Ambulatory devices to detect sleep bruxism: a narrative review

Abstract

Sleep bruxism is a masticatory muscle activity that occurs during sleep and presents as rhythmic or non‐rhythmic activities commonly seen in healthy individuals but might also represent movement or sleep disorders. Given that the clinical presentations of sleep bruxism are relevant to dentistry, the early detection of sleep bruxism is of particular interest to dentists. However, the gold standard for sleep bruxism diagnosis involves polysomnography with audio‐visual recording – a resource intensive and mostly inaccessible diagnostic method. As such, ambulatory devices to detect sleep bruxism have the potential to address the limitations of polysomnography. This review of the literature was carried out up until December 2024 on commercially available ambulatory devices in detecting sleep bruxism. Select ambulatory devices appear promising as a preliminary screening tool for sleep bruxism both in clinical practice and for domestic use.

Abbreviations

AASM

american academy of sleep medicine

AI

artificial intelligence

BEI

bruxism effort index

BPI

personal bruxism index

BTI

bruxism time index

CNN

convolutional neural network

ECG

electrocardiography

EEG

electroencephalography

EMG

electromyography

EOG

electrooculography

ICAB

international consensus on the assessment of bruxism

ICSD‐3

international classification of sleep disorders 3rd edition

ISFDD

intra‐splint force detection device

mEMG

masseter electromyography

MM

mandibular movement

MVC

maximum voluntary clench

OFA

orofacial activities

OMA

other muscular activities

OSA

obstructive sleep apnoea

PPV

positive predictive value

PSG

polysomnography

PSG‐AV

polysomnography with audio‐video recording

RMMA

rhythmic masticatory muscle activity

SB

sleep bruxism

sEMG

surface electromyography

STAB

standardized tool for the assessment of bruxism

TMJ

temporomandibular joint

XGB

extreme gradient boosting

Clinical Relevance.

Ambulatory devices offer portable and affordable alternatives for clinicians and patients in detecting and monitoring sleep bruxism enabling more comprehensive treatment planning and better quality of care. This narrative review based on the current scientific evidence evaluates commercially available ambulatory devices to detect and monitor sleep bruxism and their utility against the gold standard of polysomnography with audio‐visual recording.

INTRODUCTION

Our understanding of sleep bruxism (SB) has advanced significantly over the past few decades. ¹ SB is defined as a masticatory muscle activity during sleep, characterized as rhythmic (phasic) or non‐rhythmic (tonic). It is not classified as a movement or a sleep disorder in otherwise healthy individuals. ² The characteristic pattern noted on electromyography (EMG) is termed rhythmic masticatory muscle activity (RMMA), which is observable in the masseter and temporalis muscles. This activity might be preceded by sleep microarousals and manifests in two forms: phasic (repetitive contractions) and tonic (sustained clenching). ³ , ⁴ SB should be distinguished from awake bruxism, a different pathophysiological entity that occurs during consciousness. ¹

The prevalence of SB is reported to be 8%–16% in adults, ⁵ 3% in elderly and as high as 40% in children. ⁶ , ⁷ This high prevalence has significant implications for health outcomes as SB is a sign associated with a variety of dental and systemic disorders (Table 1). Despite extensive research, the aetiopathology of SB remains enigmatic and is likely multifactorial. ¹ , ⁵ The currently accepted model for SB suggests a central nervous system‐mediated process involving cardiac‐autonomic activation. ⁸ , ⁹ Increased sympathetic tone has been linked to sleep microarousals, elevated heart rate and respiratory events that precede SB‐related RMMA. ¹ , ³ , ¹⁰ Stress, medications and genetics are other putative factors in this model. ¹

Table 1.

Intraoral and extraoral complications of sleep bruxism

Location	Complications
Intraoral	Hard tissue: destruction of restorations, enamel or dentin attrition, implant failure, tooth fracture, tooth loss, pulp exposure, vertical enamel craze lines, 1 pulpitis, narrowing of the occlusal surface. 5 Soft tissues: periodontal changes, soft‐tissue trauma, parotid obstruction, prominent buccal linea alba (static bruxism). 1
Extraoral	Head and neck: TMJ disease including condylar flattening, masticatory myalgia, temporal headache, sleep disorders including OSA, 1 sleep disruption. 4 Systemic: hypertension, cardiovascular disorders, 1 neurological disorders. 5

TMJ = temporomandibular joint; OSA = obstructive sleep apnoea.

