Skip to main content Accessibility help
×
Home
Hostname: page-component-768ffcd9cc-727vs Total loading time: 0.439 Render date: 2022-12-01T21:33:21.103Z Has data issue: true Feature Flags: { "useRatesEcommerce": false } hasContentIssue true

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*
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

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.

Type
Original Article
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

These authors contributed equally to this work.

References

Bowman, C., Huang, Y., Walch, O. J., Fang, Y., Frank, E., Tyler, J., … Forger, D. B. (2021). A method for characterizing daily physiology from widely used wearables. Cell Reports Methods, 1(4), 100058. doi: 10.1016/j.crmeth.2021.100058CrossRefGoogle ScholarPubMed
Cho, C. H., Ahn, Y. M., Kim, S. J., Ha, T. H., Jeon, H. J., Cha, B., … Lee, H. J. (2017). Design and methods of the mood disorder cohort research consortium (MDCRC) study. Psychiatry Investigation, 14(1), 100. doi: 10.4306/pi.2017.14.1.100CrossRefGoogle Scholar
Cho, C. H., Lee, T., Kim, M. G., In, H. P., Kim, L., & Lee, H. J. (2019). Mood prediction of patients with mood disorders by machine learning using passive digital phenotypes based on the circadian rhythm: Prospective observational cohort study. Journal of Medical Internet Research, 21(4), e11029. doi: 10.2196/11029CrossRefGoogle ScholarPubMed
Darcy, A. M., Louie, A. K., & Roberts, L. W. (2016). Machine learning and the profession of medicine. Jama, 315(6), 551552. doi: 10.1001/jama.2015.18421CrossRefGoogle ScholarPubMed
Dunlap, J. C. (1999). Molecular bases for circadian clocks. Cell, 96(2), 271290. doi: 10.1016/s0092-8674(00)80566-8CrossRefGoogle ScholarPubMed
Etain, B., Meyrel, M., Hennion, V., Bellivier, F., & Scott, J. (2021). Can actigraphy be used to define lithium response dimensions in bipolar disorders? Journal of Affective Disorders, 283, 402409. doi: 10.1016/j.jad.2021.01.060CrossRefGoogle ScholarPubMed
Ethem, A. (2014). Introduction to machine learning. Cambridge, MA: The MIT Press.Google Scholar
Garcia-Ceja, E., Riegler, M., Jakobsen, P., Torresen, J., Nordgreen, T., Oedegaard, K. J., & Fasmer, O. B. (2018). Motor activity based classification of depression in unipolar and bipolar patients. In 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)(pp. 316–321). IEEE. doi: 10.1109/CBMS.2018.00062CrossRefGoogle Scholar
Harvey, A. G. (2008). Sleep and circadian rhythms in bipolar disorder: Seeking synchrony, harmony, and regulation. American Journal of Psychiatry, 165(7), 820829. doi: 10.1176/appi.ajp.2008.08010098CrossRefGoogle ScholarPubMed
Jacobson, N. C., Weingarden, H., & Wilhelm, S. (2019). Digital biomarkers of mood disorders and symptom change. NPJ Digital Medicine, 2(1), 13. doi: 10.1038/s41746-019-0078-0CrossRefGoogle ScholarPubMed
Jain, S. H., Powers, B. W., Hawkins, J. B., & Brownstein, J. S. (2015). The digital phenotype. Nature Biotechnology, 33(5), 462463. doi: 10.1038/nbt.3223CrossRefGoogle ScholarPubMed
Jaussent, I., Bouyer, J., Ancelin, M. L., Akbaraly, T., Peres, K., Ritchie, K., … Dauvilliers, Y. (2011). Insomnia and daytime sleepiness are risk factors for depressive symptoms in the elderly. Sleep, 34(8), 11031110. doi: 10.5665/SLEEP.1170CrossRefGoogle ScholarPubMed
Jeong, S., Seo, J. Y., Jeon, S., Cho, C. H., Yeom, J. W., Jeong, J., … Lee, H. J. (2020). Circadian rhythm of heart rate assessed by wearable devices tends to correlate with the circadian rhythm of salivary cortisol concentration in healthy young adults. Chronobiology in Medicine, 2(3), 109114. doi: 10.33069/cim.2020.0022CrossRefGoogle Scholar
