Emily Cox-Martin1, Jaejoon Song2, Wendy Demark-Wahnefried3, Elizabeth J Lyons4, Karen Basen-Engquist5. 1. Division of Medical Oncology, School of Medicine, University of Colorado Anschutz Medical Campus, 12801 E 17th Ave, Aurora, CO, 80045, USA. Emily.Cox-Martin@ucdenver.edu. 2. Department of Biostatistics, The University of Texas MD Anderson Cancer Center, P.O. Box 301402, Houston, TX, 77230-1402, USA. 3. Department of Nutrition Sciences, University of Alabama at Birmingham, 1675 University Blvd, Birmingham, AL, 35233, USA. 4. Department of Nutrition and Metabolism, The University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555-1124, USA. 5. Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Unit 1330, P.O. Box 301439, Houston, TX, 77230-1439, USA.
Abstract
PURPOSE: To utilize data from lifestyle intervention pilot studies for cancer survivors to elucidate demographic, disease-related, and health behavior factors that might predict enrollment in this type of research. Additionally, factors were differentially compared based on intervention design (i.e., individual versus couple-based). METHODS: Secondary data analysis was conducted regarding predictors of enrollment into lifestyle intervention studies, including Healthy Moves Weight Loss (individual participants, screened n = 89, enrolled n = 30) and Healthy Moves Couples (survivors and their partners, screened n = 197, enrolled n = 23). Due to small sample sizes, common in pilot studies, random forest analyses were used to maximize information yielded by the data. RESULTS: Results identified numerous important predictors of enrollment in individual and couple-based lifestyle interventions. Percent energy from fat and physical activity minutes were identified as important predictors for both recruitment methods. Age, cancer site, and marital status were important predictors of enrollment in the individual-based intervention. Weight, fiber consumption, and disease-related symptom severity and interference were important predictors of enrollment in the couple-based intervention. CONCLUSION: Although there was some overlap in predictors for enrollment between studies, many differential predictors were identified between individual versus couple-based study designs for lifestyle intervention in cancer survivors. Future lifestyle intervention studies for cancer survivors may benefit from targeting different predictors of enrollment based on study design to optimize recruitment. Additionally, understanding predictors may allow certain barriers to enrollment (i.e., symptom burden) to be directly addressed, making lifestyle intervention research more feasible and acceptable to difficult-to-recruit survivors.
PURPOSE: To utilize data from lifestyle intervention pilot studies for cancer survivors to elucidate demographic, disease-related, and health behavior factors that might predict enrollment in this type of research. Additionally, factors were differentially compared based on intervention design (i.e., individual versus couple-based). METHODS: Secondary data analysis was conducted regarding predictors of enrollment into lifestyle intervention studies, including Healthy Moves Weight Loss (individual participants, screened n = 89, enrolled n = 30) and Healthy Moves Couples (survivors and their partners, screened n = 197, enrolled n = 23). Due to small sample sizes, common in pilot studies, random forest analyses were used to maximize information yielded by the data. RESULTS: Results identified numerous important predictors of enrollment in individual and couple-based lifestyle interventions. Percent energy from fat and physical activity minutes were identified as important predictors for both recruitment methods. Age, cancer site, and marital status were important predictors of enrollment in the individual-based intervention. Weight, fiber consumption, and disease-related symptom severity and interference were important predictors of enrollment in the couple-based intervention. CONCLUSION: Although there was some overlap in predictors for enrollment between studies, many differential predictors were identified between individual versus couple-based study designs for lifestyle intervention in cancer survivors. Future lifestyle intervention studies for cancer survivors may benefit from targeting different predictors of enrollment based on study design to optimize recruitment. Additionally, understanding predictors may allow certain barriers to enrollment (i.e., symptom burden) to be directly addressed, making lifestyle intervention research more feasible and acceptable to difficult-to-recruit survivors.
Entities:
Keywords:
Cancer survivors; Enrollment; Lifestyle intervention; Random forest
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