Empowering Mental Wellness: A Mobile App for Early Intervention Using Biomarker Analysis
Disciplines
Data Science | Graphics and Human Computer Interfaces | Software Engineering
Abstract (300 words maximum)
Mental health is an essential part of living a balanced and fulfilling life, but it is often overlooked compared to physical health. While physical health is important for performing daily activities, mental health plays a crucial role in how we manage stress, build connections, and make decisions. Previous research studies have shown that nearly 60 million Americans experienced a mental illness in 2024, yet there were only 340 people for every one mental health provider in the U.S. Furthermore, young adults aged 18–25—who are the most digitally connected generation—suffer from the highest rates of severe mental illness yet are the least likely to seek or receive treatment. These findings highlight a growing crisis where more people are struggling with mental health issues, but the resources available to help them remain insufficient. Given this gap, there is a need for innovative technological solutions that empower young adults with self-awareness and early intervention tools to manage their mental well-being. This research investigates the use of behavioral and physiological biomarkers to predict mental health conditions and provide timely interventions. We analyze heart rate variability, blood pressure, and sleep patterns as physiological data and frequency of digital device usage and social interaction data for behavioral data. Using machine learning and statistical models, we examine correlations between these biomarkers and mental health conditions to develop predictive algorithms. The mobile application integrates these insights to offer real-time monitoring, early warnings, and personalized recommendations. Preliminary findings indicate that behavioral and physiological markers can provide meaningful insights into mental health trends. By combining data-driven analysis with user-centered design, this platform offers a practical and accessible solution for mental health self-management. This study bridges the gap in mental health care by offering a proactive and accessible solution that encourages self-awareness and early intervention.
Academic department under which the project should be listed
CCSE - Information Technology
Primary Investigator (PI) Name
Maria Valero
Empowering Mental Wellness: A Mobile App for Early Intervention Using Biomarker Analysis
Mental health is an essential part of living a balanced and fulfilling life, but it is often overlooked compared to physical health. While physical health is important for performing daily activities, mental health plays a crucial role in how we manage stress, build connections, and make decisions. Previous research studies have shown that nearly 60 million Americans experienced a mental illness in 2024, yet there were only 340 people for every one mental health provider in the U.S. Furthermore, young adults aged 18–25—who are the most digitally connected generation—suffer from the highest rates of severe mental illness yet are the least likely to seek or receive treatment. These findings highlight a growing crisis where more people are struggling with mental health issues, but the resources available to help them remain insufficient. Given this gap, there is a need for innovative technological solutions that empower young adults with self-awareness and early intervention tools to manage their mental well-being. This research investigates the use of behavioral and physiological biomarkers to predict mental health conditions and provide timely interventions. We analyze heart rate variability, blood pressure, and sleep patterns as physiological data and frequency of digital device usage and social interaction data for behavioral data. Using machine learning and statistical models, we examine correlations between these biomarkers and mental health conditions to develop predictive algorithms. The mobile application integrates these insights to offer real-time monitoring, early warnings, and personalized recommendations. Preliminary findings indicate that behavioral and physiological markers can provide meaningful insights into mental health trends. By combining data-driven analysis with user-centered design, this platform offers a practical and accessible solution for mental health self-management. This study bridges the gap in mental health care by offering a proactive and accessible solution that encourages self-awareness and early intervention.