Quality of Life Research

, Volume 28, Issue 4, pp 969–977 | Cite as

The weight-specific adolescent instrument for economic evaluation (WAItE): psychometric evaluation using a Rasch model approach

  • Yemi OluboyedeEmail author
  • Adam B. Smith
  • Andrew Hill
  • Claire Hulme



The Weight-specific Adolescent Instrument for Economic evaluation (WAItE) is a 7-item condition-specific tool assessing the impact of weight status on seven dimensions of quality of life. The content of the WAItE was developed with both treatment-seeking and non-treatment-seeking adolescents aged 11–18 years. The aim of this study was to assess the psychometric properties of the WAItE in adolescent and adult populations.


Treatment-seeking adolescents with obesity (females n = 155; males n = 123; mean age = 13.3; 13.1 years, respectively) completed the WAItE twice. An adult general population sample completed the WAItE via an online survey (females n = 236; males n = 231; mean age = 41.2; 44.3 years, respectively). The Partial Credit Model was applied to the data and item fit evaluated against published criteria.


The WAItE had a unidimensional structure both for adolescents and adults. There was no item misfit observed for either participant samples and no differential item functioning (DIF) was present by age or gender for the adolescents. Some DIF was observed across age groups for the adult sample. For the adolescent sample, stable item locations were observed over time.


The aim of the WAItE is to assess the impact of weight status on the lives of adolescents in cost-effectiveness evaluation of weight management programmes. The results of this study demonstrated that the WAItE has reliable psychometric properties. The instrument may therefore be used to aid informed decision around the identification of cost-effective weight management programmes in both adolescent and adult populations.


Obesity Quality of life Economic evaluation Adolescents Condition-specific measure Rasch analysis Adults 



We would like to acknowledge the advice and support of the following individuals: Cathy Brennan, Jenny Hewison, Donna Lamping, Christopher McCabe, David Meads, Jennifer Roberts, Katherine Stevens, Alan Tennant. We would like to acknowledge Aki Tsuchiya (PhD supervisor) for her guidance and support throughout the fellowship project. Finally, we would like to thank all the participants who took part in the research and the parents and staff who supported this research.


The work presented here was part of a National Institute for Health Research (NIHR) funded fellowship project awarded to the first author (DFR/2009/02/101). This paper presents independent research funded by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

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 and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Ethical approval was provided by the University of Leeds, School of Medicine Research Ethics Committee for both the adolescents and adult studies (Ref: HSLTLM/11/049).

Informed consent

Both of the weight management services followed their own procedures for obtaining consent. All parents and carers of adolescents provided written or oral consent for adolescents to complete the WAItE. If parents did not object, then written or oral assent (under 16 years)/consent (16 years plus) for all participating adolescents was obtained. Anonymised datasets were provided directly from weight management organisations who adhered to strict security protocols. Adult participants who were recruited from a consumer panel provided consent to the market research company to be approached and complete web surveys.


