Skip to main content
SpringerLink
Log in

Assessing the cone of economy in patients with spinal disease using only a force plate: an observational retrospective cohort study

  • Original Article
  • Published:
European Spine Journal Aims and scope Submit manuscript

Abstract

Study design

This is a retrospective cohort with multiple regression modeling.

Objective

The aim is to develop a new method for estimating cone of economy (CoE) using a force plate rather than traditional motion capture.

Background

Currently, most spinal deformity surgeons rely on static radiographic parameters for alignment, balance, and outcomes data alongside patient-reported outcome measures. The CoE, the stable region of upright posture, can be objectively measured to determine the efficiency and balance of the spine. Motion capture technology is currently used to collect data to calculate CoE, but this requires expensive and complex equipment, which is a barrier to widespread adoption and clinical use of CoE measurements. Force plates, which measure pressure, are less expensive and can be used in a clinical setting.

Methods

Motion capture and a force plate were used to quantify the CoE of 473 subjects (423 spinal surgical candidates; 50 healthy controls; 271 females; age: 58.60 ± 15.27; height: 1.69 ± 0.13; weight: 81.07 ± 20.91), and a linear multiple regression model was used to predict CoE using force plate data in a human motion laboratory setting. Patients were required to stand erect with feet together and eyes open in their self-perceived balanced and natural position for a full minute while measures of sway and center of pressure (CoP) were recorded.

Results

The CoP variable regression model successfully predicted CoE measurements. The variables that were used to predict vertical CoE were CoP coronal sway, CoP sagittal sway, and CoP total sway in several combinations. The coefficient of determination for the head total sway model indicated a 87.0% correlation (F(3,469) = 1044.14, p < 0.001). The coefficient of determination for the head sagittal sway model indicated a 69.2% correlation (F(3,469) = 351.70, p < 0.001). The coefficient of determination for the head coronal sway model indicated a 85.2% correlation (F(3,469) = 899.27, p < 0001).

Conclusion

Cone of economy was estimated from force plate data using center of pressure with high correlation without the use of motion capture in healthy controls and a variety of spine patients. This could lower the entry burden for measurement of the CoE in patients, enabling widespread use. This would provide surgeons objective global balance data, along with Haddas’ CoE classification system, that could assist with surgical decision-making and facilitate objective monitoring surgical outcomes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Fairbank JC, Pynsent PB (2000) The Oswestry Disability Index. Spine (Phila Pa 1976) 25:2940–2952; discussion 2952. https://doi.org/10.1097/00007632-200011150-00017

  2. Dolan P (1997) Modeling valuations for EuroQol health states. Med Care 35:1095–1108. https://doi.org/10.1097/00005650-199711000-00002

    Article  CAS  PubMed  Google Scholar 

  3. Jackson RP, Simmons EH, Stripinis D (1989) Coronal and sagittal plane spinal deformities correlating with back pain and pulmonary function in adult idiopathic scoliosis. Spine (Phila Pa 1976) 14:1391–1397. https://doi.org/10.1097/00007632-198912000-00018

  4. Glassman SD, Bridwell K, Dimar JR, Horton W, Berven S, Schwab F (2005) The impact of positive sagittal balance in adult spinal deformity. Spine (Phila Pa 1976) 30:2024–2029. https://doi.org/10.1097/01.brs.0000179086.30449.96

  5. Yagi M, Ohne H, Konomi T, Fujiyoshi K, Kaneko S, Takemitsu M, Machida M, Yato Y, Asazuma T (2017) Walking balance and compensatory gait mechanisms in surgically treated patients with adult spinal deformity. Spine J Official J North Am Spine Socty 17:409–417. https://doi.org/10.1016/j.spinee.2016.10.014

    Article  Google Scholar 

  6. J D (1994) Three-dimensional analysis of the scoliotic deformity. The pediatric spine: principles and practice. Raven Press Ltd, New York

  7. Schwab F, Patel A, Ungar B, Farcy JP, Lafage V (2010) Adult spinal deformity-postoperative standing imbalance: how much can you tolerate? An overview of key parameters in assessing alignment and planning corrective surgery. Spine 35:2224–2231. https://doi.org/10.1097/BRS.0b013e3181ee6bd4

    Article  PubMed  Google Scholar 

  8. Haddas R, Lieberman IH (2018) A method to quantify the “cone of economy.” Eur Spine J 27:1178–1187. https://doi.org/10.1007/s00586-017-5321-2

    Article  PubMed  Google Scholar 

  9. Savage JW, Patel AA (2014) Fixed sagittal plane imbalance. Global Spine J 4:287–296. https://doi.org/10.1055/s-0034-1394126