SB is assessed using a combination of questionnaire, clinical examination and instrumental analysis. ¹ , ¹¹ The international consensus on the assessment of bruxism (ICAB) in 2018 ² and the International Classification of Sleep Disorders 3rd edition (ICSD‐3) by the American Academy of Sleep Medicine (AASM) ⁴ criteria are the two most widely used assessment frameworks. ¹ , ² , ⁴ According to ICSD‐3, SB requires regular or frequent tooth grinding sounds during sleep (might be self‐reported) and abnormal tooth wear with or without jaw muscle pain, temporal headache and jaw locking upon waking. ⁴ The ICAB proposes that SB patients be categorized into possible, probable and definite bruxism based on positive self‐reports, clinical findings and instrumental evaluation, respectively. ² The categorization of possible and probable bruxism (i.e. non‐instrumental evaluation) are poor indicators of SB with sensitivities (49% and 22%, respectively) and specificities (80% and 89%, respectively). ¹² The ICSD‐3 criteria using self‐report was found to be a good screening tool for SB diagnosis (sensitivity 58%), with muscle fatigue and temporal headache scoring high sensitivity (78% and 67%, respectively) but lacked the diagnostic reliability to replace polysomnography (PSG) with audio‐visual recording as the current gold standard. ¹² This is supported by the inherent inaccuracy of self‐report and clinical examination which do not indicate current activity and severity. ¹¹ , ¹³ Recently, a new assessment protocol, the Standardized Tool for the Assessment of Bruxism (STAB), has been developed and comprises patient‐based, clinical‐based and instrument‐based evaluation methods including PSG and ambulatory devices to assess for sleep and awake bruxism. ¹⁴ , ¹⁵

PSG is a quantifiable analysis of sleep patterns and disturbances usually assessed over a single night. ¹⁵ There are four types of sleep studies: Type I is carried out in a sleep centre with support from a sleep technician and records 12 channels, ¹³ Type II–IV, or ambulatory monitors, are performed at home with no assistance and no audio‐visual recording. ¹³ Type II measures the same channels as Type I. ¹³ Type III has two to four channels while Type IV employs only one channel. ¹³

Type I PSG, or polysomnography with audio‐video recording (PSG‐AV) is regarded as the gold standard for assessing SB and for evaluating comorbidities with other sleep disorders such as obstructive sleep apnoea (OSA). ¹¹ , ¹⁶ It monitors electroencephalography (EEG), electromyography (EMG), electrooculography (EOG), electrocardiography (ECG), leg movements, respiratory effort, airflow and pulse oximetry plotted against sleep stages to identify patterns of sleep disturbance. ¹¹ , ¹⁷ The EMG pattern scoring to establish a polysomnographic diagnosis of SB is based on sleep microarousal associated RMMA of the masseter and temporalis muscles. ¹¹ A RMMA burst has a mean amplitude two times the baseline EMG amplitude. ⁴ Audio‐video recording improves sensitivity and specificity in SB detection by allowing the recognition and exclusion of other orofacial activities (OFA, e.g., swallowing) and other muscular activities (OMA, e.g., head movement) that would otherwise lead to the overestimation of RMMA by 23.8%. ¹¹ , ¹⁶ , ¹⁸ Sleep staging is important in distinguishing SB from awake bruxism as RMMA, OFA and OMA events also occur during brief periods of wakefulness. ¹⁶

The reference points for SB diagnosis based on EMG measurements were first validated in 1996 and have since been revised and substantiated by the AASM and retrospectively updated in 2007. ⁴ , ¹⁹ , ²⁰ An updated overview of SB diagnosis published in 2015 ⁶ synthesizes the 1996 ¹⁹ and AASM ⁴ criteria by including the additional requirement that more than two RMMA episodes per hour needs to be observed with at least one episode associated with grinding sounds. Nonetheless, the diagnostic criteria outlined by the 1996 study, ¹⁹ 2015 overview ⁶ and AASM ⁴ are commonly cited, with the first being the most frequently used. ²¹ The 1996 study's criteria propose PSG‐AV cut‐off points (four RMMA episodes per hour) for SB diagnosis and shows a sensitivity of 72% and specificity of 94%. ¹⁹ However, due to the time‐variability of SB, per hour and per night, the use of cut‐off bands around these points have been posited. ²² Further, the night‐to‐night variability of SB, and potentially, the first‐night laboratory effect decreases PSG‐AV diagnostic accuracy. ¹⁶ , ²¹ SB diagnostic accuracy is a common study limitation that might result in low internal validity. ²³ The nature of PSG‐AV requires an overnight stay in a sleep laboratory with data meticulously analysed by a sleep scientist and signed off by a sleep physician, ²¹ thus rendering the process inconvenient, ²⁴ uncomfortable, ⁵ expensive, time‐consuming and often delayed by long waiting lists. ²¹ , ²⁴ , ²⁵ As such, it is usually only performed once over a single night in foreign conditions, often non‐conducive to sleep. ²³ Indeed, its low level of applicability to clinical practice is why PSG‐AV for SB is mainly used for research purposes. ¹¹ , ¹³ , ¹⁶

