Christopher N Kaufmann1, Anda Gershon2, Colin A Depp3, Shefali Miller2, Jamie M Zeitzer2, Terence A Ketter2. 1. Division of Geriatrics and Gerontology, Department of Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, USA. Electronic address: cnkaufmann@ucsd.edu. 2. Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA. 3. Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego School of Medicine, La Jolla, CA, USA.
Abstract
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.
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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