  1. 1.
    Griffiths, L., Parsons, T., & Hill, A. (2010). Self-esteem and quality of life in obese children and adolescents: A systematic review. International Journal of Paediatric Obesity, 5(4), 282–304.CrossRefGoogle Scholar
  2. 2.
    Tsiros, M., Olds, T., Buckley, J., Grimshaw, P., Brennan, L., Walkley, J., et al. (2009). Health-related quality of life in obese children and adolescents. International Journal of Obesity, 33, 387–400.CrossRefGoogle Scholar
  3. 3.
    National-Institute-for-Clinical-Excellence. (April 2013). Guide to the methods of technology appraisal.Google Scholar
  4. 4.
    Ravens-Sieberer, U., Redegeld, M., & Bullinger, M. (2001). Quality of life after in-patient rehabilitation in children with obesity. [Research Support. Non-U.S. Gov’t]. International Journal of Obesity & Related Metabolic Disorders: Journal of the International Association for the Study of Obesity, 25(Suppl 1), S63–S65.CrossRefGoogle Scholar
  5. 5.
    Kolotkin, R., Zeller, M., Modi, A., Samsa, G., Quinlan, N., Yanovski, J., et al. (2006). Assessing weight-related quality of life in adolescents. [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t Validation Studies]. Obesity, 14(3), 448–457.CrossRefGoogle Scholar
  6. 6.
    Moorehead, M., Ardelt-Gattinger, E., Lechner, H., & Oria, H. (2003). The validation of the Moorehead-Ardelt Quality of Life Questionnaire II. Obesity Surgery, 13(5), 684–692.CrossRefGoogle Scholar
  7. 7.
    Zeller, M., & Modi, A. (2009). Development and initial validation of an obesity-specific quality-of-life measure for children: sizing me up. Obesity, 17(6), 1171–1177.Google Scholar
  8. 8.
    Morales, L., Edwards, T., Flores, Y., Barr, L., & Patrick, D. (2011). Measurement properties of a multicultural weight-specific quality-of-life instrument for children and adolescents. [Validation Studies]. Quality of Life Research, 20(2), 215–224.CrossRefGoogle Scholar
  9. 9.
    Brazier, J., Ratcliffe, J., Salomon, J., & Tsuchiya, A. (2007). Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press.Google Scholar
  10. 10.
    Oluboyede, Y., Hulme, C., & Hill, A. (2017). Development and refinement of the WAItE: A new obesity-specific quality of life measure for adolescents. Quality of Life Research, 26(8), 2025–2039. Scholar
  11. 11.
    Oluboyede, O. (2013). Quality of life assessment in adolescent obesity: Development of a new instrument for economic evaluation. Leeds: University of Leeds.Google Scholar
  12. 12.
    Prieto, L., Alonso, J., & Lamarca, R. (2003). Classical test theory versus Rasch analysis for quality of life questionnaire reduction. Health and Quality of Life Outcomes, 1(1), 27. Scholar
  13. 13.
    More-Life. Retrieved September 2017.
  14. 14.
  15. 15.
    Cole, T., Freeman, J., & Preece, M. (1995). Body mass index reference curves for the UK, 1990. Archives of Disease in Childhood, 73, 25–29.CrossRefGoogle Scholar
  16. 16.
    Gately, P., Cooke, C., Barth, J., Bewick, B., Radley, D., & Hill, A. (2005). Children’s residential weight-loss programs can work: A prospective cohort study of short-term outcomes for overweight and obese children. Pediatrics, 116(1), 73–77.CrossRefGoogle Scholar
  17. 17.
    Linacre, J. (2014). A user’s guide to Winsteps.Google Scholar
  18. 18.
    Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Chicago: University of Chicago Press. Reprinted 1980.Google Scholar
  19. 19.
    Masters, G. N. (1982). A rasch model for partial credit scoring. Psychometrika, 47(2), 149–174. Scholar
  20. 20.
    Smith, A. B., Rush, R., Fallowfield, L. J., Velikova, G., & Sharpe, M. (2008). Rasch fit statistics and sample size considerations for polytomous data. BMC Medical Research Methodology, 8, 33–33. Scholar
  21. 21.
    Raîche, G. (2005). Critical eigenvalue sizes (variances) in standardized residual principal components analysis. Rasch Measurement Transactions, 19:1, 1012.Google Scholar
  22. 22.
    Lord, F. (1980). Applications of item response theory to practical testing problems. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  23. 23.
    Lai, J.-S., Teresi, J., & Gershon, R. (2005). Procedures for the analysis of differential item functioning (DIF) for small sample sizes. Evaluation & the Health Professions, 28(3), 283–294. Scholar
  24. 24.
    Ratcliffe, J., Stevens, K., Flynn, T., Brazier, J., & Sawyer, M. (2012). An assessment of the construct validity of the CHU9D in the Australian adolescent general population. Quality of Life Research, 21(4), 717–725. Scholar
  25. 25.
    Ravens-Sieberer, U., Gosch, A., Rajmil, L., Erhart, M., Bruil, J., Power, M., et al. (2008). The KIDSCREEN-52 quality of life measure for children and adolescents: psychometric results from a cross-cultural survey in 13 european countries. Value in Health, 11(4), 645–658. Scholar
  26. 26.
    Amin, L., Rosenbaum, P., Barr, R., Sung, L., Klaassen, R. J., Dix, D. B., et al. (2012). Rasch analysis of the PedsQL: An increased understanding of the properties of a rating scale. Journal of Clinical Epidemiology, 65(10), 1117–1123. Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yemi Oluboyede
    • 1
    Email author
  • Adam B. Smith
    • 2
  • Andrew Hill
    • 3
  • Claire Hulme
    • 3
  1. 1.Institute for Health and SocietyNewcastle UniversityNewcastle Upon TyneUK
  2. 2.York Health Economics Consortium LtdUniversity of YorkYorkUK
  3. 3.Leeds Institute of Health SciencesUniversity of LeedsLeedsUK

Personalised recommendations