Quality of Life Research

, Volume 25, Issue 10, pp 2403–2415 | Cite as

New measures to capture end of life concerns in Huntington disease: Meaning and Purpose and Concern with Death and Dying from HDQLIFE (a patient-reported outcomes measurement system)

  • N. E. Carlozzi
  • N. R. Downing
  • M. K. McCormack
  • S. G. Schilling
  • J. S. Perlmutter
  • E. A. Hahn
  • J. -S. Lai
  • S. Frank
  • K. A. Quaid
  • J. S. Paulsen
  • D. Cella
  • S. M. Goodnight
  • J. A. Miner
  • M. A. Nance



Huntington disease (HD) is an incurable terminal disease. Thus, end of life (EOL) concerns are common in these individuals. A quantitative measure of EOL concerns in HD would enable a better understanding of how these concerns impact health-related quality of life. Therefore, we developed new measures of EOL for use in HD.


An EOL item pool of 45 items was field tested in 507 individuals with prodromal or manifest HD. Exploratory and confirmatory factor analyses (EFA and CFA, respectively) were conducted to establish unidimensional item pools. Item response theory (IRT) and differential item functioning analyses were applied to the identified unidimensional item pools to select the final items.


EFA and CFA supported two separate unidimensional sets of items: Concern with Death and Dying (16 items), and Meaning and Purpose (14 items). IRT and DIF supported the retention of 12 Concern with Death and Dying items and 4 Meaning and Purpose items. IRT data supported the development of both a computer adaptive test (CAT) and a 6-item, static short form for Concern with Death and Dying.


The HDQLIFE Concern with Death and Dying CAT and corresponding 6-item short form, and the 4-item calibrated HDQLIFE Meaning and Purpose scale demonstrate excellent psychometric properties. These new measures have the potential to provide clinically meaningful information about end-of-life preferences and concerns to clinicians and researchers working with individuals with HD. In addition, these measures may also be relevant and useful for other terminal conditions.


Health-related quality of life Neuro-QoL PROMIS HDQLIFE Huntington disease End of life Patient-reported outcome (PRO) 



Work on this manuscript was supported by the National Institutes of Health (NIH), National Institute of Neurological Disorders and Stroke (R01NS077946), and the National Center for Advancing Translational Sciences (UL1TR000433). In addition, a portion of this study sample was collected in conjunction with the Predict-HD study. The Predict-HD was supported by the NIH, National Institute of Neurological Disorders and Stroke (R01NS040068), the NIH, Center for Inherited Disease Research (provided supported for sample phenotyping), and the CHDI Foundation (award to the University of Iowa). We thank the University of Iowa, the Investigators and Coordinators of this study, the study participants, the National Research Roster for Huntington Disease Patients and Families, the Huntington Study Group, and the Huntington’s Disease Society of America. We acknowledge the assistance of Jeffrey D. Long, Hans J. Johnson, Jeremy H. Bockholt, Roland Zschiegner, and Jane S. Paulsen. We also acknowledge Roger Albin, Kelvin Chou, and Henry Paulsen for the assistance with participant recruitment. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. HDQLIFE Site Investigators and Coordinators are Noelle Carlozzi, Praveen Dayalu, Stephen Schilling, Amy Austin, Matthew Canter, Siera Goodnight, Jennifer Miner, Nicholas Migliore (University of Michigan, Ann Arbor, MI); Jane Paulsen, Nancy Downing, Isabella DeSoriano, Courtney Shadrick, Amanda Miller (University of Iowa, Iowa City, IA); Kimberly Quaid, Melissa Wesson (Indiana University, Indianapolis, IN); Christopher Ross, Gregory Churchill, Mary Jane Ong (Johns Hopkins University, Baltimore, MD); Susan Perlman, Brian Clemente, Aaron Fisher, Gloria, Obialisi, Michael Rosco (University of California Los Angeles, Los Angeles, CA); Michael McCormack, Humberto Marin, Allison Dicke (Rutgers University, Piscataway, NJ); Joel Perlmutter, Stacey Barton, Shineeka Smith (Washington University, St. Louis, MO); Martha Nance, Pat Ede (Struthers Parkinson’s Center); Stephen Rao, Anwar Ahmed, Michael Lengen, Lyla Mourany, Christine Reece, (Cleveland Clinic Foundation, Cleveland, OH); Michael Geschwind, Joseph Winer (University of California—San Francisco, San Francisco, CA), David Cella, Richard Gershon, Elizabeth Hahn, Jin-Shei Lai (Northwestern University, Chicago, IL).


