Research Article| Volume 17, ISSUE 5, P345-352, June 2002

Relationship between pain and vertebral motion in chronic low-back pain subjects


      Objectives. To investigate the relationship between intervertebral motion, intravertebral deformation and pain in chronic low-back pain patients.
      Design. This study measured vertebral motion of the lumbar spine and associated pain in a select group of chronic low-back pain patients as they performed a standard battery of motions in all planes.
      Background. Numerous studies have demonstrated that individuals with low-back pain have impaired spinal motion, yet few studies have examined the specific relationship between pain and motion parameters. Although it is accepted that the pain in mechanical low-back patients is due to specific spinal motions, no studies have related specific motions to pain measures.
      Methods. Percutaneous intra-pedicle screws were placed into the right and left L4 (or L5) and S1 segments of nine chronic low-back pain patients. The external fixator frame was removed following the clinical external fixation test. The 3D locations of the pedicle screws and the level of pain were recorded as the subjects performed a battery of motions. The relationship between the pain and motion parameters was assessed using linear discriminant analysis and neural network models.
      Results. The neural network model showed a strong relationship between observed and predicted pain (R2=0.997). The discriminant analysis showed a weak relationship (R2=0.5).
      Conclusions. Vertebral motion parameters are strongly predictive of pain in this select group of chronic low-back pain patients. The nature of the relationship is nonlinear and involves interactions; neural networks are able to effectively describe these relationships.Relevance Specific patterns of intervertebral motion and intravertebral deformation result in pain in chronic low-back pain patients. This substantiates the mechanical back pain aetiology.


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Clinical Biomechanics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Kang S.W.
        • Lee W.N.
        • Moon J.H.
        • Chun S.I.
        Correlation of spinal mobility with the severity of chronic lower back pain.
        Yonsei Med. J. 1995; 36: 37-44
        • Klein A.B.
        • Snyder-Mackler L.
        • Roy S.H.
        • DeLuca C.
        Comparison of spinal mobility and isometric trunk extensor forces with electromyographic spectral analysis in identifying low back pain.
        Phys. Ther. 1991; 71: 445-454
        • Marras W.S.
        • Wongsam P.E.
        Flexibility and velocity of the normal and impaired lumbar spine.
        Arch. Phys. Med. Rehabil. 1986; 67: 213-217
        • Okawa A.
        • Shinomiya K.
        • Komori H.
        • Muneta T.
        • Arai Y.
        • Nakai O.
        Dynamic motion study of the whole lumbar spine by videofluoroscopy.
        Spine. 1998; 23: 1743-1749
        • Bogduk N.
        • Twomey L.T.
        Clinical anatomy of the lumbar spine.
        second ed. Churchill Livingstone, Melbourne1991
        • Cox M.E.
        • Asselin S.
        • Gracovetsky S.A.
        • Richards M.P.
        • Newman N.M.
        • Karakusevic V.
        • et al.
        Relationship between functional evaluation measures and self-assessment in nonacute low back pain.
        Spine. 2000; 25: 1817-1826
        • McGregor A.H.
        • Dore C.J.
        • McCarthy I.D.
        • Hughes S.P.
        Are subjective clinical findings and objective clinical tests related to the motion characteristics of low back pain subjects?.
        J. Orthop. Sports Phys. Ther. 1998; 28: 370-377
        • Bishop J.B.
        • Szpalski M.
        • Ananthraman S.K.
        • McIntyre D.R.
        • Pope M.H.
        Classification of low back pain from dynamic motion characteristics using an artificial neural network.
        Spine. 1997; 22: 2991-2998
        • Gioftsos G.
        • Grieve D.W.
        The use of artificial neural networks to recognize patterns of human movement: gait patterns.
        Clin. Biomech. 1995; 10: 179-183
        • Chau T.
        A review of analytical techniques for gait data. Part 2: neural network and wavelet methods.
        Gait Posture. 2001; 13: 102-120
        • Schlaepfer F.
        • Magerl F.
        • Jacobs R.
        • Perren S.M.
        • Weber B.G.
        In vivo measurements of loads on an external fixation device for human lumbar spine fractures.
        in: Engineering aspects of the spine. Mechanical Engineering Publications Limited for The Institution of Mechanical Engineers, London1980: 57-62
      1. Wilke HJ, Claes L, Worsdorfer O. In vivo measurements at spinal stabilization implants. In: Proceedings of the 7th Meeting of the European Society of Biomechanics, Aarhus, Denmark, vol. A3. 1989

