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Sleep disturbance is a transdiagnostic risk factor that is so prevalent among young adults that it is considered a public health epidemic, which has been exacerbated by the COVID-19 pandemic. Sleep may contribute to mental health via affect dynamics. Prior literature on the contribution of sleep to affect is largely based on correlational studies or experiments that do not generalize to the daily lives of young adults. Furthermore, the literature examining the associations between sleep variability and affect dynamics remains scant.
In an ecologically valid context, using an intensive longitudinal design, we aimed to assess the daily and long-term associations between sleep patterns and affect dynamics among young adults during the COVID-19 pandemic.
College student participants (N=20; female: 13/20, 65%) wore an Oura ring (Ōura Health Ltd) continuously for 3 months to measure sleep patterns, such as average and variability in total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, and sleep onset latency (SOL), resulting in 1173 unique observations. We administered a daily ecological momentary assessment by using a mobile health app to evaluate positive affect (PA), negative affect (NA), and COVID-19 worry once per day.
Participants with a higher sleep onset latency (
Fluctuating sleep patterns are associated with affect dynamics at the daily and long-term scales. Low PA and affect variability may be potential pathways through which sleep has implications for mental health.
Sleep is a robust and transdiagnostic risk factor for various physical and mental health problems, including mood disorders [
Insufficient sleep and sleep variability may contribute to physical and mental health via affect dynamics. Positive affect (PA) and negative affect (NA)—broadband indices of emotion—are well-established predictors of well-being [
Prior literature has mainly focused on the negative consequences that sleep deprivation has for average PA and NA, finding that sleep duration significantly predicts dampened PA and elevated NA [
The aim of this study was therefore to assess (1) the daily associations between sleep and affect and (2) the long-term associations between sleep patterns (ie, average and variability in total sleep time [TST], wake after sleep onset [WASO], sleep efficiency, sleep onset latency [SOL]) and affect dynamics (ie, mean levels of PA and NA, PA and NA variability, and COVID-19 worry) dynamics among young adults during the COVID-19 pandemic across a 3-month period. Examining these associations at the between- and within-person levels will allow for an improved understanding of the development of comorbid sleep and mood disorders and support the identification of early intervention windows for at-risk individuals. This study was designed to examine sleep among young adults in a 3-month period; however, the period of assessment varied from 1 month to 3 months due to retention. The majority of previous studies have assessed subjective and objective sleep outcomes in a 14-day period [
College student participants (N=20; female: 13/20, 65%; age: mean 19.80, SD 1.0 years) were assessed daily across a 3-month period during the 2020 COVID-19 pandemic (June to November), resulting in 1173 unique observations. Participants were eligible if they met the following criteria: participants must be unmarried, be English speakers, be full-time undergraduate students aged between 18 and 22 years, and own a primary Android smartphone that is compatible with the ecological momentary assessment (EMA) phone-based survey apps and study wearable devices.
This study was part of a larger intensive longitudinal study for examining student mental health that included physiological assessments, sleep tracking, and daily emotional and behavioral reports. The procedures of this study were approved by the institutional review board (approval number: 2019-5153) at University of California, Irvine. All individuals provided written informed consent prior to participation.
Herein, we describe the procedures that are relevant to the purposes of our investigation. Participants were first instructed on how to wear the noninvasive device (ie, Oura ring [Ōura Health Ltd]) that continuously assessed sleep, activity, and physiology throughout the day and during sleep [
By using the Oura ring (specifications: 2 infrared light-emitting diode heart rate sensors, 2 negative thermal coefficient body temperature sensors, a 3-axis accelerometer, and a gyroscope), TST, WASO, sleep efficiency, and SOL were calculated through the detection and interpretation of physiological measures, including heart rate, heart rate variability, and pulse wave variability amplitude. Previous studies have compared the Oura ring to polysomnography—the gold standard of sleep measurement—and research-grade actigraphy (Philips Respironics). A study by Chee et al [
As part of the EMA phone-based surveys, participants reported daily PA and NA by using the Positive and Negative Affect Schedule (PANAS) [
To determine the variability of each sleep and affect variable, we first created a series of successive differences by calculating the difference between 2 successive observations within the same subject (eg, night 2 − night 1; night 3 − night 2; etc). Next, these values were squared. We used the square successive differences to compute a mean square successive difference score. Finally, we calculated the root mean square successive difference score for each participant. The root mean square successive difference is considered an index of variability that is similar to the intraindividual variance of a series of observations but is more sensitive to fluctuations across successive observations [
We first examined variables for normality and heteroscedasticity. To examine the association between sleep (TST, sleep efficiency, and SOL) and affect variables (PA, NA, and COVID-19 worry), we first conducted multilevel models by using the restricted maximum likelihood approach. This approach improves estimates of variance components and fixed effect SE estimates in smaller samples by separating the estimates of the fixed effects from the variance components [
A total of 20 college students (female: 13/20, 65%) completed this study, providing 1623 (mean 43.49, SD 25.51 days/person) nights of usable Oura ring sleep data. Completion rates for EMA studies were high (83%).
