Preliminary study: quantification of chronic pain from physiological data
- PMID: 36213596
- PMCID: PMC9534370
- DOI: 10.1097/PR9.0000000000001039
Preliminary study: quantification of chronic pain from physiological data
Abstract
Introduction: It is unknown if physiological changes associated with chronic pain could be measured with inexpensive physiological sensors. Recently, acute pain and laboratory-induced pain have been quantified with physiological sensors.
Objectives: To investigate the extent to which chronic pain can be quantified with physiological sensors.
Methods: Data were collected from chronic pain sufferers who subjectively rated their pain on a 0 to 10 visual analogue scale, using our recently developed pain meter. Physiological variables, including pulse, temperature, and motion signals, were measured at head, neck, wrist, and finger with multiple sensors. To quantify pain, features were first extracted from 10-second windows. Linear models with recursive feature elimination were fit for each subject. A random forest regression model was used for pain score prediction for the population-level model.
Results: Predictive performance was assessed using leave-one-recording-out cross-validation and nonparametric permutation testing. For individual-level models, 5 of 12 subjects yielded intraclass correlation coefficients between actual and predicted pain scores of 0.46 to 0.75. For the population-level model, the random forest method yielded an intraclass correlation coefficient of 0.58. Bland-Altman analysis shows that our model tends to overestimate the lower end of the pain scores and underestimate the higher end.
Conclusion: This is the first demonstration that physiological data can be correlated with chronic pain, both for individuals and populations. Further research and more extensive data will be required to assess whether this approach could be used as a "chronic pain meter" to assess the level of chronic pain in patients.
Keywords: Chronic pain; Pain quantification; Physiological data; Random forest.
Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The International Association for the Study of Pain.
Conflict of interest statement
The authors have no conflicts of interest to declare.Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
Figures






Similar articles
-
Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?Clin Orthop Relat Res. 2020 Jul;478(7):0-1618. doi: 10.1097/CORR.0000000000001251. Clin Orthop Relat Res. 2020. PMID: 32282466 Free PMC article.
-
Validation of the Spanish version of the Neck Disability Index.Spine (Phila Pa 1976). 2010 Feb 15;35(4):E114-8. doi: 10.1097/BRS.0b013e3181afea5d. Spine (Phila Pa 1976). 2010. PMID: 20110848
-
Psychological factors in the use of the neck disability index in chronic whiplash patients.Spine (Phila Pa 1976). 2010 Jan 1;35(1):E16-21. doi: 10.1097/BRS.0b013e3181b135aa. Spine (Phila Pa 1976). 2010. PMID: 20042942
-
A clinical test to assess isometric cervical strength in chronic whiplash associated disorder (WAD): a reliability study.BMC Musculoskelet Disord. 2022 Aug 1;23(1):736. doi: 10.1186/s12891-022-05703-0. BMC Musculoskelet Disord. 2022. PMID: 35915421 Free PMC article.
-
Evaluating physiological signal salience for estimating metabolic energy cost from wearable sensors.J Appl Physiol (1985). 2019 Mar 1;126(3):717-729. doi: 10.1152/japplphysiol.00714.2018. Epub 2019 Jan 10. J Appl Physiol (1985). 2019. PMID: 30629472 Free PMC article.
Cited by
-
Home-Use and Portable Biofeedback Lowers Anxiety and Pain in Chronic Pain Subjects.Am J Lifestyle Med. 2023 Dec 12:15598276231221112. doi: 10.1177/15598276231221112. Online ahead of print. Am J Lifestyle Med. 2023. PMID: 39554946 Free PMC article.
-
Development of a Pain Measurement Device Using 3D Printing and Electronic Air Pressure Control.Biomedicines. 2025 Jan 21;13(2):254. doi: 10.3390/biomedicines13020254. Biomedicines. 2025. PMID: 40002667 Free PMC article.
References
-
- Åkerblom S, Perrin S, Fischer MR, McCracken LM. Treatment outcomes in group-based cognitive behavioural therapy for chronic pain: an examination of PTSD symptoms. Eur J Pain 2020;24:807–17. - PubMed