The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Williams, A. C. D. C. (1995) · Journal of Psychosomatic Research · doi:10.1016/0022-3999(95)90016-0 · Cited by 1,472
Lewis, M. D., Haviland-Jones, J. M., & Barrett, L. F. (2000) · Cited by 1,304
Keefe, F. J., Lefebvre, J. C., Egert, J. R., Affleck, G., Sullivan, M. J., & Caldwell, D. S. (2000) · Pain · doi:10.1016/s0304-3959(00)00296-7 · Cited by 735
Facial expression of pain: An evolutionary account
Williams, A. C. D. C. (2002) · Behavioral and Brain Sciences · doi:10.1017/s0140525x02000080 · Cited by 612
Keltner, D. J., Ekman, P., Gonzaga, G. C., & Beer, J. S. (2002) · doi:10.1093/oso/9780195126013.003.0022 · Cited by 555
The consistency of facial expressions of pain: a comparison across modalities
Prkachin, K. M. (1992) · Pain · doi:10.1016/0304-3959(92)90213-u · Cited by 553
Self-citationThe facial expression of pain in patients with dementia
Miriam Kunz, A., Scharmann, S., Hemmeter, U., Schepelmann, K., & Lautenbacher, S. (2007) · Pain · doi:10.1016/j.pain.2007.09.007 · Cited by 290
Gender Differences in Pain and Pain Behavior: The Role of Catastrophizing
Sullivan, M. J. L., Tripp, D. A., & Santor, D. (2000) · Cognitive Therapy and Research · doi:10.1023/a:1005459110063 · Cited by 285
Handbook of pain assessment, 2nd ed.
Turk, D. C., & Melzack, R. (2001) · Cited by 259
The facial expression of pain.
Craig, K. D., Prkachin, K. M., & GRUNAU, R. E. (2001) · Cited by 259
Self-citation
Research works printed but not linked: 2
- Hadjistavropoulos, 1997, Toward a research outcome measure of pain in frail elderly in chronic care., Pain Clinic, 10, 71
- Prkachin, 2007, The underestimation of pain among health-care providers., Can J Nurs Res, 39, 88
Cited by
Sorted by citations
Items 401–472 of 472
THE INTERACTIONAL ORGANISATION OF PAIN DISPLAYS IN MEDICAL CONSULTATIONS
La, J. (2018) · doi:10.26686/wgtn.17134175.v1 · Cited by 1
Fabiano, D., Jaishanker, M., & Canavan, S. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.08341 · Cited by 1
Facial Expressions of Pain Among Patients in the Emergency DepartmentPreprint
Wang, K.-C., Seak, C., Tsai, F.-S., Chien, C.-Y., Lee, C.-C., Ng, C., Wang, B., & Weng, Y.-M. (2020) · doi:10.21203/rs.3.rs-94475/v1 · Cited by 1
Automatic Pain Assessment Through Facial Expressions
Seladji, I. (2020) · RIT Scholar Works (Rochester Institute of Technology) · Cited by 1
Othman, E., Werner, P., Saxen, F., Al-Hamadi, A. K., Gruss, S., & Walter, S. (2021) · Research Square · doi:10.21203/rs.3.rs-927204/v1 · Cited by 1
Computational Pain Quantification and the Effects of Age, Gender, Culture and Cause
Ostberg, C. R. (2014) · e-Publications@Marquette (Marquette University)
Pain Expression Recognition Based on Fuzzy Classification Approach
Zhu, S. (2014) · Journal of Computers · doi:10.4304/jcp.9.8.1975-1982
Perception of pain in others : the impact of a label of major depressive disorder
Turcotte, K. (2013) · cIRcle (University of British Columbia) · doi:10.14288/1.0074237
Pain Assessment through Facial Expression
Singh, S. K., Rastogi, V., & Singh, S. K. (2015) · Advances in healthcare information systems and administration book series · doi:10.4018/978-1-4666-9432-3.ch003
The Influence of Pain Intensity and Executive Functioning on Facial Pain Expression
Traxler, J. (2016) · Repository hosted by TU Delft Library (TU Delft)
Krishna, H. S., & D’souza, C. J. (2020) · Journal of Bio Innovation · doi:10.46344/jbino.2020.v09i06.22
Prediction of Intensity Level of Pain using Computational MethodsPreprint
Singh, S. K. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3349020
Facial Muscles Reactions to Other Person’s Facial Expressions of Pain
Švėgžda, A., Stanikūnas, R., Augustinaitė, K., Bliumas, R., & Vaitkevičius, H. (2021) · Psichologija · doi:10.15388/psichol.2021.24
Weik, E. (2021) · cIRcle (University of British Columbia) · doi:10.14288/1.0401490
Gomutbutra, P., Kittisares, A., Sanguansri, A., Choosri, N., Sawaddiruk, P., Fakfum, P., Lerttrakarnnon, P., & Saralamba, S. (2021) · Research Square · doi:10.21203/rs.3.rs-1010233/v1
Biele, C. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-90004-5_2
Zalk, N. V., Lalitharatne, T. D., Tan, Y., & Nanayakkara, T. (2021) · doi:10.31219/osf.io/9ukqy
