Multidimensional PROMIS Self-Efficacy Measure for Managing Chronic Conditions

Abstract

This study used a multidimensional categorical model to concurrently estimate individual’s self-efficacy for managing their chronic conditions across five related domains measured with the Patient-Reported Outcomes Measurement Information System Self-Efficacy Measure for managing chronic conditions (PROMIS-SE). A total of 1087 individuals with chronic conditions was analyzed in this study. A Diagnostic Classification Model (DCM) was applied to PROMIS-SE’s 4-item short forms measuring five behavioral domains (daily activities, emotions, medications and treatments, social interactions, and symptoms) to provide patient multidimensional categorical outcomes (high, transition, or low self-efficacy). Psychometric properties were examined using classification consistency, model fit, entropy value, domain and item-level information, and patient profiles. DCM PROMIS-SE showed adequate classification consistency, fit, and high entropy values. Five domains demonstrated different average probabilities of having high self-efficacy for patients with chronic conditions from 42.0% (emotions) to 70% (medications and treatments). Rating scale analysis indicated the rating 5 (very confident) most critically discriminated patients with high or low self-efficacy for managing chronic conditions across all domains. Only four common patient profile groups contained more than 5% of the sample. Acceptable psychometric properties indicate that DCM PROMIS-SE satisfactorily classified patients with chronic conditions. This study demonstrates a feasible approach for other existing multidimensional measures to classify patients’ conditions and support clinical judgment.

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References

  1. Bandura, A. (1997). Self-efficacy: The exercise of control. W H Freeman and Company: New York.

    Google Scholar 

  2. Bradshaw L. Diagnostic classification models. In: Rupp, A. A., & Leighton, J. P. The Wiley handbook of cognition and assessment: Frameworks, methodologies, and applications. Wiley; 2016. p. 297–327.

  3. Bradshaw, L., Izsák, A., Templin, J., & Jacobson, E. (2014). Diagnosing teachers’ understandings of rational numbers: building a multidimensional test within the diagnostic classification framework. Educational Measurement: Issues and Practice, 33, 2–14.

    Article  Google Scholar 

  4. Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R., Buysse, D., Choi, S., Cook, K., Devellis, R., DeWalt, D., Fries, J. F., Gershon, R., Hahn, E. A., Lai, J. S., Pilkonis, P., Revicki, D., Rose, M., Weinfurt, K., Hays, R., & PROMIS Cooperative Group. (2010). The patient-reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63, 1179–1194.

    Article  Google Scholar 

  5. Cella, D., Choi, S., Garcia, S., Cook, K. F., Rosenbloom, S., Lai, J.-S., Tatum, D. S., & Gershon, R. (2014). Setting standards for severity of common symptoms in oncology using the PROMIS item banks and expert judgment. Quality of Life Research, 23, 2651–2661.

    Article  Google Scholar 

  6. Clark, N. M., & Dodge, J. A. (1999). Exploring self-efficacy as a predictor of disease management. Health Education & Behavior, 26, 72–89.

    Article  Google Scholar 

  7. Clark SL, Muthén B. Relating latent class analysis results to variables not included in the analysis. 2009.

    Google Scholar 

  8. Cook, K. F., Victorson, D. E., Cella, D., Schalet, B. D., & Miller, D. (2015). Creating meaningful cut-scores for Neuro-QOL measures of fatigue, physical functioning, and sleep disturbance using standard setting with patients and providers. Quality of Life Research, 24, 575–589.

    Article  Google Scholar 

  9. Cui, Y., Gierl, M. J., & Chang, H.-H. (2012). Estimating classification consistency and accuracy for cognitive diagnostic assessment. Journal of Educational Measurement, 49, 19–38.

    Article  Google Scholar 

  10. de la Torre, J., van der Ark, L. A., & Rossi, G. (2017). Analysis of clinical data from a cognitive diagnosis modeling framework. Measurement and Evaluation in Counseling and Development, 1–16.

  11. DeCarlo, L. T. (2011). On the analysis of fraction subtraction data: the DINA model, classification, latent class sizes, and the Q-matrix. Applied Psychological Measurement, 35, 8–26.

