Abstract
Background
Obstructive Sleep Apnea-hypopnea Syndrome (OSAHS) has become a major public health challenge globally. Most patients have a concomitant voice disorder. The existing treatment methods have problems.Aims/Objectives: This study investigates the therapeutic effect of adding scientific vocalization to oropharyngeal muscle training on OSAHS patients.
Material and methods
A total of 30 patients were selected from September 2020 to October 2022. They underwent overnight polysomnography (PSG) and were identified as having mild to moderate obstructive sleep apnea hypoventilation syndrome. They were then chosen for a three-month period of modified oropharyngeal muscle training combined with scientific vocalization.
Results
Out of the 30 selected patients, 26 patients completed the training. Results showed a significant changes in multiple sleep-related indicators. he clinical outcomes were as follows: 7 cases were cured, 13 cases were effective, and 6 cases were ineffective. The overall effective rate was 76.92%.
Conclusions and significance
The combination of oropharyngeal muscle training and scientific vocalization for the treatment of mild to moderate OSAHS in adults significantly improves several measures used in the treatment of the condition. The method is easy to learn, effective, safe to use, and affordable. It provides more options for the treatment of OSAHS.
Chinese Abstract
背景
阻塞性睡眠呼吸暂停呼吸不足综合征(OSAHS)已成为全球重大公共卫生问题。 大多数患者伴有声音障碍。 现有的治疗方法是有问题的。
目的
本研究调查了在口咽肌训练的基础上添加科学发声的OSAHS 患者治疗效果。
材料与方法
在2020年9月至2022年10月期间, 共选择患者30例。他们接受了过夜多导睡眠图 (PSG), 并被确定为轻度至中度阻塞性睡眠呼吸暂停呼吸不足综合征。 然后, 他们被挑选接受为期三个月的改良口咽肌训练与科学发声相结合的治疗。
结果
在 30 名入选患者中, 26 名患者完成了训练。 结果显示多项睡眠相关指标的明显变化。 临床结果如下: 治愈7例, 有效13例, 无效6例。 总有效率为76.92%。
结论与意义
用于治疗成人轻度至中度 OSAHS的口咽肌训练与科学发声训练相结合的治疗方法显显著改善了治疗该病症的若干措施。 该方法易与掌握、有效、使用安全并且花费不高。 它为OSAHS的治疗提供了更多选择。
Ethics approval and consent to participate
The study was approved by Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital) ethics committee.
Author contributions
Study conception and design: Zuofeng Huang, Shuo Li, Yingwei Qiu. Data acquisition: Danlin Huang, Fei Liu, Junyi Liang, Zhimin Zhao, and Hui Lu. Analysis and data interpretation: Zuofeng Huang, Hui Lu, and Danlin Huang. Drafting of the manuscript—Shuo Li, Junyi Liang, Danlin Huang. Critical revision: Zuofeng Huang, Shuo Li.
Disclosure statement
We are committed to the interests of the competition does not exist.
Data availability statement
We confirm that we are not using other people’s data,