Biofeedback and Self-regulation

, Volume 20, Issue 1, pp 39–49 | Cite as

Electromyographic recordings of paraspinal muscles: Variations related to subcutaneous tissue thickness

  • Monica A. Hemingway
  • Heinz-J. Biedermann
  • James Inglis


The aim of this study was to assess the effect on EMG amplitude measures of variations in the thickness of underlying tissue between surface electrodes and the active muscle. 20 normal subjects with different amounts of subcutaneous tissue performed comparable constant force contractions for a 45-second period, during which paraspinal EMG recordings were taken. Three measures of subcutaneous tissue thickness were obtained from each subject: Body Mass Index, total body fat as calculated by Durnin's formula, and skinfold thickness at the recording sites. The results show that (i) the greater the thickness of subcutaneous tissue between the surface recording site and the contracting muscles, the lower the recorded electromyographic activity, and that (ii) up to 81.2% of the variance in the EMG measures can be explained by variation in the amount of subcutaneous tissue. These findings support the view that the absolute level of surface-recorded EMG cannot simply be taken at face value. The amplitude of the signal will be affected by, for example, the amount of body fat.

Descriptor Key words

biofeedback electromyography back pain assessment treatment 


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  1. Ahern, K. D., Follick, M. J., Council, J. R., Laser-Wolston, N., & Litchman, J. (1988). Comparison of lumbar paravertebral EMG patterns in chronic low back pain patients and nonpatient controls.Pain, 34 153–160.Google Scholar
  2. Arena, J. G., Sherman, R. A., Bruno, G. M., & Young, T. R. (1991). Electromyographic recordings of low back pain subjects and nonpain controls in six different positions: Effect of pain levels.Pain, 45 23–28.Google Scholar
  3. Arena, J. G., Sherman, R. A., Bruno, G. M., & Young, T. R. (1989). Electromyographic recordings of five types of low back pain subjects and nonpain controls in different positions.Pain, 37 57–65.Google Scholar
  4. Biedermann, H. J. (1984). Comments on the reliability of muscle activity comparisons in EMG biofeedback research with back pain patients.Biofeedback and Self-Regulation, 9(4), 451–458.Google Scholar
  5. Biedermann, H. J. (1990). Weight lifting in a postural restraining device: A reliable method to generate paraspinal constant force contractions.Clinical Biomechanics, 5 180–182.Google Scholar
  6. Biedermann, H. J., DeFoa, J. L., & Forrest, W. J. (1991). Muscle fiber directions of iliocostalis and multifidus: Male-Female differences.Journal of Anatomy, 179 163–167.Google Scholar
  7. Biedermann, H. J., & Monga, T. N. (1985). Relaxation oriented EMG biofeedback with back pain patients: Evaluation of paraspinal muscle activity.Clinical Biofeedback and Health, 8(2), 119–123.Google Scholar
  8. Biedermann, H. J., McGhie, A., Monga, T. N., & Shanks, G. L. (1987). Perceived and actual control in EMG treatment of back pain.Behavior Research and Therapy, 25(2), 137–147.Google Scholar
  9. Biedermann, H. J., Shanks, G. L., & Inglis, J. (1990). Median frequency estimates of paraspinal muscles: Reliability analysis.Electromyographic Clinical Neurophysiology, 30 83–88.Google Scholar
  10. Biedermann, H. J., Shanks, G. L., Forrest, W. J., & Inglis, J. (1991). Power spectrum analyses of electromyographic activity.Spine, 16(10), 1179–1184.Google Scholar
  11. Cohen, M. J., Swanson, G. A., Naliboff, B. D., Schandler, S. L., & McArthur, D. L. (1986). Comparison of electromyographic response patterns during posture and stress tasks in chronic low back pain patterns and control.Journal of Psychosomatic Research, 30(2), 135–141.Google Scholar
