| Regulators |
Improve access to a body of feasibility studies when assessing the appropriateness of a given technology for collecting outcomes data |
| Sponsors or clinical investigators selecting endpoints |
Helps identify endpoints that have a likelihood of meeting specific trial objectives |
| Clinical operations teams within sponsor organizations or academic sites seeking to select the most appropriate technology for their trial |
Aid the selection of the best technology for a specific endpoint |
| Clinical operations teams within sponsor organizations or academic sites seeking to evaluate operational feasibility and reduce risk of the planned trial |
Access to information that could be used to improve upon the planned trial |
| Technology manufacturers |
Promote understanding of how their tools are being used in clinical trials and how they can be improved to make them useful with other technologies |
| Patient groups |
Improve understanding of digital health technologies used in their medical area of interest |
| Individual patients |
Understand breadth of research activities using digital health technologies in their medical area of interest |
| Engineers and computer scientists (academic and other) aiming to develop algorithms to quantify novel outcomes (e.g. trial of software techniques to examine proof of concept work) |
Aid in determining whether algorithm X gives the expected output and is feasible, and whether methods are pragmatic and useful to answer a clinical question |
| IRBs and ethics boards |
Gain insight into how digital health technologies have been used in small studies in order assess potential privacy and/or safety concerns in proposed larger trials |
| Statisticians |
Provide an estimate of variance of the endpoints to support study power calculations |
| Investors and health policy planners |
Provide a snapshot of the state-of-the art of digital health technologies to help identify gaps in the field and future directions |
