Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson's Disease

Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study over 12 months

Digital measures may provide objective, sensitive, real-world measures of disease progression in Parkinson's disease (PD). However, multicenter longitudinal assessments of such measures are few. We recently demonstrated that baseline assessments of gait, tremor, finger tapping, and speech from a commercially available smartwatch, smartphone, and research-grade wearable sensors differed significantly between 82 individuals with early, untreated PD and 50 age-matched controls. Here, we evaluated the longitudinal change in these assessments over 12 months in a multicenter observational study using a generalized additive model, which permitted flexible modeling of at-home data. All measurements were included until participants started medications for PD. Over one year, individuals with early PD experienced significant declines in several measures of gait, an increase in the proportion of day with tremor, modest changes in speech, and few changes in psychomotor function. As measured by the smartwatch, the average (SD) arm swing in-clinic decreased from 25.9 (15.3) degrees at baseline to 19.9 degrees (13.7) at month 12 (P = 0.004). The proportion of awake time an individual with early PD had tremor increased from 19.3% (18.0%) to 25.6% (21.4%; P < 0.001). Activity, as measured by the number of steps taken per day, decreased from 3052 (1306) steps per day to 2331 (2010; P = 0.16), but this analysis was restricted to 10 participants due to the exclusion of those that had started PD medications and lost the data. The change of these digital measures over 12 months was generally larger than the corresponding change in individual items on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale but not greater than the change in the overall scale. Successful implementation of digital measures in future clinical trials will require improvements in study conduct, especially data capture. Nonetheless, gait and tremor measures derived from a commercially available smartwatch and smartphone hold promise for assessing the efficacy of therapeutics in early PD.

Too little or too much nocturnal movements in Parkinson's disease: A practical guide to managing the unseen

Nocturnal and sleep-related motor disorders in people with Parkinson's disease (PD) have a wide spectrum of manifestations and present a complex clinical picture. Problems can arise due to impaired movement ability (hypokinesias), e.g. nocturnal hypokinesia or early-morning akinesia, or to excessive movement (hyperkinesias), e.g. end-of-the-day dyskinesia, parasomnias, periodic limb movement during sleep and restless legs syndrome. These disorders can have a significant negative impact on the sleep, daytime functional ability, and overall quality of life of individuals with PD and their carers. The debilitating motor issues are often accompanied by a combination of non-motor symptoms, including pain and cramping, which add to the overall burden. Importantly, nocturnal motor disorders encompass a broader timeline than just the period of sleep, often starting in the evening, as well as occurring throughout the night and on awakening, and are not just limited to problems of insomnia or sleep fragmentation. Diagnosis can be challenging as, in many cases, the 'gold standard' assessment method is video polysomnography, which may not be available in all settings. Various validated questionnaires are available to support evaluation, and alternative approaches, using wearable sensors and digital technology, are now being developed to facilitate early diagnosis and monitoring. This review sets out the parameters of what can be considered normal nocturnal movement and describes the clinical manifestations, usual clinical or objective assessment methods, and evidence for optimal management strategies for the common nocturnal motor disorders that neurologists will encounter in people with PD in their clinical practice.

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.

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

BACKGROUND

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

SUMMARY

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

KEY MESSAGES

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

Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review

BACKGROUND

Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity.

OBJECTIVE

This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers.

METHODS

This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed.

RESULTS

This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials.

CONCLUSION

mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.

