understanding of PD. NMS can dominate the clinical pictures and NMS subtypes have recently been proposed, initially based on clinical observations, and later confirmed in data driven analyses of large datasets and in biomarker-based studies. In this chapter, we provide an update on what is known about three common subtypes of NMS in PD. The pain (Park-pain), sleep dysfunction (Park-sleep), and autonomic dysfunction (Park-autonomic), providing an overview of their individual classification, clinical manifestation, pathophysiology, diagnosis, and potential treatments.

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

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

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.

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.

Symptoms assessment and decision to treat patients with advanced Parkinson's disease based on wearables data

Body-worn sensors (BWS) could provide valuable information in the management of Parkinson's disease and support therapeutic decisions based on objective monitoring. To study this pivotal step and better understand how relevant information is extracted from BWS results and translated into treatment adaptation, eight neurologists examined eight virtual cases composed of basic patient profiles and their BWS monitoring results. Sixty-four interpretations of monitoring results and the subsequent therapeutic decisions were collected. Relationship between interrater agreements in the BWS reading and the severity of symptoms were analyzed via correlation studies. Logistic regression was used to identify associations between the BWS parameters and suggested treatment modifications. Interrater agreements were high and significantly associated with the BWS scores. Summarized BWS scores reflecting bradykinesia, dyskinesia, and tremor predicted the direction of treatment modifications. Our results suggest that monitoring information is robustly linked to treatment adaptation and pave the way to loop systems able to automatically propose treatment modifications from BWS recordings information.

The role of wrist-worn technology in the management of Parkinson's disease in daily life: A narrative review

Parkinson's disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Its slow and heterogeneous progression over time makes timely diagnosis challenging. Wrist-worn digital devices, particularly smartwatches, are currently the most popular tools in the PD research field due to their convenience for long-term daily life monitoring. While wrist-worn sensing devices have garnered significant interest, their value for daily practice is still unclear. In this narrative review, we survey demographic, clinical and technological information from 39 articles across four public databases. Wrist-worn technology mainly monitors motor symptoms and sleep disorders of patients in daily life. We find that accelerometers are the most commonly used sensors to measure the movement of people living with PD. There are few studies on monitoring the disease progression compared to symptom classification. We conclude that wrist-worn sensing technology might be useful to assist in the management of PD through an automatic assessment based on patient-provided daily living information.

Clinical correlates of movement disorders in adult Niemann-Pick type C patients measured via a Personal KinetiGraph

Background

Niemann-Pick type C (NPC) is an autosomal recessive progressive neurodegenerative disorder caused by mutations in the NPC1 or NPC2 genes. Patients with this disorder have variable phenotypic presentations that often include neuropsychiatric manifestations, cognitive decline, and movement disorders. There is considerable interpatient variation in movement disorders, with limited quantitative measurements describing the movements observed. Objective measurements using wearable sensors provide clinically applicable monitoring of patients with Parkinson’s disease, and hence may be utilized in patients with NPC.

Objective

To explore the relationship between objective measurements of movement obtained via the use of the Personal KinetiGraph (PKG) with the clinical information obtained via questionnaires and clinical rating tools of patients with Niemann-Pick type C.

Methods

Twelve patients with Niemann-Pick type C were recruited who wore the PKG for 6 days during regular activities. A 6-day output was provided by the manufacturer, which provided bradykinesia (BK) and dyskinesia (DK) scores. BK and DK scores were further divided into their interquartile ranges. A fluctuation score (FDS), percentage time immobile (PTI), and percent time with tremors (PTT) were also provided. Clinical assessments included Abnormal Involuntary Movement Scale (AIMS), Epworth Sleepiness Score (ESS), Falls, Neuropsychiatric Unit Assessment Tool (NUCOG), Parkinson’s disease questionnaire (PDQ), and modified Unified Parkinson’s Disease Rating Scale (UPDRS) which were performed over telehealth within 2 weeks of PKG use. Pearson’s correlation analyses were utilized to explore the relationship between DK and BK quartiles and clinical measures.

