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The structure, reliability and validity of pain expression: Evidence from patients with shoulder pain
Abstract
The present study examined psychometric properties of facial expressions of pain. A diverse sample of 129 people suffering from shoulder pain underwent a battery of active and passive range-of-motion tests to their affected and unaffected limbs. The same tests were repeated on a second occasion. Participants rated the maximum pain induced by each test on three self-report scales. Facial actions were measured with the Facial Action Coding System. Several facial actions discriminated painful from non-painful movements; however, brow-lowering, orbit tightening, levator contraction and eye closing appeared to constitute a distinct, unitary action. An index of pain expression based on these actions demonstrated test-retest reliability and concurrent validity with self-reports of pain. The findings support the concept of a core pain expression with desirable psychometric properties. They are also consistent with the suggestion of individual differences in pain expressiveness. Reasons for varying reports of relations between pain expression and self-reports in previous studies are discussed.
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Zarghami, Y., Mafeld, S. C., Conway, A., & Taati, B. (2023) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg57933.2023.10042502 · Cited by 6
State-Specific and Supraordinal Components of Facial Response to Pain
Dirupo, G., Garlasco, P., Chappuis, C., Sharvit, G., & Corradi‐Dell’Acqua, C. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2965105 · Cited by 7
Automated Assessment of Pain: Prospects, Progress, and a Path Forward
Prkachin, K. M., & Hammal, Z. (2021) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3461615.3485671 · Cited by 6
Self-citationOlugbade, T., Buono, R. A., Williams, A. C. D. C., García, S. D. O., Gold, N., Holloway, C., & Bianchi‐Berthouze, N. (2022) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii55700.2022.9953831 · Cited by 6
Application of deep learning-based facial pain recognition model for postoperative pain assessment
Zhang, J.-T., Hu, X. V., Duan, W., Ji, M., & Yang, J.-J. (2025) · Journal of Clinical Anesthesia · doi:10.1016/j.jclinane.2025.111898 · Cited by 5
Metadata
- Authors
- Prkachin, K. M., & Solomon, P. E.
- Affiliations
- University of Northern British Columbia (CA)
- McMaster University (CA)
- Corresponding author
- Prkachin, Kenneth M.
- ORCID
- Solomon, Patricia E.
- Venue
- Pain
- Published
- 2008-10-15
- Volume
- 139
- Issue
- 2
- Pages
- 267-274
- ISSN
- 0304-3959 (ISSN-L)
- OpenAlex
- W2106043670
- Cited by
- 472
- References
- 31
- Citations by year
- 2026: 29 · 2025: 27 · 2024: 39 · 2023: 27 · 2022: 29 · 2021: 44 · 2020: 40 · 2019: 37 · 2018: 41 · 2017: 21 · 2016: 33 · 2015: 32 · 2014: 24 · 2013: 16 · 2012: 7
- FWCI
- 4.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pain (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pediatric Pain Management Techniques
- Health Sciences › Medicine › Physiology (medical) › Pain Mechanisms and Treatments
- Keywords
- pain expression, facial expressions of pain, shoulder pain, facial action units, facial action coding system, test-retest reliability, concurrent validity, self-reported pain, range of motion
- MeSH terms
- Facial Expression, Female, Humans, Male, Middle Aged, Pain Measurement, Psychometrics, Sensitivity and Specificity, Reproducibility of Results, Expressed Emotion, Evidence-Based Medicine, Shoulder Pain
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