Work on this manuscript was supported by the National Institutes of Health (NIH), National Institute of Neurological Disorders and Stroke (R01NS077946), and the National Center for Advancing Translational Sciences (UL1TR000433). In addition, a portion of this study sample was collected in conjunction with the Predict-HD study. The Predict-HD was supported by the NIH, National Institute of Neurological Disorders and Stroke (R01NS040068), the NIH Center for Inherited Disease Research (provided supported for sample phenotyping), and the CHDI Foundation (award to the University of Iowa).

Compliance with ethical standards

Conflict of interest

Carlozzi, N.E. currently has research grants from the NIH; she is also supported by grant funding from the NIH, NIDILRR, and CHDI; she declares no conflicts of interest. Downing, N.R. declares no conflicts of interest. McCormack, M.K. currently has grants from the NJ Department of Health; he declare no conflicts of interest. Schilling, S.G. has a research grant from NSF. He also is supported by grant funding from NIH. He declares no conflicts of interest. Perlmutter, J.S. currently has funding from the NIH, HDSA, CHDI, and APDA. He has received honoraria from the University of Rochester, American Academy of Neurology, Movement Disorders Society, Toronto Western Hospital, St Lukes Hospital in St Louis, Emory U, Penn State, Alberta innovates, Indiana Neurological Society, Parkinson Disease Foundation, Columbia University, St. Louis University, Harvard University, and the University of Michigan. Hahn, E.A. currently has research grants from the NIH; she is also supported by grant funding from the NIH and PCORI, and by research contracts from Merck and EMMES; she declares no conflicts of interest. Lai J.-S. currently has research grants from the NIH; she declares no conflicts of interest. Frank, S. receives salary support from the Huntington Study Group for a study sponsored by Auspex Pharmaceuticals. There is no conflict of interest. Quaid, K.A. has research funding from NIH, NIA, NCRR, NINDS and CHDI. She also has funding from HDSA. She has no conflicts of interest to declare. Paulsen, J.S. currently has research grants from the NIH; she is also supported by grant funding from NIH, NINDS, and CHDI; she declares no conflicts of interest. Cella, D. receives grant funding from the National Institutes of Health and reports that he has no conflicts of interest. Goodnight, S.M. is supported by grant funding from the NIH and the Craig H. Neilsen Foundation; she declares no conflicts of interest. Miner, J.A. is supported by research grants from the NIH; she declares no conflict of interest. Nance, M.A. declares no conflicts of interest.