Results

We found bradykinesia to be a feature among this cohort of patients, with a median BKS of 22.0 (7.4). Additionally, PTI scores were elevated at 4.9 (8.2) indicating elevated daytime sleepiness. Significant correlations were demonstrated between BK25 and Falls (r =  − 0.74, 95% CI = [− 0.95, − 0.08]), BK50 and Falls (r =  − 0.79, 95% CI = [− 0.96, − 0.19]), and BK75 and Falls (r =  − 0.76, 95% CI = [− 0.95, − 0.11]). FDS correlated with PDQ (r =  − 0.7, 95% CI = [− 0.92, − 0.18]), UPDRS IV (r =  − 0.65, 95% CI = [− 0.90, − 0.09]), UPDRS (r =  − 0.64, 95% CI = [− 0.9, − 0.06]), and AIMS (r =  − 0.96, 95% CI = [− 0.99, − 0.49]). DK25 in comparison with NUCOG-A (r = 0.72, 95% CI = [0.17, 0.93]) and DK75 in comparison with NUCOG (r = 0.64, 95% CI = [0.02, 0.91]) and NUCOG-A (r = 0.63, 95% CI = [0.01, 0.90]) demonstrated significant correlations. Additionally, duration of illness in comparison with PTI (r = 0.72, 95% CI = [0.22, 0.92]) demonstrated significance.

Conclusions

Utilization of PKG measures demonstrated that bradykinesia is under recognized among NPC patients, and the bradykinetic patients were less likely to report concerns regarding falls. Additionally, the FDS rather than the DKS is sensitive to the abnormal involuntary movements of NPC—reflecting a differing neurobiology of this chorea compared to levodopa-induced dyskinesias. Furthermore, dyskinetic individuals performed better in cognitive assessments of attention which may indicate an earlier timepoint within disease progression.

Personalized Assessment of Insomnia and Sleep Quality in Patients with Parkinson's Disease

Sleep disturbances are more common in patients with Parkinson’s disease (PD) than in the general population and are considered one of the most troublesome symptoms by these patients. Insomnia represents one of the most common sleep disturbances in PD, and it correlates significantly with poor quality of life. There are several known causes of insomnia in the general population, but the complex manifestations that might be associated with PD may also induce insomnia and impact the quality of sleep. The treatment of insomnia and the strategies needed to improve sleep quality may therefore represent a challenge for the neurologist. A personalized approach to the PD patient with insomnia may help the clinician to identify the factors and comorbidities that should also be considered in order to establish a better individualized therapeutic plan. This review will focus on the main characteristics and correlations of insomnia, the most common risk factors, and the main subjective and objective methods indicated for the assessment of insomnia and sleep quality in order to offer a concise guide containing the main steps needed to approach the PD patient with chronic insomnia in a personalized manner.

The Home-Based Sleep Laboratory

Sleep disturbances are prevalent in neurodegenerative diseases in general, and in Parkinson's disease (PD) in particular. Recent evidence points to the clinical value of sleep in disease progression and improving quality of life. Therefore, monitoring sleep quality in an ongoing manner at the convenience of one's home has the potential to improve clinical research and to contribute to significantly better personalized treatment. Further, precise mapping of sleep patterns of each patient can contribute to a better understanding of the disease, its progression and the appropriate medical treatment. Here we review selective, state-of-the-art, home-based devices for assessing sleep and sleep related disorders. We highlight the large potential as well as the main challenges. In particular, we discuss medical validity, standardization and regulatory concerns that currently impede widespread clinical adoption of existing devices. Finally, we propose a roadmap with the technological and scientific steps that are required to impact PD research and treatment.

Domotics, Smart Homes, and Parkinson's Disease

Technology has an increasing presence and role in the management of Parkinson’s disease. Whether embraced or rebuffed by patients and clinicians, this is an undoubtedly growing area. Wearable sensors have received most of the attention so far. This review will focus on technology integrated into the home setting; from fixed sensors to automated appliances, which are able to capture information and have the potential to respond in an unsupervised manner. Domotics also have the potential to provide ‘real world’ context to kinematic data and therapeutic opportunities to tackle challenging motor and non-motor symptoms. Together with wearable technology, domotics have the ability to gather long-term data and record discrete events, changing the model of the cross-sectional outpatient assessment. As clinicians, our ultimate goal is to maximise quality of life, promote autonomy, and personalise care. In these respects, domotics may play an essential role in the coming years.

Digital health technology for non-motor symptoms in people with Parkinson's disease: Futile or future?

INTRODUCTION

There is an ongoing digital revolution in the field of Parkinson's disease (PD) for the objective measurement of motor aspects, to be used in clinical trials and possibly support therapeutic choices. The focus of remote technologies is now also slowly shifting towards the broad but more "hidden" spectrum of non-motor symptoms (NMS).

