State-of-the-art digital phenotyping methods for cardiometabolic risk prevention and management: a scoping review.

Abstract

Health behaviour change plays a crucial role in cardiometabolic risk prevention and management but remains challenging due to complex differences between individuals. Digital phenotyping (DP), defined as the quantification of human behaviours through digital data obtained from devices or sensors, has been used to improve health behaviours but its use for cardiometabolic disease risk prevention and management remains unclear. To provide an overview of how DP has been used to improve cardiometabolic disease risk prevention and management. A scoping review was conducted according to Arksey and O'Malley's five stage framework and reporting guidelines outlined in the PRISMA-ScR. Nine databases (CINAHL, Cochrane, Embase, Ovid (Medline), PubMed, PsycINFO, Scopus, Web of Science and ProQuest Dissertations and Theses Global) were searched for articles published from journal inception to 2024. An inductive qualitative content analysis was conducted on included articles. 73 studies were included. DP has been applied to ten cardiometabolic risk factors (i.e. adiposity, blood pressure, lipid profile, glucose control, cardiorespiratory fitness, arterial stiffness, cardiac function, adipocyte and gut-derived hormones, inflammatory markers and liver fat) through five types of health-related behaviours (physical activity, sedentary behaviours, sleep patterns, dietary habits and medication adherence), measured using five data collection modalities (accelerometers, multi-sensor wearable devices, online platforms, smartphones, and ingestible sensors). DP has potential to improve cardiometabolic risk prevention and management by identifying individualised behaviours to tailor health promoting interventions.