Ethical approval

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.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Ross, C. A., Margolis, R. L., Rosenblatt, A., Ranen, N. G., Becher, M. W., & Aylward, E. (1997). Huntington disease and the related disorder, dentatorubral-pallidoluysian atrophy (DRPLA). Medicine (Baltimore), 76(5), 305–338.CrossRefGoogle Scholar
  2. 2.
    Paulsen, J. S. (2010). Early detection of huntington disease. Future Neurology, 5(1), 85–104.CrossRefGoogle Scholar
  3. 3.
    Carlozzi, N. E., & Tulsky, D. S. (2013). Identification of health-related quality of life (HRQOL) issues relevant to individuals with Huntington disease. Journal of Health Psychology, 18(2), 212–225.PubMedCrossRefGoogle Scholar
  4. 4.
    Booij, S. J., Tibben, A., Engberts, D. P., Marinus, J., & Roos, R. A. C. (2014). Thinking about the end of life: A common issue for patients with Huntington’s disease. Journal of Neurology, 261(11), 2184–2191.PubMedCrossRefGoogle Scholar
  5. 5.
    Teno, J. M., Byock, I., & Field, M. J. (1999). Research agenda for developing measures to examine quality of care and quality of life of patients diagnosed with life-limiting illness. Journal of Pain and Symptom Management, 17(2), 75–82.PubMedCrossRefGoogle Scholar
  6. 6.
    Steinhauser, K. E., Clipp, E. C., & Tulsky, J. A. (2002). Evolution in measuring the quality of dying. Journal of Palliative Medicine, 5(3), 407–414.PubMedCrossRefGoogle Scholar
  7. 7.
    Mularski, R. A., Dy, S. M., Shugarman, L. R., Wilkinson, A. M., Lynn, J., Shekelle, P. G., et al. (2007). A systematic review of measures of end-of-life care and its outcomes. Health Services Research, 42(5), 1848–1870.PubMedPubMedCentralCrossRefGoogle Scholar
  8. 8.
    van Soest-Poortvliet, M. C., van der Steen, J. T., Zimmerman, S., Cohen, L. W., Reed, D., Achterberg, W. P., et al. (2013). Selecting the best instruments to measure quality of end-of-life care and quality of dying in long term care. Journal of the American Medical Directors Association, 14(3), 179–186.PubMedCrossRefGoogle Scholar
  9. 9.
    Albers, G., Echteld, M. A., de Vet, H. C., Onwuteaka-Philipsen, B. D., van der Linden, M. H., & Deliens, L. (2010). Content and spiritual items of quality-of-life instruments appropriate for use in palliative care: A review. Journal of Pain and Symptom Management, 40(2), 290–300.PubMedCrossRefGoogle Scholar
  10. 10.
    Klager, J., Duckett, A., Sandler, S., & Moskowitz, C. (2008). Huntington’s disease: A caring approach to the end of life. Care Management Journal, 9(2), 75–81.CrossRefGoogle Scholar
  11. 11.
    Huntington Society of Canada. (2010). Factsheets for healthcare professionals: Palliative care for individuals in late-stage Huntington disease.
  12. 12.
    Downing, N. R., Williams, J. K., & Paulsen, J. S. (2010). Couples’ attributions for work function changes in prodromal Huntington disease. Journal of Genetic Counseling, 19(4), 343–352.PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    McMillan, S. C., & Weitzner, M. (1998). Quality of life in cancer patients: Use of a revised Hospice Index. Cancer Practice, 6(5), 282–288.PubMedCrossRefGoogle Scholar
  14. 14.
    Krause, S., Rydall, A., Hales, S., Rodin, G., & Lo, C. (2015). Initial validation of the death and dying distress scale for the assessment of death anxiety in patients with advanced cancer. Journal of Pain and Symptom Management, 49(1), 126–134.PubMedCrossRefGoogle Scholar
  15. 15.
    Lo, C., Hales, S., Zimmermann, C., Gagliese, L., Rydall, A., & Rodin, G. (2011). Measuring death-related anxiety in advanced cancer: Preliminary psychometrics of the death and dying distress scale. Journal of Pediatric Hematology/Oncology, 33(Suppl 2), S140–S145.PubMedCrossRefGoogle Scholar
  16. 16.
    Cohen, S. R., Mount, B. M., Bruera, E., Provost, M., Rowe, J., & Tong, K. (1997). Validity of the McGill quality of life questionnaire in the palliative care setting: A multi-centre Canadian study demonstrating the importance of the existential domain. Palliative Medicine, 11(1), 3–20.PubMedCrossRefGoogle Scholar
  17. 17.
