Evaluating the psychometric properties of two-item and four-item short forms of the Japanese Pain Self-Efficacy Questionnaire: a cross-sectional study
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The Pain Self-Efficacy Questionnaire is a valid measure assessing self-efficacy in individuals with chronic pain. Short-form versions of the measure have been developed to decrease the assessment burden. However, few studies have evaluated the psychometric properties of the short forms in languages other than English. The aim of this study was to evaluate two 2-item short forms and one 4-item short form of the Japanese Pain Self-Efficacy Questionnaire in terms of internal consistency, criterion validity, structural validity, and construct validity.
This was a cross-sectional study. Data from 150 individuals with mixed chronic pain at a pain management center in a university hospital were extracted from clinical records and analyzed. The data included the information of the original version and short forms of the Japanese Pain Self-Efficacy Questionnaire, and other pain-related measures assessing pain intensity, pain interference, anxiety, depression and pain catastrophizing.
Item statistics supported the item selection for each of the three short forms. All the short forms demonstrated adequate internal consistency and criterion validity. With respect to construct validity, one of the 2-item short forms failed to meet the criterion regarding the change in the magnitude of correlation with a depression scale. The 4-item short form met all the criteria including structural validity.
The study findings provide evidence for the reliability and validity of 2- and 4-item versions of the Japanese Pain Self-Efficacy Questionnaire for use in clinical and research settings.
KeywordsPain Self-Efficacy Questionnaire Self-efficacy Chronic pain Short form
We would like to thank Professor Michael Nicholas for granting us permission to develop and evaluate the PSEQ-J short forms and also to Professor Mark P Jensen for his detailed comments on an earlier draft of this manuscript.
Compliance with ethical standards
Conflict of interest
All authors declare no conflict of interest related to the present study.
- 3.Bandura A. Social foundations of action and thought: a social cognitive view. Englewood Cliffs: Prentice Hall; 1986.Google Scholar
- 6.Lee H, Hübscher M, Moseley GL, Kamper SJ, Traeger AC, Mansell G, McAuley JH. How does pain lead to disability? A systematic review and meta-analysis of mediation studies in people with back and neck pain. Pain. 2015;156:988–97.Google Scholar
- 13.Rasmussen MU, Rydahl-Hansen S, Amris K, Danneskiold Samsøe B, Mortensen EL. The adaptation of a Danish version of the Pain Self-Efficacy Questionnaire: reliability and construct validity in a population of patients with fibromyalgia in Denmark. Scan J Caring Sci. 2016;30:202–10.CrossRefGoogle Scholar
- 20.Dworkin RH, Turk DC, Farrar JT, Haythornthwaite JA, Jensen MP, Katz NP, Kerns RD, Stucki G, Allen RR, Bellamy N, Carr DB, Chandler J, Cowan P, Dionne R, Galer BS, Hertz S, Jadad AR, Kramer LD, Manning DC, Martin S, McCormick CG, McDermott MP, McGrath P, Quessy S, Rappaport BA, Robbins W, Robinson JP, Rothman M, Royal MA, Simon L, Stauffer JW, Stein W, Tollett J, Wernicke J, Witter J. IMMPACT. Core outcome measures for chronic pain clinical trials: IMMPACT recommendations. Pain. 2005;113:9–19.CrossRefGoogle Scholar
- 21.Treede R-D, Rief W, Barke A, Aziz Q, Bennett MI, Benoliel R, Cohen M, Evers S, Finnerup NB, First MB. A classification of chronic pain for ICD-11. Pain. 2015;156:1003.Google Scholar
- 25.Kitamura T. Hospital anxiety depression scale. Arch Psychatr Diagn Clin Eval. 1993;4:371–2.Google Scholar
- 30.Field A. Discovering statistics using SPSS. London: Sage publications; 2009.Google Scholar
- 31.Kline RB. Principles and practice of structural equation modeling. 3rd ed. New York: Guilford publications; 2011.Google Scholar
- 33.Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, Bouter LM, De Vet HC. The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study. Qual life Res. 2010;19:539–49.CrossRefGoogle Scholar
- 34.Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Mahwah: Lawrence Earlbaum Associates; 1988.Google Scholar
- 35.R core team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2017.Google Scholar
- 36.Muthén L, Muthén B. Mplus User’s Guide, 8th Edn. Los Angeles: 1998–2017.Google Scholar