Optimizing measurement of vision-related quality of life: a computerized adaptive test for the impact of vision impairment questionnaire (IVI-CAT)

  • Eva K. Fenwick
  • Bao Sheng Loe
  • Jyoti Khadka
  • Ryan E. K. Man
  • Gwyn Rees
  • Ecosse L. LamoureuxEmail author



To compare the results from a simulated computerized adaptive test (CAT) for the 28-item Impact of Vision Impairment (IVI) questionnaire and the original paper–pencil version in terms of efficiency (main outcome), defined as percentage item reduction.


Using paper–pencil IVI data from 832 participants across the spectrum of vision impairment, item calibrations of the 28-item IVI instrument and its associated 20-item vision-specific functioning (VSF) and 8-item emotional well-being (EWB) subscales were generated with Rasch analysis. Based on these calibrations, CAT simulations were conducted on 1000 cases, with ‘high’ and ‘moderate’ precision stopping rules (standard error of measurement [SEM] 0.387 and 0.521, respectively). We examined the average number of items needed to satisfy the stopping rules and the corresponding percentage item reduction, level of agreement between person measures estimated from the full IVI item bank and from the CAT simulations, and item exposure rates (IER).


For the overall IVI-CAT, 5 or 9.7 items were required, on average, to obtain moderate or high precision estimates of vision-related quality of life, corresponding to 82.1 and 65.4% item reductions compared to the paper–pencil IVI. Agreement was high between the person measures generated from the full IVI item bank and the IVI-CAT for both the high precision simulation (mean bias, − 0.004 logits; 95% LOA − 0.594 to 0.587) and moderate precision simulation (mean bias, 0.014 logits; 95% LOA − 0.828 to 0.855). The IER for the IVI-CAT in the moderate precision simulation was skewed, with six EWB items used > 40% of the time.


Compared to the paper–pencil IVI instrument, the IVI-CATs required fewer items without loss of measurement precision, making them potentially attractive outcome instruments for implementation into clinical trials, healthcare, and research. Final versions of the IVI-CATs are available.


Vision-related quality of life Vision impairment Computerized adaptive testing Item bank Impact of Vision Impairment questionnaire 


Author contributions

EKF contributed to the conception of the work, interpretation of the data, and drafting of the manuscript; EKF and BSL conducted the data analysis, assisted with interpretation of results, and drafted sections of the paper; JK contributed to the methodology and study design and revised the manuscript critically for important intellectual content; GW revised the manuscript critically for important intellectual content; ELL contributed to the conception of the work, interpretation of the data, and revised the manuscript critically for important intellectual content. He will act as a overall guarantor of the study.


Prof. Ecosse Lamoureux was supported by an Australian National Health and Medical Research Council (NHMRC) Senior Research Fellowship (#1045280). Dr Gwyn Rees was funded by an NHMRC Career Development Fellowship (#1061801). The funding organizations had no role in the design or conduct of this research or preparation of this manuscript. The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian Government.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict 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 (Royal Victorian Eye and Ear Hospital Human Research Ethics Committee #04/556H, #09/923H) 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.

