Age-specific differences in the time-frequency representation of surface electromyographic data recorded during a submaximal cyclic back extension exercise: a promising biomarker to detect early signs of sarcopenia

Abstract

Purpose: Motivated by the goal of developing new methods to detect early signs of sarcopenia, we investigated if surface electromyographic (SEMG) data recorded during the performance of cyclic, submaximal back extensions are marked by age-specific differences in their time and frequency characteristics. Furthermore, day-today retest reliability of the EMG measures was examined. Methods: A total of 86 healthy volunteers used a back dynamometer to perform a series of three maximal voluntary contractions (MVC) consisting of isometric back extensions, followed by an isometric back extension at 80% MVC, and finally 25 slow cyclic back extensions at 50% MVC. SEMG data was recorded bilaterally at L1, L2, and L5 from the iliocostalis lumborum, longissimus, and multifidus muscles, respectively. Tests were repeated two days and six weeks later. A linear mixed-effects model with fixed effects "age, sex, test number" and the random effect "person" was performed to investigate age-specific differences in both the initial value and the time-course (as defined by the slope of the regression line) of the root mean square (RMS-SEMG) values and instantaneous median frequency (IMDF-SEMG) values calculated separately for the shortening and lengthening phases of the exercise cycles. Generalizability Theory was used to examine reliability of the EMG measures. Results: Back extensor strength was comparable in younger and older adults. The initial value of RMS-SEMG and IMDF-SEMG as well as the RMS-SEMG time-course did not significantly differ between the two age groups. Conversely, the IMDF-SEMG time-course showed more rapid changes in younger than in older individuals. Absolute and relative reliability of the SEMG time-frequency representations were comparable in older and younger individuals with good to excellent relative reliability but variable absolute reliability levels.

The Back Muscle Surface Electromyography-Based Fatigue Index: A Digital Biomarker of Human Neuromuscular Aging?

“…The trunk range of motion was monitored by utilizing the sagittal angular displacement data derived from a geometrical procedure, using gravity as reference, as described in previous work [ 19 , 50 ]. To calculate individual angles, the accelerometer data collected using all the probes were combined ( Figure 1 ).…”

“…We recently suggested that an index derived from surface electromyographic (SEMG) data collected during a fatigue test could be used as a digital biomarker of the elevated risk of an early onset of neuromuscular aging processes [ 18 , 19 ]. This method estimates—via a time–frequency transformations—the compression over time of the SEMG frequency spectrum associated with localized muscle fatigue.…”

“…This protocol complies with both evidence-based recommendations for resistance training in novice trainees [ 30 , 31 ], and the recommended level of muscle contraction to generate blood flow occlusion and muscle fatigue detectable using SEMG data [ 25 ]. By relying on ad hoc algorithms to analyze the time–frequency content of SEMG data collected during dynamic contractions [ 32 , 33 ], our research group demonstrated that this method may indicate neuromuscular aging processes in healthy pain-free volunteers [ 19 ]. However, given known alterations in trunk sensorimotor control and muscle morphology with LBP [ 10 , 34 ], it remains uncertain as to whether this novel test is marked by sufficient age-specific discriminatory sensitivity in cLBP persons.…”

Sarcopenia: Technological Advances in Measurement and Rehabilitation

“…On the basis of the data obtained by Habenicht et al [21] in their study on back extension, a diagnostic algorithm for assessing the first signs of muscle weakness related to back extension may be developed [21]. Subsequently, Gennaro et al [23], in their ES, defined "corticomuscular coherence" (CMC), obtained during locomotion by simultaneously measuring EEG and EMG, and suggested it as a new feature for the diagnosis of sarcopenia [23], reporting that it has a high sensitivity and specificity.…”

Comparative Analysis of Supervised Classifiers for the Evaluation of Sarcopenia Using a sEMG-Based Platform

“…After the trial, the authors concluded that dynamometer and sEMG values were highly correlated, although no statistically significant difference in muscle strength with or without sarcopenia was evidenced. On the other hand, the primary purpose of the research reported in [ 26 ] was to examine whether age-specific effects could be observed in the sEMG representation of healthy individuals engaged in cyclic back extension exercise, seeking to develop new biomarkers that could be used to screen for very early forms of sarcopenia.…”