Prediction of impending mood episode recurrence using real-time digital phenotypes in major depression and bipolar disorders in South Korea: a prospective nationwide cohort study

Sep 23, 2022Psychological medicine

Using real-time digital data to predict mood episode return in major depression and bipolar disorder in South Korea

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Abstract

Prediction accuracies for impending mood episodes are 90.1% for major depressive episodes, 92.6% for manic episodes, and 93.0% for hypomanic episodes.

A total of 495 patients with mood disorders were followed for an average of 279.7 days.
Two hundred seventy mood episodes recurred in 135 subjects during the follow-up.
Digital phenotypes related to circadian rhythm were used to predict mood episode recurrences.
The area under the curve values for predicting mood episodes were 0.937 for major depressive episodes, 0.957 for manic episodes, and 0.963 for hypomanic episodes.
Circadian rhythm misalignment was identified as a significant factor in predicting mood episode recurrences.

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BACKGROUND: Mood disorders require consistent management of symptoms to prevent recurrences of mood episodes. Circadian rhythm (CR) disruption is a key symptom of mood disorders to be proactively managed to prevent mood episode recurrences. This study aims to predict impending mood episodes recurrences using digital phenotypes related to CR obtained from wearable devices and smartphones.

METHODS: The study is a multicenter, nationwide, prospective, observational study with major depressive disorder, bipolar disorder I, and bipolar II disorder. A total of 495 patients were recruited from eight hospitals in South Korea. Patients were followed up for an average of 279.7 days (a total sample of 75 506 days) with wearable devices and smartphones and with clinical interviews conducted every 3 months. Algorithms predicting impending mood episodes were developed with machine learning. Algorithm-predicted mood episodes were then compared to those identified through face-to-face clinical interviews incorporating ecological momentary assessments of daily mood and energy.

RESULTS: Two hundred seventy mood episodes recurred in 135 subjects during the follow-up period. The prediction accuracies for impending major depressive episodes, manic episodes, and hypomanic episodes for the next 3 days were 90.1, 92.6, and 93.0%, with the area under the curve values of 0.937, 0.957, and 0.963, respectively.

CONCLUSIONS: We predicted the onset of mood episode recurrences exclusively using digital phenotypes. Specifically, phenotypes indicating CR misalignment contributed the most to the prediction of episodes recurrences. Our findings suggest that monitoring of CR using digital devices can be useful in preventing and treating mood disorders.

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Prediction of impending mood episode recurrence using real-time digital phenotypes in major depression and bipolar disorders in South Korea: a prospective nationwide cohort study

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