Using smartphone-tracked behavioural markers to recognize depression and anxiety symptoms: Digital phenotyping in the Netherlands Study of Depression and Anxiety (Preprint)

BACKGROUND Depression and anxiety are prevalent but commonly missed and misdiagnosed, an important concern because many patients do not experience spontaneous recovery and duration of untreated illness is associated with worse outcomes. OBJECTIVE This study explores the potential of using smartphone-tracked behavioral markers to support diagnostics and improve recognition of these disorders. METHODS We used the dedicated Behapp digital phenotyping platform to passively track location and app usage in 217 individuals, comprising symptomatic (n=109; depression/anxiety diagnosis or symptoms) and asymptomatic individuals (n=108; no diagnosis/symptoms). After quantifying 46 behavioural markers (e.g., % time at home), we applied a machine learning approach to (1) determine which markers are relevant for depression/anxiety recognition and (2) develop and evaluate diagnostic prediction models for doing so. RESULTS Our analysis identifies the total number of GPS-based trajectories as a potential marker of depression/anxiety, where individuals with fewer trajectories are more likely symptomatic. Models using this feature in combination with demographics or in isolation outperformed demographics-only models (AUCMdn=0.60 vs 0.60 vs 0.51). CONCLUSIONS Collectively, these findings indicate that smartphone-tracked behavioural markers have limited discriminant ability in our study but potential to support future depression/anxiety diagnostics.