Reducing patient burden in experience sampling studies: A simulation study to validate the personalized missingness design

Successful personalized treatment requires a thorough understanding of the complex dynamic processes underlying disorders. Intensive longitudinal methods (e.g., experience sampling) that ask patients to complete multiple-item questionnaires several times a day are ideally suited for this. However, collecting such data entails severe patient burden, especially for those with low energy and little concentration (e.g., patients suffering from chronic cancer-related fatigue and/or psychological disorders such as somatic symptom disorder). This burden is currently predominantly lightened with single-item measures, but these cannot validly capture complex conditions, leading to a catch-22 situation: Capturing complex dynamic processes and effective personalized treatment require intensive longitudinal patient data on multiple-item questionnaires, but patients cannot provide this type of data because it is too taxing. To solve this problem, we developed a personalized missingness design that presents an individualized and time-varying minimal subset of items on each occasion, thereby striking an optimal balance between thoroughly mapping patients’ symptoms and keeping the number of items a person needs to answer to a minimum. The design builds on multilevel factor analyses to determine which sets of items are most informative, which can change over time. Expert-informed simulations validated our new design. While the design can be universally applied to any measurement of (psychological) symptoms (e.g., to inform cognitive behavioral therapy), we tailored our simulations to patients suffering from chronic cancer-related fatigue in collaboration with experts in psycho-oncology. In the near future, the design will be implemented in the widely used experience sampling app m-Path in collaboration with the developers.