Table S1 in
The multilevel models of the relation between sleep and affect revealed that participants with a higher SOL (
The regression model for predicting PA from average TST accounted for 34% of the variance in average PA (adjusted
SOL and sleep efficiency predicted COVID-19 worry variability. The multiple regression model for predicting COVID-19 worry variability from the average SOL and sex accounted for 38% of the variance in COVID-19 worry variability (adjusted
The multiple regression models for predicting affect variability from TST variability, while controlling for sex, accounted for 36% of the variance in PA variability (adjusted
The hierarchical regression models showed that TST variability predicted PA variability above and beyond the average TST (adjusted
Sleep patterns across the daily and long-term scales were associated with daily and average affect and affect variability, which were assessed over a 3-month period. These findings are consistent with those of prior work suggesting that poor sleep confers a heightened risk for affective disturbances that are prevalent in mood disorders, such as depression [
Individuals with longer sleep times on the previous night experienced lower PA on the next day. Similarly, individuals with longer average TSTs over the study period reported lower PA. Previous studies however have suggested a positive association between sleep duration and greater PA (eg, a study by Galambos et al [
TST variability predicted higher affect variability across all affect domains (ie, PA, NA, COVID-19 worry); thus, it may be a proximal predictor of mood disturbances. Affect variability is prevalent in various mood-related disorders, such as depression, bipolar disorder, and anxiety disorders [
Our findings also provide specific relevance to the COVID-19 context. Individuals with a higher SOL experienced higher COVID-19 worry variability, and individuals with a higher sleep efficiency reported lower COVID-19 worry variability. The specificity of the associations between SOL and sleep efficiency, and COVID-19 worry variability (ie, not NA in general) may suggest that the association between sleep and arousal-related affect is unique. Sleep disturbances may contribute to the lower capacity to adaptively overcome stress and therefore may be associated with higher stress-related sleep reactivity and cognitive presleep hyperarousal [
Our findings carry some limitations. First, whether sleep causally predicts affect remains unclear. Daily affect may also predict multiple sleep indices [
Fluctuating sleep patterns are associated with affect dynamics, such as average PA and affect variability, across all affect domains (ie, PA, NA, COVID-19 worry) at the daily and long-term scales. Low PA and affect variability may be potential pathways through which sleep has implications for mental health. Interventions that target sleep stability may indirectly reduce affect variability and therefore prevent mood disorders. The stabilization of affect may be an early marker or predictor of the efficacy of transdiagnostic sleep interventions that target mood disorders with anhedonic features.
Means, SDs, and correlations between sleep and affect variables.
Adjusted estimates for predicting average affect from objective sleep, gender, and age.
Adjusted estimates for predicting affect variability from objective sleep, gender, and age.
Unstandardized coefficient estimates in models for predicting daily sleep by daily affect.
ecological momentary assessment
negative affect
positive affect
Positive and Negative Affect Schedule
sleep onset latency
total sleep time
wake after sleep onset
This study was funded by Donald Bren Foundation (principal investigator: RCJ) as well as the University of California, Irvine, Undergraduate Research Opportunities Program (faculty sponsor: JLB).
ZAM was responsible for study conceptualization and design, conducted the data analysis, and drafted the manuscript. JL assisted with study conceptualization and design, edited the manuscript, and contributed to the analytic plan. KS contributed to study conceptualization, to contextualizing the contribution of the study within the literature, and to manuscript editing. APR, AY, and SH assisted with data preparation and manuscript writing. SL and SJ created computing models that enabled data collection and assisted with data preparation and manuscript writing. NDD, RCJ, and AMR assisted with study conceptualization and design, created computing models that enabled data collection, and assisted with data preparation and manuscript writing. JLB assisted with study conceptualization and design, edited the manuscript, and contributed to the analytic plan. All authors edited and approved the manuscript.
None declared.