The research team led by Chief Investigator Kim Jae Kyoung (IBS Biomedical Mathematics Group, and Professor at KAIST) and Professor Lee Heon-Jeong (Korea University College of Medicine) has developed a novel model that can predict mood episodes in mood disorder patients using only sleep and circadian rhythm data collected from wearable devices.

Mood disorders are closely associated with irregularities in sleep and circadian rhythms. With the growing popularity of wearable devices like smartwatches, it is now easier than ever to collect health data in everyday life, highlighting the importance of analyzing sleep-wake patterns for predicting mood episodes. However, existing models require diverse data types, making data collection costly and limiting practical application.

To overcome these limitations, the research team developed a model that predicts mood episodes using only sleep-wake pattern data. By analyzing 429 days of data from 168 mood disorder patients, the team extracted 36 sleep and circadian rhythm features. Applying these features to machine learning algorithms, they achieved highly accurate predictions for depressive, manic, and hypomanic episodes (AUCs: 0.80, 0.98, and 0.95, respectively). The paper is published in npj Digital Medicine,

The study found that daily changes in circadian rhythm are a key predictor of mood episodes. Specifically, delayed circadian rhythms increase the risk of depressive episodes, while advanced circadian rhythms increase the risk of manic episodes. This discovery opens new possibilities for tracking individual circadian rhythm changes to predict future mood episodes.

Professor Heon-Jeong commented, “This study demonstrates the potential of using only sleep-wake data from wearable devices to predict mood episodes, increasing the feasibility of real-world applications. We envision a future where mood disorder patients can receive personalized sleep pattern recommendations through a smartphone app to prevent mood episodes.”

Chief Investigator Kyoung added, “By developing a model that predicts mood episodes based solely on sleep-wake pattern data, we have reduced the cost of data collection and significantly improved clinical applicability. This study offers new possibilities for cost-effective diagnosis and treatment of mood disorder patients.”