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dc.creatorRosar, João Vicente-
dc.creatorMarquezin, Maria Carolina Salomé-
dc.creatorPizzolato, Aianne Souto-
dc.creatorKobayashi, Fernanda Yukie-
dc.creatorBussadori, Sandra Kalil-
dc.creatorPereira, Luciano José-
dc.creatorCastelo, Paula Midori-
dc.date.accessioned2022-05-30T12:27:18Z-
dc.date.available2022-05-30T12:27:18Z-
dc.date.issued2021-05-01-
dc.identifier.citationROSAR, J. V. et al. Identifying predictive factors for sleep bruxism severity using clinical and polysomnographic parameters: a principal component analysis. Journal of Clinical Sleep Medicine, [S.l.], v. 17, n. 5, p. 949-956, May 2021. DOI: 10.5664/jcsm.9078.pt_BR
dc.identifier.urihttps://jcsm.aasm.org/doi/10.5664/jcsm.9078pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/50057-
dc.description.abstractSTUDY OBJECTIVES: the aim was to identify predictive factors for sleep bruxism (SB) severity among polysomnographic parameters, salivary cortisol levels, temporomandibular disorders, age, and sex. METHODS: young adults (19–30 years) were screened for self-/roommate reports of teeth grinding/clenching during sleep associated with clinical signs of tooth wear. Individuals positive for both conditions were administered a polysomnographic exam to provide a definite diagnosis of SB (n = 28). Healthy participants without SB signs/symptoms were also included (n = 15). The Research Diagnostic Criteria for Temporomandibular Disorders was applied to determine functional, muscular, and articular domains of the Temporomandibular Index. Cortisol awakening levels were measured in saliva. Principal component analysis was used to extract the latent components emerging from polysomnographic results, and 2 regression models were adjusted to predict the number and duration of bruxism episodes. RESULTS: principal component analysis resulted in 4 components-C1: %N1, total sleep time, sleep efficiency, arousals/microarousals; C2: %N2, %N3; C3: periodic limb movements and apneas; C4: %REM and REM latency. The number of SB episodes/h was predicted by increasing muscular scores and C2 (decrease in %N2 and increase in %N3) (adjusted R2 = 45%; P =.001). The total time of SB episodes was predicted by decreased articular and increased functional scores, age, and female sex (adjusted R2 = 36%; P = 0.010). Salivary cortisol levels were not associated with SB severity and did not differ between groups. CONCLUSIONS: the findings showed that SB severity was predicted by muscular and functional scores, female sex, and distinct polysomnographic patterns, contributing to the deeper knowledge of the underlying pathophysiology of SB severity; additionally, the findings can help to formulate health approaches that are specific to the patient and will better assist in treating this condition.pt_BR
dc.languageen_USpt_BR
dc.publisherAmerican Academy of Sleep Medicine (AASM)pt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceJournal of Clinical Sleep Medicine (JCSM)pt_BR
dc.subjectBruxismpt_BR
dc.subjectSleep disturbancept_BR
dc.subjectTemporomandibular disorderspt_BR
dc.titleIdentifying predictive factors for sleep bruxism severity using clinical and polysomnographic parameters: a principal component analysispt_BR
dc.typeArtigopt_BR
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