Digital phenotyping enables continuous passive monitoring of behavior and physiology, offering a promising paradigm for early detection of psychotic relapse. In this work, we develop and systematically study two smartwatch-based frameworks for daily relapse detection. The first forecasts cardiac dynamics and flags deviations between predicted and observed features as indicators of abnormality. The second adopts a multi-task formulation that fuses sleep with motion and cardiac-derived signals, learning time-aware embeddings and predicting measurement timing. Both pipelines use Transformer encoders and output a daily anomaly score, derived from predictive uncertainty estimated via an ensemble of multilayer perceptrons to improve robustness to real-world wearable variability. While each framework independently demonstrates strong predictive power, we show that they capture complementary physiological signatures. Consequently, we propose a late-fusion strategy that synergistically combines the anomaly signals from both architectures into a unified decision score. We benchmark our methodology on the 2nd e-Prevention Grand Challenge dataset, where our fused model achieves a 8% relative improvement over the competition-winning baseline. Our results, supported by extensive ablation studies, suggest that the integration of diverse digital phenotypes, cardiac, motion, and sleep, is essential for the high-fidelity detection of psychotic relapse in real-world settings.

翻译：数字表型技术能够连续被动监测行为与生理状态，为精神疾病复发的早期检测提供了极具前景的范式。本研究开发并系统性地研究了两种基于智能手表的日常复发检测框架：第一种框架通过预测心脏动力学特征，将预测值与观测值之间的偏差标记为异常指标；第二种框架采用多任务学习架构，融合睡眠、运动及心脏衍生信号，学习时间感知嵌入并预测测量时序。两种流程均采用Transformer编码器，并通过多层感知机集成估计的预测不确定性输出每日异常分数，以增强对真实世界可穿戴数据变异性的鲁棒性。尽管各框架独立展现出强预测能力，但研究表明它们捕获了互补的生理信号特征。为此，我们提出一种晚期融合策略，将两种架构的异常信号协同整合为统一决策分数。在第二届e-Prevention Grand Challenge数据集上的基准测试表明，我们的融合模型相较于竞赛优胜基线取得8%的相对性能提升。通过广泛消融实验验证的结果表明，整合数字表型中的心脏、运动与睡眠多维信号，对于在真实环境下实现高保真度精神疾病复发检测具有关键意义。