METHODS

A narrative review of digital health technologies for measuring NMS in people with PD was conducted. These digital technologies were defined as assessment tools for NMS offered remotely in the form of a wearable, downloadable as a mobile app, or any other objective measurement of NMS in PD that did not require a hospital visit and could be performed remotely. Searches were performed using peer-reviewed literature indexed databases (MEDLINE, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane CENTRAL Register of Controlled Trials), as well as Google and Google Scholar.

RESULTS

Eighteen studies deploying digital health technology in PD were identified, for example for the measurement of sleep disorders, cognitive dysfunction and orthostatic hypotension. In addition, we describe promising developments in other conditions that could be translated for use in PD.

CONCLUSION

Unlike motor symptoms, non-motor features of PD are difficult to measure directly using remote digital technologies. Nonetheless, it is currently possible to reliably measure several NMS and further digital technology developments are underway to offer further capture of often under-reported and under-recognised NMS.

Non-motor complications in late stage Parkinson's disease: recognition, management and unmet needs

Introduction: The burden of non-motor symptoms (NMS) is a major determinant of health-related quality of life in Parkinson's disease (PD), particularly at its late stage.Areas covered: The late stage is usually defined as the period from unstable advanced to the palliative stage, characterized by a combination of emerging treatment-resistant axial motor symptoms (freezing of gait, postural instability, falls and dysphagia), as well as both non-dopaminergic and dopaminergic NMS: cognitive decline, neuropsychiatric symptoms, aspects of dysautonomia, pain and sleep disturbances (insomnia and excessive day-time sleepiness). Here, the authors summarize the current knowledge on NMS dominating the late stage of PD and propose a pragmatic and clinically focused approach for their recognition and treatment.Expert opinion: The NMS progression pattern is complex and remains under-researched. While dopamine-dependent NMS may improve with dopamine replacement therapy, non-dopamine dependent NMS worsen progressively and culminate at the late stages of PD. Furthermore, some PD specific features could interact negatively with other comorbidities, multiple medication use and frailty - the evaluation of these aspects is important in the creation of personalized management plans in the late stage of PD.

Assessment of real life eating difficulties in Parkinson's disease patients by measuring plate to mouth movement elongation with inertial sensors

Parkinson’s disease (PD) is a neurodegenerative disorder with both motor and non-motor symptoms. Despite the progressive nature of PD, early diagnosis, tracking the disease’s natural history and measuring the drug response are factors that play a major role in determining the quality of life of the affected individual. Apart from the common motor symptoms, i.e., tremor at rest, rigidity and bradykinesia, studies suggest that PD is associated with disturbances in eating behavior and energy intake. Specifically, PD is associated with drug-induced impulsive eating disorders such as binge eating, appetite-related non-motor issues such as weight loss and/or gain as well as dysphagia—factors that correlate with difficulties in completing day-to-day eating-related tasks. In this work we introduce Plate-to-Mouth (PtM), an indicator that relates with the time spent for the hand operating the utensil to transfer a quantity of food from the plate into the mouth during the course of a meal. We propose a two-step approach towards the objective calculation of PtM. Initially, we use the 3D acceleration and orientation velocity signals from an off-the-shelf smartwatch to detect the bite moments and upwards wrist micromovements that occur during a meal session. Afterwards, we process the upwards hand micromovements that appear prior to every detected bite during the meal in order to estimate the bite’s PtM duration. Finally, we use a density-based scheme to estimate the PtM durations distribution and form the in-meal eating behavior profile of the subject. In the results section, we provide validation for every step of the process independently, as well as showcase our findings using a total of three datasets, one collected in a controlled clinical setting using standardized meals (with a total of 28 meal sessions from 7 Healthy Controls (HC) and 21 PD patients) and two collected in-the-wild under free living conditions (37 meals from 4 HC/10 PD patients and 629 meals from 3 HC/3 PD patients, respectively). Experimental results reveal an Area Under the Curve (AUC) of 0.748 for the clinical dataset and 0.775/1.000 for the in-the-wild datasets towards the classification of in-meal eating behavior profiles to the PD or HC group. This is the first work that attempts to use wearable Inertial Measurement Unit (IMU) sensor data, collected both in clinical and in-the-wild settings, towards the extraction of an objective eating behavior indicator for PD.

