Skip to main content
Log in

Development of a physical function outcome measure to harmonize comparisons between three Asian adult populations

  • Published:
Quality of Life Research Aims and scope Submit manuscript

Abstract

Purpose

The purpose of this study was to use modern measurement techniques and create a precise functional status metric for Asian adults.

Methods

The study subjects included Asian American adults from the 2012 Health and Retirement Study (n = 211), Chinese adults in the China Health and Retirement Longitudinal Study (n = 13,649), and Korean adults in the Korean Longitudinal Study of Aging (n = 7,486). The Rasch common-item equating method with nine self-care and mobility items from the three databases were used to create a physical function measure across the three Asian adult populations.

Results

The created physical function measure included 23 self-care and mobility tasks and demonstrated acceptable psychometric properties (unidimensional, local independence, no misfit, no differential item functioning). A significant group difference in the estimated physical function across the three Asian adult populations (\({F}_{\mathrm{2,21242}}\) = 445.21, p < 0.0001) was identified. The American Asian adults (5.16 logits) had better physical function compared to the Chinese (4.15 logits) and Korean adults (3.32 logits).

Conclusion

Since the outcome measure was calibrated with the population-representative Asian samples, this derived physical function measure can be used for cross-national comparisons between the three countries. Using this precise functional status metric can help to identify factors that influence health outcomes in other Asian countries (China and Korea). This has the potential to generate numerous benefits, such as international disability monitoring and health-related policy development, improved shared decision making, and international syntheses of research findings.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

The Health and Retirement Study (HRS), China Health and Retirement Longitudinal Study (CHARLS), and Korean Longitudinal Study of Aging are public use and available at https://hrs.isr.umich.edu, http://charls.pku.edu.cn/index/en.html, and https://survey.keis.or.kr/eng/klosa/klosa01.jsp.

References

  1. Sonnega, A., Faul, J. D., Ofstedal, M. B., Langa, K. M., Phillips, J. W., & Weir, D. R. (2014). Cohort profile: The health and retirement study (HRS). International Journal of Epidemiology, 43(2), 576–585. https://doi.org/10.1093/ije/dyu067

    Article  PubMed  PubMed Central  Google Scholar 

  2. Wong, R., Michaels-Obregon, A., & Palloni, A. (2015). Cohort profile: The Mexican health and aging study (MHAS). International Journal of Epidemiology, 46(2), e2. https://doi.org/10.1093/ije/dyu263

    Article  PubMed Central  Google Scholar 

  3. Minicuci, N., Naidoo, N., Chatterji, S., & Kowal, P. (2016). Data resource profile: Cross-national and cross-study sociodemographic and health-related harmonized domains from SAGE plus ELSA, HRS and SHARE (SAGE+, Wave 1). International Journal of Epidemiology, 45(5), 1403–1403j. https://doi.org/10.1093/ije/dyw181

    Article  PubMed  PubMed Central  Google Scholar 

  4. Börsch-Supan, A., Brandt, M., Hunkler, C., Kneip, T., Korbmacher, J., Malter, F., et al. (2013). Data resource profile: The survey of health, ageing and retirement in Europe (SHARE). International Journal of Epidemiology, 43(4), 992–1001.

    Article  Google Scholar 

  5. Park, J. H., Lim, S., Lim, J., Kim, K., Han, M., Yoon, I. Y., et al. (2007). An overview of the Korean longitudinal study on health and aging. Psychiatry Investigation, 4(2), 84.

    Google Scholar 

  6. Zhao, Y., Hu, Y., Smith, J. P., Strauss, J., & Yang, G. (2012). Cohort profile: The China health and retirement longitudinal study (CHARLS). International Journal of Epidemiology, 43(1), 61–68.

    Article  Google Scholar 

  7. 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. https://doi.org/10.1136/bmjopen-2016-011842

    Article  Google Scholar 

  8. 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.

    Article  Google Scholar 

  9. Díaz-Venegas, C., Reistetter, T. A., & Wong, R. (2016). Differences in the progression of disability: A US–Mexico comparison. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 73(5), 913–922. https://doi.org/10.1093/geronb/gbw082

    Article  PubMed Central  Google Scholar 

  10. Hoeffel, E. M., Rastogi, S., Kim, M. O., & Hasan, S. (2012). The Asian population: 2010: US department of commerce Economics and Statistics Administration. Marylands: US Census Bureau.

