As is the case in many fields, but particularly those that have undergone a period of rapid expansion, there is enormous variability in the methodology associated with using digital tools in clinical research. From a technological standpoint, for example, it is common to see the same or similar tools with varying sampling units, frequencies, epoch durations, and data processing algorithms. From a study design and reporting standpoint, the recent CTTI systematic review of feasibility studies promoting the effective use of mobile technologies noted substantial variability in the way that digital tools were deployed in clinical research studies, as well as the endpoints reported within a single clinical domain. Although there will – and should – always be some variability in how a particular tool is used in research to maximize the likelihood of a successful study, the current level of variability in the field of digital medicine has drawbacks including, for example, the unnecessary confusion that comes from adopting different units for a given sensor, the inability to make comparisons across studies, and the inability to pool different data sources for meta-analyses.
The purpose of this study is to discuss the advantages of adopting common methodologies in the field, and establishing common data elements allowing for straightforward comparison and merging of data. This project could include collection of original data allowing comparison of the various methodological decisions that are made during the study design phase. The overall goal is to highlight the advantages of common methodologies, not necessarily to recommend what those common methodologies should be.
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Two key factors behind the successful adoption of digital medicine tools are: strong science and patient/participant adherence. Volumes are written about study design and reporting quality; however, data regarding patient/participant adherence is often not reported, considered either non-essential or possibly as a perceived failure. Adherence to a digital tool may be influenced by many factors including, but not limited to:
This project will involve a systematic review and meta-analysis of published data, in order to summarize adherence to various technologies that have been used to collect digital data in clinical research studies.
To explore the various factors that enable optimal adherence in successful trials, as well as factors that may have negatively impacted adherence and how these may be addressed in future studies. Given that poor adherence is often the root cause of a failed research study, this project will emphasize the importance of monitoring and addressing adherence as a means of promoting the wider adoption of digital tools in clinical research.
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The use of digital medicine tools in clinical research has become increasingly common in recent years, a trend that has continued during the COVID-19 pandemic as investigators look for efficient ways to conduct research in remote settings. Alongside the many advantages of this new research paradigm, there are important challenges associated with selecting and utilizing digital medicine tools appropriately while maintaining scientific rigor. The digital medicine field today is reminiscent of the field of laboratory assay based biomarkers twenty years ago. Many elements of the “fit-for-purpose” concept developed for laboratory assay based biomarkers can be extended and applied to digitally-measured biomarkers.
The goal of this research project was to summarize historical perspectives and lessons learned from the early days of the laboratory-lab biomarker field, highlighting the commonalities to digitally-measured biomarkers as well as areas in which the two modalities differ. The resulting manuscript contains several recommendations for future considerations, which we consider to be critical to the appropriate adoption of scalable digital medicine solutions. The manuscript “Fit‐for‐Purpose Biometric Monitoring Technologies (BioMeT): Leveraging the Laboratory Biomarker Experience” was peer-reviewed and published in the Clinical and Translational Science journal in August 2020.
Project Lead: Elena S. Izmailova; First Authors: Alan Godfrey, Benjamin Vandendriessche Contributors: Jessie P. Bakker, Cheryl Fitzer-Attas, Ninad Gujar, Matthew Hobbs, Qi Liu, Carrie A Northcott, Virginia Parks, William A Wood, Vadim Zipunnikov, John A. Wagner
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