GRAPHICAL ABSTRACT

INTRODUCTION

Parkinson’s disease (PD) is characterized by classical motor symptoms such as bradykinesia, rigidity, and resting tremor, as well as by a wide range of nonmotor symptoms [1]. Among these manifestations, sleep disturbances represent a core feature that profoundly impacts the quality of life of patients, affecting up to 80% of individuals with PD within the first 5 years of diagnosis and 96.5% of PD patients in cross-sectional studies [2,3]. Sleep-related symptoms in PD patients are heterogeneous and encompass various pathophysiological entities, including disturbed nocturnal sleep quality and fragmentation, REM sleep behavior disorder (RBD), nocturnal immobility, excessive daytime sleepiness (EDS), insomnia, circadian rhythm disturbances, obstructive sleep apnea (OSA), and restless leg syndrome (RLS).

For objective sleep assessment in PD patients, laboratory-based video-polysomnography (vPSG) has long been regarded as the reference standard. Actigraphy, which was first applied to sleep monitoring in the late 1970s, provided an earlier alternative for ambulatory sleep-wake estimation [4].

Despite their long-standing utility, these conventional approaches exhibit several limitations. Although it is diagnostically comprehensive, laboratory-based vPSG is costly, burdensome, and susceptible to the “first-night effect,” in which the unfamiliar clinical environment distorts normal sleep architecture and may yield nonrepresentative data. Although it is more accessible, classical actigraphy systematically overestimates sleep duration in parkinsonian patients, as the characteristic nocturnal immobility of PD is frequently misclassified as sleep by threshold-based algorithms [5,6]. These shortcomings collectively limit the ecological validity and scalability of conventional sleep assessments, particularly when longitudinal, home-based monitoring is needed.

Over the past decade, the convergence of miniaturized sensing hardware, ubiquitous mobile connectivity, and advances in machine learning and artificial intelligence (AI) has fundamentally transformed the landscape of sleep medicine. The classical reference standards (including vPSG and actigraphy) have themselves been transformed by these advances. Polysomnography (PSG) has progressively evolved toward ambulatory and AI-assisted configurations [7,8]. Moreover, actigraphy has advanced from single-axis, threshold-based devices to multiaxis inertial measurement units (IMUs) with AI classifiers. Additionally, entirely novel sensing paradigms have emerged, including ultrawideband (UWB) radar systems that achieve contactless remote sensing of respiratory signals.

Despite this technological advancement, the application of emerging digital technologies to the specific domain of sleep assessment in PD has lagged considerably behind both the general sleep medicine field and the PD motor symptom domain. This disparity is not a novel phenomenon; specifically, the nonmotor symptoms of PD, including autonomic dysfunction, cognitive impairment, neuropsychiatric features, and sleep disorders, have historically received substantially less investigative attention than motor symptoms, and the infrastructure of validated biomarker tools, structured clinical trials, and regulatory pathways has correspondingly been less developed [9].

Based on this background, this scoping review aims to systematically map the current state-of-the-art digital sleep technologies for patients with PD by specifically identifying the translational gap in technological application between the general population and PD-specific clinical requirements. For this purpose, we adopted a technology-driven framework and classified digital sleep technologies into four tiers based on technical maturity and regulatory status. The technologies are then mapped across 4 major technological lineages and across major PD-related sleep symptom domains.

METHODS

Study design

This study was conducted as a scoping review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) [10,11]. No formal registration was sought for this review; however, a structured internal protocol was followed throughout the review process.

Technology classification

To address the immense heterogeneity and wide spectrum of digital interventions, this review employed a dedicated tier system to systematically categorize the current technological landscape. The identified technologies were categorized into a fourtier framework based on their clinical validation status, technical modality, and intended use environment (Table 1). Tier 1 (T1) comprises classic technologies established as the clinical reference standards that are recognized by regulatory bodies and clinical guidelines, such as the classic full-channel in-lab PSG. Tier 2 (T2) consists of clinically validated digital technologies that offer portable or ambulatory alternatives to T1 by utilizing established algorithms to ensure clinical equivalence in remote settings, as exemplified by conventional actigraphy. Tier 3 (T3) represents AI-enhanced and multimodal platforms that leverage machine learning for automated scoring or sensor fusion; this category includes prominent consumer wearables such as the Apple Watch. Finally, Tier 4 (T4) encompasses emerging contactless or ambient sensing modalities, including UWB radar and undermattress ballistocardiography (BCG).

With this review’s emphasis on novel technological advancements, the selection process mainly focused on studies involving Tiers 2, 3, and 4. In addition, beyond the hierarchical tiers, technologies were systematically subcategorized according to their technical lineages into PSG and respiratory, IMU-based, electroencephalography (EEG)-based, and other modalities. These technical modalities were then mapped across 7 major sleep-symptom domains of PD: RBD, sleep quality/fragmentation and insomnia, circadian rhythm disturbance, EDS, nighttime immobility, sleep-related respiratory disorders, and sleeprelated movement disorders.

Search strategy

The PubMed/MEDLINE, Embase, Web of Science, and Scopus databases were searched for original research articles published in English before September of 2025. A three-block Boolean search strategy was developed comprising 1) population terms encompassing PD; 2) technology terms encompassing the full spectrum of digital sleep monitoring devices and methods; and 3) sleep outcome terms encompassing the sleep symptom domains of interest. The detailed search terms are listed in Supplementary Table 1.

