Discussion
nonadherence, breast cancer
BREAST CANCER
Using an online factorial screening experiment, we optimized an information leaflet intervention to increase beliefs about the necessity of AET and reduce concerns about AET. The optimized information leaflet contained four out of five of the candidate components; diagrams explaining how AET works We have demonstrated that it is feasible and beneficial to optimize an information leaflet using an online factorial experiment. Compared with a classical approach (i.e., using an RCT to evaluate the leaflet as a package), the optimization phase provided an insight into the contributions of individual components of the leaflet in isolation and combined. From this, we know that the leaflet supports medication beliefs, which is a known barrier to AET adherence [The strategies we tested appear to be effective in changing medication beliefs, which builds on the limited existing evidence. These strategies could be applied in other contexts where medication beliefs are a barrier to adherence behaviors. However, our results suggest these strategies had more impact on increasing necessity beliefs than reducing concerns. While this was still effective in improving the cost-benefit analysis (differential) which has been found to be a more consistent predictor of nonadherence than necessity beliefs or concerns alone [The The only candidate component screened out of the optimized information leaflet was the The synergistic interaction between the Our study had limitations. Women with breast cancer reported significantly higher necessity beliefs at baseline than women without breast cancer (We used a rigorous approach to optimize an information leaflet to increase necessity beliefs and reduce concerns in women taking AET. Our approach has enabled refinement of our conceptual model, and has led to the development of a more efficient information leaflet, removing components that are negatively impacting the outcome. Factorial experimental designs offer a highly efficient way of optimizing multicomponent intervention packages such as information leaflets. Optimization, guided by MOST, can enhance our overall understanding of behavioral interventions.