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Disentangling trait versus state characteristics of the Pain Catastrophizing Scale and the PHQ-8 Depression Scale.

Publication ,  Journal Article
Dumenci, L; Kroenke, K; Keefe, FJ; Ang, DC; Slover, J; Perera, RA; Riddle, DL
Published in: Eur J Pain
September 2020

BACKGROUND: Research on the role of trait versus state characteristics of a variety of measures among persons experiencing pain has been a focus for the past few decades. Studying the trait versus state nature of the Pain Catastrophizing Scale (PCS) and the Patient Health Questionnaire (PHQ-8) depression scale would be highly informative given both are commonly measured in pain populations and neither scale has been studied for trait/state contributions. METHODS: The PHQ-8 and PCS were obtained on persons undergoing knee arthroplasty at baseline, 2-, 6- and 12-month post-surgery (N = 402). The multi-trait generalization of the latent trait-state model was used to partition trait and state variability in PCS and PHQ-8 item responses simultaneously. A set of variables were used to predict trait catastrophizing and trait depression. RESULTS: For total scores, the latent traits and latent states explain 63.2% (trait = 43.2%; state = 20.0%) and 50.2% (trait = 29.4%; state = 20.8%) of the variability in PCS and PHQ-8, respectively. Patients with a high number of bodily pain sites, high levels of anxiety, young patients and African-American patients had high levels of trait catastrophizing and trait depression. The PCS and the PHQ-8 consist of both enduring trait and dynamic state characteristics, with trait characteristics dominating for both measures. CONCLUSION: Clinicians and researchers using these scales should not assume the obtained measurements solely reflect either trait- or state-based characteristics. SIGNIFICANCE: Clinicians and researchers using the PCS or PHQ-8 scales are measuring both state and trait characteristics and not just trait- or state-based characteristics.

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Published In

Eur J Pain

DOI

EISSN

1532-2149

Publication Date

September 2020

Volume

24

Issue

8

Start / End Page

1624 / 1634

Location

England

Related Subject Headings

  • Prospective Studies
  • Patient Health Questionnaire
  • Pain Measurement
  • Humans
  • Depression
  • Catastrophization
  • Anesthesiology
  • 3214 Pharmacology and pharmaceutical sciences
  • 3209 Neurosciences
  • 3202 Clinical sciences
 

Citation

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Dumenci, L., Kroenke, K., Keefe, F. J., Ang, D. C., Slover, J., Perera, R. A., & Riddle, D. L. (2020). Disentangling trait versus state characteristics of the Pain Catastrophizing Scale and the PHQ-8 Depression Scale. Eur J Pain, 24(8), 1624–1634. https://doi.org/10.1002/ejp.1619
Dumenci, Levent, Kurt Kroenke, Francis J. Keefe, Dennis C. Ang, James Slover, Robert A. Perera, and Daniel L. Riddle. “Disentangling trait versus state characteristics of the Pain Catastrophizing Scale and the PHQ-8 Depression Scale.Eur J Pain 24, no. 8 (September 2020): 1624–34. https://doi.org/10.1002/ejp.1619.
Dumenci L, Kroenke K, Keefe FJ, Ang DC, Slover J, Perera RA, et al. Disentangling trait versus state characteristics of the Pain Catastrophizing Scale and the PHQ-8 Depression Scale. Eur J Pain. 2020 Sep;24(8):1624–34.
Dumenci, Levent, et al. “Disentangling trait versus state characteristics of the Pain Catastrophizing Scale and the PHQ-8 Depression Scale.Eur J Pain, vol. 24, no. 8, Sept. 2020, pp. 1624–34. Pubmed, doi:10.1002/ejp.1619.
Dumenci L, Kroenke K, Keefe FJ, Ang DC, Slover J, Perera RA, Riddle DL. Disentangling trait versus state characteristics of the Pain Catastrophizing Scale and the PHQ-8 Depression Scale. Eur J Pain. 2020 Sep;24(8):1624–1634.
Journal cover image

Published In

Eur J Pain

DOI

EISSN

1532-2149

Publication Date

September 2020

Volume

24

Issue

8

Start / End Page

1624 / 1634

Location

England

Related Subject Headings

  • Prospective Studies
  • Patient Health Questionnaire
  • Pain Measurement
  • Humans
  • Depression
  • Catastrophization
  • Anesthesiology
  • 3214 Pharmacology and pharmaceutical sciences
  • 3209 Neurosciences
  • 3202 Clinical sciences