Students’ Mental Health: The Social Network Experience Sampling Model for Detecting Early Warning Signals of Worsening Mental Health [Seed Funding]

College students report increased rates of mental health issues that have only worsened during the Covid-19 pandemic. This is alarming because mental health problems do not only affect the student but also fellow students and university staff, the campus, and society. Preventing mental illness in students is therefore a priority. To guide prevention efforts, this project investigates early warning signals (EWS) of worsening mental health.

This project focuses on socio-affective EWS because the transition to college involves significant social challenges (e.g., establishing new relationships) and social maladjustment which fuels mental health issues. This project addresses two questions.

  • What shapes students’ social networks over time? People have an intrinsic need to be accepted by their peers, motivating them to build social relationships. Because positive emotions have social communicative functions, we hypothesize that positive emotions influence relationship building. We introduce a new method to unravel the exchange between positive emotion and developing social networks in daily life. This will identify targets to improve social interaction patterns on campus.
  • Which social interaction patterns predict mental health? Identifying EWS of worsening mental health may reveal when preventive efforts have the biggest impact. We will leverage artificial neural network modeling with feature selection to identify which social interaction patterns in developing social networks detect anxiety/depression at various timescales (at the group-level and person-level). Social interaction patterns refer to students’ temporal and spatial interaction features (e.g., diversity/regularities in social partners/venues visited).

To answer these questions, we introduce the novel Social Network Experience Sampling Model. SN-ESM combines Experience Sampling Methodology, Relational Event Modeling, and Smartphone Sensor technology to study the dynamic exchange between positive emotion, social network development, and anxiety/depression.

Team Composition

  • Jonas Everaert studies cognitive/affective/social risk factors for psychopathology using experience sampling/smartphone sensing. The quality of his work is reflected in high-end publications (e.g., Clinical Psychological Science), prestigious editorial board memberships (Journal of Abnormal Psychology), and awards (APS Rising Star).
  • Joris Mulder develops Bayesian statistical methods to answer challenging questions for social-behavioral research. His current work focuses on relational event models to study social interaction dynamics in temporal networks (funded by NWO Vidi and ERC StG). He publishes in high-impact journals (e.g., Social Networks).
  • Yasemin Erbas studies emotions in daily life and interpersonal contexts. She publishes in leading journals (e.g., Social Psychological and Personality Science) and received prestigious grants (FWO).
  • Egon Dejonckheere studies emotions and their role in mental wellbeing in daily life. He is a core-member of m-Path, the platform that will be used in this project.
  • Marianne van Woerkom designs and implements well-being interventions in various contexts. She publishes in high-impact journals (e.g., Journal of Applied Psychology).
  • Tom Smeets has extensive experience in research on stress/anxiety and developing expertise of just-in-time adaptive interventions. He publishes in outstanding journals (e.g., Computers in Human Behavior).

Cross-cutting themes

The Herbert Simon Research Institute for Health, Well-being, and Adaptiveness is a research center devoted to carrying out excellent, state of the art research in order to contribute to healthy and resilient people. We have selected three themes, which involve the collaboration between various Departments  and address actual themes in need of both fundamental and applied research.