Digital Biomarker of Aging
Long-term lifestyle behaviors like physical activity, circadian rhythms, and sleep are closely linked to health outcomes, chronic disease incidence, and biological aging. Scalable digital devices, such as smartwatches and smartphones, facilitate continuous data collection to quantify these behaviors and explore their relationship to aging. Our research focuses on developing digital biomarkers of aging by applying advanced statistical methods, machine learning, and data science to wearable-derived accelerometer data. This work has led to publications in Scientific Reports and npj Digital Medicine, as well as presentations at IEEE ICHI 2023 and IEEE EMBC 2024. Additionally, we are developing an open-source package to enable researchers to apply our biological age algorithm using their own wearable or smartwatch data.