Background: Daily life tracking has proven to be of great help in the assessment of patients with bipolar disorder. Although there are many smartphone apps for tracking bipolar disorder, most of them lack academic verification, privacy policy and long-term maintenance.
Methods: Our developed app, MoodSensing, aims to collect users' digital phenotyping for assessment of bipolar disorder. The data collection was approved by the Institutional Review Board. This study collaborated with professional clinicians to ensure that the app meets both clinical needs and user experience requirements. Based on the collected digital phenotyping, deep learning techniques were applied to forecast participants' weekly HAM-D and YMRS scale scores.
Results: In experiments, the data collected by our app can effectively predict the scale scores, reaching the mean absolute error of 0.84 and 0.22 on the scales. The statistical data also demonstrate the increase in user engagement.
Conclusions: Our analysis reveals that the developed MoodSensing app can not only provide a good user experience, but also the recorded data have certain discriminability for clinical assessment. Our app also provides relevant policies to protect user privacy, and has been launched in the Apple Store and Google Play Store.
Keywords: App; Bipolar disorder; Digital phenotyping; HAM-D; Smartphones; YMRS.
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