The results of a survey of students’ responses to the COVID-19 pandemic will be used by Texas A&M University researchers working to identify and document the prevalence and severity of mental health challenges as they develop a wearable continuous monitoring tool.
The survey of 2,000 students conducted by researchers in the Wm Michael Barnes ’64 Department of Industrial and Systems Engineering showed the respondents experienced a marked increase of mental health issues during the pandemic: 71% reported heightened levels of stress and anxiety, and 91% expressed fear and worry about their own health and the health of their loved ones.
Drawing from these results, researchers will explore the effectiveness of the Mental Health Evaluation and Lookout (mHELP) program, which uses advanced machine learning and a wide range of sensors provided on commercial smartwatches. The wearable device would be able to detect indicators of anxiety and prompt the wearer of the smartwatch to resources.
“I am passionate about, and have been personally affected by, college students’ mental health,” said Farzan Sasangohar, assistant professor in the industrial and systems engineering department and the director of Applied Cognitive Ergonomics Lab (ACE-Lab). “During the initial peaks of COVID-19, we noticed a significant uptick of mental health issues among our students and were determined to investigate it further.”
The research team designed and developed mHELP to investigate the effectiveness of using a combination of wearable sensors, mobile health and machine learning to monitor students’ mental health and provide support. The program is built partly off of the team’s previous research that focused on continual monitoring of veterans’ mental health.
Heightened levels of stress and anxiety can cause high blood pressure, abnormal heart rhythms and stroke, and exacerbate issues including depression, anxiety and personality disorders.
“These findings show the importance of supporting self-management of mental health,” Sasangohar said. “Our ongoing research addresses this need by developing self-management technologies that use non-invasive sensors, mainly provided on smartphones or watches, bundled with advanced machine-learning tools for detection of mental health anomalies, and equipping users with a suite of therapeutic and self-assessment tools on mobile health platforms.”
In addition, the team has identified evidence to show the underutilization of mental health services, as well as the prevalence of strategies that render ineffective against treating mental health.
The research team also includes Alec Smith, doctoral student in industrial and systems engineering; Sudeep Hedge, assistant research engineer in the ACE-Lab; and Changwon Son and Xiaomei Wang, postdoctoral researchers in the ACE-Lab.