Monitoring behavioural drift, a 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 activities, sleep, and communication provide complementary, non-redundant insights into an individual’s well-being. This work establishes the methodological basis for integrating multimodal sensing data to monitor mental health trajectories, providing a scalable path towards early intervention in digital health.
Tolosa et al. (Fri,) studied this question.