From Patterns to Deviations: Detecting Behavioural Drift for Mental Health Monitoring Using Smartphone and Wearable Data

Monitoring behavioural drift, the sustained shift in an individual’s daily activity, sleep, or social patterns offers a significant lens for early mental health intervention. However, detecting these drifts in free-living settings remains challenging due to the absence of ground-truth labels, the temporal complexity of human behaviour, and fragmentation across heterogeneous sensing modalities. This paper proposes a multimodal approach to quantify and detect behavioural drift using longitudinal data from over 500 university students in the NetHealth cohort. We extract personalised, longitudinal features spanning three behavioural domains physical activity, sleep hygiene, and communication diversity and model deviations relative to rolling, individual-specific statistical baselines. To differentiate transient anomalies from meaningful behavioural change, we introduce a sustained streak mechanism that identifies persistent drift episodes. We evaluate three temporal modelling strategies Isolation Forest, Convolutional Neural Networks, and Long Short-Term Memory networks across both single-modality and fused approaches. Our findings indicate that recurrent models offer the strongest performance, highlighting the necessity of capturing temporal dependencies in behavioural data. Furthermore, we find that cross-modal correlations between drift signals are weak, confirming that activity, sleep, and communication provide complementary, non-redundant insights into an individual's wellbeing. This work establishes a robust methodological basis for integrating multimodal sensing data to monitor mental health trajectories, providing a scalable path toward early intervention in digital health.