Advancements and critical steps for statistical analyses in blood pressure response to resistance training in...

Repeated measures analysis of covariance and three-way analysis of variance with repeated measures are common statistical methods. For a valid interpretation of blood pressure (BP) response to exercise, a variety of additional statistical methods must be implemented. Four additional statistical methods are presented: technical error of measurement (SEM), smallest real difference (SRD), magnitude-based inference and mixed effect modeling technique (MEM). The aim of this perspective article is to demonstrate how to apply already known statistical analyses regarding BP responsiveness in order to improve interpretation and achieve higher reliability for future studies in exercise science.

A total of 27 hypertensive older women (aged 68.37 ± 5.55 years) participated in the present study. A whole-body resistance training (RT) program was performed on two nonconsecutive days per week for 10 weeks. BP was monitored during the 10-week RT intervention and after 15 weeks of detraining. First, individuals were classified as high and low responders, then statistical methods to analyze data included the use of SEM, SRD, magnitude-based inference and MEM.

When magnitude-based inference was used to classify responsiveness, most participants displayed a trivial response. Decrements in SBP between 1 and 10 mmHg were not clinically meaningful but fell within the measurement error of the SBP measurements. Baseline SBP and time of training predicted post-SBP response.

Changes over time and declines in SBP might not be a SRD and fell in the SEM. Moreover, SBP responsiveness was the result of inappropriate control of covariates such as period of training.