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Reaching a diagnosis of Parkinson’s disease or related disorders provides an opportunity for clinician and patient discussions focused on maintaining and improving quality of life. By the time a clinician is consulted for diagnosis, the motor symptoms typical of synucleinopathies like Parkinson’s disease have often advanced enough to affect a patient’s quality of life. After receiving a diagnosis, patients or their caregivers may recall, in retrospect, that non-motor symptoms such as constipation, loss of sense of smell, or sleep disturbances, may have been present years or even decades before major motor-related issues emerged. These non-motor features are increasingly recognized as prodromal indicators of synucleinopathies, including a specific form of disrupted sleep: idiopathic or isolated REM sleep behavior disorder (iRBD).¹

REM sleep behavior disorder (RBD) is characterized by dream-enactment behaviors that occur alongside a loss of the normal muscle atonia that accompanies REM sleep. These behaviors may include vocalizations or complex motor movements that may pose safety risks for individuals and their partners. An estimated one million people in the United States are affected by iRBD, most commonly middle-aged and older adults.^1,2

A large multicenter study has shown a cumulative conversion rate from iRBD to a neurodegenerative synucleinopathy of 73.50% at 12 years and an overall annual conversion rate of 6.30%.³ Conditions associated with progression include Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA).

RBD is typically diagnosed through a combination of clinical history and polysomnography (PSG), demonstrating REM sleep without normal muscle atonia. Diagnosis is often delayed or missed due to under recognition of early symptoms, limited access to sleep specialists, and the cost and availability constraints of PSG and sleep lab centers.^1,4

Emerging wearable technologies may help address these diagnostic challenges. By enabling multi-night, home-based monitoring, wearable devices can capture data that a single-night PSG may miss, while also expanding access to testing and increasing likelihood of timely treatment initiation. A recent 2025 study demonstrated that a wrist-worn actigraphy device paired with a machine-learning model could detect isolated RBD with as few as seven nights of data collection.⁴

A recent case report in JAMA Neurology, ”Self-Diagnosed REM Sleep Behavior Disorder Using a Consumer Device” illustrates how wearable device data may further serve as an early warning sign for RBD.⁵ In this case, an initial PSG and evaluation by a sleep specialist did not clarify the cause of a patient’s progressively worsening sleep-related vocalizations and movements. When data from the patient’s sleep tracking device revealed that these nocturnal activities coincided with transitions from REM sleep to wakefulness, he sought a second opinion, after which further clinical assessment and PSG confirmed the diagnosis. A subsequent skin biopsy showed phosphorylated α-synuclein (P-SYN) deposition, supporting the presence of an underlying synucleinopathy.

This case demonstrates how wearable devices can aid in RBD diagnosis and highlights skin biopsy-based alpha-synuclein detection as a beneficial tool for advancing early detection and improving understanding of synucleinopathies. Baseline data from the NIH-funded Syn-Sleep Study, an ongoing 24-month longitudinal investigation sponsored by CND Life Sciences, recently reported P-SYN detection in 75.0% of patients with iRBD who had no evidence of other neurodegenerative disease.⁶

A diagnosis of RBD can help patients take steps to improve sleep quality, through avoidance of exacerbating factors, implementation of sleep-safety measures, and consideration of trials of pharmaceutical therapies aimed at reducing the frequency of RBD episodes. In a recent survey of patients, 90% of individuals with iRBD indicated that they wanted prognostic information about their condition at the time of diagnosis.⁷Understanding the long-term implications of RBD may encourage patients to adopt positive lifestyle changes and health-related behaviors that could support better long-term health outcomes and quality of life. Initiatives such as the Proactive Brain Health Alliance offer resources and educational materials to support patients in this process.

Receiving a diagnosis may also create opportunities for participation in research aimed at delaying progression to more advanced clinical manifestations of synucleinopathies, particularly as disease-modifying therapeutics become available. NAPS is a consortium of researchers dedicated to advancing this goal.

