Predicting anxiety state using smartphone-based passive sensing

[Display omitted] •Predict the change of anxiety using various smartphone’s passive sensing logs.•Create co-occurring combination of features by categorization of features.•Collect both daily anxiety state and smartphone logs from 20 healthy participants.•Co-occurring feature contributes great improvement on F-score (70.2->74.2%).•Viewing the smartphone in dark is the key in detecting increases in anxiety. This study predicts the change of stress levels using real-world and online behavioral features extracted from smartphone log information. Previous studies of stress detection using smartphone data focused on a single feature and did not consider all features simultaneously. We propose a method to extract a co-occurring combination of a user’s real-world and online behavioral features by converting raw sensor data into categorical features. We conducted an experiment in which the State Trait Anxiety Inventory (STAI) was used to assess the anxiety-related stress levels of 20 healthy participants. The participants installed a log-collecting application on their smartphones and answered the STAI questions once a day for one month. The proposed method showed an F-score of 74.2%, which is 4.0% higher than the F-score of previous studies (70.2%) that used single non-combined features. The results demonstrate that anxiety-related stress levels can be predicted using combined features extracted from smartphone log data.