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On Running Late in Life

On Running Late in Life We read with interest the article by Chakravarty and coworkers.1 Although the concept of salutary effects of running is appealing, there are some features of this study that require the findings to be interpreted with caution. The Health Assessment Questionnaire Disability Index2 (HAQ-DI) comprises 8 categories, each of which has at least 2 component questions, totaling 20 items. There are 4 possible responses for each item, scored 0 to 3, and a category score is determined from the highest item score in that category. The sum of the 8 category scores is divided by 8, yielding a single disability index from 0 to 3. Thus, the HAQ-DI will tend to inflate when a subject has answered more than 1 item within a category with a nonzero score. Furthermore, since the categories are correlated, a person having difficulties within one category will often have difficulties in other categories. Both these properties make it likely that the HAQ-DI should exhibit nonlinear behavior. This was indeed the findings of a study on HAQ-DI in 1530 adults from the general Finnish population,3 demonstrating that the HAQ-DI increased exponentially with age older than 50 years for both men and women and at a faster rate for women. In the study by Chakravarty et al,1 the group of runners comprises 19% women, whereas the control group comprises 44% women. In addition, the control group is older than the runners, and the longer the study lasts, the greater will be the impact of age.4 The sex difference is even more striking when the subjects are categorized into “ever runners” (20% women) vs “never runners” (60% women). The linear model used by Chakravarty and coworkers1 will not handle the nonlinear properties of data correctly. There were differences in disability levels among completers and dropouts in the control group but not among runners. If, as the authors claim, running improve, health and quality of life, you would expect to see similar patterns for noncompleters among runners as among controls. The different dropout patterns and the significant differences between groups at baseline should be properly included in the statistical analysis. Also, the investigators chose to include covariates in the multivariable model if they met P < .05. This is not a good method,5 and is particularly dubious in observational studies, in which it is pivotal to include all important covariates. We acknowledge the important work of Chakravarty and coworkers1 but caution is required when extrapolating their findings to the general population. Correspondence: Dr Höglund, Competence Centre for Clinical Research, Lund University Hospital, SE-221 85 Lund, Sweden (peter.hoglund@skane.se). References 1. Chakravarty EFHubert HBLingala VBFries JF Reduced disability and mortality among aging runners: a 21-year longitudinal study [published correction appears in Arch Intern Med. 2008;168(22):1948-1949]. Arch Intern Med 2008;168 (15) 1638- 1646PubMedGoogle ScholarCrossref 2. Stanford University School of Medicine, The health assessment questionnaire. http://aramis.stanford.edu/HAQ.html. Accessed August 11, 2008 3. Krishnan ESokka THäkkinen AHubert HHannonen P Normative values for the Health Assessment Questionnaire Disability Index: benchmarking disability in the general population. Arthritis Rheum 2004;50 (3) 953- 960PubMedGoogle ScholarCrossref 4. Krishnan EHäkkinen ASokka THannonen P Impact of age and comorbidities on the criteria for remission and response in rheumatoid arthritis. Ann Rheum Dis 2005;64 (9) 1350- 1352PubMedGoogle ScholarCrossref 5. Harrell FE Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York, NY Springer2001; http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Internal Medicine American Medical Association

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References (6)

Publisher
American Medical Association
Copyright
Copyright © 2009 American Medical Association. All Rights Reserved.
ISSN
0003-9926
eISSN
1538-3679
DOI
10.1001/archinternmed.2009.35
Publisher site
See Article on Publisher Site

Abstract

We read with interest the article by Chakravarty and coworkers.1 Although the concept of salutary effects of running is appealing, there are some features of this study that require the findings to be interpreted with caution. The Health Assessment Questionnaire Disability Index2 (HAQ-DI) comprises 8 categories, each of which has at least 2 component questions, totaling 20 items. There are 4 possible responses for each item, scored 0 to 3, and a category score is determined from the highest item score in that category. The sum of the 8 category scores is divided by 8, yielding a single disability index from 0 to 3. Thus, the HAQ-DI will tend to inflate when a subject has answered more than 1 item within a category with a nonzero score. Furthermore, since the categories are correlated, a person having difficulties within one category will often have difficulties in other categories. Both these properties make it likely that the HAQ-DI should exhibit nonlinear behavior. This was indeed the findings of a study on HAQ-DI in 1530 adults from the general Finnish population,3 demonstrating that the HAQ-DI increased exponentially with age older than 50 years for both men and women and at a faster rate for women. In the study by Chakravarty et al,1 the group of runners comprises 19% women, whereas the control group comprises 44% women. In addition, the control group is older than the runners, and the longer the study lasts, the greater will be the impact of age.4 The sex difference is even more striking when the subjects are categorized into “ever runners” (20% women) vs “never runners” (60% women). The linear model used by Chakravarty and coworkers1 will not handle the nonlinear properties of data correctly. There were differences in disability levels among completers and dropouts in the control group but not among runners. If, as the authors claim, running improve, health and quality of life, you would expect to see similar patterns for noncompleters among runners as among controls. The different dropout patterns and the significant differences between groups at baseline should be properly included in the statistical analysis. Also, the investigators chose to include covariates in the multivariable model if they met P < .05. This is not a good method,5 and is particularly dubious in observational studies, in which it is pivotal to include all important covariates. We acknowledge the important work of Chakravarty and coworkers1 but caution is required when extrapolating their findings to the general population. Correspondence: Dr Höglund, Competence Centre for Clinical Research, Lund University Hospital, SE-221 85 Lund, Sweden (peter.hoglund@skane.se). References 1. Chakravarty EFHubert HBLingala VBFries JF Reduced disability and mortality among aging runners: a 21-year longitudinal study [published correction appears in Arch Intern Med. 2008;168(22):1948-1949]. Arch Intern Med 2008;168 (15) 1638- 1646PubMedGoogle ScholarCrossref 2. Stanford University School of Medicine, The health assessment questionnaire. http://aramis.stanford.edu/HAQ.html. Accessed August 11, 2008 3. Krishnan ESokka THäkkinen AHubert HHannonen P Normative values for the Health Assessment Questionnaire Disability Index: benchmarking disability in the general population. Arthritis Rheum 2004;50 (3) 953- 960PubMedGoogle ScholarCrossref 4. Krishnan EHäkkinen ASokka THannonen P Impact of age and comorbidities on the criteria for remission and response in rheumatoid arthritis. Ann Rheum Dis 2005;64 (9) 1350- 1352PubMedGoogle ScholarCrossref 5. Harrell FE Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York, NY Springer2001;

Journal

Archives of Internal MedicineAmerican Medical Association

Published: Apr 13, 2009

Keywords: hospitals, university,disability,sex characteristics,quality of life

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