Quality of Life Research

, Volume 28, Issue 1, pp 199–210 | Cite as

Adapting summary scores for the PROMIS-29 v2.0 for use among older adults with multiple chronic conditions

  • Wenjing HuangEmail author
  • Adam J. Rose
  • Elizabeth Bayliss
  • Lesley Baseman
  • Emily Butcher
  • Rosa-Elena Garcia
  • Maria Orlando Edelen



The patient-reported outcomes measurement information system 29-item profile (PROMIS-29 v2.0) is a widely used health-related quality of life (HRQoL) measure. Summary scores for physical and mental HRQoL have recently been developed for the PROMIS-29 using a general population. Our purpose was to adapt these summary scores to a population of older adults with multiple chronic conditions.


We collected the PROMIS-29 v2.0 for 1359 primary care patients age 65+ with at least 2 of 13 chronic conditions. PROMIS-29 has 7 domains, plus a single-item pain intensity scale. We used exploratory factor analysis (EFA), followed by confirmatory factor analysis (CFA), to examine the number of factors that best captured these eight scores. We used previous results from a recent study by Hays et al. (Qual Life Res 27:1885–1891, 2018) to standardize scoring coefficients, normed to the general population.


The mean age was 80.7, and 67% of participants were age 80 or older. Our results indicated a 2-factor solution, with these factors representing physical and mental HRQoL, respectively. We call these factors the physical health score (PHS) and the mental health score (MHS). We normed these summary scores to the general US population. The mean MHS for our population of was 50.1, similar to the US population, while the mean PHS was 42.2, almost a full standard deviation below the US population.


We describe the adaptation of physical and mental health summary scores of the PROMIS-29 for use with a population of older adults with multiple chronic conditions.


Physical health Mental health Quality of life PROMIS PROMIS-29 Comorbidity Geriatrics 



Funded by the National Institute on Aging (contract #HHSN271201500064C NIH NIA, PI: Edelen). The funder had no role in data collection, data analysis, interpretation, manuscript drafting, manuscript revision, or decision to submit for publication.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no relevant conflicts of interest.

Ethical approval

Approved by the Human Subjects Research Protection Committee of the RAND Corporation and the Institutional Review Board of Kaiser Permanente Colorado. The authors declare that this study was conducted in accordance with appropriate ethical standards for research, including the Declaration of Helsinki.

Informed consent

Participants provided informed consent, with a waiver of documentation of informed consent.