A blinded, controlled trial of objective measurement in Parkinson's disease

Medical conditions with effective therapies are usually managed with objective measurement and therapeutic targets. Parkinson’s disease has effective therapies, but continuous objective measurement has only recently become available. This blinded, controlled study examined whether management of Parkinson’s disease was improved when clinical assessment and therapeutic decisions were aided by objective measurement. The primary endpoint was improvement in the Movement Disorder Society-United Parkinson’s Disease Rating Scale’s (MDS-UPDRS) Total Score. In one arm, objective measurement assisted doctors to alter therapy over successive visits until objective measurement scores were in target. Patients in the other arm were conventionally assessed and therapies were changed until judged optimal. There were 75 subjects in the objective measurement arm and 79 in the arm with conventional assessment and treatment. There were statistically significant improvements in the moderate clinically meaningful range in the MDS-UPDRS Total, III, IV scales in the arm using objective measurement, but not in the conventionally treated arm. These findings show that global motor and non-motor disability is improved when management of Parkinson’s disease is assisted by objective measurement.

More Sensitive Identification for Bradykinesia Compared to Tremors in Parkinson's Disease Based on Parkinson's KinetiGraph (PKG)

The effective management and therapies for Parkinson's disease (PD) require appropriate clinical evaluation. The Parkinson's KinetiGraph (PKG) is a wearable sensor system that can monitor the motion characteristics of PD objectively and continuously. This study was aimed to assess the correlations between PKG data and clinical scores of bradykinesia, rigidity, tremor, and fluctuation. It also aims to explore the application value of identifying early motor symptoms. An observational study of 100 PD patients wearing the PKG for ≥ 6 days was performed. It provides a series of data, such as the bradykinesia score (BKS), percent time tremor (PTT), dyskinesia score (DKS), and fluctuation and dyskinesia score (FDS). PKG data and UPDRS scores were analyzed, including UPDRS III total scores, UPDRS III-bradykinesia scores (UPDRS III-B: items 23-26, 31), UPDRS III-rigidity scores (UPDRS III-R: item 22), and scores from the Wearing-off Questionnaire (WOQ-9). This study shows that there was significant correlation between BKS and UPDRS III scores, including UPDRS III total scores, UPDRS III-B, and UPDRS III-R scores (r = 0.479-0.588, p ≤ 0.001), especially in the early-stage group (r = 0.682, p < 0.001). Furthermore, we found that BKS in patients with left-sided onset (33.57 ± 5.14, n = 37) is more serious than in patients with right-sided onset (29.87 ± 6.86, n = 26). Our findings support the feasibility of using the PKG to detect abnormal movements, especially bradykinesia in PD. It is suitable for the early detection, remote monitoring, and timely treatment of PD symptoms.

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.

Smartwatch-based Activity Analysis During Sleep for Early Parkinson's Disease Detection

Parkinson's Disease (PD) is the second most common neurodegenerative disorder with the non-motor symptoms preceding the motor impairment that is needed for clinical diagnosis. In the current study, an angle-based analysis that processes activity data during sleep from a smartwatch for quantification of sleep quality, when applied on controls and PD patients, is proposed. Initially, changes in their arm angle due to activity are captured from the smartwatch triaxial accelerometry data and used for the estimation of the corresponding binary state (awake/sleep). Then, sleep metrics (i.e., sleep efficiency index, total sleep time, sleep fragmentation index, sleep onset latency, and wake after sleep onset) are computed and used for the discrimination between controls and PD patients. A process of validation of the proposed approach when compared with the PSG-based ground truth in an in-the-clinic setting, resulted in comparable state estimation. Moreover, data from 15 early PD patients and 11 healthy controls were used as a test set, including 1,376 valid sleep recordings in-the-wild setting. The univariate analysis of the extracted sleep metrics achieved up to 0.77 AUC in early PD patients vs. healthy controls classification and exhibited a statistically significant correlation (up to 0.46) with the clinical PD Sleep Scale 2 counterpart Items. The findings of the proposed method show the potentiality to capture non-motor behavior from users' nocturnal activity to detect PD in the early stage.

