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Psychometric evaluation of the PainCAS Interference with Daily Activities, Psychological/Emotional Distress, and Pain scales

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The PainCAS is a web-based clinical tool for assessing and tracking pain and opioid risk in chronic pain patients. Despite evidence for its utility within the clinical setting, the PainCAS scales have never been subject to psychometric evaluation. The current study is the first to evaluate the psychometric properties of the PainCAS Interference with Daily Activities, Psychological/Emotional Distress, and Pain scales.


Patients (N = 4797) from treatment centers and hospitals in 16 different states completed the PainCAS as part of routine clinical assessment. A subsample (n = 73) from two hospital-based treatment centers also completed comparator measures. Rasch Rating Scale Models were employed to evaluate the Interference with Daily Activities and Psychological/Emotional Distress scales, and empirical evaluation included assessment of dimensionality, discrimination, item fit, reliability, information, and person-to-item targeting. Additionally, convergent and discriminant validity were evaluated through classical test theory approaches. Convergent validity of the Pain scales was evaluated through correlations with corresponding comparator items.


One Interference with Daily Activities item was removed due to poor functioning and discrimination. The retained items from the Interference with Daily Activities and Psychological/Emotional Distress scales conformed to unidimensional Rasch measurement models, yielding satisfactory item fit, reliability, precision, and coverage. Further, results provided support for the convergent and discriminant validity of these two scales. Convergent validity between the PainCAS Pain and BPI Pain items was also strong.


Taken together, results provide strong psychometric support for these PainCAS Pain scales. Strengths and limitations of the current study are discussed.

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  1. Of note, this comparison could also be viewed as evidence of alternate forms of reliability as the PainCAS Pain scales are highly similar in content to the BPI Pain items.

  2. Item discrimination was estimated outside of the Rasch model.

  3. It should be noted that given the fact that this sample of chronic pain patients is older (i.e., > 50 years), pain interfering with childcare may be less of an issue than it would be for a younger population.

  4. Note that thereare a 0.985 and a 0.994 correlation between the raw interference and distress scores and their Rasch scores. Therefore, for ease of calculating total scores, raw scores are used instead of Rasch.


  1. Butler, S. F., et al. (2016). Impact of an electronic pain and opioid risk assessment program: Are there improvements in patient encounters and clinic notes? Pain Medicine, 17(11), 2047–2060.

    Article  PubMed  Google Scholar 

  2. Zacharoff, K., Butler, S. F., Jamison, R., Budman, S., Charity, S., & Yiu, E. (2010). Development of the pain assessment interview network-clinical advisory system (painCAS), a systematic computer-administered assessment of chronic pain patients. The Journal of Pain, 11(4), S3.

    Article  Google Scholar 

  3. Butler, S. F., Fernandez, K., Benoit, C., Budman, S. H., & Jamison, R. N. (2008). Validation of the revised screener and opioid assessment for patients with pain (SOAPP-R). The Journal of Pain, 9(4), 360–372.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Butler, S. F., et al. (2007). Development and validation of the current opioid misuse measure. Pain, 130(1–2), 144–156.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Butler, S. F., Zacharoff, K., Charity, S., Lawler, K., & Jamison, R. N. (2014). Electronic opioid risk assessment program for chronic pain patients: Barriers and benefits of implementation. Pain Practice, 14, E98–E105.

    Article  PubMed  Google Scholar 

  6. Bond, T., & Fox, C. (2015). Applying the Rasch model: Fundamental measurement in the human sciences. Mahwah, NJ: Lawrence Erlbaum Associates, Publishers.

    Google Scholar 

  7. Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists (4th ed.). Mahwah, NJ: L. Erlbaum Associates.

    Google Scholar 

  8. Cleeland, C. S., & Ryan, K. M. (1994). Pain assessment: Global use of the brief pain inventory. Annals of the Academy of Medicine, Singapore, 23(0304–4602), 129–138.

    CAS  PubMed  Google Scholar 

  9. McNair, D., Lorr, M., & Droppleman, L. (1971). Manual for the profile of mood states. San Diego, CA: Educational and Industrial Testing Service.

    Google Scholar 

  10. Haythornthwaite, J. A., & Edwards, R. R. (2007). Profile of mood states (POMS), Presented at the fourth meeting of the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials.

  11. Andrich, D. (1978). Application of a psychometric model to ordered categories which are scored with successive integers. Applied Psychological Measurement, 2(4), 581–594.

    Article  Google Scholar 

  12. Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York: The Guilford Press.

    Google Scholar 

  13. Linacre, J. M. (2015). A user’s guide to WINSTEPS/MINISTEP Rasch-Model Computer Programs (3.91.0). Retrieved Jan 25, 2016 from

  14. IBM Corp. (2013). IBM SPSS statistics for windows, version 22.0. Armonk, NY: IBM Corp.

    Google Scholar 

  15. Reckase, M. (1979). Unifactor latent trait models applied to multifactor tests: Results and implications. Journal of Educational Statistics, 4, 207–230.

    Article  Google Scholar 

  16. Linacre, J. M. (2000). Item discrimination and infit mean-squares. Rasch Measurement Transactions, 14(2), 743.

    Google Scholar 

  17. Smith, R. M. (1996). Polytomous mean-square fit statistics. Rasch Measurement Transactions, 10(3), 516–517.

    Google Scholar 

  18. Linacre, J. M. (2017). Winsteps® Rasch measurement computer program user’s guide. Beaverton:

    Google Scholar 

  19. Institute of Medicine. (2012). Relieving pain in America: A blueprint for transforming prevention, care, education, and research. Retrieved Mar 23, 2016 from

Download references


This research was supported by NIDA R43 and R44 DA026359.

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Correspondence to Stacey A. McCaffrey.

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At the time this research was conducted, the authors were consultants or employees of Inflexxion, Inc., which owns copyright to the PainCAS.

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McCaffrey, S.A., Black, R.A., Butler, S.F. et al. Psychometric evaluation of the PainCAS Interference with Daily Activities, Psychological/Emotional Distress, and Pain scales. Qual Life Res 27, 835–843 (2018).

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