Due to the vast diversity of digital tools and the inherent risk of omitting relevant literature through standardized search queries, we supplemented our methodology with a meticulous manual curation process and cross-referencing. This strategy included reviewing technologies that target the general population to identify the gap between current commercial products and PDspecific clinical applications.

In accordance with the Population-Concept-Context (PCC) framework of the PRISMA-ScR, the inclusion criteria were defined as follows: 1) population: studies involving patients with PD or parkinsonism exhibiting any sleep-related symptoms or disorders; 2) concept: novel digital technologies applied for the monitoring or assessment of sleep; and 3) context: original research, including clinical trials, observational studies, and pilot feasibility studies published in peer-reviewed journals. The exclusion criteria included studies with nondigital outcomes, those not involving sleep or PD/parkinsonism, and nonoriginal articles. Furthermore, traditional gold-standard assessments (i.e., Tier 1 technologies) were excluded unless they were utilized as a reference standard for validating a novel digital tool (Figure 1).

Data charting and synthesis of results

Study selection was conducted by two independent reviewers (KWP and JHS) with title and abstract screening, followed by full-text review. Afterward, the technologies were categorized according to the dedicated Tier system defined above. Disagreements were resolved through discussion and consensus. Data were extracted and charted according to several predefined variables to characterize both the technological and clinical landscapes of the included studies. Specifically, we recorded the technical category and assigned the technology tier alongside the underlying technology lineage. Device-specific information, including brand names and hardware models, was documented where available. Furthermore, we documented the key protocols of each study, population, major clinical findings, and reference details.

Given the extensive scope of this review, the primary summary table (Table 2) focuses exclusively on technologies categorized as Tier 2 to Tier 4 that have been validated within PD populations. The narrative text further includes sleep technological advances in the general population to supplement the PDspecific data presented in the table.

RESULTS

Search results

A total of 19 study outcomes were identified for the final synthesis, with one study overlapping in the sleep quality and night immobility domains (Figure 1 and Table 2). Among the sleep domains, RBD and nighttime immobility (representing the most characteristic symptoms of PD) were the most frequently represented domains. The technological distribution comprised 10 Tier 2, 7 Tier 3, and 2 Tier 4 studies. Although broad search terms were employed to encompass atypical parkinsonism, relevant literature was exceptionally rare, yielding only two studies that focused on respiratory audio analysis for stridor in multiple system atrophy (MSA). This synthesis of the Tier system and its integration with sleep domains can be observed in Figure 2. The following sections provide a comprehensive evaluation of these findings, stratified by sleep domain and contextualized by comparative evidence from the general population.

Sleep technology lineages

The identified technologies were categorized into four primary lineages based on their signal acquisition methods: 1) PSG and respiratory-based signals; 2) accelerometer-based signals, including actigraphy; 3) EEG-based signals; and 4) novel, ambient, or contactless sensing modalities.

PSG & respiratory-based signals

Within the PSG and respiratory lineage, full-channel, in-laboratory video PSG with a conventional manual sleep scoring system is classified as Tier 1 (T1). This method remains the diagnostic gold standard for a comprehensive assessment of sleep architecture, sleep quality, and various disorders, such as sleepdisordered breathing, movement disorders, and narcolepsy. This technique is characterized by continuous overnight supervision and the use of 15 to 20 sensors, including EEG, electrooculogram (EOG), electromyogram (EMG), electrocardiogram (ECG), respiratory effort, and infrared cameras.

Emerging home-based portable PSG devices are currently used that can utilize approximately 10–15 sensors to replicate the diagnostic outputs of in-lab video PSG in a domestic setting. These portable/ambulatory PSG devices are classified as Tier 2 (T2), as they provide the portability of standard in-lab ancestry. Although ambulatory PSG devices are validated with reference to standard in-lab PSG in providing comparable sleep staging and architecture data, the absence of professional supervision during the test introduces greater potential for technical artifacts or data inaccuracies.

Furthermore, AI-based automated scoring systems for PSG are categorized as Tier 3 (T3). Although these technologies are being increasingly validated for their ability to achieve scoring parity with standard PSG, their current evidence base primarily focuses on general scoring accuracy rather than specific validation within the PD population.

IMU-based wearables

IMU sensors refer to electronic devices that measure an object’s specific force, angular rate, and orientation. Historically, actigraphy preceded the current ubiquity of smartphones and smartwatches, thereby establishing this methodology as a reg-ulated medical device that integrates specialized hardware with algorithmic software. These systems process digital signals through filtering to distinguish physiological activity from environmental noise and quantify movement within predefined epochs. Since the late 1970s, regulatory-approved sleep-wake scoring algorithms have enabled at-home continuous monitoring [4]. Thus, actigraphy is a foundational precursor to modern digital neurotechnologies. In clinical practice, it is primarily utilized to quantify circadian rhythm, insomnia, and EDS.

Within our framework, these actigraphy devices utilizing traditional threshold-based algorithms are categorized as T2. If platforms apply automated AI models to raw accelerometer data from these devices, they are classified as T3. In this sense, regulatory-cleared smart devices or apps that are specifically engineered for the longitudinal monitoring of PD motor symptoms can also be included in the T3 category. These platforms were primarily developed to quantify PD motor symptoms using smartwatches or dedicated accelerometer sensors. Consequently, their actigraphy-like potential is often utilized to measure sleep-rest rhythm as an ancillary function.