Non‐specialized ambulatory devices have recently been developed to substitute PSG‐AV. ²⁵ Its ability to be used at home presents an attractive alternative as it allows multi‐night recordings in a natural environment thus more accurately reflecting typical behaviour, is easier to operate, relatively inexpensive and has a short waiting time. ²⁴ , ²⁶ However, the absence of audio‐video recording in portable devices presents a significant limitation as they tend to overestimate SB events due to OMA and OFA confounders leading to poorer accuracy (sensitivity 55% and specificity 80%). ¹³ , ¹⁶ Moreover, they are unable to differentiate between primary (idiopathic) and secondary SB including SB associated with potentially life‐threatening medical conditions. ²⁷ Relying solely on ambulatory devices could overlook the underlying cause of SB, underscoring the importance of comprehensive diagnostic methods like PSG‐AV. ²⁸ A systematic review in 2014 ²⁹ found that the available information on portable devices is insufficient to validate the use of any non‐PSG technique to substitute PSG‐AV for SB diagnosis.

This article employs a narrative review approach to enable a broad exploratory analysis of ambulatory devices, particularly given the paucity of high‐quality scientific data required for a systematic review. This review highlights the application of commercially available ambulatory devices to detect SB and the limitations surrounding its use and generate avenues for future research.

METHODS

A comprehensive review of the literature was carried out up until December 2024 on the current commercially available ambulatory devices that claim to be able to detect SB. Databases searched include PubMed, Scopus and grey literature sources such as Google Scholar. Search terms included but were not limited to, bruxism, sleep bruxism, polysomnography, PSG‐AV and ambulatory devices. The search was limited to articles published in English with no restrictions on publication dates. Articles were included based on relevance and quality of evidence – those that assess the individual devices in the detection of SB. All other articles were excluded. The findings outlined in this article provide insight into the clinical utility and role of ambulatory devices for clinical practice and domestic use.

Diagnostic capabilities of non‐instrumental approaches to diagnose SB can achieve a sensitivity and specificity of 78% and 73%, respectively. ¹⁸ An instrumental approach should be more accurate than a non‐instrumental approach, otherwise the former would incur an increased use of resources without any benefit. Hence, an ambulatory device will be considered acceptable if it has a diagnostic sensitivity and specificity of at least 80% when compared with PSG‐AV (gold standard). These values are based on the device's ability to detect a mild level of SB as per the PSG‐AV definition of SB (two RMMA episodes per hour), to differentiate between bruxers and non‐bruxers. ⁶

RESULTS

13 ambulatory devices were found through PubMed, Scopus and grey literature (Google Scholar) and five excluded due to non‐commercial availability. eight remaining ambulatory devices were searched for their SB diagnostic accuracy compared with PSG‐AV in the above databases. If there was no comparison to PSG‐AV, other relevant validation studies were included. Studies were excluded due to poor relevance and low quality of evidence (e.g. case studies).

Based on the above, this review was synthesized from 40 articles (Table 2).

Table 2.

Number of articles included for each ambulatory device that claims to detect sleep bruxism

Device	Number of articles
Bitestrip™	4
dia‐BRUXO®	2
GrindCare®	7
NOX T3™	1
Sleep Profiler™	3
Bruxoff®	4
Sunrise®	3
Intra‐splint force detection devices	16

The following are commercially available ambulatory devices for detecting SB:

Bitestrip™

Bitestrip™ (Scientific Laboratory Products, Ltd., Tel Aviv, Israel) is a disposable, single recording surface electromyography (sEMG) device that is a single‐channel analogue device that adheres to the left masseter muscle to record masseter EMG (mEMG) events based on a maximum voluntary clench (MVC) – registering episodes when the left masseter muscle contraction exceeds 30% of MVC and diagnosing SB when there are over 40 of such episodes per hour of sleep. ²⁸

Two studies ²⁸ , ³⁰ evaluated SB episode‐by‐episode agreement of the Bitestrip™ device and PSG‐AV. Compared with PSG‐AV, Bitestrip™ can detect SB episodes with a positive predictive value (PPV) of 75%–100% and sensitivity of 72%–84.2%, but accuracy diminishes with increasing bruxism severity. ²⁸ , ³⁰ Its poor accuracy in identifying bruxism severity might be attributed to several limitations. ²⁸ First, Bitestrip™ is only capable of detecting unilateral masseteric activity without audio‐visual recording. ²⁸ As an EMG‐only setup, it is also unable to differentiate OFAs from RMMA, decreasing SB detection accuracy. ¹⁶ , ¹⁸ , ²⁸ Moreover, Bitestrip™ records only 5 h of sleep compared with 8 h by PSG‐AV, thus potentially losing 3 h of data. ²⁸

