Search

Purpose/Objective: Lack of patient participation and engagement remains a barrier to implementing effective online self-management and behavioral health interventions. Identifying patient characteristics associated with engagement rates may lead to interventions that improve engagement in traditional and online self-management programs. In this study, two online self-management and recovery programs were evaluated to identify factors that predict patient engagement. Research Method/Design: Predictors were collected in a questionnaire at baseline before 435 participants started either of the two interventions. One or two online lessons were completed per week with seven or eight total lessons to complete in each program, and each lesson took about 20–30 min to finish. Full patient engagement was defined as completing all lessons and assessments in the program and partial engagement as attempting at least one lesson or assessment. Results: Predictors of full patient engagement were self-rated confidence in completing the program or being over 60 years of age. Predictors of at least partial patient engagement were experienced ordering online or being over 50 years of age. Conclusions/Implications: Identifying profiles of individuals who predict poor engagement may improve implementation and the health outcomes of intervention programs. Impact and Implications: Online delivery of self-management and cognitive behavioral therapy-based interventions is being implemented to improve access to care. Utilization of the programs is variable and identifying factors that predict engagement is needed. Older age and greater confidence in completing the program independently predicted a greater likelihood of patients fully engaging in two online self-management and recovery programs. Older age and having experience purchasing items online independently predicted a greater likelihood of patients at least partially engaging in two online self-management and recovery programs. Future research should focus on individual attributes that predict patient engagement and on developing interventions to promote engagement. [ABSTRACT FROM AUTHOR]