EEG-based signals

The third lineage consists of EEG-based signals, which utilize a reduced electrode montage to provide high-fidelity neurophysiological monitoring outside of traditional laboratory settings. Although these devices do not meet the clinical criteria for full-channel PSG because of the absence of synchronized EOG and EMG, they are being increasingly recognized for their “near-PSG” capabilities, thereby offering a portable surrogate for brainwave-derived sleep staging. This category primarily features specialized headbands and forehead patches, such as the Dreem headband, SleepGraph, and Sleep Profiler. Within our framework, these technologies are predominantly classified as T3 because they transition away from manual expert scoring in favor of AI-driven, automated sleep-staging algorithms [12,13].

Novel and multimodal signals

The final lineage encompasses novel and multimodal sensing paradigms that redefine the traditional boundaries of sleep monitoring by shifting toward ambient, contactless, or highly integrated physiological assessment.

This category includes three major technological axes [14,15]. First, radar-based systems (UWB and frequency-modulated continuous wave [FMCW] systems, among others) enable blanket-penetrating, contactless monitoring and can simultaneously estimate sleep posture, body movement, and cardiorespiratory signals such as heart rate and respiratory rate. Second, camera-based approaches provide detailed information on body and head position, movement patterns, and transition mapping, although privacy concerns, lighting conditions, and occlusion remain important limitations. Third, undermattress and smart bed systems based on ballistocardiogram (BCG), piezoelectric, pressure, or pneumatic sensing are currently the most scalable methods for long-term home use, thus offering practical estimates of body movement, bed occupancy, sleep-wake behavior, and even sleep-disordered breathing (in some platforms). Collectively, these technologies provide the methodological foundation for next-generation digital biomarkers of sleep, with potential applications spanning sleep architecture, nocturnal movement, cardiorespiratory monitoring, and long-term home-based assessment. Given their emerging status and varying levels of clinical validation, these technologies are primarily classified as Tier 4.

Sleep domains

REM Sleep behavior disorder

RBD is a parasomnia characterized by the loss of physiological muscle atonia during REM sleep, thus resulting in dream enactment behavior. Among patients with PD, RBD is among the most prevalent and clinically significant nonmotor comorbidities, occurring in approximately 25%–58% of PD patients. Idiopathic RBD (iRBD) is now firmly established as the most specific prodromal marker of α-synucleinopathy. Longitudinal studies have demonstrated that a majority of iRBD patients will phenoconvert to overt α-synucleinopathy at a rate of approximately 7%–9% per year and most commonly convert to PD. In an effort to develop disease-modifying strategies for PD, there is a growing consensus that accurate and early detection of iRBD with precise polysomnographic confirmation is paramount for the early inclusion of the prodromal PD population.

According to the International Classification of Sleep Disorders, Third Edition (ICSD-3) [16], PSG recording to demonstrate REM sleep without atonia (RSWA) is a mandate to diagnose RBD (regardless of PD status) [17]. Thus, full-night in-lab video-PSG is a well-established T1 reference standard. Recent advances in home-based portable PSG devices (T2) and AI-based automatic PSG scoring algorithms (T3) have been developed to overcome the complexity of in-lab PSG equipment, environmental constraints, and the time-intensive nature of manual scoring [18-20]. Such modern PSG techniques have been primarily validated in common sleep disorders, most notably including OSA [21]. Formal validation in patients with PD remains largely absent from the literature, particularly with respect to RSWA scoring. However, given that the underlying PSG signal processing and sleep-staging algorithms are agnostic to the etiology of sleep pathology, it is biologically plausible that their performance would be generalizable to PD populations. Nonetheless, disease-specific validation in this cohort is warranted before broad clinical implementation.

Tier 2-level actigraphies have been explored for the detection of RBD in PD patients but have generally demonstrated relatively low sensitivity and accuracy (Table 2) [22,23]. Within the T3 category, the most prevalent technologies involve machine learning-based modeling of actigraphy signals to predict REM sleep stages or RSWA. These tools are trained by using vPSG as the ground truth to predict RBD as a proxy, thus circumventing the need for traditional EEG or EOG signals. Machine learningtrained wrist actigraphy features have demonstrated high efficacy in discriminating PD patients with RBD from those without RBD, thereby achieving an accuracy of 92.9% during inlaboratory recordings and 100% over 2-week home recordings in the PD cohort [24].

For iRBD detection in mixed clinical and community cohorts, an actigraphy-based machine learning model combined with a standard questionnaire yielded a sensitivity of 88.1% and a positive predictive value of 100% [25]. The same group subsequently reported a fully automated, questionnaire-free actigraphy classifier combining nighttime activity and 24-hour rest-activity rhythm (RAR) disruption in an iRBD cohort, which achieved a sensitivity of 78.9% and a specificity of 96.4% [26]. Another multicenter study compared three commercially available actigraphy devices and demonstrated that the sleep-based iRBD detection model was well generalizable across devices and populations, with areas under the receiver operating characteristic curve (AUCs) of 0.838–0.865 being reported (regardless of device resolution) [27]. Among these studies, PD patients with RBD were included in only one study [24]. The remaining studies were conducted exclusively in the iRBD cohort, which highlights a critical research gap in the validation of actigraphy-based RBD detection within PD populations.