Significantly, Bitestrip™ lacks an evaluation of its diagnostic sensitivity and specificity, and as such, requires further validation in this area. Despite being currently unable to replace PSG‐AV, Bitestrip™ was noted to be a potentially useful preliminary screening tool for SB in two systematic reviews. ²¹ , ²⁹

dia‐BRUXO®

dia‐BRUXO® (Biotech Novations, Sanremo, Italy) is a single‐channel sEMG device designed to diagnose and monitor bruxism by capturing EMG signals from the left masseter muscle. ³¹ Unlike Bitestrip,™ which places the electrode directly on the masseter muscle, dia‐BRUXO® electrodes are placed under the left earlobe anterior to the ear, aligning with the cutaneous projection of the masseter muscle. ³²

dia‐BRUXO® claims to differentiate between bruxism activities (both tonic and phasic) and OFAs such as swallowing, chewing, speaking and yawning, through software analysis of the continuous EMG trace. ³² By excluding non‐bruxism activities, its software generates several indices to quantify bruxism activity: bruxism time index (BTI), bruxism effort index (BEI) and personal bruxism index (BPI). ³³ BTI refers to the duration of bruxing activities across the recording period, BEI calculates the force of muscle contraction during bruxing while BPI assesses individual specific bruxism severity taking into account BTI, BEI and symptoms experienced. ³³

The dia‐BRUXO® device records sEMG data over 24 h, claiming to facilitate diagnosis for both sleep and awake bruxism. ³² dia‐BRUXO® has not been validated against PSG‐AV, however, a preliminary study ³² reported initial BTI and BEI values for sleep and awake bruxism. Another study ³³ found statistically significant BTI and BEI values for self‐reported possible bruxism compared with self‐reported non‐bruxism, thus being able to assess possible bruxism as ‘definite bruxism’. Though notably, a non‐PSG‐AV diagnosis of definite bruxism questions the validity of this study.

The device claims to filter OFAs, however, EMG‐only devices have been shown to have lower fidelity in detecting bruxism events or might misinterpret non‐bruxism‐related activity as bruxism. ¹⁶ Thus, comparison against PSG‐AV and validation by other independent groups is pertinent in verifying its dedicated software algorithms and substantiating its claims in SB diagnosis.

GrindCare®

The GrindCare® (Sunstar Suisse S.A., Etoy, Switzerland) device was designed to manage SB. It consists of a single‐channel sEMG which has two functions – measuring RMMA and producing electrical impulses intended to relax bruxing muscles based on a biofeedback algorithm. The device attaches to the skin over the anterior part of the temporalis muscle with a gel pad worn during sleep. An EMG event is recognized by the device when the EMG signal detected exceeds 20% MVC for a duration of 100 ms up to 1 s, any longer will be recorded as additional events. ³⁴ , ³⁵ When an EMG event is recognized, the device outputs an electrical impulse to relax the muscle, inhibiting the SB event.

Three studies assessed the accuracy of GrindCare® in SB detection against PSG‐AV. ³⁵ , ³⁶ , ³⁷ In adults, the number of EMG events recorded by the device is correlated with SB events measured by PSG‐AV. ³⁵ , ³⁶ In terms of the device's validity in diagnosing SB compared with PSG‐AV, a sensitivity of 60% and specificity of 60% was achieved. ³⁵ The sensitivity of the GrindCare® device decreases from 60% to 30% as the number of EMG events per hour increases. ³⁵

The GrindCare® system studies, however, had limitations in their designs that could have compromised their results. One of the studies ³⁶ did not use the physical GrindCare® device but instead simulated the device's EMG analysis algorithm based on retrospective EMG data from a PSG‐AV dataset. This method assumes that the recording and processing of the EMG data by the GrindCare® device can be accurately simulated using retrospective PSG‐AV data, questioning the study's internal validity. Two studies ³⁵ , ³⁷ compared PSG‐AV and GrindCare® data from different nights, which incurred the influence of night‐to‐night variability, ¹⁶ , ²¹ and diminished the validity of the results. In children using GrindCare®, EMG episodes were also overestimated and did not correlate with PSG‐AV detected number of SB episodes. ³⁷ Although the use of GrindCare® can reduce EMG activity during sleep, ³⁸ , ³⁹ , ⁴⁰ evidence to support its ability to detect SB is limited – a finding consistent with other EMG‐only devices.