Shedding Light on Nocturnal Movements in Parkinson's Disease: Evidence from Wearable Technologies

In Parkinson’s disease (PD), abnormal movements consisting of hypokinetic and hyperkinetic manifestations commonly lead to nocturnal distress and sleep impairment, which significantly impact quality of life. In PD patients, these nocturnal disturbances can reflect disease-related complications (e.g., nocturnal akinesia), primary sleep disorders (e.g., rapid eye movement behaviour disorder), or both, thus requiring different therapeutic approaches. Wearable technologies based on actigraphy and innovative sensors have been proposed as feasible solutions to identify and monitor the various types of abnormal nocturnal movements in PD. This narrative review addresses the topic of abnormal nocturnal movements in PD and discusses how wearable technologies could help identify and assess these disturbances. We first examine the pathophysiology of abnormal nocturnal movements and the main clinical and instrumental tools for the evaluation of these disturbances in PD. We then report and discuss findings from previous studies assessing nocturnal movements in PD using actigraphy and innovative wearable sensors. Finally, we discuss clinical and technical prospects supporting the use of wearable technologies for the evaluation of nocturnal movements.

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.

Qualitative Evaluation of the Personal KinetiGraphTM Movement Recording System in a Parkinson's Clinic

Background:

Wearable sensors provide accurate, continuous objective measurements, quantifying the variable motor states of patients with Parkinson’s disease (PD) in real time.

Objectives:

To evaluate the impact of using continuous objective measurement using the Personal KinetiGraph™ (PKG^®) Movement Recording System in the routine clinical care of patients with PD (PwP).

Methods:

Physicians employed the use of the PKG in patients for whom they were seeking objective measurement. Patients wore a PKG data logger for ≥6 days during routine daily living activities. During the survey period of December 2015 through July 2016, physician surveys were completed by four Movement Disorder Specialists for whom measurements from the PKG were available during a subsequent routine clinic visit.

Results:

Of 112 completed physician surveys, 46 (41%) indicated the PKG provided relevant additional information sufficient to consider adjusting their therapeutic management plan; 66 (59%) indicated the PKG provided no further information to support a therapeutic decision differing from that made during a routine clinical evaluation. Upon further review of these 46 surveys, 36 surveys (78%) revealed the information provided by the PKG ultimately resulted in adjusting the patient’s medical management.

Conclusions:

The PKG provided novel additional information beyond that captured during a routine clinic visit sufficient to change the medical management of PwP. Physicians adjusted treatment nearly a third of the time based on data provided by real-time, remote monitoring outside the clinic setting. The use of the PKG may provide for better informed therapeutic decisions, improving the quality of life for PwP.

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.

Technology-Enabled Clinical Trials

The complexity and costs associated with traditional randomized, controlled trials have increased exponentially over time, and now threaten to stifle the development of new drugs and devices. Nevertheless, the growing use of electronic health records, mobile applications, and wearable devices offers significant promise for transforming clinical trials, making them more pragmatic and efficient. However, many challenges must be overcome before these innovations can be implemented routinely in randomized, controlled trial operations. In October of 2018, a diverse stakeholder group convened in Washington, DC, to examine how electronic health record, mobile, and wearable technologies could be applied to clinical trials. The group specifically examined how these technologies might streamline the execution of clinical trial components, delineated innovative trial designs facilitated by technological developments, identified barriers to implementation, and determined the optimal frameworks needed for regulatory oversight. The group concluded that the application of novel technologies to clinical trials provided enormous potential, yet these changes needed to be iterative and facilitated by continuous learning and pilot studies.

Dopaminergic Effect on Non-Motor Symptoms in Late Stage Parkinson's Disease

BACKGROUND

Non-motor symptoms (NMS) are common in late stage Parkinson's disease (PD), as the frequency and severity of most of these symptoms increase with advancing disease.

OBJECTIVE

To assess effect of dopaminergic therapy on NMS in late stage PD and to investigate relationships between dopaminergic effect on NMS and on motor function.

METHOD

Thirty PD patients in Hoehn and Yahr (HY) stages IV and V in "on" were included. Dopaminergic effect on non-motor symptomatology was assessed by the modified version of the Non-Motor Symptoms Scale (NMSS) in the "off" and the "on" state during a standardized L-dopa test, in parallel also assessing motor function.

RESULTS

NMS were common and many of the symptoms occurred in >80% of the individuals. The highest NMSS scores were seen within the NMSS domains 3: mood/apathy and 7: urinary in both the "off" and the "on" state. There was a statistically significant (p < 0.001) improvement in the modified NMSS total score (median) from 79 in "off" to 64 in "on". There were statistically significant differences between the "off" and the "on" state for domains 2: sleep/fatigue, 3: mood/apathy, 5: attention/memory, 6: gastrointestinal and 7: urinary. The differences in the NMSS score between the "off" and the "on" state were in general larger for motor responders than for motor non-responders. In motor non-responders, differences of the NMSS score between the "off" and the "on" state were found for the total score, domain 3: mood/apathy and its item 11-flat moods.