    Article  PubMed  PubMed Central  Google Scholar 

  10. Haddas R, Satin A, Lieberman I (2020) What is actually happening inside the “cone of economy”: compensatory mechanisms during a dynamic balance test. European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society. https://doi.org/10.1007/s00586-020-06411-w

  11. Ruhe A, Fejer R, Walker B (2011) Center of pressure excursion as a measure of balance performance in patients with non-specific low back pain compared to healthy controls: a systematic review of the literature. Eur Spine J 20:358–368. https://doi.org/10.1007/s00586-010-1543-2

    Article  PubMed  Google Scholar 

  12. Haddas R, Ju KL, Belanger T, Lieberman IH (2018) The use of gait analysis in the assessment of patients afflicted with spinal disorders. Eur Spine J 27:1712–1723. https://doi.org/10.1007/s00586-018-5569-1

    Article  PubMed  Google Scholar 

  13. Haddas R, Ju KL (2019) Gait Alteration in Cervical Spondylotic Myelopathy Elucidated by Ground Reaction Forces. Spine (Phila Pa 1976) 44:25–31. https://doi.org/10.1097/BRS.0000000000002732

  14. Haddas R, Lieberman I, Boah A, Arakal R, Belanger T, Ju KL (2019) Functional balance testing in cervical spondylotic myelopathy patients. Spine 44:103–109. https://doi.org/10.1097/BRS.0000000000002768

    Article  PubMed  Google Scholar 

  15. Haddas R, Satin A, Lieberman I (2020) What is Actually Happening inside the “Cone of Economy”: Compensatory Mechanisms during a Dynamic Balance Test. Eur Spine J

  16. Leslie G, Portney MPW (2009) Foundation of clinical research: applications to practice. Julie Levin Alexander, Upper Saddle River, New Jersy

    Google Scholar 

  17. Haddas R, Ju KL, Belanger T, Lieberman IH (2018) The use of gait analysis in the assessment of patients afflicted with spinal disorders. Eur Spine J. https://doi.org/10.1007/s00586-018-5569-1

    Article  PubMed  Google Scholar 

  18. Haddas R, Lieberman IH (2019) The change in Sway and neuromuscular activity in adult degenerative scoliosis patients pre and post surgery compared with controls. Spine 44:E899-e907. https://doi.org/10.1097/brs.0000000000003009

    Article  PubMed  Google Scholar 

  19. O’Sullivan S, Schmitz T (2007) Physical Rehabilitation.Davis Company, Philadelphia

    Google Scholar 

  20. Diebo BG, Shah NV, Pivec R, Naziri Q, Patel A, Post NH, Assi A, Godwin EM, Lafage V, Schwab FJ, Paulino CB (2018) From static spinal alignment to dynamic body balance: utilizing motion analysis in spinal deformity surgery. JBJS Rev 6:e3. https://doi.org/10.2106/jbjs.rvw.17.00189

    Article  PubMed  Google Scholar 

  21. Arima H, Yamato Y, Hasegawa T, Togawa D, Kobayashi S, Yasuda T, Banno T, Oe S, Matsuyama Y (2017) Discrepancy between standing posture and sagittal balance during walking in adult spinal deformity patients. Spine (Phila Pa 1976) 42:E25-e30. https://doi.org/10.1097/brs.0000000000001709

  22. Karimi MT, Kavyani M, Kamali M (2016) Balance and gait performance of scoliotic subjects: a review of the literature. J Back Musculoskelet Rehabil 29:403–415. https://doi.org/10.3233/bmr-150641

    Article  PubMed  Google Scholar 

  23. Brumagne S, Cordo P, Lysens R, Verschueren S, Swinnen S (2000) The role of paraspinal muscle spindles in lumbosacral position sense in individuals with and without low back pain. Spine (Phila Pa 1976) 25:989–994. https://doi.org/10.1097/00007632-200004150-00015

  24. Moseley GL, Hodges PW (2005) Are the changes in postural control associated with low back pain caused by pain interference? Clin J Pain 21:323–329. https://doi.org/10.1097/01.ajp.0000131414.84596.99

    Article  PubMed  Google Scholar 

  25. Corbeil P, Blouin JS, Teasdale N (2004) Effects of intensity and locus of painful stimulation on postural stability. Pain 108:43–50. https://doi.org/10.1016/j.pain.2003.12.001

    Article  PubMed  Google Scholar 

  26. Luoto S, Taimela S, Hurri H, Aalto H, Pyykkö I, Alaranta H (1996) Psychomotor speed and postural control in chronic low back pain patients A controlled follow-up study. Spine (Phila Pa 1976) 21:2621–2627. https://doi.org/10.1097/00007632-199611150-00012

  27. Ruhe A, Fejer R, Walker B (2010) The test-retest reliability of centre of pressure measures in bipedal static task conditions—a systematic review of the literature. Gait Posture 32:436–445. https://doi.org/10.1016/j.gaitpost.2010.09.012

    Article  PubMed  Google Scholar 

  28. Haddas R, Sambhariya V, Kosztowski T, Block A, Lieberman I (2021) Cone of economy classification: evolution, concept of stability, severity level, and correlation to patient-reported outcome scores. European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society. https://doi.org/10.1007/s00586-020-06678-z

Download references

Author information

Authors and Affiliations

  1. Texas Back Institute, 6020 West Parker Road, Plano, TX, 75093, USA

    Ram Haddas, Isador Lieberman & Peter B. Derman

  2. UNT Health Science Center, Fort Worth, TX, USA

    Addison Wood

  3. John Peter Smith Hospital, Fort Worth, TX, USA

    Addison Wood

Authors
  1. Ram Haddas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Addison Wood
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Isador Lieberman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter B. Derman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ram Haddas.

Ethics declarations

Conflict of interest

The authors disclose no potential conficts of interest.

Human and animal participants

IRB Approval: The study was approved by the Western Institutional Review Board for the Protection of Human Subjects (IRB#: 20,152,881).

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 11 kb)

Appendices

Appendix A Linear regression models to predict head total sway of the cone of economy

Equation 4: Head Total Sway Regression Model for Spine Patients and Controls (N = 473)

$$\widehat{{\text{Head Total Sway}}} = 9.35 + \left( {1.41 \times {\text{CoP Coronal Sway}}} \right) + \left( {2.23 \times {\text{CoP Sagittal Sway}}} \right) + \left( {1.22 \times {\text{CoP Total Sway}}} \right)$$

Equation 5: Head Total Sway Regression Model for Spine Patients (N = 423).

$$\widehat{{\text{Head Total Sway}}} = 8.90 + \left( {1.45 \times {\text{CoP Coronal Sway}}} \right) + \left( {2.36 \times {\text{CoP Sagittal Sway}}} \right) + \left( {1.22 \times {\text{CoP Total Sway}}} \right)$$

Equation 6: Head Total Sway Regression Model for Controls (N = 50)

$$\widehat{{\text{Head Total Sway}}} = 8.31 - \left( 0.48 \times {\text{CoP Coronal Sway}} \right) - \left( 0.54 \times {\text{CoP Sagittal Sway}} \right) + \left( {1.72 \times {\text{CoP Total Sway}}} \right)$$

Equation 7: Head Total Sway Regression Model for Spine Patients and Controls (N = 473)

$$\widehat{{\text{Head Total Sway}}} = 12.50 + \left( {1.42 \times {\text{CoP Total Sway}}} \right)$$

Equation 8: Head Total Sway Regression Model for Spine Patients (N = 423)

$$\widehat{{\text{Head Total Sway}}} = 12.47 + \left( {1.42 \times {\text{CoP Total Sway}}} \right)$$

Equation 9: Head Total Sway Regression Model for Controls (N = 50)

$${\widehat{{\text{Head Total Sway}}}} = 7.54 + \left( 1.67 \times {\text{CoP Total Sway}} \right)$$

CoP: Center of Pressure.

Appendix B. Linear regression models to predict head sagittal sway of the cone of economy

Equation 10: Head Sagittal Sway Regression Model for Spine Patients and Controls (N = 473)

$$\widehat{{\text{Head Sagittal Sway}}} = 1.74 + \left( {0.21 \times CoP Coronal Sway} \right) + \left( {1.03 \times CoP Sagittal Sway} \right) + \left( {0.02 \times CoP Total Sway} \right)$$

Equation 11: Head Sagittal Sway Regression Model for Spine Patients (N = 423)

$$\widehat{{\text{Head Sagittal Sway}}} = 1.83 + \left( {0.20 \times {\text{CoP Coronal Sway}}} \right) + \left( {1.02 \times {\text{CoP Sagittal Sway}}} \right) + \left( {0.02 \times CoP Total Sway} \right)$$

Equation 12: Head Sagittal Sway Regression Model for Controls (N = 50)

$$\widehat{\text{Head Sagittal Sway}} = 0.93 - \left( 0.98 \times {\text{CoP Coronal Sway}} \right) - \left( 1.17 \times {\text{CoP Sagittal Sway}} \right) - \left( 0.01 \times {\text{CoP Total Sway}} \right)$$

Equation 13: Head Sagittal Sway Regression Model for Spine Patients and Controls (N = 473)

$$\widehat{{\text{Head Sagittal Sway}}} = 1.76 + \left( {0.30 \times {\text{CoP Coronal Sway}}} \right) + \left( {1.16 \times {\text{CoP Sagittal Sway}}} \right)$$

