Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Machine learning models for predicting treatment outcomes in chronic non-specific back pain patients undergoing lumbar extension traction
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 30 January 2026

Machine learning models for predicting treatment outcomes in chronic non-specific back pain patients undergoing lumbar extension traction

  • Ibrahim M. Moustafa1,2,3,
  • Dilber Uzun Ozsahin4,5,6,
  • Mubarak Taiwo Mustapha6,
  • Shima Zadeh1,2,
  • Iman Khowailed1,2,
  • Paul A. Oakley7,8,9 &
  • …
  • Deed E. Harrison7 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Health care
  • Medical research

Abstract

Conservative treatment for chronic non-specific low back pain (CLBP) includes lumbar extension traction (LET) to re-align lumbar lordosis (LL). This study explores the use of machine learning (ML) models to predict post-treatment outcomes in patients with CLBP undergoing LET and how these predictions can support clinical decision-making. We utilized a retrospective database of 431 consecutive patients with uncomplicated CLBP. Post-treatment variables predicted included LL, NRS pain score, and Oswestry Disability Index (ODI). Input model variables included pre-treatment LL, sacral base angle (SBA), ratio of LL/SBA fit type, NRS, ODI, frequency, duration, LET compliance, and demographic variables of age and BMI. Initial variables were analyzed to predict post-treatment outcomes. Three ML models—Random Forest (RF), XGBoost, and Multilayer Perceptron (MLP)—were employed to handle both continuous and categorical variables, and performance was evaluated for predictive accuracy. Factors affecting outcomes were identified using Shapley Additive Explanations. Treatment was a multimodal spine rehabilitation program featuring LET applied 3–6 times per week, varied between 4 and 10 weeks, and follow-up was performed at the end of care. Improvements in LL, NRS, and ODI were − 11.5° to − 23.6°, 7.3/10 to 3.3/10, and 33.2% to 10.4%, respectively. Among the ML models, XGBoost demonstrated the highest predictive accuracy for lumbar lordotic angle (R2 = 0.728) and pain score (R2 = 0.648), while Random Forest slightly outperformed XGBoost for ODI (0.631 vs. 0.616). MLP performed poorly for ODI predictions (R2 = 0.201), indicating difficulty in capturing functional disability patterns. SHAP analysis identified fit type, compliance, traction frequency, pre-treatment lumbar curve, and BMI as the most influential predictors. These predictors offer actionable insight for clinical decision-making by allowing clinicians to stratify patients based on predicted responsiveness, tailor LET frequency and duration, and educate patients on the importance of compliance. This study demonstrates that ML models, particularly XGBoost and Random Forest, can effectively predict LET outcomes, supporting personalized treatment strategies for CLBP patients.

Data availability

Data is available upon request from the corresponding author.

References

  1. Wu, A. et al. Global low back pain prevalence and years lived with disability from 1990 to 2017: estimates from the global burden of disease study 2017. Ann. Transl. Med. 8 (6). (2020).

  2. Foster, N. E. et al. Prevention and treatment of low back pain: evidence, challenges, and promising directions. Lancet 391 (10137), 2368–2383 (2018).

    Google Scholar 

  3. Ketenci, A. & Zure, M. Pharmacological and non-pharmacological treatment approaches to chronic lumbar back pain. Turkish J. Phys. Med. Rehabilitation. 67 (1), 1 (2021).

    Google Scholar 

  4. Guo, Y. et al. Bibliometric analysis of research on manual therapy for low back pain from 2013 to 2023. Med. (Baltim). 104 (8), e41618. https://doi.org/10.1097/MD.0000000000041618 (2025).

    Google Scholar 

  5. Hurwitz, E. L., Haldeman, S. & Cedraschi, C. The global spine care initiative: applying evidence-based guidelines on the non-invasive management of back and neck pain to low- and middle-income communities. Eur. Spine J. 27 (Suppl 6), 851–860. https://doi.org/10.1007/s00586-017-5433-8 (2018). Epub 2018 Feb 19.

    Google Scholar 

  6. WHO guideline for non-surgical management of chronic primary low back pain in adults in primary and community care settings. ISBN 978-92-4-008178-9 (electronic version). (Accessed 13 March 2025). https://www.who.int/publications/i/item/9789240081789 (2023).

  7. Harrison, D. E., Cailliet, R., Harrison, D. D., Janik, T. J. & Holland, B. Changes in sagittal lumbar configuration with a new method of extension traction: nonrandomized clinical controlled trial. Arch. Phys. Med. Rehabil. 83 (11), 1585–1591. https://doi.org/10.1053/apmr.2002.35485 (2002).

