Summary: Researchers have developed a machine-learning model that predicts mood episodes in mood disorder patients using only sleep and circadian rhythm data collected from wearable devices. By analyzing over 400 days of data from 168 patients, the model demonstrated high accuracy in predicting depressive, manic, and hypomanic episodes, reducing the need for costly and complex data collection. The findings highlight the importance of circadian rhythm changes in mood episode prediction and pave the way for cost-effective, real-world applications in managing mood disorders.
Key Takeaways:
- Sleep Data Predicts Mood Episodes: The model uses sleep and circadian rhythm data from wearable devices to accurately predict depressive, manic, and hypomanic episodes.
- Circadian Rhythms as Key Indicators: Delayed circadian rhythms were linked to depressive episodes, while advanced rhythms were associated with manic episodes, offering new insights into mood episode triggers.
- Real-World Application Feasibility: By relying solely on sleep-wake data, the model reduces costs and increases practicality, enabling personalized recommendations through wearable devices or smartphone apps for mood disorder patients, according to researchers.
Researchers have developed a 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 led by chief investigator Kim Jae Kyoung, PhD, of IBS Biomedical Mathematics Group and a professor at KAIST and professor Lee Heon-Jeong of Korea University College of Medicine 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).
Circadian Rhythm Changes Predictive of Mood Episodes
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.
“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,” says Heon-Jeong in a release. “We envision a future where mood disorder patients can receive personalized sleep pattern recommendations through a smartphone app to prevent mood episodes.”
Jae Kyoung adds in a release, “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.”
The results of this study are published in npj Digital Medicine.
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