Bringing real-time stress detection to scale: Development of a biosensor driven, stress detection classifier for smartwatches

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PROJECT SUMMARY/ABSTRACT The goals of this mentored, patient-oriented, research career development award are two-fold: 1) Characterize the autonomic nervous system correlates of stress-reactivity, both in laboratory and ambulatory contexts in order to inform the development of a biosensor driven, stress-detection classifier algorithm that can run on commercially available smartwatches, and 2) establish the principal investigator as an independent researcher at Massachusetts General Hospital - Harvard Medical School. The specific aims of this research will be accomplished through an innovative study leveraging the strengths of traditional laboratory-based, psychophysiological research, and cutting-edge, in natura monitoring of stress and stress’ autonomic correlates using a combination of ecological momentary assessment of affect, and ambulatory electrocardiogram monitoring. This research will inform the development of a stress-detection algorithm that will run on commercially available smartwatches. The clinical and health applications for real-time stress detection are numerous, but this technology holds particular promise for individuals in early recovery from alcohol use disorder for whom unchecked stress heightens risk for alcohol use and engagement in other maladaptive coping behaviors. The smartwatch-embedded stress detection algorithm developed in this research will ultimately be linked to existing smartphone-based relapse prevention apps that will prompt patients with real-time coaching to mitigate alcohol use risk. Aims of the principal investigator’s career development and training plan include, 1) learning fundamental principles of machine learning with an emphasis on biosensor technologies, 2) gaining facility with Matlab programming, with an emphasis on signal analysis and psychophysiological model building, 3) broadening expertise in cardiovascular waveform and interval analysis with particular emphasis on artefact management, and 4) acquiring skills in the development and application of mHealth-based clinical interventions. These goals will be achieved through a training plan comprised of mentorship, formal coursework, seminars, conferences, and manuscript preparation. Knowledge gained via the training plan will be augmented by the research undertaken. Drs. John Kelly, Paolo Bonato, Gari Clifford, and Bettina Hoeppner will serve as mentors on this award, and will provide targeted expertise in machine learning approaches, Matlab programing, artefact management, and mHealth treatment development. Massachusetts General Hospital - Harvard Medical School provides an exceptional environment in which to conduct this training and research. By the end of the 5-year award period, the goals are to have a working stress-detection classifier algorithm ready for R01 testing, and for the principal investigator to be established as an independent investigator. This award is consistent with NIH's goal of increasing and maintaining a strong cohort of investigators to address the nation's clinical research needs.