    Cohen, S. R., Mount, B. M., Strobel, M. G., & Bui, F. (1995). The mcgill quality of life questionnaire: A measure of quality of life appropriate for people with advanced disease. A preliminary study of validity and acceptability. Palliative Medicine, 9(3), 207–219.PubMedCrossRefGoogle Scholar
  18. 18.
    Lo, C., Burman, D., Swami, N., Gagliese, L., Rodin, G., & Zimmermann, C. (2011). Validation of the QUAL-EC for assessing quality of life in patients with advanced cancer. European Journal of Cancer, 47(4), 554–560.PubMedCrossRefGoogle Scholar
  19. 19.
    Heyland, D. K., Jiang, X., Day, A. G., Cohen, S. R., & Canadian Researchers at the End of Life Network. (2013). The development and validation of a shorter version of the Canadian Health Care Evaluation Project Questionnaire (CANHELP Lite): A novel tool to measure patient and family satisfaction with end-of-life care. Journal of Pain and Symptom Management, 46(2), 289–297.PubMedCrossRefGoogle Scholar
  20. 20.
    McCaffrey, N., Skuza, P., Breaden, K., Eckermann, S., Hardy, J., Oaten, S., et al. (2014). Preliminary development and validation of a new end-of-life patient-reported outcome measure assessing the ability of patients to finalise their affairs at the end of life. PLoS One, 9(4), e94316.PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Byock, I. R., & Merriman, M. P. (1998). Measuring quality of life for patients with terminal illness: The Missoula-VITAS quality of life index. Palliative Medicine, 12(4), 231–244.PubMedCrossRefGoogle Scholar
  22. 22.
    Rudilla, D., Oliver, A., Galiana, L., & Barreto, P. (2016). A new measure of home care patients’ dignity at the end of life: The palliative patients’ dignity scale (PPDS). Palliative and Support Care, 14(2), 99–108.Google Scholar
  23. 23.
    Buzgova, R., Kozakova, R., Sikorova, L., Zelenikova, R., & Jarosova, D. (2016). Development and psychometric evaluation of patient needs assessment in palliative care (PNAP) instrument. Palliative and Support Care, 14(2), 129–137.Google Scholar
  24. 24.
    Lawton, M. P., Moss, M., Hoffman, C., Kleban, M. H., Ruckdeschel, K., & Winter, L. (2001). Valuation of life: A concept and a scale. Journal of Aging and Health, 13(1), 3–31.PubMedCrossRefGoogle Scholar
  25. 25.
    Steinhauser, K. E., Bosworth, H. B., Clipp, E. C., McNeilly, M., Christakis, N. A., Parker, J., & Tulsky, J. A. (2002). Initial assessment of a new instrument to measure quality of life at the end of life. Journal of Palliative Medicine, 5(6), 829–841.PubMedCrossRefGoogle Scholar
  26. 26.
    Heyland, D. K., Cook, D. J., Rocker, G. M., Dodek, P. M., Kutsogiannis, D. J., Skrobik, Y., et al. (2010). The development and validation of a novel questionnaire to measure patient and family satisfaction with end-of-life care: The Canadian Health Care Evaluation Project (CANHELP) questionnaire. Palliative Medicine, 24(7), 682–695.PubMedCrossRefGoogle Scholar
  27. 27.
    Cella, D., Gershon, R., Lai, J. S., & Choi, S. (2007). The future of outcomes measurement: Item banking, tailored short-forms, and computerized adaptive assessment. Quality of Life Research, 16(Suppl 1), 133–141.PubMedCrossRefGoogle Scholar
  28. 28.
    Carlozzi, N. E., Schilling, S. G., Lai, J.-S., Paulsen, J. S., Hahn, E. A., Perlmutter, J. S., Ross, C. A., Downing, N. R., Kratz, A. L., McCormack, M. K., Nance, M. A., Quaid, K. A., Stout, J., Gershon, R. C., Ready, R., Miner, J. A., Barton, S. K., Perlman, S. L., Rao, S. M., Frank, S., Shoulson, I., Marin, H., Geschwind, M. D., Dayalu, P., Foroud, T., Goodnight, S. M., & Cella, D. (Under Review). HDQLIFE: Development and assessment of health-related quality of life in Huntington disease (HD). Quality of Life Research.Google Scholar
  29. 29.
    Novack, T. (2000). The orientation log. Retrieved March 3, 2015, from
  30. 30.
    Hanauer, D. A., Mei, Q., Law, J., Khanna, R., & Zheng, K. (2015). Supporting information retrieval from electronic health records: A report of University of Michigan’s nine-year experience in developing and using the electronic medical record search engine (EMERSE). Journal of Biomedical Informatics, 55, 290–300.PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Paulsen, J. S., Hayden, M., Stout, J. C., Langbehn, D. R., Aylward, E., Ross, C. A., et al. (2006). Preparing for preventive clinical trials: The predict-HD study. Archives of Neurology, 63(6), 883–890.PubMedCrossRefGoogle Scholar