NOX T3™

The NOX T3™ (NOX Medical, Reykjavík, Iceland) device is a Type II ambulatory PSG that records at least nine different signals including audio, EMG, ECG, EEG, pulse oximetry, pulse and plethysmography. ⁴¹ It analyses sleep data via the Noxturnal® software and Nox BodySleep™ algorithm and claims to be able to detect SB via its EMG signal. ⁴¹

The only available study ⁴² compared its ability to detect SB events against PSG‐AV by using mEMG data from the device and comparing it to EMG and video data from PSG‐AV. The study found that the NOX T3™ device achieved a sensitivity of 48.3%, specificity of 81.2%, PPV of 51.9% and NPV of 78.9% in detecting SB events. Thus, the accuracy of NOX T3™'s EMG channel at detecting SB events is limited. As the study only assessed the device's EMG channel, it is not surprising that the study found that the NOX T3™ device tended to overestimate SB events. Future research using data from more channels of the NOX T3™ device could potentially increase its SB event detection accuracy.

Sleep Profiler™

The Sleep Profiler™ (Advanced Brain Monitoring, Inc., Carlsbad, United States) device is a Type II ambulatory PSG that has up to eight channels that can record EEG, EOG, EMG, head positioning, head movement and snoring sounds. ⁴³ It is also advertised to have automated sleep staging comparable in accuracy to manual PSG staging. ⁴³ , ⁴⁴ , ⁴⁵

The Sleep Profiler™ was compared with PSG‐AV for SB detection in one recent study. ⁴⁶ The device tended to overestimate SB episodes when assessing episode‐by‐episode agreement, with approximately 40% of detected episodes being false‐positives when compared with PSG‐AV. In terms of SB diagnosis, the overestimation was compensated for by increasing the device's diagnostic threshold (EMG bursts per hour greater than 36 compared with the standard 25 for PSG‐AV), yielding a sensitivity of 100% and specificity of 93.3% when SB was manually scored by a sleep technologist. When SB was scored automatically by the device's software, the sensitivity remained at 100% but the specificity decreased to 86.7%, both still above the acceptable threshold of 80%.

A limitation of the study ⁴⁶ was that SB was definitively diagnosed if the PSG‐AV recorded an RMMA index greater than four which is a different PSG‐AV criteria of SB diagnosis than that outlined in this review. While the Sleep Profiler™ showed excellent accuracy in detecting SB, the study is limited by its use of non‐standard PSG‐AV criteria for the diagnosis of SB and a small sample size (n = 20). ⁴⁶

Bruxoff®

Bruxoff® (OT Bioelettronica, Turin, Italy) is a portable device capable of recording bilateral mEMG activity as well as ECG signals. The device uses disposable CoDe® (Spes Medica, Battipaglia, Italy) electrodes for the detection of EMG activity of the masseter muscles, which claims to selectively monitor the target muscle and is not affected by issues arising from misalignment of muscle fibres. ⁴⁷ , ⁴⁸ The device also comes with downloadable software, Bruxmeter®, which can automatically detect SB episodes based on pre‐established thresholds, ⁹ , ¹¹ though it can be adjusted according to clinician preferences. Clinicians also have the option to manually evaluate the device's outputs. ⁴⁷

Current evidence for the accuracy and validity of the device is promising but limited. Bruxoff®'s integration of EMG with ECG monitoring lends better accuracy than EMG alone, ⁴⁹ and showed an ability to record parameters with little intrasubject variability across multiple nights, hence indicating good reliability. ⁴⁸

Two studies ⁵⁰ , ⁵¹ compared this device to PSG systems. A high sensitivity (92.3%) and specificity (91.6%) was achieved when the device was evaluated against a Type II PSG system (no audio‐visual recording). ⁵⁰ However, comparison against a PSG‐AV system yielded an acceptable sensitivity (83.3%) but a poor specificity (72.2%). ⁵¹

Despite integration with ECG leads, Bruxoff® remains limited by its tendency to overestimate RMMA and the diagnosis of SB. ⁵¹ Additionally, the device is suggested to be less accurate at detecting SB when thresholds for high‐frequency bruxers were taken as reference, ⁴⁹ , ⁵¹ showing reduced agreement with PSG‐AV as the number of recorded events increased. ⁵¹ The sensitivity of the device drops from 83.3% at 2.1 events per hour to 33% at four events per hour. ⁵¹ Thus, while it has good reliability in determining the presence of SB in an individual, the diagnosis of higher frequency SB is less accurate. In summary, although Bruxoff® might be feasible, ⁴⁹ it does not have an acceptable specificity value (greater than 80%) to detect SB when compared against PSG‐AV.

