Dynamic Consent for Sensor-Driven Research

Abstract

Dynamic consent is a term initially emerged in biomedical research that involves a large-scale, long-term participant engagement for continuous data collection (e.g., bio-samples, health records). Dynamic consent is a wider concept of informed consent that enables granular consent in dealing with personal data. Dynamic consent is typically incorporated into a personalized digital platform that allows participants to tailor and manage their own consent preferences. This feature leads to improved transparency and proactive privacy management. Due to such benefits, dynamic consent offers potential applications in other domains that collect diverse data that require multiple consents over time. One possible testbed is digital health, where there have been several attempts to track symptoms and diagnose mental illnesses (e.g., depression) with data collected from mobile and wearable devices (i.e., digital phenotyping). As these sensors continuously collect personal data, users may feel uncomfortable in certain private contexts. However, the current status of the studies only provides one-off informed consent without consideration of specific user contexts, which calls for context-aware fine-grained control. Thus, this paper explores the feasibility of dynamic consent in sensor-driven research and suggests a future outlook of dynamic consent usage in mobile and ubiquitous computing.