Influence of Arm Fat on the Indirect Measurement of Blood Pressure: A Statistical and Machine Learning Approach

Abstract Background The indirect measurement of blood pressure (BP) is known to be influenced by many factors such as the technique, observer, and equipment; however, the influence of arm composition has not been investigated yet. Objective To identify the influence of arm fat on the indirect measurement of blood pressure using statistical inference and machine learning models. Methods Cross-sectional study, with 489 healthy young adults aged from 18 to 29 years old. Measurements of arm length (AL), arm circumference (AC) and arm fat index (AFI) were taken. BP was measured in both arms simultaneously. Data were processed using Python 3.0 and its specific packages for descriptive analysis, regression and cluster analysis. Significance levels: 5% for all calculations. Results BP and anthropometric measurements were different between the hemi-bodies. In the right arm, systolic blood pressure (SBP), AL and AFI were higher, while AC was similar compared with the left arm. AL and AC showed positive correlation with SBP. According to the regression model, for a fixed value of AC and AL, SBP readings could be reduced by a mean of 1.80 mmHg in the right arm, and 1.62 mmHg in the left arm for every 10% increase in AFI. Clustering analysis corroborated the regression results. Conclusion There was a significant influence of AFI on BP readings. SBP had a positive correlation with AL and AC, and a negative correlation with AFI, suggesting the need for further investigations on the relationship between BP and percentages of arm muscle and fat.