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Supplement: The Examination of Two Short Dietary Assessment Methods, within the Context of Multiple Behavioral Change Interventions in Adult Populations

Use of Signal Detection Methodology to Identify Subgroups of Dietary Supplement Use in Diverse Populations^1,2

³ Department of Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, MI 48109-2029; ⁴ Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD 20892-7344; ⁵ Program in Public Health Nutrition, Department of Nutrition, and Department of Society, Human Development, and Health, Harvard School of Public Health, Boston, MA 02115; ⁶ Department of Clinical Nutrition, Rush University Medical Center, Chicago, IL 60612; ⁷ Hunt Consulting Associates, Consultant to Harvard School of Public Health, Program in Public Health Nutrition, Lyme, NH 03768; ⁸ Cancer Prevention and Control Program, Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208; ⁹ Department of Nutrition and Food Sciences, University of Rhode Island, Kingston, RI 02881; ¹⁰ Illinois Institute of Technology, Institute of Psychology, Chicago, IL 60616; ¹¹ Departments of Medicine, Clinical and Social Sciences in Psychology, Psychiatry, University of Rochester, Rochester, NY 14642; ¹² Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD 20892-7344; ¹³ Oregon Research Institute, Eugene, OR 97403; ¹⁴ Division of Health Promotion and Sports Medicine, Oregon Health & Science University, Portland, OR 97239; and ¹⁵ Office of Dietary Supplements, NlH, Bethesda, MD 20892-7517

^* To whom correspondence should be addressed. E-mail: reda{at}umich.edu.

Despite widespread use of dietary supplements, little is known about correlates and determinants of their use. Using a diverse sample from 7 interventions participating in the Behavior Change Consortium (n = 2539), signal detection methodology (SDM) demonstrated a method for identifying subgroups with varying supplement use. An SDM model was explored with an exploratory half of the entire sample (n = 1268) and used 5 variables to predict dietary supplement use: cigarette smoking, fruit and vegetable intake, dietary fat consumption, BMI, and stage of change for physical activity. A comparison of rates of supplement use between the exploratory model groups and comparably identified groups in the reserved, confirmatory sample (n = 1271) indicates that these analyses may be generalizable. Significant indicators of any supplement use included smoking status, percentage of energy from fat, and fruit and vegetable consumption. Although higher supplement use was associated with healthy behaviors overall, many of the identified groups exhibited mixed combinations of healthy and unhealthy behaviors. The results of this study suggest that patterns of dietary supplement use are complex and support the use of SDM to identify possible population characteristics for targeted and tailored health communication interventions.

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