  12. Cram, J. R. (1990).Clinical EMG for surface recordings (Vol. 2). Nevada City, CA: Clinical Resources.Google Scholar
  13. Cram, J. R., & Steger, J. C. (1983). EMG scanning in the diagnosis of chronic pain.Biofeedback and Self-Regulation, 8(2), 229–241.Google Scholar
  14. DeFoa, J. L., Forrest, W., & Biedermann, H. J. (1989). Muscle fiber direction of longissimus, iliocostalis, and multifidus: Landmark derived reference lines.Journal of Anatomy, 163 243–247.Google Scholar
  15. DeLuca, C. J. (1979). Physiology and mathematics of myoelectric signals.IEEE Transactions on Biomedical Engineering, BME-6 313–325.Google Scholar
  16. DeLuca, C. J. (1984). Myoelectric manifestations of localized muscular fatigue in humans.CRC Critical Review in Biomedical Engineering, 11 251–279.Google Scholar
  17. Dolce, J. J., & Raczynski, J. M. (1985). Neuromuscular activity and electromyography in painful backs: Psychological and biomechanical models in assessment and treatment.Psychological Bulletin, 97 502–520.Google Scholar
  18. Durnin, J. V. G. A., & Womersley, J. (1974). Body fat assessed from total body density and its estimation from skinfold thickness: Measurements on 481 men and women aged from 16 to 72 years.British Journal of Nutrition, 32 77–97.Google Scholar
  19. Flor, H., Haag, G., & Turk, D. C. (1986). Long-term efficacy of EMG biofeedback for chronic rheumatic back pain.Pain, 27 195–202.Google Scholar
  20. Flor, H., & Turk, D. C. (1984). Etiological theories and treatments for chronic back pain. I. Somatic models and interventions.Pain, 19 105–121.Google Scholar
  21. Flor, H., Turk, D. C., & Birbaumer, N. (1985). Assessment of stress-related psychophysiological reactions in chronic back pain patients.Journal of Consulting and Clinical Psychology, 53 354–364.Google Scholar
  22. Gilmore, L. D., & DeLuca, C. J. (1985). Muscle fatigue monitor: Second generation.IEEE Transaction on Biomedical Engineering, BME-32 75–78.Google Scholar
  23. Hays, W. L. (1973).Statistics for the social sciences (2nd ed.). New York: Holt, Rinehart & Winston.Google Scholar
  24. National Institute on Drug Abuse (1981).New approaches to treatment of chronic pain: A review of multidisciplinary pain clinics and pain centers. Research monograph series, 36. Rockville, MD: Department of Health and Human Services, Public Health Service.Google Scholar
  25. Nouwen, A., & Solinger, J. W. (1979). The effectiveness of EMG biofeedback training in low back pain.Biofeedback and Self-Regulation, 4 103–111.Google Scholar
  26. Roy, S. H., DeLuca, C. J., & Scheider, J. (1986). Effects of electrode location on myoelectric conduction velocity and median frequency estimates.Journal of Applied Physiology, 61 1510–1517.Google Scholar
  27. Sihvonen, T., Partanen, J., & Hänninen, O. (1988). Averaged (rms) surface EMG in testing back function.Electromyographic Clinical Neurophysiology, 28 335–339.Google Scholar
  28. SAS Institute Inc. (1988).SAS user's guide: Statistics, version 5 edition. Cary, NC: SAS.Google Scholar
  29. Tabachnick, B. G., & Fidell, L. S. (1988).Using multivariate statics. New York: Harper and Row.Google Scholar
  30. Teufel, R., & Traue, H. C. (1989). Myogenic factors in low back pain. In C. Bischoff, H. C. Traue, & J. Zenz (Eds.),Clinical perspectives on headache and low back pain. Toronto: Hogrefe & Huber.Google Scholar
  31. Womersley, J., & Durnin, J. V. (1977). A comparison of the skinfold method with the extent of overweight and various weight and height relationships in the assessment of obesity.British Journal of Nutrition, 38 271–284.Google Scholar

Copyright information

© Plenum Publishing Corporation 1995

Authors and Affiliations

  • Monica A. Hemingway
    • 1
  • Heinz-J. Biedermann
    • 1
  • James Inglis
    • 1
  1. 1.Queen's UniversityUSA

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