Commercial smartwatch-grade devices for inferring RBD can be categorized as T3 in this review. For example, a previous study utilized the accelerometry and heart rate signals from the ASUS VivoWatch BP to classify sleep stages with machine learning [28]. Another study in a PD cohort revealed that Empatica’s E4 wristband accelerometry-derived nocturnal motor activity metrics could capture symptom burden that is not fully reflected in self-reported RBD, with high internal consistency being observed when sensor data were integrated with questionnaire items [29].

Other T3 technologies include EEG-based wearable devices that retain EEG and/or EOG signals but lack full laboratory PSG montages to assess RBD and RSWA. A proof-of-concept study simultaneously recorded a self-applicable two-channel portable EEG/EOG device alongside in-lab PSG in 8 PD patients, with the results demonstrating no significant difference in sleep staging parameters, along with demonstrating RSWA detection agreement in six of eight patients (κ=0.686), thereby supporting the feasibility of home-based RBD screening in PD patients [30]. Another headband-based EEG device was utilized to develop and validate an automated RSWA detection algorithm, which demonstrated concordance with expert visual RSWA scoring in iRBD patients during both in-laboratory and multinight in-home recordings [31].

Notably, several T4-categorizable contactless camera-based automated video analyses have emerged for RBD detection. A 3D time-of-flight camera signal could automatically quantify movement across body regions during REM sleep in iRBD patients, thereby achieving a test accuracy of 0.866.32 A similar conventional 2D clinical camera-based approach on 172 vPSG recordings demonstrated a peak accuracy of 91.9% in detecting RBD events [33]. However, these studies were conducted only in iRBD cohorts, thus demonstrating that the validation of contactless RBD detection in the PD population is an open area for further investigation.

Sleep quality/fragmentation & insomnia

Sleep quality impairment, sleep fragmentation, and insomnia represent the most prevalent sleep disturbances in PD patients, with insomnia alone being reported in 27%–80% of PD patients. Sleep quality and fragmentation represent multidimensional, partially subjective constructs that emerge across multiple sleep disorders and reflect disrupted sleep continuity and insufficient sleep depth (rather than a discrete diagnostic category within the ICSD-3). For this reason, these sleep disturbances have traditionally been assessed through a combination of validated subjective instruments, including the Pittsburgh Sleep Quality Index and the Insomnia Severity Index. More objective parameters include PSG-derived features such as sleep efficiency (SE), the arousal index, N1 percentage, wake after sleep onset (WASO), and total sleep time (TST), among other parameters. More recently, home-based actigraphies have been widely used, as they enable the multinight, ecologically valid quantification of restactivity fragmentation and sleep-wake patterns.

With respect to PD, one Tier 2-level wrist-worn IMU measured the nocturnal bradykinesia score as a measure of sleep quality [34]. Among the Tier 3 devices evaluated in PD, the Verily Study Watch provides the most direct evidence that wearablederived sleep metrics meaningfully reflect the clinical burden of sleep fragmentation and insomnia in PD. In 149 Parkinson’s Progression Markers Initiative (PPMI) participants, this wearable-measured TST was significantly associated with cognitive performance, and increased WASO was independently associated with worse motor complications. These findings suggest that passively collected, multinight sleep fragmentation metrics capture PD nonmotor disease progression [35]. One study used the combination of actigraphy and vPSG in 50 PD patients undergoing deep brain stimulation to demonstrate that objective improvements in sleep continuity (↑SE, ↓WASO, and ↓arousal index) following stimulation were tightly coupled to reductions in daytime sleepiness and improvements in subjective sleep quality [36]. Their methods demonstrate that multimodal wearable-augmented monitoring can be used to sensitively track sleep quality responses to therapeutic interventions.

Consumer-grade smartwatches and smart rings are increasingly capable of estimating TST, SE, WASO, and nocturnal awakening frequency in healthy adults with reasonable concordance to PSG; however, the clinical relevance of these metrics in PD has not been formally assessed with these devices. Critically, actigraphy-based studies in PD have already established that standard wrist-worn movement-based algorithms overestimate sleep and underestimate WASO and fragmentation, particularly in PD patients with greater disease severity and associated nocturnal immobility [5]. This observation is attributed to the fact that algorithm-detected immobility is often misclassified as sleep in the context of PD rigidity and akinesia. This fundamental confounding factor warrants further validation of these consumer wearables in the PD population.

A similar critical research gap is evident in the Tier 4 contactless sensing technologies with respect to differentiating between PD and healthy adults. Undermattress BCG devices, radar systems, or smart beds offer a theoretically compelling approach to sleep fragmentation and insomnia monitoring in PD precisely due to the fact that they completely eliminate the wearability burden. The Withings Sleep Analyzer has demonstrated the ability to estimate TST, SE, sleep fragmentation, and the apneahypopnea index from BCG and microphone signals in older adults and dementia patients. However, formal prospective validation of these novel devices against PSG-confirmed sleep fragmentation metrics or ICSD-3-defined insomnia in a PD-specific cohort has not been reported.

