Our lab focuses on digital phenotyping and digital interventions with mHealth data, predictors of health outcomes including social determinants with big data, identifying sub-groups of treatment responders using machine learning, clinical trials in mental and behavioral health, and multivariate methodology. We have established collaborations with the Institute of Geriatric Psychiatry, the ALACRITY Research Center, the Department of Population Health Services and the Cornell Center for Health Equity.
New Research
See our new manuscript, currently under review and resubmission at Statistics in Medicine, "A co-segmentation algorithm to predict emotional stress from passively sensed mHealth data." Our data-driven segmentation algorithm identifies stress periods more accurately than traditional ML methods. With this algorithm, we plan to develop a just-in-time, adaptive intervention (JITAI) to prevent the experience of an emotionally stressful period in middle-aged and older adults with chronic pain and depression.