  32. 32.
    Paulsen, J. S., Langbehn, D. R., Stout, J. C., Aylward, E., Ross, C. A., Nance, M., et al. (2008). Detection of Huntington’s disease decades before diagnosis: The Predict-HD study. Journal of Neurology, Neurosurgery and Psychiatry, 79(8), 874–880.PubMedCrossRefGoogle Scholar
  33. 33.
    Paulsen, J. S., Long, J. D., Johnson, H. J., Aylward, E. H., Ross, C. A., Williams, J. K., et al. (2014). Clinical and biomarker changes in premanifest Huntington disease show trial feasibility: A decade of the PREDICT-HD study. Front Aging Neuroscience, 6, 78.CrossRefGoogle Scholar
  34. 34.
    Cella, D., Nowinski, C., Peterman, A., Victorson, D., Miller, D., Lai, J.-S., & Moy, C. (2011). The neurology quality of life measurement (Neuro-QOL) initiative. Archives of Physical Medicine and Rehabilitation, Supplement, 92(Suppl 1), S28–S36.CrossRefGoogle Scholar
  35. 35.
  36. 36.
    PROMIS® Instrument Development and Psychometric Evaluation Scientific Standards.
  37. 37.
    Shoulson, I., & Fahn, S. (1979). Huntington disease—clinical care and evaluation. Neurology, 29(1), 1–3.PubMedCrossRefGoogle Scholar
  38. 38.
    Huntington Study Group. (1996). Unified Huntington’s disease rating scale: Reliability and consistency. Movement Disorders, 11(2), 136–142.CrossRefGoogle Scholar
  39. 39.
    Muthen, B. (1978). Contributions to factor-analysis of dichotomous variables. Psychometrika, 43(4), 551–560.CrossRefGoogle Scholar
  40. 40.
    Muthen, B., Du Toit, S. H. C., & Spisic, D. (1997). Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes.
  41. 41.
    Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford Press.Google Scholar
  42. 42.
    Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246.PubMedCrossRefGoogle Scholar
  43. 43.
    Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.CrossRefGoogle Scholar
  44. 44.
    Hatcher, L. (1994). A step-by-step approach to using SAS for factor analysis and structural equation modeling. Cary: SAS Institute Inc.Google Scholar
  45. 45.
    McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah: Lawrence Erlbaum Associates Inc.Google Scholar
  46. 46.
    Reise, S. P., Morizot, J., & Hays, R. D. (2007). The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Quality of Life Research, 16(Suppl 1), 19–31.PubMedCrossRefGoogle Scholar
  47. 47.
    Cook, K. F., Kallen, M. A., & Amtmann, D. (2009). Having a fit: Impact of number of items and distribution of data on traditional criteria for assessing IRT’s unidimensionality assumption. Quality of Life Research, 18(4), 447–460.PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Muthén, L. K., & Muthén, B. O. (2011). Mplus user’s guide. Los Angeles: Muthén & Muthén.Google Scholar
  49. 49.
    Samejima, F., van der Liden, W. J., & Hambleton, R. (1996). The graded response model. In W. J. van der Liden (Ed.), Handbook of modern item response theory (pp. 85–100). New York, NY: Springer.Google Scholar
  50. 50.
    Cai, L., Thissen, D., & du Toit, S. H. C. (2011). IRTPRO for windows [Computer software]. Lincolnwood: Scientific Software International.Google Scholar
  51. 51.
    Crane, P. K., Gibbons, L. E., Jolley, L., & van Belle, G. (2006). Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar. Medical Care, 44(11 Suppl 3), S115–S123.PubMedCrossRefGoogle Scholar
  52. 52.
    R Core Team. (2014). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
  53. 53.
    Choi, S. W. (2009). Firestar: Computerized adaptive testing simulation program for polytomous item response theory models. Applied Psychological Measurement, 33(8), 644–645.CrossRefGoogle Scholar
  54. 54.
    Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores (Psychometric Monograph No. 17). Richmond, VA: Psychometric Society.Google Scholar
  55. 55.
    Bryant, F. B., & Yarnold, P. R. (1995). Principal components analysis and exploratory and confirmatory factor analysis. In L. G. Grimm & R. R. Yarnold (Eds.), Reading and understanding multivariate statistics (pp. 99–136). Washington, DC: American Psychological Association.Google Scholar
  56. 56.
    Everitt, B. S. (1975). Multivariate analysis: The need for data, and other problems. British Journal of Psychiatry, 126, 237–240.PubMedCrossRefGoogle Scholar