Supplementary material

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  1. 1.
    Fenwick, E. K., Man, R. E., Ong, P. G., Sabanayagam, C., Gupta, P., Cheng, C. Y., et al. (2016). Association of changes in visual acuity with vision-specific functioning in the Singapore Malay Eye Study. JAMA Ophthalmology,134(11), 1299–1305.CrossRefGoogle Scholar
  2. 2.
    Fenwick, E., Ong, P., Man, R., Cheng, C.-Y., Sabanayagam, C., Wong, T., et al. (2016). Association of vision impairment and major eye diseases with mobility and independence in a Chinese population. JAMA Ophthalmology,134(10), 1087–1093.CrossRefGoogle Scholar
  3. 3.
    Rees, G., Tee, H. W., Marella, M., Fenwick, E., Dirani, M., & Lamoureux, E. L. (2010). Vision-specific distress and depressive symptoms in people with vision impairment. Investigative Ophthalmology & Visual Science,51(6), 2891–2896.CrossRefGoogle Scholar
  4. 4.
    Snyder, C. F., Jensen, R. E., Segal, J. B., & Wu, A. W. (2013). Patient-reported outcomes (PROs): Putting the patient perspective in patient-centered outcomes research. Medical Care,51(8 Suppl 3), S73–S79.CrossRefGoogle Scholar
  5. 5.
    Snyder, C. F., Aaronson, N. K., Choucair, A. K., Elliott, T. E., Greenhalgh, J., Halyard, M. Y., et al. (2012). Implementing patient-reported outcomes assessment in clinical practice: A review of the options and considerations. Quality of Life Research,21(8), 1305–1314.CrossRefGoogle Scholar
  6. 6.
    Greenhalgh, J. (2009). The applications of PROs in clinical practice: What are they, do they work, and why? Quality of Life Research,18(1), 115–123.CrossRefGoogle Scholar
  7. 7.
    Lamoureux, E., Pallant, J. F., Pesudovs, K., Hassell, J. B., & Keeffe, J. E. (2006). The impact of vision impairment questionnaire: An evaluation of its measurement properties using Rasch analysis. Investigative Ophthalmology & Visual Science,47, 4732–4741.CrossRefGoogle Scholar
  8. 8.
    Lamoureux, E., Pallant, J. F., Pesudovs, K., Rees, G., Hassell, J. B., & Keeffe, J. E. (2007). The impact of vision impairment questionnaire: An assessment of its domain structure using confirmatory factor analysis and Rasch analysis. Investigative Ophthalmology & Visual Science,48, 1001–1006.CrossRefGoogle Scholar
  9. 9.
    Fenwick, E. K., Man, R. E., Rees, G., Keeffe, J., Wong, T. Y., & Lamoureux, E. L. (2017). Reducing respondent burden: Validation of the brief impact of vision impairment questionnaire. Quality of Life Research,26(2), 479–488.CrossRefGoogle Scholar
  10. 10.
    Khadka, J., McAlinden, C., & Pesudovs, K. (2013). Quality assessment of ophthalmic questionnaires: Review and recommendations. Optometry and Vision Science,90(8), 720–744.CrossRefGoogle Scholar
  11. 11.
    Gibbons, R. D., Weiss, D. J., Kupfer, D. J., Frank, E., Fagiolini, A., Grochocinski, V. J., et al. (2008). Using computerized adaptive testing to reduce the burden of mental health assessment. Psychiatric Services (Washington, D. C.),59(4), 361–368.CrossRefGoogle Scholar
  12. 12.
    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), 1133–1141.Google Scholar
  13. 13.
    Forkmann, T., Boecker, M., Norra, C., Eberle, N., Kircher, T., Schauerte, P., et al. (2009). Development of an item bank for the assessment of depression in persons with mental illnesses and physical diseases using Rasch analysis. Rehabilitation Psychology,54(2), 186–197.CrossRefGoogle Scholar
  14. 14.
    Revicki, D. A., & Cella, D. F. (1997). Health status assessment for the twenty-first century: Item response theory, item banking and computer adaptive testing. Quality of Life Research,6(6), 595–600.CrossRefGoogle Scholar
  15. 15.
    Reeve, B. B., Hays, R. D., Bjorner, J. B., Cook, K. F., Crane, P. K., Teresi, J. A., et al. (2007). Psychometric evaluation and calibration of health-related quality of life item banks: Plans for the patient-reported outcomes measurement information system (PROMIS). Medical Care,45(5 Suppl 1), S22–S31.CrossRefGoogle Scholar
  16. 16.
    Lamoureux, E. L., Pallant, J. F., Pesudovs, K., Rees, G., Hassell, J. B., & Keeffe, J. E. (2007). The effectiveness of low-vision rehabilitation on participation in daily living and quality of life. Investigative Ophthalmology & Visual Science,48(4), 1476–1482.CrossRefGoogle Scholar
  17. 17.
    Goldstein, J., E. Fenwick, R. Finger, V.K. Gothwal, M.L. Jackson, E. Lamoureux, G. Rees, and R.W. Massof. (2018). Calibrating the impact of vision impairment (IVI): Creation of a sample-independent visual function measure for patient-centered outcomes research. Translational Vision Science & Technology, In pressGoogle Scholar
  18. 18.
    Weih, L. M., Hassell, J. B., & Keeffe, J. (2002). Assessment of the impact of vision impairment. Investigative Ophthalmology & Visual Science,43(4), 927–935.Google Scholar
  19. 19.
    Rees, G., Xie, J., Chiang, P. P., Larizza, M. F., Marella, M., Hassell, J. B., et al. (2015). A randomised controlled trial of a self-management programme for low vision implemented in low vision rehabilitation services. Patient Education and Counseling,98(2), 174–181.CrossRefGoogle Scholar
  20. 20.
    Rees, G., Xie, J., Holloway, E. E., Sturrock, B. A., Fenwick, E. K., Keeffe, J. E., et al. (2013). Identifying distinct risk factors for vision-specific distress and depressive symptoms in people with vision impairment. Investigative Ophthalmology & Visual Science,54(12), 7431–7438.CrossRefGoogle Scholar
  21. 21.
    Linacre, J. (1994). Sample size and item calibration stability. Rasch Measurement Transactions,7(4), 328.Google Scholar
  22. 22.
    Linacre, J. M. (2005). A user’s guide to Winsteps/Ministeps Rasch-model programs. Chicago: MESA Press.Google Scholar
  23. 23.
    Andrich, D. (1978). Rating formulation for ordered response categories. Psychometrica,43, 561–573.CrossRefGoogle Scholar
  24. 24.
    Chen, S. K., & Cook, K. F. (2009). simpolycat: An SAS program for conducting CAT simulation based on polytomous IRT models. Behavior Research Methods,41(2), 499–506.CrossRefGoogle Scholar
  25. 25.
    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
  26. 26.
    Latimer, S., Meade, T., & Tennant, A. (2014). Development of item bank to measure deliberate self-harm behaviours: Facilitating tailored scales and computer adaptive testing for specific research and clinical purposes. Psychiatry Research,217(3), 240–247.CrossRefGoogle Scholar
  27. 27.
    Revuelta, J., & Ponsoda, V. (1998). A comparison of item exposure control methods in computerized adaptive testing. Journal of Educational Measurement,35, 311–327.CrossRefGoogle Scholar
  28. 28.
    Embretson, S., & Reise, S. P. (2000). Item response theory for psychologists. New Jersey London: Lawrence Erlbaun Associates.Google Scholar
  29. 29.
    Scalise, K., & Allen, D. (2015). Use of open-source software for adaptive measurement: Concerto as an R-based computer adaptive development and delivery platform. British Journal of Mathematical and Statistical Psychology,68, 478–496.CrossRefGoogle Scholar
  30. 30.
    Kingsbury, G., & Zara, A. (1989). Procedures for selecting items for computerized adaptive tests. Applied Measurement in Education,2(4), 359–375.CrossRefGoogle Scholar
  31. 31.
    Fenwick, E., Barnard, J., Gan, A., Loe, B. S., Khadka, J., Pesudovs, K., Man, R. E . K., Lee, S. Y., Tan, G., Wong, T., & Lamoureux, E. L. (2019). Computerized adaptive tests: an innovative, efficient, and precise method to assess the patient-centred impact of diabetic retinopathy. Under review. Google Scholar
  32. 32.
    Gershon, R. C. (2005). Computer adaptive testing. Journal of Applied Measurement,6(1), 109–127.PubMedGoogle Scholar
  33. 33.
    Haley, S. M., Ni, P., Jette, A. M., Tao, W., Moed, R., Meyers, D., et al. (2009). Replenishing a computerized adaptive test of patient-reported daily activity functioning. Quality of Life Research,18(4), 461–471.CrossRefGoogle Scholar
  34. 34.
    Finger, R., Tellis, B., Crewe, J., Keeffe, J., Ayton, L., & Guymer, R. (2014). Developing the impact of vision impairment-very low vision (IVI-VLV) questionnaire as part of the LoVADA protocol. Investigative Ophthalmology & Visual Science,55(10), 6150–6158.CrossRefGoogle Scholar
  35. 35.
    Bradley, S., Rumsfeld, J., & Ho, P. (2016). Incorporating health status in routine care to improve health care value: The VA patient reported health status assessment (PROST) system. JAMA,316(5), 487–488.CrossRefGoogle Scholar
  36. 36.
    Wu, A.W., R.E. Jensen, C. Salzberg, and C.F. Snyder, Advances in the use of patient reported outcome measures in electronic health records including case studies. 2013, In support of the PCORI National Workshop to Advance the Use of PRO measures in Electronic Health Records: Atlanta, USA.Google Scholar
  37. 37.
    Lim, A. S., & Bishop, G. D. (2000). The role of attitudes and beliefs in differential health care utilisation among Chinese in Singapore. Psychology and Health,14(6), 965–977.CrossRefGoogle Scholar
  38. 38.
    Fenwick, E., Man, R., Cheung, C., Sabanayagam, C., Cheng, C.-Y., Neelam, K., et al. (2017). Ethnic differences in the association between age-related macular degeneration and vision-specific functioning. JAMA Ophthalmology,135(5), 469–476.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Eye Research Australia, Royal Victorian Eye and Ear HospitalUniversity of MelbourneMelbourneAustralia
  2. 2.Singapore Eye Research Institute, Singapore National Eye CentreSingaporeSingapore
  3. 3.Duke-NUS Medical SchoolSingaporeSingapore
  4. 4.School of PsychologyUniversity of CambridgeCambridgeUK
  5. 5.University of South AustraliaAdelaideAustralia
  6. 6.Registry of Older South Australians, South Australian Health and Medical Research InstituteAdelaideAustralia
  7. 7.College of Nursing and Health SciencesFlinders UniversityAdelaideAustralia
  8. 8.Department of OphthalmologyNational University of SingaporeSingaporeSingapore

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