RAINBOW-ENGAGE

About the RAINBOW-ENGAGE project

Effective and personalized precision medicine approaches to achieving sustained behavior change are currently outside routine clinical practice. Yet, changing health behaviors is fundamental to managing complex lifestyle-related chronic conditions such as depression and obesity - two top contributors to the global burden of disease and disability1-3. Depression and obesity also commonly co-occur4-15; their combined disease burden is exacerbated and often intractable13,14,16-18.

Behavior change interventions are mainstay among the treatment options for both, and yet the intervention effects tend to be variable and modest. Advanced knowledge of the nature and variability of brain-behavior relations between and within individuals is essential to developing behavioral interventions that are precise, proactive, and personalized – and consequently more effective. Framed within the precision medicine paradigm, our central premise is that behavior change can only be better understood and optimized, when defined in relation to its neurobiological underpinnings, how these underpinnings are expressed in individual choices of daily living, and how they are shaped by targeted interventions. 

By leveraging the ongoing RAINBOW study (Research Aimed at Improving Both Mood and Weight), ENGAGE aims to establish the malleability of self-regulation in successful long term behavioral change, as well as the individualized mechanisms underlying these changes. Eventually, we hope to use our findings to match patients demonstrating self-regulatory dysfunction to the most efficacious treatment possible for that individual. To achieve these goals, we are evaluating longitudinal participant data using functional and structural neuroimaging, virtual reality assays, and continous passive smart phone sampling.

ENGAGE is funded under the Science of Behavior Change NIH priority initiative. More information on the ENGAGE project, the Science of Behavior Change Initiative, and the other projects in the Science of Behavior Change Network can be found on their new website.


Virtual Reality


Through our partnership with the Virtual Human Interaction Lab, we've acquired a virtual reality system for use at our scanning facility. Our setup with an Oculus CV1, a Falcon Northwest Tiki, and Fruit Ninja VR is pictured. Currently, we are investigating self-reflective tendencies, emotional regulation, and cognitive control through our customized virtual reality environments.

Neuroimaging  


The ENGAGE study uses the Center for Cogntive and Neurobiological Imaging for our functional and structural MRI measures. We are primarily interested in four neural circuits implicated in self-regulation in previous research by Dr. Williams and others. We will be looking at both between participant variations and within-participant variations over the two-year enrollment period.

Passive Sampling


We are utilizing Mindstrong for the ENGAGE study, a continous passive sampling smartphone application developed and tested by Paul Dagum, MD, PhD. Dr. Dagum has demonstrated that certain phone-use variables can be noninvansively collected and used to predict various mental health outcomes, a finding we hope to replicate and expand upon in ENGAGE.

Project Highlights

On Sept 18 and 19, the investigators and personnel on the ENGAGE project hosted the first annual NIH visit.  We were delighted to host the visit of Dr.  Susan  Czajkowsi, who is a leader in the NIH Science of Behavior Change (SOBC) initiative and Chief of the National Cancer Institute’s Health Behaviors Research Branch and Behavior Research Program.

Dr. Czajkowski visited each of the research labs and groups that collaborate on ENGAGE, including the Panlab,  PAMFRI, Wandell’s CNI, and the Bailenson Virtual Reality Lab.  She also joined us for the DSMB meeting on Sept 19.

Dr. Czajkowski presented a very stimulating task on the SOBC initiative and the projects (including ENGAGE) that have been funded from the initiative.

Collaborators

Sources

1. The Global BMI Mortality Collaboration (2016). Body-mass index and all-cause mortality: individual participant-data meta-analysis of 239 prospective studies in four continents. The Lancet, 388(10046), 776-786.

2. Global Burden of Disease 2015 Disease and Injury Incidence and Prevalence Collaborators (2016). Global, regional, and national incidence, prevalence, and years lived with disability for 310 disease and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1545-1602.

3. Global Burden of Disease 2015 Risk Factors Collaborators (2016). Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1659-1724.

4. Atlantis, E., Baker, M. (2008). Obesity effects on depression: systematic review of epidemiological studies. International Journal of Obesity, 32, 881-891.

5. Carpenter, K.M., Hasin, D.S., Allison, D.B., Faith, M.S. (2000). Relationships between obesity and DSM-IV major depressive disorder, suicide ideation, and suicide attempts: results from a general population study. American Journal of Public Health, 90, 251-257.

6. Dragan, A., Akhtar-Danesh, N. (2007). Relation between body mass index and depression: a structural equation modeling approach. BMC Medical Research Methodology, 7, 17.

7. Faith, M.S., Matz, P.E., Jorge, M.A. (2002). Obesity-depression associations in the population. Journal of Psychosomatic Research, 53, 935-942.

8. Friedman, M.A., Brownell, K.D. (1995). Psychological correlates of obesity: moving to the next research generation. Psychological Bulletin, 117, 3-20.

9. Heo, M., Pietrobelli, A., Fontaine, K.R., Sirey, J.A., & Faith, M.S. (2006). Depressive mood and obesity in US adults: comparison and moderation by sex, age, and race. International journal of obesity30(3), 513-519.

10. Istvan, J., Zavela, K., & Weidner, G. (1992). Body weight and psychological distress in NHANES I. International journal of obesity and related metabolic disorders: journal of the International Association for the Study of Obesity16(12), 999-1003.

11. Onyike, C.U., Crum, R.M., Lee, H.B., Lyketsos, C.G., & Eaton, W.W. (2003). Is obesity associated with major depression? Results from the Third National Health and Nutrition Examination Survey. American journal of epidemiology158(12), 1139-1147.

12. Markowitz, S., Friedman, M.A., & Arent, S.M. (2008). Understanding the relation between obesity and depression: causal mechanisms and implications for treatment. Clinical Psychology: Science and Practice15(1), 1-20.

13. Simon, G.E., Ludman, E.J., Linde, J.A., Operskalski, B.H., Ichikawa, L., Rohde, P., ... Jeffery, R.W. (2008). Association between obesity and depression in middle-aged women. General hospital psychiatry30(1), 32-39.

14. Strine, T.W., Mokdad, A.H., Dube, S. R., Balluz, L.S., Gonzalez, O., Berry, J.T., … Kroenke, K. (2008). The association of depression and anxiety with obesity and unhealthy behaviors among community-dwelling US adults. General hospital psychiatry30(2), 127-137.

15. Bjerkesset, O., Romundstad, P., Evans, J. & Gunnell, D. (2008). Association of adult body mass index and height with anxiety, depression, and suicide in the general population: the HUNT study. American Journal of Epidemiology, 167(2), 193-202.

16. Blaine, B. (2008). Does depression cause obesity?: A meta-analysis of longitudinal studies of depression and weight control. Journal of Health Psychology, 13(8), 1190-1197.

17. de Wit, L., Luppino, F., van Straten, A., Penninx, B., Zitman, F., & Cuijpers, P. (2010) Depression and obesity: a meta-analysis of community-based studies. Psychiatry Research, 178(2), 230-235.

18. Ma, J., & Xiao, L. (2010) Obesity and depression in US women: results from the 2005-2006 National Health and Nutritional Examination Survey. Obesity,18(2), 348-353.