Summary:
A joint research team from KAIST in Korea and the University of Michigan has developed a smartwatch-based technology to track circadian rhythm disruptions and predict symptoms of depression. This data filtering technology creates a “digital twin” of the circadian clock, enabling predictions of mood changes and depression-related symptoms such as sleep disturbances and appetite changes. Tested on approximately 800 shift workers, the technology demonstrated its ability to forecast mood disruptions and mental health risks. The research provides a non-invasive, continuous method for mental health monitoring, with potential to overcome barriers faced by socially disadvantaged groups in accessing traditional care.
Key Takeaways:
- Smartwatch Data Predicts Mental Health Risks: Researchers have developed a technology that uses smartwatch data to track circadian rhythm disruptions, enabling predictions of mood changes and depression symptoms.
- Shift Workers Tested for Mood Predictions: A large-scale study involving 800 shift workers validated the technology’s ability to forecast symptoms like sleep disturbances, appetite changes, and decreased concentration.
- Non-Invasive Approach to Mental Health Monitoring: The smartwatch-based tool provides a less invasive, more accessible alternative to traditional methods like polysomnography and melatonin testing.
A joint research team from Korea and the US has developed a technology that uses biometric data collected through wearable devices to predict tomorrow’s mood and, further, to predict the possibility of developing symptoms of depression.
The research team under Dae Wook Kim, PhD, from the department of brain and cognitive sciences at KAIST and the team under professor Daniel B. Forger, PhD, from the department of mathematics at the University of Michigan have developed a technology to predict symptoms of depression such as sleep disorders, depression, loss of appetite, overeating, and decreased concentration in shift workers from the activity and heart rate data collected from smartwatches.
According to the World Health Organization, a promising new treatment direction for mental illness focuses on the sleep and circadian timekeeping system located in the hypothalamus of the brain, which directly affects impulsivity, emotional responses, decision-making, and overall mood.
Limitations of Current Mental Health Monitoring
However, in order to measure endogenous circadian rhythms and sleep states, blood or saliva must be drawn every 30 minutes throughout the night to measure changes in the concentration of the melatonin hormone in our bodies and polysomnography (PSG) must be performed.
As such treatments require hospitalization, and most psychiatric patients only visit for outpatient treatment, there has been no significant progress in developing treatment methods that take these two factors into account. In addition, the cost of the PSG test leaves mental health treatment considering sleep and circadian rhythms out of reach for the socially disadvantaged.
The solution to overcome these problems is to employ wearable devices for the easier collection of biometric data such as heart rate, body temperature, and activity level in real-time without spatial constraints. However, current wearable devices have the limitation of providing only indirect information on biomarkers required by medical staff, such as the phase of the circadian clock.
Developing a Digital Twin of the Circadian Clock
The joint research team developed a filtering technology that accurately estimates the phase of the circadian clock, which changes daily, such as heart rate and activity time series data collected from a smartwatch. This is an implementation of a digital twin that precisely describes the circadian rhythm in the brain, and it can be used to estimate circadian rhythm disruption.
The possibility of using the digital twin of this circadian clock to predict the symptoms of depression was verified through collaboration with the research team of Srijan Sen, MD, PhD, of the Michigan Neuroscience Institute and Amy Bohnert, PhD, of the department of psychiatry of the University of Michigan.
Large-Scale Study Validates Technology
The collaborative research team conducted a large-scale prospective cohort study involving approximately 800 shift workers and showed that the circadian rhythm disruption digital biomarker estimated through the technology can predict tomorrow’s mood as well as six symptoms, including sleep problems, appetite changes, decreased concentration, and suicidal thoughts, which are representative symptoms of depression.
“It is very meaningful to be able to conduct research that provides a clue for ways to apply wearable biometric data using mathematics that have not previously been utilized for actual disease management,” Kim says in a release. “We expect that this research will be able to present continuous and non-invasive mental health monitoring technology. This is expected to present a new paradigm for mental health care. By resolving some of the major problems socially disadvantaged people may face in current treatment practices, they may be able to take more active steps when experiencing symptoms of depression, such as seeking counsel before things get out of hand.”
The results of this study were published in npj Digital Medicine.
Photo caption: Schematic diagram of the research results. Based on the biometric data collected by a smartwatch, a mathematical algorithm that solves the inverse problem to estimate the brain’s circadian phase and sleep stages has been developed. This algorithm can estimate the degrees of circadian disruption, and these estimates can be used as digital biomarkers to predict depression risks.
Photo credit: KAIST Theoretical and Computational Brain Science Lab
Leave a Reply