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Types of Research Conducted by the DGC

Depression affects more than 300 million people each year and is the leading cause of disability, with 1 in 5 people receiving a diagnosis in their lifetime. But little is understood about depression’s many causes and types, and the different trajectories depression can take from person to person. This gap in knowledge and understanding motivated UCLA to create the Depression Grand Challenge (DGC), an ambitious endeavor to cut the global burden of depression on human health and wellbeing in half by 2050.

To disentangle the causes and courses of depression and further scientific understanding, the DGC team conducts different types of research studies, each aligned with one of three research objectives: to discover depression’s causes and trajectories, to elucidate depression’s underlying mechanisms, and to develop new and better depression treatments. 

Below, we overview the many types of research efforts undertaken by the DGC team to advance understanding of depression and depression treatment. Note that types of research described below are not mutually exclusive; in other words, the same project may include more than one of these features.

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Pilots & demonstration studies

A pillar of the DGC’s 4-step playbook, pilot and demonstration studies are small-scale projects conducted to assess scientific feasibility and de-risk operational components— such as recruitment strategies, study infrastructure and operational flows  — before significant investment. These early-stage projects minimize risk and increase efficiency because they provide researchers with a testing ground to identify barriers and course-correct before scaling up to a large-scale study, where such corrections would be much more costly and time-intensive. 

In practice: Beginning with pilot and demonstration projects is the DGC’s standard operating procedure — each active, full-scale DGC project was preceded by a pilot or demonstration project. For example, the Digital Mental Health Study began with two pilot phases involving several hundred participants recruited through UCLA Health. Insights from these early phases informed the study’s full launch, which ultimately enrolled more than 4,000 participants.

Clinical trials

Clinical trials are research studies that test how new treatment approaches work in people when compared against a standard method of care. These studies are designed to answer specific research questions — for instance, whether a new treatment works better than an existing treatment, whether a new treatment is as effective as a more intensive treatment, or whether a new treatment helps certain groups more than others. Clinical trials bridge the gap between scientific discover and real-world care and are essential to advancing new and better treatments.

When joining a clinical trial, study participants are randomly assigned to one of two treatment interventions, also called conditions. One group of participants, known as the control group, receives the standard intervention; the other group of participants receive an alternative intervention. Researchers track participants’ outcomes over time, then evaluate the effects of the alternative intervention relative to the standard method of care. 

Beyond helping researchers understand a treatment’s effectiveness, findings from clinical trials may inform how the treatment might be implemented in real-world settings or assess challenges that need to be overcome for the treatment to be feasibly scaled. These studies also may generate data that reveal who benefits most — or least — from a given approach, paving the way for more personalized and equitable interventions. 

In practice: DGC researchers have conducted clinical trials to assess the effectiveness of the STAND system of care. In the New Moms Mood Tracking & Wellbeing study, new parents experiencing depression and anxiety symptoms before or after pregnancy were assigned to receive either perinatal psychiatric care or a specialized version of the STAND system of care. Researchers measured changes in symptom severity to determine the comparative impact of each treatment approach, ultimately showing that a version of STAND customized for helping birth parents experiencing depression before and after giving birth was as effective in relieving symptoms as standard care from an expert psychiatrist.

Research investigating biological mechanisms

Foundational or basic science research advances fundamental underlying scientific understanding in a field and catalyzes new lines of inquiry and research questions. In depression research, this work frequently investigates the biological processes that underlie depression symptoms and supports the development of future research questions and hypotheses. 

Some foundational studies focus on making new discoveries, searching to identify patterns, associations, or targets that are not yet known. This work often involves exploratory analyses that reveal features such as genetic variants, molecular pathways, or neural signatures. By revealing new phenomena or relationships, foundational research helps generate hypotheses about how a condition like depression develops and how it might be treated.

Other studies focus on mechanisms, building on existing observations to better understand how exactly identified biological signals and processes happen within the body. Understanding mechanisms allows researchers to move beyond correlation and toward a more precise understanding of how and why a condition like depression manifests in the body.

Together, this research furthers scientific understanding of biological mechanisms, catalyzing future research and identifying targets for new interventions. It links the identification of biological signals and processes with the explanation of their effects, which ultimately supports the development of more precise treatment and clarifies why existing treatments may work for some individuals but not others. 

In practice: Because antidepressant effectiveness varies widely during pregnancy and the gut microbiome is known to influence both brain signaling and drug metabolism, the Microbial Modifiers study (led by Elaine Hsiao in collaboration with the DGC) examines how variations in the gut microbiome influence the biological effects of antidepressant medications, with the goal of uncovering mechanisms that affect treatment responses in maternal depression.

