One of the biggest drivers of the nation’s rising healthcare spending is providing care for patients with chronic diseases, many of which are linked to health behaviors such as poor diet, lack of exercise, and smoking. A key strategy for making self-care and preventive measures more achievable has been the integration of passive monitoring into everyday life via mobile sensors and providing personalized information and guidance to patients.
The tools developed by mDOT transform the ability of researchers to leverage the full range of available sensors and mobile technologies, allowing them to deliver dynamically personalized and temporally-precise mHealth interventions to individuals. These interventions can precipitate a much-needed transformation by enabling patients to initiate and sustain the lifestyle choices necessary to prevent or successfully manage the burden of chronic health conditions.
mDOT develops methods of identifying the ideal moments when health risks are elevated yet mitigatable. Our technologies have a significant potential to advance the fundamental understanding of health and behavior by supporting the analysis of complex, longitudinal, mHealth data.
To maximize the chances of success, mobile health intervention content is optimized to address the key drivers of current risk. mDOT addresses these challenges by developing advanced reinforcement learning methods so that they can be applied to the case of mHealth interventions.
mDOT resources make remote care possible for patients who have traditionally required close involvement of clinicians, marking a major transformation in care delivery. Our deliverables enable much greater personalization of mHealth interventions by expanding access to emerging biomarkers.