    Google Scholar 

  8. Diab, A. A. & Moustafa, I. M. The efficacy of lumbar extension traction for sagittal alignment in mechanical low back pain: a randomized trial. J. Back Musculoskelet. Rehabil. 26 (2), 213–220. https://doi.org/10.3233/BMR-130372 (2013).

    Google Scholar 

  9. Diab, A. A. & Moustafa, I. M. Lumbar lordosis rehabilitation for pain and lumbar segmental motion in chronic mechanical low back pain: a randomized trial. J. Manipulative Physiol. Ther. 35 (4), 246–253. https://doi.org/10.1016/j.jmpt.2012.04.021 (2012).

    Google Scholar 

  10. Lee, C. H., Heo, S. J., Park, S. H., Jeong, H. S. & Kim, S. Y. Functional changes in patients and morphological changes in the lumbar intervertebral disc after applying Lordotic Curve-Controlled traction: A Double-Blind randomized controlled study. Med. (Kaunas). 56 (1), 4. https://doi.org/10.3390/medicina56010004 (2019).

    Google Scholar 

  11. Moustafa, I. M. & Diab, A. A. Extension traction treatment for patients with discogenic lumbosacral radiculopathy: a randomized controlled trial. Clin. Rehabil. 27 (1), 51–62. https://doi.org/10.1177/0269215512446093 (2013).

    Google Scholar 

  12. Chun, S. W., Lim, C. Y., Kim, K., Hwang, J. & Chung, S. G. The relationships between low back pain and lumbar lordosis: a systematic review and meta-analysis. Spine J. 17 (8), 1180–1191. https://doi.org/10.1016/j.spinee.2017.04.034 (2017).

    Google Scholar 

  13. Sadler, S. G., Spink, M. J., Ho, A., De Jonge, X. J. & Chuter, V. H. Restriction in lateral bending range of motion, lumbar lordosis, and hamstring flexibility predicts the development of low back pain: a systematic review of prospective cohort studies. BMC Musculoskelet. Disord. 18 (1), 179. https://doi.org/10.1186/s12891-017-1534-0 (2017).

    Google Scholar 

  14. Cardoso, L. et al. Computational modeling of posteroanterior lumbar traction by an automated massage bed: predicting intervertebral disc stresses and deformation. Front. Rehabil Sci. 3, 931274. https://doi.org/10.3389/fresc.2022.931274 (2022).

    Google Scholar 

  15. Lee, C. H., Heo, S. J. & Park, S. H. The real time geometric effect of a Lordotic Curve-Controlled spinal traction device: A randomized cross over study. Healthc. (Basel). 9 (2), 125. https://doi.org/10.3390/healthcare9020125 (2021).

    Google Scholar 

  16. de Andrada Pereira, B. et al. Influence of lumbar lordosis on posterior rod strain in long-segment construct during Biomechanical loading: a cadaveric study. Neurospine 18 (3), 635 (2021).

    Google Scholar 

  17. Moustafa, I. M. et al. Utilizing machine learning to predict post-treatment outcomes in chronic non-specific neck pain patients undergoing cervical extension traction. Sci. Rep. 14 (1), 11781 (2024).

    Google Scholar 

  18. Tangsrivimol, J. A. et al. Artificial intelligence in neurosurgery: a state-of-the-art review from past to future. Diagnostics (Basel) 13 (14), 2429. https://doi.org/10.3390/diagnostics13142429 (2023).

  19. Javaid, M., Haleem, A., Pratap Singh, R., Suman, R. & Rab, S. Significance of machine learning in healthcare: Features, pillars and applications. Int. J. Intell. Networks. 3, 58–73. https://doi.org/10.1016/J.IJIN.2022.05.002 (2022).

    Google Scholar 

  20. Tschuggnall, M. et al. Machine learning approaches to predict rehabilitation success based on clinical and patient-reported outcome measures. Inf. Med. Unlocked 24 https://doi.org/10.1016/J.IMU.2021.100598 (2021).

  21. Tagliaferri, S. D. et al. Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews. Npj Digit. Med. 3, 1–16. https://doi.org/10.1038/s41746-020-0303-x (2020).

    Google Scholar 

  22. Shanahan, C., Ward, A. R. & Robertson, V. J. Comparison of the analgesic efficacy of interferential therapy and transcutaneous electrical nerve stimulation. Physiotherapy 92, 247–253. https://doi.org/10.1016/J.PHYSIO.2006.05.008 (2006).

    Google Scholar 

  23. Robertson, V., Ward, A., Low, J. & Reed, A. Electrotherapy explained, Principles and Practice 4th edn (Heinemann, 2006).

  24. Kisner, C. & Colby, L. A. Therapeutic Exercise: Foundation and Techniques 5th edn (F.A.Davis Company, 2007).

  25. Horton, W. C. et al. Is there an optimal patient stance for obtaining a lateral 36 radiograph? A critical comparison of three techniques. Spine (Phila Pa. 1976). 30 (4), 427–433. https://doi.org/10.1097/01.brs.0000153698.94091.f8 (2005).

