Digital sensing technologies — like smart watches and smartphones — hold transformative potential for mental health research and care. By passively collecting many types of objective data in real-world environments, these tools can help researchers improve understanding, detection and treatment of depression and anxiety.
Below, we provide an overview of digital sensing and the promise it holds for mental health research and treatment, and we outline how the UCLA Depression Grand Challenge is involved in field of digital sensing through its research, infrastructure and leadership.
Digital sensing & its importance to mental health
Currently, the standard of care for depression and anxiety relies on trial and error, meaning individuals spend months or years testing different treatments in hopes of finding one that works for them. Accelerating an individual’s path to the right treatment requires tools capable of furthering understanding and revealing depression’s varied causes and trajectories. With this information, clinicians could successfully match a patient to the right treatment more often.
Digital sensing technology enables the continuous monitoring of individuals’ behaviors and physiological factors (e.g., physical activity, sleep quantity and quality, heart rate) within their everyday environments, typically through sensors embedded in wearable devices. These technologies have massive potential and are presently being leveraged to revolutionize mental health research and treatment.
In research
In research, digital sensing technology has the potential to provide answers to persisting questions about depression’s myriad causes, types and trajectories. Researchers can leverage the massive quantity of objective data collected by digital sensors to illuminate the relationship between these data and the causes, symptoms and trajectories of depression and anxiety. For example, researchers may compare objective data on sleep quantity with data on depression trajectories to better understand the relationship between sleep and depression.
Digital sensing technology also allows researchers to account for depression’s great degree of heterogeneity — its causes and symptoms may be different or even opposite from individual to individual. For instance, a certain behavior (e.g., increased sleep) may indicate worsening symptoms of depression for one individual and improving symptoms for another.
Critically, digital sensing technologies offer the precision, scalability and accessibility researchers need to conduct high-quality, high-powered human studies and make significant breakthroughs in furthering understanding of depression.
In clinical care
In clinical practice, this increased understanding could give health care providers the power to identify causes of depression; to prevent the onset of depressive episodes through earlier detection and intervention; and to guide personalized and more effective treatments.
The dual application of digital sensor technology in both research and clinical settings creates opportunities to bridge gaps between the two, fostering innovations in mental health care and advancing evidence-based practices and common standards for digital sensing for mental health.
Further reading
If you would like to learn more about digital sensing, you may access the following materials produced by or in collaboration with the DGC or DGC-affiliated researchers:
- “Advancing the use of sensor-based digital health technologies for mental health research and clinical practice,” a 2024 Wellcome-funded report that overviews current barriers and needs to advancing digital sensing for mental health and provides evidence-based, actionable recommendations to drive interest, investment and direction for sensor-based digital health technology research and innovation.
- “Advancing digital sensing in mental health research,” an npj Digital Medicine publication overviewing what must be done to overcome critical challenges in the field, such as limited data standards and underpowered studies.
- Reports series produced by Digital Sensing Workshop workgroups, which provide stakeholders’ recommendations on five respective topics: 1) digital infrastructure, 2) data flow, 3) research study design, 4) reporting and 5) users’ perspective.
All publications & reports
- Advancing the use of sensor-based digital health technologies for mental health research and clinical practice
- Behind the paper: ‘Advancing digital sensing in mental health research’
- Advancing digital sensing in mental health research
- Personalized mood prediction from patterns of behavior collected with smartphones
- 2023 Digital Sensing Workshop reports
