Q&A: How a wearable monitoring tool may revolutionize mental health care
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With mental health issues becoming more prevalent on college campuses, researchers are working to help students more easily obtain mental health care by developing a wearable continuous monitoring tool.
The tool will utilize advanced machine learning and sensors on commercial off-the-shelf smartwatches to detect high anxiety symptoms and direct the wearer to resources. The device is triggered by negative indicators, including self-reports by the wearer and anxiety patterns of heart rate. Upon detection of these indicators, it would prompt the wearer to engage in therapeutic activities.
Healio spoke with Farzan Sasangohar, PhD, lead researcher for the intervention and assistant professor in the department of industrial and systems engineering at Texas A&M University, about the Mental Health Evaluation and Lookout (mHELP) pilot program and how this type of continuous monitoring tool will benefit college students and the mental health community as a whole. – by Kate Burba
Question: Can you provide some background regarding the stigma around mental health care that may keep students away from the help they need?
Answer: Unfortunately, mental health conditions are commonly viewed with a negative connotation by the general population. In some cultures, depression is even considered a sign of weakness. College students are often looking for social acceptance and tend to avoid anything that's perceived as negative, and I think this contributes to the mental health stigma.
Q: What prompted the development of this device?
A: I have witnessed an increasing prevalence of mental health issues among students, both when I was a student and now as faculty. I think the trend is going to be here for a long time if it’s not dealt with systematically.
Q: How will this device be implemented, and what will it look like?
A: In the mHELP pilot study, we’re investigating the efficacy of implementing this technology using participatory ergonomics. We engaged a wide range of stakeholders to understand and document their readiness, technology acceptance, any sort of implementation barriers and expectations of these technologies. We are also collecting usage analytics from the devices. We conduct frequent iterative usability testing in our labs, bringing in students and having them go through detailed tasks, and we address any usability issues with the tool that we observe.
Q: Do you foresee any barriers to implementation? If so, how might this device be tailored to overcome them?
A: One of the biggest barriers is integrating this technology into the current IT infrastructure and the mental care processes here, on a large campus, and almost everywhere else. Ideally, we’d like to have a fully integrated solution that connects students to providers to detect episodes of depressive moments or hyperarousal. That is the ideal, but there are so many electronic privacy concerns that make this very challenging.
Q: What role will mental health providers play in this intervention?
A: We’d like providers to have access to the data collected by these devices to inform their therapeutic efforts. This needs more work to investigate necessary changes to how they do things in their current work practices and current workflows. That's why we engage providers very early on in the design process to ensure that those transitions are smooth.
Q: How could this program be used as a model for integrating mobile-enabled technologies into mental health care in other communities?
A: This pilot program can help us understand the efficacy of remote monitoring technologies for mental health and how they augment our capabilities in remote monitoring and real time detection of depressive and stress episodes, providing therapy, and enabling self-management of mental health conditions. I think this is a model that that can be adopted easily on other campuses.
I also hope we use this to initiate programs for vulnerable populations, such as those in underserved communities or veterans. Our Center for Remote Health Technologies and Systems at Texas A&M University, has excellent infrastructure and various test beds for underserved communities to implement these technologies. My goal is to utilize our capabilities to expand the program in the next 5 years to many campuses and also underserved communities where there’s a great need for self-management.
Q: Might this technology have implications for clinical practice?
A: I think the tool can support providers in two ways. Number one, it triggers the need for in-person or virtual therapy with that detection piece. Number two, it provides them with objective and self-evaluated mental health status data that improve quality of care. I think the technology will augment providers’ capabilities. This is going to provide much more timely intervention and revolutionize the practice.
Disclosure: Sasangohar reports no relevant financial disclosures.