Digital health technologies offer new opportunities for personalized health management and disease prevention. In this retrospective, long-term longitudinal study of over 20,000 users of a digital health platform (DHP), we aimed to determine whether improvements in health-related biomarkers could be observed in users and, if so, how they were maintained over time. We further explored whether genetic predisposition and physiological patterns, such as sleep and activity, were associated with variability in these biomarker responses. The DHP evaluates a user’s individual biological profile, consisting of blood biomarkers, polygenic risk scores (PGS), and fitness tracker data, and provides personalized lifestyle interventions based on knowledge about nutrition, supplements, exercise, and recovery collected from over 7,000 clinical studies. Here, we show improvement in suboptimal levels of the primary outcome, key blood biomarkers that are sustained or increased in the long-term with DHP use. We additionally show the correlation of biomarker improvement with secondary outcomes, including specific sleep and activity patterns in users. Lastly, we find significant correlations between polygenic risk and both baseline levels and longitudinal change in biomarkers, including low-density lipoprotein cholesterol (LDL-c), suggesting that genetic predisposition for a negative trait (e.g., elevated LDL-c) could make it more difficult to improve that trait. This longitudinal, integrated biomarker dataset highlights the potential of digital health tools in fostering improvements in health-related biomarkers through personalized data analytics and targeted behavioral interventions.
Schneider et al. (Tue,) studied this question.