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(Stroke. 2002;33:1348.)
© 2002 American Heart Association, Inc.

Original Contributions

Quality of Life Measurement After Stroke

Uses and Abuses of the SF-36

Jeremy C. Hobart, PhD; Linda S. Williams, MD; Kimberly Moran, MS Alan J. Thompson, MD

From the Neurological Outcome Measures Unit (J.C.H., A.J.T.), Institute of Neurology, Queen Square, London, United Kingdom; Roudebush Veterans Affairs Medical Centre (L.S.W.), Indianapolis, Ind; Department of Neurology (L.S.W., K.M.), Indiana University School of Medicine, Indianapolis; and Regenstrief Institute for Health Care (L.S.W.), Indianapolis.

Correspondence to Jeremy Hobart, PhD, MRCP, Consultant Neurologist, Derriford Hospital, Plymouth, Devon PL6 8DH, UK. E-mail jeremy.hobart{at}phnt.swest.nhs.uk

Background and Purpose— The Medical Outcomes Study 36-item Short-Form Health Survey (SF-36) is widely used to measure health status after stroke. However, a fundamental assumption for its valid use after stroke has not been comprehensively tested: is it legitimate to generate scores for 8 scales and 2 summary measures using the standard algorithms? We tested this assumption.

Methods— SF-36 data from 177 people after stroke were examined (71% male; mean age, 62). We tested 6 scaling criteria to determine the legitimacy of generating the 8 SF-36 scale scores using Likert’s method of summed ratings, and we tested 2 scaling criteria to determine the appropriateness of the standard SF-36 algorithms for weighting and combining scale scores to generate 2 summary measures (physical and mental).

Results— Scaling assumptions were fully satisfied for 6 of the 8 scales, but 3 of these 6 scales had notable floor and/or ceiling effects. Assumptions for generating 2 SF-36 summary measures were not satisfied.

Conclusions— In this sample, 5 of the 8 SF-36 scales had limited validity as outcome measures after stroke, and the reporting of physical and mental summary scores was not supported. Results raise questions about the use of the SF-36 in stroke, and the SF-12 that is developed from it, and highlight the importance of testing scaling assumptions when applying existing scales to new populations.

Key Words: health measurement • psychometrics • quality of life • SF-36 • stroke outcome

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