Greater variability in daily sleep efficiency predicts depression and anxiety in young adults: Estimation of depression severity using the two-week sleep quality records of wearable devices

Jae-A Lim; Je-Yeon Yun; Soo-Hee Choi; Susan Park; Hye Won Suk; Joon Hwan Jang

doi:10.3389/fpsyt.2022.1041747

Greater variability in daily sleep efficiency predicts depression and anxiety in young adults: Estimation of depression severity using the two-week sleep quality records of wearable devices

Front Psychiatry. 2022 Nov 7:13:1041747. doi: 10.3389/fpsyt.2022.1041747. eCollection 2022.

Authors

Jae-A Lim^{1

2

3}, Je-Yeon Yun^{4

5}, Soo-Hee Choi⁶, Susan Park¹, Hye Won Suk^{2

3}, Joon Hwan Jang^{1

7}

Affiliations

¹ Department of Psychiatry, Seoul National University Health Service Center, Seoul, South Korea.
² Department of Psychology, Sogang University, Seoul, South Korea.
³ Institute for Hope Research, Sogang University, Seoul, South Korea.
⁴ Seoul National University Hospital, Seoul, South Korea.
⁵ Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, South Korea.
⁶ Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea.
⁷ Department of Human Systems Medicine, Seoul National University College of Medicine, Seoul, South Korea.

Abstract

Objectives: Sleep disturbances are associated with both the onset and progression of depressive disorders. It is important to capture day-to-day variability in sleep patterns; irregular sleep is associated with depressive symptoms. We used sleep efficiency, measured with wearable devices, as an objective indicator of daily sleep variability.

Materials and methods: The total sample consists of 100 undergraduate and graduate students, 60% of whom were female. All were divided into three groups (with major depressive disorder, mild depressive symptoms, and controls). Self-report questionnaires were completed at the beginning of the experiment, and sleep efficiency data were collected daily for 2 weeks using wearable devices. We explored whether the mean value of sleep efficiency, and its variability, predicted the severity of depression using dynamic structural equation modeling.

Results: More marked daily variability in sleep efficiency significantly predicted levels of depression and anxiety, as did the average person-level covariates (longer time in bed, poorer quality of life, lower extraversion, and higher neuroticism).

Conclusion: Large swings in day-to-day sleep efficiency and certain clinical characteristics might be associated with depression severity in young adults.

Keywords: depression; dynamic structural equation models; sleep efficiency; variability; wearable device.