To help patients manage their mental well-being between appointments, researchers at Texas A&M University have developed a smart device-based electronic platform that can continuously monitor hyperarousal status, one of the signs of psychiatric distress. They said this advanced technology could read facial cues, analyze voice patterns, and integrate readings from vital sign sensors integrated into smartwatches to determine if a patient is under stress.
In addition, the researchers noted that the technology could provide feedback and alert healthcare teams if there is a sudden deterioration in the patient’s mind. Health.
“Mental health can change very quickly and many of these changes remain hidden from providers or advisors,” Dr. Farzan Sasangohar, Assistant Professor in the Department of Industrial and Systems Engineering at Wm Michael Barnes ’64. “Our technology will provide providers and advisors with ongoing access to patient variables and status, and I believe it will have life-saving implications, as they can reach patients when they need it. In addition, it will allow patients to manage your mental health better. “
The integrated electronic monitoring and feedback platform for researchers is described in the paper Journal of Psychiatric Practice.
Unlike some physical ailments that can usually be treated with some doctor visits, people with mental health needs may require an extended period of care. Between visits to one reference professional, missing information about a patient’s mental health status. Therefore, unforeseen deterioration in mental health has a limited likelihood of treatment. For example, a patient with anxiety disorder may experience a stressful life event, which causes extreme irritability and restlessness, which may require immediate medical attention. But this patient may be between appointments. On the other hand, health professionals they have no way of knowing about their patients ’ongoing struggle with mental health, which can prevent them from providing proper care.
Therefore, patient-reported outcomes between visits are critical to designing effective mental health care interventions so that patient well-being is further improved. To fill that gap, Sasangohar and his team worked with doctors and researchers in the Department of Psychiatry at Houston Methodist Hospital to develop a smart electronic platform that would help assess a patient’s mental well-being.
“The hospital has the largest hospital psychiatric clinic in the Houston area,” Sasangohar said. “With this collaboration, we could include thousands of patients who had given their consent for psychiatric control.”
Sasangohar collaborators at Houston Methodist Hospital were already using a patient navigation tool, called CareSense. This software can be used to send reminders and follow-up questions to patients to better assess their well-being. For example, people at risk for self-harm may be asked to periodically take questionnaires for major depressive disorder.
Instead of relying solely on patients ’subjective assessment of their mental health, Sasangohar and his team also developed a whole suite of automated hyperarousal analysis software that can be easily installed on smartphones. and smart watches. These programs collect input from voice and face recognition applications and sensors and sensors already integrated into smartwatches, such as heart rate sensors and pedometers. The data from all these sources then train machine learning algorithms to recognize patterns aligned with the normal state of excitation. Once trained, algorithms can continuously look at readings from sensors and recognition applications to determine if the individual is in a high state of arousal.
“The key here is triangulation,” Sasangohar said. “Each of these methods alone, for example, the analysis of facial feeling, shows a promise to detect the mental state, albeit with limitations. But when this information is combined with the analysis of the feeling of voice, as well as physiological indicators of anxiety, diagnosis and inference being much more powerful and clearer ”.
Sasangohar noted that both the subjective assessment of the mental state and the objective assessment of machine learning algorithms are integrated to make a final assessment of the state of arousal for a given individual.
While the prototype of their technology is ready, the researchers said they still need to improve the battery life of the smartphones that carry their software, as the algorithms have a lot of power. In addition, they noted that they must address usability issues, that is, issues that prohibit patients to use their technology, such as difficulties navigating your application.
“Because of the stigma surrounding mental illness, we wanted to build a mental health monitoring device that was very discreet,” Sasangohar said. “So we chose over-the-counter products, such as smartphones, and then created sophisticated apps that work within these devices to control mental health discreet. ”
J. Christopher Fowler et al, Improving psychiatric care through integrated digital technologies, Journal of Psychiatric Practice (2021). DOI: 10.1097 / PRA.0000000000000535
Texas A&M University
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