        • Sahni I.K.
        • Hipp J.A.
        • Kirking B.C.
        • Alexander J.W.
        • Esses S.I.
        Use of percutaneous transpedicular external fixation pins to measure intervertebral motion.
        Spine. 1999; 24: 1890-1893
        • Bednar D.A.
        • Raducan V.
        External spinal skeletal fixation as a prognostic aid in the management of problem low back pain: A prospective randomized clinical trial.
        Clin. Orthop. 1996; 322: 131-139
        • Esses S.I.
        • Botsford D.J.
        • Kostuik J.P.
        The role of external spinal skeletal fixation in the assessment of low-back disorders.
        Spine. 1989; 14: 594-601
        • van der Schaaf D.B.
        • van Limbeek J.
        • Pavlov P.W.
        Temporary external transpedicular fixation of the lumbosacral spine.
        Spine. 1999; 24: 481-484
        • Magerl F.
        External spinal skeletal fixation.
        in: Weber B.G Magerl F. The external fixator. Springer-Verlag, Berlin1985: 290-365
        • Axelsson P.
        • Johnsson R.
        • Stromqvist B.
        Mechanics of the external fixation test in the lumbar spine––A Roentgen stereophotogrammetric analysis.
        Spine. 1996; 21: 330-333
      2. Nydegger T, Lund T, Schlenzka D, Oxland TR. Can Schanz screws be used to measure in vivo lumbar intervertebral motion? Orthop. Res. Soc. 2000. Paper #380

        • Woltring H.J.
        • Huiskes R.
        • de Lange A.
        • Veldpaus F.E.
        Finite centroid and helical axis estimation from noisy landmark measurements in the study of human joint kinematics.
        J. Biomech. 1985; 18: 379-389
        • Steffen T.
        • Rubin R.K.
        • Baramki H.G.
        • Antoniou J.
        • Marchesi D.
        • Aebi M.
        A new technique for measuring lumbar segmental motion in vivo––method, accuracy, and preliminary results.
        Spine. 1997; 22: 156-166
        • Kemp R.A.
        • MacAulay C.
        • Palcic B.
        Opening the black box: the relationship between neural networks and linear discriminant functions.
        Anal. Cell Pathol. 1997; 14: 19-30
        • Savelberg H.H.
        • de Lange A.L.
        Assessment of the horizontal, fore-aft component of the ground reaction force from insole pressure patterns by using artificial neural networks.
        Clin. Biomech. 1999; 14 (Novel award third prize paper): 585-592
        • Marras W.S.
        • Parnianpour M.
        • Ferguson S.A.
        • Kim J.Y.
        • Crowell R.R.
        • Bose S.
        • et al.
        The classification of anatomic- and symptom-based low back disorders using motion measure models.
        Spine. 1995; 20: 2531-2546
        • Mann H.N.
        • Brown M.D.
        Artificial intelligence in the diagnosis of low back pain.
        Orthop. Clin. N. Am. 1991; 22: 303-314
        • Sanders N.W.
        • Mann III N.H.
        Automated scoring of patient pain drawings using artificial neural networks: efforts toward a low back pain triage application.
        Comput. Biol. Med. 2000; 30: 287-298
        • Pesonen E.
        Is neural network better than statistical methods in diagnosis of acute appendicitis?.
        Stud. Health Technol. Inform. 1997; 43 Pt A: 377-381
        • Reibnegger G.
        • Weiss G.
        • Werner-Felmayer G.
        • Judmaier G.
        • Wachter H.
        Neural networks as a tool for utilizing laboratory information: comparison with linear discriminant analysis and with classification and regression trees.
        Proc. Natl. Acad. Sci. USA. 1991; 88: 11426-11430
        • Wu W.L.
        • Su F.C.
        • Cheng Y.M.
        • Chou Y.L.
        Potential of the genetic algorithm neural network in the assessment of gait patterns in ankle arthrodesis.
        Ann. Biomed. Eng. 2001; 29: 83-91
        • Willems P.C.
        • Nienhuis B.
        • Sietsma M.
        • van der Schaaf D.B.
        • Pavlov P.W.
        The effect of a plaster cast on lumbosacral joint motion. An in vivo assessment with precision motion analysis system.
        Spine. 1997; 22: 1229-1234
        • Green T.P.
        • Allvey J.C.
        • Adams M.A.
        Spondylolysis bending of the inferior articular processes of lumbar vertebrae during simulated spinal movements.
        Spine. 1994; 19: 2683-2691
        • Kaigle A.M.
        • Wessberg P.
        • Hansson T.H.
        Muscular and kinematic behavior of the lumbar spine during flexion–extension.
        J. Spinal Disord. 1998; 11: 163-174