Cardiovascular diseases (CVD) are the leading cause of death in women, accounting for 29% of deaths in Switzerland in 2022. 

In addition to the traditional CVD risk factors that apply to both sexes, there are female-specific CVD risk factors, such as adverse pregnancy outcome (including gestational diabetes or pre-eclampsia), POCS or premature menopause, which additionally increase the CVD risk. Unlike in men, diabetes is one of the strongest CVD risk factors in women. Moreover, some chronic diseases that increase the CVD risk profile (e.g. rheumatoid arthritis or migraine) are more prevalent in women than in men. 

Despite the increasing evidence base, women-specific aspects are not sufficiently considered in current cardiometabolic prevention. In particular, routinely used CVD risk calculators do not take into account any female-specific factors apart from biological sex and there are no cardiometabolic prevention measures tailored to women, although there is even evidence that the positive effect of lifestyle modification (e.g. increasing physical activity) on CVD risk profile is larger in women than in men. 

The interdisciplinary FemTech Digital project, funded by a strategy grant from the Faculty of Medicine, aims to close this critical care gap in cardiometabolic prevention in women with innovative technology. Modern AI approaches and multi-dimensional individual data collection (including the use of wearable sensor technologies in everyday life) make it possible to digitally map cardiometabolic health and lifestyle behavior and to simulate the effects of lifestyle measures and medication (so-called digital twin approach) on specific health outcomes. This information can be used to generate personalised predictions and recommendations. For example, a Smartwach detects that someone has been sitting in the office all day and recommends increasing the daily number of steps to positively influence the CVD risk profile. This approach differs from general health recommendations on exercise and nutrition. 

The technology developed in the project aims to develop an AI-powered decision support system for female-specific, personalised CVD risk prediction and prevention. This should optimize cardiometabolic prevention for women of all ages, promote general health literacy and minimize the years of health lost due to premature death or disability caused by CVD.