Sunrise® (Australia, Europe and United Kingdom version)

The Sunrise® system (Sunrise SA, Namur, Belgium) is a wireless sensor designed to adhesively attach to the patient's mentolabial sulcus to record their mandibular movements (MM) during sleep. ⁵² It is a certified medical device that claims to be able to diagnose OSA as well as measure other sleep data with comparable accuracy to a professional sleep laboratory. ⁵³ , ⁵⁴ , ⁵⁵ , ⁵⁶ The device acquires six channels of raw MM data from the sensor which is transferred to the cloud and processed by the Sunrise® software using a machine learning algorithm that analyses the data and generates a detailed sleep report, including an RMMA index. ⁵² , ⁵⁶

Solely using MM data from Sunrise® to identify SB episodes via its machine learning algorithms seems to be promising. ⁵² , ⁵⁷ The first study to assess the device's ability to detect SB was reported in an abstract supplement in which a convolutional neural network (CNN) based classifier was used, a type of artificial neural network to recognize patterns in images, using PSG‐AV and MM data from patients with SB. ⁵⁷ The CNN classifier achieved an accuracy of 91% for distinguishing SB events from other RMMA (microarousals and respiratory effort) which led to the conclusion that SB could be automatically identified using artificial intelligence (AI) technology.

The initial Sunrise® study's ⁵⁷ positive results set the basis for another study ⁵² which used an extreme gradient boosting (XGB) instead of a CNN based classifier, involving MM and PSG‐AV data. XGB is a machine learning library that uses decision trees for large datasets. The new study achieved an overall accuracy of 86.5% and a sensitivity of 84.3% in detecting SB episodes. Though the XGB classifier tended to slightly underestimate RMMA when the RMMA index was greater than 15, SB episodes could be quantified with good agreement (sensitivity and specificity greater than 80%) compared with PSG‐AV in those with OSA.

These two studies have shown that SB episodes can be identified using MM data with high accuracy and sensitivity in OSA populations. However, the specific machine learning algorithm used to identify SB episodes in the commercially available Sunrise® device has not been disclosed, so its validity might differ from that found in the above studies which used unique algorithms and machine learning training datasets. As SB detection is achieved by using MM analysis, tonic episodes of SB (teeth clenching) that do not involve movement might not be identified accurately by the Sunrise® device potentially resulting in the underestimation of SB events, limiting its diagnostic validity. Moreover, some authors in the studies disclosed potential conflict of interest in the Sunrise® system, highlighting the need for future independent research regarding the validity of the commercially available Sunrise® device in detecting SB both in OSA and non‐OSA populations.

Intra‐splint Force Detection Devices

Occlusal splints are a non‐specific SB management option often prescribed to protect against dental attrition, however, its effect on SB activity in the long‐term lacks evidence. ⁵⁸ , ⁵⁹ , ⁶⁰ , ⁶¹ A systematic review ⁶¹ found the efficacy of full‐occlusion biofeedback splints in reducing SB episodes to be promising, suggesting the feasibility of Intra‐splint Force Detection Devices (ISFDDs) to also act as active therapeutic devices. Hence, ISFDDs possess potential in the management of SB in addition to SB detection and monitoring.

Current literature on ISFDDs lacks standardization in device design and comparison to PSG‐AV. Much of the ISFDDs developed in the last two decades involve a maxillary splint embedded with piezoresistive or piezoelectric pressure sensors at the canine and/or premolar‐molar region. ⁶² , ⁶³ , ⁶⁴ Moreover, the proposed criteria and thresholds for how SB episodes are to be identified by the ISFDDs lack consensus. Approaches include a percentage of a calibrated MVC or of maximum bite force established via analysis of baseline nocturnal recordings. ⁶⁵ , ⁶⁶ , ⁶⁷ , ⁶⁸ Some thresholds were determined arbitrarily ⁶⁷ or through visual evaluation where satisfactory elimination of background noise in the output signal was achieved. ⁶² , ⁶⁶ Identification of SB episodes was often validated by comparing the device with EMG by analysing the concurrent signal outputs, ⁶² , ⁶³ , ⁶⁶ , ⁶⁷ , ⁶⁹ , ⁷⁰ with some studies conducting visual discrimination of the ISFDD signals alone based on a generated report. ⁶⁴ , ⁷¹

Concordance of SB identification between ISFDDs and EMG were primarily evaluated in awake participants simulating various SB‐related oromotor behaviours such as teeth clenching, grinding and tapping. ⁶² , ⁶³ , ⁶⁴ , ⁶⁶ , ⁶⁹ , ⁷⁰ , ⁷¹ Output from ISFDD reports showed visually distinct signal profiles of bruxing events which can be discriminated from normal oromotor patterns. A pilot study ⁶⁴ indicated good sensitivity (80–100%) and specificity (75–80%) in visual identification alone. Furthermore, studies that evaluated simultaneous EMG recordings during these exercises indicated good correlations in output signals and concurrent SB episode detection. ⁶² , ⁶⁶ , ⁶⁹ , ⁷⁰ It was also noted that grinding behaviour was better identified with ISFDD. Similarly, non‐SB‐related oromotor behaviours recorded by EMG were not visible in the ISFDD's recordings – suggesting ISFDD to have better specificity than EMG recordings. ⁶⁹ However, such studies by awake subjects are of limited usefulness in validating ISFDDs for SB detection. Additionally, two of the studies ⁶⁹ , ⁷⁰ have disclosed potential conflict of interest in their ISFDD design.