    Google Scholar 

  11. Department of Homeland Security. (2014). 2013 Yearbook of immigration statistics. Washington: Homeland Security.

    Google Scholar 

  12. Passel, J., & Rohal, M. (2015). Modern immigration wave brings 59 million to US, Driving population growth and change through 2065. Pew Research Center. Retrieved from https://www.pewresearch.org/hispanic/2015/09/28/modern-immigration-wave-brings-59-million-to-u-s-driving-population-growth-and-change-through-2065/#:~:text=Immigration%20since%201965%20has%20swelled,record%2045%20million%20in%202015.&text=This%20fast%2Dgrowing%20immigrant%20population,projected%20record%2018%25%20in%202065

  13. Keppel, K. G. (2007). Ten largest racial and ethnic health disparities in the United States based on healthy people 2010 objectives. American Journal of Epidemiology, 166(1), 97–103. https://doi.org/10.1093/aje/kwm044

    Article  PubMed  Google Scholar 

  14. Cruz-Flores, S., Rabinstein, A., Biller, J., Elkind, M. S. V., Griffith, P., Gorelick, P. B., et al. (2011). Racial-ethnic disparities in stroke care: The American experience A statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 42(7), 2091–2116. https://doi.org/10.1161/STR.0b013e3182213e24

    Article  PubMed  Google Scholar 

  15. Nakagawa, T., Cho, J., & Yeung, D. (2020). Successful aging in East Asia: Comparison among China, Korea, and Japan. Journals of Gerontology: Series B. https://doi.org/10.1093/geronb/gbaa042.

  16. French, D. J., Browning, C., Kendig, H., Luszcz, M. A., Saito, Y., Sargent-Cox, K., et al. (2012). A simple measure with complex determinants: Investigation of the correlates of self-rated health in older men and women from three continents. BMC Public Health, 12, 649. https://doi.org/10.1186/1471-2458-12-649

    Article  PubMed  PubMed Central  Google Scholar 

  17. Lee, S. Y., Kim, S. J., Yoo, K. B., Lee, S. G., & Park, E. C. (2016). Gender gap in self-rated health in South Korea compared with the United States. International Journal of Clinical and Health Psychology, 16(1), 11–20. https://doi.org/10.1016/j.ijchp.2015.08.004

    Article  PubMed  Google Scholar 

  18. Halford, C., Wallman, T., Welin, L., Rosengren, A., Bardel, A., Johansson, S., et al. (2012). Effects of self-rated health on sick leave, disability pension, hospital admissions and mortality. A population-based longitudinal study of nearly 15,000 observations among Swedish women and men. BMC Public Health, 12, 1103. https://doi.org/10.1186/1471-2458-12-1103

    Article  PubMed  PubMed Central  Google Scholar 

  19. Mair, C. A. (2013). Family ties and health cross-nationally: The contextualizing role of familistic culture and public pension spending in Europe. Journals of Gerontology Series B Psychological Sciences and Social Sciences, 68(6), 984–996. https://doi.org/10.1093/geronb/gbt085

    Article  Google Scholar 

  20. Wright, B. D., & Stone, M. H. (1979). Best test design. Chicago: Mesa Press.

    Google Scholar 

  21. Wright, B. D., & Masters, G. N. (1982). Rating scale analysis (1st ed.). Oxford: Pluribus Press.

    Google Scholar 

  22. 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.

    Article  Google Scholar 

  23. Organisation for Economic Co-operation and Development. (2015). Health at a glance 2015: OECD indicators. Paris: OECD Publishing.

    Google Scholar 

  24. Hong, I., Simpson, A. N., Simpson, K. N., Brotherton, S. S., & Velozo, C. A. (2018). Comparing disability levels for community-dwelling adults in the United States and the Republic of Korea using the Rasch Model. Journal of Applied Measurement, 19(2), 114–128.

    PubMed  Google Scholar 

  25. Wright, B. D., & Linacre, J. M. (1989). Observations are always ordinal; measurements, however, must be interval. Archives of Physical Medicine and Rehabilitation, 70(12), 857–860.

    CAS  PubMed  Google Scholar 

  26. Jette, A. M. (1994). Physical disablement concepts for physical therapy research and practice. Physical Therapy, 74(5), 380–386.

    Article  CAS  Google Scholar 

  27. Hong, I., Reistetter, T. A., Díaz-Venegas, C., Michaels-Obregon, A., & Wong, R. (2018). Cross-national health comparisons using the Rasch model: Findings from the 2012 US Health and Retirement Study and the 2012 Mexican Health and Aging Study. Quality of Life Research, 27(9), 2431–2441. https://doi.org/10.1007/s11136-018-1878-4

    Article  PubMed  PubMed Central  Google Scholar 

  28. Fielder, R. (1998). Uniform data system for medical rehabilitation. American Journal of Physical Medicine & Rehabilitation, 77, 444–450.

    Article  Google Scholar 

  29. Jacobzone, S., Cambois, E., Chaplain, E., & Robine, J. M. (1999). The health of older persons in OECD countries. OECD Labour Market and Social Policy Occasional Papers, No. 37, OECD Publishing, Paris, https://doi.org/10.1787/066187831020

  30. Díaz-Venegas, C., Reistetter, T. A., Wang, C.-Y., & Wong, R. (2016). The progression of disability among older adults in Mexico. Disability and Rehabilitation, 38(20), 2016–2027.

