OBJECTIVES : Since 2002, the intensive behavioral modification Diabetes Prevention Program (DPP) has demonstrated effective delays in progression to type 2 diabetes in high risk individuals. In this study, a modified DPP was delivered using telehealth technology to rural communities. The purpose of this study was to develop a predictive model for successful weight loss using self-reported readiness to change (RtC) behavior and actual behavior change during the program.

METHODS : Three modified DPP comprised of 16 weekly core sessions (CS) and 6 monthly or bi-monthly post-core sessions (PCS)were delivered in 2011-2012 to nine communities. A cluster analysis identified three subpopulations (low, moderate, and high RtC subgroups) based on patient baseline responses to RtC diet and exercise questions using the Transtheoretical Model of Change. Using path analyses, three predictors (RtC subgroups, age, program attended), three mediators (session attendance, documented daily fat consumption (FC) and minutes of moderate activity (MA)), and ≥5% weight loss were used to develop the predictive model.

RESULTS : The analysis included 171 participants, approximately 90% female. During the CS, age (p<0.0001) and the program attended (p<0.05) were positively associated with the documentation of FC and of MA. FC (p<0.0001), moderate RtC (p=0.0191), and high RtC (p=0.0213) were positively associated with program attendance. Session attendance was a significant predictor of weight loss during CS (p<0.0001). During the entire program, the high RtC (p=0.0001) and age (p=0.0001) were positively associated with CS attendance. Age (p=0.0520) and program attended (p=0.0006) were positively associated with PCS attendance. Both CS (p=0.0001) and PCS (p=0.0001) attendance were positively associated with FC and MA documentation. CS (p<0.0001) and PCS (p=0.0067) attendance were significant predictors of achieving weight loss goal.

CONCLUSIONS : The newly identified RtC subgroups based on two RtC questions may help target the patient population who can benefit from labor-intensive DPP resources and improve healthcare outcomes.