PDF(Pubmed)
Abstract：
BACKGROUND: The existing respiratory fit test panels (RFTPs) are based on Bivariate and Principal Component Analysis (PCA) which utilise American and Chinese head and facial dimensions. As RFTPs based on local facial anthropometric data for Malaysia are not available, this study was conducted with the aim to develop new RFTPs using Malaysian data.
METHODS: A cross-sectional study was conducted across Malaysia among 3,324 participants of the study of National Health and Morbidity Survey 2020 aged 18 and above. Ten head and facial dimensions were measured. Face length and face width were used to construct bivariate facial panel, whereas the scores from the first two PCA were used to develop the PCA panel.
RESULTS: This study showed that Malaysians have the widest upper limit for facial width. It also found that three factors could be reduced from the PCA analysis. However only 2 factors were selected with PCA 1 representing head and facial size and PCA 2 representing facial shape. Our bivariate panel could accommodate 95.0% of population, while our PCA panel accommodated 95.6%.
CONCLUSIONS: This was the first study to use Malaysian head and facial anthropometry data to create bivariate and PCA panels. Respirators constructed using these panels are likely to fit ≥ 95.0% of Malaysia\'s population.
METHODS: A cross-sectional study was conducted across Malaysia among 3,324 participants of the study of National Health and Morbidity Survey 2020 aged 18 and above. Ten head and facial dimensions were measured. Face length and face width were used to construct bivariate facial panel, whereas the scores from the first two PCA were used to develop the PCA panel.
RESULTS: This study showed that Malaysians have the widest upper limit for facial width. It also found that three factors could be reduced from the PCA analysis. However only 2 factors were selected with PCA 1 representing head and facial size and PCA 2 representing facial shape. Our bivariate panel could accommodate 95.0% of population, while our PCA panel accommodated 95.6%.
CONCLUSIONS: This was the first study to use Malaysian head and facial anthropometry data to create bivariate and PCA panels. Respirators constructed using these panels are likely to fit ≥ 95.0% of Malaysia\'s population.
摘要：
背景：现有的呼吸配合测试面板（RFTP）基于双变量和主成分分析（PCA），利用美国和中国的头部和面部尺寸。由于没有基于马来西亚当地面部人体测量数据的RFTP，本研究旨在利用马来西亚数据开发新的RFTP.
方法：在马来西亚进行了一项横断面研究，纳入了2020年全国健康和发病率调查18岁及以上的3,324名参与者。测量十个头部和面部尺寸。面部长度和面部宽度用于构建双变量面部面板，而来自前两个PCA的分数用于开发PCA小组。
结果：这项研究表明，马来西亚人的面部宽度上限最宽。研究还发现，从PCA分析中可以减少三个因素。但是，仅选择了2个因素，PCA1代表头部和面部大小，PCA2代表面部形状。我们的双变量面板可以容纳95.0%的人口，而我们的PCA面板容纳了95.6%。
结论：这是第一个使用马来西亚头部和面部人体测量数据创建双变量和PCA面板的研究。使用这些面板建造的呼吸器可能适合马来西亚95.0％以上的人口。
方法：在马来西亚进行了一项横断面研究，纳入了2020年全国健康和发病率调查18岁及以上的3,324名参与者。测量十个头部和面部尺寸。面部长度和面部宽度用于构建双变量面部面板，而来自前两个PCA的分数用于开发PCA小组。
结果：这项研究表明，马来西亚人的面部宽度上限最宽。研究还发现，从PCA分析中可以减少三个因素。但是，仅选择了2个因素，PCA1代表头部和面部大小，PCA2代表面部形状。我们的双变量面板可以容纳95.0%的人口，而我们的PCA面板容纳了95.6%。
结论：这是第一个使用马来西亚头部和面部人体测量数据创建双变量和PCA面板的研究。使用这些面板建造的呼吸器可能适合马来西亚95.0％以上的人口。
