Using Network Science to Examine Temporal Relationships between 24-Hour Movement Behaviors and Depression During the...

Area of Science:

• Psychiatry and Behavioral Science
• Chronobiology
• Data Science

Background:

• College transition is a peak period for depression onset, coinciding with significant shifts in daily movement behaviors.
• Existing research on depression risk factors often uses limited self-reported, cross-sectional data, hindering understanding of temporal dynamics and symptom specificity.
• Novel integration of compositional data analysis and network psychometrics offers a path to dissect complex relationships between time-use behaviors and mental health.

Purpose of the Study:

• To investigate the dynamic interplay between 24-hour movement behaviors (sleep, sedentary time, physical activity) and specific depressive and anxiety symptoms in first-year college students.
• To apply compositional data analysis and network modeling to understand within-person and between-person associations between daily behavior patterns and symptom fluctuations.
• To identify critical periods and behavioral profiles associated with heightened risk for depression symptom onset and progression during the crucial college transition.

Main Methods:

• A 16-week prospective cohort study (N=144) of first-year undergraduates utilized a hybrid panel-burst design.
• Daily assessments of depressive (PHQ-8) and anxiety (GAD-7) symptoms, alongside objective 24-hour movement data (sleep, sedentary, physical activity) from wearables (Fitbit Charge 6).
• Compositional data analysis and multilevel vector autoregressive models were employed to analyze time-use data and symptom dynamics.

Main Results:

• Preliminary analyses focus on modeling daily movement behavior compositions and their associations with individual depressive and anxiety symptoms.
• Secondary analyses will explore how symptom-behavior networks vary across different movement profiles and change over the semester.
• The study aims to reveal specific behavioral patterns linked to distinct symptoms and identify periods of increased vulnerability.

Conclusions:

• This research moves beyond aggregate depression scores to offer precision behavioral psychiatry by linking specific movement behaviors to distinct symptoms.
• Findings will elucidate individual differences in these associations and pinpoint when risk for depression symptoms intensifies during the college transition.
• The study design provides a framework for predictive modeling and developing just-in-time adaptive interventions for early depression detection and prevention in emerging adults.