    Article  Google Scholar 

  31. Hong, I., Pryor, L., Wong, R., Ottenbacher, K. J., & Reistetter, T. A. (2020). Comparisons of the association of family and social factors with functional limitations across the United States, Mexico, and South Korea: Findings from the HRS family of surveys. Journal of Aging and Health, 32(9), 1042–1051. https://doi.org/10.1177/0898264319878549

    Article  PubMed  Google Scholar 

  32. Christensen, K. B., Kreiner, S., & Mesbah, M. (2013). Rasch models in health. New Jersey: Wiley Online Library.

    Google Scholar 

  33. Müller, M. (2020). Item fit statistics for Rasch analysis: Can we trust them? Journal of Statistical Distributions and Applications, 7(1), 1–12.

    Article  Google Scholar 

  34. 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), S22-31. https://doi.org/10.1097/01.mlr.0000250483.85507.04

    Article  PubMed  Google Scholar 

  35. Linda K. Muthén, & Bengt O. Muthén (2015). Mplus. (7.4 ed.,). Los Angeles, CA.

  36. Fischer, G. H. (1995). Rasch models: Foundations, recent developments, and applications. New York: Springer-Verlag.

    Book  Google Scholar 

  37. Christensen, K. B., Makransky, G., & Horton, M. (2016). Critical values for Yen’s Q3: Identification of local dependence in the Rasch model using residual correlations. Applied Psychological Measurement, 41(3), 178–194. https://doi.org/10.1177/0146621616677520

    Article  PubMed  PubMed Central  Google Scholar 

  38. Linacre, J. M. (2002). What do infit and outfit, mean-square and standardized mean? Rasch Measurement Transactions, 16(2), 878.

    Google Scholar 

  39. Wright, B. D., Linacre, J. M., Gustafson, J., & Martin-Lof, P. (1994). Reasonable mean-square fit values. Rasch Measurement Transactions, 8(3), 370.

    Google Scholar 

  40. Penfield, R. D. (2001). Assessing differential item functioning among multiple groups: A comparison of three Mantel–Haenszel procedures. Applied Measurement in Education, 14(3), 235–259.

    Article  Google Scholar 

  41. Linacre, J. M. (2017). A user’s guide to WINSTEPS® 3.93.2

  42. SAS Institute Inc. (2015). SAS for Windows (9.4). Cary, NC: SAS Institute.

    Google Scholar 

  43. Shih, R. A., Jinkook, L., & Lopamudra, D. (2012). Harmonization of cross-national studies of aging to the health and retirement study: Cognition. Santa Monica, CA: RAND Corporation.

    Book  Google Scholar 

  44. Hong, I., Kim, Y. J., Sonnenfeld, M. L., Grattan, E., & Reistetter, T. A. (2018). Disability measurement for Korean community-dwelling adults with stroke: Item-level psychometric analysis of the Korean Longitudinal Study of Ageing. Annals of Rehabilitation Medicine, 42(2), 336–345. https://doi.org/10.5535/arm.2018.42.2.336

    Article  PubMed  PubMed Central  Google Scholar 

  45. Hong, I., Yoo, E. Y., Kazley, A. S., Lee, D., Li, C. Y., & Reistetter, T. A. (2018). Development and validation of the activities of daily living short form for community-dwelling Korean stroke survivors. Evaluation and Health Professions, 41(1), 44–66. https://doi.org/10.1177/0163278717695863

    Article  Google Scholar 

Download references

Acknowledgements

We thank for Kenneth Ottenbacher, PhD, OTR (University of Texas Medical Branch) and Rebeca Wong, PhD (University of Texas Medical Branch) and Timothy A. Restarter, PhD, OTR (University of Texas Health Science Center at San Antonio) for serving as mentors on this study. Sarah Toombs Smith, PhD, ELS (University of Texas Medical Branch) provided assistance in proofreading and editing the manuscript. John Michael Linacre, PhD (Research Director, Winsteps.com) provided technical supports. The authors would like to thank other members of the research team for their efforts, including Ms. Suyeong Bae and Mr. Sanghun Nam.

Funding

This research was supported in part by Grant # K12 HD055929 from the National Institutes of Health (NIH) and Grant # P30-AG024832 from the University of Texas Medical Branch (UTMB) Claude D. Pepper Older American Independence Centers (OAICS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH nor UTMB Claude D. Pepper OAICS. This research was also supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE) of the Republic of Korea and National Research Foundation of Korea (NRF) (Big Data Specialized Education and Research Team for Cognitive Health and Social Integration of Community-Dwelling Older Adults). The Health and Retirement Study (HRS) is sponsored by the National Institute on Aging (NIA U01AG009740) and the Social Security Administration.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ickpyo Hong.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hong, I., Hreha, K.P., Hilton, C.L. et al. Development of a physical function outcome measure to harmonize comparisons between three Asian adult populations. Qual Life Res 31, 281–291 (2022). https://doi.org/10.1007/s11136-021-02909-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-021-02909-y

Keywords

Navigation