Major depressive disorder (MDD) is one of the most prevalent and debilitating mental disorders in the world. Clinical trials recruiting depressed individuals are instrumental in the development and evaluation of effective treatments for depression. The safety of participants in clinical trials is overseen by the Institutional review boards (IRBs). Concerns about abilities of people with depression to make an informed decision about research participation have been an ongoing source of debate. Many, including IRB reviewers, presume that mental disorders compromise decision-making. However, empirical data regarding decision-making abilities of depressed persons in research context are scant. IRBs must therefore make recommendations based on accepted """"""""wisdom"""""""" rather than empirical evidence. This likely results in the variability in recommendations of IRBs regarding the appropriate level of protection of depressed individuals. Whereas instruments assessing the individuals'capacity to give informed consent exist, they can only determine whether a person knows how to make a decision, but not whether a decision is consistent with a person's preferences and is in his or her best interest. Thus, the question of whether depression affects the quality of decisions remains unaddressed. The proposed project aims to provide data that will ultimately help those entrusted with the well-being of research participants to select appropriate degree of safeguards for depressed individuals enrolling in research. This will be accomplished by using tools from health economics and health decision-making to understand the influence of MDD on the evaluation of risks and benefits of hypothetical clinical trials. Decisions made by depressed individuals will be examined for consistency with their preferences for better health states, and compared to decisions made by individuals with chronic pain (a comparably disabling non-psychiatric condition), with comorbid MDD and pain, and to decisions of healthy controls. This research is responsive to NIMH Strategic Objectives 3 and 4. Results of this study have the potential to streamline research involving depressed persons by providing major stakeholders the data which either supports the need for additional protection, or determines that no additional safeguards are necessary. It may shed light on the difficulties depressed individuals experience with decision-making, thereby providing a new focus of interventions. This project describes a five-year program to develop an academic career in psychology under the mentorship of Dr. Laura Dunn, who has extensive experience in bioethics and issues related to capacity to provide informed consent. In addition, Dr. Ricardo Muqoz, a co-mentor, will provide guidance on aspects of the project related to depression, and Dr. Jonathan Baron, a consultant, will provide assistance and training in decision-making. Ultimately, this project will allow the establishment of a line of inquiry combining psychopathology and decision-making in a mutually informative relationship;the scientific development afforded by this award will provide the skills necessary to become a successful independent investigator.

Public Health Relevance

Despite scant data about decision-making abilities of depressed individuals, many, including those entrusted with the protection of depressed research participants, presume that depression compromises the abilities to make an informed decision about research participation. This project will study the quality of decisions made by depressed individuals, and examine whether they make decisions that those with a non-psychiatric disorder do not. Results of this investigation will provide data needed to determine the appropriate level and types of protection and safeguards for depressed individuals enrolling in research.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Clinical Investigator Award (CIA) (K08)
Project #
1K08MH091501-01
Application #
7953505
Study Section
Special Emphasis Panel (ZRG1-HDM-B (90))
Program Officer
Hill, Lauren D
Project Start
2010-08-01
Project End
2015-05-31
Budget Start
2010-08-01
Budget End
2011-05-31
Support Year
1
Fiscal Year
2010
Total Cost
$178,344
Indirect Cost
Name
University of California San Francisco
Department
Psychiatry
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
Naushad, Nida; Dunn, Laura B; Muñoz, Ricardo F et al. (2018) Depression increases subjective stigma of chronic pain. J Affect Disord 229:456-462
Muñoz, R F; Leykin, Y; Barrera, A Z et al. (2017) The impact of phone calls on follow-up rates in an online depression prevention study. Internet Interv 8:10-14
Leykin, Yan; Dunn, Laura B; Muñoz, Ricardo F (2017) The effect of depression on the decision to join a clinical trial. J Consult Clin Psychol 85:751-756
Elefant, Ashley B; Contreras, Omar; Muñoz, Ricardo F et al. (2017) Microinterventions produce immediate but not lasting benefits in mood and distress. Internet Interv 10:17-22
Rutter, Tara M; Flentje, Annesa; Dilley, James W et al. (2016) Sexual orientation and treatment-seeking for depression in a multilingual worldwide sample. J Affect Disord 206:87-93
Aguilera, Adrian; Schueller, Stephen M; Leykin, Yan (2015) Daily mood ratings via text message as a proxy for clinic based depression assessment. J Affect Disord 175:471-4
Liu, Nancy H; Contreras, Omar; Muñoz, Ricardo F et al. (2014) Assessing suicide attempts and depression among Chinese speakers over the Internet. Crisis 35:322-9
Leykin, Yan; Muñoz, Ricardo F; Contreras, Omar et al. (2014) Results from a trial of an unsupported internet intervention for depressive symptoms. Internet Interv 1:175-181
Gross, Margaret S; Liu, Nancy H; Contreras, Omar et al. (2014) Using Google AdWords for international multilingual recruitment to health research websites. J Med Internet Res 16:e18
Gill, Supria; Contreras, Omar; Muñoz, Ricardo F et al. (2014) Participant retention in an automated online monthly depression rescreening program: patterns and predictors. Internet Interv 1:20-25

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