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Influence of Breathing Modes and Facial Growth Patterns on Electromyographic Fatigue of Masticatory Muscles in Children.

Yulieth Paulina Stave GomezNathalisa de Morais RockenbachAnaelena Bragança de MoraesEliane Castilhos CorrêaAna Maria Toniolo da SilvaAngela Ruviaro Busanello-Stella
Published in: International archives of otorhinolaryngology (2023)
Introduction  Changes in breathing patterns affect the harmonious development of the structures of the craniofacial system, leading to changes in posture, occlusion, and facial growth patterns. However, little is known about how these changes influence the muscle contraction patterns, either at rest or while functioning, and either in a normal or unbalanced condition. Objective  To study the masseter and anterior temporal muscles fatigue during mastication in nasal- and mouth-breathing children, also considering their facial growth patterns. Methods: A total of 70 children aged 6 to 12 years old who met the study criteria were assessed. Speech-language-hearing, otorhinolaryngologic, and cephalometric assessments were performed to divide them into groups. In the electromyographic assessment, the children were asked to chew gum following a metronome until they felt fatigued. The median frequency of the muscles was analyzed at 15, 30, 45, and 60 seconds of mastication. The reported time of fatigue perception was recorded. The data were analyzed with analysis of variance (ANOVA) and the Kruskal-Wallis and the Mann-Whitney U tests. Results  There were no median frequency decrease patterns nor differences in the myoelectric manifestations and reported time of fatigue between the groups. Conclusion  The masticatory muscles did not reveal fatigue in the electromyographic analysis; however, the fatigue time was reported, despite the absence of physiological fatigue. The breathing mode, the facial growth pattern, and the association between them did not interfere with the behavior of the median frequency of the electromyographic signal and the fatigue time perception.
Keyphrases
  • sleep quality
  • young adults
  • soft tissue
  • depressive symptoms
  • autism spectrum disorder
  • high resolution
  • single cell
  • genome wide
  • artificial intelligence
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