Self-assessment and rest-activity monitoring for effective bipolar disorder management: a longitudinal actigraphy study

BackgroundRecurrent course and disruption of circadian rhythms are among the core features of bipolar disorder (BD). Thus, ongoing symptom monitoring is an essential part of good clinical management. ObjectiveWe conducted a study to validate the English version of the ASERT (Aktibipo questionnaire), a tool for self-assessment of mood symptoms. We also analyzed the relationship of self-assessed symptoms with clinician ratings and actigraphy measures, and investigated the possibility of predicting depressive episodes using subjective and digital measures. MethodsThis was a longitudinal study of individuals with BD, followed for up to 11 months. The participants completed weekly mood self-assessments (ASERT) using a smartphone app and wore wrist actigraphs. During monthly appointments, the severity of their mood symptoms was rated by clinicians, and the participants completed questionnaires addressing overall functioning (FAST), and biological rhythms (BRIAN). ResultsThe study confirmed the validity and reliability of the ASERT as a measure of subjective mood. Additionally, we found significant associations between ASERT responses, clinical scales, and actigraphy data. In our analysis, a combination of self-assessment and actigraphy data detected depression relapse with 67% sensitivity, 90% specificity, and 81% balanced accuracy. Furthermore, we observed a strong correlation between the stability of daily routine and overall functioning, emphasizing the significance of circadian rhythm disruptions in BD. ConclusionThis study highlights the potential of digital tools, such as digitally administered self-assessments and actigraphy, to enhance the management of BD by providing valuable insights into mood states and detecting relapse. Further research is needed to refine and optimize these tools for widespread clinical application, such as informing personalized treatment plans.