  57. 57.
    Gorsuch, R. L. (1983). Factor analysis. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  58. 58.
    Clauser, B. E., & Hambleton, R. K. (1994). Review of differential item functioning, P. W. Holland, H. Wainer. Journal of Educational Measurement, 31(1), 88–92.CrossRefGoogle Scholar
  59. 59.
    Kirwin, J. L., & Edwards, R. A. (2013). Helping patients articulate end-of-life wishes: A target for interprofessional participation. Annals of Palliative Medicine, 2(2), 95–97.PubMedGoogle Scholar
  60. 60.
    Booij, S. J., Engberts, D. P., Rodig, V., Tibben, A., & Roos, R. A. (2013). A plea for end-of-life discussions with patients suffering from Huntington’s disease: The role of the physician. Journal of Medical Ethics, 39(10), 621–624.PubMedCrossRefGoogle Scholar
  61. 61.
    Booij, S. J., Tibben, A., Engberts, D. P., & Roos, R. A. (2014). Perhaps the subject of the questionnaire was too sensitive: Do we expect too much too soon? Wishes for the end of life in Huntington’s disease—The perspective of European physicians. J Huntingtons Disease, 3(3), 229–232.Google Scholar
  62. 62.
    Dellefield, M. E., & Ferrini, R. (2011). Promoting excellence in end-of-life care: Lessons learned from a cohort of nursing home residents with advanced Huntington disease. Journal of Neuroscience Nursing, 43(4), 186–192.PubMedCrossRefGoogle Scholar
  63. 63.
    Williams, J. K., Erwin, C., Juhl, A. R., Mengeling, M., Bombard, Y., Hayden, M. R., et al. (2010). In their own words: Reports of stigma and genetic discrimination by people at risk for Huntington disease in the International RESPOND-HD study. American Journal of Medical Genetics B Neuropsychiatric Genetics, 153B(6), 1150–1159.PubMedGoogle Scholar
  64. 64.
    Boersma, I., Miyasaki, J., Kutner, J., & Kluger, B. (2014). Palliative care and neurology: Time for a paradigm shift. Neurology, 83(6), 561–567.PubMedPubMedCentralCrossRefGoogle Scholar
  65. 65.
    Tibben, A. (2007). Predictive testing for Huntington’s disease. Brain Research Bulletin, 72(2–3), 165–171.PubMedCrossRefGoogle Scholar
  66. 66.
    Duff, K., Paulsen, J. S., Beglinger, L. J., Langbehn, D. R., Wang, C., Stout, J. C., et al. (2010). “Frontal” behaviors before the diagnosis of Huntington’s disease and their relationship to markers of disease progression: Evidence of early lack of awareness. Journal of Neuropsychiatry and Clinical Neurosciences, 22(2), 196–207.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • N. E. Carlozzi
    • 1
  • N. R. Downing
    • 2
  • M. K. McCormack
    • 3
  • S. G. Schilling
    • 4
  • J. S. Perlmutter
    • 5
    • 6
  • E. A. Hahn
    • 7
  • J. -S. Lai
    • 8
    • 9
  • S. Frank
    • 10
  • K. A. Quaid
    • 11
  • J. S. Paulsen
    • 12
    • 13
    • 14
  • D. Cella
    • 7
    • 8
    • 9
  • S. M. Goodnight
    • 1
  • J. A. Miner
    • 1
  • M. A. Nance
    • 15
  1. 1.Department of Physical Medicine and RehabilitationUniversity of MichiganAnn ArborUSA
  2. 2.College of NursingThe University of IowaIowa CityUSA
  3. 3.Department of PathologyRowan UniversityPiscatawayUSA
  4. 4.Institute for Social ResearchUniversity of MichiganAnn ArborUSA
  5. 5.Departments of Neurology, Radiology, and Anatomy and NeurobiologyWashington University School of MedicineSt. LouisUSA
  6. 6.Program in Occupational Therapy and Program in Physical TherapyWashington University School of MedicineSt. LouisUSA
  7. 7.Department of Medical Social SciencesNorthwestern UniversityChicagoUSA
  8. 8.Center on Outcomes, Research and Education, Evanston Northwestern HealthcareNorthwestern UniversityEvanstonUSA
  9. 9.Institute for Health Services Research and Policy Studies, Feinberg School of MedicineNorthwestern UniversityChicagoUSA
  10. 10.Beth Israel Deaconess Medical CenterBostonUSA
  11. 11.Department of Medical and Molecular GeneticsIndiana UniversityIndianapolisUSA
  12. 12.Department of Psychiatry, Carver College of MedicineThe University of IowaIowa CityUSA
  13. 13.Department of Neurology, Carver College of MedicineThe University of IowaIowa CityUSA
  14. 14.Department of PsychologyThe University of IowaIowa CityUSA
  15. 15.Hennepin County Medical CenterMinneapolisUSA

Personalised recommendations