Imagine you’re at the store, and suddenly you start to get really anxious. The moment passes, but your doctor has told you to keep track of these events and report them so you can manage your anxiety better. The only problem is that the details of the moment get fuzzy, and you can’t say exactly what triggered the attack. You might have been thinking about something at work or at home. Or maybe it was a stray conversation you heard that reminded you of something you dread. You’re not even really sure how anxious you were.
But what if a wearable device could monitor and record your body’s response to stress?
Researchers at the University of Illinois Urbana-Champaign have been using U.S. National Science Foundation (NSF) ACCESS-allocated resources at the National Center for Supercomputing Applications (NCSA) to develop wearable technology that can capture a person’s physiological state in real time. Perhaps, by having one of these wearable sensors, they’d be able to deliver “just-in-time interventions” – those reminders you might get from your smartwatch to sit down and take a break if your heart is racing.
Manuel Hernandez, teaching associate professor in biomedical and translational sciences at the Carle Illinois College of Medicine, is part of a team of researchers who have been researching and testing these types of wearable technologies.
“This work stemmed from observing the impact of the COVID pandemic on mental health in society, and the need to better monitor mental health changes and to aid in the delivery of just-in-time interventions in adults across the lifespan, and particularly those in high-stress environments,” said Hernandez.
Hernandez’s team identified a key issue with treating anxiety – the information doctors mainly have to work from is reported to them by patients, well after an event occurs.
“Anxiety is a common mental health condition that can significantly impair daily functioning, especially for university students in STEM,” said Hernandez. “State anxiety is a situational emotional response and is typically assessed through self-reported questionnaires and clinical interviews. These traditional methods only capture discrete snapshots of an individual’s emotional state and rely heavily on retrospective reporting. To overcome the limitations of self-reporting, we use wearable and contactless sensors for continuous monitoring and the development of objective markers of mental health status in our research.”
This type of work greatly benefits from access to high-performance computing (HPC) resources, such as the GPUs and CPUs available through the ACCESS program – in this case, the team used NCSA’s Delta supercomputer. With HPC resources, researchers can process months of data in minutes.
Hernandez’s team was able to easily get an allocation for compute resources through the ACCESS program. “NCSA resources helped our team fast-track the algorithm development and data analysis of both machine learning and deep learning models for use in state anxiety detection,” said Hernandez.
The team has made significant progress with their research. In addition to their published work in Sensors, they’ve also published findings in IEEE Transactions on Affective Computing and Applied Sciences. But the team’s work isn’t finished – they have plans to keep evolving the research going forward.
“Our next steps are to evaluate what interventions may be most effective at alleviating the negative impacts of stress on mental health, and to evaluate how effectively we can monitor changes in anxiety while we carry out everyday activities,” said Hernandez.
You can find more details about this research in the original article posted here: Using Delta to Help Shape Better Mental Health.
Resource Provider Institution(s): National Center for Supercomputing Applications (NCSA)
Resources Used: Delta
Affiliations: University of Illinois
Funding Agency: NSF
Grant or Allocation Number(s): CIS220101
The science story featured here was enabled by the U.S. National Science Foundation’s ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.