Repeatability of surface EMG variables in the sternocleidomastoid and anterior scalene muscles

Deborah Falla, Paul Dall'Alba, Alberto Rainoldi, Roberto Merletti, Gwendolen Jull

Research output: Contribution to journalArticlepeer-review

70 Citations (Scopus)


In this study we examined the repeatability and reliability of the surface electromyographic (sEMG) signal mean frequency (MNF), average rectified value (ARV) and conduction velocity (CV) measured for the sternocleidomastoid (SCM) and the anterior scalene (AS) muscles in nine healthy volunteers during 15-s isometric cervical flexion contractions at 50% of the maximal voluntary contraction level over 3 non-consecutive days. Repeatability and reliability estimates were obtained for the initial values and rates of change of each sEMG variable by using both the Intraclass Correlation Coefficient (ICC) and the normalised standard error of the mean (nSEM). Results from SCM indicated good levels of repeatability for the initial value and slope of ARV (ICC>65%). For the AS, high levels of repeatability were identified for the initial value of MNF (ICC>70%) and the slope of ARV (ICC>75%). Values of nSEM in the range 2.8-7.2% were obtained for the initial values of MNF and CV for both SCM and AS, indicating clinically acceptable measurement precision. The low value obtained for the nSEM of the initial value of MNF for the AS, in combination with the high ICC, indicates that of all of the variables examined, this variable could offer the best normative index to distinguish between subjects with and without neck pain, and represents the sEMG variable of choice for future evaluation purposes.

Original languageEnglish
Pages (from-to)542-9
Number of pages8
JournalEuropean Journal of Applied Physiology
Issue number6
Publication statusPublished - Oct 2002


  • Adult
  • Electromyography
  • Female
  • Humans
  • Male
  • Muscle Contraction
  • Muscle Fatigue
  • Neck Muscles
  • Neck Pain
  • Reproducibility of Results
  • Journal Article
  • Research Support, Non-U.S. Gov't
  • Validation Studies


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