Circadian rhythm disturbances

Circadian disruption is being increasingly recognized as a core sleep symptom in PD that multidirectionally affects autonomic, mood, and cognitive symptoms, as well as overall quality of life. Circadian biomarkers typically include wrist actigraphy with 24-hour RAR metrics and sometimes biofluid markers such as circadian levels of melatonin or cortisol. These biomarkers are correlated with poor sleep continuity and efficiency; additionally, they often accompany insomnia and sleep fragmentation. With respect to the Tier 2 category, a large cohort study of 239 patients with PD (in which 7 days of continuous actigraphy was used) demonstrated fragmented sleep and disrupted circadian rhythms [37].

One study that focused on the circadian rhythm itself in PD employed an ambulatory circadian monitoring device, which we classified as a T3 device because of its multimodal biosignals, including skin temperature, triaxial accelerometry, body position, and light exposure. They reported reduced daytime activity, fragmented sleep, and circadian vulnerability in a PD population [38]. Notably, fully fledged AI pipelines trained on circadian features have been developed in non-PD cohorts to distinguish insomnia subtypes and delayed sleep-wake phase disorder using similar devices [39]. However, these frameworks have not yet been applied or validated in PD patients. Similarly, new contactless Tier 4 sensing modalities for quantifying circadian rhythm disruption have not yet been validated in PD patients.

Excessive daytime sleepiness

EDS is defined as the inability to maintain wakefulness and alertness during the daytime despite a circadian drive that normally promotes wakefulness [40]. In PD, EDS is a common nonmotor symptom observed across the disease course (ranging from prodromal to advanced stages) and has been reported in approximately 20%–50% of patients [41-43]. In addition to its high prevalence, EDS substantially impairs daily functioning and quality of life; moreover, it causes cognitive dysfunction, increases the risk of falls and may also represent a prodromal feature associated with the risk of PD.

For a T1 gold standard evaluation, lab-based PSG followed by standardized daytime testing remains the reference approach. The most established physiological tools include the Multiple Sleep Latency Test (MSLT) and the Maintenance of Wakefulness Test (MWT) [44]. T2 approaches extend this framework, and daytime actigraphy enables the objective quantification of napping behavior; moreover, it demonstrates a dissociation between subjective sleepiness and objectively measured daytime sleep in PD patients (Table 2) [45]. Wrist actigraphy has similarly been used to characterize sleep-wake disturbance patterns in PD in relation to clinical symptoms [46]. In addition, actigraphybased daytime napping has been observed to be associated with future PD risk in older adults [47]. Beyond PD, psychomotor vigilance testing provides a validated digital measure of sustained attention and reaction-time lapses associated with daytime sleepiness in OSA [48].

Among the T3 category studies, no studies have been performed that included a PD population. However, for the general population, wrist-worn wearable sensors have been observed to classify drowsy versus alert states in real time [49]; additionally, wearable glass systems combined with convolutional neural networks have enabled eye-closure and blink-based drowsiness detection using a compact, body-worn platform [50]. Moreover, machine learning models applied to continuous actigraphy have demonstrated the feasibility of classifying sleep and wake states over 24-hour intervals without predefined rest windows [51], and proximity-sensor-based wearable systems have demonstrated that blink behavior and behavioral slowing can detect real-world drowsiness states [52]. Longitudinal smartwatch monitoring has further enabled the extraction of digital biomarkers in hypersomnolence cohorts, thereby supporting the feasibility of prolonged passive monitoring [53].

No studies included the PD population in the T4 category. However, various T4 technologies have been adopted for the evaluation of daytime sleepiness. Camera-based systems using eyelid closure metrics such as percentage of eyelid closure (PERCLOS), together with facial physiological signals, detected drowsiness in real time without body-worn sensors [54]. More recent deep learning frameworks, including transformer-based architectures, have further improved contactless classification of drowsy states from visual input alone [55]. In addition, ultralongterm subcutaneous EEG has been proposed as a continuous home-monitoring platform for sleepiness-related conditions, thereby potentially enabling objective longitudinal biomarkers of daytime sleep propensity [56]. Collectively, these findings indicate that although PD-specific EDS research still strongly relies on T1–2 approaches, T3–4 technologies in the broader population are rapidly advancing toward scalable, passive, and continuous monitoring.

Nighttime immobility

Nighttime immobility (also referred to as nocturnal hypokinesia or impaired bed mobility) is a PD-specific nocturnal motor symptom characterized by difficulty with turning in bed or changing body position during the night. Clinically, it may manifest not only as impaired axial turning itself but also as pain, cramping, sleep fragmentation, caregiver burden, and complications related to prolonged immobility. Due to its close relationship with nocturnal wearing-off and early-morning off, nighttime immobility has important therapeutic implications and may prompt adjustments of nocturnal dopaminergic treatment [57,58].

For objective assessment, T1 laboratory-based PSG remains the gold standard [59]. In this context, nocturnal mobility is typically assessed via manual video-based quantification of turning events and body position changes. However, this approach is labor intensive and not optimized for capturing specific nocturnal motor deficits, such as nocturnal hypokinesia or early-morning akinesia [60,61].

With respect to the T2 category, single or multisite inertial sensor systems have demonstrated the ability to quantify turning movements in bed, movement velocity, and getting-out-ofbed performance in PD patients (Table 2) [62-64]. These metrics are associated with sleepiness and mood [62]. These approaches have also been extended to treatment-response monitoring, thereby exhibiting improvement in nocturnal mobility following apomorphine infusion [65]. Collectively, these studies establish wearable inertial sensing devices as the core validated digital biomarker framework for night-time immobility in PD.

