Body Composition And Ultrasound Measurements As Valuable Tools To Maximize The Recruitment Of Participants With Relevant Number Of Identifiable Motor Units During Submaximal Strength Test

PURPOSE: High-density surface EMG (HDsEMG) provides valuable information about motor units (MUs) properties and their adaptability non-invasively. However, great intersubject variability in identification of MUs exists due to anatomical constraints of the muscle, individual volume conductor properties and the contraction intensity. A recent study showed that greater muscle-electrode distance (MED) negatively influences EMG signal decomposition outcome, at low force levels only. Here we investigated the influence of body composition and anatomical features on the extraction of MUs number in vastus lateralis (VL) during voluntary contractions. METHODS: Fourty-six healthy subjects (age 23.8±3 yr; Male: 91%) took part in this study. Participants performed submaximal isometric knee extensions at 15, 35, 50, and 70% of maximal voluntary contraction. Concurrently, HDs-EMG was used to record right leg (RL) VL muscle activity. EMG signals were decomposed, and the MUs number was extracted. Body composition was assessed with BIA and DXA scan. Ultrasonography (US) was used to determine MED. Correlations and regressions analyses between average MUs number and all body composition features were assessed. RESULTS: A total of 1082 MUs were identified from VL (average 28 MUs/subject). Negative correlations were found between MUs number and Fat Mass (FM) estimated by BIA (15%: p<.001, r=-.58; 35%: p=.002, r=-.48) and DXA (15%: p=.021, r=-.37; 35%: p=.017, r=-.38), RL FM measured by DXA (15%: p<.001, r=-.51; 35%: p<.001, r=-.60; 50%: p<.001, r=-.33; 70%: p=.001, r=-.39), and MED (15%: p<.001, r=-0.69; 35%: p<.001, r=-.68; 50%: p<.001, r=-.59; 70%: p=.002, r=-.48). None of the main anthropometrical features (i.e., BMI) correlated with MUs number. CONCLUSIONS: Our results revealed the possibility to partly predict the quality of HDsEMG decomposition, i.e., number of identified MUs/subject. In particular, the current analysis confirmed that having higher levels of body and localized fat mass, assessed with BIA or DXA, decreases the number of detectable MUs. Overall, we showed that the single most predictive parameter for MUs number is the MED, therefore its quantification through US may be recommended prior to HDsEMG recording.