Training intensity quantification of core stability exercises based on a smartphone accelerometer

Although core stability (CS) training is largely used to enhance motor performance and prevent musculoskeletal injuries, the lack of methods to quantify CS training intensity hinders the design of CS programs and the comparison and generalization of their effects. The aim of this study was to analyze the reliability of accelerometers integrated into smartphones to quantify the intensity of several CS isometric exercises. Additionally, this study analyzed to what extent the pelvic acceleration data represent the local stability of the core structures or the whole-body postural control. Twenty-three male and female physically-active individuals performed two testing-sessions spaced one week apart, each consisting of two 6-second trials of five variations of frontal bridge, back bridge, lateral bridge and bird-dog exercises. In order to assess load intensity based on the postural control challenge of CS exercises, a smartphone accelerometer and two force platforms were used to measure the mean pelvic linear acceleration and the mean velocity of the centre of pressure displacement, respectively. Reliability was assessed through the intra-class correlation coefficient (ICC(3,1)) and the standard error of measurement (SEM). In addition, Pearson coefficient was used to analyze the correlation between parameters. The reliability analysis showed that most CS exercise variations obtained moderate-to-high reliability scores for pelvic acceleration (0.71<ICC<0.88; 13.23%≤SEM≤22.99%) and low-to-moderate reliability scores for centre of pressure displacement (0.24<ICC<0.89; 9.88%≤SEM≤35.90%). Regarding the correlation analysis, correlations between pelvic acceleration and centre of pressure displacement were moderate-to-high (0.52≤r≤0.81). Based on these results, smartphone accelerometers seem reliable devices to quantify isometric CS exercise intensity, which is useful to identify the individuals’ CS status and to improve the dose-response characterization of CS programs.