In T3, several algorithmic approaches have been applied to PD and prodromal PD for the assessment of nocturnal hypokinesia. Wearable-based machine learning models have demonstrated that immobile bouts can predict abnormal polysomnographic findings with clinically relevant accuracy [66], whereas trunk sensor-based monitoring has revealed reduced nocturnal motor activity in both PD and iRBD patients [67]. Large-scale multimodal smartwatch data from observational cohorts further support the feasibility of passive nocturnal motor monitoring in real-world PD settings [35]. Additionally, for the general healthy population, in-bed movements classified using pressure-based sensing and algorithmic frameworks [68] and the wrist kinematics-based detection of posture transitions and immobility have been investigated [69].

In T4, load-cell systems placed under the bed have demonstrated the ability to detect and classify nocturnal movements without body-worn sensors [70], whereas posture-tracking frameworks enable the classification of lying positions and transitions [71]. Bed-based sensor technologies have further demonstrated feasibility for the continuous overnight monitoring of movement in real-world environments for spinal cord injury/stroke patients [72] and in Alzheimer’s dementia patients [73]. However, these technologies have not been studied or validated in PD.

Sleep-related respiratory symptoms

Sleep-related respiratory disorders, including OSA and nocturnal stridor, are increasingly recognized as clinically relevant nonmotor features in PD and related synucleinopathies, especially in MSA. These conditions contribute to sleep fragmentation, hypoxemia, and disease burden; moreover, in certain cases (such as stridor in MSA), these conditions may be associated with increased mortality risk. Therefore, objective characterization has primarily relied on PSG, which enables simultaneous measurements of airflow, respiratory effort, oxygen saturation, and associated motor phenomena. PSG-based studies in PD and other synucleinopathies have demonstrated the presence of sleep-disordered breathing and its clinical correlates, thus establishing a physiological foundation for respiratory biomarker development [74-76]. These T1 approaches remain essential for accurate phenotyping, as they capture disease-specific pathophysiology that cannot be inferred from indirect signals. With respect to the T2 category, studies incorporating laryngeal EMG in conjunction with PSG have demonstrated abnormal vocal cord activation patterns underlying nocturnal stridor in the MSA population [77,78].

In contrast, T3 technologies developed in the general population have focused on scalable and automated detection of respiratory events using wearable and reduced-sensor systems. Deep learning approaches leveraging signals such as photoplethysmography and oxygen saturation have demonstrated the feasibility of detecting OSA by using smartwatch-based platforms [79], whereas wearable multimodal systems integrating physiological signals have further expanded the scope of ambulatory monitoring [80]. These approaches aim to approximate apnea-related events without full PSG setups, thereby offering substantial advantages in terms of accessibility and long-term monitoring, although their validation remains largely confined to nonneurodegenerative populations.

T4 approaches extend this paradigm to fully contactless monitoring using ambient sensing technologies. However, to date, no studies have systematically evaluated these approaches in PD. In the general population, radar-based systems (including FMCW and mmWave platforms) enable the detection of respiratory motion and apnea-related events without physical contact, with recent studies demonstrating agreement with PSGderived metrics [81,82]. Similarly, bed-embedded sensors based on BCG or pressure signals allow for the passive monitoring of respiratory patterns during sleep [83,84], and audio-based systems for measuring breathing abnormalities have been studied in the general population [85]. The audio-based approach has been applied to MSA, with this approach being used to detect stridor and to support both diagnostic and prognostic assessments [86,87].

Monitoring of sleep-related movement

RLS is a common neurological disorder with an estimated prevalence of approximately 5%–10% in the general population, whereas in PD patients, pooled estimates suggest a prevalence of approximately 14% (despite heterogeneity being observed across studies) [88]. Periodic limb movements in sleep (PLMS), which is an objective polysomnographic endophenotype of RLS, are also frequently observed, with PLMS indices exceeding 15/hour in approximately 25%–40% of PD patients [88]. Clinically, these conditions contribute to sleep fragmentation and daytime dysfunction; importantly, subjective RLS symptoms are not consistently correlated with objective PLMS, particularly in PD patients, thus highlighting the need for physiology-based biomarkers [88].

T1 technology for vPSG with anterior tibialis EMG remains the gold standard for PLMS quantification in the PD population [89,90]. More scalable T3 approaches extend this framework to ambulatory settings through wearable and home-based monitoring systems. These systems include reduced-sensor EMG pipelines for automated sleep-event detection [91]; machine-learning approaches that classify PLMS-related phenotypes from video-PSG and autonomic features [92]; and actigraphy-based approaches that estimate PLMS from limb movement signals [93]. Device-independent ankle-actigraphy algorithms can detect PLMS in ambulatory settings [94], and newer wearable systems (such as SOMNOwatch) can achieve encouraging agreement with PSG-derived PLMS measures [95]. Moreover, automated PSGbased algorithms have demonstrated that periodic and nonperiodic limb movements can be computationally separated [96]. T4 approaches extend beyond body-worn sensors toward contactless monitoring. Three-dimensional video analysis has demonstrated the feasibility of PLMS detection with reasonable agreement against PSG [97], and radar-based systems have more recently been proposed for noncontact PLMS monitoring [98]. Additional exploratory systems include textile multisensor ankle bands [99] and ultrasonic sensing approaches [100]. The concept of bed-based movement monitoring can be traced back to the static chargesensitive bed [101]; however, these methods primarily detect gross movements rather than limb-specific periodic activity and consequently cannot yet replace EMG-based PLMS quantification. Thus, their role is currently exploratory, and further validation against EMG-based PLMS metrics is needed before they can be considered as reliable digital biomarkers for periodic limb movement disorder (PLMD).

