RESUMEN
Background Self-efficacy is an important determinant of treatment adherence, and peer modelling of success can provide vicarious self-efficacy. A series of patient stories ('talking heads' videos) were developed with people with cystic fibrosis (CF) as part of the CFHealthHub multi-component adherence intervention, aiming to demonstrate success with daily therapy in 'people like me'. Methodology One-to-one semi-structured interviews exploring patients' experiences, barriers and facilitators of nebuliser adherence were audio and video-recorded between October 2015 and August 2016. Interview transcripts were reviewed to identify descriptions of problem-solving and sustained treatment success. Positive stories potentially providing vicarious descriptions of success were selected as video clips. Results In total, 14 adults with CF were recruited from five UK CF centres. Each participant contributed a median of five (interquartile range: 3-6) video clips, and a total of 57 unique clips were uploaded onto the CFHealthHub digital platform. Nine of those clips spanned two categories, hence, there were 66 clips across 16 categories. Conclusions The videos were well received though some adults were concerned that comparisons with peers might create anxiety by highlighting the possibility of future decline or current relative underperformance. It is important to sensitively support choice when providing resources aiming to increase vicarious self-efficacy. Our experience may guide the development of similar videos for people with other long-term conditions.
RESUMEN
BACKGROUND: The purpose of using preventative inhaled treatments in cystic fibrosis is to improve health outcomes. Therefore, understanding the relationship between adherence to treatment and health outcome is crucial. Temporal variability, as well as absolute magnitude of adherence affects health outcomes, and there is likely to be a threshold effect in the relationship between adherence and outcomes. We therefore propose a pragmatic algorithm-based clustering method of objective nebulizer adherence data to better understand this relationship, and potentially, to guide clinical decisions. METHODS TO CLUSTER ADHERENCE DATA: This clustering method consists of three related steps. The first step is to split adherence data for the previous 12 months into four 3-monthly sections. The second step is to calculate mean adherence for each section and to score the section based on mean adherence. The third step is to aggregate the individual scores to determine the final cluster ("cluster 1" = very low adherence; "cluster 2" = low adherence; "cluster 3" = moderate adherence; "cluster 4" = high adherence), and taking into account adherence trend as represented by sequential individual scores. The individual scores should be displayed along with the final cluster for clinicians to fully understand the adherence data. THREE ILLUSTRATIVE CASES: We present three cases to illustrate the use of the proposed clustering method. CONCLUSION: This pragmatic clustering method can deal with adherence data of variable duration (ie, can be used even if 12 months' worth of data are unavailable) and can cluster adherence data in real time. Empirical support for some of the clustering parameters is not yet available, but the suggested classifications provide a structure to investigate parameters in future prospective datasets in which there are accurate measurements of nebulizer adherence and health outcomes.