    Google Scholar 

  26. Betz, J. W. et al. Reliability of the Biomechanical assessment of the sagittal lumbar spine and pelvis on radiographs used in clinical practice: A systematic review of the literature. J. Clin. Med. 13 (16), 4650. https://doi.org/10.3390/jcm13164650 (2024).

    Google Scholar 

  27. Harrison, D. E., Haas, J. W., Moustafa, I. M., Betz, J. W. & Oakley, P. A. Can the mismatch of measured pelvic morphology vs. Lumbar lordosis predict chronic low back pain patients? J. Clin. Med. 13 (8), 2178. https://doi.org/10.3390/jcm13082178 (2024).

    Google Scholar 

  28. Kobayashi, T. M. D., Atsuta, Y. M. D., Matsuno, T. M. D. & Takeda Naoki MD†. A longitudinal study of congruent sagittal spinal alignment in an adult cohort. Spine 29 (6), 671–676. https://doi.org/10.1097/01.BRS.0000115127.51758.A2 (2004).

  29. Ostelo, R. W. et al. Interpreting change scores for pain and functional status in low back pain: towards international consensus regarding minimal important change. Spine (Phila Pa. 1976). 33 (1), 90–94. https://doi.org/10.1097/BRS.0b013e31815e3a10 (2008).

    Google Scholar 

  30. Fritz, J. M. & Irrgang, J. J. A comparison of a modified Oswestry low back pain disability questionnaire and the Quebec back pain disability scale. Phys. Ther. 81 (2), 776–788. https://doi.org/10.1093/ptj/81.2.776 (2001).

    Google Scholar 

  31. Raptis, S., Ilioudis, C. & Theodorou, K. From pixels to prognosis: unveiling radiomics models with SHAP and LIME for enhanced interpretability. Biomedical Phys. Eng. Express. 10 (3), 035016 (2024).

    Google Scholar 

  32. Hartvigsen, J. et al. Lancet low back pain series working Group. What low back pain is and why we need to pay attention. Lancet 391, 2356–2367 (2018).

    Google Scholar 

  33. Buchbinder, R. et al. Lancet low back pain series working Group. low back pain: a call for action. Lancet 391, 2384–2388 (2018).

    Google Scholar 

  34. Nuckols, T. K. et al. Rigorous development does not ensure that guidelines are acceptable to a panel of knowledgeable providers. J. Gen. Intern. Med. 23, 37–44 (2008).

    Google Scholar 

  35. Oakley, P. A., Ehsani, N. N., Moustafa, I. M. & Harrison, D. E. Restoring lumbar lordosis: a systematic review of controlled trials utilizing chiropractic bio Physics® (CBP®) non-surgical approach to increasing lumbar lordosis in the treatment of low back disorders. J. Phys. Ther. Sci. 32 (9), 601–610. https://doi.org/10.1589/jpts.32.601 (2020).

    Google Scholar 

  36. Haas, M., Vavrek, D., Peterson, D., Polissar, N. & Neradilek, M. B. Dose-response and efficacy of spinal manipulation for care of chronic low back pain: a randomized controlled trial. Spine J. 14 (7), 1106-16. https://doi.org/10.1016/j.spinee.2013.07.468 (2014).

  37. Haas, M., Groupp, E. & Kraemer, D. F. Dose-response for chiropractic care of chronic low back pain. Spine J. 4 (5), 574–583. https://doi.org/10.1016/j.spinee.2004.02.008 (2004).

  38. Legaye, J., Duval-Beaupère, G., Hecquet, J. & Marty, C. Pelvic incidence: a fundamental pelvic parameter for three-dimensional regulation of spinal sagittal curves. Eur. Spine J. 7 (2), 99–103. https://doi.org/10.1007/s005860050038 (1998).

    Google Scholar 

  39. Mendoza-Lattes, S., Ries, Z., Gao, Y. & Weinstein, S. L. Natural history of spinopelvic alignment differs from symptomatic deformity of the spine. Spine (Phila Pa. 1976). 35 (16), E792–E798. https://doi.org/10.1097/BRS.0b013e3181d35ca9 (2010).

    Google Scholar 

  40. Maiers, M. J., Albertson, A. K., Major, C., Mendenhall, H. & Petrie, C. P. The association between individual radiographic findings and improvement after chiropractic spinal manipulation and home exercise among older adults with back-related disability: a secondary analysis. Chiropr. Man. Th. 33 (1), 2. https://doi.org/10.1186/s12998-024-00566-9 (2025).