DISCUSSION

In this review, we summarize recent advances in digital biomarkers for sleep symptoms applied to PD and parkinsonism, with these advances being structured according to a tiered technological framework.

Sleep disturbances are among the most prevalent and disabling nonmotor manifestations of PD, encompassing RBD, insomnia and sleep fragmentation, circadian rhythm disturbance, EDS, nocturnal immobility, sleep-related respiratory disorders, and sleep-related movement disorders [2]. Rather than representing isolated complaints, these symptoms collectively reflect degeneration across dopaminergic, cholinergic, brainstem, hypothalamic, and autonomic networks, thus supporting the concept of sleep as a composite nonmotor marker of PD burden and progression [2,102]. Clinically, sleep symptoms are associated with poor quality of life, worsened cognitive performance, high caregiver burden, and impaired daily function; however, many sleep symptoms remain underrecognized because they fluctuate over time and overlap with fatigue, depression, apathy, autonomic dysfunction, and medication effects [2,3].

Need for novel tools beyond conventional vPSG

Laboratory-based vPSG remains the reference standard because it simultaneously captures EEG, EOG, EMG, cardiorespiratory, body position, and nocturnal behavior data, thus enabling the diagnosis of parasomnias, respiratory disorders, PLMS, and physiological sleepiness when combined with MSLT or MWT [16]. However, vPSG is expensive, labor intensive, and poorly suited to repeated or longitudinal assessments in real-world settings [65]. These constraints are particularly problematic in PD, wherein symptoms such as nocturnal hypokinesia, early-morning akinesia, fragmented sleep, or daytime somnolence may vary substantially from night to night or may be missed by single-night laboratory recordings [63]. Classic actigraphy partially addresses the issue of accessibility; however, in PD, it can overestimate sleep by misclassifying quiet wakefulness and rigidityrelated immobility as sleep, thus again emphasizing the need for objective and longitudinal digital biomarkers [46]. In this context, novel sleep technologies that are more accessible, scalable, and quantitatively robust are being rapidly developed, thereby corresponding to the T3–T4 categories outlined in our review [14,15,103].

Uneven translation of Tier 3–4 technologies into PD and synucleinopathies

Our review revealed that PD-specific applications of Tier 3–4 technologies remain largely limited and are unevenly distributed across sleep symptoms. The strongest evidence is concentrated in RBD and nocturnal immobility (Table 2). In RBD, wearable actigraphy and smartwatch-based approaches have been observed to be effective in PD or iRBD cohorts, including home actigraphy classifiers and smartwatch-derived REM-related motor signatures (Table 2) [24,28]. In terms of nocturnal immobility, validated wearable sensor studies have objectively quantified turning movements in bed, movement velocity, positional risk, and getting-out-of-bed performance; moreover, these studies have even demonstrated treatment responsiveness after nocturnal dopaminergic therapy [62,63,65]. In contrast, digital biomarker evaluation is relatively sparse for insomnia, EDS, and sleep-related respiratory disorders, as well as circadian rhythm disorders or PLMD in PD patients (Table 2).

Tier 3 technologies: multimodal, scalable technologies for sleep monitoring

Tier 3 technologies provide an avenue by incorporating multimodal integration and utilizing machine learning algorithms. In the general population, machine learning models now combine movement, heart rate, photoplethysmography, respiratory, or behavioral response signals to infer sleep stages, drowsiness, or respiratory events at scale [49,51]. Consumer-facing ecosystems have also rapidly expanded; specifically, a 2025 review of home sleep monitoring technologies evaluated 21 smartphone applications, 16 smartwatches, and 9 smart mattresses, thereby highlighting the broad application of these technologies to sleep tracking, snoring, fragmentation, and physiological monitoring [14]. As noted above, in PD, the deployment of Tier 3 technologies remains largely restricted to selected domains such as RBD and nocturnal immobility. Nevertheless, multimodal and scalable Tier 3 applications are particularly important in PD because the biology of PD-related sleep dysfunction is inherently multimodal2 and not biologically or clinically independent. For example, distinguishing daytime somnolence from immobility or sleep fragmentation from nocturnal akinesia often requires the integrated interpretation of movement, neurophysiological, and cardiopulmonary signals [93-95]. Similarly, in respiratory sleep disorders, auditory analysis alone may identify stridor but not fully characterize airway physiology or broader sleep architecture [86,87]. For this reason, Tier 3 systems that integrate mobility with EEG, cardiorespiratory, or behavioral signals are likely to provide greater clinical relevance and information, particularly in PD and related synucleinopathies.