    Article  Google Scholar 

  12. Galesic, M., & Bosnjak, M. (2009). Effects of questionnaire length on participation and indicators of response quality in a web survey. Public Opinion Quarterly, 73, 349–360.

    Article  Google Scholar 

  13. Gao, M., Miller, M. D., & Liu, R. (2017). The impact of Q-matrix misspecification and model misuse on classification accuracy in the generalized DINA model. Journal of Measurement and Evaluation in Education and Psychology, 8, 391–403.

    Google Scholar 

  14. Gayoso-Diz, P., Otero-González, A., Rodriguez-Alvarez, M. X., Gude, F., García, F., De Francisco, A., et al. (2013). Insulin resistance (HOMA-IR) cut-off values and the metabolic syndrome in a general adult population: effect of gender and age: EPIRCE cross-sectional study. BMC Endocrine Disorders, 13, 47.

    Article  Google Scholar 

  15. Gruber-Baldini, A. L., Velozo, C., Romero, S., & Shulman, L. M. (2017). Validation of the PROMIS® measures of self-efficacy for managing chronic conditions. Quality of Life Research, 26, 1915–1924.

    Article  Google Scholar 

  16. Henson, R. A., Templin, J. L., & Willse, J. T. (2009). Defining a family of cognitive diagnosis models using log-linear models with latent variables. Psychometrika., 74, 191–210.

    Article  Google Scholar 

  17. Holman, H., & Lorig, K. (1992). Perceived self-efficacy in self-management of chronic disease. In R. Schwarzer (Ed.), Self-efficacy thought control of action (Vol. 1, pp. 305–324).

    Google Scholar 

  18. Hong, I., Velozo, C. A., Li, C.-Y., Romero, S., Gruber-Baldini, A. L., & Shulman, L. M. (2016). Assessment of the psychometrics of a PROMIS item bank: self-efficacy for managing daily activities. Quality of Life Research, 25, 2221–2232.

    Article  Google Scholar 

  19. Jaeger, J., Tatsuoka, C., Berns, S. M., & Varadi, F. (2006). Distinguishing neurocognitive functions in schizophrenia using partially ordered classification models. Schizophrenia Bulletin, 32, 679–691.

    Article  Google Scholar 

  20. Jurich, D. P., & Bradshaw, L. P. (2014). An illustration of diagnostic classification modeling in student learning outcomes assessment. International Journal of Testing, 14, 49–72.

    Article  Google Scholar 

  21. Kroenke, K., & Spitzer, R. L. (2002). The PHQ-9: A new depression diagnostic and severity measure. Psychiatric Annals, 32, 509–515.

    Article  Google Scholar 

  22. Kunina-Habenicht, O., Rupp, A. A., & Wilhelm, O. (2009). A practical illustration of multidimensional diagnostic skills profiling: comparing results from confirmatory factor analysis and diagnostic classification models. Studies in Educational Evaluation, 35, 64–70.

    Article  Google Scholar 

  23. Lee, M. J., Romero, S., Velozo, C. A., Gruber-Baldini, A. L., & Shulman, L. M. (2019). Multidimensionality of the PROMIS self-efficacy measure for managing chronic conditions. Quality of Life Research, 1–9.

  24. Liu, R., Huggins-Manley, A. C., & Bulut, O. (2018). Retrofitting diagnostic classification models to responses from IRT-based assessment forms. Educational and Psychological Measurement, 78, 357–383.

    Article  Google Scholar 

  25. Lorig, K. R., Sobel, D. S., Ritter, P. L., Laurent, D., & Hobbs, M. (2001). Effect of a self-management program on patients with chronic disease. Effective Clinical Practice: ECP, 4, 256–262.

    Google Scholar 

  26. Morgan, E. M., Mara, C. A., Huang, B., Barnett, K., Carle, A. C., Farrell, J. E., & Cook, K. F. (2017). Establishing clinical meaning and defining important differences for patient-reported outcomes measurement information system (PROMIS®) measures in juvenile idiopathic arthritis using standard setting with patients, parents, and providers. Quality of Life Research, 26, 565–586.