    Google Scholar 

  41. Haslam-Larmer, L. et al. Gleaning a lot from the history and physical exam, and reasonably confident without imaging: a qualitative study of primary care clinicians’ management of patients with low back pain. BMC Prim. Care. 26 (1), 26. https://doi.org/10.1186/s12875-025-02726-z (2025).

    Google Scholar 

  42. Williams, B., Gichard, L., Johnson, D. & Louis, M. An investigation into the chiropractic practice and communication of routine, repetitive radiographic imaging for the location of postural misalignments. J. Clin. Imaging Sci. 14, 28. https://doi.org/10.25259/JCIS_68_2024 (2024).

    Google Scholar 

  43. Noshchenko, A. et al. Spinopelvic parameters in asymptomatic subjects without spine disease and deformity: A systematic review with Meta-Analysis. Clin. Spine Surg. 30, 392–403 (2017).

    Google Scholar 

  44. Fujishiro, T. et al. European spine study Group, ESSG. Decision-making factors in the treatment of adult spinal deformity. Eur. Spine J. 27 (9), 2312–2321 (2018).

    Google Scholar 

  45. Banno, T. et al. The cohort study for the determination of reference values for spinopelvic parameters (T1 pelvic angle and global tilt) in elderly volunteers. Eur. Spine J. 25, 3687–3693 (2016).

    Google Scholar 

  46. Tominaga, R. et al. Dose-response relationship between spino-pelvic alignment determined by sagittal modifiers and back pain-specific quality of life. Eur. Spine J. 30 (10), 3019–3027 (2021). https://doi.org/10.1007/s00586-021-06965-3

    Google Scholar 

Download references

Acknowledgements

Partial funding for this project was received from The NCMIC Foundation and CBP NonProfit for funding of open access fees if accepted for publication.

Author information

Authors and Affiliations

  1. Department of Physiotherapy, College of Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates

    Ibrahim M. Moustafa, Shima Zadeh & Iman Khowailed

  2. Neuromusculoskeletal Rehabilitation Research Group, RIMHS–Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates

    Ibrahim M. Moustafa, Shima Zadeh & Iman Khowailed

  3. Faculty of Physical Therapy, Cairo University, Giza, 12613, Egypt

    Ibrahim M. Moustafa

  4. Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah, UAE

    Dilber Uzun Ozsahin

  5. Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, UAE

    Dilber Uzun Ozsahin

  6. Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia, 99138, Turkey

    Dilber Uzun Ozsahin & Mubarak Taiwo Mustapha

  7. CBP Nonprofit (a Spine Research Foundation), Eagle, ID, 83616, USA

    Paul A. Oakley & Deed E. Harrison

  8. Private Practice, Newmarket, ON, L3Y 8Y8, Canada

    Paul A. Oakley

  9. Kinesiology and Health Science, York University, Toronto, ON, M3J 1P3, Canada

    Paul A. Oakley

Authors
  1. Ibrahim M. Moustafa
    View author publications

    Search author on:PubMed Google Scholar

  2. Dilber Uzun Ozsahin
    View author publications

    Search author on:PubMed Google Scholar

  3. Mubarak Taiwo Mustapha
    View author publications

    Search author on:PubMed Google Scholar

  4. Shima Zadeh
    View author publications

    Search author on:PubMed Google Scholar

  5. Iman Khowailed
    View author publications

    Search author on:PubMed Google Scholar

  6. Paul A. Oakley
    View author publications

    Search author on:PubMed Google Scholar

  7. Deed E. Harrison
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Authors I.M.M., D.U.O., M.T.M., S.Z., I.K., P.A.O., and D.E.H. all participated in the research idea and participated in its design. I.M.M., D.U.O., M.T.M., and D.E.H. contributed to the statistical analysis. I.M.M., D.U.O., M.T.M., S.Z., I.K. participated in data collection and supervision. I.M.M., D.U.O., M.T.M., S.Z., I.K., P.A.O., and D.E.H. All contributed to the interpretation of the results and wrote the drafts. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Deed E. Harrison.

Ethics declarations

Competing interests

PAO is a paid consultant for CBP NonProfit, Inc. DEH teaches rehabilitation methods and is the CEO of a company that distributes spine rehabilitation equipment to physicians in the U.S.A. as used in this manuscript. All the other authors declare that they have no competing interests.

Additional information

Publisher’s note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moustafa, I.M., Ozsahin, D.U., Mustapha, M.T. et al. Machine learning models for predicting treatment outcomes in chronic non-specific back pain patients undergoing lumbar extension traction. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38059-9

Download citation

  • Received: 18 March 2025

  • Accepted: 28 January 2026

  • Published: 30 January 2026

  • DOI: https://doi.org/10.1038/s41598-026-38059-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Low back pain
  • Lordosis
  • Extension traction
  • Disability
  • Prediction
  • Machine learning
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on Twitter
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing