Advertisement

Quality of Life Research

, Volume 26, Issue 10, pp 2867–2875 | Cite as

Item-level psychometrics of the ADL instrument of the Korean National Survey on persons with physical disabilities

  • Ickpyo Hong
  • Mi Jung Lee
  • Moon Young Kim
  • Hae Yean ParkEmail author
Article

Abstract

Purpose

The aim of this study is to investigate the psychometrics of the 12 items of an instrument assessing activities of daily living (ADL) using an item response theory model.

Methods

A total of 648 adults with physical disabilities and having difficulties in ADLs were retrieved from the 2014 Korean National Survey on People with Disabilities. The psychometric testing included factor analysis, internal consistency, precision, and differential item functioning (DIF) across categories including sex, older age, marital status, and physical impairment area.

Results

The sample had a mean age of 69.7 years old (SD = 13.7). The majority of the sample had lower extremity impairments (62.0%) and had at least 2.1 chronic conditions. The instrument demonstrated unidimensional construct and good internal consistency (Cronbach’s alpha = 0.95). The instrument precisely estimated person measures within a wide range of theta values (−2.22 logits < θ < 0.27 logits) with a reliability of 0.9. Only the changing position item demonstrated misfit (χ2 = 36.6, df = 17, p = 0.0038), and the dressing item demonstrated DIF on the impairment type (upper extremity/others, McFadden’s Pseudo R 2 > 5.0%).

Conclusions

Our findings indicate that the dressing item would need to be modified to improve its psychometrics. Overall, the ADL instrument demonstrates good psychometrics, and thus, it may be used as a standardized instrument for measuring disability in rehabilitation contexts. However, the findings are limited to adults with physical disabilities. Future studies should replicate psychometric testing for survey respondents with other disorders and for children.

Keywords

Activities of daily living Assessment Psychometrics Item response theory 

Notes

Compliance with ethical standards

Conflict of interest

Ickpyo Hong, Mi Jung Lee, Moon-Young Kim, and Hae Yean Park 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 and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was exempted by the Institutional review boards (IRB) of Yonsei University because the research is a study of an existing dataset, the 2014 Korean National Survey on People with Disabilities which is publicly available. The study dataset is de-identified, such that subjects cannot be identified directly, or through identifiers linked to the subjects.

Informed consent

Not applicable.

Supplementary material

11136_2017_1637_MOESM1_ESM.docx (16 kb)
Supplementary material 1 (DOCX 15 kb)

References

  1. 1.
    Korean Institute for Health and Social Affairs (2014). National Survey on People with Disabilities.Google Scholar
  2. 2.
    Kim, H.-O., & Joung, K. H. (2007). A study on the needs of health & community services among the disabled at home in rural areas. Journal of Korean Academy of Community Health Nursing, 18(3), 480–491.Google Scholar
  3. 3.
    Osborn, R., Moulds, D., Squires, D., Doty, M. M., & Anderson, C. (2014). International survey of older adults finds shortcomings in access, coordination, and patient-centered care. Health Affairs, 33(12), 2247–2255. doi: 10.1377/hlthaff.2014.0947.CrossRefPubMedGoogle Scholar
  4. 4.
    Verbrugge, L. M., & Jette, A. M. (1994). The disablement process. Social Science & Medicine, 38(1), 1–14.CrossRefGoogle Scholar
  5. 5.
    Grotle, M., Brox, J. I., & Vøllestad, N. K. (2005). Functional status and disability questionnaires: What do they assess?: A systematic review of back-specific outcome questionnaires. Spine, 30(1), 130–140. doi: 10.1097/01.brs.0000149184.16509.73.CrossRefPubMedGoogle Scholar
  6. 6.
    Potkin, S. G. (2002). The ABC of Alzheimer’s disease: ADL and improving day-to-day functioning of patients. International Psychogeriatrics, 14(S1), 7–26. doi: 10.1017/S1041610203008640.CrossRefPubMedGoogle Scholar
  7. 7.
    Andersen, C. K., Wittrup-Jensen, K. U., Lolk, A., Andersen, K., & Kragh-Sørensen, P. (2004). Ability to perform activities of daily living is the main factor affecting quality of life in patients with dementia. Health and Quality of Life Outcomes, 2(1), 1–7. doi: 10.1186/1477-7525-2-52.CrossRefGoogle Scholar
  8. 8.
    Stineman, M. G., & Granger, C. V. (1997). A modular case-mix classification system for medical rehabilitation illustrated. Health Care Financing Review, 19(1), 87–103.PubMedPubMedCentralGoogle Scholar
  9. 9.
    Carter, G. M., Relies, D. A., Ridgeway, G. K., & Rimes, C. M. (2003). Measuring function for medicare inpatient rehabilitation payment. Health Care Financing Review, 24(3), 25–44.PubMedPubMedCentralGoogle Scholar
  10. 10.
    Mackenbach, J. P., Simon, J. G., Looman, C. W., & Joung, I. M. (2002). Self-assessed health and mortality: Could psychosocial factors explain the association? International Journal of Epidemiology, 31(6), 1162–1168. doi: 10.1093/ije/31.6.1162.CrossRefPubMedGoogle Scholar
  11. 11.
    Buz, J., & Cortés-Rodríguez, M. (2016). Measurement of the severity of disability in community-dwelling adults and older adults: Interval-level measures for accurate comparisons in large survey data sets. British Medical Journal Open, 6(9), e011842. doi: 10.1136/bmjopen-2016-011842.Google Scholar
  12. 12.
    Cook, C., & Pietrobon, R. (2006). Item analysis of the NHANES ADL instrument in a sample of patients reporting frequent severe headaches. Physiotherapy Research International, 11(2), 84–92. doi: 10.1002/pri.324.CrossRefPubMedGoogle Scholar
  13. 13.
    Cook, C. E., Richardson, J. K., Pietrobon, R., Braga, L., Silva, H. M., & Turner, D. (2006). Validation of the NHANES ADL scale in a sample of patients with report of cervical pain: Factor analysis, item response theory analysis, and line item validity. Disability and Rehabilitation, 28(15), 929–935. doi: 10.1080/09638280500404263.CrossRefPubMedGoogle Scholar
  14. 14.
    Cieza, A., Oberhauser, C., Bickenbach, J., Jones, R. N., Üstün, T. B., Kostanjsek, N., et al. (2015). The English are healthier than the Americans: Really? International Journal of Epidemiology, 44(1), 229–238. doi: 10.1093/ije/dyu182.CrossRefPubMedGoogle Scholar
  15. 15.
    Curtin, L., Mohadjer, L., Dohrmann, S., Montaquila, J., Kruszan-Moran, D., Mirel, L., et al. (2012). The national health and nutrition examination survey: Sample design, 1999–2006. Vital and health statistics. Series 2, Data Evaluation and Methods Research, 155, 1–39.Google Scholar
  16. 16.
    Won, C. W., Rho, Y. G., Kim, S. Y., Cho, B. R., & Lee, Y. S. (2002). The validity and reliability of Korean Activities of Daily Living (K-ADL) scale. Journal of the Korean Geriatrics Society, 6(2), 98–106.Google Scholar
  17. 17.
    Won, C. W., Rho, Y. G., SunWoo, D., & Lee, Y. S. (2002). The validity and reliability of Korean Instrumental Activities of Daily Living (K-IADL) scale. Journal of the Korean Geriatrics Society, 6(4), 273–280.Google Scholar
  18. 18.
    Shin, S.-M., & Chun, J.-S. (2011). A study on donning and doffing independence of the person with disabilities on upper-limbs. Research Journal of the Costume Culture, 19(1), 42–53.Google Scholar
  19. 19.
    Hambleton, R. K. (1991). Fundamentals of item response theory (Measurement methods for the social sciences series). Newbury Park: Sage Publications.Google Scholar
  20. 20.
    Wright, B. D., & Stone, M. H. (1979). Best test design. Chicago: Mesa Press.Google Scholar
  21. 21.
    Bond, T. G., & Fox, C. M. (2001). Applying the Rasch model fundamental measurement in the human sciences. Mahwah: Lawrence Erlbaum Associates Inc.Google Scholar
  22. 22.
    De Ayala, R. J. (2013). The theory and practice of item response theory. New York: Guilford.Google Scholar
  23. 23.
    Linacre, J. M. (1998). Detecting multidimensionality: which residual data-type works best? Journal of Outcome Measurement, 2(3), 266–283.PubMedGoogle Scholar
  24. 24.