Equation 14: Head Sagittal Sway Regression Model for Spine Patients (N = 423)

$$\widehat{{\text{Head Sagittal Sway} }}= 1.87 + \left( {0.29 \times {\text{CoP Coronal Sway}}} \right) + \left( {1.15 \times {\text{CoP Sagittal Sway}}} \right)$$

Equation 15: Head Sagittal Sway Regression Model for Controls (N = 50)

$$\widehat{{\text{Head Sagittal Sway}}} = 0.87 + \left( {0.95 \times {\text{CoP Coronal Sway}}} \right) + \left( {1.16 \times {\text{CoP Sagittal Sway}}} \right)$$

Equation 16: Head Sagittal Sway Regression Model for Spine Patients and Controls (N = 473)

$$\widehat{{\text{Head Sagittal Sway}}} = 1.72 + \left( 1.34 \times {\text{CoP Sagittal Sway}} \right)$$

Equation 17: Head Sagittal Sway Regression Model for Spine Patients (N = 423)

$$\widehat{{\text{Head Sagittal Sway}}} = 1.85 + \left( {1.32 \times {\text{CoP Sagittal Sway}}} \right)$$

Equation 18 Head Sagittal Sway Regression Model for Controls (N = 50)

$$\widehat{{\text{Head Sagittal Sway}}} = 1.42 + \left( {1.27 \times {\text{CoP Sagittal Sway}}} \right)$$

CoP: Center of Pressure.

Appendix C: Linear regression models to predict head coronal sway of the cone of economy

Equation 19: Head Coronal Sway Regression Model for Spine Patients and Controls (N = 473)

$$\widehat{{\text{Head Coronal Sway}}} = 0.48 + \left( {1.11 \times {\text{CoP Coronal Sway}}} \right) + \left( {0.02 \times {\text{CoP Sagittal Sway}}} \right) + \left( {0.01 \times {\text{CoP Total Sway}}} \right)$$

Equation 20: Head Coronal Sway Regression Model for Spine Patients (N = 423)

$$\widehat{{\text{Head Coronal Sway}}} = 0.48 + \left( {1.11 \times {\text{CoP Coronal Sway}}} \right) + \left( {0.02 \times {\text{CoP Sagittal Sway}}} \right) + \left( {0.01 \times {\text{CoP Total Sway}}} \right)$$

Equation 21: Head Coronal Sway Regression Model for Controls (N = 50)

$$\widehat{{\text{Head Coronal Sway}}} = 0.38 + \left( {1.21 \times {\text{CoP Coronal Sway}}} \right) + \left( {0.02 \times {\text{CoP Sagittal Sway}}} \right) + \left( {0.02 \times {\text{CoP Total Sway}}} \right)$$

Equation 22: Head Coronal Sway Regression Model for Spine Patients and Controls (N = 473)

$$\widehat{{\text{Head Coronal Sway}}} = 0.49 + \left( {1.17 \times {\text{CoP Coronal Sway}}} \right) + \left( {0.10 \times {\text{CoP Sagittal Sway}}} \right)$$

Equation 23: Head Coronal Sway Regression Model for Spine Patients (N = 423)

$$\widehat{{\text{Head Coronal Sway}}} = 0.50 + \left( {1.17 \times {\text{CoP Coronal Sway}}} \right) + \left( {0.10 \times {\text{CoP Sagittal Sway}}} \right)$$

Equation 24: Head Coronal Sway Regression Model for Controls (N = 50)

$$\widehat{{\text{Head Coronal Sway}}} = 0.58 + \left( {1.29 \times {\text{CoP Coronal Sway}}} \right) + \left( {0.02 \times {\text{CoP Sagittal Sway}}} \right)$$

Equation 25: Head Coronal Sway Regression Model for Spine Patients and Controls (N = 473)

$$\widehat{{\text{Head Coronal Sway}}} = 0.74 + \left( {1.23 \times {\text{CoP Coronal Sway}}} \right)$$

Equation 26: Head Coronal Sway Regression Model for Spine Patients (N = 423)

$$\widehat{{\text{Head Coronal Sway}}} = 0.76 + \left( {1.23 \times {\text{CoP Coronal Sway}}} \right)$$

Equation 27: Head Coronal Sway Regression Model for Controls (N = 50)

CoP: Center of pressure.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Haddas, R., Wood, A., Lieberman, I. et al. Assessing the cone of economy in patients with spinal disease using only a force plate: an observational retrospective cohort study. Eur Spine J 30, 2504–2513 (2021). https://doi.org/10.1007/s00586-021-06836-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00586-021-06836-x

Keywords

Search

Navigation