Beyond supporting early detection of iRBD and early risk of synucleinopathies, wearable technology also holds promise for the longitudinal monitoring of sleep health. Modern wearable devices can provide continuous multi-dimensional insights that can help guide personalized lifestyle interventions. Sleep quality and duration are increasingly recognized as important contributors to brain health, influencing processes such as protein clearance, inflammation, neuroplasticity, oxidative stress, and dopamine signaling, all of which are associated with progression of synucleinopathies.⁸

Questions regarding the accuracy of wearable technology remain. The accuracy of three consumer devices (Oura Gen3, Fitbit Sense 2, Apple Watch Series 8) was studied over a single night in healthy adults, compared to concurrent PSG.⁹ While all devices were useful for sleep detection and total sleep time with moderate, device-dependent accuracy in sleep stage estimates, the Oura Ring demonstrated the highest sensitivity and substantial agreement with PSG by (sensitivity range ≈ 76.0–79.5%) across sleep stages and the closest nightly totals to PSG.⁸ However, PSG-based sleep analysis remains unmatched. Wearable devices cannot replicate the detailed physiologic data captured from various sensors used in PSG studies that monitor brain activity, eye movements, muscle activity, breathing patterns, and heart rhythm.

While wearable consumer devices are not a substitute for the multitude of sleep-related metrics available with PSG or specialized sleep expertise, the devices can provide individuals with useful insights into sleep trends and the effects of lifestyle and habits. As wearable technology continues to improve in response to consumer demand, its potential to support earlier interventions for synucleinopathies will also advance. With increasingly reliable and actionable data at our fingertips, the prospect of improved clinical outcomes for synucleinopathies is on the horizon.

<sup>References

1Postuma RB, Berg D. Advances in markers of prodromal Parkinson disease. Nat Rev Neurol. 2016;12(11):622-634. doi:10.1038/nrneurol.2016.152
2</sup>Haba-Rubio J, Frauscher B, Marques-Vidal P, et al. Prevalence and determinants of rapid eye movement sleep behavior disorder in the general population. Sleep. 2018;41(2):zsx197. doi:10.1093/sleep/zsx197
³Postuma RB, Iranzo A, Hu M, et al. Risk and predictors of dementia and parkinsonism in idiopathic REM sleep behaviour disorder: a multicentre study. Brain. 2019;142(3):744-759. doi:10.1093/brain/awz030
⁴Zhou L, Brink-Kjaer A, Gunter K, et al. Actigraphy-based detection of isolated REM sleep behavior disorder: multicenter validation across devices and populations. NPJ Digit Med. 2025;8(1):634. Published 2025 Oct 29. doi:10.1038/s41746-025-01999-z
⁵Marwaha S, Schenck CH, During EH. Self-Diagnosed REM Sleep Behavior Disorder Using a Consumer Device. JAMA Neurol. Published online September 22, 2025. doi:10.1001/jamaneurol.2025.3400
⁶T Levine, B Bellaire, C Gibbons, et al. 1276 The Syn-Sleep Study: Detection of Cutaneous Phosphorylated Alpha-Synuclein in REM Sleep Behavior Disorder, Sleep, Volume 48, Issue Supplement_1, May 2025, Pages A549–A550, https://doi.org/10.1093/sleep/zsaf090.1276
⁷Pérez-Carbonell L, Simonet C, Chohan H, et al. The Views of Patients with Isolated Rapid Eye Movement Sleep Behavior Disorder on Risk Disclosure. Mov Disord. 2023;38(6):1089-1093. doi:10.1002/mds.29403
⁸Bohnen NI, Hu MTM. Sleep Disturbance as Potential Risk and Progression Factor for Parkinson’s Disease. J Parkinsons Dis. 2019;9(3):603-614. doi:10.3233/JPD-191627
⁹Robbins R, Weaver MD, Sullivan JP, et al. Accuracy of Three Commercial Wearable Devices for Sleep Tracking in Healthy Adults. Sensors (Basel). 2024;24(20):6532. Published 2024 Oct 10. doi:10.3390/s24206532