Skip to main content
main-content
Top

Tip

Swipe om te navigeren naar een ander artikel

Gepubliceerd in: Quality of Life Research 3/2018

22-12-2017

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

Auteurs: Stacey A. McCaffrey, Ryan A. Black, Stephen F. Butler, Inflexxion, Inc.

Gepubliceerd in: Quality of Life Research | Uitgave 3/2018

Log in om toegang te krijgen
share
DELEN

Deel dit onderdeel of sectie (kopieer de link)

  • Optie A:
    Klik op de rechtermuisknop op de link en selecteer de optie “linkadres kopiëren”
  • Optie B:
    Deel de link per e-mail

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusion

Taken together, results provide strong psychometric support for these PainCAS Pain scales. Strengths and limitations of the current study are discussed.
Voetnoten
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.
 
Literatuur
1.
go back to reference 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. CrossRefPubMed 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. CrossRefPubMed
2.
go back to reference 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. CrossRef 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. CrossRef
3.
go back to reference 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. CrossRefPubMedPubMedCentral 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. CrossRefPubMedPubMedCentral
5.
go back to reference 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. CrossRefPubMed 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. CrossRefPubMed
6.
go back to reference Bond, T., & Fox, C. (2015). Applying the Rasch model: Fundamental measurement in the human sciences. Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Bond, T., & Fox, C. (2015). Applying the Rasch model: Fundamental measurement in the human sciences. Mahwah, NJ: Lawrence Erlbaum Associates, Publishers.
7.
go back to reference Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists (4th ed.). Mahwah, NJ: L. Erlbaum Associates. Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists (4th ed.). Mahwah, NJ: L. Erlbaum Associates.
8.
go back to reference 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. PubMed 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. PubMed
9.
go back to reference McNair, D., Lorr, M., & Droppleman, L. (1971). Manual for the profile of mood states. San Diego, CA: Educational and Industrial Testing Service. McNair, D., Lorr, M., & Droppleman, L. (1971). Manual for the profile of mood states. San Diego, CA: Educational and Industrial Testing Service.
10.
go back to reference 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. 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.
go back to reference Andrich, D. (1978). Application of a psychometric model to ordered categories which are scored with successive integers. Applied Psychological Measurement, 2(4), 581–594. CrossRef Andrich, D. (1978). Application of a psychometric model to ordered categories which are scored with successive integers. Applied Psychological Measurement, 2(4), 581–594. CrossRef
12.
go back to reference Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York: The Guilford Press. Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York: The Guilford Press.
14.
go back to reference IBM Corp. (2013). IBM SPSS statistics for windows, version 22.0. Armonk, NY: IBM Corp. IBM Corp. (2013). IBM SPSS statistics for windows, version 22.0. Armonk, NY: IBM Corp.
15.
go back to reference Reckase, M. (1979). Unifactor latent trait models applied to multifactor tests: Results and implications. Journal of Educational Statistics, 4, 207–230. CrossRef Reckase, M. (1979). Unifactor latent trait models applied to multifactor tests: Results and implications. Journal of Educational Statistics, 4, 207–230. CrossRef
16.
go back to reference Linacre, J. M. (2000). Item discrimination and infit mean-squares. Rasch Measurement Transactions, 14(2), 743. Linacre, J. M. (2000). Item discrimination and infit mean-squares. Rasch Measurement Transactions, 14(2), 743.
17.
go back to reference Smith, R. M. (1996). Polytomous mean-square fit statistics. Rasch Measurement Transactions, 10(3), 516–517. Smith, R. M. (1996). Polytomous mean-square fit statistics. Rasch Measurement Transactions, 10(3), 516–517.
18.
go back to reference Linacre, J. M. (2017). Winsteps® Rasch measurement computer program user’s guide. Beaverton: Winsteps.com. Linacre, J. M. (2017). Winsteps® Rasch measurement computer program user’s guide. Beaverton: Winsteps.com.
Metagegevens
Titel
Psychometric evaluation of the PainCAS Interference with Daily Activities, Psychological/Emotional Distress, and Pain scales
Auteurs
Stacey A. McCaffrey
Ryan A. Black
Stephen F. Butler
Inflexxion, Inc.
Publicatiedatum
22-12-2017
Uitgeverij
Springer International Publishing
Gepubliceerd in
Quality of Life Research / Uitgave 3/2018
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-017-1766-3