CONCLUSION

There is an effect of dopaminergic medication on NMS in late stage PD, to some extent also for those with a non-significant response on motor function during L-dopa test. It is therefore of importance to optimize dopaminergic therapy in order to give the most effective symptomatic treatment possible.

Non-motor correlates of wrist-worn wearable sensor use in Parkinson's disease: an exploratory analysis

Wearable sensors are becoming increasingly more available in Parkinson’s disease and are used to measure motor function. Whether non-motor symptoms (NMS) can also be measured with these wearable sensors remains unclear. We therefore performed a retrospective, exploratory, analysis of 108 patients with a diagnosis of idiopathic Parkinson’s disease enroled in the Non-motor Longitudinal International Study (UKCRN No. 10084) at King’s College Hospital, London, to determine the association between the range and nature of NMS and an accelerometer-based outcome measure of bradykinesia (BKS) and dyskinesia (DKS). NMS were assessed by the validated NMS Scale, and included, e.g., cognition, mood and sleep, and gastrointestinal, urinary and sexual problems. Multiple linear regression modelling was used to identify NMS associated with BKS and DKS. We found that BKS was associated with domains 6 (gastrointestinal tract; p = 0.006) and 8 (sexual function; p = 0.003) of the NMS scale. DKS was associated with domains 3 (mood/cognition; p = 0.016), 4 (perceptual problems; p = 0.025), 6 (gastrointestinal tract; p = 0.029) and 9 (miscellaneous, p = 0.003). In the separate domains, constipation was significantly associated with BKS. Delusions, dysphagia, hyposmia, weight change and hyperhidrosis were identified as significantly associated with DKS. None of the NMSS domains were associated with disease duration (p ≥ 0.08). In conclusion, measures of BKS and DKS were mainly associated with gastrointestinal problems, independent of disease duration, showing the potential for wearable devices to pick up on these symptoms. These exploratory results deserve further exploration, and more research on this topic in the form of comprehensive large-scale studies is needed.

The use of accelerometry as a tool to measure disturbed nocturnal sleep in Parkinson's disease

Sleep disturbances are common in Parkinson’s disease (PD). We used the Parkinson’s KinetiGraph (PKG), an objective movement recording system for PD to assess night time sleep in 155 people aged over 60 and without PD (controls), 72 people with PD (PwP) and 46 subjects undergoing a Polysomnogram (PSG: 36 with sleep disorder and 10 with normal sleep). The PKG system uses a wrist worn logger to capture acceleration and derive a bradykinesia score (BKS) every 2 min over 6 days. The BKS ranges from 0–160 with higher scores associated with lesser mobility. Previously we showed that BKS > 80 were associated with day time sleep and used this to produce scores for night time sleep: Efficiency (Percent time with BKS > 80), Fragmentation (Average duration of runs of BKS > 80) and Sleep Quality (BKS > 111 as a representation of atonia). There was a fair association with BKS score and sleep level as judged by PSG. Using these PKG scores, it was possible to distinguish between normal and abnormal PSG studies with good Selectivity (86%) and Sensitivity (80%). The PKG’s sleep scores were significantly different in PD and Controls and correlated with a subject’s self-assessment (PDSS 2) of the quality, wakefulness and restlessness. Using both the PDSS 2 and the PKG, it was apparent that sleep disturbances were apparent early in disease in many PD subjects and that subjects with poor night time sleep were more likely to have day time sleepiness. This system shows promise as a quantitative score for assessing sleep in Parkinson’s disease.

A movement recording system reveals the occurrence of sleep disturbances in the early stages of Parkinson’s disease (PD). Malcolm Horne, a movement disorders expert at the University in Melbourne, and colleagues assessed night time sleep in 72 patients with PD using a wrist-worn device that captures movement patterns. The Parkinson’s KinetiGraph (PKG) system derives scores that are associated with sleep stages and correlate with patients’ self-assessment of sleep quality, wakefulness and restlessness. Significant differences between the PKG sleep scores of PD patients and age-matched healthy controls confirmed that night time sleep disturbances and day time sleepiness worsen as the disease progresses. Abnormal PKG scores were found in patients affected by the disease for only 3 years highlighting the extent to which sleep is disrupted in early-stage PD.