Chaudhuri's Dashboard of Vitals in Parkinson's syndrome: an unmet need underpinned by real life clinical tests

We have recently published the notion of the "vitals" of Parkinson's, a conglomeration of signs and symptoms, largely nonmotor, that must not be missed and yet often not considered in neurological consultations, with considerable societal and personal detrimental consequences. This "dashboard," termed the Chaudhuri's vitals of Parkinson's, are summarized as 5 key vital symptoms or signs and comprise of (a) motor, (b) nonmotor, (c) visual, gut, and oral health, (d) bone health and falls, and finally (e) comorbidities, comedication, and dopamine agonist side effects, such as impulse control disorders. Additionally, not addressing the vitals also may reflect inadequate management strategies, leading to worsening quality of life and diminished wellness, a new concept for people with Parkinson's. In this paper, we discuss possible, simple to use, and clinically relevant tests that can be used to monitor the status of these vitals, so that these can be incorporated into clinical practice. We also use the term Parkinson's syndrome to describe Parkinson's disease, as the term "disease" is now abandoned in many countries, such as the U.K., reflecting the heterogeneity of Parkinson's, which is now considered by many as a syndrome.

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.

Use of remote monitoring and integrated platform for the evaluation of sleep quality in adult-onset idiopathic cervical dystonia

BACKGROUND

Up to 70% of individuals diagnosed with adult-onset idiopathic focal cervical dystonia (AOIFCD) report difficulties with sleep. Larger cohort studies using wrist-worn accelerometer devices have emerged as an alternative to smaller polysomnography studies, in order to evaluate sleep architecture.

METHODS

To measure activity during the sleep/wake cycle, individuals wore a consumer-grade wrist device (Garmin vivosmart 4) continuously over 7 days on their non-dominant wrist, while completing a daily sleep diary and standardised sleep and non-motor questionnaires via a dedicated app. Sleep measures were derived from the captured raw triaxial acceleration and heart rate values using previously published validated algorithms.

RESULTS

Data were collected from 50 individuals diagnosed with AOIFCD and 47 age- and sex-matched controls. Those with AOIFCD self-reported significantly higher levels of excessive daytime sleepiness (p = 0.04) and impaired sleep quality (p = 0.03), while accelerometer measurements found the AOIFCD cohort to have significantly longer total sleep times (p = 0.004) and time spent in NREM sleep (p = 0.009), compared to controls. Overall, there was limited agreement between wearable-derived sleep parameters, and self-reported sleep diary and visual analogue scale records.

DISCUSSION

This study shows the potential feasibility of using consumer-grade wearable devices in estimating sleep measures at scale in dystonia cohorts. Those diagnosed with AOIFCD were observed to have altered sleep architecture, notably longer total sleep time and NREM sleep, compared to controls. These findings suggest that previously reported disruptions to brainstem circuitry and serotonin neurotransmission may contribute to both motor and sleep pathophysiology.

Investigating the combined effect of ALAN and noise on sleep by simultaneous real-time monitoring using low-cost smartphone devices

The association between artificial light at night (ALAN) and noise, on the one hand, and sleep, on the other, is well established. Yet studies investigating these associations have been infrequent and mostly conducted in controlled laboratory conditions. As a result, little is known about the applicability of their results to real-world settings. In this paper, we attempt to bridge this knowledge gap by carrying out an individual-level real-world study, involving 72 volunteers from different urban localities in Israel. The survey participants were asked to use their personal smartphones and smartwatches to monitor sleep patterns for 30 consecutive days, while ALAN and noise exposures were monitored in parallel, with inputs reported each second. The volunteers were also asked to fill in a questionnaire about their individual attributes, daily habits, room settings, and personal health, to serve as individual-level controls. Upon cointegration, the assembled data were co-analyzed using bivariate and multivariate statistical tools. As the study reveals, the effect of ALAN and noise on sleep largely depends on when the exposure occurred, that is, before sleep or during sleep. In particular, the effect of ALAN exposure was found to be most pronounced if it occurred before sleep, while exposure to noise mattered most if it occurred during the sleep phase. As the study also reveals, the effects of ALAN and noise appear to amplify each other, with a 14-15.3% reduction in sleep duration and an 8-9% reduction in sleep efficiency observed at high levels of ALAN-noise exposures. The study helped to assemble a massive amount of real-time observations, enabling a robust individual-level analysis.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.