    Article  Google Scholar 

  27. Nagaraja, V., Mara, C., Khanna, P. P., Namas, R., Young, A., Fox, D. A., Laing, T., McCune, W. J., Dodge, C., Rizzo, D., Almackenzie, M., & Khanna, D. (2018). Establishing clinical severity for PROMIS® measures in adult patients with rheumatic diseases. Quality of Life Research, 27, 755–764.

    Article  Google Scholar 

  28. Newman, S., Steed, E., & Mulligan, K. (2008). Chronic physical illness: self-management and behavioural interventions: Self management and behavioural interventions. UK: McGraw-Hill Education.

    Google Scholar 

  29. PROMIS. http://www.healthmeasures.net/explore-measurement-systems/promis. Accessed 15 Apr 2018.

  30. Ramachandran, V. S. (Ed.). (2012). Encyclopedia of human behavior (2nd ed.). Oxford (England): Elsevier.

    Google Scholar 

  31. Rupp, A. A., & Templin, J. (2008). The effects of Q-matrix misspecification on parameter estimates and classification accuracy in the DINA model. Educational and Psychological Measurement, 68, 78–96.

    Article  Google Scholar 

  32. Rupp, A. A., Templin, J., & Henson, R. A. (2010). Diagnostic measurement: Theory, methods, and applications. Guilford Press.

  33. Sahlqvist, S., Song, Y., Bull, F., Adams, E., Preston, J., et al. (2011). Effect of questionnaire length, personalisation and reminder type on response rate to a complex postal survey: randomised controlled trial. BMC Medical Research Methodology, 11, 62.

    Article  Google Scholar 

  34. Siegert, R. J., & Levack, W. M. (2014). Rehabilitation goal setting: Theory, practice and evidence. CRC Press.

  35. Standard Setting. http://www.healthmeasures.net/score-and-interpret/interpret-scores/standard-setting. Accessed 28 Apr 2018.

  36. Strecher, V. J., McEvoy DeVellis, B., Becker, M. H., & Rosenstock, I. M. (1986). The role of self-efficacy in achieving health behavior change. Health Education Quarterly, 13, 73–92.

    Article  Google Scholar 

  37. Tatsuoka, K. K. (1983). Rule space: an approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement, 20, 345–354.

    Article  Google Scholar 

  38. Templin, J. L., & Henson, R. A. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11, 287–305.

    Article  Google Scholar 

  39. Tu, D., Gao, X., Wang, D., & Cai, Y. (2017). A new measurement of internet addiction using diagnostic classification models. Frontiers in Psychology, 8, 1768.

    Article  Google Scholar 

  40. Turner, J., & Kelly, B. (2000). Emotional dimensions of chronic disease. The Western Journal of Medicine, 172, 124–128.

    Article  Google Scholar 

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Correspondence to Sergio Romero.

Ethics declarations

The study was funded by the National Institutes of Health, Grant 1U01AR057967–01, “Development and Validation of a Self–Efficacy Item Bank,” awarded to Lisa Shulman (Principal Investigator) and Ann Gruber-Baldini, Sergio Romero, and Craig Velozo (Co-Investigators). The results and conclusions presented in this paper are those of the authors and are independent from the funding source. The scales and item banks described in this paper are freely available at: http://www.healthmeasures.net/explore-measurement-systems/promis.

Conflict of Interest

Mi Jung Lee declares that she has no conflict of interest. Sergio Romero declares that he has no conflict of interest. Ren Liu declares that he has no conflict of interest. Craig Velozo declares that he has no conflict of interest. Ann L. Gruber-Baldini declares that she has no conflict of interest. Lisa M. Shulman declares that she has no conflict of interest.

Ethical Approval

This study was approved by the Institutional review boards (IRB) of the Medical University of South Carolina (#Pro00033397), the University of Florida (#261–2010), and the University of Maryland (#HP-000432550). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Lee, M.J., Romero, S., Liu, R. et al. Multidimensional PROMIS Self-Efficacy Measure for Managing Chronic Conditions. Applied Research Quality Life (2020). https://doi.org/10.1007/s11482-020-09842-1

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Keywords

  • Patient-reported outcome measure
  • Self-efficacy
  • PROMIS
  • And multidimensionality