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  • Evaluating the Impact of Cannabinoids on Sleep Parameters in Patients Undergoing Sleep Studies Jacksonville, Fla.

    The overarching purpose of this study is to advance our understanding of how cannabinoids interact with sleep physiology, potentially offering insights into their therapeutic or detrimental roles in sleep disorders. By elucidating the specific effects of cannabinoids on different aspects of sleep, this research could inform clinical practices and guide the development of cannabinoid-based interventions for sleep-related issues.

Closed for Enrollment

  • Clinical Pilot of Augmented Human Intelligence in Major Depressive Disorder (AHI/Depression Pilot) Rochester, Minn., Jacksonville, Fla.

    The primary purpose of this study is to evaluate the degree of statistical agreement between observed clinical outcomes (non-response/remission) after 8 weeks of treatment and the outcomes predicted by an Augmented Human Intelligence (AHI)-based clinical decision support tool after 2 weeks of follow up.
  • Multi-Omic, Clinomic and Digitomic Attributes of Major Depression for Integrative Analytics: the MACADAMIA Pilot Study Rochester, Minn., Jacksonville, Fla.

    The primary study objective is to develop a longitudinal multi-omic and digitomic dataset suitable for the identification of novel biological and physiological markers of transitions between depressive states (categorically defined) and depressive symptoms (as a continuous measure).
  • Pilot to Assess Effectiveness of and Satisfaction With Brief Synchronous Tele-psychiatry Consult Jacksonville, Fla.

    This study is being conducted to assess the effectiveness and satisfaction of getting mental health consults using remote video consults through an iPad.
  • Wearable Augmented Prediction of Burnout in Nurses: A Synergy of Engineering, Bioethics, Nursing and Wellness Sciences Rochester, Minn., Jacksonville, Fla.

    This research project’s vision is to develop a technology to predict burnout in RNs by combining workplace, psychological, and physiological factors, and exploring the barriers to adopting such a technology.

    Aim 1 is to create a unique, open-access, de-identified dataset that transforms the science of burnout internationally and informs the interaction of continuous physiological measures (measured from smart watches) and repeated psychological and work-related factors for predicting burnout in RNs at Mayo Clinic’s Florida and Rochester sites.

    Aim 2 is to develop an analytical framework combining probabilistic graphical models (PGMs) and multitask learning (MTL) to derive interpretable predictions of burnout using the data gathered in Aim 1. 

    Finally, two supplemental analytical aims of this project are to examine burnout impact on absenteeism and related costs for RNs, and to assess the impact of burnout on nursing-sensitive outcomes.
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