Large-scale & population-level studies

Large-scale studies are research efforts that engage a large number of participants, often across one or more backgrounds, such as race, age, gender and symptom severity. 

Population-level studies are a type of Iarge-scale study where researchers specifically seek to engage a sample of participants closely representative of a broad population. 

Large-scale studies are significant because they empower researchers with the robust data sets and statistical power needed to detect meaningful patterns, uncover relationships between data, and understand differences in findings across different groups and settings. This at-scale approach also strengthens the reliability and reproducibility of research findings. Population-level approaches extend this — by representing the diversity of entire populations within a study, researchers can ensure the generalizability of study findings across multiple communities and identities.

Large-scale studies are critical for advancing scientific understanding of depression, given that depression is highly heterogeneous; its causes, symptoms, and trajectories are variable or even contradictory from one person to the next. To address this complexity, researchers require studies that include participants who are representative of the general population, which may be accomplished with focusing on a specific population at a time, or ensuring that the sample is representative of the entire population. 

In practice: Both Nelson Freimer and Jonathan Flint have conducted population-level genetic studies in South Korea and Colombia respectively to better understand biological risk factors for depression across a national population and related racial and ethnic populations.

Digital sensing studies

Digital sensing studies are studies that leverage sensors embedded in smartphones and wearable devices to continuously collect real-time, objective data on participants’ behaviors and physiology as they go about their daily lives, such as movement, sleep and heart rate. This passive and continuous data collection allows researchers to observe participants in real-world environments instead of relying solely on self-reported symptoms, which can be inaccurate.

Using digital sensing in mental health research offers the promise of helping uncover complex relationships between behavioral or physiological patterns and symptoms of depression and anxiety. Because depression manifests differently across individuals, digital sensing offers a way to capture these diverse causes and trajectories and better understand how symptoms vary between individuals. For example, a spike in sleep duration may correlate with the onset of depression symptoms for one person while correlating with the improvement of depression symptoms in another.

With the ability to collect high-quality, real-world data at scale, digital sensing studies have the power to expand both the precision and reach of mental health research. They provide the foundation for developing predictive models, improving early detection, and informing more personalized, accessible care. 

In practice: In a first-of-its-kind study in collaboration with Apple, the DGC’s Digital Mental Health Study (and the follow-on National Digital Mental Health Study) is a digital sensing research effort leveraging digital sensors in iPhone and Apple Watch to illuminate the relationship between objective, everyday measures (e.g., sleep, heart rate) and symptoms of depression and anxiety.

Systematic review & scoping review projects

What distinguishes evidence-synthesis research efforts from the other types of research described above is that they do not collect new data, nor do they involve study participants. Instead, they aim to gather and review published research findings about a specific topic to synthesize the current state of the field, including identifying gaps in knowledge and potential weaknesses in the evidence known to date.  

Scoping reviews are preliminary assessments of the existing literature on a specific topic. They aim to overview and map available evidence to answer broad questions and better understand the general state of a specific scientific field or focus area. 

Systematic reviews have a narrower focus than scoping reviews. As their name suggests, these reviews are systematic and follow close to a predetermined protocol. They collect literature that aligns with specific eligibility criteria, like study size, scope, rigor, and matching search terms — studies that do not meet these criteria are not included in the review. These explicit search methods can help minimize researcher bias, ensuring reliable findings from which conclusions can be drawn. 

In practice: The DGC team contributed to a scoping research project led by DiMe DATAcc on digital sensing for mental health, which sought to identify 1) relevant digital sensing measures for depression and anxiety, and 2) which features of digital sensing technologies are most effective for capturing these measures. Researchers conducted a scientific literature review and held interviews with individuals experiencing mental illness, their care partners and clinician-researchers with expertise in mental illness. Findings from this scoping research project were published in a report providing recommendations to drive interest, investment, and direction for digital sensing research and innovation.

In summary, the strength of a research project lies in the deliberate alignment of its objectives with the appropriate structural design. Different research objectives require different tools, from exploratory foundational studies to clinical trials to population-scale data collection.

Together, these research approaches reflect the DGC’s deliberate strategy of aligning scientific questions with the methods best suited to answer them. By designing each project with this in mind, the DGC ensures that resources are deployed efficiently and that findings meaningfully advance understanding of depression’s causes and trajectories, underlying mechanisms, and best treatments. 

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