title: Derivation and Evaluation of a Risk-Scoring Tool to Predict Participant Attrition in a Lifestyle Intervention Project.
abstract: Participant attrition in clinical trials and community-based interventions is a serious, common, and costly problem. In order to develop a simple predictive scoring system that can quantify the risk of participant attrition in a lifestyle intervention project, we analyzed data from the Special Diabetes Program for Indians Diabetes Prevention Program (SDPI-DP), an evidence-based lifestyle intervention to prevent diabetes in 36 American Indian and Alaska Native communities. SDPI-DP participants were randomly divided into a derivation cohort (n = 1600) and a validation cohort (n = 801). Logistic regressions were used to develop a scoring system from the derivation cohort. The discriminatory power and calibration properties of the system were assessed using the validation cohort. Seven independent factors predicted program attrition: gender, age, household income, comorbidity, chronic pain, site's user population size, and average age of site staff. Six factors predicted long-term attrition: gender, age, marital status, chronic pain, site's user population size, and average age of site staff. Each model exhibited moderate to fair discriminatory power (C statistic in the validation set: 0.70 for program attrition, and 0.66 for long-term attrition) and excellent calibration. The resulting scoring system offers a low-technology approach to identify participants at elevated risk for attrition in future similar behavioral modification intervention projects, which may inform appropriate allocation of retention resources. This approach also serves as a model for other efforts to prevent participant attrition.
authors.affiliation: Department of Epidemiology, School of Medicine, University of California Irvine, 205B Irvine Hall, Irvine, CA, 92697-7550, USA. lhjiang@uci.edu.|Department of Epidemiology and Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX, USA. lhjiang@uci.edu.
authors.forename: Luohua
authors.initials: L
authors.lastname: Jiang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Rural Public Health, Texas A&M Health Science Center, College Station, TX, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Yang
authors.affiliation: Centers for American Indian and Alaska Native Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
authors.forename: Haixiao
authors.initials: H
authors.lastname: Huang
authors.affiliation: Centers for American Indian and Alaska Native Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
authors.forename: Ann
authors.initials: A
authors.lastname: Johnson
authors.affiliation: Department of Psychology, University of Colorado Denver, Denver, CO, USA.
authors.forename: Edward J
authors.initials: EJ
authors.lastname: Dill
authors.affiliation: Centers for American Indian and Alaska Native Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
authors.forename: Janette
authors.initials: J
authors.lastname: Beals
authors.affiliation: Centers for American Indian and Alaska Native Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
authors.forename: Spero M
authors.initials: SM
authors.lastname: Manson
authors.affiliation: Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, USA.
authors.forename: Yvette
authors.initials: Y
authors.lastname: Roubideaux
mesh_terms: D000293:Adolescent; D000328:Adult; D005260:Female; D006801:Humans; D008019:Life Style; D008297:Male; D008875:Middle Aged; D011584:Psychology; D018570:Risk Assessment; D055815:Young Adult
keywords: Lifestyle modifications; Multi-site study; Retention; Risk prediction models; Weight loss program