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Artificial intelligence (AI)-Enabled behavioral health application for college students: Pilot study protocol.

Edlin Garcia Colato¹, Aijia Yuan², Sagar Samtani²

¹Department of Health and Wellness Design, Indiana University School of Public Health, Bloomington, Indiana, United States of America.

|October 30, 2025

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This study develops an automated mental health screening tool using smartphone sensor data to detect depressive behaviors in young adults. Early detection can support proactive mental wellness initiatives in universities.

Area of Science:

Digital mental health
Computational psychiatry
Machine learning in healthcare

Background:

Depression is prevalent in young adults (18-25), necessitating accessible mental health support in higher education.
Current mental health screening methods lack privacy and proactive capabilities.
Mobile technology offers a platform for continuous, private mental health monitoring.

Purpose of the Study:

To develop and validate an automated screening tool for depressive behaviors using smartphone sensor data.
To identify specific behavioral patterns indicative of depression in university students.
To enhance proactive mental health self-awareness among young adults.

Main Methods:

Recruitment of ~1,000 first-year undergraduate students from two US universities.

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Collection of survey data and passive, continuous sensor-based behavioral data (physical activity, social interaction, sleep).

Application of deep learning and machine learning algorithms to analyze behavioral data and survey responses.

Main Results:

Analysis of relationships between sensor-derived behaviors and self-reported mental health.
Identification of key behavioral patterns associated with depressive symptoms.
Development of a predictive model for depressive behaviors.

Conclusions:

Smartphone sensor data can be leveraged to create an automated, private mental health screening tool.
This technology can facilitate early identification and intervention for depression in young adults.
The study highlights the potential of digital phenotyping for mental wellness in higher education.