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Digital phenotyping promises to transform psychiatry by using multimodal, densely sampled data. However, its potential is hindered by the lack of focus on identifying and validating digital biomarkers that accurately reflect mental states before evaluating their impact on outcomes. This longitudinal study used explainable machine learning to analyze multivariate, densely sampled data from 133 bipolar disorder (BD) participants over a median of 251 days, identifying robust digital biomarkers defining depressive episodes. The analysis included features from email-based daily self-reported mood, energy, and anxiety, as well as passively collected activity and sleep data using an Oura ring. The most robust descriptors of depressive episodes were lower daily mood variability, lower daily activity variability, and higher daily sleep onset latency variability. Self-reported daily mood features achieved the highest performance (AU-ROC: 0.82 ± 0.03). Our results establish the value of multimodal data and represent a critical first step toward automated detection and prediction of illness episodes in BD.