Gomutbutra, P., Kittisares, A., Sanguansri, A., Choosri, N., Sawaddirak, P., Fakfum, P., Lerttrakarnnon, P., & Soralamb, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1652828/v1
Gomutbutra, P., Kittisares, A., Sanguansri, A., Choosri, N., Sawaddirak, P., Fakfum, P., Soralamb, S., & Lettrakarnnon, P. (2022) · Research Square · doi:10.21203/rs.3.rs-1652828/v2
A Novel Pain Level Assessment Approach Using Multi Modal Bayesian Network Modeling
Hou, Y., Yang, A., Yan, R., Guo, W., Feng, C., Tao, R., & Zhou, X. (2022) · Chinese Control Conference (CCC) · doi:10.23919/ccc55666.2022.9902304
Behavioural, Cognitive and Neural Mechanisms of Human Social InteractionPreprint
Hamilton, A. F. D. C. (2023) · doi:10.31234/osf.io/e5npf
Mostafa, R., McNair, N. A., Tan, W., Saunders, C., Colagiuri, B., & Barnes, K. (2023) · doi:10.31234/osf.io/ev2y5
The intensive care unit pain rating scale: a brief introduction
Bakhtadze, M. A., & Schevchenko, E. I. (2024) · Russian Journal of Pain · doi:10.17116/pain20242201184
Gao, F. Q., Qing, L., Li, L., Xiong, K., Xu, R., & Gao, L. (2024) · Research Square · doi:10.21203/rs.3.rs-4193042/v1
Facial Pain Expression Detection System using TXQEDA-Deep for Pain EvaluationPreprint
Aliradi, R., Chenni, N., Touami, M., Ejbali, R., & Aoun, N. B. (2024) · Preprints.org · doi:10.20944/preprints202405.0659.v1
Complex issues in vocational rehabilitation with functional capacity evaluation results
Allison, S., Leslie, T. P., & Dalgin, R. S. (2024) · Journal of Vocational Rehabilitation · doi:10.3233/jvr-240024
Milliet, M. M., Meyer, C. M., Dunham, K., & Winkelmann, Z. K. (2024) · International Journal of Athletic Therapy & Training · doi:10.1123/ijatt.2023-0092
Ceccarelli, I., Bagnis, A., Ottaviani, C., Thayer, J. F., & Mattarozzi, K. (2024) · Cognitive Research Principles and Implications · doi:10.1186/s41235-024-00588-0 · Cited by 1
A Critical Review About the Application of Artificial Intelligence in Pain Assessment
Farghal, M. (2024) · Premier Journal of Artificial Intelligence · doi:10.70389/pjai.100002
A Review of Deep Learning Methods for Automatic Pain Assessment
Lu, Z., Jasin, A. M., Zulkifle, F. A., & Ul–Saufie, A. Z. (2024) · International Conference on Data Science and Information Technology (DSIT) · doi:10.1109/dsit61374.2024.10881665
Integrative Human-Computer Interaction System Using Eyes and Facial Movement
Park, J., Jang, J.-W., Park, D., Ju, Y.-G., Kim, K.-T., Kim, C.-H., Lee, J., & Won, D.-O. (2025) · International Conference on Consumer Electronics · doi:10.1109/icce63647.2025.10929911
Lightweight Multimodal Algorithm for Automated Pain Detection
Yu, Y., Zhu, H., Li, X., & Jin, N. (2025) · International Conference on Software Engineering and Computer Science (CSECS) · doi:10.1109/csecs64665.2025.11009282
Katranzopoulou, M., Delibasis, K. K., & Maglogiannis, I. G. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-88486-3_8
Gao, F. Q., Qing, L., Li, L., Zhao, W. D., & Gao, L. (2025) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc66358.2025.11200378
Multimodal pain assessment with transformers
Benavent-Lledó, M., Lopez-Valle, M. D., Ortiz-Pérez, D., Mulero-Pérez, D., García‐Rodríguez, J., & Ψαρρού, Α. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132066
Adibuzzaman, M. (2015) · e-Publications@Marquette (Marquette University)
The Delaware pain database: A set of painful expressions and corresponding norming dataPreprint
Qu-Lee, J. W., Mende‐Siedlecki, P., Drain, A., & Goharzad, A. (2020) · OSF Preprints (OSF Preprints)
Pain Detection Using Deep Networks and Visual Transformers
Pinto, M. A. S., Spitale, F. R., Araujo, F. A. S., Filho, C. F. F. C., & Costa, M. G. F. (2025) · International Conference on Pattern Recognition Systems (ICPRS) · doi:10.1109/icprs66293.2025.11302853
Lu, Z., Zulkifle, F. A., Japeri, A. Z. U.-S. M., Shamsuddin, M. R., & Jasin, A. M. (2025) · International Journal of Electrical and Electronics Research · doi:10.37391/ijeer.130422
Dissecting Pain: A Study on Multiple Modalities Using Advanced CNN and Transformer Models
Solanki, S., Jadhav, T. R., Gupta, P., & Ghane, S. (2025) · International Conference on Contemporary Computing · doi:10.1109/ic366947.2025.11290165
Barnes, K., McNair, N. A., Saunders, C., Tan, W., & Colagiuri, B. (2026) · Pain · doi:10.1097/j.pain.0000000000003903
