Daytime midpoint as a digital biomarker for chronotype in bipolar disorder

Sep 2, 2018Journal of affective disorders

Midday timing from digital data as a marker of daily rhythm type in bipolar disorder

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Abstract

Participants with bipolar disorder had a significantly delayed daytime midpoint of 16:49:07 compared to healthy controls at 16:12:51 (p < 0.01).

Bipolar disorder is associated with greater variability in daytime activity midpoints compared to healthy controls.
Females and adolescents with bipolar disorder exhibited more pronounced delays and variability in daytime midpoints.
Adults with bipolar disorder showed increased variability in daytime activity midpoints relative to controls.
Daytime midpoints calculated from wrist actigraphy correlated highly with sleep midpoints, indicating potential overlap in sleep and activity patterns.
The daytime midpoint mean was also significantly correlated with results from the Composite Scale of Morningness.

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BACKGROUND: Bipolar disorder (BD) is associated with later sleep and daily activity (evening rather than morning chronotype). Objective chronotype identification (e.g., based on actigraphs/smartphones) has potential utility, but to date, chronotype has mostly been assessed by questionnaires. Given the ubiquity of accelerometer-based devices (e.g. actigraphs/smartphones) worn/used during daytime and tendency to recharge rather than wear at night, we assessed chronotype using daytime (rather than sleep) interval midpoints.

METHODS: Sixty-one participants with BD type I (BD-I) or II (BD-II) and 61 healthy controls completed 25-50 days of continuous actigraphy. The Composite Scale of Morningness (CSM) was completed by a subset of this group. Daytime activity midpoint was calculated for each daytime interval, excluding naps. Evening chronotype was defined as having a daytime interval midpoint at or after 16:15:00 (4:15:00 PM).

RESULTS: BD versus controls had delayed daytime midpoint (mean ± standard deviation) (16:49:07 ± 01:26:19 versus 16:12:51 ± 01:02:14, p < 0.01), and greater midpoint variability (73.3 ± 33.9 min versus 58.1 ± 18.3 min, p < 0.01). Stratifying by gender and age, females and adolescents with BD had delayed and more variable daytime midpoints versus controls. Adults with BD had greater midpoint variability than controls. Within-person mean and standard deviations of daytime midpoints were highly correlated with sleep midpoints (r = 0.99, p < 0.01 and r = 0.86, p < 0.01, respectively). Daytime midpoint mean was also significantly correlated with the CSM (r = -0.56, p < 0.01).

LIMITATIONS: Small sample size; analyses not fully accounting for daytime napping.

CONCLUSIONS: Wrist actigraphy for determination of daytime midpoints is a potential tool to identify objective chronotype. Exploration of the use of consumer devices (wearables/smartphones) is needed.

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Daytime midpoint as a digital biomarker for chronotype in bipolar disorder

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