The particular strength of Tier 3 systems is based not only in the collection of multiple signals but also in the use of AI-based models to identify latent patterns across modalities, improve classification performance, and capture complex physiological states that are difficult to resolve with any single channel alone [104]. In our review, Tier 2 simple IMU-based detection of RBD in PD demonstrated lower sensitivity and accuracy [22,23]; however, when this method was combined with machine learning analysis, the clinical accuracy significantly increased [24]. For other examples, wearable multimodal systems integrating acceleration, ECG, and skin temperature have outperformed conventional actigraphy for sleep-stage classification [105], whereas deep learning models combining cardiorespiratory and body-movement features have enabled meaningful estimation of sleep architecture from a reduced set of noninvasive signals [106]. More recently, benchmark studies have suggested that incorporating respiratory information in addition to actigraphy and cardiac dynamics can further improve machine-learning-based sleep-stage classification beyond movement- or cardiac-only approaches [107]. Accordingly, the future development of sleep biomarkers in PD will likely depend on multimodal digital assessments of mobility integrated with machine learning-based analytical frameworks, as exemplified by Tier 3 systems.

Tier 4 technologies: ambient and contactless monitoring

Tier 4 technologies extend sleep assessment into fully passive and contactless monitoring. Radar-based systems using UWB, FMCW, or mmWave signals now allow for blanket-penetrating respiratory and movement monitoring. Additionally, camerabased systems using RGB, infrared, depth, or thermal imaging can classify posture, movement, and sleep stages. Furthermore, undermattress or bed-embedded systems can also estimate movement, sleep stages, respiration, and heart rate without the need for body-worn sensors [14,15,103]. The major advantage of Tier 4 systems is their ability to achieve high ecological validity with minimal patient burden. Due to the fact that they do not require the attachment of sensors or overnight laboratory admission, these approaches are particularly well suited for repeated, longterm monitoring in the home environment, where natural sleep behavior can be observed more accurately than in the sleep laboratory setting. Recent reviews have emphasized the notion that ambient and bed-embedded monitoring platforms are attractive precisely due to the fact that they improve comfort, reduce behavioral interference, and make continuous physiological surveillance more feasible at scale [103]. In addition, consumer-oriented reviews suggest that smart mattresses and other passive systems may offer a particularly favorable balance between usability and longitudinal adherence, even if their current clinical validation remains more limited than that of laboratory PSG or some wearables [14]. Collectively, Tier 4 technologies highlight the convergence of ambient sensing and AI-driven signal interpretation, which enables the evaluation of sleep symptoms while preserving their nonintrusive nature. These features are particularly important for PD and related synucleinopathies, wherein progressive decreases in mobility and frailty limit the feasibility of conventional assessments of traditional Tier 1 PSG-based or Tier 2 wearable approaches. In this context, Tier 4 biomarkers may enable continuous and longitudinal monitoring in patients with PD. However, it should also be noted that Tier 4 technologies are relatively newly developed tools, and their rapid expansion (particularly in the consumer sleep-tracking market) has outpaced rigorous scientific evaluation [108]. Accordingly, further validation is needed, including careful assessments of their accuracy and agreement with established reference standards such as PSG.

Clinical gap and future directions for PD

The applications of Tier 3 and Tier 4 technologies in PD remain sparse, and the major clinical gap does not involve the lack of available technology; rather, it involves the lack of PDspecific validation. In the general population, sleep evaluation platforms currently span nearly the full spectrum of sleep-monitoring modalities, including smartphone applications, smartwatches, smart mattresses, radar-based systems, and camerabased approaches [14]. In contrast, studies in PD remain concentrated in a limited number of symptom domains and are typically characterized by small cohorts, short or incomplete longitudinal follow-ups, and insufficient multimodal benchmarking, as reviewed in this article. In the future, Tier 3–4 technologies will be prospectively validated against domain-specific reference standards in PD and related synucleinopathies. Furthermore, multimodal digital frameworks should be explicitly developed to resolve clinically important ambiguities. Along with the development of computer vision and machine learning technology, digital sleep monitoring could transition from being an exploratory adjunct to becoming a scalable tool for longitudinal phenotyping and therapeutic monitoring in PD. These techniques could ultimately enable comprehensive assessments of sleep in elderly individuals and prodromal PD patients, thereby facilitating early stratification of high-risk groups. In addition, these measures may serve as nonmotor outcome measures in clinical trials for PD.

Supplementary Materials

Notes

Conflicts of Interest

The authors have no financial conflicts of interest.

Funding Statement

None

Acknowledgments

None

Author Contributions

Conceptualization: Kye Won Park, Jung Hwan Shin. Data curation: Kye Won Park, Jung Hwan Shin. Investigation: Kye Won Park, Jung Hwan Shin. Methodology: Kye Won Park, Jung Hwan Shin, Han-Joon Kim. Project administration: Kye Won Park, Jung Hwan Shin. Resources: Jung Hwan Shin. Software: Kye Won Park, Jung Hwan Shin. Supervision: Ki-Young Jung, Han-Joon Kim. Writing—original draft: Kye Won Park, Jung Hwan Shin. Writing—review & editing: Ki-Young Jung, Han-Joon Kim.

Figure 1.

PRISMA-ScR flowchart. PD, Parkinson’s disease.

Figure 2.

Integration of sleep technologies within the Tier system across sleep domains and summarized review findings. PD, Parkinson’s disease; AI, artificial intelligence.

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