While there are no studies comparing ISFDDs to PSG‐AV, those that compared ISFDDs to EMG‐only devices have included analyses of nocturnal recordings of sleeping subjects as well. ⁶⁵ , ⁶⁶ , ⁶⁷ , ⁶⁹ From this, the ISFDDs were able to detect SB episodes with a sensitivity of 86.1%–89%. ⁶⁵ , ⁶⁶ , ⁶⁷ Only one study ⁶⁵ reported on a specificity value (84.5%), though details on how this value was calculated were not provided by the authors. Another study ⁶⁶ showed an overestimation of SB episode numbers and duration by 30%. Furthermore, studies on ISFDDs are limited by unvalidated detection criteria and small sample size which prevented specificity calculations in most studies. ⁶⁶ , ⁶⁷

Overall, ISFDDs appear to have comparable value to EMG‐only devices in detecting SB activity albeit with limited scientific data and a tendency for overestimation. However, such a device might offer advantages unavailable to EMG such as ease of use and direct detection of SB‐related occlusal forces. Furthermore, there is significant potential for the integration of ISFDDs with biofeedback therapy and AI‐guided systems. ⁶¹ , ⁶⁵ , ⁶⁹ , ⁷² Hence, while ISFDDs have limited diagnostic validity, they can be a convenient clinical tool for the screening, monitoring and management of SB.

DISCUSSION

The use of ambulatory devices for detecting SB as an alternative to PSG‐AV appears promising, though it remains an evolving area requiring ongoing development.

The current commercially available devices as outlined in this review are Bitestrip™, dia‐BRUXO®, GrindCare®, NOX T3™, Sleep Profiler™, Bruxoff®, Sunrise® and ISFDD (Table 3). Many of these devices currently only show potential as screening tools to detect SB rather than as a definitive diagnostic instrument. To date, these devices are unable to replace the gold standard, PSG‐AV, due to inherent limitations in the individual device designs and the lack of or inadequate validation studies.

Table 3.

Summary of commercially available ambulatory devices to detect sleep bruxism

Device	Modality	Number of channels	Episodic a /diagnostic b sensitivity & specificity compared with PSG‐AV
Bitestrip™	sEMG of the masseter 28	1	Sensitivity (episodic): 72–84.2% 28 , 30 Specificity: No comparison to PSG‐AV
dia‐BRUXO®	sEMG of the masseter 31	1	Sensitivity: No comparison to PSG‐AV Specificity: No comparison to PSG‐AV
GrindCare®	sEMG of the anterior temporalis	1	Sensitivity (diagnostic): 60% 35 Specificity (diagnostic): 60% 35
NOX T3™	Type II PSG 13 , 41	9+	Sensitivity (diagnostic): 48.3% 42 Specificity (diagnostic): 81.2% 42
Sleep Profiler™	Type II PSG 46	Up to 8	Sensitivity (diagnostic): 100% 46 Specificity (diagnostic): 93.3% 46
Bruxoff®	Concurrent sEMG of the masseter and ECG monitoring device	2	Sensitivity (diagnostic): 83.3% 51 Specificity (diagnostic): 72.2% 51
Sunrise®	Sensors to track mandibular movements 52	6	Sensitivity (episodic): 84.3% 52 Specificity: No comparison to PSG‐AV
Intra‐splint Force Detection Devices	Pressure sensors embedded in a maxillary splint 73	1	Sensitivity: No comparison to PSG‐AV Specificity: No comparison to PSG‐AV

PSG‐AV = polysomnography with audio and visual recording; PSG = polysomnography; SB = sleep bruxism; EMG = electromyography; sEMG = single‐surface electromyography.

^a SB episode detection sensitivity.

^b SB diagnosis of bruxism and non‐bruxism sensitivity and specificity.

Of these devices, only Sleep Profiler™ shows both acceptable sensitivity and specificity (greater than 80%) in diagnosing SB when compared against PSG‐AV. ⁴⁶ However, the use of this device might pose challenges for clinical practice and individual use due to its many channels. Also, its only validation study ⁴⁶ is limited by a small sample size and non‐standard PSG definition for SB diagnosis. The study used a diagnostic cut‐off point of more than four RMMA per hour, indicative of severe bruxism as per the current understanding of SB. ¹¹ , ⁴⁶ As such, further larger studies utilizing an RMMA index of greater than two per hour as per the diagnostic PSG definition might improve the visibility and utility of Sleep Profiler™ for SB diagnosis.

