Harnessing Digital Phenotyping to Advance University Student Mental Health (Brightline): Study Protocol for a Prospective Observational Study

Introduction

Mental health issues such as depression and anxiety are highly and disproportionally prevalent among university students. Beyond the academic rigor, stressors imposed by a new environment result in them being vulnerable to the onset and manifestation of mental health symptomatology. Leveraging smartphones and wearables for digital phenotyping capabilities is an innovative approach for monitoring and intervening in the mental health conditions of university students. This provides a unique opportunity to collect and identify digital and behavioral biomarkers, subsequently enabling the development of predictive models to identify university students at risk.

Methods and analysis

This study — Brightline— will employ an observational study design over a 6-month period, recruiting 500 students from a major public university in Singapore. Passive data collection will occur continuously throughout the monitoring period through a wearable device (Fitbit Charge 6) and smartphone sensors via the Brightline app, which utilizes a digital phenotyping data collection platform. Active data collection will consist of self-report questionnaires to be completed at the beginning of the study and follow-up assessments at one, three, and six months after. The passive and active data collected will be analyzed to identify the digital biomarkers associated with depression, anxiety, stress, loneliness, and affect among university students. Predictive models of these mental health issues will also be developed.

Ethics and dissemination

This study was approved by the Nanyang Technological University Institutional Review Board (IRB-2023-894). Findings from this study will be published in peer-reviewed journals and presented at academic conferences.

Trial registration

NCT06770075.

Strengths and limitations of this study

• Brightline is a large-scale longitudinal study that aims to examine the mental health of university students over time using smartphones and wearables.

• This study has significant clinical implications, offering insights into the early warning signs and trajectories of university student’s mental health.

• These findings may inform the development of personalized and preventative interventions for integration into university mental health services.

• Challenges in adhering to active and passive data collection may lead to missing data and participant dropout, potentially affecting the development of predictive models.