    Velozo, C. A., Kielhofner, G., & Lai, J. S. (1999). The use of Rasch analysis to produce scale-free measurement of functional ability. American Journal Occupational Therapy, 53(1), 83–90.CrossRefGoogle Scholar
  25. 25.
    Muthén, L. K., & Muthén, B. O. (2015). Mplus. Los Angeles: Muthén & Muthén.Google Scholar
  26. 26.
    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. doi: 10.1097/01.mlr.0000250483.85507.04.CrossRefPubMedGoogle Scholar
  27. 27.
    Tabchnick, B. G., & Fidell, L. S. (2006). Using multivariate statistics. Boston: Allyin & Bacon.Google Scholar
  28. 28.
    Hays, R. D., Morales, L. S., & Reise, S. P. (2000). Item response theory and health outcomes measurement in the 21st century. Medical Care, 38(9 Suppl), II28.PubMedPubMedCentralGoogle Scholar
  29. 29.
    De Ayala, R. J. (2009). The theory and practice of item response theory. Methodology in the social sciences. New York: Guilford Press.Google Scholar
  30. 30.
    Orlando, M., & Thissen, D. (2003). Further investigation of the performance of S − X 2: An item fit index for use with dichotomous item response theory models. Applied Psychological Measurement, 27(4), 289–298. doi: 10.1177/0146621603027004004.CrossRefGoogle Scholar
  31. 31.
    Dodd, B. G., Koch, W. R., & De Ayala, R. J. (1989). Operational characteristics of adaptive testing procedures using the graded response model. Applied Psychological Measurement, 13(2), 129–143.CrossRefGoogle Scholar
  32. 32.
    Adams, R. J. (1987). Adaptive testing, information, and the partial credit model. Melbourne: University of Melbourne.Google Scholar
  33. 33.
    Biddle, R. E. (1993). How to set cutoff scores for knowledge tests used in promotion, training, certification, and licensing. Public Personnel Management, 22, 63–79.CrossRefGoogle Scholar
  34. 34.
    McHorney, C. A., & Tarlov, A. R. (1995). Individual-patient monitoring in clinical practice: are available health status surveys adequate? Quality of Life Research, 4(4), 293–307.CrossRefPubMedGoogle Scholar
  35. 35.
    Holland, P. W., & Wainer, H. (2012). Differential item functioning. Princeton: Routledge.Google Scholar
  36. 36.
    Choi, S. W., Gibbons, L. E., & Crane, P. K. (2016). Logistic regression differential item functioning using IRT. Retrieved from https://cran.r-project.org/web/packages/lordif/lordif.pdf. Accessed 2 July 2017.
  37. 37.
    Choi, S. W., Gibbons, L. E., & Crane, P. K. (2011). lordif: An R package for detecting differential item functioning using iterative hybrid ordinal logistic regression/item response theory and monte carlo simulations. Journal of Statistical Software, 39(8), 1–30.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Cai, L., Thissen, D., & du Toit, S. H. C. (2011). IRTPRO for Windows (21st ed.). Lincolnwood: Scientific Software International.Google Scholar
  39. 39.
    SAS Institute Inc. (2015). SAS for Windows (94th ed.). Cary: SAS Institute.Google Scholar
  40. 40.
    Granger, C. V., Hamilton, B. B., Keith, R. A., Zielezny, M., & Sherwin, F. S. (1986). Advances in functional assessment for medical rehabilitation. Topics in Geriatric Rehabilitation, 1(3), 59–74.CrossRefGoogle Scholar
  41. 41.
    Velozo, C. A., Byers, K. L., Wang, Y. C., & Joseph, B. R. (2007). Translating measures across the continuum of care: using Rasch analysis to create a crosswalk between the Functional Independence Measure and the Minimum Data Set. Journal of Rehabilitation Research and Development, 44(3), 467–478.CrossRefPubMedGoogle Scholar
  42. 42.
    Granger, C. V., Hamilton, B. B., Linacre, J. M., Heinemann, A. W., & Wright, B. D. (1993). Performance profiles of the functional independence measure. American Journal of Physical Medicine and Rehabilitation, 72(2), 84–89.CrossRefPubMedGoogle Scholar
  43. 43.
    Embretson, S. E., & Reise, S. P. (2013). Item response theory. London: Psychology Press.Google Scholar
  44. 44.
    Austin, P. C., Escobar, M., & Kopec, J. A. (2000). The use of the Tobit model for analyzing measures of health status. Quality of Life Research, 9(8), 901–910.CrossRefPubMedGoogle Scholar
  45. 45.
    Spector, W. D., & Fleishman, J. A. (1998). Combining activities of daily living with instrumental activities of daily living to measure functional disability. Journals of Gerontology, 53(1), S46–S57.CrossRefPubMedGoogle Scholar
  46. 46.
    Cabrero-Garcia, J., & Lopez-Pina, J. A. (2008). Aggregated measures of functional disability in a nationally representative sample of disabled people: analysis of dimensionality according to gender and severity of disability. Quality of Life Research, 17(3), 425–436. doi: 10.1007/s11136-008-9313-x.CrossRefPubMedGoogle Scholar
  47. 47.
    Linacre, J. (2010). Two perspectives on the application of Rasch models. European Journal of Physical and Rehabilitation Medicine, 46(2), 309–310.PubMedGoogle Scholar
  48. 48.
    Merbitz, C., Morris, J., & Grip, J. C. (1989). Ordinal scales and foundations of misinference. American Journal of Physical Medicine and Rehabilitation, 70(4), 308–312.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ickpyo Hong
    • 1
  • Mi Jung Lee
    • 2
  • Moon Young Kim
    • 3
  • Hae Yean Park
    • 3
    Email author
  1. 1.Division of Rehabilitation SciencesUniversity of Texas Medical BranchGalvestonUSA
  2. 2.Department of Occupational TherapyUniversity of FloridaGainesvilleUSA
  3. 3.Department of Occupational Therapy, College of Health ScienceYonsei UniversityWonju-siSouth Korea

Personalised recommendations