Irregular sleep and event schedules are associated with poorer self-reported well-being in US college students

Abstract

Study objectives: Sleep regularity, in addition to duration and timing, is predictive of daily variations in well-being. One possible contributor to changes in these sleep dimensions are early morning scheduled events. We applied a composite metric-the Composite Phase Deviation (CPD)-to assess mistiming and irregularity of both sleep and event schedules to examine their relationship with self-reported well-being in US college students.

Methods: Daily well-being, actigraphy, and timing of sleep and first scheduled events (academic/exercise/other) were collected for approximately 30 days from 223 US college students (37% females) between 2013 and 2016. Participants rated well-being daily upon awakening on five scales: Sleepy-Alert, Sad-Happy, Sluggish-Energetic, Sick-Healthy, and Stressed-Calm. A longitudinal growth model with time-varying covariates was used to assess relationships between sleep variables (i.e. CPDSleep, sleep duration, and midsleep time) and daily and average well-being. Cluster analysis was used to examine relationships between CPD for sleep vs. event schedules.

Results: CPD for sleep was a significant predictor of average well-being (e.g. Stressed-Calm: b = -6.3, p < 0.01), whereas sleep duration was a significant predictor of daily well-being (Stressed-Calm, b = 1.0, p < 0.001). Although cluster analysis revealed no systematic relationship between CPD for sleep vs. event schedules (i.e. more mistimed/irregular events were not associated with more mistimed/irregular sleep), they interacted upon well-being: the poorest well-being was reported by students for whom both sleep and event schedules were mistimed and irregular.

Conclusions: Sleep regularity and duration may be risk factors for lower well-being in college students. Stabilizing sleep and/or event schedules may help improve well-being.

Clinical trial registration: NCT02846077.

Keywords: intra-individual variability; mental health; mood; public health; sleep and stress; sleep regularity; social jet lag; stress; well-being.

© Sleep Research Society 2019. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Figures

Figure 1.

Example Composite Phase Deviation for sleep (CPDSleep) and for first scheduled event (FSE) timing (CPDFSE). (a) Raster plot of one individual showing daily sleep episodes, FSEs, and well-being assessments over 30 days. Days 1 and 7 are missing sleep data (gray bars from left to right). MSFsc = chronotype (midsleep on weekends, corrected for sleep loss on weekdays). Panels b and d show an enlarged section of panel a with only midsleep and FSE information. (b) Enlarged section of panel a (days 1–7) for midsleep times with ΔChronotype (ΔCT) and ΔDay-to-Day (ΔDD). Note that days 1 and 7 are missing, resulting in missing CPD data. (c) CPD plot for sleep. The arrows exemplify vectors from the origin to a data point. The CPD value of this data point is quantified by the length of the corresponding vector. Colored contour lines connect areas of equal data point density. (d) As in panel b but for FSEs. (e) As in panel c but for FSEs with ΔEvent and ΔDD.

Figure 2.

Associations of well-being and CPDSleep with chronotype and sex. A late chronotype (MSFsc) was associated with (a) higher Composite Phase Deviation (CPDSleep) and (b) younger age. Males scored higher (“better”) on (c) Sleepy–Alert, (d) Sad–Happy, (e) Sluggish–Energetic, and (f) Stressed–Calm. r = rank correlation coefficient Spearman’s rho. Sex comparisons in panels c–f are based on nonparametric Mann–Whitney U tests. Horizontal lines denote significant group differences: *p < 0.05, ***p < 0.001.

Figure 3.

Cluster analysis. Divisive hierarchical clustering [36] was used to examine the relationship between sleep schedules and event schedules. Sleep schedules were assessed by Composite Phase Deviation using midsleeps (CPDSleep), whereas event schedules were assessed by Composite Phase Deviation using FSE times (CPDFSE). (a) The dendrogram shows a two-clusters and a four-clusters solution, depending on where the dendrogram is cut. (b) The two-clusters solution groups the data into low-CPDFSE (aligned and regular event schedules, Cluster 1) and high-CPDFSE (mistimed and irregular event schedules, Cluster 2) clusters. Axes show z-scaled CPDSleep and CPDFSE values, i.e. a value of −1 equals 1 sd below the sample mean. (c) The four-clusters solution further splits the data along the horizontal axis: aligned/regular sleepers on aligned/regular schedules (low-CPDSleep/low-CPDFSE) (Cluster 1, n = 48), aligned/regular sleepers on mistimed/irregular schedules (low-CPDSleep/high-CPDFSE) (Cluster 2, n = 73), mistimed/irregular sleepers on aligned/regular schedules (high-CPDSleep/low-CPDFSE) (Cluster 3, n = 61), and mistimed/irregular sleepers on mistimed/irregular schedules (high-CPDSleep/high-CPDFSE) (Cluster 4, n = 41). The four colored circles mark the four individuals shown in panels e–h. (d) Characteristics of the four clusters by sleep duration (SDur), chronotype (MSFsc, sleep loss-corrected midsleep on weekends), standard deviation of midsleeps (MS (sd)), and standard deviation of first scheduled events (FSE (sd)). Colored boxes (gray and red) mark statistical differences between clusters (Kruskal–Wallis, p < 0.05). Effect sizes (ε 2) for cluster comparisons were as follows: SDε 2 = 0.02, MSFsc ε 2 = 0.20, MS (sd) ε 2 = 0.60, and FSE (sd) ε 2 = 0.67. Raster plots are shown of one individual from each cluster (note that individuals were selected to illustrate differences): (e) Cluster 3, (f) Cluster 4, (g) Cluster 1, (h) Cluster 2. Black bars = sleep episodes. Red dots = midsleeps. Red line = chronotype (MSFsc, sleep loss-corrected midsleep on weekends). Blue dots = first scheduled events (FSEs). Blue line = average start time of FSE.

Figure 4.

Well-being by clusters. Scores (mean ± SE) were compared among the four clusters on scales (a) Sleepy–Alert, (b) Sad–Happy, (c) Sluggish–Energetic, (d) Sick–Healthy, and (e) Stressed–Calm. Cluster 1: aligned/regular sleepers on aligned/regular event schedules (low-CPDSleep/low-CPDFSE). Cluster 2: aligned/regular sleepers on mistimed/irregular event schedules (low-CPDSleep/high-CPDFSE). Cluster 3: mistimed/irregular sleepers on aligned/regular event schedules (high-CPDSleep/low-CPDFSE). Cluster 4: mistimed/irregular sleepers on mistimed/irregular event schedules (high-CPDSleep/high-CPDFSE). Mistimed/irregular sleepers on mistimed/irregular event schedules (Cluster 4) reported the poorest well-being, whereas aligned/regular sleepers on mistimed/irregular event schedules (Cluster 2) reported the best well-being. The latter may be explained by later average start times of first scheduled events on mistimed/irregular event schedules (Clusters 2 and 4) compared to aligned/regular event schedules (Clusters 1 and 3), as shown in panel f. Horizontal lines denote significant group differences with *p < 0.05 and **p < 0.01, derived from linear regression models with Cluster 4 as reference.