Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder

Background Traditionally, assessment of psychiatric symptoms has been relying on their retrospective report to a trained interviewer. The emergence of smartphones facilitates passive sensor-based monitoring and active real-time monitoring through time-stamped prompts; however there are few validated self-report measures designed for this purpose. Methods We introduce a novel, compact questionnaire, Mood Zoom (MZ), embedded in a customized smartphone application. MZ asks participants to rate anxiety, elation, sadness, anger, irritability and energy on a 7-point Likert scale. For comparison, we used four standard clinical questionnaires administered to participants weekly to quantify mania (ASRM), depression (QIDS), anxiety (GAD-7), and quality of life (EQ-5D). We monitored 48 Bipolar Disorder (BD), 31 Borderline Personality Disorder (BPD) and 51 Healthy Control (HC) participants to study longitudinal (median±iqr: 313±194 days) variation and differences of mood traits by exploring the data using diverse time-series tools. Results MZ correlated well (|R| > 0.5, p 0.3, p Limitations All reported scores rely on self-assessment; there is a lack of ongoing clinical assessment by experts to validate the findings. Conclusions MZ could be used for efficient, long-term, effective daily monitoring of mood instability in clinical psychiatric practice.