Judd, L. L., Akiskal, H. S., Schettler, P. J., Endicott, J., Maser, J., Solomon, D. A., … Keller, M. B. (2002). The long-term natural history of the weekly symptomatic status of bipolar I disorder. Archives of General Psychiatry, 59(6), 530537. doi: 10.1001/archpsyc.59.6.530CrossRefGoogle ScholarPubMed
Knowles, J. B., Cairns, J., MacLean, A. W., Delva, N., Prowse, A., Waldron, J., & Letemendia, F. J. (1986). The sleep of remitted bipolar depressives: Comparison with sex and age-matched controls. The Canadian Journal of Psychiatry, 31(4), 295298. doi: 10.1177/070674378603100402CrossRefGoogle ScholarPubMed
Kraepelin, E. (1906). Über sprachstörungen im traume. Leipzig: Engelmann.Google Scholar
Lee, H. J. (2019). Circadian misalignment and bipolar disorder. Chronobiology in Medicine, 1(4), 132136. doi: 10.33069/cim.2019.0027CrossRefGoogle Scholar
McClung, C. A. (2007). Circadian genes, rhythms and the biology of mood disorders. Pharmacology & Therapeutics, 114(2), 222232. doi: 10.1016/j.pharmthera.2007.02.003CrossRefGoogle ScholarPubMed
Moon, J. H., Cho, C. H., Son, G. H., Geum, D., Chung, S., Kim, H., … Lee, H. J. (2016). Advanced circadian phase in mania and delayed circadian phase in mixed mania and depression returned to normal after treatment of bipolar disorder. EBioMedicine, 11, 285295. doi: 10.1016/j.ebiom.2016.08.019CrossRefGoogle ScholarPubMed
Pavlova, B., & Uher, R. (2020). Assessment of psychopathology: Is asking questions good enough? JAMA Psychiatry, 77(6), 557558. doi: 10.1001/jamapsychiatry.2020.0108CrossRefGoogle ScholarPubMed
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 28252830. doi: 10.5555/1953048.2078195Google Scholar
Saeb, S., Lattie, E. G., Schueller, S. M., Kording, K. P., & Mohr, D. C. (2016). The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ, 4, e2537. doi: 10.7717/peerj.2537CrossRefGoogle ScholarPubMed
Saeb, S., Zhang, M., Karr, C. J., Schueller, S. M., Corden, M. E., Kording, K. P., & Mohr, D. C. (2015). Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: An exploratory study. Journal of Medical Internet Research, 17(7), e4273. doi: 10.2196/jmir.4273 PMID: 26180009CrossRefGoogle ScholarPubMed
Seabold, S., & Perktold, J. (2010). Statsmodels: Econometric and statistical modeling with python. In Proceedings of the 9th Python in Science Conference (Vol. 57, p. 61).CrossRefGoogle Scholar
Shapley, L. S. (1953). A value for n-person games. In Kuhn, H. W. & Tucker, A. W. (Eds.), Contributions to the theory of games (AM-28) (Vol II, pp. 307318). New Jersey, NY: Princeton University Press.Google Scholar
Silver, R., & Kriegsfeld, L. J. (2014). Circadian rhythms have broad implications for understanding brain and behavior. European Journal of Neuroscience, 39(11), 18661880. doi: 10.1111/ejn.12593CrossRefGoogle ScholarPubMed
Supplementary material: File

Lee et al. supplementary material

Lee et al. supplementary material

Download Lee et al. supplementary material(File)
File 46 KB
1
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

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
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

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
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

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
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *