Advancing Understanding and Implementation of Digital Mindfulness Applications in Colombian Cancer Care

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PROJECT SUMMARY/ABSTRACT There are approximately 680,000 adolescent and young adult cancer survivors (AYACS) in the U.S., and the survival rate in this group is increasing. AYACS experience a myriad of deleterious treatment-related side effects, higher rates of physical and emotional problems and are at a heighted risk of early morbidity and mortality compared to non-cancer age-matched controls. Increased moderate to vigorous intensity physical activity (MVPA) is consistently associated with enhanced quality of life (QOL), reduced cancer treatment- related side effects and chronic disease risk, and improved cancer prognosis in cancer survivors. Yet, the majority of AYACS (~60%) do not meet cancer-specific MVPA recommendations (i.e., 150 mins/week), and very little research on MVPA in cancers survivors has focused specifically on AYACS. Existing studies of MVPA in survivors are largely resource-intensive, costly, and deliver multiple components (i.e., coaching calls, supervised exercise) simultaneously limiting scalability. Three evidence-based strategies have been identified that may be particularly useful for overcoming these barriers and increasing MVPA in AYACS: 1) electronic delivery of a MVPA program, 2) social support, and 3) mindfulness training. However, little is known on the unique roles of social support and mindfulness in MVPA promotion. More granular information about the discrete effects of these components is needed to optimize intervention configuration in order to maximize an effective and scalable MVPA program for AYACS. The purpose of this study is to apply the Multiphase Optimization Strategy (MOST), to determine which types of social support and mindfulness training intervention components optimally increase MVPA in a 12-week mHealth MVPA intervention with a 12 week follow-up. MOST is a framework adapted from engineering that uses highly efficient factorial experiments to evaluate individual, and combined, effects of intervention components to determine which ones can be reduced, eliminated or replaced to improve efficiency. Inactive AYACS (n=304) will receive a core intervention consisting of the OPTIMAL AYAO app and Fitbit and be randomly assigned to 4 components under consideration for inclusion in the optimized intervention: 1) general mindfulness training; 2) MVPA specific mindfulness training; 3) e-coach and 4) engagement of a buddy. We will also examine the effects of increasing MVPA on symptom burden, intensities of other activities (i.e. light and sedentary), and sleep quality and duration and potential mediators and moderators of component effects. The proposed study represents the first systematic effort to use MOST to design an optimized, scalable mHealth MVPA intervention for AYACS. This study will lead to an improved understanding of how to effectively change AYACS' MVPA and ultimately, improve health and disease outcomes in this population.