Participants
Ninety healthy volunteers (49 women and 41 men; mean age 20.7 ± 0.7 years) wererecruited from college students of Fujian Medical University who fulfilled the following criteria: (1) centred midline and no marked restriction and deviation of mouth opening and closing; (2) overjet and overbite of 1-3 mm, bilateral molar support with molar and cusp relation of Angle’s class I, and (3) presence of complete permanent dentition, except third molars.
Healthy subjects were excluded if they met one or more of the following exclusion criteria: (1) history of local or general trauma; (2) presence of systemic diseases, neurological or psychiatric disorders, muscular diseases, cervical pain, or TMD based on the Research Diagnostic Criteria (RDC) [15];(3) pregnancy; (4) consumption of anti-inflammatory, analgesic, antidepressant, or myorelaxant drugs; (5) presence of parafunctional facets and anamnesis of parafunctional tooth clenching, bruxism, or unilateral chewing; (6) presence of obvious dentition crowding or spacing, malposed, supernumerary or fractured tooth, visible caries, tooth abrasion/hypersensitivity, toothache, periodontal disease, or occlusal discomfort; (7) fixed or removable restorations, tooth filling, or occlusal adjustment that affected the occlusal surfaces; and (8) previous or concurrent orthodontic, orthognathic, or TMJ treatment.
Twenty-six TMD participants were recruited through public invitations, via recruitment posters and personal contacts (19 women and 7 men; mean age 29.7 ± 4.8 years). Patients with unilateral or bilateral TMJ disc displacement symptoms, such as TMJ clicking, TMJ locking, and limitation in opening mouth were included in the study. The study did not cover the patients with muscle disorders (myofascial pain), with arthralgia, osteoarthritis, and osteoarthrosis, with TMJ fracture, with dentofacial deformity, with systemic disease affecting TMJ, and undergone TMD treatments. They were further divided into reversible anterior disk displacement (RADD) group (n=13) and anterior disc displacement without reduction (ADDWR) group (n=13) by magnetic resonance imaging (MRI) images.
Recruitment and tests took place from February 2022 to July 2022. All subjects received the test instructions and were randomly allocated by selecting sealed opaque envelopes before the test. The testers had no information about the allocation pattern.
Surface electromyography
The sEMG of the anterior temporalis (TA), masseter (MM), sternocleidomastoid (cervical group, CG), and digastric (DA) were recorded simultaneously with an sEMG device (K7/EMG, Myotronics-Noromed, Inc., Tukwila WA, USA) using disposable silver/silver chloride bipolar surface electrodes (Duo-Trode, Myotronics-Noromed, Inc., Tukwila WA, USA).
The tests were carried out in a quiet environment, with the subjects comfortably seated on a fixed chair with a straight back, closed eyes, and relaxed head position. The locations of the electrodes were determined by palpation of muscle contraction (Fig. 1A) [11], and the ground electrodes were positioned on the subject’s clavicle bone to provide a common reference to the differential input of the amplifier. Each recording site was thoroughly cleaned and dried, and each pair of electrodes was placed on the site over the belly of the muscle and parallel with the muscle fibers, and conductive paste was used for good electrical contact with the skin. The electrode noise test was conducted to ensure the proper connection of the K7 EMG ground wire to the patient ground electrode, proper cleansing of the skin, quality of electrodes and electrode leads, and low electrical noise caused by an electric motor. The sEMG recording was started only when the software (K7 Program 15.0, Myotronics-Noromed, Inc., Tukwila WA, USA) revealed the absence of noise.
The raw sEMG data were recorded with a sampling frequency of 2000 Hz, and all recorded signals were band-pass filtered with a high- and low-pass Hamming filter with cut-off frequencies of, respectively, 15 and 650 Hz, and an additional 60 Hz 110 dB/decade notch filter. Three consecutive tracks, with a duration of 15 seconds and without any voluntary movement, were recorded. The root mean square (RMS) value of sEMG (in μV) for each channel was displayed at the end of the tracing. First, sEMG was conducted on four pairs of muscles (TA, MM, CG, and DA) in the MPP, and subjects were guided to maintain the face and jaw as relaxed as possible (Fig. 2A) [16]. The results showed that the relative resting activity of the masticatory/cervical muscles and the motor unit activity were averaged over time to quantify the amount of electrical activity.
To determine the relative efficiency of muscle function, the second test was performed to measure the sEMG of TA and MM during the maximal biting force against natural dentition (TA-ND and MM-ND) or cotton rolls (TA-CR and MM-CR). The cotton rolls were used as standard food substitution with a length of 3.0 cm and a diameter of 1.0 mm. Each subject was asked to clench twice with maximum force, hold for 2.0 seconds, and relax for 1.0 second, followed by another clench as before after two wet cotton rolls were placed on the posterior teeth (Fig. 3A).
In the third test, subjects were guided to clench three times to monitor early motor unit recruitment as they closed from rest through freeway space to initial tooth contact. Partial asymmetry indices (PAI) at the peak sEMG values of TA and MM (pTA and pMM) were automatically generated by software (Fig. 3B), and total asymmetry indices (TAI) during maximal voluntary clenching (MVC) were calculated using the following formula to determine muscle balance. 11, 12 For PAI and TAI, a zero value reflects similar muscle activity on the left and right sides.
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)
Mandibular kinematics
Kinesiographic recordings were performed using a kinesiograph (K7/CMS, Myotronics-Noromed, Inc., Tukwila WA, USA) that measured mandibular movements with an accuracy of 0.1 mm in the vertical, anteroposterior (AP), and lateral axes. An array with eight magnetic sensors mounted on the subject’s head tracked the motion of the small magnet attached to the labial surface of the lower central incisor at a sample rate of 125 Hz (Fig. 1B). The maximum mouth opening (MMO) and opening and closing velocities were measured.
The first tracing was a simultaneous sagittal and frontal trace of the trajectory that the mandibular traversed during mouth opening and closing. Each subject was asked to open their mouth as wide as possible and then close it (Fig. 4A). Then they opened and closed their mouths starting from intercuspal position (ICP) and back as fast as possible, the sagittal, frontal, and velocity tracings were simultaneously recorded. Meanwhile, the maximum velocity of terminal tooth contact (MVTTC) was measured, defined as the closing speed at 0.8 mm below the centric occlusion (CO) (Fig. 4B).
All measurements were repeated twice, with a gap of 15 minutes between two recordings, and the average values were calculated. Additionally, each participant was assessed by two examiners at an interval of three days. Reliability was calculated using the intraclass correlation coefficient (ICC). The inter-and interrater reliability was calculated with ICC using the average data of each test per session.
Statistical analysis
Statistical analysis was performed using SPSS 27.0 software (IBM Corp., Armonk, NY, USA). The level of significance was set at P≤0.05 for all tests. Shapiro-Wilk and Levene tests were used to assess the normality of data and homogeneity of variance, respectively. Significant differences in sEMG values and mandibular kinematics between sexes and between healthy and TMD subjects were established by the analysis of variance (ANOVA) or the Mann‐Whitney U test for non-parametric data. Further, the receiver operator characteristic (ROC) curve was used to analyse their diagnostic efficiency for TMD, and their effect on the severity of TMD was also investigated using Spearman correlation.