Anticipating mood episodes in bipolar disorder using the BipoSense dataset: exploring multiple digital phenotyping approaches.
Anticipating mood episodes in patients with bipolar disorder (BD) is a critical challenge, with digital phenotyping holding significant promise as a tool for early detection. This talk will explore different methodological approaches to predicting emerging mood episodes in patients with BD.
Using the BipoSense dataset, which includes 12 months of daily e-diary entries and continuous passive sensing data collected via smartphone, and bi-weekly expert interviews to assess affective status, the talk will cover methods such as Critical Slowing Down as an early warning sign for emerging mood episodes, and Statistical Process Control, which adapts techniques from industrial production processes to detect ‘out-of-bounds’ behaviors in passive sensing data.
The goal is to identify reliable predictors for emerging mood episodes, with the potential for future clinical application. This talk will synthesize insights from these approaches and discuss future directions for leveraging digital phenotyping to enhance personalized care for BD patients.
Organised by: the ECNP Digital Health Network.
Speaker: Vera Ludwig, Germany
About the GetDigital webinar series
Digital approaches are fundamentally changing traditional ways of diagnosis, monitoring, management, and treatment of brain disorders worldwide. GetDigital is a webinar series on mHealth organised by the ECNP Digital Health Applied to the Clinical Research of Brain Disorders Network.
This online series aim at sharing and distributing new and high-quality research to researchers and clinicians, educating Early Career Scientists across Europe, and foster collaborations across research groups.
Explore the GetDigital series here.
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