Background N-of-1 trials have emerged as a personalized approach to patient-centered care, where patients can compare evidence-based treatments using their own data. However, little is known about optimal methods to present individual-level data from medication-related N-of-1 trials to patients to promote decision-making. Objectives We conducted qualitative interviews with patients with heart failure with preserved ejection fraction undergoing N-of-1 trials to iterate, refine, and optimize a patient-facing data visualization tool for displaying the results of N-of-1 medication trials. The goal of optimizing this tool was to promote patients' understanding of their individual health information and to ultimately facilitate shared decision-making about continuing or discontinuing their medication. Methods We conducted 32 semistructured qualitative interviews with 9 participants over the course of their participation in N-of-1 trials. The N-of-1 trials were conducted to facilitate a comparison of continuing versus discontinuing a β-blocker. Interviews were conducted in person or over the phone after each treatment period to evaluate participant perspectives on a data visualization tool prototype. Data were coded using directed content analysis by two independent reviewers and included a third reviewer to reach a consensus when needed. Major themes were extracted and iteratively incorporated into the patient-facing data visualization tool. Results Nine participants provided feedback on how their data were displayed in the visualization tool. After qualitative analysis, three major themes emerged that informed our final interface. Participants preferred: (1) clearly stated individual symptom scores, (2) a reference image with labels to guide their interpretation of symptom information, and (3) qualitative language over numbers alone conveying the meaning of changes in their scores (e.g., better, worse). Conclusion Feedback informed the design of a patient-facing data visualization tool for medication-related N-of-1 trials. Future work should include usability and comprehension testing of this interface on a larger scale.
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Mangal et al. (2024) studied this question.
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