Pain Outweighs Negative Emotions in the Perception of Facial Configuration Sequences
Tessier, M.-H., Marcoux, A., & Jackson, P. L. (2026) · Journal of Nonverbal Behavior · doi:10.1007/s10919-026-00508-4
Reading Faces. Using Hard Multi-Task Metric Learning for Kernel Regression
Nicolle, J. (2016) · theses.fr (ABES) · doi:10.70675/8a22a6caz95d0z4cb8zaeadz043ba01a754d
Deep learning for automatic facial expression assessment with application to the expression of pain
Vu, M. T. (2022) · theses.fr (ABES) · doi:10.70675/b3a4930cz2ae5z4f53z9602ze781cb8f524a
New Artificial Intelligence techniques for Computer vision based medical diagnosis
Morabit, S. E. (2023) · theses.fr (ABES) · doi:10.70675/8e7e57eaz99b2z4837za83dz6076e06ce2d0
Paincontrol: identity-preserving pain expression transfer with generative diffusion models
Zarghami, Y., Muzammil, M., Adeli, V., Reimer, H., Hadjistavropoulos, T., & Taati, B. (2026) · BioMedical Engineering OnLine · doi:10.1186/s12938-026-01561-2
An exploratory study of headache pain intensity using facial expressions and APEX frames
Kim, J., Sohn, J., Lee, J.-J., Kim, H., Kim, Y. C., Song, G. H., Shin, S. E., Hou, J.-U., & Kwon, Y.-S. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02617-2
Towards Intelligent Pain Monitoring Systems: A Survey of Recent Technologies and Methods
Naseer, A., Tayyib, N., & Rashid, S. (2026) · Sensors · doi:10.3390/s26082447
Uejima, K., Takahashi, T., Matsui, M., Fukushima, S., & Nishi, Y. (2026) · Cureus · doi:10.7759/cureus.107883
Tsan, Y., Wiryasaputra, R., Hsieh, Y.-J., Zhang, Q.-X., Liu, H. T., & Yang, C. (2026) · Diagnostics · doi:10.3390/diagnostics16091346
Cross-spectral fusion of thermal and RGB imaging for objective pain estimation
Othmani, O. E., & Naouali, S. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001424
Othmani, O. E., Naouali, S., & Ouersighni, R. (2026) · IEEE International Conference on Computing and Machine Intelligence (ICMI) · doi:10.1109/icmi68585.2026.11539759
Bi, N., Wang, T., Yin, L., & Sharma, V. (2026) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg67764.2026.11556981
Pain in 3D: Controllable Generation of Synthetic Faces for Automated Pain Assessment
Lin, X., Mehraban, S., Moturu, A., & Taati, B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31933-3_22
Fauzi, A. R., Handayani, T., Sazkia, A. C., & Ardiansyah, M. (2026) · Clinical Simulation in Nursing · doi:10.1016/j.ecns.2026.102049
Zero-shot multimodal pain estimation via synthetic pain simulation and domain-invariant learning
Othmani, O. E., & Naouali, S. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1827727
Lisio, F. D., Sharif, M. N., Santoriello, V., Guerra, C., Feo, R. D., Piazza, O., & Cascella, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-33500-5_37
Re-Evaluating Single-Backbone Transfer Learning for Pain Assessment from Facial Expressions
AL-Edwan, R., & Jafar, I. (2026) · IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies · doi:10.1109/aeect69724.2026.11657863
Cross-Corpus Stability of Frozen-Feature Conclusions About Facial Affect DynamicsPreprint
Huynh, V. T., Nguyen, H. H., Nguyen, Q.-T., Yang, H.-J., & Kim, S.-H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7389279
Li, H., Wang, C., Xu, Y., Wang, X., & Zhao, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7459680
AlamViT: A Lightweight Spatiotemporal Facial Expression Model for Automated Pain Recognition
Alabdouli, A., Rabie, T. F., Baziyad, M. S., & Bonny, T. (2026) · Mathematics · doi:10.3390/math14183376
Facial Expression Based Pain Recognition Using Imbalance-Aware Deep Learning
Kaur, R., Bhatia, M. P. S., & Kumar, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-30957-0_19
Tosun, E., Eksinar, N., Ghasemkhani, B., Özdemir, A., & Ekinci, M. (2026) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap71869.2026.11727886
Chary, P. S., Velmurugan, P., Parthiban, B., & Ganapathy, N. (2026) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw71585.2026.11712109
Der wechselseitige Einfluss von Schmerz- und Emotionsverarbeitung
Reicherts, P., Gerdes, A. B. M., Pauli, P., & Wieser, M. J. (2014) · Der Schmerz · doi:10.1007/s00482-014-1481-8
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review