Annals of Neurology: Actigraphy enables home screening for REM behavioral disorders in Parkinson's disease

Rapid eye movement sleep behavior disorder (RBD) is a sleep disorder
that affects up to 70% of people with Parkinson's disease (PD).
People with RBD exhibit motor and dream-making behaviors during sleep, which can be violent, sometimes violent, and harmful
.
Diagnosing and treating RBD is critical to
preventing serious injuries to patients and their bed partners.

Figure 1: Cover image of the paper

Isolated RBD represents the early stages of PD or other synaptic proteinopathy and may appear
years before the more pronounced clinical manifestations of these disorders.
Its early diagnosis provides a unique window into evaluating the disease-changing effects
of upcoming treatments.
In addition, the PD phenotype associated with RBD tends to be more aggressive and exhibits more motor complications
.
They are also more often accompanied by symptoms of
cognitive, behavioral, and autonomic disorders.
Therefore, identifying RBD in PD can provide fundamental insights that inform clinical practice
from a therapeutic and prognostic perspective.
RBD associated with synaptic protein disease remains neglected and under-recognized, even
among movement disorder specialists.
Awareness of RBD among the general population and healthcare workers still needs to be improved
.
Diagnosing RBD requires nighttime video polysomnography (VPSG), an expensive, time-consuming test that can only be performed by specialized centers, and is a burden
on patients.
Current screening tools rely on questionnaires or interviews
.
These methods are often subjective and may not be available to people living in the community, or require the presence
of a bed companion.
In PD patients, their reliability in capturing RBD is not well proven
.

Although RBD behavior is known to be more blunt and violent than that observed in PD patients while awake, there has been no objective and systematic description
.
In addition to PSG, the lack of objective measures limits the development of
screening tools for diagnosing RBD in the context of daily living.
To better understand the manifestations of RBD and how it changes over time, and to assess efficacy in clinical trials and clinical routines, home screening will be a major tool
.

The wrist kinesimeter is a promising RBD screening tool
.
In a recent study, visual analysis of motion recordings using pattern recognition could distinguish idiopathic RBD from other motor activities during sleep and identify isolated RBD subjects
in the general population.
In this study, they comprehensively described the motor characteristics of RBD from wrist behavioral signals and extracted those features that best distinguished RBD from non-RBD PD patients
.
A novel, convenient, at-home RBD screening method
was then designed and validated.
Combining action map technology and machine learning algorithms, it is optimized in a controlled clinical environment and translated into a home environment
.
Finally, the method validated accuracy at home in non-PD patients with insomnia, either alone or associated with other sleep disorders, and without a history of
RBD.

In this way, Flavio Raschellà and others of Onera Health in Eindhoven, the Netherlands, explored that rapid eye movement sleep behavior disorder (RBD) is a potentially harmful and often overlooked sleep disorder that affects up to 70% of people with
Parkinson's disease.
Current diagnostics rely on nighttime video polysomnography, an expensive and cumbersome test that requires specific clinical expertise
.
They explored the use of wrist behavior to achieve automatic diagnosis
of RBD in the home environment.

Twenty-six Parkinson's patients underwent a two-week home wrist force map followed by two evaluations
in the lab.
Patients were divided into RBD and non-RBD
based on dream promulgation history and video polysomnography.
They used behavioral mapping signals to comprehensively describe the patient's movement patterns
during sleep.
A machine learning classification algorithm was then trained to distinguish patients
with or without RBD with the most relevant features.
The classification performance of laboratory and home records was quantified
for clinical diagnosis.
Performance
was further validated in a control group consisting of non-PD patients with other sleep disorders.

To determine the characteristics of RBD, behavioral characteristics extracted from (i) individual motor events and (ii) overall nocturnal activity are key
.
Patients with RBD are generally more active and exhibit shorter, more amplitude, and more dispersed exercise times
.

Figure 2: Graph of paper results

Using these features, their classification algorithm achieved an accuracy of 92.
9±8.
16% in clinical tests
.
When home recordings from Parkinson's disease patients were verified, the accuracy was 100% over a two-week period and 94.
4%
in non-Parkinson's control patients.
These characteristics show robustness
under different tests and conditions.

The significance of the study lies in its findings: opening up new prospects for faster, cheaper and more regular screening for sleep disorders, both in routine clinical practice and in clinical trials
.

Raschellà F, Scafa S, Puiatti A, Martin Moraud E, Ratti P.
Actigraphy enables home screening of REM behavior disorder in Parkinson disease.
_Annals of Neurology_.
Published online October 4, 2022:ana.
26517.
doi:[10.
1002/ana.
26517](https://doi.
org/10.
1002/ana.
26517)