All other devices had insufficient sensitivity and/or specificity (greater than 80%) or have not had their diagnostic accuracy validated against PSG‐AV. GrindCare®, Bruxoff® and NOX T3™ lack acceptable sensitivity and/or specificity. GrindCare® confers direct therapeutic benefits when a bruxism event is detected in the form of an electrical impulse transmitted to relax the temporalis muscle to reduce the level of parafunctional activity but not eradicate parafunctional activity completely. ³⁸ , ³⁹ , ⁴⁰ However, previous studies compared against PSG‐AV produced a sensitivity and specificity of less than 80%. ³⁵ While Bruxoff® provides better accuracy with an added ECG channel, has good reliability and multi‐night recording, its overestimation of RMMA yields less than 80% specificity. The PSG‐AV study evaluating NOX T3™ only used the device's EMG channel, despite it offering more than nine other channels. As a Type II PSG device, it shares similarities with Sleep Profiler™, therefore further research using data from more channels is necessary to adequately assess the device.

Bitestrip™ and Sunrise® have been assessed for their episode‐by‐episode (episodic) SB detection sensitivity but not their SB diagnostic sensitivity or specificity. Both devices yielded episodic sensitivity values greater than 80%, ²⁸ , ⁵² However, diagnostic sensitivity provides more clinical utility than episodic sensitivity when detecting SB. Notably, the lack of specificity values ignores the presence of false‐positives which limits the diagnostic accuracy of the devices. The Sunrise® device's novel MM tracking approach might not detect tonic SB (e.g. teeth clenching) nor has it been evaluated in a non‐OSA population. ⁵² Therefore, future assessment of the diagnostic accuracy of Bitestrip™ and Sunrise® against PSG‐AV for SB detection merits consideration.

dia‐BRUXO® and ISFDDs have not been evaluated against PSG‐AV. dia‐BRUXO® combines awake and sleep bruxism diagnosis into one device, ³² though further studies into its SB diagnostic accuracy are needed to support its clinical utility. The ISFDDs, however, have been evaluated against EMG‐only device systems and achieved a sensitivity and specificity greater than 80%. ⁶⁵ , ⁶⁶ , ⁶⁷ It might have better specificity than EMG‐only devices in detecting non‐bruxism events and better identifies teeth‐grinding behaviour. Despite this, inconsistencies in the criteria and threshold for SB episodes limit its validity. As such, evaluation against PSG‐AV with a standardized cut‐off point for SB detection is warranted.

Bitestrip™, dia‐BRUXO® and GrindCare® are EMG‐only devices. While simple and user‐friendly, EMG‐only devices without audio‐visual recording are unable to exclude OFAs and OMAs, thus overestimating SB events. ¹⁶ This is often compensated for by elevating thresholds for SB episodes and diagnosis optimized using receiver operating curve analysis. However, the use of different thresholds and measures (e.g. the use of MVC rather than RMMA) decreases the devices' diagnostic accuracy, a similarity among almost all devices outlined in this review. When compared against PSG‐AV, the accuracy of the devices (Bitestrip™, ²⁸ GrindCare®, ³⁵ Bruxoff®, ⁵¹ Sunrise® ⁵² ) in detecting SB episodes were diminished with increasing SB severity.

CONCLUSION

Select commercially available ambulatory devices currently offer promising results as alternatives to PSG‐AV recordings to detect SB in the context of clinical practice and domestic use. These devices might have a role as a screening tool with wider utility than PSG‐AV. However, further research to assess the accuracy and reliability of these devices evaluated against the gold standard, PSG‐AV, in diverse populations is necessary prior to widespread adoption in clinical practice and for domestic use.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

FUNDING INFORMATION

This research received no specific grant from any funding agency in the public, commercial, or not‐for‐profit sectors.

AUTHOR CONTRIBUTIONS

C Li: Writing – original draft; writing – review and editing. S Yap: Writing – original draft; writing – review and editing. A Loh: Writing – original draft; writing – review and editing. YJ Yap: Writing – original draft; writing – review and editing. O Kujan: Supervision; writing – review and editing. R Balasubramaniam: Supervision; conceptualization; writing – review and editing.

ETHICS STATEMENT

Ethics approval was not sought for the present study because this article is a literature review and did not involve human participants nor animal subjects. Therefore, ethics approval was not required.

DATA AVAILABILITY STATEMENT

This study is based on a review of previously published literature data. All data supporting the findings are publicly available in the cited articles and sources.