Data integrity is of utmost importance for effective clinical decision-making and health management. Digital health projects increasingly involves the assessment of physiological and health parameters using mobile sensors such as mobile medical devices, connected rapid diagnostic and point of care tests, and sensors directly integrated into mobile phones or wearables. However, using biomedical sensing technologies in remote and uncontrolled settings inherently introduces new challenges for assuring data integrity. For example, digital health intervention with physiological monitoring are challenged by the high degree of measurement uncertainty, unknown training of the user, and higher risks for misuse of the technology. While the goal of any digital health implementation is to provide better access to health services and consequently improve health outcomes, this cannot be achieved if the technology itself is causing a lack of data integrity. Therefore, evaluating data integrity should be considered and evaluated in the early preparation phase of a digital health implementation project.
The ETH Sensor Based Digital Health Checklist can help implementers to systematically evaluate their projects for potential data quality shortcomings and establish a quality control workflow in the design and implementation phase of projects.
The checklist targets implementers, but can also be used in participatory planning workshops with key stakeholders, such as study coordinators, software developers, patients etc.
The checklist is designed to be used during the preparation phase of a digital health project. i.e. from the initial planningn phase to the release of the mHealth tools and can be also used as monitoring tool to verify implementation updates.
The checklist is available as an interactive online form implemented in RedCap. The questionnaire results can be exported as pdf and retrieved anytime with the unique code obtained via email.
Interactive checklist on RedCap
A printable version of the checklist is available for download.
PDF of checklist
If you are using this checklist for your work, please cite
- Zhang J, Tüshaus L, Nuno N, et al. Data Integrity-Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis. JMIR Mhealth Uhealth 2018;6(12):e11896 doi:10.2196/11896.
In collaboration with the Household Health Systems Research Group at SwissTPH and the Universidad Peruana Cayetano Heredia, Lima, Peru, we developed a checklist that can assist implementers to systematically evaluate potential risks of their projects.
Following persons have actively contributed to the development of this checklist:
Jia Zhang, Prof. Walter Karlen, Nestor Nuno, Dr. Daniel Mäusezahl, Afua Adjekum, Dr. Daniel Cobos, Dr. Kristina Keitel, Dr. Beth Payne, Simon Hofstede, Jenny Brown, and Dustin Dunsmuir.
Feedback and contact
We strive to continuously improve this checklist and provide an easy to use and meaningful tool to plan your mHealth project. If you encounter any issues, have suggestions for improvement, or success stories to share, we would love to hear it!
Please send a message to Prof. Karlen.