Getting a good night sleep? The importance of recognizing and treating nocturnal hypokinesia in Parkinson's disease

When Parkinson's disease (PD) patients are asked about the quality of their sleep, their answers are dominated by difficulties associated with impaired mobility in bed, medically referred to as nocturnal hypokinesia. Nocturnal hypokinesia is symptomatic from the mid-stage of the disease, affecting up to 70% of PD patients, and contributes to poor sleep quality, and increased carer burden. Here we explore four areas of nocturnal hypokinesia that are relevant to clinical practice, namely: manifestations and definition; clinical assessment and objective monitoring; etiologies and contributing factors; and evidence-based therapeutic approaches. In addition, we provide an operational definition of what constitutes nocturnal hypokinesia and outline different methods of assessment, ranging from clinical interviews and rating scales to objective night-time monitoring with inertial sensors. Optimal management of nocturnal hypokinesia in PD begins with recognizing its manifestation by inquiring about cardinal symptoms and contributing factors from, not only patients, but also carers, followed by formal assessment, and the application of individualized evidence-based treatment. Night-time dopaminergic treatment is the primary therapy; however, careful clinical judgment is required to balance the benefits with the potential adverse events related to nocturnal dopaminergic stimulation. Future studies are needed to explore the practicality of home-based objective assessment of nocturnal hypokinesia, new therapeutic options not limited to dopaminergic medications, and non-pharmacologic approaches, including training on compensatory strategies and bedroom adaptations.

Technologies Assessing Limb Bradykinesia in Parkinson's Disease

Background: The MDS-UPDRS (Movement Disorders Society – Unified Parkinson’s Disease Rating Scale) is the most widely used scale for rating impairment in PD. Subscores measuring bradykinesia have low reliability that can be subject to rater variability. Novel technological tools can be used to overcome such issues.

Objective: To systematically explore and describe the available technologies for measuring limb bradykinesia in PD that were published between 2006 and 2016.

Methods: A systematic literature search using PubMed (MEDLINE), IEEE Xplore, Web of Science, Scopus and Engineering Village (Compendex and Inspec) databases was performed to identify relevant technologies published until 18 October 2016.

Results: 47 technologies assessing bradykinesia in PD were identified, 17 of which offered home and clinic-based assessment whilst 30 provided clinic-based assessment only. Of the eligible studies, 7 were validated in a PD patient population only, whilst 40 were tested in both PD and healthy control groups. 19 of the 47 technologies assessed bradykinesia only, whereas 28 assessed other parkinsonian features as well. 33 technologies have been described in additional PD-related studies, whereas 14 are not known to have been tested beyond the pilot phase.

Conclusion: Technology based tools offer advantages including objective motor assessment and home monitoring of symptoms, and can be used to assess response to intervention in clinical trials or routine care. This review provides an up-to-date repository and synthesis of the current literature regarding technology used for assessing limb bradykinesia in PD. The review also discusses the current trends with regards to technology and discusses future directions in development.

Clinical Assessments in Parkinson's Disease: Scales and Monitoring

Measurement of disease state is essential in both clinical practice and research in order to assess the severity and progression of a patient's disease status, effect of treatment, and alterations in other relevant factors. Parkinson's disease (PD) is a complex disorder expressed through many motor and nonmotor manifestations, which cause disabilities that can vary both gradually over time or come on suddenly. In addition, there is a wide interpatient variability making the appraisal of the many facets of this disease difficult. Two kinds of measure are used for the evaluation of PD. The first is subjective, inferential, based on rater-based interview and examination or patient self-assessment, and consist of rating scales and questionnaires. These evaluations provide estimations of conceptual, nonobservable factors (e.g., symptoms), usually scored on an ordinal scale. The second type of measure is objective, factual, based on technology-based devices capturing physical characteristics of the pathological phenomena (e.g., sensors to measure the frequency and amplitude of tremor). These instrumental evaluations furnish appraisals with real numbers on an interval scale for which a unit exists. In both categories of measures, a broad variety of tools exist. This chapter aims to present an up-to-date summary of the most relevant characteristics of the most widely used scales, questionnaires, and technological resources currently applied to the assessment of PD. The review concludes that, in our opinion: (1) no assessment methods can substitute the clinical judgment and (2) subjective and objective measures in PD complement each other, each method having strengths and weaknesses.