Both are associated with other serious health risks such as  cardiovascular disease and diabetes.
The economic burden in the US due to depression and obesity were estimated by the Centers for Disease Control and Prevention to be $210.5 billion and $150 billion respectively. 

How can be better understand depression and obesity and intervene?


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. We hypothesize three domains for self-regulation subtypes in depression and obesity and highlight implications for effective clinical intervention:

​​To achieve these goals, we are evaluating longitudinal participant data using functional and structural neuroimaging, virtual reality assays, and continous passive smart phone sampling:


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.
Abnormalities in activation and connectivity in the default mode, affective, and cognitive control circuits are hypothesized to underlie differential depression symptom profiles.

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.

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.

Stanford Research Team

(From left to right, front to back):
Sarah Chang, Leanne Williams, PhD., Andrea N Goldstein-Piekarski, PhD.,
Matthew D Sacchet, PhD., Carlos Correa, Monica Kullar
Not pictured: Adam Pines (Lab Alum)

Papers and Presentations

1. Williams LM, Pines A, Goldstein-Piekarski AN, Rosas LG, Kullar M, Sacchet MD, Gevaert O, Bailenson J, Lavori PW, Dagum P, Wandell B, Correa C, Greenleaf W, Suppes T, Perry LM, Smyth JM, Lewis MA, Venditti EM, Snowden M, Simmons JM, Ma J. The ENGAGE study: Integrating neuroimaging, virtual reality and smartphone sensing to understand self-regulation for managing depression and obesity in a precision medicine model. Behaviour Research and Therapy (2017), S0005-7967(17)30202-4. 
2.    Pines AR*, Sacchet MD*. Kullar M, Ma J, Williams LM (in review). Multi-level relations among neural, self-report, and behavioral correlates of emotion regulation in comorbid depression and obesity. Scientific Reports (*equal contributions)
3. Williams, LM. VR, Brain Circuits and Precision Psychiatry. 3rd Annual Innovations in Psychiatry and Behavioral Health: Virtual Reality and Behavior Change. Stanford, CA.


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 .

Grant: “ENGAGE” Project: UH2HL132368


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 obesity, 30(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 Obesity, 16(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 epidemiology, 158(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 Practice, 15(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 psychiatry, 30(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 psychiatry, 30(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.