Adherence to Electronic Patient-Reported Outcome Measures: A Null-Results Review of Measure-Related Effects on Response Rates in Routine Care

This systematic review focuses on measure-related effects of PROMs on patient acceptance, adherence, and response rates in routine clinical care. The review received no external funding and was registered on medRxiv [medRxiv 2024.06.13.24308883] prior to its execution. It was conducted in accordance with PRISMA guidelines. Studies in English or German from the past ten years including patients ≥18 years in routine care with multiple measurement time points were included. Reports on clinical trials in terms of drug or intervention research as well as study protocols were excluded. A literature search across MEDLINE, Embase [Ovid], CINAHL, PsychArticles, PsycInfo [Ebsco], Scopus and Web of Science identified n = 420 potentially relevant publications. The data were retrieved from the data banks on 31st of January 2024. In the full-text screening, no study met the predefined inclusion criteria, therefore no risk of bias assessment or result synthesis was performed. This unexpected finding underscores a significant research gap. It indicates that the effects of instrument-inherent properties (like structure or wording) on patient adherence to PRO assessments have not been evaluated in routine care settings. The design of PROMs can have a potential influence on patient participation and the resulting data quality, which is especially relevant in light of the rising use of PRO assessments in routine care and the growing interest in real-world evidence data. Due to its search window of 10 years, its restriction to articles published in English or German, and its focus on routine care settings, the review may have excluded qualitative research and early works on adherence in routine care. Overall our review highlights the need for future research focusing on intrinsic PROM characteristics and how they relate to patients' acceptance and adherence in order to explore the full potential of real-world data.