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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

Published online by Cambridge University Press:  23 September 2022

Heon-Jeong Lee Open the ORCID record for Heon-Jeong Lee [Opens in a new window] ,
Chul-Hyun Cho ,
Taek Lee ,
Jaegwon Jeong ,
Ji Won Yeom ,
Sojeong Kim ,
Sehyun Jeon ,
Ju Yeon Seo ,
Eunsoo Moon  and
Ji Hyun Baek
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Heon-Jeong Lee*
Affiliation:
Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea Chronobiology Institute, Korea University, Seoul, Republic of Korea
Chul-Hyun Cho
Affiliation:
Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea Chronobiology Institute, Korea University, Seoul, Republic of Korea
Taek Lee*
Affiliation:
Department of Convergence Security Engineering, Sungshin University, Seoul, Republic of Korea
Jaegwon Jeong
Affiliation:
Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea Chronobiology Institute, Korea University, Seoul, Republic of Korea
Ji Won Yeom
Affiliation:
Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea Chronobiology Institute, Korea University, Seoul, Republic of Korea
Sojeong Kim
Affiliation:
Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea Chronobiology Institute, Korea University, Seoul, Republic of Korea
Sehyun Jeon
Affiliation:
Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea Chronobiology Institute, Korea University, Seoul, Republic of Korea
Ju Yeon Seo
Affiliation:
Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea Chronobiology Institute, Korea University, Seoul, Republic of Korea
Eunsoo Moon
Affiliation:
Department of Psychiatry, Pusan National University School of Medicine, Busan, Republic of Korea
Ji Hyun Baek
Affiliation:
Department of Psychiatry, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
Dong Yeon Park
Affiliation:
Department of Psychiatry, National Center for Mental Health, Seoul, Republic of Korea
Se Joo Kim
Affiliation:
Department of Psychiatry and Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
Tae Hyon Ha
Affiliation:
Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
Boseok Cha
Affiliation:
Department of Psychiatry, Gyeongsang National University College of Medicine, Jinju, Republic of Korea
Hee-Ju Kang
Affiliation:
Department of Psychiatry, Chonnam National University College of Medicine, Gwangju, Republic of Korea
Yong-Min Ahn
Affiliation:
Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
Yujin Lee
Affiliation:
Seoul Metropolitan Eunpyeong Hospital, Seoul, Republic of Korea
Jung-Been Lee
Affiliation:
Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea Chronobiology Institute, Korea University, Seoul, Republic of Korea
Leen Kim
Affiliation:
Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea Chronobiology Institute, Korea University, Seoul, Republic of Korea
*
Author for correspondence: Heon-Jeong Lee, E-mail: leehjeong@korea.ac.kr; Taek Lee, E-mail: comtaek@sungshin.ac.kr
Author for correspondence: Heon-Jeong Lee, E-mail: leehjeong@korea.ac.kr; Taek Lee, E-mail: comtaek@sungshin.ac.kr
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Abstract

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.

Keywords

Circadian rhythmsmachine learningmood disorderspredictionwearable devices
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 12 , September 2023 , pp. 5636 - 5644
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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Footnotes

*

These authors contributed equally to this work.

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