摘要
Maximum Voluntary Ventilation (MVV), one of the components of Pulmonary Function Testing (PFT), has multiple uses. Various factors including the inspiratory muscle strength (IMS) influence its magnitude. Our aim was to quantify the IMS indirectly using an economical and non invasive bedside assessment tool, determine its association with MVV and then develop a predictive equation for MVV. 41 healthy non-athletic physical therapy students participated in the study. IMS measurement was performed with a sphygmomanometer. Average of the three net deflections in sphygmomanometer following deepest possible breaths was taken as indirect measurement of IMS in mm of Hg. MVV was measured according to ATS guidelines using a spirometer. Results from the data analysis revealed a significant correlation between IMS and MVV(r = 0.83, p < 0.001) and the coefficient of determination = 0.68. So, we developed a regression equation: Y = 1.9669(X) + 49.838 with SEE: 13.02L/min and ANOVA for the equation was (F=68.9, p < 0.001). Hence, it can be concluded that a strong correlation between the indirect IMS and MVV was established and a predictive equation to estimate MVV was developed. This equation proved to have a high predictive value with a small error of estimation. This indicates that the value of the indirect IMS measurement obtained using the sphygmomanometer can be used to estimate MVV in normal healthy individuals without the use of a conventional spirometer.
Maximum Voluntary Ventilation (MVV), one of the components of Pulmonary Function Testing (PFT), has multiple uses. Various factors including the inspiratory muscle strength (IMS) influence its magnitude. Our aim was to quantify the IMS indirectly using an economical and non invasive bedside assessment tool, determine its association with MVV and then develop a predictive equation for MVV. 41 healthy non-athletic physical therapy students participated in the study. IMS measurement was performed with a sphygmomanometer. Average of the three net deflections in sphygmomanometer following deepest possible breaths was taken as indirect measurement of IMS in mm of Hg. MVV was measured according to ATS guidelines using a spirometer. Results from the data analysis revealed a significant correlation between IMS and MVV(r = 0.83, p < 0.001) and the coefficient of determination = 0.68. So, we developed a regression equation: Y = 1.9669(X) + 49.838 with SEE: 13.02L/min and ANOVA for the equation was (F=68.9, p < 0.001). Hence, it can be concluded that a strong correlation between the indirect IMS and MVV was established and a predictive equation to estimate MVV was developed. This equation proved to have a high predictive value with a small error of estimation. This indicates that the value of the indirect IMS measurement obtained using the sphygmomanometer can be used to estimate MVV in normal healthy individuals without the use of a conventional spirometer.