  1. 1.
    Cella, D. F. (1995). Measuring quality of life in palliative care. Seminars in Oncology, 22(2 Suppl 3), 73–81.
  2. 2.
    Schipper, H., Clinch, J. J., & Olweny, C. L. M. (1996). Quality of life studies: Definitions and conceptual issues. In B. Spilker (Ed.), Quality of life and pharmacoeconomics in clinical trials (pp. 11–23). Philadelphia: Lippincott-Raven Publishers.Google Scholar
  3. 3.
    Bevans, M., Ross, A., & Cella, D. (2014). Patient-reported outcomes measurement information system (PROMIS): Efficient, standardized tools to measure self-reported health and quality of life. Nursing Outlook, 62(5), 339–345. Scholar
  4. 4.
    Chen, J., Ou, L., & Hollis, S. J. (2013). A systematic review of the impact of routine collection of patient reported outcome measures on patients, providers and health organizations in an oncologic setting. BMC Health Services Research, 13, 211. Scholar
  5. 5.
    Ware, J. E. Jr., & Sherbourne, C. D. (1992). The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Medical Care, 30(6), 473–483.CrossRefGoogle Scholar
  6. 6.
    Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., et al. (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(11), 1179–1194. Scholar
  7. 7.
    Hays, R. D., Alonso, J., & Coons, S. J. (1998). Possibilities for summarizing health-related quality of life when using a profile instrument. In M. Staquet, R. Hays & P. Fayers (Eds.), Quality of life assessment in clinical trials: Methods and practice (153, p. 143). Oxford: Oxford University Press.Google Scholar
  8. 8.
    Farivar, S. S., Cunningham, W. E., & Hays, R. D. (2007). Correlated physical and mental health summary scores for the SF-36 and SF-12 health survey, V. 1. Health and Quality of Life Outcomes, 5:54. 18.CrossRefGoogle Scholar
  9. 9.
    Hays, R. D., Marshall, G. N., Wang, E. Y. I., & Sherbourne, C. D. (1994). Four-year cross-lagged associations between physical and mental health in the medical outcomes study. Journal of Consulting and Clinical Psychology, 62, 441–449.CrossRefGoogle Scholar
  10. 10.
    Hays, R. D., Spritzer, K. L., Schalet, B., & Cella, D. (2018). PROMIS®-29 v2.0 physical and mental health summary scores. Quality of Life Research, 27(7), 1885–1891. Scholar
  11. 11.
    Rose, A. J., et al. (2018). Evaluating the PROMIS-29 v2.0 for use among older adults with multiple chronic conditions. Quality of Life Research. Google Scholar
  12. 12.
    Tarlov, A. R., et al. (1989). The medical outcomes study: An application of methods for monitoring the results of medical care. JAMA, 262(7), 925–930.CrossRefGoogle Scholar
  13. 13.
    Edelen, M. O., Rose, A. J., Bayliss, E., Baseman, L., Butcher, E., Garcia, R. E., Tabano, D., & Stucky, B. D. (2017). Patient-reported outcome-based performance measures for older adults with multiple chronic conditions. Santa Monica: RAND Corporation.
  14. 14.
    Embretson, S. E., & Reise, S. P. (2000). item response theory for psychologists. London: Psychology Press.Google Scholar
  15. 15.
    Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. Scholar
  16. 16.
    Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141–151. Scholar
  17. 17.
    Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1, 245–276. Scholar
  18. 18.
    SAS (9.4 ed.). Cary, NC: SAS Corporation.Google Scholar
  19. 19.
    Muthén, L. K., & Muthén, B. O. (1998–2017). Mplus User’s Guide. Eighth Edition. Los Angeles: Muthén & Muthén.Google Scholar
  20. 20.
    Hays, R. D., & Stewart, A. L. (1990). The structure of self-reported health in chronic disease patients. Psychological Assessment, 2, 22–30. Scholar
  21. 21.
    Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. Scholar
  22. 22.
    Beaumont, J. L., Cella, D., Phan, A. T., Choi, S., Liu, Z., & Yao, J. C. (2012). Comparison of health-related quality of life in patients with neuroendocrine tumors with quality of life in the general US population. Pancreas, 41(3), 461–466. Scholar
  23. 23.
    Kazis, L. E., Miller, D. R., Skinner, K. M., Lee, A., Ren, X. S., Clark, J. A. el al (2004). Patient-Reported Measures of Health: The Veterans Health Study. The Journal of ambulatory care management, 27(1), 70–83.
  24. 24.
    Revicki, D. A., Kawata, A. K., Harnam, N., Chen, W.-H., Hays, R. D., & Cella, D. (2009). Predicting EuroQol (EQ-5D) scores from the patient-reported outcomes measurement information system (PROMIS) global items and domain item banks in a United States sample. Quality of Life Research, 18, 783–791.CrossRefGoogle Scholar
  25. 25.
    Hays, R. D., Revicki, D. A., Feeny, D., Fayers, P., Spritzer, K. L., & Cella, D. (2016). Using linear equating to map PROMIS global health items and the PROMIS-29 V. 2 Profile measure to the health utilities index—mark 3. Pharmacoeconomics, 34, 1015–1022.CrossRefGoogle Scholar
  26. 26.
    Craig, B. M., Reeve, B. B., Brown, P. M., Cella, D., Hays, R. D., Lipscomb, J., Pickard, A. S., & Revicki, D. A. (2014). US valuation of health outcomes measured using the PROMIS-29. Value in Health, 17(8), 846–853.CrossRefGoogle Scholar
  27. 27.
    Hanmer, J., Cella, D., Feeny, D., Fischhoff, B., Hays, R. D., Hess, R., et al. (2017). Selection of key health domains from PROMIS® for a generic preference-based scoring system. Quality of Life Research, 26, 3377–3385.CrossRefGoogle Scholar
  28. 28.
    Hanmer, J., Feeny, D., Fischoff, B., Hays, R. D., Hess, R., Pilkonis, P., et al. (2015). The PROMIS of QALYs. Health and Quality of Life Outcomes, 13, 122.CrossRefGoogle Scholar
  29. 29.
    Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J. C., Saunders, L. D., Beck, C. A., Feasby, T. E., & Ghali, W. A. (2005). Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical Care, 43(11), 1130–1139. Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.RAND CorporationSanta MonicaUSA
  2. 2.RAND CorporationBostonUSA
  3. 3.Section of General Internal MedicineBoston University School of MedicineBostonUSA
  4. 4.Institute for Health ResearchKaiser Permanente ColoradoDenverUSA
  5. 5.Department of Family MedicineUniversity of Colorado School of MedicineAuroraUSA
  6. 6.RAND CorporationArlingtonUSA

Personalised recommendations