Health risk behavior, including poor diet, physical inactivity, tobacco and other substance use, causes as much as 40% of the illness, suffering, and early death related to chronic diseases. Non-adherence to medical regimens is an important exemplar of the challenges in changing health behavior and its associated impact on health outcomes. Although an array of interventions has been shown to be effective in promoting initiation and maintenance of health behavior change, the mechanisms by which they actually work are infrequently systematically examined. One promising domain of mechanisms to be examined across many populations and types of health behavior is self-regulation. Self-regulation involves identifying one’s goals, and maintaining goal-directed behavior. A large scientific literature has identified the role of self-regulation as a potential causal mechanism in promoting health behavior.
Advances in digital technologies have created unprecedented opportunities to assess and modify self-regulation and health behavior. The proposed project is designed to identify valid and replicable assays of mechanisms of self-regulation across populations to inform an ontology of self-regulation that can ultimately inform development of health behavior interventions of maximal efficacy and potency.
Center for Technology and Behavioral Health
Dr. Marsch is the Director of the Center for Technology and Behavioral Health (CTBH; a P30 “Center of Excellence” supported by the National Institute on Drug Abuse), the Director of the Northeast Node of the National Drug Abuse Clinical Trials Network, and the Andrew G. Wallace Professor within the Geisel School of Medicine at Dartmouth College. As a national interdisciplinary Center, CTBH uses science to inform the development, evaluation, and strategic implementation of technology (web, mobile)-based self-regulation tools for substance use disorders and related behavioral health issues. These tools are designed to deliver engaging and effective self-monitoring and self-management interventions to promote behavioral health and are designed to collectively lead to transformations in the delivery of science-based health care – by improving quality of care, access to care, and health outcomes, while reducing costs of care. Dr. Marsch serves on the National Advisory Council to the National Institute on Drug Abuse.
Department of Psychology
Dr. Poldrack is the Albert Ray Lang Professor of Psychology at Stanford University and Director of the Center for Reproducible Neuroscience His research uses brain imaging to understand the brain systems supporting decision making, executive control, and behavior change. His lab also develops informatics tools to help make sense of the growing body of neuroimaging data (including the OpenfMRI.org and neurovault.org data sharing projects and the Cognitive Atlas ontology) as well as tools to help improve the reproducibility of neuroimaging research (including the Brain Imaging Data Structure and BIDS-Apps projects). He also has one of the most intensely studied individual human brains ever, having been imaged more than 100 times as part of the MyConnectome project.
Lead Project Scientist
1. Thompson, W. H., Wright, J., & Bissett, P. G. (2020). Open exploration. eLife, 9.
2. Enkavi, A. Z., Eisenberg, I. W., Bissett, P. G., Mazza, G. L., MacKinnon, D. P., Marsch, L. A., & Poldrack, R. A. (2019). Large-scale analysis of test–retest reliabilities of self-regulation measures. Proceedings of the National Academy of Sciences, 116(12), 5472-5477.
3. Steinkamp, J. M., Goldblatt, N., Borodovsky, J. T., LaVertu, A., Kronish, I. M., Marsch, L. A., & Schuman-Olivier, Z. (2019). Technological interventions for medication adherence in adult mental health and substance use disorders: A systematic review. JMIR mental health, 6(3), e12493.
4. Eisenberg, I. W., Bissett, P. G., Enkavi, A. Z., Li, J., MacKinnon, D. P., Marsch, L. A., & Poldrack, R. A. (2019). Uncovering the structure of self-regulation through data-driven ontology discovery. Nature Communications, 10(1), 2319.
5. Pelham III, W. E., Gonzalez, O., Metcalf, S. A., Whicker, C. L., Scherer, E. A., Witkiewitz, K., ... & Mackinnon, D. P. (2019). Item response theory analysis of the five facet mindfulness questionnaire and its short forms. Mindfulness, 10(8), 1615-1628.
6. Pelham, W. E., Petras, H., & Pardini, D. A. (2019). Can Machine Learning Improve Screening for Targeted Delinquency Prevention Programs?. Prevention Science, 1-13.
7. Bari, R., Adams, R. J., Rahman, M. M., Parsons, M. B., Buder, E. H., & Kumar, S. (2018). rconverse: Moment by moment conversation detection using a mobile respiration sensor. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2(1), 2.
8. Eisenberg, I. W., Bissett, P. G., Canning, J. R., Dallery, J., Enkavi, A. Z., Whitfield-Gabrieli, S., ... & Kim, S. J. (2017). Applying novel technologies and methods to inform the ontology of self-regulation. Behaviour research and therapy.
9. Sochat, V. V., Eisenberg, I. W., Enkavi, A. Z., Li, J., Bissett, P. G., & Poldrack, R. A. (2016). The experiment factory: standardizing behavioral experiments. Frontiers in psychology, 7.